CN109409541A - The method for realizing abandoned car battery reverse logistic feasibility assessment - Google Patents
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Abstract
The present invention is a kind of method for realizing abandoned car battery reverse logistic feasibility assessment, establish the fuzzy neural network method optimized based on particle swarm algorithm, comprise the steps of: S1, by identifying to risk factors, abandoned car battery reverse logistic feasibility assessment index system is constructed, and questionnaire method is combined to carry out data collection and processing；S2, feasibility assessment is carried out to abandoned car battery reverse logistic using Field Using Fuzzy Comprehensive Assessment；S3, the reverse transmittance nerve network of particle group optimizing is trained using the extent feasible that model of fuzzy synthetic evaluation calculates, using the reverse transmittance nerve network of trained particle group optimizing to other intelligent Evaluations with similar characteristics electron wastes dismantling enterprise's feasibility；S4, alarm differentiation is carried out according to evaluation result, and proposes corresponding feasibility control strategy.
Description
Technical Field
The invention relates to a feasibility evaluation method, in particular to a method for realizing feasibility evaluation of reverse logistics of a scraped car storage battery.
Background
With the continuous improvement of national economic level, the demand of consumers for automobiles is on a remarkable rising trend, the scale of the automobile industry is gradually enlarged, and the national automobile keeping amount reaches 1.94 hundred million by 2016 and is increased by 13 percent on year-by-year basis. At the same time, a large number of scrapped cars is also associated. According to statistics, the number of scrapped automobiles in China reaches 600 thousands in 2016, 1200 thousands of automobiles are expected to be broken through in 2020, and the problems of recycling, abandonment and the like of scrapped automobiles are increasingly serious. The storage battery is used as a main hazardous waste product, the problems of few recovery channels, poor recovery standardization, low recycling rate, large pollution in disassembly treatment and the like are more and more prominent, and the storage battery gradually draws attention of governments and related enterprises.
Because the recycling reverse logistics of the scrapped automobile storage battery have uncertainty in occurrence time, place and quantity and are dispersed, the problems of low operation efficiency and low economic benefit are faced to production enterprises or recycling enterprises, and potential environmental pollution risks are presented to governments and the public. The existing evaluation methods are hundreds of, and how to select one or more methods which are most suitable for the feasibility evaluation of the abandoned automobile reverse logistics from a plurality of methods is very important to sum up, so that an effective feasibility evaluation method is needed to guide enterprises to make reasonable reverse logistics operation decisions, reference basis is provided for government-issued related reverse logistics policies, and support is provided for green cycle sustainable development.
Disclosure of Invention
The invention aims to provide a method for realizing the feasibility evaluation of the reverse logistics of a scrapped automobile storage battery, one or more methods which are most suitable for the feasibility evaluation of the reverse logistics of scrapped automobiles are selected from a plurality of methods, and the effectiveness of the method is evaluated.
In order to achieve the above object, the present invention provides the following technical solutions.
A method for realizing the feasibility evaluation of the reverse logistics of a scraped car storage battery is used for establishing a fuzzy neural network method based on particle swarm optimization and comprises the following steps:
s1, identifying risk factors, constructing a scraped car storage battery reverse logistics feasibility evaluation index system, and collecting and processing data by combining an questionnaire method;
s2, performing feasibility evaluation on the reverse logistics of the scrapped automobile storage battery by using a fuzzy comprehensive evaluation method;
s3, training the particle swarm optimized back propagation neural network by using the feasibility degree calculated by the fuzzy comprehensive evaluation model, and intelligently evaluating the feasibility of other electronic waste dismantling enterprises with similar characteristics by using the trained particle swarm optimized back propagation neural network;
and S4, performing alarm judgment according to the evaluation result, and proposing a corresponding feasibility control strategy.
Preferably, the step S1 specifically includes the following steps:
s1.1, constructing a scrapped automobile storage battery reverse logistics feasibility evaluation index system, and constructing the scrapped automobile storage battery reverse logistics feasibility evaluation index system based on an active reverse logistics management theory;
s1.2, adopting an questionnaire method to enable experts to score the influence degree of each index on the reverse logistics of the scrapped automobile storage battery implemented by enterprises;
and S1.3, analyzing the reliability and the validity of the data by adopting SPSS software.
