Chemical enterprise limited space operation risk early warning method and system

Abstract

The invention provides a chemical enterprise limited space operation risk early warning method, which comprises the steps of selecting chemical enterprise limited space operation accident cases, obtaining accident causes, and constructing a limited space operation accident fault tree; carrying out statistics on basic events in the accident fault tree, determining the frequency of accidents caused by each basic event, and determining the occurrence frequency of each basic event; based on the fault tree, determining a target layer, a criterion layer and an index layer of an analytic hierarchy process; respectively constructing a criterion layer judgment matrix and an index layer judgment matrix according to the frequency of the basic event of the accident tree; performing normalization processing on the judgment matrix; performing consistency check on the judgment matrix, and after the consistency check is passed, obtaining key indexes which have great influence on the operation risk in the limited space of the chemical enterprise; by monitoring the change of the key index, the risk situation of the limited space operation of the chemical enterprise is objectively evaluated. According to the method, the accident fault tree and the analytic hierarchy process are organically combined, so that the accuracy and reliability of risk early warning are effectively improved.

Description

Technical Field

[0001] This application relates to the technical field of chemical production, and particularly to a risk early warning method and system for confined space operations in chemical enterprises. Background Art

[0002] In the production of chemical enterprises, the risks of confined space operations are high and safety accidents occur frequently. Confined spaces such as reaction kettles and pipelines are characterized by enclosed spaces and poor ventilation, which are prone to accumulating toxic and harmful gases and flammable and explosive substances, and may also be oxygen-deficient, seriously threatening the lives of operating personnel.

[0003] At present, the problems of common risk early warning methods are prominent. The experience judgment method relies on personal experience, has strong subjectivity, and there are large differences in judgments among different personnel. It is easy to make misjudgments in the face of complex or new risks. The single-index monitoring method only focuses on individual parameters, such as only measuring the concentration of oxygen or a certain toxic gas, and it is difficult to reflect the overall risk. The static assessment method cannot update the risk assessment in real time when the environment, equipment, etc. change during the operation after the pre-operation assessment. However, the chemical production environment is dynamically changeable, and changes in raw materials, equipment status, etc. often lead to changes in risks.

[0004] Therefore, there is an urgent need for a more scientific, accurate, and real-time risk early warning method and system. Combining advanced technologies to improve the safety of confined space operations in chemical enterprises is the starting point of this patent research. Summary of the Invention

[0005] This application provides a risk early warning method and system for confined space operations in chemical enterprises, and its advantage is that it can more comprehensively warn of the risks of confined space operations and improve the accuracy and reliability of early warning.

[0006] To solve the above technical problems, the technical solution of the present invention is as follows:

[0007] On the one hand, this application provides a risk early warning method for confined space operations in chemical enterprises, including the following steps:

[0008] Step 1: Select accident cases of confined space operations in chemical enterprises from the OSHA case library, determine the accidents and their causes, and construct a fault tree for confined space operation accidents based on this;

[0009] Step 2: Statistically analyze the basic events in the accident fault tree, count the frequencies of each basic event causing the accident to occur, and then determine the occurrence frequency of each basic event;

[0010] Step 3: Based on the fault tree, determine the target layer, criterion layer, and index layer of the analytic hierarchy process;

[0011] Step 4: Construct a criterion layer judgment matrix and an index layer judgment matrix respectively according to the frequencies of the basic events of the accident tree;

[0012] Step 5: Normalize the judgment matrix and calculate the weights of each index;

[0013] Step 6: Conduct a consistency test on the judgment matrix. After passing the consistency test, obtain the key indicators that have a greater impact on the risk of confined space operations in chemical enterprises;

[0014] Step 7: Objectively evaluate the current risk status of confined space operations in chemical enterprises by monitoring the changes in key indicators.

[0015] Furthermore, in Step 2, the frequency P of the basic event is calculated as follows:

[0016]

[0017] where M is the frequency of secondary causes and f n is the frequency of tertiary causes.

[0018] Furthermore, take the top event as the target layer of the analytic hierarchy process model, take the primary causes as the criterion layer of the analytic hierarchy process model, and take the tertiary causes as the index layer of the analytic hierarchy process model;

[0019] Furthermore, in Step 4, construct a judgment matrix based on the occurrence frequencies of each basic event and the importance scale table of the analytic hierarchy process model.

