CN112508416A - Oil and gas storage and transportation station safety grade evaluation method based on cloud fuzzy analytic hierarchy process - Google Patents

Oil and gas storage and transportation station safety grade evaluation method based on cloud fuzzy analytic hierarchy process Download PDF

Info

Publication number
CN112508416A
CN112508416A CN202011453486.6A CN202011453486A CN112508416A CN 112508416 A CN112508416 A CN 112508416A CN 202011453486 A CN202011453486 A CN 202011453486A CN 112508416 A CN112508416 A CN 112508416A
Authority
CN
China
Prior art keywords
cloud
index
sub
factors
oil
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202011453486.6A
Other languages
Chinese (zh)
Inventor
陈国华
门金坤
周利兴
饶小惠
罗琛南
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
South China University of Technology SCUT
Original Assignee
South China University of Technology SCUT
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by South China University of Technology SCUT filed Critical South China University of Technology SCUT
Priority to CN202011453486.6A priority Critical patent/CN112508416A/en
Publication of CN112508416A publication Critical patent/CN112508416A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services
    • G06Q50/265—Personal security, identity or safety

Landscapes

  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Engineering & Computer Science (AREA)
  • Strategic Management (AREA)
  • Development Economics (AREA)
  • Educational Administration (AREA)
  • Economics (AREA)
  • Tourism & Hospitality (AREA)
  • General Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • Theoretical Computer Science (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Marketing (AREA)
  • Physics & Mathematics (AREA)
  • Game Theory and Decision Science (AREA)
  • Quality & Reliability (AREA)
  • Operations Research (AREA)
  • Computer Security & Cryptography (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

本发明公开了基于云模糊层次分析法的油气储运站场安全等级评估方法，包括：基础信息采集；构建油气储运站场安全评估的多层次综合评估结构；确定指标评语变量，并相应确定指标评语变量值区间；计算获得子指标因子综合权重；专家针对指标进行打分，收集打分数据；生成综合云；对所述标准云和所述综合云进行相似度计算；将获得的所述相似度的值与所述指标评语变量值区间进行比较，判别油气储运场站安全级别。本发明是一种定性与定量相结合的系统分析方法，同时引入了多层次评估因素的随机性与模糊性，可有效避免专家的主观影响因素，能够提供可靠的油气储运站场安全水平诊断结果，使油气储运站场能够及时发现安全隐患，积极改进，避免事故的发生。

Figure 202011453486

The invention discloses a method for evaluating the safety level of oil and gas storage and transportation stations based on a cloud fuzzy analytic hierarchy process. Index comment variable value interval; calculate and obtain the comprehensive weight of sub-indicator factors; experts score the index and collect scoring data; generate a comprehensive cloud; perform similarity calculation on the standard cloud and the comprehensive cloud; The value of is compared with the variable value interval of the index comment to determine the safety level of the oil and gas storage and transportation station. The present invention is a system analysis method combining qualitative and quantitative analysis, and at the same time, randomness and ambiguity of multi-level evaluation factors are introduced, which can effectively avoid the subjective influencing factors of experts, and can provide reliable diagnosis of the safety level of oil and gas storage and transportation stations. As a result, the oil and gas storage and transportation station can discover hidden safety hazards in time, make active improvements, and avoid accidents.