Preferably, in step S1.3, reliability analysis is performed on questionnaire data by using the colonbach Alpha coefficient, and then KMO test and bartlett sphere test are performed on the data to determine validity.
Preferably, the step S2 specifically includes the following steps:
s2.1, establishing an evaluation factor set U ═ U1，u2，…unN is the number of the influence factors of the criterion layer;
evaluation of factor u for i-th layeriIs further divided into ui＝{ui1，ui2，…uimM is the number of the influence factors of the index layer;
s2.2, establishing an evaluation grade discourse domain V ═ V1，v2，…vn}；
S2.3, establishing a weight a of the criterion layer evaluation index as (a1, a2, a3), and performing single factor evaluation;
s2.4, establishing a fuzzy relation matrix R;
s2.5, determining the comprehensive weight of the index layer evaluation index, and performing secondary evaluation;
wherein,representing a fuzzy operator;
and S2.6, performing overall sequencing on the evaluation indexes according to the maximum membership rule.
Preferably, in the step S2.2, the evaluation index is divided into five grades according to the degree of influence of the evaluation index on the reverse logistics of the electronic waste by using a lecott five-grade scale.
Preferably, the step S3 specifically includes the following steps:
s3.1, initializing the number of neurons in each layer of the BP neural network, the number of layers of hidden layers, the position vector of particles, the dimension of velocity vector, the scale of particle swarm, and learning factor c1And c2Inertial weight ω, velocity of each particle, individual extremum pbestAnd global optimum gbestA fitness function;
s3.2, carrying out forward propagation calculation on each particle by using a training sample to calculate a training error, and then calculating the fitness of the particle according to a fitness function;
s3.3, updating the individual extreme value, the global optimum value, the speed and the position of each particle according to the fitness value of each particle;
and S3.4, judging whether the fitness of the particle swarm reaches a preset error standard or the maximum iteration number.
Preferably, the step S3 is to program the fuzzy neural network algorithm based on PSO optimization through C language, and run the fuzzy neural network algorithm on MATLAB R2018a version for simulation.
Preferably, the step S4 specifically includes the following steps:
s4.1, feasibility evaluation and alarm judgment are carried out, the influence degree of each factor on implementation of reverse logistics of the storage battery can be directly obtained by combining a maximum membership principle, and corresponding alarm judgment is obtained according to the influence degree;
and S4.2, judging according to different alarms to obtain a corresponding feasibility control strategy.
Preferably, the alarm set in step S4.2 comprises:
white alarm indicates that the feasibility of the reverse logistics project of the storage battery of the enterprise is good, and the operation environment of each link inside and outside is good;
blue alarm shows that the feasibility of the reverse logistics project of the enterprise storage battery is good, and the internal and external operating environments are good;
yellow alarm, which indicates that the feasibility of the reverse logistics project of the enterprise storage battery is general and certain risk exists in the internal and external operating environments;
orange alarm, which indicates that the feasibility of the reverse logistics project of the storage battery of the enterprise is poor, and some links may cause more serious loss and need to cause high attention of a manager;
and a red alarm indicates that the enterprise storage battery reverse logistics project is poor in feasibility and is in a high crisis state.
The method has the advantages that the fuzzy comprehensive evaluation method can exert good advantages when processing subjective factors with fuzziness, and the BP neural network has high-speed self-learning, good self-adaptive capacity and fault tolerance and forms good complementarity with the fuzzy comprehensive evaluation method to a certain extent. Meanwhile, considering that the BP neural network has low convergence speed and is easy to fall into a local extremum, a Particle Swarm Optimization (PSO) is adopted to optimize a gradient descent method in the BP neural network.
Drawings
FIG. 1 is a flow chart of a structural model for evaluating the feasibility of reverse logistics of a scraped car storage battery in the invention;
FIG. 2 is a flow chart of a particle swarm optimization BP neural network algorithm.
Detailed Description
In order to make the technical means, the original characteristics, the achieved purposes and the effects of the invention easy to understand, the invention is further explained in detail with the accompanying drawings and the specific embodiments, but the scope of the invention is not limited in any way.
An embodiment of the present invention is described in detail below with reference to fig. 1 and 2.