[0020] Furthermore, in Step 5, the normalization process of the judgment matrix is calculated as follows:

[0021]

[0022] Furthermore, in Step 5, sum the rows of the normalized vector matrix, and the calculation formula is as follows:

[0023]

[0024] Furthermore, in Step 5, calculate the weights of each index, and the calculation formula is as follows:

[0025]

[0026] Furthermore, in Step 6, conduct a consistency test, and the calculation formula is as follows:

[0027] Calculate the maximum eigenvalue of the vector matrix:

[0028]

[0029] Calculate the consistency index:

[0030]

[0031] Calculate the consistency ratio CR:

[0032]

[0033] On the other hand, the present application provides a risk early warning system for confined space operations in chemical enterprises, including: a memory and a processor; wherein, the memory stores a computer program, and when the program is executed by the processor, it can implement the risk early warning method for confined space operations in chemical enterprises described in any one of claims 1-8.

[0034] To sum up, the beneficial effects of the present application are as follows: The present invention selects accident cases of confined space operations in chemical enterprises from the OSHA case library, deeply analyzes and obtains the accident causes, and constructs a fault tree for confined space operation accidents based on this; counts the basic events in the accident fault tree, clarifies the frequency of each basic event leading to the accident, and further determines the occurrence frequency of each basic event; based on the fault tree, determines the target layer, criterion layer, and index layer of the analytic hierarchy process; constructs the criterion layer judgment matrix and index layer judgment matrix respectively according to the frequency of the basic events of the accident tree; normalizes the judgment matrix; conducts a consistency test on the judgment matrix, and after passing the consistency test, obtains the key indicators that have a greater impact on the risk of confined space operations in chemical enterprises; objectively evaluates the current risk status of confined space operations in chemical enterprises by monitoring the changes of the key indicators. By organically combining the accident fault tree and the analytic hierarchy process, the present invention effectively improves the accuracy and reliability of risk early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 is a flowchart of the risk early warning method for confined space operations in chemical enterprises of the present invention;

[0036] Figure 2 is a fault tree diagram of confined space operations in chemical enterprises of the present invention;

[0037] Figure 3 is a warning dimension diagram of confined space operations in chemical enterprises of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0038] The following will describe in detail the specific embodiments of the present application with reference to the accompanying drawings.

[0039] A specific embodiment of the present application provides a warning method for confined space operations in chemical enterprises. Referring to Figure 1 , the method includes the following steps:

[0040] Step 1: Select accident cases of confined space operations in chemical enterprises from the OSHA case library, deeply analyze and obtain the accident causes, and construct a fault tree for confined space operation accidents based on this.

[0041] In this embodiment, as Figure 2As shown in the figure, taking the accident cases of confined space operations in chemical enterprises in the OSHA case database as the starting point, the entire accident process is deeply analyzed, and all the causative factors covering the accident are comprehensively sorted out. On this basis, the poisoning and asphyxiation accident in confined space operations is set as the top event, and analysis and sorting are carried out from the four dimensions of "human factors, object factors, environmental factors, and management factors" to explore the intermediate events leading to the occurrence of the top event. Subsequently, these intermediate events are further disassembled to find the direct causes of the accident and convert them into basic events, thus successfully constructing a fault tree for confined space operation accidents. Figure 2 The meanings represented by the symbols in the accident fault tree are shown in Table 1.

[0042] In terms of human factors, in the scenario of confined space operations, human factors are one of the key factors leading to accidents. Among them, unsafe behavior, as the core of human factors, largely depends on the individual state. The unsafe behavior of operators can directly trigger accidents. We divide the unsafe behavior of construction workers into two categories: violations and human errors. A violation refers to an operator deviating from relevant norms during operation, such as habitual violations, lack of safety awareness, working without a license, not following the plan during construction, and risky behaviors. Human error refers to an operator making a wrong judgment and failing to correctly identify dangerous situations during confined space operations, such as physical discomfort.

[0043] Table 1 Accident Fault Tree Symbol Table

[0044] Event Name Number Event Name Number Event Name Number Poisoning and Suffocation Accident T Lack of Daily Management <![CDATA[M 42 > Excessive Concentration of Harmful Gases <![CDATA[X 12 > Human Factors <![CDATA[M1]]> Habitual Violation <![CDATA[X1]]> Failure to Ventilate and Replace <![CDATA[X 13 > Object Factors <![CDATA[M2]]> Working Without a License <![CDATA[X2]]> Insufficient Lighting <![CDATA[X 14 > Environmental Factors <![CDATA[M3]]> Failure to Construct According to the Construction Plan <![CDATA[X3]]> Severe Weather <![CDATA[X 15 > Management Factors <![CDATA[M4]]> Risky Entry into Confined Spaces <![CDATA[X4]]> Failure to Detect Gas Concentration Before Operation <![CDATA[X 16 > Violation <![CDATA[M 11 > Operation Error <![CDATA[X5]]> Failure to Equip with Emergency Rescue Equipment <![CDATA[X 17 > Human Error <![CDATA[M 12 > Physical Discomfort <![CDATA[X6]]> No On-site Supervision <![CDATA[X 18 > Failure of Safety Facilities <![CDATA[M 21 > Failure of Gas Detection Equipment <![CDATA[X7]]> Insufficient Technical Disclosure <![CDATA[X 19 > Insufficient Protective Measures <![CDATA[M 22 > Failure of Ventilation and Exhaust Equipment <![CDATA[X8]]> Insufficient Pre-job Training <![CDATA[X 20 > Harsh Construction Environment <![CDATA[M 31 > Failure to Wear Protective Equipment <![CDATA[X9]]> No Emergency Drill <![CDATA[X 21 > Harsh Natural Environment <![CDATA[M 32 > Unqualified Protective Equipment <![CDATA[X 10 > Failure to Develop Operating Procedures <![CDATA[X 22 > Lack of On-site Management <![CDATA[M 41 > Accidental Detachment of Protective Equipment <![CDATA[X 11 >

