CN113379238A - Risk assessment method and device and electronic equipment - Google Patents
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Abstract
The invention relates to a risk assessment method, a risk assessment device and electronic equipment, wherein the method comprises the following steps: acquiring data of an object to be evaluated, a plurality of evaluation factors and a plurality of evaluation indexes; respectively carrying out weight calculation on each evaluation factor and each evaluation index, and determining the weight of each evaluation factor and each evaluation index; constructing a plurality of comprehensive measurement functions of multiple evaluation indexes according to the data of the object to be evaluated, the weight of each evaluation factor and the weight of each evaluation index; determining corresponding confidence degrees based on the comprehensive measurement vectors so as to determine corresponding risk evaluation levels of the object to be evaluated; and comparing the risk evaluation levels corresponding to the comprehensive measurement functions of the object to be evaluated so as to perform key monitoring on the object to be evaluated with different risk evaluation levels in the comparison result. By establishing a comprehensive measurement function with multiple evaluation indexes and comprehensively considering evaluation levels under different functions, the accuracy of risk evaluation is further ensured, and objects with different results are subjected to key monitoring.
Description
Technical Field
The invention relates to the technical field of safety production, in particular to a risk assessment method and device and electronic equipment.
Background
In some important infrastructures and predictive protection of emergency environments, risk assessment is often involved to ensure smooth operation of equipment and prevent accidents. Specifically, the natural gas pipeline construction develops rapidly, but with the accelerated construction of gas pipe networks in cities at all levels and the continuous aging of partial pipelines, the safety problem in the gas pipeline system is increasingly prominent. Risk factors threatening the safety of the urban gas pipeline are numerous, the surrounding environment of the pipeline is complex and various, and if the stable and reliable operation of the pipeline is ensured and accidents are prevented, risk evaluation research must be carried out on the urban gas pipeline. However, in the process of risk assessment of the gas pipeline, there are many factors influencing safety risk, and there are many complexities and uncertainties, and the existing assessment method cannot be used for processing the uncertainties of the influencing factors.
Disclosure of Invention
In view of this, embodiments of the present invention provide a risk assessment method, an apparatus, and an electronic device, so as to solve the problem that the existing risk assessment cannot handle the uncertainty of the influencing factors.
According to a first aspect, an embodiment of the present invention provides a risk assessment method, including:
acquiring data of an object to be evaluated, a plurality of evaluation factors and a plurality of evaluation indexes;
respectively carrying out weight calculation on each evaluation factor and each evaluation index, and determining the weight of each evaluation factor and each evaluation index;
according to the data of the object to be evaluated, the weight of each evaluation factor and the weight of each evaluation index, constructing a plurality of comprehensive measurement functions of multiple evaluation indexes so as to respectively determine comprehensive measurement vectors of the object to be evaluated corresponding to different comprehensive measurement functions;
determining corresponding confidence degrees based on the comprehensive measurement vectors so as to determine corresponding risk evaluation levels of the object to be evaluated;
and comparing the risk evaluation levels corresponding to the comprehensive measurement functions of the objects to be evaluated, and determining a comparison result so as to perform key monitoring on the objects to be evaluated with different risk evaluation levels in the comparison result.
According to the risk assessment method provided by the embodiment of the invention, aiming at the uncertainty of multiple influence factors in the risk assessment process, the comprehensive measure functions of multiple assessment indexes are established, the risk assessment grades corresponding to the comprehensive measure functions of different objects to be assessed are compared, so that the risk assessment result is determined, the assessment grades under different functions are comprehensively considered, the accuracy of the risk assessment is further ensured, and the objects with different results are subjected to key monitoring and recalculation.
With reference to the first aspect, in a first embodiment of the first aspect, the performing weight calculation on each evaluation factor and each evaluation index, and determining the weight of each evaluation factor and each evaluation index includes:
establishing a hierarchical structure model of the object to be evaluated, wherein the hierarchical structure model comprises various evaluation factors and evaluation indexes and is used for representing the risk evaluation level of the object to be evaluated;
constructing a judgment matrix by using the hierarchical structure model;
and respectively calculating the corresponding evaluation factors and the weights of the evaluation indexes on the basis of the judgment matrix.
With reference to the first aspect or the first implementation manner of the first aspect, in a second implementation manner of the first aspect, after the constructing a determination matrix by using the hierarchical structure model, the method further includes:
acquiring a corresponding relation between the matrix order and the consistency correction index;
determining a consistency correction index of the judgment matrix according to the order of the judgment matrix and the corresponding relation;
calculating a consistency index of the judgment matrix, correcting the consistency index by using a consistency correction index of the judgment matrix, and determining a consistency ratio;
and when the consistency ratio does not meet the preset requirement, reconstructing the judgment matrix until the consistency ratio meets the preset requirement.
With reference to the first aspect, in a third implementation manner of the first aspect, the constructing multiple comprehensive measurement functions of multiple evaluation indexes according to the data of the object to be evaluated, the weight of each evaluation factor, and the weight of each evaluation index to determine a comprehensive measurement vector of the object to be evaluated corresponding to different comprehensive measurement functions, respectively includes:
determining a corresponding measure function type according to the data of the object to be evaluated;
constructing different single index measurement functions of each evaluation index based on the measurement function types to determine index measurement vectors corresponding to each evaluation index under different single index measurement functions;
constructing a comprehensive measure function of a plurality of multiple evaluation indexes corresponding to the single index measure function by using the index measure vector and the weight of each evaluation index;
and respectively calculating comprehensive measurement vectors of the object to be evaluated corresponding to different comprehensive measurement functions according to the comprehensive measurement functions of the multiple evaluation indexes and the weight of each evaluation factor.