Figure 202011453486

Description

Oil and gas storage and transportation station safety grade evaluation method based on cloud fuzzy analytic hierarchy process
Technical Field
The invention belongs to the technical field of oil and gas storage and transportation safety, and particularly relates to an oil and gas storage and transportation station safety grade evaluation method based on a cloud fuzzy analytic hierarchy process.
Background
With the continuous promotion of the modern construction of our country, the petrochemical industry has become an important component in the industrial economy of our country. The use amount of energy sources such as petroleum, natural gas and the like is increased year by year, and the economic development is promoted, and meanwhile, serious potential safety hazards are brought. In the petroleum industry, oil and gas storage and transportation stations are hubs connecting production links, and the safety problem is more and more emphasized. During the storage, transportation and production of oil and gas, the leakage of materials can easily cause disastrous accidents such as fire, explosion, personnel poisoning and the like. Therefore, providing reliable safety level diagnosis results of the oil and gas storage and transportation station has great significance for the safe production of the oil and gas storage and transportation station.
In recent years, researchers at home and abroad have been striving to advance The development of Safety assessment theory to support and guide Safety analysis in hazardous production sites (Song Q et al, The application of closed model combined with a nonlinear and analytical Process [ J ]. Process Safety and Environmental Protection, 2021, 145: 12-22). With the development of interdisciplines of fuzzy mathematics, information science, management science, etc., most studies, such as the "a adaptation for environmental breakdown of engineered nanomaterials using Analytical Hierarchy Process (AHP) and fuzzy information science [ J ] environmental international, 2016, 92: 334-347, yoyo fly et al "risk assessment of air traffic safety based on entropy weight and fuzzy analysis [ J ]. aeronautical computing techniques, 2013, 43 (04): 1-5, witch et al "a set of comprehensive safety evaluation methods [ P ] applied to safety pre-evaluation: CN104915888A, 2015-09-16, Aicong et al, "a comprehensive pipe gallery operation management safety evaluation method [ P ]. Zhejiang province: CN111738612A, 2020-10-02 adopts analytic hierarchy process as multi-criterion decision tool to determine security level. The method has the advantages of small calculated amount, simple process and easy operation. However, the analytic hierarchy process lacks quantitative analysis, is highly subjective, and only gives relative risk. An oil and gas storage and transportation station is a production system with a complex structure, and the traditional analytic hierarchy process cannot perform quantitative comprehensive evaluation on the complex structure system.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an oil and gas storage and transportation station safety level evaluation method based on a cloud fuzzy analytic hierarchy process, and a multi-level comprehensive evaluation structure for oil and gas storage and transportation station safety evaluation is constructed by comprehensively considering 4 main safety influence factors in management, personnel, facilities and environment. And finally, a reliable safety level quantization result of the oil and gas storage and transportation station is provided through similarity, so that potential safety hazards can be found in time in the oil and gas storage and transportation station, positive improvement is realized, and accidents are avoided.
In order to achieve the purpose of the invention, the safety grade evaluation method of the oil and gas storage and transportation station based on the cloud fuzzy analytic hierarchy process comprises the following steps:
acquiring basic information, including acquiring safety influence factor information in 4 aspects of management, personnel, facilities and environment;
a multilevel comprehensive evaluation structure for safety evaluation of an oil and gas storage and transportation station is constructed, and main factors and sub-index factors of a lower layer belonging to each main factor are determined;
determining an index comment variable value and correspondingly determining an index comment variable value interval;
generating a standard cloud based on the index comment variable value interval;
calculating to obtain the comprehensive weight of the sub-index factors;
the expert scores the indexes and collects scoring data;
generating a comprehensive cloud based on the sub-index factor comprehensive weight and the grading data;
similarity calculation is carried out on the standard cloud and the comprehensive cloud;
and comparing the obtained similarity value with the index comment variable value interval, and judging the safety level of the oil and gas storage and transportation station.
Further, the main factors in step 2 include management, personnel, facilities and environment, and the sub-index factors belonging to the lower layer of each main factor include:
the main management factors comprise three sub-index factors of a safety operation procedure, a supervision and inspection system and a safety policy;
