CN117993702A - Enterprise equipment safety performance evaluation method and system - Google Patents
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Abstract
The embodiment of the invention provides an enterprise equipment safety performance evaluation method and system, and belongs to the technical field of enterprise management. The method comprises the following steps: collecting equipment operation data, and performing classification processing on the operation data to obtain an evaluation data set; under a preset evaluation index system, determining subjective weight and objective weight of each evaluation item; the preset evaluation index system is a multi-level evaluation system, each level comprises a plurality of evaluation items, and each evaluation item comprises a plurality of evaluation indexes; obtaining comprehensive weights of all evaluation items based on the subjective weights and the objective weights, and obtaining variable weights corresponding to all the evaluation items according to the comprehensive weights of all the evaluation items based on index properties of all the evaluation items; and evaluating the equipment safety performance based on the variable weight of each evaluation item and the evaluation data set. The scheme of the invention realizes quantitative evaluation of the enterprise equipment management level.
Description
Technical Field
The invention relates to the technical field of enterprise management, in particular to an enterprise equipment safety performance evaluation method and an enterprise equipment safety performance evaluation system.
Background
Since the consequences of refinery accidents are extremely serious, it is an industry-accepted fact to guarantee equipment safety performance indicators for refining enterprises. The equipment integrity management is one of important elements of the process safety management, so the equipment safety is also one of important professional safety of process safety performance of refining enterprises. At present, the research on complex relations among equipment safety performance index systems and indexes is less. The method for determining the index system, the index relation weight and the index threshold has important significance for evaluating the safety performance of the smelting enterprise equipment. In the face of a large number of refining enterprises, a large amount of manpower and material resources are input in the checking and auditing process every year to evaluate the equipment safety management level of the enterprises and discover management defects. If the equipment safety state of the enterprise can be judged in real time through a scientific index system, the problems of the enterprise can be dynamically found and early-warned in time, the frequency of checking and auditing can be reduced, and the monitoring effect and efficiency are improved. At present, some methods for monitoring the safety state of equipment in a refining enterprise exist, and the methods are mainly aimed at an equipment integrity management method or an equipment integrity performance index system construction method, but are still lacking at present for an equipment safety-related performance index system and how to quantify equipment safety performance evaluation results. Aiming at the problem that quantitative evaluation cannot be performed in the existing refining enterprise equipment safety evaluation method, a new enterprise equipment safety performance evaluation method needs to be created.
Disclosure of Invention
The embodiment of the invention aims to provide an enterprise equipment safety performance evaluation method and system, which at least solve the problem that quantitative evaluation cannot be performed in the existing refining enterprise equipment safety evaluation method.
In order to achieve the above object, a first aspect of the present invention provides an enterprise equipment safety performance evaluation method, which is applied to equipment safety performance evaluation of a refining enterprise, the method comprising: collecting equipment operation data, and performing classification processing on the operation data to obtain an evaluation data set; under a preset evaluation index system, determining subjective weight and objective weight of each evaluation item; the preset evaluation index system is a multi-level evaluation system, each level comprises a plurality of evaluation items, and each evaluation item comprises a plurality of evaluation indexes; obtaining comprehensive weights of all evaluation items based on the subjective weights and the objective weights, and obtaining variable weights corresponding to all the evaluation items according to the comprehensive weights of all the evaluation items based on index properties of all the evaluation items; and evaluating the equipment safety performance based on the variable weight of each evaluation item and the evaluation data set.
Optionally, the subjective weight determining rule of each evaluation item is: setting initial weights for all evaluation items based on a subjective assignment method, and obtaining an initial index weight set; and in the same level, performing pairwise comparison of each evaluation item to obtain a comparison judgment matrix, wherein the comparison judgment matrix is expressed as:
Ai＝(aij)n×n
Wherein, Representing the relative weight of the ith evaluation index and the jth evaluation index; normalizing each evaluation index in the comparison judgment matrix, and carrying out consistency check on the comparison judgment matrix after normalization; if the check is not passed, reconstructing the comparison judgment matrix until a comparison judgment matrix with the consistency check is passed is obtained.
Optionally, the processing rule of normalizing each vector in the comparison and judgment matrix is as follows:
Wherein w i is the weight coefficient of the ith evaluation index; based on the characteristic that the comparison judgment matrix is positive, calculating and obtaining the weight coefficient of each evaluation index, so that the obtained weight coefficient meets the following rule:
Aw＝λmaxw
Wherein A is a comparison judgment matrix; lambda max is the maximum eigenvalue in the comparison judgment matrix.