[0045] Step 2: Count the basic events in the accident fault tree, clarify the frequency of each basic event leading to the accident, and then determine the frequency of occurrence of each basic event;

[0046] In this embodiment, according to the structure of the accident fault tree, the accident causative factors are classified into levels and made to correspond to each other. Among them, intermediate events correspond to primary and secondary causative factors, and basic events correspond to tertiary causative factors. The specific corresponding relationship is shown in Table 2.

[0047] Based on the existing data, we organize and count the frequency of the accident causative factors leading to the accident, and then calculate the frequency of the tertiary causative factors leading to the accident, that is, the frequency of the basic events. The specific calculation method is as follows:

[0048] The frequency of the secondary causative factors leading to the accident is M, {f1, f2, ···, f n} is the frequency of the tertiary causative factors leading to the accident, and P is the frequency of the basic event, then:

[0049]

[0050] Table 2 Accident Causes and Their Corresponding Occurrence Probabilities

[0051]

[0052] Step 3: Based on the fault tree, determine the target layer, criterion layer, and index layer of the analytic hierarchy process;

[0053] Take the top event as the target layer of the analytic hierarchy model, the primary causes as the criterion layer of the analytic hierarchy model, and the tertiary causes as the index layer of the analytic hierarchy model; the specific corresponding relationships are shown in Table 3.

[0054] Table 3 Construction Table of Analytic Hierarchy Model

[0055]

[0056]

[0057] Step 4: Construct the criterion layer judgment matrix and the index layer judgment matrix respectively according to the frequencies of the basic events of the fault tree;

[0058] An important task of the analytic hierarchy process is to establish the corresponding judgment matrix. Through the matrix form, the importance degrees among various risk index factors can be identified. The matrix form can clearly determine the correlation relationship between two factors through the row and column method. Table 4 is the judgment table of each index weight.

[0059] Table 4 Importance Scale Table of Analytic Hierarchy Model

[0060] Scale Meaning and Explanation 1 The importance of element i and element j to the upper-level factor is the same 3 Element i is slightly more important than element j 5 Element i is more important than element j 7 Element i is much more important than element j 9 Element i is extremely more important than element j 2n The importance of element i and element j is between 2n - 1 and 2n + 1 Reciprocal The result of comparing index i and index j

[0061] Construct the criterion layer judgment matrix as follows:

[0062]

[0063] Construct the index layer judgment matrix as follows:

[0064]

[0065] Step 5, normalize the judgment matrix and calculate the weights of each index. The calculation formula:

[0066]

[0067] Take matrix B1 as an example for normalization. The result after normalizing the judgment matrix is as follows:

[0068]

[0069] Sum the normalized vector matrix by rows. The calculation formula is as follows:

[0070]

[0071] In matrix B1, ω1 = 2.0232, ω2 = 1.3825, ω3 = 0.8024, ω4 = 0.8024, ω5 = 0.5548, ω6 = 0.4344

[0072] Calculate the weights of each index. The calculation formula is as follows:

[0073]

[0074] The weights of each index are obtained as shown in Table 5:

[0075] Table 5 Weights of Each Index

[0076]

[0077]

[0078] In step 6, perform a consistency test.

[0079] The analysis process of the consistency test for the constructed matrix is as follows:

[0080] 1) Calculate the maximum eigenvalue of the vector matrix

[0081] 2) Calculate the consistency index CI ≤ 0.1.

[0082] 3) Find the corresponding average random consistency index RI. For n = 1, 2, 3,..., 9, the values of RI are given in Table 6.

[0083] 4) Calculate the consistency ratio CR, CR ≤ 0.1. When CR ≤ 0.1, it is judged that the consistency of the matrix is within the acceptable range; otherwise, the judgment matrix should be appropriately corrected.

[0084] Table 6 RI and the Corresponding Critical Eigenvalues

[0085]

[0086] Calculate the values of CI and CR according to the critical eigenvalues of RI in Table 6 as follows:

[0087] CI = 0.0236, CR = 0.0265

[0088] CI1 = 0.0302, CR1 = 0.0246

[0089] CI2 = 0.0344, CR2 = 0.0313

[0090] CI3 = 0.0237, CR3 = 0.0266

[0091] CI4 = 0.0460, CR4 = 0.0351

[0092] Both the matrix consistency and the consistency ratio are less than 0.1, and it can pass the compatibility test from the perspective of the theoretical analysis value.

[0093] As can be seen from Table 5, the key indicators that have a greater impact on the risk of confined space operations in chemical enterprises include habitual violations, working without a license, no emergency drills, not wearing protective equipment, no on-site supervision, not constructing according to the construction plan, and taking risks to enter confined spaces, etc. By monitoring the weight changes of the key indicators affecting the confined space operations in chemical enterprises, the poisoning and asphyxiation accidents during the confined space operations in chemical enterprises can be predicted and pre-warned.

[0094] The present invention uses a method that combines the analytic hierarchy process model with the accident fault tree to analyze and predict the risks of confined space operations. Compared with the traditional fault tree analysis method, this method is more accurate and efficient. At the same time, by predicting potential accident causes with the analytic hierarchy process model, the operation plan can be adjusted in a targeted manner in advance, effectively reducing the operation risks and eliminating potential safety hazards.

[0095] Another specific embodiment of the present application provides a risk early warning system for confined space operations in chemical enterprises, including: a memory and a processor; wherein, the memory stores a computer program, and when the program is executed by the processor, it can implement the risk early warning method for confined space operations in chemical enterprises as described above.

[0096] The parts not involved in the present invention are the same as or implemented by the prior art.

[0097] The above are only the preferred embodiments of the present application. It should be noted that for those of ordinary skill in the art, without departing from the creative concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application.

Claims

1. A confined space operation risk warning method for a chemical enterprise, characterized in that: The following steps are involved: Step 1: Select accident cases of confined space operations in chemical enterprises, determine the accidents and their causes, and construct a fault tree for confined space operations accidents; Step 2: Count the basic events in the accident fault tree, count the frequency of accidents caused by each basic event, and then determine the frequency of each basic event; Step 3: Based on the fault tree, determine the target layer, criterion layer and indicator layer of the hierarchical analysis method; Step 4: According to the frequency of basic events in the accident tree, construct the criterion layer judgment matrix and the indicator layer judgment matrix respectively; Step 5: Normalize the judgment matrix and calculate the weight of each indicator; Step 6: Conduct consistency test on the judgment matrix. After the consistency test is passed, the key indicators with greater impact on the risk of confined space operations in chemical enterprises are obtained; Step 7: Evaluate the risk status of confined space operations in chemical companies by monitoring changes in key indicators.

2. The method for early warning of risks in confined space operations in chemical enterprises according to claim 1 is characterized in that: In step 2, the frequency P of the basic event is calculated as follows: Among them, M is the frequency of secondary causes, f n It is the frequency of the third-level cause.

3. The method for early warning of risks in confined space operations in chemical enterprises according to claim 1 is characterized in that: In step 3, the top event is used as the target layer of the hierarchical analysis model, the first-level cause is used as the criterion layer of the hierarchical analysis model, and the third-level cause is used as the indicator layer of the hierarchical analysis model.

4. The method for early warning of risks in confined space operations in chemical enterprises according to claim 1 is characterized in that: In step 4, a judgment matrix is ​​constructed based on the occurrence frequency of each basic event and the importance scale table of the hierarchical analysis model.

5. The method for early warning of risks in confined space operations in chemical enterprises according to claim 1 is characterized in that: In step 5, the judgment matrix is ​​normalized, and the calculation formula is as follows:

6. The method for early warning of risks in confined space operations in chemical enterprises according to claim 5 is characterized in that: In step 5, the normalized vector matrix is ​​summed row by row, and the calculation formula is as follows:

7. The method for early warning of risks in confined space operations in chemical enterprises according to claim 6 is characterized in that: In step 5, the weight of each indicator is calculated using the following formula:

8. The method for early warning of risks in confined space operations in chemical enterprises according to claim 1 is characterized in that: In step 6, a consistency check is performed, and the calculation formula is as follows: Compute the largest eigenvalue of a vector matrix: Calculate the consistency index: Calculate the consistency ratio CR:

9. A confined space operation risk warning system for chemical enterprises, characterized in that: include: A memory and a processor; wherein the memory stores a computer program, and when the program is executed by the processor, it can implement the risk warning method for confined space operations in a chemical enterprise as described in any one of claims 1 to 8.

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