the main factors of the personnel comprise four sub-index factors of pre-post training, personnel allocation, fatigue, skill and knowledge;
the main factors of the facility comprise four sub-index factors of facility layout, maintenance condition, measurement instrument inspection and corrosion resistance of the facility;
the main environmental factors include four sub-index factors of temperature, humidity, noise and sanitary condition.
Further, the index comment variable value interval is established, the safety level of the oil and gas storage and transportation station is qualitatively described, and then a quantization value interval is set for each level.
Further, the determining an index comment variable value and correspondingly determining an index comment variable value interval includes:
the index comment variable is qualitative description of the safety level of the oil and gas storage and transportation station and is determined as 'poor', 'normal', 'good' and 'excellent', and the quantitative value intervals of all levels are respectively defined as [0, 4 ], [4, 7 ], [7, 9) and [9, 10 ].
Further, the generating a standard cloud specifically includes:
calculating standard cloud digital features according to the index comment variable interval, wherein the standard cloud digital features comprise expected Ex, entropy En and super-entropy He, the expected Ex represents distribution of qualitative description in a domain of discourse, the entropy En reflects randomness and fuzziness of a qualitative concept, the super-entropy He is a measure of the cohesiveness of the index comment variables, and the calculation method of the digital features comprises the following steps:
Figure BDA0002832427510000021
Figure BDA0002832427510000022
He＝λEn
in the formula, xmaxAnd xminRespectively indicating an upper limit and a lower limit of a variable interval of the comment; λ is a coefficient for expressing a linear relationship between entropy En and super entropy He;
inputting standard cloud digital feature expectation Ex, entropy En and super-entropy He into a forward cloud generator, and outputting N cloud drops Drop (x) by the forward cloud generatori，yi) Form a standard cloud in which yiThe calculation method of (2) is as follows:
Figure BDA0002832427510000031
in the formula, S is a normal random number with Ex as an expectation and He as a standard deviation.
Further, the calculating to obtain the sub-indicator factor comprehensive weight specifically includes:
step 5.1: quantizing the importance degree of each main factor and each sub-index factor by adopting a triangular fuzzy number, respectively constructing a fuzzy consistent judgment matrix about the main factor and a fuzzy consistent judgment matrix about the sub-index factor, and respectively and correspondingly obtaining a main factor weight and a sub-index factor weight;
step 5.2: consistency check, if the two fuzzy consistent judgment matrixes are consistent, entering a step 5.3, otherwise, returning to the step 5.1 to perform importance degree comparison again;
step 5.3: and obtaining the comprehensive weight of the sub-index factors according to the weight of the main factor factors and the weight of the sub-index factors.
Further, the sub-indicator factor comprehensive weight can be obtained by multiplying the main factor weight and the sub-indicator factor weight.
Further, step 7.1: respectively calculating and evaluating cloud digital characteristics of each sub-index factor according to grading data of experts;
step 7.2: calculating the comprehensive cloud digital characteristics according to the cloud digital characteristics and the comprehensive weights of the sub index factors;
step 7.3: and the comprehensive cloud digital characteristics are used as input of a forward cloud generator to generate cloud droplets of a certain scale to form a comprehensive cloud.
Further, in the step 7.1, in the cloud digital features of each sub-index factor, the cloud digital features of any sub-index factor include an evaluation cloud expected ExedEntropy EnedEntropy of HeedThe calculation of each cloud digital feature is as follows:
Figure BDA0002832427510000032
Figure BDA0002832427510000033
Figure BDA0002832427510000034
wherein E is the number of experts and V is the sample variance;
further, in step 7.2, the comprehensive cloud digital characteristics are calculated according to the comprehensive weights of the evaluation cloud digital characteristics and the sub index factors, and the comprehensive cloud expectation ExsEntropy EnsEntropy of HesThe calculation method is as follows:
Figure BDA0002832427510000041
Figure BDA0002832427510000042
Figure BDA0002832427510000043
in the formula, D is the number of sub-index factors, omegadIs the sub-index factor integrated weight.
Further, performing similarity calculation on the standard cloud and the comprehensive cloud, specifically including:
step 8.1: randomly selecting a cloud Drop (x) in the comprehensive cloudi，yi)。
Step 8.2: calculating secondary cloud Drop (x) in a standard cloudi，θi)。
Repeating step 8.1 and step 8.2 to generate N thetaiThe similarity calculation method is as follows:
Figure BDA0002832427510000044
compared with the prior art, the invention can realize the following beneficial effects:
the invention combines the complex structure characteristics of the production system of the oil and gas storage and transportation station, comprehensively considers 4 main safety influence factors in the aspects of management, personnel, facilities and environment, constructs a multilevel comprehensive evaluation structure of the safety evaluation of the oil and gas storage and transportation station, is a system analysis method combining the qualitative and quantitative evaluation, simultaneously introduces the randomness and the fuzziness of the multilevel evaluation factors, can effectively avoid the subjective influence factors of experts, can provide a reliable safety level diagnosis result of the oil and gas storage and transportation station, enables the oil and gas storage and transportation station to find potential safety hazards in time, improves actively and avoids accidents.
Drawings
Fig. 1 is a schematic flow chart of a security level assessment method according to an embodiment of the present invention.
Fig. 2 is a schematic view of a multi-level comprehensive evaluation structure for safety evaluation of an oil and gas storage and transportation station in the embodiment of the invention.
Fig. 3 is a schematic diagram of a forward cloud generator according to an embodiment of the present invention.
Detailed Description
For ease of understanding, the present invention is specifically described with reference to FIGS. 1-3.
The safety grade evaluation method for the oil and gas storage and transportation station based on the cloud fuzzy analytic hierarchy process comprises the following steps:
step 1: and collecting basic information. The collection includes 4 aspects of safety influence factor information of management, personnel, facilities and environment.
Step 2: a multilevel comprehensive evaluation structure for safety evaluation of an oil and gas storage and transportation station is constructed, and 4 main factors of management, personnel, facilities and environment and 15 sub-index factors of the lower layer are determined.
In this embodiment, the sub-indicator factor of the lower layer of the main factor M includes a safety operation procedure M1Supervision and inspection system M2And security policy M3(ii) a Sub-index factors under the main factor H of the personnel include pre-post training H1Staffing equipment H2Fatigue degree H3And skill and knowledge H4(ii) a Facility principal factor F underlying factors including facility layout F1Maintenance condition F2Measurement instrument test F3And facility corrosion resistance F4(ii) a Environmental main factor E the factors underlying layer include temperature E1Humidity E2Noise E3And sanitary conditions E4。
And step 3: and determining an index comment variable value, and correspondingly determining an index comment variable value interval. The safety level of the oil and gas storage and transportation station is qualitatively described, namely index comment variables are determined, and then a quantitative value interval is set for each index comment variable.
In this embodiment, "poor", "general", "good", and "excellent" are used as qualitative descriptions of the security level of the oil and gas storage and transportation yard, and the quantization value intervals of each level are defined as [0, 4 ], [4, 7 ], [7, 9 ], and [9, 10 ], respectively. It should be understood that other indicator comment variables and intervals may be set in other embodiments.
And 4, step 4: generating a standard cloud specifically as follows:
step 4.1: calculating standard cloud digital characteristics according to the index comment variable value interval, wherein the standard cloud digital characteristics comprise: ex, entropy En, super entropy He is expected. Wherein, it is expected that Ex represents the distribution of qualitative description of the security level in the domain of discourse, entropy En reflects the randomness and ambiguity of the qualitative description of the security level, and super-entropy He is the measure of the cohesiveness of the index variable, and each digital feature calculation method is as follows:
Figure BDA0002832427510000051
Figure BDA0002832427510000052
He＝λEn
in the formula, xmaxAnd xminRespectively indicating an upper limit and a lower limit of a variable interval of the comment; λ is a coefficient for expressing a linear relationship between En and He. The quantization intervals are calculated as a whole, so that x in the present inventionmax＝10，xmin＝0。
Step 4.2: and generating cloud drops of a certain scale by using a forward cloud generator to form a standard cloud. The input of the forward cloud generator is 3 standard cloud digital features, and the output is N cloud Drop (x)i，yi) Formation of a standard cloud, xi，yiRepresenting the position of the cloud droplet in the universe of discourse. Wherein, yiThe calculation method of (2) is as follows:
Figure BDA0002832427510000053
in the formula, S is a normal random number with Ex as an expectation and He as a standard deviation.
And 5: calculating the index factor weight specifically as follows:
step 5.1: and respectively calculating the weights of the main index factors and the sub index factors. Comparing the importance of index factors pairwise, wherein each index factor is only compared with the factor of the same layer, and determining the index factors according to a triangular fuzzy 9-scale methodThe relative degree of importance. Wherein, the scale is
Figure BDA0002832427510000054
