CN109325704B - Risk evaluation method for hazardous waste disposal process based on rough set-GRNN algorithm - Google Patents
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Abstract
The invention relates to the field of hazardous waste disposal, and discloses a risk evaluation method for a hazardous waste disposal process based on a rough set-GRNN algorithm, which is used for identifying risks from multiple angles so as to more effectively manage and control the hazardous waste disposal process. The method comprises the following steps of firstly establishing a risk index system of the hazardous waste treatment process, wherein the risk index system comprises the following steps: determining a danger processing and sorting flow, and splitting the danger processing and sorting flow into a plurality of sub-flows; identifying risk influence factors existing in each sub-process from the aspects of environment, human factors, technology, organization and process; determining specific evaluation indexes of the risk influence factors to obtain a risk factor set consisting of the evaluation indexes; then screening risk influence factors in the dangerous waste treatment process through a rough set; and finally, calculating a risk assessment value based on the GRNN algorithm and the screened risk influence factors. The method is suitable for risk evaluation of the hazardous waste disposal process.
Description
Technical Field
The invention relates to the field of hazardous waste disposal, in particular to a risk evaluation method for a hazardous waste disposal process based on a rough set-GRNN algorithm.
Background
With the development of industry, the discharge of dangerous waste (referred to as hazardous waste) in industrial production process is increasing day by day. It is estimated that the world produces hundreds of millions of tons of hazardous waste annually. Because of the serious pollution and potential serious influence brought by hazardous wastes, the hazardous wastes are called as 'political wastes' in industrially developed countries, the public is very sensitive to the problem of the hazardous wastes, the hazardous waste disposal sites are set in the areas where the public lives, and in addition, the disposal cost of the hazardous wastes is high, and some enterprises do not comply with the relevant national regulations on the hazardous wastes in the hazardous waste disposal. Meanwhile, in the industrialized development process, the environmental risk problem is increasingly serious due to the disordered management of the hazardous waste, and the hazardous waste is possibly leaked in each link of collection, transportation, storage, final treatment and the like to influence the ecological environment and endanger the human health. Therefore, the risk management of the hazardous waste treatment process becomes a main subject of research in the industry, and a complete risk assessment system is provided as a premise for enhancing the risk management.
The conventional risk evaluation method for the dangerous waste treatment process only divides the whole process into sub-process identification risk factors, does not classify and identify the factors, and has a single visual angle during risk evaluation and inaccurate final results.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the risk evaluation method for the hazardous waste disposal process based on the rough set-GRNN algorithm is provided, risks are identified from multiple angles, and the hazardous waste disposal process is controlled more effectively.
In order to solve the problems, the invention adopts the technical scheme that: the risk evaluation method for the hazardous waste disposal process based on the rough set-GRNN algorithm comprises the following steps:
step 1, establishing a risk index system of a hazardous waste treatment process, comprising the following steps:
step 11, determining a dangerous waste treatment process, and splitting the dangerous waste treatment process into a plurality of sub-processes;
step 12, identifying risk influence factors existing in each sub-process from the aspects of environment, human factors, technology, organization and process;
step 13, determining specific evaluation indexes of the risk influence factors to obtain a risk factor set consisting of the evaluation indexes;
step 2, screening risk influence factors in the dangerous waste treatment process by the rough set, wherein the screening comprises the following steps:
according toThe indexes in the risk factor set are sorted according to the importance of the indexes, the index with the minimum importance is deleted, and if the risk factor set meets the requirements after the indexes are deleted
Continuing to select the index with the minimum importance in the risk factor set for deletion until the risk factor set does not meet the requirements after the indexes are deleted
Wherein alpha is1Number of incompatible samples, α, introduced after removing index a for the set of risk factors0Deleting the number of samples in the risk factor table before the index a for the risk factor set, wherein epsilon is a threshold value;
and 3, calculating a risk assessment value based on the GRNN algorithm and the risk influence factors screened in the step 2.
Further, step 11 may be implemented by splitting the hazardous waste disposal process into sub-processes of collection, transportation, storage and disposal according to the commonly used hazardous waste disposal process.
As a feasible way, step 13 may employ a delphire expert consulting method to determine the specific evaluation index of the risk influencing factor.
Further, step 3 may specifically include: determining a network structure of GRNN, taking the risk influence factors screened in the step 2 as data input, and calculating a risk evaluation value through a GRNN algorithm;
the network structure of GRNN includes:
an input layer having a number of neurons equal to the dimension of the input variable in the sample;
the number of the neurons in the mode layer is equal to the dimension of the input vector in the sample, namely the number of indexes in a dangerous and useless process risk assessment system;
the summation layer is used for carrying out weighted summation on the neurons of all the mode layers;
and the number of the neurons in the output layer is equal to the dimension of the output vector in the sample, namely the risk assessment value of the dangerous waste treatment process.