As shown in the attached figure 1, the method for establishing the fuzzy neural network based on particle swarm optimization specifically comprises the following steps: s1, identifying risk factors, constructing a scraped car storage battery reverse logistics feasibility evaluation index system, and collecting and processing data by combining an questionnaire method; s2, performing feasibility evaluation on the reverse logistics of the scrapped automobile storage battery by using a fuzzy comprehensive evaluation method; s3, training the particle swarm optimized back propagation neural network by using the feasibility degree calculated by the fuzzy comprehensive evaluation model, and intelligently evaluating the feasibility of other electronic waste dismantling enterprises with similar characteristics by using the trained particle swarm optimized back propagation neural network; and S4, performing alarm judgment according to the evaluation result, and proposing a corresponding feasibility control strategy.
The step S1 specifically includes the following steps:
s1.1, constructing a scrapped automobile storage battery reverse logistics feasibility evaluation index system, based on an active reverse logistics management theory, taking reduction of storage battery reverse logistics risks and improvement of storage battery Recovery efficiency and profit rate as a core, following a PPT-SIR principle, namely forecasting (Predict), prevention (Predict), tracking (Track), Speed (Speed), identification (Identify) and correction (Recovery), and constructing the scrapped automobile storage battery reverse logistics feasibility evaluation index system by identifying risk factors, as shown in Table 1.1.
TABLE 1.1 evaluation index system for reverse logistics feasibility of scraped car storage batteries
S1.2, adopting an questionnaire method, mainly taking two forms of actual access and questionnaire filling on the internet as main forms, and enabling experts to score the influence degree of each index on the reverse logistics of the scrapped automobile storage battery implemented by an enterprise, wherein an investigation object is mainly an expert scholars in the fields of storage batteries, electronic wastes, automobile industry, reverse logistics, reverse supply chains, full life cycles and the like; the index distinguishes between positive and negative indices.
S1.3, analyzing the reliability and the validity of the data by adopting SPSS software, firstly, analyzing the reliability of the questionnaire data by adopting a Crohn Bach Alpha coefficient, and then, carrying out KMO (Kernel-based expert system) test and Butterworth spherical test on the data to judge the validity of the data.
The step S2 specifically includes the following steps:
s2.1, establishing an evaluation factor set U ═ U1，u2，…unN is the number of the influence factors of the criterion layer;
evaluation of factor u for i-th layeriCan be further divided into ui＝{ui1，ui2，…uimAnd m is the number of the influence factors of the index layer.
S2.2, establishing an evaluation grade discourse domain V ═ V1，v2，…vn}. The invention adopts a Lekt five-level scale to divide the evaluation index into five levels according to the degree of influence of the evaluation index on the reverse logistics of the electronic waste from 'minimal influence' to 'maximal influence', and an investigated person beats 31 indexes in the table 1.1 according to the actual situationAnd dividing and quantizing the fuzzy statement.
S2.3, the weight a of the criterion layer evaluation index is set to (a1, a2, a3), and the one-factor evaluation is performed.
And S2.4, establishing a fuzzy relation matrix R, namely a judgment matrix. Quantifying the evaluated objects one by one from each factor, wherein rijAnd (4) representing the degree of membership of the ith evaluation factor to the jth evaluation level.
And S2.5, determining the comprehensive weight of the index layer evaluation index, and performing secondary evaluation. Comprehensive weight omega of secondary indexijIs a criterion layer index uiWeight r ofiSecondary index u corresponding to the criterion layerijWeight r ofijProduct of the multiplication and fuzzy operator
And S2.6, performing overall sequencing on the evaluation indexes according to the maximum membership rule.
As shown in fig. 2, the step S3 specifically includes:
s3.1, initializing the number of neurons in each layer of the BP neural network, the number of layers of hidden layers, the position vector of particles, the dimension of velocity vector, the scale of particle swarm, and learning factor c1And c2Inertial weight ω, velocity of each particle, individual extremum pbestAnd global optimum gbestA fitness function;
s3.2, carrying out forward propagation calculation on each particle by using a training sample to calculate a training error, and then calculating the fitness of the particle according to a fitness function;
s3.3, updating the individual extreme value, the global optimum value, the speed and the position of each particle according to the fitness value of each particle;
and S3.4, judging whether the fitness of the particle swarm reaches a preset error standard or the maximum iteration number.