Indicating that the index factor A and the index factor B have the same importance; scale division
Figure BDA0002832427510000055
Indicating that index factor a is slightly more important than index factor B; scale division
Figure BDA0002832427510000061
Indicating that index factor A is more important than index factor B; scale division
Figure BDA0002832427510000062
The index factor A is obviously more important than the index factor B; scale division
Figure BDA0002832427510000063
Indicating that index factor a is more important than index factor B. Other scales
Figure BDA0002832427510000064
Figure BDA0002832427510000065
Is an intermediate importance between the scales described above. Scale division
Figure BDA0002832427510000066
To
Figure BDA0002832427510000067
Represented by triangular blur numbers. After all index factors are compared pairwise, a fuzzy consistent judgment matrix can be established, and the obtained mean value of the fuzzy importance scales is the factor weight.
By the method, the fuzzy consistent judgment matrix related to the main factor and the fuzzy consistent judgment matrix related to the sub-index factor can be obtained respectively, and the main factor weight and the sub-index factor weight are obtained correspondingly.
Step 5.2: and (5) consistency check, if the two fuzzy consistency judgment matrixes are consistent, entering a step 5.3, and if not, returning to the step 5.1 to perform importance degree comparison again.
Step 5.3: and calculating the sub index factor comprehensive weight. The sub-indicator factor integrated weight is obtained by multiplying the main indicator factor weight and the sub-indicator factor weight obtained in step 5.1.
Step 6: and inviting experts to score the indexes, scoring each index of the evaluation object by the experts according to own experience, and collecting scoring data.
And 7: generating a comprehensive cloud, which comprises the following specific steps:
step 7.1: and respectively calculating and evaluating the cloud digital characteristics of each sub-index factor according to the grading data of the experts. For any sub-index factor d, the evaluation cloud expectation Ex thereofedEntropy EnedEntropy of HeedThe calculation method is as follows:
Figure BDA0002832427510000068
Figure BDA0002832427510000069
Figure BDA00028324275100000610
wherein E is the number of experts, V is the sample variance, ExeIs desired for the sample.
Step 7.2: and calculating the comprehensive cloud digital characteristics according to the cloud digital characteristics and the comprehensive weight of the sub index factors. Synthetic cloud expectation ExsEntropy EnsEntropy of HesThe calculation method is as follows:
Figure BDA00028324275100000611
Figure BDA00028324275100000612
Figure BDA0002832427510000071
in the formula, D is the number of sub-index factors.
Step 7.3: and the comprehensive cloud digital characteristics are used as input of a forward cloud generator to generate cloud droplets of a certain scale to form a comprehensive cloud.
And 8: and calculating the similarity. The method comprises the following specific steps:
step 8.1: randomly selecting a cloud Drop (x) in the comprehensive cloudi，yi)。
Step 8.2: calculating secondary cloud Drop (x) in a standard cloudi，θi)，θiIs to distinguish from yiI.e. for the same xiIn the integrated cloud, cloud Drop (x) is generatedi，yi) In the standard cloud, a secondary cloud Drop (x) is generatedi，θi)。
Repeating step 8.1 and step 8.2 to generate N thetaiThe similarity calculation method is as follows:
Figure BDA0002832427510000072
and step 9: and judging the security level of the oil and gas storage and transportation station by comparing the similarity of the comprehensive cloud and the standard cloud.
Figure BDA0002832427510000073
The temporal security level is "poor";
Figure BDA0002832427510000074
the temporal security level is "normal";
Figure BDA0002832427510000075
the temporal security level is "good";
Figure BDA0002832427510000076
the security level is "excellent".
To sum up, in order to avoid the influence of subjectivity, the method provided by this embodiment collects, sorts and analyzes potential safety hazard footprints from a complex system structure of an oil and gas storage and transportation station, comprehensively considers 4 main safety influence factors in the aspects of management, personnel, facilities and environment, constructs a multilevel comprehensive evaluation structure for evaluating the safety level of the oil and gas storage and transportation station, obtains a sub-index factor comprehensive weight by adopting fuzzy consistency discrimination matrix calculation, and performs similarity calculation through the generated standard cloud and comprehensive cloud to finally obtain a quantitative result of the safety level of the oil and gas storage and transportation station. The method combines the qualitative evaluation with the quantitative evaluation, and introduces the randomness and the fuzziness of multi-level evaluation factors, so that the subjective influence factors of experts can be effectively avoided, and the reliable safety level diagnosis result of the oil and gas storage and transportation station can be provided, so that the oil and gas storage and transportation station can find potential safety hazards in time, actively improve and avoid accidents.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.