With reference to the third implementation manner of the first aspect, in the fourth implementation manner of the first aspect, the constructing a comprehensive measure function of multiple evaluation indexes corresponding to the single index measure function by using the index measure vector and the weights of the evaluation indexes includes:
calculating single-factor measure vectors corresponding to the evaluation factors according to the index measure vectors and the weights of the evaluation indexes;
and constructing a plurality of comprehensive measurement functions of multiple evaluation indexes corresponding to the single index measurement function by using the single-factor measurement vectors.
With reference to the first implementation manner of the first aspect, in a fifth implementation manner of the first aspect, the determining a corresponding confidence based on each of the comprehensive measurement vectors to determine a corresponding risk evaluation level of the object to be evaluated includes:
obtaining uncertainty measure of each classification layer in the hierarchical structure model;
and determining the corresponding risk evaluation level of the object to be evaluated according to the comprehensive measurement vector and each uncertainty measurement.
With reference to the fifth implementation manner of the first aspect, in the sixth implementation manner of the first aspect, the determining, according to the comprehensive measurement vector and each uncertainty measurement, a risk evaluation level corresponding to the object to be evaluated includes:
calculating Euclidean distances between the comprehensive measure vector and each uncertainty measure;
and determining the level corresponding to the minimum distance value in the Euclidean distances as the risk evaluation level of the object to be evaluated.
According to a second aspect, an embodiment of the present invention further provides a risk assessment apparatus, including:
the first processing module is used for acquiring data of an object to be evaluated, a plurality of evaluation factors and a plurality of evaluation indexes;
the second processing module is used for respectively carrying out weight calculation on each evaluation factor and each evaluation index and determining the weight of each evaluation factor and each evaluation index;
the third processing module is used for constructing a plurality of comprehensive measurement functions of multiple evaluation indexes according to the data of the object to be evaluated, the weight of each evaluation factor and the weight of each evaluation index so as to respectively determine comprehensive measurement vectors of the object to be evaluated corresponding to different comprehensive measurement functions;
the fourth processing module is used for determining corresponding confidence degrees based on the comprehensive measurement vectors so as to determine the corresponding risk evaluation level of the object to be evaluated;
and the fifth processing module is used for comparing the risk evaluation grades corresponding to the comprehensive measurement functions of the objects to be evaluated and determining a comparison result so as to perform key monitoring on the objects to be evaluated with different risk evaluation grades in the comparison result.
According to a third aspect, an embodiment of the present invention provides an electronic device, including: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory having stored therein computer instructions, and the processor executing the computer instructions to perform the risk assessment method according to the first aspect or any one of the embodiments of the first aspect.
According to a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to perform the risk assessment method according to the first aspect or any one of the implementation manners of the first aspect.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow diagram of a risk assessment method according to an embodiment of the present invention;
FIG. 2 is a flow diagram of a risk assessment method according to an embodiment of the present invention;
FIG. 3 is a flow diagram of a risk assessment method according to an embodiment of the present invention;
FIG. 4 is a flow diagram of a risk assessment method according to an embodiment of the present invention;
FIG. 5 is a diagram of a quantitative indicator-pipeline burial depth measure function according to an embodiment of the invention;
FIG. 6 is a schematic diagram of a qualitative indicator-pipeline line identification metric function according to an embodiment of the present invention;
FIG. 7 is a block diagram of a risk assessment device according to an embodiment of the present invention;
fig. 8 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the electronic device in the embodiment of the present invention may be an intelligent interactive device, for example, a mobile phone, a tablet, and the like, and is not limited herein.
In this embodiment, a natural gas pipeline is taken as an example for explanation, and many risk assessment methods are used in the existing security risk assessment, and some achievements are obtained. Common safety risk assessment methods include an expert scoring method, a Delphi method, an analytic hierarchy process, a fuzzy comprehensive assessment method, a fault tree analysis method, a gray comprehensive assessment method, a support vector machine method, a TOPS IS method, an artificial neural network method and a material element expansion method. The risk evaluation process of the gas pipeline has more factors influencing safety risk, and has a plurality of complexities and uncertainties, however, the evaluation methods cannot be used for processing the uncertainties. Therefore, to solve the above technical problem, the risk assessment method of the present embodiment is proposed.
In accordance with an embodiment of the present invention, there is provided a risk assessment method embodiment, it being noted that the steps illustrated in the flowchart of the figure may be performed in a computer system, such as a set of computer-executable instructions, and that while a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than here.
In this embodiment, a risk assessment method is provided, which can be used in the electronic device described above, and fig. 1 is a flowchart of a risk assessment method according to an embodiment of the present invention, as shown in fig. 1, the flowchart includes the following steps:
and S11, acquiring the data of the object to be evaluated, a plurality of evaluation factors and a plurality of evaluation indexes.
Wherein, let a1，a2，…，anIs n factors located within the evaluation space set a, the evaluation space can be written as: a ═ a1，a2，…，an}. For any evaluation factor ai(i-1, 2, …, n), if m indices evaluate it, the evaluation factor aiThe evaluation index space B of (a) is represented by B1，b2，…，bnSet of m indices, i.e. B ═ B1，b2，…，bn}. If with aijIndicates the evaluation factor aiAbout the index bj(j ═ 1,2, …, m), then aiCan be expressed as an m-dimensional vector: a isi＝{ai1，ai2，…，aim}。
Is provided with C1,C2,…,CpIs aiAny sub-item a ofijP evaluation levels, the evaluation space C can be written as: c ═ C1,C2,…,Cp). For the k-th evaluation level CkIf it is higher than Ck+1Then, it is recorded as Ck>Ck+1Otherwise, it is recorded as Ck<Ck+1. If satisfy C1>C2>…>CpOr (C)1<C2<…<Cp) Then called { C1，C2，…，CpIs the ordered partition class of the evaluation space C.
For example, the actual conditions, operation risk properties and other factors of the urban gas pipeline are comprehensively considered, and the risk condition is classified into four grades: grade I, low risk; class ii, general risk; class iii, greater risk; grade iv, significant risk. Ordered evaluation space C ═ C, which together form an uncertain measure function1,C2,C3,C4}。
The risk evaluation system of the gas pipeline is an extremely complex system, reasonable and accurate evaluation is realized, and a complete and scientific evaluation index system must be established. If the evaluation index is too large, the complexity of the evaluation process is increased, and if the evaluation index is too small, the objective condition of the gas pipeline cannot be comprehensively reflected. In actual engineering, factors influencing the risk of a gas pipeline are many, and each factor is difficult to take into account. As shown in Table 1, the comprehensive evaluation index system for the risk assessment of the gas pipeline consists of 3 layers of index systems, and comprises 5 first-level evaluation factors and 34 second-level evaluation indexes.
The evaluation indexes can be divided into two types according to different properties: quantitative and qualitative indicators. The qualitative index cannot be directly used for pipeline evaluation, and needs to be quantized first. There are many ways to quantify the qualitative index, which mainly include: a grading standard quantification method, an expert research method, a fuzzy processing method and the like. The hierarchical standard quantization method has the characteristics of simplicity, convenience and effectiveness, and is widely applied, so that the hierarchical standard quantization method is adopted to convert qualitative indexes into quantitative indexes.
TABLE 1 Risk evaluation index grading Table
The data, the evaluation factors and the evaluation indexes of the object to be evaluated may be acquired by the electronic device from an external acquisition device, or acquired by integrating the acquisition device into the electronic device, and the manner of acquiring the personnel information by the electronic device is not limited, and only the electronic device is required to acquire the personnel information, which is not limited in this embodiment.
S12, a weight calculation is performed for each evaluation factor and each evaluation index, and the weight of each evaluation factor and each evaluation index is determined.
The weight is a sign for describing the degree of importance of the evaluation index in the index system. The method for calculating the weight mainly comprises the following steps: empirical weighting, analytic hierarchy process, principal component analysis, entropy weight process, etc. The qualitative evaluation indexes are more according to the evaluation index grades divided in the foregoing, and the analytic hierarchy process can quantitatively reduce the influence of subjective factors of people on the qualitative indexes, so that the evaluation results are more scientific and reasonable and accord with objective practice. The manner of calculation of the weights in particular will be described below.
S13, according to the data of the object to be evaluated, the weight of each evaluation factor and the weight of each evaluation index, a plurality of comprehensive measurement functions of multiple evaluation indexes are constructed, so that comprehensive measurement vectors of the object to be evaluated corresponding to different comprehensive measurement functions are respectively determined.
Specifically, the value a is setijBelonging to the k-th evaluation grade CkDegree of (d), denoted as ωijk＝ω(aij∈Ck) And ω should satisfy the following condition: (1)0 is not more than omega (a)ij∈Ck) 1 or less, representing the interval [0, 1%]A certain number above describes the value aijBelonging to the evaluation class CkThe degree of (d); (2) omega (a)ij∈Ck) 1, ω satisfies the "normalization" condition with respect to the evaluation space C; (3)
indicating that ω satisfies the "additivity" condition with respect to the evaluation space C. Where i is 1,2, …, n, j is 1,2, …, m, k is 1,2, …, p. ω satisfying the above three conditions at the same time is called as an undetermined measure, which is referred to as measure for short.
The method is characterized in that a scientific and reasonable uncertain measure function is constructed, and is the key for describing the uncertain state of things by applying an uncertain theory. In the process of solving the actual problem by using the uncertain measure theory, an evaluator constructs an uncertain measure function according to the grasped related information of the evaluation object, the actual measurement value and personal experience.
Assume that the attribute value of an evaluation object at the initial stage is diThen the attribute is in the i state. At attribute value from diIs changed into di+1The state of the attribute of the evaluation object is changed along with the change of the state of the evaluation object, the state i is in a weakening trend, and the state i +1 is in an enhancement trend. When the attribute value of the evaluation object becomes di+1When the i state of the attribute to be evaluated disappears completely to become 0, the i +1 state of the attribute increases to 1. The form of the uncertain measure reflects the change condition of the attribute state of the evaluation object, and an evaluator constructs an uncertain measure function corresponding to the state change intensity of the evaluation object according to the state change intensity of the evaluation object. There are four common cases of uncertain measure function distributions: linear, parabolic, exponential and sinusoidal distributions, the specific graphical and functional expressions are shown in table 2.
As can be seen from Table 2, the function expression ωi(x) At diIs 0, at di+1,di+2]Image and function ω ofi+1(x) In (d)i,di+1]The images are consistent; function omegai+1(x) At di+1Has a left interval value of 0, in the interval [ d ]i-1,di]Function image of (3) and function ωi(x) In (d)i,di+1]The images above are identical. In any non-zero interval, the measurement distribution functions are simultaneously presented pairwise, and the conditions of nonnegativity, normalization and additivity are met.
Therefore, a plurality of comprehensive measurement functions of multiple evaluation indexes can be constructed according to the data of the object to be evaluated, the weight of each evaluation factor and the weight of each evaluation index, so as to respectively determine the comprehensive measurement vectors of the object to be evaluated corresponding to different comprehensive measurement functions. The process of specifically constructing the uncertain measure function will continue with the detailed steps described below.
TABLE 2 common uncertain measure function models
And S14, determining corresponding confidence degrees based on the comprehensive measurement vectors so as to determine the corresponding risk evaluation level of the object to be evaluated.
In this embodiment, the confidence corresponding to each comprehensive measurement vector is first determined, and then the risk evaluation level corresponding to the object to be evaluated is determined according to the confidence. Wherein the division of the rating scale is ordered C1<C2<C3<C4As the level increases, the risk level of the system increases. The concept of combining uncertain measures is known as { C1,C2,C3,C4And the risk level is determined according to the confidence criterion when the ordered space is no longer suitable for the maximum membership criterion.
And S15, comparing the risk evaluation levels corresponding to the comprehensive measurement functions of the object to be evaluated, and determining a comparison result so as to perform key monitoring on the object to be evaluated with different risk evaluation levels in the comparison result.
In this embodiment, the four different undetermined measurement functions determine the risk evaluation level corresponding to the current object to be evaluated, the risk evaluation level can be directly determined for the objects to be evaluated with the same comparison result, and the objects to be evaluated with different comparison results need to pay attention. Specific implementations are described below.
According to the risk assessment method provided by the embodiment, aiming at the uncertainty of multiple influence factors in the risk assessment process, the comprehensive measurement functions with multiple assessment indexes are established, the risk assessment grades corresponding to the comprehensive measurement functions with different objects to be assessed are compared, the risk assessment result is further determined, the assessment grades under different functions are comprehensively considered, the accuracy of the risk assessment is further ensured, and the objects with different results are subjected to key monitoring and recalculation.
In this embodiment, a risk assessment method is provided, which can be used in the electronic device described above, and fig. 2 is a flowchart of a risk assessment method according to an embodiment of the present invention, as shown in fig. 2, the flowchart includes the following steps:
and S21, acquiring the data of the object to be evaluated, a plurality of evaluation factors and a plurality of evaluation indexes.
Please refer to S11 in fig. 1, which is not described herein again.
S22, a weight calculation is performed for each evaluation factor and each evaluation index, and the weight of each evaluation factor and each evaluation index is determined.
Specifically, the above S22 may include the following steps:
s221, establishing a hierarchical structure model of the object to be evaluated, wherein the hierarchical structure model comprises each evaluation factor and each evaluation index and is used for representing the risk evaluation level of the object to be evaluated.
In this embodiment, an Analytic Hierarchy Process (AHP) is used to calculate the weight, the first step of the specific Process is to establish a hierarchical structure model of the object to be evaluated, where the hierarchical structure model includes each evaluation factor and evaluation index, the hierarchical structure model is used to represent the risk evaluation level of the object to be evaluated, and the above example illustrates that the establishment of the division is the ordered C1<C2<C3<C4The hierarchical structure model of (1).
S222, a judgment matrix is constructed by utilizing the hierarchical structure model.
On the basis of a risk evaluation index system, an evaluation index x is assumed to be comparediAnd xjFor the relative importance of the factor X on its upper layer, a 1-9 scale is usually employed. The method quantifies the comparison result between the indexes, and numerically represents the relative importance degree of the two indexes at the lower layer relative to the factors at the upper layer, as shown in Table 3.
TABLE 3 Scale and implications of decision matrix
| Means of | Assignment of value |
| Contrast factor xiAnd xjBoth of the |
1 |
| Contrast factor xiAnd xjThe former being slightly more important than the latter | 3 |
| Contrast factor xiAnd xjThe former being significantly more important than the latter | 5 |
| Contrast factor xiAnd xjThe former being more important than the latter | 7 |
| Contrast factor xiAnd xjThe former being of extreme importance than the latter | 9 |
| Represents a contrast factor xiAnd xjIs intermediate between the above adjacent judgments | 2,4,6,8 |
Let gijDenotes xiAnd xjThe ratio of relative importance of (c), the established judgment matrix G:
in another specific embodiment, after the step S222 and before the step S223, the risk assessment method of this embodiment further includes:
(1) and acquiring the corresponding relation between the matrix order and the consistency correction index.
The judgment matrix constructed by using the scaling method is prone to deviation caused by subjective factors of people, in order to ensure the accuracy of the weight, the consistency of the matrix needs to be verified, a corresponding relationship between the order of the matrix and the consistency correction index RI is obtained first, and the values of the corresponding RI under different matrix orders are shown in table 4, wherein the corresponding relationship can be adjusted according to the requirements of actual items, which is not limited in this embodiment.
TABLE 4 RI value and order comparison table
| Order of the |
1 | 2 | 3 | 4 | 5 | 6 | 7 |
| RI value | 0 | 0 | 0.52 | 0.89 | 1.12 | 1.24 | 1.36 |
| Order of the scale | 8 | 9 | 10 | 11 | 12 | 13 | 14 |
| RI value | 1.41 | 1.46 | 1.49 | 1.52 | 1.54 | 1.56 | 1.58 |
(2) And determining the consistency correction index of the judgment matrix according to the order and the corresponding relation of the judgment matrix. Specifically, the consistency correction index RI may be determined by looking up in table 4.
(3) And calculating a consistency index of the judgment matrix, correcting the consistency index by using a consistency correction index of the judgment matrix, and determining a consistency ratio.
The consistency ratio CR is an important standard for checking and judging whether the matrix meets the consistency condition, and a calculation formula is as follows: CR is CI/RI, CI indicates a consistency index, and RI indicates a consistency correction index. Wherein CI may be represented by the formula: CI ═ λmaxN)/(n-1). Wherein λ ismaxTo determine the maximum characteristic root of the matrix G, n is the matrix order.
(4) And when the consistency ratio does not meet the preset requirement, reconstructing the judgment matrix until the consistency ratio meets the preset requirement.