The steps are to program a fuzzy neural network algorithm based on PSO optimization through C language, and run simulation on MATLABR2018a version. The invention relates to a membership degree matrix R of 9 index layers with economic benefit1As input of training samples, the evaluation result B is used1Membership matrix R from the ecological benefit as output of training samples2And extracting 9 index layers as input of verification samples, training the neural network, and checking the error between a simulation evaluation target value and an actual evaluation target value output by the neural network after the training is finished so as to test the scientificity and effectiveness of the storage battery reverse logistics risk evaluation model.
The step S4 specifically includes the following steps:
and S4.1, feasibility evaluation and alarm judgment are carried out, the influence degree of each factor on implementation of reverse logistics of the storage battery can be directly obtained by combining the maximum membership principle, and corresponding alarm judgment is obtained according to the influence degree.
TABLE 4.1 feasibility assessment and alarm discrimination
And S4.2, judging according to different alarms to obtain a corresponding feasibility control strategy.
As shown in table 4.1, the white alarm indicates that the feasibility of the reverse logistics project of the storage battery of the enterprise is good, the operation environment of each link inside and outside is good, and the prevention can be performed only by paying attention to the white alarm. Blue alarm shows that the feasibility of the reverse logistics project of the storage battery of the enterprise is good, the internal and external operation environments are still good, and the problems of abnormal information and the like possibly exist in individual links in an index layer, so that attention needs to be paid and effective analysis needs to be carried out, and adverse effects caused by the index are avoided as much as possible. The yellow alarm shows that the feasibility of the reverse logistics project of the storage battery of the enterprise is general, certain loss is possibly caused due to the fact that certain risks exist in the internal and external operating environments, detailed analysis and diagnosis of high-influence factors are recommended, and pre-established control measures are adopted to prevent and reduce the loss. Orange alarm shows that the feasibility of the reverse logistics project of the storage battery of the enterprise is poor, some links can cause serious loss, high attention of a manager needs to be paid, high influence factors in the process are analyzed in detail, deep defense is adopted according to sources, and the purpose of reducing loss is achieved. The red alarm shows that the feasibility of the reverse logistics project of the storage battery of the enterprise is poor and the storage battery is in a high-risk state, which means that the enterprise can possibly bear huge loss, enterprise managers need to comprehensively deal with the loss, analyze the mechanism in detail, find out the critical condition and adopt targeted risk treatment measures to control the loss.
While the present invention has been described in detail by way of the foregoing preferred examples, it is to be understood that the above description is not to be taken as limiting the invention. Various modifications and alterations to this invention will become apparent to those skilled in the art upon reading the foregoing description. Accordingly, the scope of the invention should be determined from the following claims.
Claims (9)
1. A method for realizing the feasibility evaluation of the reverse logistics of a scraped car storage battery is characterized in that a fuzzy neural network method based on particle swarm optimization is established, and the method comprises the following steps:
s1, identifying risk factors, constructing a scraped car storage battery reverse logistics feasibility evaluation index system, and collecting and processing data by combining an questionnaire method;
s2, performing feasibility evaluation on the reverse logistics of the scrapped automobile storage battery by using a fuzzy comprehensive evaluation method;
s3, training the particle swarm optimized back propagation neural network by using the feasibility degree calculated by the fuzzy comprehensive evaluation model, and intelligently evaluating the feasibility of other electronic waste dismantling enterprises with similar characteristics by using the trained particle swarm optimized back propagation neural network;
and S4, performing alarm judgment according to the evaluation result, and proposing a corresponding feasibility control strategy.
2. The method for realizing the feasibility evaluation of reverse logistics of the scrapped automobile storage battery according to claim 1, wherein the step S1 specifically comprises the following steps:
s1.1, constructing a scrapped automobile storage battery reverse logistics feasibility evaluation index system, and constructing the scrapped automobile storage battery reverse logistics feasibility evaluation index system based on an active reverse logistics management theory;
s1.2, adopting an questionnaire method to enable experts to score the influence degree of each index on the reverse logistics of the scrapped automobile storage battery implemented by enterprises;
and S1.3, analyzing the reliability and the validity of the data by adopting SPSS software.
3. The method for evaluating the feasibility of reverse logistics of scrapped automobile storage batteries according to claim 2, wherein in the step S1.3, reliability analysis is performed on questionnaire data by using a Crohn Bach Alpha coefficient, and then KMO (K-nearest neighbor) inspection and Batterit spherical inspection are performed on the data to judge the effectiveness.
4. The method for realizing the feasibility evaluation of reverse logistics of the scrapped automobile storage battery according to claim 1, wherein the step S2 specifically comprises the following steps:
s2.1, establishing an evaluation factor set U ═ U1，u2，...unN is the number of the influence factors of the criterion layer;
evaluation of factor u for i-th layeriIs further divided into ui＝{ui1，ui2，...uim} of whichM is the number of influencing factors of the index layer;
s2.2, establishing an evaluation grade discourse domain V ═ V1，v2，...vn}；
S2.3, establishing a weight a of the criterion layer evaluation index as (a1, a2, a3), and performing single factor evaluation;
s2.4, establishing a fuzzy relation matrix R;
s2.5, determining the comprehensive weight of the index layer evaluation index, and performing secondary evaluation;
wherein,representing a fuzzy operator;
and S2.6, performing overall sequencing on the evaluation indexes according to the maximum membership rule.
5. The method for evaluating the feasibility of reverse logistics of scrapped automobile storage batteries according to claim 4, wherein in the step S2.2, a Leketed five-grade scale is adopted to divide the evaluation indexes into five grades according to the influence degree of the evaluation indexes on the reverse logistics of electronic wastes.
6. The method for realizing the feasibility evaluation of reverse logistics of the scrapped automobile storage battery according to claim 1, wherein the step S3 specifically comprises the following steps:
s3.1, initializing the number of neurons in each layer of the BP neural network, the number of layers of hidden layers, the position vector of particles, the dimension of velocity vector, the scale of particle swarm, and learning factor c1And c2Inertial weight ω, velocity of each particle, individual extremum PbestAnd global optimum gbestA fitness function;
s3.2, carrying out forward propagation calculation on each particle by using a training sample to calculate a training error, and then calculating the fitness of the particle according to a fitness function;
s3.3, updating the individual extreme value, the global optimum value, the speed and the position of each particle according to the fitness value of each particle;
and S3.4, judging whether the fitness of the particle swarm reaches a preset error standard or the maximum iteration number.
7. The method for evaluating the feasibility of reverse logistics of scrapped automobile batteries according to claim 6, wherein the step S3 is implemented by programming a PSO-based optimized fuzzy neural network algorithm through C language and running simulation on MATLABR2018a version.
8. The method for realizing the feasibility evaluation of reverse logistics of the scrapped automobile storage battery according to claim 1, wherein the step S4 specifically comprises the following steps:
s4.1, feasibility evaluation and alarm judgment are carried out, the influence degree of each factor on implementation of reverse logistics of the storage battery can be directly obtained by combining a maximum membership principle, and corresponding alarm judgment is obtained according to the influence degree;
and S4.2, judging according to different alarms to obtain a corresponding feasibility control strategy.
9. The method for evaluating the feasibility of reverse logistics of scrapped automobile batteries according to claim 8, wherein the alarm set in the step S4.2 comprises:
white alarm indicates that the feasibility of the reverse logistics project of the storage battery of the enterprise is good, and the operation environment of each link inside and outside is good;
blue alarm shows that the feasibility of the reverse logistics project of the enterprise storage battery is good, and the internal and external operating environments are good;
yellow alarm, which indicates that the feasibility of the reverse logistics project of the enterprise storage battery is general and certain risk exists in the internal and external operating environments;
orange alarm, which indicates that the feasibility of the reverse logistics project of the storage battery of the enterprise is poor, and some links may cause more serious loss and need to cause high attention of a manager;
and a red alarm indicates that the enterprise storage battery reverse logistics project is poor in feasibility and is in a high crisis state.
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| CN112098873A (en) * | 2020-08-17 | 2020-12-18 | 四川大学 | Lithium battery health state estimation method based on charging voltage curve geometric characteristics |
| CN113964409A (en) * | 2021-09-17 | 2022-01-21 | 江苏大学 | Automatic recovery system for electric vehicle battery and control method |
| CN117339913A (en) * | 2023-10-31 | 2024-01-05 | 科立鑫(珠海)新能源有限公司 | Waste battery recovery system |
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