Claims (10)

1.基于云模糊层次分析法的油气储运站场安全等级评估方法，其特征在于，包括以下步骤：1. the oil and gas storage and transportation station safety level assessment method based on cloud fuzzy analytic hierarchy process, is characterized in that, comprises the following steps: 基础信息采集，包括采集管理、人员、设施及环境4个方面安全影响因素信息；Basic information collection, including the collection of information on four aspects of safety influencing factors: management, personnel, facilities and environment; 构建油气储运站场安全评估的多层次综合评估结构，确立了主因素以及隶属于每个主因素的下层的子指标因素；Build a multi-level comprehensive evaluation structure for the safety evaluation of oil and gas storage and transportation stations, and establish the main factors and the sub-index factors that belong to the lower layers of each main factor; 确定指标评语变量，并相应确定指标评语变量值区间；Determine the index comment variable, and determine the value interval of the index comment variable accordingly; 基于所述指标评语变量值区间，生成标准云；generating a standard cloud based on the variable value interval of the index comment; 计算获得子指标因子综合权重；Calculate and obtain the comprehensive weight of sub-indicator factors; 专家针对指标进行打分，收集打分数据；Experts score indicators and collect scoring data; 基于所述子指标因子综合权重和所述打分数据，生成综合云；generating a comprehensive cloud based on the comprehensive weights of the sub-index factors and the scoring data; 对所述标准云和所述综合云进行相似度计算；performing similarity calculation on the standard cloud and the comprehensive cloud; 将获得的所述相似度值与所述指标评语变量值区间进行比较，判别油气储运场站安全级别。The obtained similarity value is compared with the variable value interval of the index comment to determine the safety level of the oil and gas storage and transportation station. 2.根据权利要求1所述的基于云模糊层次分析法的油气储运站场安全等级评估方法，其特征在于，步骤2中所述主因素包括管理、人员、设施及环境，所述隶属于每个主因素的下层的子指标因素，包括：2. The method for evaluating the safety level of oil and gas storage and transportation stations based on cloud fuzzy AHP according to claim 1, wherein the main factors described in step 2 include management, personnel, facilities and the environment, and the The sub-indicator factors below each main factor include: 管理主因素包括安全操作规程、监督检查制度及安全政策三个子指标因素；The main factors of management include three sub-index factors of safety operation procedures, supervision and inspection system and safety policy; 人员主因素包括岗前培训、人员配备、疲劳度及技能和知识四个子指标因素；The main factor of personnel includes four sub-indicator factors of pre-job training, staffing, fatigue, and skills and knowledge; 设施主因素包括设施布局、检修情况、计测仪表检验及设施耐腐蚀性四个子指标因素；The main factors of facilities include four sub-index factors of facility layout, maintenance situation, measurement instrument inspection and facility corrosion resistance; 环境主因素包括温度、湿度、噪声及卫生情况四个子指标因素。The main environmental factors include four sub-index factors of temperature, humidity, noise and sanitation. 3.根据权利要求1所述的基于云模糊层次分析法的油气储运站场安全等级评估方法，其特征在于，所述确定指标评语变量，并相应确定指标评语变量值区间，包括：3. The method for evaluating the safety level of oil and gas storage and transportation stations based on cloud fuzzy analytic hierarchy process according to claim 1, is characterized in that, described determining the index comment variable, and correspondingly determining the index comment variable value interval, comprising: 所述指标评语变量为对油气储运场站安全级别进行的定性描述，确定为“差”、“一般”、“良好”以及“优秀”，各级别的量化值区间分别定义为[0，4)、[4，7)、[7，9)和[9，10)。The index comment variable is a qualitative description of the safety level of oil and gas storage and transportation stations, which is determined as "poor", "average", "good" and "excellent", and the quantitative value interval of each level is defined as [0, 4 ), [4, 7), [7, 9) and [9, 10). 4.根据权利要求1所述的基于云模糊层次分析法的油气储运站场安全等级评估方法，其特征在于，所述生成标准云，具体包括：4. The method for evaluating the safety level of oil and gas storage and transportation stations based on cloud fuzzy AHP according to claim 1, wherein the generating standard cloud specifically includes: 根据指标评语变量区间计算标准云数字特征，所述标准云数字特征包括期望Ex，熵En和超熵He，其中，期望Ex代表定性描述在论域的分布，熵En反映了定性概念的随机性和模糊性，超熵He是指标评语变量凝聚性的度量，各数字特征计算方法如下：Calculate the standard cloud numerical features according to the variable interval of the index comment, the standard cloud numerical features include expectation Ex, entropy En and super entropy He, wherein, expectation Ex represents the distribution of qualitative description in the universe of discourse, and entropy En reflects the randomness of qualitative concepts and fuzziness, the super entropy He is a measure of the cohesion of the index comment variables, and the calculation method of each digital feature is as follows:
Figure FDA0002832427500000011
Figure FDA0002832427500000011
Figure FDA0002832427500000021
Figure FDA0002832427500000021
 He＝λEhHe=λEh 式中，xmax与xmin分别是指标评语变量区间的上限与下限；λ是一个用于表示熵En与超熵He之间线性关系的系数；In the formula, x max and x min are the upper and lower limits of the index comment variable interval, respectively; λ is a coefficient used to represent the linear relationship between entropy En and super-entropy He; 将标准云数字特征期望Ex，熵En和超熵He输入前向云生成器，前向云生成器输出N个云滴Drop(xi，yi)形成标准云，其中，yi的计算方法如下：The standard cloud digital feature expectation Ex, entropy En and super entropy He are input into the forward cloud generator, and the forward cloud generator outputs N cloud droplets Drop(x i , y i ) to form a standard cloud, where, the calculation method of y i as follows:
Figure FDA0002832427500000022
Figure FDA0002832427500000022