The invention has the beneficial effects that: compared with the prior art, the method identifies risks from five aspects of environment, human factors, technology, organization, process and the like according to the dangerous waste treatment process, resolves the complex risk management model facing the dangerous waste treatment process into each sub-domain model, establishes an index system through the sub-process five-dimensional risk model, and performs risk evaluation on the dangerous waste treatment process by using a rough set-GRNN algorithm, so that the technical range of the dangerous waste technical field is expanded, and the technical blank is filled. The method can accurately and comprehensively evaluate the risk level of the dangerous waste treatment process, can comprehensively know the effect and the defect of the dangerous waste treatment process, can promote the reduction of the risk through improvement, and is beneficial to improving the treatment efficiency of the dangerous waste.
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FIG. 1 is a schematic diagram of the steps embodied in the present invention;
FIG. 2 is a flow chart of a conventional hazardous waste treatment;
fig. 3 is a schematic diagram of the risk factors of the present invention.
Detailed Description
The invention combines the rough set and the GRNN algorithm to evaluate the risk of the hazardous waste treatment process. Firstly, identifying risk factors in the dangerous waste treatment process from the viewpoints of human factors (H), technologies (T), organizations (O), environments (E) and processes (P), then preprocessing the risk factor set by using a rough set, and finally determining a risk evaluation result of the dangerous waste treatment process by using a GRNN algorithm. The specific steps of the invention are shown in fig. 1, and comprise:
step 1, establishing a risk index system of a hazardous waste treatment process, comprising the following steps:
step 11, determining a dangerous waste treatment process, and splitting the dangerous waste treatment process into a plurality of sub-processes; according to the conventional hazardous waste treatment process shown in fig. 2, the hazardous waste treatment process can be divided into sub-processes of collection, transportation, storage and disposal;
and step 12, as shown in FIG. 3, identifying risk influencing factors existing in each sub-process from the aspects of environment, human factors, technology, organization and process:
1) analyzing risk elements of the sub-processes from external environments and internal environments, wherein the external environments comprise risk elements existing in market environments, strategic environments and the like; the internal environment comprises risk elements existing in enterprise sites, facilities and the like;
2) analyzing risk elements in the sub-process from external human factors and internal human factors, wherein the external part has risk elements related to clients, and the internal part has risk elements related to employees;
3) analyzing risk elements of the sub-processes from the types of the technologies, wherein the risk elements comprise risk elements existing in processing technologies, platform technologies and the like;
4) analyzing risk elements in the sub-processes from enterprises, departments, involved units, and the like;
5) analyzing risk elements in the sub-processes from process types, process controls, and the like;
and step 13, determining specific evaluation indexes of the risk influence factors by using an expert consultation method to obtain a risk factor set consisting of the evaluation indexes. The specific risk factors can be determined by means of expert delphire consultation here, which includes the following steps:
(1) determining survey questions and drawing up survey synopsis;
(2) forming an expert group, and determining the number of experts according to the knowledge range required by the topic;
(3) the questions to be predicted and the related requirements are put forward to all experts;
(4) each expert puts forward own prediction opinions according to the materials received by the experts;
(5) the first judgment opinions of the experts are gathered and listed into a chart for comparison, and then sent to each expert, so that the experts compare different opinions of the experts with other people and modify the opinions and judgment of the experts;
(6) collecting the modification opinions of all experts, collecting the modification opinions, and distributing the modification opinions to each expert again so as to carry out second modification. Collecting opinions in turns and feeding back information without experts is a main link of Delphi. Collecting opinions and information hair generally goes through three or four rounds. This process is repeated until each expert no longer changes his opinion;
(7) and comprehensively processing the opinions of the experts. And constructing a risk evaluation index system suitable for the dangerous waste treatment process according to the risk factors with high representativeness and combining with expert opinions.
Step 2, screening risk influence factors in the dangerous waste treatment process by the rough set, wherein the screening comprises the following steps:
the indexes in the risk factor set are sorted according to the importance of the indexes, namely R ═ { R ═ R1,R2…RzAnd deleting the index with the minimum importance after sorting, and if the risk factor set after deleting the index meets the requirement
The index with the minimum importance in the risk factor set is continuously selected for deletion until the risk factor set R is { R ═ R after the index is deleted1,R2…RmZ is not satisfied
Wherein alpha is1Number of incompatible samples, α, introduced after removing index a for the set of risk factors0Deleting the number of samples in the risk factor table before the index a for the risk factor set, wherein epsilon is a threshold value;
and 3, calculating a risk assessment value based on the GRNN algorithm and the risk influence factors screened in the step 2. The method comprises the following steps:
firstly, determining a network structure of GRNN, then inputting the risk influence factors screened in the step 2 as data, and finally calculating a risk evaluation value through a GRNN algorithm. Wherein, the network structure of GRNN includes:
an input layer having a number of neurons equal to the dimension of the input variable in the sample;
the number of the neurons in the mode layer is equal to the dimension of the input vector in the sample, namely the number of indexes in a dangerous and useless process risk assessment system;
the summation layer is used for carrying out weighted summation on the neurons of all the mode layers;
and the number of the neurons in the output layer is equal to the dimension of the output vector in the sample, namely the risk assessment value of the dangerous waste treatment process.