In this embodiment, for example, the preset requirement is determined to be 0.1, and when CR is less than 0.1, it indicates that the determination matrix is acceptable; when CR is more than or equal to 0.1, the judgment matrix is not acceptable. The decision matrix needs to be modified or reconstructed until CR <0.1 is satisfied. It should be noted that the preset requirement may be adjusted according to actual engineering requirements, and the embodiment is not limited thereto.
S223, based on the determination matrix, weights of the corresponding evaluation factors and evaluation indexes are calculated, respectively.
The weight is solved by the model by a sum-product method, and the method comprises the following steps: normalizing the matrix G by columns to obtain a matrix H ═ Hij)n×n，
② summing the matrix G by rows to obtain the vector O ═ O (O)1,O2,…,On)T，
Normalizing the vector O to obtain a weight vector
Fourthly, calculating the maximum eigenvalue lambda of the matrix Hmax，
It should be noted that, this embodiment only exemplifies that the sum-product method is used to solve the weight, and other existing methods may be used to calculate the weight in practical application, and this embodiment is not limited to this.
Based on the basic principle of the analytic hierarchy process, the actual condition of the pipeline and the statistical accident data are considered, and the weight values of all factors and indexes in the evaluation index system are calculated. Taking the first-order factor in the index system as an example, the first-order factor is compared pairwise by using a 1-9 scale method. The weights of the indexes corresponding to each factor are respectively obtained according to the processes, and the specific results are shown in tables 5-10.
TABLE 5 first-level factor Scale index decision matrix and weights
Wherein CR is 0.0220<0.1, the judgment matrix of the primary factor scale index meets the requirement of consistency.
TABLE 6 third party corruption secondary index decision matrix and weights
And if CR is 0.0304<0.1, the judgment matrix of the third party destruction secondary index meets the consistency requirement.
TABLE 7 Corrosion failure Secondary index decision matrix and weights
And if CR is 0.0181<0.1, the judgment matrix of the misoperation secondary index meets the consistency requirement.
TABLE 8 determination of matrix and weight for secondary indicators of misoperation
And if CR is 0.0031 and is less than 0.1, the judgment matrix of the secondary index of the pipeline defect meets the consistency requirement.
TABLE 9 determination matrix and weight of secondary indexes of pipeline defects
TABLE 10 determination matrix and weights for secondary indicators of pipeline defects
Wherein, CR is 0.0227<0.1, the judgment matrix of the natural disaster secondary index meets the consistency requirement.
S23, according to the data of the object to be evaluated, the weight of each evaluation factor and the weight of each evaluation index, a plurality of comprehensive measurement functions of multiple evaluation indexes are constructed, so that comprehensive measurement vectors of the object to be evaluated corresponding to different comprehensive measurement functions are respectively determined.
Please refer to S13 in fig. 1, which is not described herein again.
And S24, determining corresponding confidence degrees based on the comprehensive measurement vectors so as to determine the corresponding risk evaluation level of the object to be evaluated.
Please refer to S14 in fig. 1, which is not described herein again.
And S25, comparing the risk evaluation grades corresponding to the comprehensive measurement functions of the object to be evaluated to determine a risk evaluation result.
Please refer to S15 in fig. 1, which is not described herein again.
According to the risk assessment method provided by the embodiment, aiming at the uncertainty of multiple influence factors in the risk assessment process, the comprehensive measurement functions with multiple assessment indexes are established, the risk assessment grades corresponding to the comprehensive measurement functions with different objects to be assessed are compared, the risk assessment result is further determined, the assessment grades under different functions are comprehensively considered, the accuracy of the risk assessment is further ensured, and the objects with different results are subjected to key monitoring and recalculation.
In this embodiment, a risk assessment method is provided, which can be used in the electronic device described above, and fig. 3 is a flowchart of a risk assessment method according to an embodiment of the present invention, as shown in fig. 3, the flowchart includes the following steps:
and S31, acquiring the data of the object to be evaluated, a plurality of evaluation factors and a plurality of evaluation indexes.
Please refer to S21 in fig. 2 for details, which are not described herein.
S32, a weight calculation is performed for each evaluation factor and each evaluation index, and the weight of each evaluation factor and each evaluation index is determined.
Please refer to S22 in fig. 2 for details, which are not described herein.
S33, according to the data of the object to be evaluated, the weight of each evaluation factor and the weight of each evaluation index, a plurality of comprehensive measurement functions of multiple evaluation indexes are constructed, so that comprehensive measurement vectors of the object to be evaluated corresponding to different comprehensive measurement functions are respectively determined.
And S331, determining a corresponding measure function type according to the data of the object to be evaluated.
Some quantitative indexes in the gas pipeline risk evaluation model have higher risk level when the value is larger, and some indexes have higher risk level when the value is smaller. The index of which the risk level is increased along with the increase of the value is called as a maximum index, and conversely, the index is an extremely small index. When constructing an uncertain measure function, an evaluator determines a corresponding measure function according to the variation condition of the index risk level along with the value.
Very large index:
very small scale index:
wherein x isiAnd xjRespectively representing a maximum quantitative index and a minimum quantitative index, wherein x is an index value, C1、C2、C3、C4Four risk levels of partitioning, respectively, where the function of the unknowns measure is the same for each class.
S332, constructing different single-index measurement functions of each evaluation index based on the measurement function types to determine index measurement vectors corresponding to each evaluation index under different single-index measurement functions.
Such as: evaluation factor space F ═ F1,F2,F3Factors inFiThere are m indices { fi1,fi2,…,fimIt is evaluated. Constructing a measure function omega (a) corresponding to each index according to an evaluation index grading table (see table 1 in detail)ij∈Ck) (i-1, 2,3, j-1, 2, …, m, k-1, 2,3,4), determining index value by combining with the actual situation of the major hazard enterprise, further calculating index measure vector, and establishing factor XiThe single index measure matrix P:
s333, constructing a comprehensive measure function of a plurality of multi-evaluation indexes corresponding to the single-index measure function by using the index measure vector and the weight of each evaluation index.
Specifically, in another specific embodiment, the step S331 may further include the steps of:
(1) and calculating the single-factor measure vector corresponding to each evaluation factor according to the index measure vector and the weight of each evaluation index. Combining the matrix P of the index measure vector according to the calculated weight of each evaluation indexiCalculating the evaluation factor FiOf the one-factor measure vector Qi：
Qi＝Oi·Pi＝[ωi1 ωi2 ωi3 ωi4] (3)
Wherein, OiIs a factor FiWeight vector, P, of each indexiIs a matrix of index measure vectors.
(2) And constructing a plurality of comprehensive measurement functions of multiple evaluation indexes corresponding to the single index measurement function by using each single-factor measurement vector.
The multi-index comprehensive measurement matrix formed by all risk evaluation factors of the urban gas pipeline is as follows:
wherein, Q is a multi-index comprehensive measure matrix, QiFor comprehensive measure vectors, omega, of different evaluation factors of the evaluation objectij(i-1, 2, 3; j-1, 2,3,4) are metric vectors of different levels of different evaluation factors i of the evaluation object.
Integrated measure vector of evaluation object:
μ＝O·Q＝[ω1 ω2 ω3 ω4] (5)
wherein mu is a comprehensive measure vector, O is a weight vector of an evaluation factor, Q is a multi-index comprehensive measure matrix, and omega isj(j ═ 1,2,3,4) is a metric vector for different levels of the evaluation object.
And S334, respectively calculating comprehensive measurement vectors of the object to be evaluated corresponding to different comprehensive measurement functions according to the comprehensive measurement functions of the multiple evaluation indexes and the weights of the evaluation factors. And calculating corresponding comprehensive measure vectors aiming at different comprehensive measure functions.
And S34, determining corresponding confidence degrees based on the comprehensive measurement vectors so as to determine the corresponding risk evaluation level of the object to be evaluated.
Please refer to S24 in fig. 2 for details, which are not described herein.
And S35, comparing the risk evaluation grades corresponding to the comprehensive measurement functions of the object to be evaluated to determine a risk evaluation result.
Please refer to S25 in fig. 2 for details, which are not described herein.
According to the risk assessment method provided by the embodiment, aiming at the uncertainty of multiple influence factors in the risk assessment process, the comprehensive measurement functions with multiple assessment indexes are established, the risk assessment grades corresponding to the comprehensive measurement functions with different objects to be assessed are compared, the risk assessment result is further determined, the assessment grades under different functions are comprehensively considered, the accuracy of the risk assessment is further ensured, and the objects with different results are subjected to key monitoring and recalculation.
In this embodiment, a risk assessment method is provided, which can be used in the electronic device described above, and fig. 4 is a flowchart of the risk assessment method according to the embodiment of the present invention. In this embodiment, the split screen writing is described in detail, and as shown in fig. 4, the process includes the following steps:
and S41, acquiring the data of the object to be evaluated, a plurality of evaluation factors and a plurality of evaluation indexes.
Please refer to S31 in fig. 3 for details, which are not described herein.
S42, a weight calculation is performed for each evaluation factor and each evaluation index, and the weight of each evaluation factor and each evaluation index is determined.
Please refer to S32 in fig. 3 for details, which are not described herein.
S43, according to the data of the object to be evaluated, the weight of each evaluation factor and the weight of each evaluation index, a plurality of comprehensive measurement functions of multiple evaluation indexes are constructed, so that comprehensive measurement vectors of the object to be evaluated corresponding to different comprehensive measurement functions are respectively determined.
Please refer to S32 in fig. 3 for details, which are not described herein.
And S44, determining corresponding confidence degrees based on the comprehensive measurement vectors so as to determine the corresponding risk evaluation level of the object to be evaluated.
In some optional implementations of this embodiment, the step S44 may include the following steps:
s441, the uncertainty measure of each classification layer in the hierarchical structure model is obtained. Specifically, uncertainty measures of each classification layer in the hierarchical structure model are preset.
And S442, determining the corresponding risk evaluation level of the object to be evaluated according to the comprehensive measurement vector and each uncertainty measurement.
The confidence criterion lambda has subjectivity, and when the values are different, the judgment results are possibly inconsistent, and the evaluation results cannot be objectively reflected. Thus, using Euclidean distance for the calculation, the basic idea of distance discrimination analysis is to determine to which population the sample belongs by comparing the minimum distances between the sample and the population.
If the hierarchical space U is adopted, the uncertainty measure of each classification level in the hierarchical space U is as follows:
A1＝(1,0,…,0),A2＝(0,1,…,0),…,Ak＝(0,0,…,1)。
specifically, the step S442 further includes the following steps:
(1) and calculating Euclidean distances between the comprehensive measure vector and each uncertainty measure.
(2) And determining the level corresponding to the minimum distance value in the Euclidean distances as the risk evaluation level of the object to be evaluated.
By multi-index combined measure (mu)1×kEuclidean distance of dkDiscrimination uncertainty measure AkCorresponding rating.
Determining the minimum distance value min (D) d in the Euclidean distancep，p∈[1,k]And p is an integer, and p is an evaluation object discrimination level.
And S45, comparing the risk evaluation levels corresponding to the comprehensive measurement functions of the object to be evaluated, and determining a comparison result so as to perform key monitoring on the object to be evaluated with different risk evaluation levels in the comparison result.
Wherein, the step S45 further includes, in a specific embodiment:
s451, comparing the risk evaluation levels corresponding to the comprehensive measurement functions of the object to be evaluated, and recalculating the risk evaluation levels of the object to be evaluated by using other existing methods when the risk evaluation levels corresponding to the comprehensive measurement functions are different until the risk evaluation levels corresponding to the comprehensive measurement functions of the object to be evaluated are consistent to determine a risk evaluation result.
Specifically, in practical applications, values of the indexes are determined by investigating pipeline related technical data and combining specific experience according to the established grading standards of the evaluation indexes, as shown in table 11.
Table 11 value-taking table for each pipe section evaluation index
According to the grading standard of the indexes, a measure function of each index is constructed, the value of each index is determined according to the actual situation, the measure vector of each index is obtained, and an uncertain measure matrix of each index is established. Since the indicators include quantitative indicators and qualitative indicators, the pipeline burial depth in the quantitative indicators is shown in fig. 5, and the pipeline line identifiers in the qualitative indicators are used as examples to respectively establish the measure functions as shown in fig. 6.
According to the euclidean distance method, the minimum distance is determined.
Table 12 risk evaluation results of each pipe section under different uncertain measure functions under Euclidean distance
Taking the pipeline 1# as an example, the evaluation results calculated by the four measurement functions are inconsistent, and the evaluation results are not taken as key monitoring objects due to the low level I and level II risk degrees. The risk evaluation models constructed by different measure functions are adopted to carry out risk evaluation on the same object, and the obtained evaluation results may not be consistent. Therefore, an unknown measurement function of each index is established by adopting an unknown measurement theory, and the analysis results are different when the comprehensive evaluation is carried out. Therefore, when the unknown measurement theory is adopted for comprehensive evaluation, evaluation results under different measurement functions need to be considered, and objects with different results are selectively monitored in an important mode.
According to the risk assessment method provided by the embodiment, aiming at the uncertainty of multiple influence factors in the risk assessment process, the comprehensive measurement functions with multiple assessment indexes are established, the risk assessment grades corresponding to the comprehensive measurement functions with different objects to be assessed are compared, the risk assessment result is further determined, the assessment grades under different functions are comprehensively considered, the accuracy of the risk assessment is further ensured, and the objects with different results are subjected to key monitoring and recalculation.
In this embodiment, a risk assessment device is further provided, and the device is used to implement the above embodiments and preferred embodiments, which have already been described and are not described again. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
The present embodiment provides a risk assessment apparatus, as shown in fig. 7, including:
the first processing module 51 is configured to obtain data of an object to be evaluated, a plurality of evaluation factors, and a plurality of evaluation indexes;
the second processing module 52 is configured to perform weight calculation on each evaluation factor and each evaluation index, and determine the weight of each evaluation factor and each evaluation index;
a third processing module 53, configured to construct multiple comprehensive measurement functions of multiple evaluation indexes according to the data of the object to be evaluated, the weight of each evaluation factor, and the weight of each evaluation index, so as to determine comprehensive measurement vectors of the object to be evaluated corresponding to different comprehensive measurement functions, respectively;
a fourth processing module 54, configured to determine a corresponding confidence based on each comprehensive measurement vector, so as to determine a risk evaluation level corresponding to the object to be evaluated;
and the fifth processing module 55 is configured to compare risk evaluation levels corresponding to the comprehensive measurement functions of the objects to be evaluated, determine a comparison result, and perform key monitoring on the objects to be evaluated with different risk evaluation levels in the comparison result.
The risk assessment device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC circuit, a processor and memory executing one or more software or fixed programs, and/or other devices that may provide the above-described functionality.
Further functional descriptions of the modules are the same as those of the corresponding embodiments, and are not repeated herein.
An embodiment of the present invention further provides an electronic device, which has the risk assessment apparatus shown in fig. 7.
Referring to fig. 8, fig. 8 is a schematic structural diagram of an electronic device according to an alternative embodiment of the present invention, and as shown in fig. 8, the electronic device may include: at least one processor 61, such as a CPU (Central Processing Unit), at least one communication interface 63, memory 64, at least one communication bus 62. Wherein a communication bus 62 is used to enable the connection communication between these components. The communication interface 63 may include a Display (Display) and a Keyboard (Keyboard), and the optional communication interface 63 may also include a standard wired interface and a standard wireless interface. The Memory 64 may be a high-speed RAM Memory (volatile Random Access Memory) or a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The memory 64 may optionally be at least one memory device located remotely from the processor 61. Wherein the processor 61 may be in connection with the apparatus described in fig. 7, an application program is stored in the memory 64, and the processor 61 calls the program code stored in the memory 64 for performing any of the above-mentioned method steps.
The communication bus 62 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The communication bus 62 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in FIG. 8, but this is not intended to represent only one bus or type of bus.
The memory 64 may include a volatile memory (RAM), such as a random-access memory (RAM); the memory may also include a non-volatile memory (english: non-volatile memory), such as a flash memory (english: flash memory), a hard disk (english: hard disk drive, abbreviated: HDD) or a solid-state drive (english: SSD); the memory 64 may also comprise a combination of the above types of memory.
The processor 61 may be a Central Processing Unit (CPU), a Network Processor (NP), or a combination of CPU and NP.
The processor 61 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a Programmable Logic Device (PLD), or a combination thereof. The PLD may be a Complex Programmable Logic Device (CPLD), a field-programmable gate array (FPGA), a General Array Logic (GAL), or any combination thereof.
Optionally, the memory 64 is also used to store program instructions. Processor 61 may invoke program instructions to implement a risk assessment method as shown in the embodiments of fig. 1-4 of the present application.
The embodiment of the invention also provides a non-transitory computer storage medium, wherein the computer storage medium stores computer executable instructions, and the computer executable instructions can execute the risk assessment method in any method embodiment. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD) or a Solid State Drive (SSD), etc.; the storage medium may also comprise a combination of memories of the kind described above.
Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope defined by the appended claims.
Claims (10)
1. A method of risk assessment, the method comprising:
acquiring data of an object to be evaluated, a plurality of evaluation factors and a plurality of evaluation indexes;
respectively carrying out weight calculation on each evaluation factor and each evaluation index, and determining the weight of each evaluation factor and each evaluation index;
according to the data of the object to be evaluated, the weight of each evaluation factor and the weight of each evaluation index, constructing a plurality of comprehensive measurement functions of multiple evaluation indexes so as to respectively determine comprehensive measurement vectors of the object to be evaluated corresponding to different comprehensive measurement functions;
determining corresponding confidence degrees based on the comprehensive measurement vectors so as to determine corresponding risk evaluation levels of the object to be evaluated;
and comparing the risk evaluation levels corresponding to the comprehensive measurement functions of the objects to be evaluated, and determining a comparison result so as to perform key monitoring on the objects to be evaluated with different risk evaluation levels in the comparison result.
2. The method according to claim 1, wherein the performing a weight calculation on each evaluation factor and each evaluation index to determine the weight of each evaluation factor and each evaluation index comprises:
establishing a hierarchical structure model of the object to be evaluated, wherein the hierarchical structure model comprises various evaluation factors and evaluation indexes and is used for representing the risk evaluation level of the object to be evaluated;
constructing a judgment matrix by using the hierarchical structure model;
and respectively calculating the corresponding evaluation factors and the weights of the evaluation indexes on the basis of the judgment matrix.
3. The method of any of claims 1-2, wherein after said constructing a decision matrix using the hierarchical model, the method further comprises:
acquiring a corresponding relation between the matrix order and the consistency correction index;
determining a consistency correction index of the judgment matrix according to the order of the judgment matrix and the corresponding relation;
calculating a consistency index of the judgment matrix, correcting the consistency index by using a consistency correction index of the judgment matrix, and determining a consistency ratio;
and when the consistency ratio does not meet the preset requirement, reconstructing the judgment matrix until the consistency ratio meets the preset requirement.
4. The method according to claim 1, wherein the constructing a plurality of comprehensive measurement functions of multiple evaluation indexes according to the data of the object to be evaluated, the weight of each evaluation factor, and the weight of each evaluation index to determine a comprehensive measurement vector of the object to be evaluated corresponding to different comprehensive measurement functions respectively comprises:
determining a corresponding measure function type according to the data of the object to be evaluated;
constructing different single index measurement functions of each evaluation index based on the measurement function types to determine index measurement vectors corresponding to each evaluation index under different single index measurement functions;
constructing a comprehensive measure function of a plurality of multiple evaluation indexes corresponding to the single index measure function by using the index measure vector and the weight of each evaluation index;
and respectively calculating comprehensive measurement vectors of the object to be evaluated corresponding to different comprehensive measurement functions according to the comprehensive measurement functions of the multiple evaluation indexes and the weight of each evaluation factor.
5. The method of claim 4, wherein constructing a composite measure function of a plurality of multiple evaluation indicators corresponding to the single-indicator measure function using the indicator measure vector and weights of the respective evaluation indicators comprises:
calculating single-factor measure vectors corresponding to the evaluation factors according to the index measure vectors and the weights of the evaluation indexes;
and constructing a plurality of comprehensive measurement functions of multiple evaluation indexes corresponding to the single index measurement function by using the single-factor measurement vectors.
6. The method of claim 2, wherein said determining a corresponding confidence level based on each of said comprehensive measurement vectors to determine a corresponding risk assessment rating of said object to be assessed comprises:
obtaining uncertainty measure of each classification layer in the hierarchical structure model;
and determining the corresponding risk evaluation level of the object to be evaluated according to the comprehensive measurement vector and each uncertainty measurement.
7. The method of claim 6, wherein determining a risk assessment rating for the object to be assessed based on the combined measurement vector and each of the uncertainty measurements comprises:
calculating Euclidean distances between the comprehensive measure vector and each uncertainty measure;
and determining the level corresponding to the minimum distance value in the Euclidean distances as the risk evaluation level of the object to be evaluated.
8. A risk assessment device, characterized in that the device comprises:
the first processing module is used for acquiring data of an object to be evaluated, a plurality of evaluation factors and a plurality of evaluation indexes;
the second processing module is used for respectively carrying out weight calculation on each evaluation factor and each evaluation index and determining the weight of each evaluation factor and each evaluation index;
the third processing module is used for constructing a plurality of comprehensive measurement functions of multiple evaluation indexes according to the data of the object to be evaluated, the weight of each evaluation factor and the weight of each evaluation index so as to respectively determine comprehensive measurement vectors of the object to be evaluated corresponding to different comprehensive measurement functions;
the fourth processing module is used for determining corresponding confidence degrees based on the comprehensive measurement vectors so as to determine the corresponding risk evaluation level of the object to be evaluated;
and the fifth processing module is used for comparing the risk evaluation grades corresponding to the comprehensive measurement functions of the objects to be evaluated and determining a comparison result so as to perform key monitoring on the objects to be evaluated with different risk evaluation grades in the comparison result.
9. An electronic device, comprising:
a memory and a processor, the memory and the processor being communicatively coupled to each other, the memory having stored therein computer instructions, the processor executing the computer instructions to perform the risk assessment method of any one of claims 1-7.
10. A computer-readable storage medium having stored thereon computer instructions for causing a computer to perform the risk assessment method of any one of claims 1-7.
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