 式中，S为一个以Ex为期望，He为标准差的正态随机数。In the formula, S is a normal random number with Ex as the expectation and He as the standard deviation. 5.根据权利要求1所述的基于云模糊层次分析法的油气储运站场安全等级评估方法，其特征在于，所述计算获得子指标因子综合权重，具体包括：5. The method for evaluating the safety level of oil and gas storage and transportation stations based on cloud fuzzy AHP according to claim 1, wherein the calculation obtains the comprehensive weight of the sub-index factor, specifically comprising: 步骤5.1：采用三角模糊数量化各主因素和各子指标因素的重要程度，分别构造关于主因素的模糊一致判别矩阵和关于子指标因素的模糊一致判别矩阵，并分别相应获得主因素因子权重和子指标因子权重；Step 5.1: Use triangular fuzzy quantification to quantify the importance of each main factor and each sub-indicator factor, respectively construct a fuzzy consistent discriminant matrix about the main factor and a fuzzy consistent discriminant matrix about the sub-indicator factors, and obtain the main factor factor weights and sub-indicator factors accordingly. index factor weight; 步骤5.2：一致性检验，若两个模糊一致判别矩阵是一致的，则进入步骤5.3，否则，返回步骤5.1重新进行重要程度比较；Step 5.2: Consistency test, if the two fuzzy consistency discriminant matrices are consistent, go to step 5.3, otherwise, go back to step 5.1 to compare the importance again; 步骤5.3：根据所述主因素因子权重和所述子指标因子权重，获得子指标因子综合权重。Step 5.3: According to the main factor factor weight and the sub-indicator factor weight, obtain the comprehensive weight of the sub-indicator factor. 6.根据权利要求5所述的基于云模糊层次分析法的油气储运站场安全等级评估方法，其特征在于，通过将所述主因素因子权重和所述子指标因子权重相乘即可获得所述子指标因子综合权重。6. The method for evaluating the safety level of oil and gas storage and transportation stations based on cloud-fuzzy AHP according to claim 5, wherein the method can be obtained by multiplying the weight of the main factor factor and the weight of the sub-index factor. Comprehensive weights of the sub-index factors. 7.根据权利要求1所述的基于云模糊层次分析法的油气储运站场安全等级评估方法，其特征在于，所述生成综合云，具体包括：7. The method for evaluating the safety level of oil and gas storage and transportation stations based on cloud fuzzy AHP according to claim 1, wherein the generation of a comprehensive cloud specifically includes: 步骤7.1：根据专家的打分数据，分别计算评价各子指标因素的云数字特征；Step 7.1: Calculate and evaluate the cloud digital features of each sub-indicator factor according to the expert's scoring data; 步骤7.2：根据云数字特征与子指标因子综合权重计算综合云数字特征；Step 7.2: Calculate the comprehensive cloud digital features according to the comprehensive weight of cloud digital features and sub-index factors; 步骤7.3：将综合云数字特征作为前向云生成器的输入，生成一定规模的云滴形成综合云。Step 7.3: The synthetic cloud digital feature is used as the input of the forward cloud generator, and a certain scale of cloud droplets is generated to form a synthetic cloud. 8.根据权利要求7所述的基于云模糊层次分析法的油气储运站场安全等级评估方法，其特征在于，步骤7.1中所述分别计算评价各子指标因素的云数字特征中，对于任一子指标因素，其云数字特征均包括评价云期望Exed，熵Ened，超熵Heed，各云数字特征的计算如下：8. The method for evaluating the safety level of oil and gas storage and transportation stations based on cloud fuzzy analytic hierarchy process according to claim 7, is characterized in that, in step 7.1, calculate and evaluate respectively in the cloud digital feature of each sub-index factor, for any A sub-index factor, its cloud digital features include evaluation cloud expectation Ex ed , entropy En ed , super entropy He ed , the calculation of each cloud digital feature is as follows:
Figure FDA0002832427500000031
Figure FDA0002832427500000031
Figure FDA0002832427500000032
Figure FDA0002832427500000032
Figure FDA0002832427500000033
Figure FDA0002832427500000033
 式中，E为专家数量，V是样本方差。where E is the number of experts and V is the sample variance. 9.根据权利要求7所述的基于云模糊层次分析法的油气储运站场安全等级评估方法，其特征在于，步骤7.2中所述根据评价云数字特征与子指标因子综合权重计算综合云数字特征，综合云期望Exs，熵Ens，超熵Hes计算方法如下：9. The method for evaluating the safety level of oil and gas storage and transportation stations based on cloud fuzzy analytic hierarchy process according to claim 7, is characterized in that, described in step 7.2, according to evaluating cloud number feature and sub-index factor comprehensive weight calculation comprehensive cloud number characteristics, the comprehensive cloud expectation Ex s , the entropy En s , and the super entropy He s are calculated as follows:
Figure FDA0002832427500000034
Figure FDA0002832427500000034
Figure FDA0002832427500000035
Figure FDA0002832427500000035
Figure FDA0002832427500000036
Figure FDA0002832427500000036
 式中，D为子指标因素个数，ωd为子指标因子综合权重。In the formula, D is the number of sub-indicator factors, and ω d is the comprehensive weight of sub-indicator factors. 10.根据权利要求1-9任一所述的基于云模糊层次分析法的油气储运站场安全等级评估方法，其特征在于，对所述标准云和所述综合云进行相似度计算，具体包括：10. The method for evaluating the safety level of oil and gas storage and transportation stations based on the cloud fuzzy analytic hierarchy process according to any one of claims 1-9, characterized in that, similarity calculation is performed on the standard cloud and the comprehensive cloud, specifically include: 步骤8.1：在综合云中随机选取一个云滴Drop(xi，yi)；Step 8.1: Randomly select a cloud drop Drop(x i , y i ) in the comprehensive cloud; 步骤8.2：在标准云中计算次生云滴Drop(xi，θi)；Step 8.2: Calculate the secondary cloud droplet Drop( xi , θ i ) in the standard cloud; 重复步骤8.1与步骤8.2生成N个θi，然后计算相似度：Repeat steps 8.1 and 8.2 to generate N θ i , and then calculate the similarity:
Figure FDA0002832427500000037
Figure FDA0002832427500000037
CN202011453486.6A 2020-12-11 2020-12-11 Oil and gas storage and transportation station safety grade evaluation method based on cloud fuzzy analytic hierarchy process Pending CN112508416A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011453486.6A CN112508416A (en) 2020-12-11 2020-12-11 Oil and gas storage and transportation station safety grade evaluation method based on cloud fuzzy analytic hierarchy process