The present invention is further illustrated by the following examples.
The embodiment provides a risk evaluation method for a hazardous waste disposal process based on a rough set-GRNN algorithm, which comprises the following steps:
1. and establishing a risk index system of the hazardous waste treatment process. The method mainly comprises the following steps:
(1) after the whole dangerous waste treatment process is obtained, the whole dangerous waste treatment process is divided into sub-processes of collection, transportation, storage and disposal.
(2) Risks of each sub-process are identified from the environment, human causes, techniques, organizations, and processes in all dimensions:
1) analyzing risk elements of the sub-processes from external environments and internal environments, wherein the external environments comprise risk elements existing in market environments, strategic environments and the like; the internal environment comprises risk elements existing in enterprise sites, facilities and the like;
2) analyzing risk elements in the sub-process from external human factors and internal human factors, wherein the external part has risk elements related to clients, and the internal part has risk elements related to employees;
3) analyzing risk elements of the sub-processes from the types of the technologies, wherein the risk elements comprise risk elements existing in processing technologies, platform technologies and the like;
4) analyzing risk elements in the sub-processes from enterprises, departments, involved units, and the like;
5) risk elements in sub-processes are analyzed from process types, process controls, and the like.
(3) Five layers of the sub-process are analyzed, and a Delphi expert consulting method is applied to determine specific risk factors, wherein the specific risk factors comprise:
1) determining survey questions and drawing up survey synopsis;
2) forming an expert group, and determining the number of experts according to the knowledge range required by the topic;
3) the questions to be predicted and the related requirements are put forward to all experts;
4) each expert puts forward own prediction opinions according to the materials received by the experts;
5) the first judgment opinions of the experts are gathered and listed into a chart for comparison, and then sent to each expert, so that the experts compare different opinions of the experts with other people and modify the opinions and judgment of the experts;
6) collecting the modification opinions of all experts, collecting the modification opinions, and distributing the modification opinions to each expert again so as to carry out second modification. Collecting opinions in turns and feeding back information without experts is a main link of Delphi. Collecting opinions and information hair generally goes through three or four rounds. This process is repeated until each expert no longer changes his opinion;
7) and comprehensively processing the opinions of the experts, and constructing a risk evaluation index system suitable for the dangerous waste treatment process according to the risk factors with high representativeness and combining the opinions of the experts to obtain a risk factor set consisting of evaluation indexes.
2. And screening risk influence factors in the dangerous waste treatment process by the rough set. The method mainly comprises the following steps:
(1) and discretizing the sample. And discretizing the sample data of all indexes in the risk influence factor set by using a frequency division method, namely uniformly dividing the sample data into L equal parts, wherein the number of the samples in each part is the same. In an actual database, more sample data are continuous attributes, and the existing algorithms for mining many data can only process discrete attributes, so that the discretization of the continuous attributes is a premise for applying the algorithms.
(2) Reduction (deletion) of indices in a risk index system. The method comprises the following steps:
1) determining each risk evaluation index RiAnd comprehensively evaluating C and solving C to RiDegree of dependence of
Wherein, card (U) represents the cardinality of the set,
the positive domain of U for C is as follows:
2) index R in risk-finding index systemiThe importance of (c). Index RiIs important toThe term "property" is understood to mean that the index R is removed from the evaluation index setiThen, the degree of change of the size of the decision result is considered, and the larger the change is, the index R in the risk factors is showniThe more important. RiThe formula for calculating the importance of:
3) sorting R according to index importance1,R2…RzReducing the index with the minimum importance, and if the risk factor set after reducing the index meets the requirement
And continuing to select the index with the minimum importance in the risk evaluation index system after reduction for reduction, otherwise, stopping operation. Wherein alpha is1As a deletion index RiNumber of incompatible samples introduced afterwards, α0As a deletion index RiThe number of samples in the previous risk indicator system, ε is the threshold. This results in a new risk factor set R ═ { R ═ R1,R2…Rm}(m＜＜z)。
3. And (4) taking the risk influence factors screened in the step (2) as data input, and calculating a risk evaluation value by adopting a GRNN algorithm.
GRNN (generalized recurrent neural network) is composed of an input layer, a pattern layer, a summation layer, and an output layer, where:
an input layer: the number of the neurons in the input layer is equal to the dimension of the input vector in the learning sample, and each neuron is a simple distribution unit and directly transmits an input variable to the mode layer for reduced index content in a risk evaluation index system.
Mode layer: the number of neurons in the mode layer is equal to the dimension of an input vector in a learning sample, each neuron corresponds to different samples for reduced index content in a risk evaluation index system, and the transfer function of the mode layer is as follows:
in the formula, X is a network input variable; xiThe learning sample corresponding to the ith neuron.
And a summation layer: performing arithmetic summation on the outputs of all the neurons in the mode layer, wherein the connection weight of the mode layer and each neuron is 1, and the transfer function is as follows:
an output layer: the number of neurons in the output layer is equal to the dimension of the output vector in the learning sample, namely the risk assessment result, each neuron divides the output of the summation layer, and the output of the neuron j corresponds to the estimation result
The jth element of (i.e.
Y' is the predicted output of Y under the condition that the input is X:
applying Parzen non-parametric estimation, from a sample data set
The density function f (X, y) is estimated as follows:
in the formula: xi,YiRespectively representing the ith training input vector and the corresponding output; n is the sample capacity and p is the dimension of the random variable X. Sigma is called the smoothing factor, which is Gaussian in natureStandard deviation of the function.
Due to the fact that
After simplification, the following results are obtained:
in the above formula, hiRepresenting a gaussian radial basis function, expressed as:
compared with the prior art, the embodiment establishes a set of five-dimensional risk evaluation model of environment, human factor, technology, organization and process for risk management of the hazardous waste treatment process. The complex risk management model facing the dangerous waste treatment process is decomposed into each sub-domain model, the sub-process five-dimensional risk model is used for carrying out coordination management, the management difficulty is reduced, meanwhile, the rough set-GRNN algorithm is used for carrying out risk evaluation on the dangerous waste treatment process, the technical range of the dangerous waste technical field is expanded, and the technical blank is filled. The set of theoretical method can accurately and comprehensively evaluate the risk level of the dangerous waste treatment process, can comprehensively know the effect and the deficiency of the dangerous waste treatment process, can promote the reduction of the risk through improvement, and is beneficial to improving the dangerous waste treatment efficiency.
Claims (4)
1. The risk evaluation method for the hazardous waste disposal process based on the rough set-GRNN algorithm is characterized by comprising the following steps of:
step 1, establishing a risk index system of a hazardous waste treatment process, comprising the following steps:
step 11, determining a dangerous waste treatment process, and splitting the dangerous waste treatment process into a plurality of sub-processes;
step 12, identifying risk influence factors existing in each sub-process from the aspects of environment, human factors, technology, organization and process;
step 13, determining specific evaluation indexes of the risk influence factors to obtain a risk factor set consisting of the evaluation indexes;
step 2, screening risk influence factors in the dangerous waste treatment process by the rough set, wherein the screening comprises the following steps:
according to the importance of the indexes, the indexes in the risk factor set are sorted, the index with the minimum importance is deleted, and if the risk factor set meets the requirements after the indexes are deleted
Continuing to select the index with the minimum importance in the risk factor set for deletion until the risk factor set does not meet the requirements after the indexes are deleted
Wherein alpha is1Number of incompatible samples, α, introduced after removing index a for the set of risk factors0Deleting the number of samples in the risk factor set before the index a for the risk factor set, wherein epsilon is a threshold value;
and 3, calculating a risk assessment value based on the GRNN algorithm and the risk influence factors screened in the step 2.
2. The method of claim 1, wherein step 11 is performed by dividing the hazardous waste disposal process into collection, transportation, storage and disposal sub-processes.
3. The risk assessment method for hazardous waste disposal process based on rough set-GRNN algorithm as claimed in claim 1, wherein step 13 employs delphire expert consulting method to determine specific assessment indexes of risk influencing factors.
4. The risk assessment method of a hazardous waste disposal process based on rough set-GRNN algorithm as claimed in claim 1, wherein step 3 comprises: determining a network structure of GRNN, taking the risk influence factors screened in the step 2 as data input, and calculating a risk evaluation value through a GRNN algorithm;
the network structure of GRNN includes:
an input layer having a number of neurons equal to the dimension of the input variable in the sample;
the number of the neurons in the mode layer is equal to the dimension of the input vector in the sample, namely the number of indexes in a dangerous and useless process risk assessment system;
the summation layer is used for carrying out weighted summation on the neurons of all the mode layers;
and the number of the neurons in the output layer is equal to the dimension of the output vector in the sample, namely the risk assessment value of the dangerous waste treatment process.
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| Risk Evaluation in Water Quality Management of a River System;V.R.Subbarao Vemula等;《Journal of Water Resources Planning and Management》;20040901;第130卷(第5期);第411-423页 * |
| 我国危险固体废弃物处理处置现状分析;张旭光;《绿色科技》;20141225(第12期);第190-193页 * |
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| CN109325704A (en) | 2019-02-12 |
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