Optionally, the performing consistency check on the comparison judgment matrix after normalization processing includes: and calculating and obtaining a consistency index based on the maximum eigenvalue in the comparison and judgment matrix, wherein the calculation rule is as follows:
Wherein CI is a consistency index; n is the order of the comparison judgment matrix; determining an average random consistency index of the comparison judgment matrix based on the order of the comparison judgment matrix; calculating to obtain a random consistency ratio based on the consistency index and the average random consistency index, wherein the calculation rule is as follows:
Wherein CR is a consistency ratio; RI is the average random consistency index; and comparing the random consistency ratio with a preset value of the random consistency ratio, and when the random consistency ratio is smaller than the preset value of the random consistency ratio, judging that the consistency check of the comparison judgment matrix after normalization processing is failed.
Optionally, the objective weight determining rule of each evaluation item is to calculate the characteristic specific gravity of each evaluation index under each evaluation item; calculating entropy values of all evaluation items based on the characteristic proportion, wherein the larger the difference between the operation data observation value and the predicted value of the corresponding evaluation item is, the smaller the entropy value is; calculating the difference coefficient of each evaluation item based on the entropy value, wherein the larger the difference between the operation data observation value and the predicted value of the corresponding evaluation item is, the smaller the difference coefficient is; and determining objective weights of all evaluation items based on the difference coefficients.
Optionally, the calculation rule of the characteristic specific gravity of each evaluation index is:
wherein, p ij is the characteristic proportion of the j-th evaluation index under the i-th evaluation item; b ij is the normalized number of the observed value of the operation data of the j-th evaluation index under the i-th evaluation item.
Optionally, the rule for calculating the entropy value of each evaluation item is:
Wherein e i is the entropy value of the i-th evaluation item; the calculation rule of the difference coefficient of each evaluation item is as follows:
gi＝1-ei(i＝1,2,…,n)
wherein g i is the coefficient of difference of the i-th evaluation item; the objective weight calculation rule of each evaluation item is as follows:
Wherein v i is the objective weight of the i-th evaluation item.
Optionally, the calculation rule for obtaining the comprehensive weight of each evaluation item based on the subjective weight and the objective weight is as follows:
Wi＝λwi+(1-λ)vi
Wherein W i is the comprehensive weight of the ith evaluation item; lambda is a preset constant.
Optionally, obtaining the variable weight corresponding to each evaluation item according to the comprehensive weight of each evaluation item includes: determining the index property of each evaluation item as a benefit type index or a cost type index; wherein, the larger the index value of the benefit index is, the larger the weight is; the smaller the index value of the cost index is, the larger the weight is; the calculation rule of the variable weight of the benefit index is as follows:
the calculation rule of the variable weight of the cost index is as follows:
When alpha is more than 0, the state variable weight vector is a punishment state variable weight vector, and at the moment, alpha represents punishment level, and the greater the alpha is, the more obvious the punishment effect is; when alpha is less than 0, the state variable weight vector is an excitation type state variable weight vector, at the moment, the excitation level is represented, and the larger the alpha is, the more obvious the excitation effect is; beta i is a preset empirical value.
Optionally, evaluating the device safety performance based on the variable weight of each evaluation item and the evaluation data set includes: performing uniform processing on the evaluation data set to obtain a uniform processed evaluation data set; calculating the evaluation score of each evaluation item step by step upwards based on the comprehensive weight of each evaluation item and the evaluation data set after the consistency processing and a preset evaluation index system until the evaluation score of the equipment safety performance evaluation item is obtained by calculation; and carrying out equipment safety performance evaluation based on the evaluation score of the equipment safety performance evaluation item.
Optionally, the performing a reconciliation process on the evaluation dataset includes: determining each operation data as a maximum index or a minimum index, and converting the minimum index into the maximum index, wherein the conversion rule is as follows:
xij'＝Mi-xij
Wherein, The index value x ij of the ith item in the m evaluation objects is the largest; performing dimensionality removal treatment on all operation data based on a proportional conversion method; for the extremely large index, the dimensionality removal processing rule is as follows:
For the very small index, the dimensionality removal processing rule is as follows:
and preprocessing the operation data subjected to dimensionalization processing by adopting a proportional conversion method to obtain an evaluation data set subjected to uniform processing.
Optionally, the step-by-step calculation of the evaluation score of each evaluation item based on the comprehensive weight of each evaluation item and the evaluation data set after the consistency processing based on the preset evaluation index system includes: the comprehensive score calculation rules of the rest evaluation items except the equipment safety performance evaluation items are as follows:
Wherein h j is the composite score of the j-th evaluation item; x ij * is observation data corresponding to the i-th evaluation index in the evaluation data set after the consistency processing under the j-th evaluation item; omega ij' is the variable weight vector of the ith evaluation index under the jth evaluation item, and the calculation rule is as follows:
wherein w i is the comprehensive weight of the ith evaluation index; s i (X) is the variable weight of the ith evaluation index; the calculation rule of the equipment safety performance evaluation item is as follows:
Omega j is the comprehensive weight of the j-th evaluation index of the equipment safety performance evaluation item; h j is the composite score of the j-th evaluation index of the equipment safety performance evaluation item.
A second aspect of the present invention provides an enterprise equipment safety performance evaluation system, applied to equipment safety performance evaluation of a refining enterprise, the system comprising: the collecting unit is used for collecting equipment operation data, and performing classification processing on the operation data to obtain an evaluation data set; a processing unit for: under a preset evaluation index system, determining subjective weight and objective weight of each evaluation item; the preset evaluation index system is a multi-level evaluation system, each level comprises a plurality of evaluation items, and each evaluation item comprises a plurality of evaluation indexes; obtaining comprehensive weights of all evaluation items based on the subjective weights and the objective weights, and obtaining variable weights corresponding to all the evaluation items according to the comprehensive weights of all the evaluation items based on index properties of all the evaluation items; and the evaluation unit is used for evaluating the equipment safety performance based on the variable weight of each evaluation item and the evaluation data set.
In another aspect, the present invention provides a computer readable storage medium having instructions stored thereon, which when run on a computer, cause the computer to perform the enterprise equipment safety performance assessment method described above.
According to the technical scheme, the method provided by the scheme is a method for evaluating the safety performance of equipment of a refining enterprise based on a variable weight theory and an AHP-entropy weight method, and the quantitative evaluation of the safety performance level of the equipment is realized by constructing a more scientific quantitative index system for representing the safety performance of the equipment, automatically calculating indexes from acquired fields in the existing system, establishing a multi-dimensional diagnosis model of the safety performance of the equipment.
Additional features and advantages of embodiments of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings are included to provide a further understanding of embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain, without limitation, the embodiments of the invention. In the drawings:
Fig. 1 is a flowchart of steps in an enterprise equipment safety performance evaluation method according to an embodiment of the present invention;
FIG. 2 is a flowchart of the steps for confirming the supervisor weights and objective weights of the evaluation items according to one embodiment of the present invention;
FIG. 3 is a schematic diagram of a preset evaluation index system according to an embodiment of the present invention;
Fig. 4 is a system configuration diagram of an enterprise equipment safety performance evaluation system according to an embodiment of the present invention.
Detailed Description
The following describes specific embodiments of the present invention in detail with reference to the drawings. It should be understood that the detailed description and specific examples, while indicating and illustrating the invention, are not intended to limit the invention.
Since the consequences of refinery accidents are extremely serious, it is an industry-accepted fact to guarantee equipment safety performance indicators for refining enterprises. At present, an HSE quantitative index system is established by international large petrochemical enterprises, and safety management execution effectiveness, management defects and safety production trend are dynamically displayed. Some enterprises have also introduced a corporate HSE performance index system that includes recordable events, process safety incidents, process and equipment management indices, and the like. The equipment integrity management is one of important elements of the process safety management, so the equipment safety is also one of important professional safety of process safety performance of refining enterprises. At present, the research on complex relations among equipment safety performance index systems and indexes is less. The method for determining the index system, the index relation weight and the index threshold has important significance for evaluating the safety performance of the smelting enterprise equipment.
In the face of a large number of refining enterprises, a large amount of manpower and material resources are input in the checking and auditing process every year to evaluate the equipment safety management level of the enterprises and discover management defects. If the equipment safety state of the enterprise can be judged in real time through a scientific index system, the problems of the enterprise can be dynamically found and early-warned in time, the frequency of checking and auditing can be reduced, and the monitoring effect and efficiency are improved. At present, some methods for monitoring the safety state of equipment in a refining enterprise exist, and the methods are mainly aimed at an equipment integrity management method or an equipment integrity performance index system construction method, but are still lacking at present for an equipment safety-related performance index system and how to quantify equipment safety performance evaluation results.
Aiming at the problem that the existing refining enterprise equipment safety evaluation method cannot perform quantitative evaluation, the invention provides a novel enterprise equipment safety performance evaluation method, and the method provided by the scheme of the invention is a method for refining enterprise equipment safety performance evaluation based on a variable weight theory and an AHP-entropy weight method.
Fig. 1 is a flowchart of a method for evaluating safety performance of an enterprise device according to an embodiment of the present invention. As shown in fig. 1, an embodiment of the present invention provides a method for evaluating safety performance of an enterprise device, where the method includes:
step S10: and collecting equipment operation data, and performing classification processing on the operation data to obtain an evaluation data set.
Specifically, the operating state of the device must be represented by operating data, and because of the reduced operating efficiency caused by the equipment failure, there must be a change in the data value in the final operating data. Based on the above, when the equipment safety performance evaluation is performed, information related to the equipment operation process and the equipment management process needs to be acquired. The equipment operation process data may show whether the equipment operation process has the problem of operation with diseases, and the equipment management information can see whether the equipment management process meets the requirement of safety management.
Based on this, in order to perform enterprise equipment safety performance evaluation, equipment operation data acquisition is required, for example, data is automatically acquired from equipment KPI systems of a refining enterprise according to the belonging hierarchy, year and month. Further, in order to evaluate the evaluation indexes in each category, the operation data needs to be classified so that each evaluation item corresponds to its own operation data. And then, preprocessing the data such as data cleaning, exception handling, mechanism docking and the like to obtain an evaluation data set.
Step S20: under a preset evaluation index system, determining subjective weight and objective weight of each evaluation item.
Specifically, in order to realize quantitative evaluation of equipment safety performance, it is necessary to ensure consistency of the whole evaluation system, i.e. design an evaluation index system capable of being widely applied, and then propose a corresponding quantitative evaluation method for the system, so that the whole scheme can be conveniently popularized, and all refining enterprises are served. Based on the above, the scheme of the invention needs to construct a corresponding equipment safety evaluation index system. Specifically, as shown in fig. 2:
Step S201: and constructing a preset evaluation index system.
As shown in fig. 3, the delta film is preferably used to score each key performance index of the equipment, and an equipment safety performance evaluation index system is determined and constructed, wherein the first-level index of the evaluation system comprises unplanned shutdown caused by the equipment, equipment reliability, dynamic equipment, static equipment, electric equipment, instrument equipment and the like.
Embodiment one:
And (3) carrying out 1-5 assignment on the importance of each index based on a historical data rule by adopting a 5-level scoring method, and assigning points according to the importance: 5=important, 4=important, 3=generally, 2=unimportant, 1=very unimportant. In a specific embodiment, the corresponding evaluation questionnaires are arranged based on the historical data, the evaluation questionnaires are pushed to expert equipment ends in the corresponding fields, expert input information is recovered, the positive coefficients of the experts are measured through the questionnaire recovery rate, the expert opinion concentration degree is generally represented by index importance scoring arithmetic mean and full score frequency, and the professional opinion coordination degree is generally represented by index importance scoring variation coefficient.
Preferably, the preset evaluation index system is a multi-level evaluation system, each level includes a plurality of evaluation items, and each evaluation item includes a plurality of evaluation indexes. That is, in the self-evaluation layer, it is evaluated as an evaluation item, but the evaluation item is regarded as an evaluation index of the previous stage.
Step S202: subjective weights of the evaluation items are determined.
Specifically, setting initial weights for all evaluation items based on a subjective assignment method, and obtaining an initial index weight set; and in the same level, performing pairwise comparison of each evaluation item to obtain a comparison judgment matrix, wherein the comparison judgment matrix is expressed as:
Ai＝(aij)n×n
Wherein, Representing the relative weight of the ith evaluation index and the jth evaluation index; normalizing each evaluation index in the comparison judgment matrix, and carrying out consistency check on the comparison judgment matrix after normalization; if the check is not passed, reconstructing the judgment matrix until a judgment matrix that the consistency check passes is obtained.
The processing rules for carrying out normalization processing on each vector in the comparison and judgment matrix are as follows:
Wherein w i is the weight coefficient of the ith evaluation index; based on the characteristic that the comparison judgment matrix is positive, calculating and obtaining the weight coefficient of each evaluation index, so that the obtained weight coefficient meets the following rule:
Aw＝λmaxw
Wherein A is a comparison judgment matrix; lambda max is the maximum eigenvalue in the comparison judgment matrix.
In one possible implementation, a quantization criterion for the indicator is determined. Judging the relative importance degree of different indexes by adopting a traditional 1-9 scale method, and forming a judgment matrix through expert assignment quantization. Setting the relative importance ratio as a ij, representing the importance ratio of the index A i to the index A j, obtaining a consistency judgment matrix of A= (a ij)nxn, using the matrix to obtain weights corresponding to different indexes of corresponding levels, secondly, determining initial weights, wherein the determination of the weights usually adopts a method of combining qualitative analysis and quantitative analysis, then, processing the initial weights, establishing a judgment matrix A, carrying out pairwise comparison on evaluation indexes by an expert, wherein the initial weights form a judgment matrix A, and elements X ij of an ith row and a jth column in the judgment matrix A represent scale coefficients obtained by comparing the index X i with the index X j. Because A is a positive matrix, the positive and negative matrix must have a maximum eigenvalue lambda max, and the eigenvector X corresponding to lambda max is a positive vector, namely:
AX＝λmaxX
Using the formula And (5) carrying out normalization processing. Finally, the consistency of the judgment matrix is checked. In the analytic hierarchy process, the ratio of the consistency index of the judgment matrix to the same-order average random consistency index is the random consistency ratio of the judgment matrix, and when the random consistency ratio is lower than 0.1, the judgment matrix is generally considered to have satisfactory consistency, otherwise, the judgment matrix needs to be reconstructed.
The method comprises the steps of obtaining a consistency index based on the calculation of the maximum eigenvalue in a comparison judgment matrix, wherein the calculation rule is as follows:
Wherein CI is a consistency index; n is the order of the comparison judgment matrix; determining an average random consistency index of the comparison judgment matrix based on the order of the comparison judgment matrix; calculating to obtain a random consistency ratio based on the consistency index and the average random consistency index, wherein the calculation rule is as follows:
Wherein CR is a consistency ratio; RI is the average random consistency index;
and comparing the random consistency ratio with a preset value of the random consistency ratio, and when the random consistency ratio is smaller than the random consistency ratio, judging that the consistency check of the comparison judgment matrix after normalization processing is not passed.
Step S203: and determining objective weights of all the evaluation items.
Specifically, calculating the characteristic specific gravity of each evaluation index under each evaluation item; calculating entropy values of all evaluation items based on the characteristic proportion, wherein the larger the difference between the operation data observation value and the predicted value of the corresponding evaluation item is, the smaller the entropy value is; calculating the difference coefficient of each evaluation item based on the entropy value, wherein the larger the difference between the operation data observed value and the predicted value of the corresponding evaluation item is, the smaller the difference coefficient is, and the more important the i-th index is; and determining objective weights corresponding to the evaluation items based on the difference coefficients.
Further, the calculation rule of the characteristic specific gravity of each evaluation index is as follows:
wherein, p ij is the characteristic proportion of the j-th evaluation index under the i-th evaluation item; b ij is the normalized number of the observed value of the operation data of the j-th evaluation index under the i-th evaluation item.
Further, the rule for calculating the entropy value of each evaluation item is as follows:
Wherein e i is the entropy value of the i-th evaluation item; the calculation rule of the difference coefficient of each evaluation item is as follows:
gi＝1-ei(i＝1,2,…,n)
wherein g i is the coefficient of difference of the i-th evaluation item; the objective weight calculation rule of each evaluation item is as follows:
Wherein v i is the objective weight of the i-th evaluation item.
Further, the calculation rule for obtaining the comprehensive weight of each evaluation item based on the subjective weight and the objective weight is as follows:
Wi＝λwi+(1-λ)vi
Wherein W i is the comprehensive weight of the ith evaluation item; lambda is a preset constant.
Step S30: and obtaining the comprehensive weight of each evaluation item based on the subjective weight and the objective weight, and obtaining the variable weight corresponding to each evaluation item according to the comprehensive weight of each evaluation item based on the index property of each evaluation item.
Specifically, a weight calculation method for combining weighting by an AHP-entropy weighting method is provided aiming at the advantages and disadvantages of an analytic hierarchy process and an entropy weighting method. Meanwhile, the advantages of the two are taken to make up the respective defects, subjective factors and objective factors are considered, and the defects of a single assignment method are avoided. The calculation rule for obtaining the comprehensive weight of each evaluation item based on the subjective weight and the objective weight is as follows:
Wi＝λwi+(1-λ)vi
Wherein W i is the comprehensive weight of the ith evaluation item; lambda is a preset constant.
Further, although the subjective and objective combination weighting method avoids the disadvantage of single weighting to some extent, in practice, the importance level between indexes is not fixed, but will change with the change of the state value of the indexes. In the performance evaluation index system, the risk loss degree of some indexes is extremely high, and if the normal weight is used for comprehensive evaluation, the final comprehensive evaluation result may show that the risk level is normal and cannot objectively reflect the risk level although the weight of the indexes is high. For example, the larger the value of the failure rate index of the large unit is, the higher the corresponding risk degree is, and after the failure rate of the large unit reaches a certain value, the weight is increased, so that the influence of the index on the performance evaluation result is enhanced. Therefore, considering the disadvantage of the constant weight coefficient, it is considered to correct Chang Quan by using the local weighting principle of combining excitation and penalty, i.e. if a certain index weight increases with the increase of the state value, giving the index an appropriate excitation, and if a certain index weight decreases with the increase of the state value, giving the index an appropriate penalty. And a variable weight theory is introduced to adjust Chang Quanchong coefficients in real time, so that the judgment result is more real and accurate. Because the exponential function has the property that the change amount of the independent variable is smaller when the change range of the independent variable is smaller, and conversely, the change weight of the index can be better reflected by adopting the exponential function to determine the excitation-penalty function. Setting a state variable weight vector of the index:
S(X)＝[s1(X),s2(X),…,sn(X)]
Further, the obtaining the variable weight of each evaluation item according to the comprehensive weight of each evaluation item includes: determining the index property of each evaluation item as a benefit type index or a cost type index; wherein, the larger the index value of the benefit index is, the larger the weight is; the smaller the index value of the cost index is, the larger the weight is; the calculation rule of the variable weight of the benefit index is as follows:
the calculation rule of the variable weight of the cost index is as follows:
When alpha is more than 0, the state variable weight vector is a punishment state variable weight vector, and at the moment, alpha represents punishment level, and the greater the alpha is, the more obvious the punishment effect is; when alpha is less than 0, the state variable weight vector is an excitation type state variable weight vector, at the moment, the excitation level is represented, and the larger the alpha is, the more obvious the excitation effect is; beta i is a preset empirical value. Beta i can also be a target value for the index specified by the national relevant standard, such as preset and ruled lines.
Step S40: and evaluating the equipment safety performance based on the variable weight of each evaluation item and the evaluation data set.
Specifically, performing a unification process on the evaluation data set to obtain a uniformed evaluation data set; calculating the evaluation score of each evaluation item step by step upwards based on the comprehensive weight of each evaluation item and the evaluation data set after the consistency processing and a preset evaluation index system until the evaluation score of the equipment safety performance evaluation item is obtained by calculation; and carrying out equipment safety performance evaluation based on the evaluation score of the equipment safety performance evaluation item.
Further, the performing a reconciliation process on the evaluation dataset includes: determining each operation data as a maximum index or a minimum index, and converting the minimum index into the maximum index, wherein the conversion rule is as follows:
xij'＝Mi-xij
Wherein, The index value x ij of the ith item in the m evaluation objects is the largest; performing dimensionality removal treatment on all operation data based on a proportional conversion method; for the extremely large index, the dimensionality removal processing rule is as follows:
For the very small index, the dimensionality removal processing rule is as follows:
Or (b)
And preprocessing the operation data subjected to dimensionalization processing by adopting a proportional conversion method to obtain an evaluation data set subjected to uniform processing.
Further, the step-by-step calculation of the evaluation score of each evaluation item based on the comprehensive weight of each evaluation item and the evaluation data set after the unification processing based on the preset evaluation index system comprises the following steps:
The comprehensive score calculation rules of the rest evaluation items except the equipment safety performance evaluation items are as follows:
Wherein h j is the composite score of the j-th evaluation item; x ij * is observation data corresponding to the i-th evaluation index in the evaluation data set after the consistency processing under j evaluation items; omega ij' is the variable weight vector of the ith evaluation index under the jth evaluation item, and the calculation rule is as follows:
Wherein w i is the comprehensive weight of the ith evaluation index; s i (X) is the variable weight of the ith evaluation index; for the data obtained by mapping, the mapping condition can be changed according to the actual situation so as to achieve the purpose of increasing or decreasing the data value, thereby controlling the comprehensive score.
The calculation rule of the equipment safety performance evaluation item is as follows:
Omega j is the comprehensive weight of the j-th evaluation index of the equipment safety performance evaluation item; h j is the composite score of the j-th evaluation index of the equipment safety performance evaluation item.
In the embodiment of the invention, the scheme is based on equipment security profession, hierarchical classification is carried out on the equipment security profession, and meanwhile, the specific performance indexes under each level of hierarchy are defined. The scheme of the invention is based on analytic hierarchy process and entropy weight method analysis, realizes the combination of expert experience and objective analysis, simultaneously realizes the quantification of comprehensive evaluation value of equipment safety performance, and is beneficial to enterprises to further improve the equipment safety management level. In the scheme of the invention, in an index system for evaluating the safety performance of equipment, the importance degree among indexes is not fixed, and the importance degree can be changed along with the change of the state value of the indexes, so that the invention introduces a weight-changing theory.
Fig. 4 is a system configuration diagram of an enterprise equipment safety performance evaluation system according to an embodiment of the present invention. As shown in fig. 4, an embodiment of the present invention provides an enterprise equipment safety performance evaluation system, which includes: the collecting unit is used for collecting equipment operation data, and performing classification processing on the operation data to obtain an evaluation data set; a processing unit for: under a preset evaluation index system, determining subjective weight and objective weight of each evaluation item; the preset evaluation index system is a multi-level evaluation system, each level comprises a plurality of evaluation items, and each evaluation item comprises a plurality of evaluation indexes; obtaining comprehensive weights of all evaluation items based on the subjective weights and the objective weights, and obtaining variable weights corresponding to all the evaluation items according to the comprehensive weights of all the evaluation items based on index properties of all the evaluation items; and the evaluation unit is used for evaluating the equipment safety performance based on the variable weight of each evaluation item and the evaluation data set.
The embodiment of the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores instructions, and when the computer is run on the computer, the computer is caused to execute the enterprise equipment safety performance evaluation method.
Those skilled in the art will appreciate that all or part of the steps in a method for implementing the above embodiments may be implemented by a program stored in a storage medium, where the program includes several instructions for causing a single-chip microcomputer, chip or processor (processor) to perform all or part of the steps in a method according to the embodiments of the invention. And the aforementioned storage medium includes: a usb 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.
The alternative embodiments of the present invention have been described in detail above with reference to the accompanying drawings, but the embodiments of the present invention are not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solutions of the embodiments of the present invention within the scope of the technical concept of the embodiments of the present invention, and all the simple modifications belong to the protection scope of the embodiments of the present invention. In addition, the specific features described in the above embodiments may be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the various possible combinations of embodiments of the invention are not described in detail.
In addition, any combination of the various embodiments of the present invention may be made, so long as it does not deviate from the idea of the embodiments of the present invention, and it should also be regarded as what is disclosed in the embodiments of the present invention.
Claims (14)
1. An enterprise equipment safety performance evaluation method applied to equipment safety performance evaluation of a refining enterprise is characterized by comprising the following steps:
collecting equipment operation data, and performing classification processing on the operation data to obtain an evaluation data set;
under a preset evaluation index system, determining subjective weight and objective weight of each evaluation item; wherein,
The preset evaluation index system is a multi-level evaluation system, each level comprises a plurality of evaluation items, and each evaluation item comprises a plurality of evaluation indexes;
Obtaining comprehensive weights of all evaluation items based on the subjective weights and the objective weights, and obtaining variable weights corresponding to all the evaluation items according to the comprehensive weights of all the evaluation items based on index properties of all the evaluation items;
And evaluating the equipment safety performance based on the variable weight of each evaluation item and the evaluation data set.
2. The method of claim 1, wherein the subjective weight determination rule for each evaluation item is:
Setting initial weights for all evaluation items based on a subjective assignment method, and obtaining an initial index weight set;
and in the same level, performing pairwise comparison of each evaluation item to obtain a comparison judgment matrix, wherein the comparison judgment matrix is expressed as:
Ai＝(aij)n×n
Wherein, Representing the relative weight of the ith evaluation index and the jth evaluation index;
Normalizing each evaluation index in the comparison judgment matrix, and carrying out consistency check on the comparison judgment matrix after normalization;
If the check is not passed, reconstructing the comparison judgment matrix until a comparison judgment matrix with the consistency check is passed is obtained.
3. The method according to claim 2, wherein the processing rule for normalizing each vector in the comparison and judgment matrix is:
Wherein w i is the weight coefficient of the ith evaluation index;
based on the characteristic that the comparison judgment matrix is positive, calculating and obtaining the weight coefficient of each evaluation index, so that the obtained weight coefficient meets the following rule:
Aw＝λmaxw
Wherein A is a comparison judgment matrix;
Lambda max is the maximum eigenvalue in the comparison judgment matrix.
4. The method according to claim 2, wherein the performing consistency check on the normalized comparison judgment matrix includes:
and calculating and obtaining a consistency index based on the maximum eigenvalue in the comparison and judgment matrix, wherein the calculation rule is as follows:
Wherein CI is a consistency index;
n is the order of the comparison judgment matrix;
determining an average random consistency index of the comparison judgment matrix based on the order of the comparison judgment matrix;
Calculating to obtain a random consistency ratio based on the consistency index and the average random consistency index, wherein the calculation rule is as follows:
Wherein CR is a consistency ratio;
RI is the average random consistency index;
And comparing the random consistency ratio with a preset value of the random consistency ratio, and when the random consistency ratio is smaller than the preset value of the random consistency ratio, judging that the consistency check of the comparison judgment matrix after normalization processing is failed.
5. The method of claim 1, wherein the objective weight determination rule for each evaluation item is:
calculating the characteristic specific gravity of each evaluation index under each evaluation item;
calculating entropy values of all evaluation items based on the characteristic proportion, wherein the larger the difference between the operation data observation value and the predicted value of the corresponding evaluation item is, the smaller the entropy value is;
calculating the difference coefficient of each evaluation item based on the entropy value, wherein the larger the difference between the operation data observation value and the predicted value of the corresponding evaluation item is, the smaller the difference coefficient is;
And determining objective weights of all evaluation items based on the difference coefficients.
6. The method according to claim 5, wherein the calculation rule of the characteristic specific gravity of each evaluation index is:
Wherein, p ij is the characteristic proportion of the j-th evaluation index under the i-th evaluation item;
b ij is the normalized number of the observed value of the operation data of the j-th evaluation index under the i-th evaluation item.
7. The method of claim 5, wherein the entropy calculation rule of each evaluation item is:
wherein e i is the entropy value of the i-th evaluation item;
the calculation rule of the difference coefficient of each evaluation item is as follows:
gi＝1-ei(i＝1,2,…,n)
Wherein g i is the coefficient of difference of the i-th evaluation item;
The objective weight calculation rule of each evaluation item is as follows:
Wherein v i is the objective weight of the i-th evaluation item.
8. The method according to claim 1, wherein the calculation rule for obtaining the comprehensive weight of each evaluation item based on the subjective weight and the objective weight is:
Wi＝λwi+(1-λ)vi
Wherein W i is the comprehensive weight of the ith evaluation item;
Lambda is a preset constant.
9. The method of claim 1, wherein obtaining the variable weight for each evaluation item based on the integrated weight for each evaluation item comprises:
determining the index property of each evaluation item as a benefit type index or a cost type index; wherein,
The larger the index value of the benefit index is, the larger the weight is;
the smaller the index value of the cost index is, the larger the weight is;
the calculation rule of the variable weight of the benefit index is as follows:
the calculation rule of the variable weight of the cost index is as follows:
when alpha is more than 0, the state variable weight vector is a punishment state variable weight vector, and at the moment, alpha represents punishment level, and the greater the alpha is, the more obvious the punishment effect is;
When alpha is less than 0, the state variable weight vector is an excitation type state variable weight vector, at the moment, the excitation level is represented, and the larger the alpha is, the more obvious the excitation effect is;
beta i is a preset empirical value.
10. The method of claim 1, wherein evaluating device safety performance based on the varying weights of the evaluation items and the evaluation dataset comprises:
Performing uniform processing on the evaluation data set to obtain a uniform processed evaluation data set;
Calculating the evaluation score of each evaluation item step by step upwards based on the comprehensive weight of each evaluation item and the evaluation data set after the consistency processing and a preset evaluation index system until the evaluation score of the equipment safety performance evaluation item is obtained by calculation;
and carrying out equipment safety performance evaluation based on the evaluation score of the equipment safety performance evaluation item.
11. The method of claim 10, wherein said performing a reconciliation process on said evaluation dataset comprises:
Determining each operation data as a maximum index or a minimum index, and converting the minimum index into the maximum index, wherein the conversion rule is as follows:
xij'＝Mi-xij
Wherein, The index value x ij of the ith item in the m evaluation objects is the largest;
performing dimensionality removal treatment on all operation data based on a proportional conversion method; wherein,
For the very large index, the dimensionality removal processing rule is as follows:
For the very small index, the dimensionality removal processing rule is as follows:
Or (b)
And preprocessing the operation data subjected to dimensionalization processing by adopting a proportional conversion method to obtain an evaluation data set subjected to uniform processing.
12. The method according to claim 10, wherein the step-by-step calculation of the evaluation score of each evaluation item based on the preset evaluation index system based on the integrated weight of each evaluation item and the uniformly processed evaluation data set comprises:
The comprehensive score calculation rules of the rest evaluation items except the equipment safety performance evaluation items are as follows:
Wherein h j is the composite score of the j-th evaluation item;
x ij * is observation data corresponding to the i-th evaluation index in the evaluation data set after the consistency processing under the j-th evaluation item;
Omega ij' is the variable weight vector of the ith evaluation index under the jth evaluation item, and the calculation rule is as follows:
wherein w i is the comprehensive weight of the ith evaluation index;
s i (X) is the variable weight of the ith evaluation index;
The calculation rule of the equipment safety performance evaluation item is as follows:
Omega j is the comprehensive weight of the j-th evaluation index of the equipment safety performance evaluation item;
h j is the composite score of the j-th evaluation index of the equipment safety performance evaluation item.
13. An enterprise equipment safety performance evaluation system applied to equipment safety performance evaluation of a refining enterprise, the system comprising:
The collecting unit is used for collecting equipment operation data, and performing classification processing on the operation data to obtain an evaluation data set;
a processing unit for:
under a preset evaluation index system, determining subjective weight and objective weight of each evaluation item; wherein,
The preset evaluation index system is a multi-level evaluation system, each level comprises a plurality of evaluation items, and each evaluation item comprises a plurality of evaluation indexes;
Obtaining comprehensive weights of all evaluation items based on the subjective weights and the objective weights, and obtaining variable weights corresponding to all the evaluation items according to the comprehensive weights of all the evaluation items based on index properties of all the evaluation items;
And the evaluation unit is used for evaluating the equipment safety performance based on the variable weight of each evaluation item and the evaluation data set.
14. A computer readable storage medium having instructions stored thereon, which when run on a computer causes the computer to perform the enterprise equipment safety performance assessment method of any one of claims 1-12.
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