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011453486.6A CN112508416A (en) 2020-12-11 2020-12-11 Oil and gas storage and transportation station safety grade evaluation method based on cloud fuzzy analytic hierarchy process

Publications (1)

Publication Number Publication Date
CN112508416A true CN112508416A (en) 2021-03-16

Family

ID=74973267

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011453486.6A Pending CN112508416A (en) 2020-12-11 2020-12-11 Oil and gas storage and transportation station safety grade evaluation method based on cloud fuzzy analytic hierarchy process

Country Status (1)

Country Link
CN (1) CN112508416A (en)

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113221332A (en) * 2021-04-20 2021-08-06 自然资源部第三海洋研究所 Coastal erosion vulnerability assessment method based on cloud model theory
CN113420147A (en) * 2021-06-22 2021-09-21 中国特种设备检测研究院 Special equipment accident reason identification method and system
CN114037290A (en) * 2021-11-11 2022-02-11 江苏安防科技有限公司 Comprehensive pipe gallery operation health degree evaluation method based on cloud model
CN115062935A (en) * 2022-06-02 2022-09-16 华南理工大学 Method for constructing hydrogen fuel cell automobile safety evaluation system
CN115982960A (en) * 2022-12-08 2023-04-18 国家管网集团北方管道有限责任公司 Intelligent risk prevention and control capability evaluation method for pipeline oil transportation station

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105260861A (en) * 2015-11-25 2016-01-20 海南电网有限责任公司 Comprehensive risk assessment method for electric vehicle battery replacement station
CN110705841A (en) * 2019-09-12 2020-01-17 杭州电子科技大学 A safety assessment method for chemical production based on improved fuzzy analytic hierarchy process
US20200104440A1 (en) * 2018-09-30 2020-04-02 Wuhan University Method for evaluating state of power transformer

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105260861A (en) * 2015-11-25 2016-01-20 海南电网有限责任公司 Comprehensive risk assessment method for electric vehicle battery replacement station
US20200104440A1 (en) * 2018-09-30 2020-04-02 Wuhan University Method for evaluating state of power transformer
CN110705841A (en) * 2019-09-12 2020-01-17 杭州电子科技大学 A safety assessment method for chemical production based on improved fuzzy analytic hierarchy process

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113221332A (en) * 2021-04-20 2021-08-06 自然资源部第三海洋研究所 Coastal erosion vulnerability assessment method based on cloud model theory
CN113420147A (en) * 2021-06-22 2021-09-21 中国特种设备检测研究院 Special equipment accident reason identification method and system
CN113420147B (en) * 2021-06-22 2024-01-26 中国特种设备检测研究院 Special equipment accident cause identification method and system
CN114037290A (en) * 2021-11-11 2022-02-11 江苏安防科技有限公司 Comprehensive pipe gallery operation health degree evaluation method based on cloud model
CN115062935A (en) * 2022-06-02 2022-09-16 华南理工大学 Method for constructing hydrogen fuel cell automobile safety evaluation system
CN115982960A (en) * 2022-12-08 2023-04-18 国家管网集团北方管道有限责任公司 Intelligent risk prevention and control capability evaluation method for pipeline oil transportation station
CN115982960B (en) * 2022-12-08 2023-09-01 国家管网集团北方管道有限责任公司 Intelligent risk prevention and control capability evaluation method for pipeline oil delivery station

Similar Documents

Publication Publication Date Title
CN112508416A (en) Oil and gas storage and transportation station safety grade evaluation method based on cloud fuzzy analytic hierarchy process
CN102496069B (en) Cable multimode safe operation evaluation method based on fuzzy analytic hierarchy process (FAHP)
CN110659814A (en) A method and system for risk assessment of power grid operations based on entropy weight method
CN103246762A (en) Method of comprehensive evaluation for simulation credibility of electric propulsion system
CN101853320A (en) A Fuzzy Comprehensive Evaluation Method Applicable to Aircraft Structure Corrosion Damage
CN107909277A (en) A kind of substation's Environmental Protection Level appraisal procedure based on Fuzzy AHP
CN111797364A (en) Landslide multilevel safety evaluation method based on composite cloud model
CN115481792A (en) Tunnel geological forecasting method and system based on rough set and cloud model
CN109086997A (en) A kind of gas distribution station safe evaluation method based on BP neural network
CN110009241B (en) Method and device for evaluating fire safety level of in-service power cable channel
CN104218571B (en) A kind of running status appraisal procedure of wind power plant
CN105912857A (en) Selection and configuration method of distribution equipment state monitoring sensors
CN117196286A (en) Building construction safety risk pressure evaluation method based on toughness city theory
CN103198362A (en) Method for coal mine safety evaluation
CN117575307A (en) A dynamic cycle grading system and method for power grid security risk assessment
CN111882238A (en) Structural health assessment method of portal crane based on cloud model and EAHP
CN115953046A (en) Method for evaluating comprehensive capability of 10kV distribution network uninterrupted operating personnel
CN106709522B (en) High-voltage cable construction defect classification method based on improved fuzzy trigonometric number
CN117575312A (en) Port facility operation security risk evaluation method based on fuzzy comprehensive evaluation
CN105912775A (en) Multimodal modeling method for vehicle axle load data of bridge weight-in-motion system
CN112184040B (en) A platform for software engineering capability assessment based on behavior and learning data
CN116090869A (en) Sustainable urban physical examination method and system
CN115640990A (en) Semi-quantitative evaluation method for water damage disaster risk of oil and gas pipeline crossing river
Yu et al. Fuzzy comprehensive approach based on AHP and entropy combination weight for pipeline leak detection system performance evaluation
CN112990501A (en) Delphi-FCE-based general vehicle equipment maintenance guarantee capability assessment method

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination