Important material transportation risk assessment and prevention method

Abstract

The invention relates to the technical field of railway important material transportation, in particular to an important material transportation risk assessment and prevention method, which qualitatively performs working structure-risk structure decomposition (WBS-RBS decomposition) on important material transportation in a transition stage, improves a 0-1 coupling matrix of a traditional WBS-RBS according to a triangular intuition fuzzy number theory, and improves the risk assessment and prevention of important material transportation. And converting into a triangular intuitionistic fuzzy number based on an expert evaluation language, constructing a linear programming model according to an evaluation result, solving the weight occupied by each expert, and finally obtaining a work-risk comprehensive sorting value. And according to the obtained work-risk sorting value, improvement suggestions and measures are put forward in a targeted manner. According to the method, the risk degree of each work flow and risk factor in the important material transportation in the transition stage can be visually expressed, and improvement measures can be determined and implemented.

Description

Technical Field

[0001] The present invention relates to the technical field of railway important material transportation, and in particular to a method for assessing and preventing risks in the transportation of important materials. Background Art

[0002] As a vital component of national economic development, railway transportation's risk assessment and prevention technologies are crucial for ensuring transportation safety and improving operational efficiency. However, as my country's society and economy enter a transitional phase, new challenges facing railway transportation are gradually emerging. Traditional risk management methods are no longer fully adaptable to these new demands, necessitating in-depth research and the development of new technical solutions tailored to the specific characteristics of this transitional phase.

[0003] The transition phase refers to the gradual return of social and economic activities to a new stable state after a large-scale abnormal event. Railway transportation in this phase differs from both normal operations and emergency transportation. Instead, it involves scattered and uncertain abnormal transportation demands on the basis of normal transportation. For the railway transportation system, this requires greater agility and adaptability in risk assessment and management to cope with these complex and changing demands.

[0004] Current railway transportation risk assessment techniques primarily focus on risk identification, modeling, assessment, and prevention. During the risk identification phase, traditional methods use research and historical data analysis to qualitatively or quantitatively describe potential risk factors within the transportation process. However, traditional risk identification methods are often limited to static scenarios and fail to fully explore emerging risk characteristics during transitional periods. These emerging risks primarily manifest in increased volatility in transportation demand, the increasing frequency of fragmented transportation tasks, and the growing uncertainty in transportation resource allocation. Traditional risk identification frameworks struggle to fully capture these complex factors.

[0005] During the risk modeling phase, traditional classification methods are typically based on a single dimension, such as categorizing risks into equipment, environment, personnel, or management. This model is relatively effective for routine transportation tasks, but during the transition phase, due to the diversity, dynamism, and uncertainty of transportation tasks, a single-dimensional decomposition approach struggles to reveal the correlations between risks and potential hazards within complex transportation processes. Furthermore, the construction of risk models typically relies on historical data or existing risk databases, but risks during the transition phase may not yet have mature historical data references, limiting the applicability of risk models in this scenario.

[0006] Risk assessment methods are a core component of existing railway transport risk management. Traditional assessment methods often employ tools such as fuzzy mathematics, the analytic hierarchy process (AHP), and comprehensive weighting methods. These methods can rank risk factors and provide quantitative assessment results. However, these methods expose two problems in transitional scenarios: First, expert evaluation plays an important role in risk assessment, but existing methods fail to fully consider the subjective emotions and linguistic ambiguity of expert opinions. Experts may give different scores for the same risk in different emotional states, and traditional methods often ignore such differences, resulting in assessment results that may deviate from reality. Second, fuzzy mathematical models are insufficiently capable of expressing ambiguity and uncertainty when dealing with linguistic descriptions. This, especially when dealing with complex transport risk scenarios, restricts the accuracy and stability of assessment results.

[0007] Furthermore, effectively linking risk assessment results with risk prevention measures remains a persistent challenge. Current risk assessment systems typically prioritize identification and prioritization, lacking specific prevention plans tailored to specific risk factors. This creates a disconnect between assessment results and actual operations. During this transitional period, fragmented transportation operations require flexible and efficient risk prevention strategies, a need that is not adequately addressed in traditional risk assessment methods.

[0008] In response to the above problems, the present invention proposes a method for risk assessment and prevention of important material transportation. Based on the characteristics of the transportation of important materials in the transition stage, a new technical solution that can be used to assess and prevent the transportation of important materials in the transition stage is provided through WBS-RBS and triangular intuitionistic fuzzy number theory. Summary of the Invention

[0009] The purpose of the present invention is to provide a method for assessing and preventing risks in the transportation of important materials, qualitatively perform work structure-risk structure decomposition (WBS-RBS decomposition) on the transportation of important materials in the transition phase, improve the 0-1 coupling matrix of the traditional WBS-RBS based on the theory of triangular intuitionistic fuzzy numbers, convert it into triangular intuitionistic fuzzy numbers based on expert evaluation language, and construct a linear programming model based on its evaluation results to solve the weight of each expert, and finally obtain a comprehensive work-risk ranking value. Based on the obtained work-risk ranking value, targeted improvement suggestions and measures are proposed. The present invention can intuitively express the risk level of each work process and risk factor in the transportation of important materials in the transition phase, which is conducive to determining and implementing improvement measures.

[0010] In order to achieve the above technical objectives and the above technical effects, the present invention is implemented through the following technical solutions:

[0011] A method for assessing and preventing risks in the transportation of important materials includes the following steps:

[0012] S1: Clarify each link in the transportation process, identify possible risk factors, classify and refine the risk factors, and form a comprehensive risk structure.

[0013] S2: Decompose the work structure of the transportation process and clarify the subtasks of each link; associate risk factors with subtasks to form a WBS-RBS matrix; each cell in the matrix represents the correlation between a subtask and a risk factor.

[0014] The work structure is broken down into five main work processes: processing and packaging, receiving and warehousing, warehouse management, sorting and outbound delivery, and transportation and distribution, which correspond to 13 sub-tasks. The risk structure is broken down into five main risk factors: personnel factors, equipment factors, material factors, environmental factors, and management factors, which correspond to 25 sub-risk factors.

[0015] S3: Collect experts’ risk evaluation of each subtask, convert the language description into triangular intuitionistic fuzzy numbers, fill in the WBS-RBS matrix, and form a risk assessment matrix.

[0016] S4: Construct a linear programming model to calculate the weight of each expert, consider the expert's emotional state, optimize the weight distribution, and reduce evaluation bias;

[0017] S5: Calculate the comprehensive ranking value of each subtask and identify high-risk tasks based on the ranking value;

[0018] S6: Propose improvement suggestions for high-risk tasks; establish a dynamic adjustment mechanism to monitor risk changes in real time; formulate emergency plans to ensure rapid response to sudden risks.

[0019] Beneficial effects of the present invention:

[0020] This invention establishes a comprehensive risk identification framework through a detailed work breakdown structure (WBS) and risk breakdown structure (RBS). First, the WBS decomposes the transportation process layer by layer, covering every detail from processing and packaging to transportation and distribution. This breakdown not only ensures comprehensive coverage of transportation tasks but also clarifies operational requirements and key milestones for each specific task, thereby improving the accuracy of risk identification. Using the RBS, identified risk factors are systematically categorized according to their source and nature, such as personnel risk, equipment risk, material risk, environmental risk, and management risk. This classification approach not only clarifies the sources of risk, making risk management more targeted, but also ensures that each risk factor is correctly classified and identified through the development of risk mappings (for example, mapping operational errors to personnel risks using the function f: R→C). The underlying mechanism lies in the formation of a comprehensive risk structure by mapping task operations to potential risks, ensuring a systematic and comprehensive risk identification process.

[0021] This invention excels in establishing a correlation between risks and tasks. Using the WBS-RBS decomposition model, it organically links specific transportation tasks with relevant risk factors. The WBS defines the decomposition hierarchy of transportation tasks, detailing the specific operational activities and responsibilities for each task module. The RBS categorizes identified risks into a hierarchical structure, clarifying different risk categories and their specific sources. Using the WBS-RBS decomposition matrix, the solution meticulously associates each subtask with potential risk factors, forming a task-risk mapping. For example, within the transportation and distribution process, the task "Transport of Important Materials" is linked to the risk "Climate Factors" through a matrix, identifying specific risk exposure points. Risk prioritization is determined by calculating the probability of occurrence and the degree of impact, further ranking risks (e.g., using RPI = P × I to determine high-priority risks). This approach not only enhances the hierarchical nature of risk assessment but also allows for quantifiable analysis of the risk exposure of each task.

[0022] During the risk assessment phase, this method introduces triangular intuitionistic fuzzy numbers to address the subjective bias introduced by verbal descriptions in traditional risk assessments. Experts' verbal descriptions of the importance of task risks (e.g., "strong" or "weak") are converted into triangular intuitionistic fuzzy numbers. These values, composed of membership, non-membership, and hesitation, quantitatively reflect the experts' perception of the risk level. During the calculation of the comprehensive fuzzy numbers, a weighted average of the experts' opinions is used to ensure the objectivity and consistency of the assessment results. The underlying theoretical basis of this method lies in converting the experts' verbal evaluations into quantitative values ​​using triangular intuitionistic fuzzy numbers (e.g., "strong" is converted to T6 = (0.8, 0.2, 0.0)). This method retains the ambiguity of verbal evaluations while introducing a mathematical model to quantify them, effectively eliminating subjective bias. During the assessment, by calculating the subtask risk values ​​(e.g., R = ai - bi), accurate ranking of subtask risks is achieved, providing a scientific basis for the subsequent formulation of preventive measures.

[0023] During the expert evaluation process, the present invention effectively improves the scientific nature of the evaluation by optimizing the expert weight distribution. The initial weights are allocated based on the expert's professional field and project role, but the special feature of the scheme is the introduction of a credibility assessment mechanism. The expert's historical accuracy and consistency analysis are quantified as a credibility score, and weighted correction is performed in combination with the initial weights, so that the optimized weights reflect the contribution of more credible experts to the evaluation results. This weight optimization method minimizes the expert evaluation bias through linear programming, and dynamically corrects the expert's emotional state in the scheme to ensure that the weight distribution is fair and reasonable. This process significantly improves the accuracy and reliability of the evaluation results by adapting the triangular intuitionistic fuzzy number calculation in risk assessment through the adjusted weights. Through this optimization technology, the scheme reflects scientificity and rationality in the decision-making process, so that risk assessment does not only rely on the subjective judgment of experts, but improves the robustness of decision-making through optimization algorithms.

[0024] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0026] Figure 1 It is a schematic diagram of the overall steps of the present invention;

[0027] Figure 2 This is a diagram of the WBS decomposition tree for the important material logistics process;

[0028] Figure 3 This is a risk decomposition diagram for the transportation process of important materials. DETAILED DESCRIPTION

[0029] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0030] Example 1

[0031] The method for assessing and preventing risks in the transportation of important materials described in this embodiment includes the following steps:

[0032] S1: Risk identification and classification: clarify each link in the transportation process, identify possible risk factors, classify and refine the risk factors to form a comprehensive risk structure.

[0033] S2: Construct a WBS-RBS decomposition model: decompose the work structure of the transportation process and clarify the subtasks of each link; associate risk factors with subtasks to form a WBS-RBS matrix; each cell in the matrix represents the correlation between a subtask and a risk factor.

[0034] S3: Risk assessment and triangular intuitionistic fuzzy numbers: Collect experts’ risk evaluation of each subtask, convert the language description into triangular intuitionistic fuzzy numbers, fill in the WBS-RBS matrix, and form a risk assessment matrix.

[0035] S4: Optimize expert weight distribution: Build a linear programming model to calculate the weight of each expert, consider the expert's emotional state, optimize the weight distribution, and reduce evaluation bias;

[0036] S5: Calculate the comprehensive ranking value of work-risk: Calculate the comprehensive ranking value of each subtask and identify high-risk tasks based on the ranking value;

[0037] S6: Develop targeted preventive measures: Propose improvement suggestions for high-risk tasks; establish a dynamic adjustment mechanism to monitor risk changes in real time; develop emergency plans to ensure rapid response to sudden risks.

[0038] In this embodiment, the risk identification and classification step S1 specifically includes the following sub-steps:

[0039] S1.1: Analyze and decompose the transportation process in detail, clarifying the operational requirements, inputs and outputs, and key nodes of each link. This step is carried out through the Work Breakdown Structure (WBS) and flow charts, dividing the transportation process into specific operational links; including:

[0040] W1: Processing and packaging: including packaging, labeling and sealing of materials.

[0041] W2: Receiving and warehousing: Materials are unloaded from transport vehicles to the warehouse and registered for warehousing.

[0042] W3: Warehouse management: monitoring of storage environment, stacking and protection of materials.

[0043] W4: Sorting and delivery: sort materials according to order requirements and load them for transportation.

[0044] W5: Transportation and distribution: Transport vehicles depart from the warehouse and complete terminal delivery.

[0045] Define the transport task set as W = {W1, W2, W3, W4, W5}. Each task Wi contains specific operation activities and is associated with input and output.

[0046] S1.2: After clarifying the processes of each link, identify the possible risk factors in each link one by one. The logic of risk identification is to match the tasks and operations in the transportation process with their potential risks one by one, ensuring that all possible risks are identified without omission; including:

[0047] Processing and Packaging (W1): Insufficient packaging materials or non-compliant specifications. Packaging equipment (e.g., packaging machines, labeling machines) malfunctioning or experiencing performance degradation. Operator fatigue or negligence leading to packaging errors.

[0048] Receiving and Warehousing (W2): Mechanical failure of loading and unloading equipment (e.g., forklift) during the receiving process. Errors in registering incoming materials, resulting in inaccurate data. Wet or contaminated materials, affecting incoming materials quality.

[0049] Warehouse Management (W3): Warehouse equipment (such as shelves and temperature control equipment) may be damaged due to aging or insufficient maintenance. Abnormal storage environments (such as temperature and humidity) may lead to material deterioration. Irregular stacking of materials may pose safety risks.

[0050] Sorting out (W4): Human error during the sorting process leads to sorting errors. Information not synchronized during material outbound delivery leads to data errors. Loading is delayed due to vehicle equipment issues.

[0051] Transportation and Delivery (W5): Delivery vehicles are unable to operate normally due to mechanical failure or fuel shortage. Delivery encounters inclement weather or road obstructions. Goods are damaged during transportation, impacting delivery quality.

[0052] Define the risk factor set for task Wi as Ri = {Ri1, Ri2, ..., Rim}, and map each operational activity to a risk point. For example, the risk factor set for the processing and packaging task (W1) is: R1 = {insufficient materials, equipment failure, operational error}. The final risk factor set for all tasks is: R = R1 ∪ R2 ∪ R3 ∪ R4 ∪ R5, where R encompasses all potential risks in the transportation process.

[0053] S1.3: Systematically classify all identified risk factors according to their source and nature. The classification criteria are divided into the following five categories:

[0054] 1. Human risk (C1): Risks caused by operator error, fatigue or lack of skills.

[0055] 2. Equipment risk (C2): Risks arising from equipment failure, aging or inadequate maintenance.

[0056] 3. Material Risk (C3): Risks arising from material quality issues, improper storage or packaging defects.

[0057] 4. Environmental risks (C4): Risks caused by the natural environment (weather, humidity, temperature, etc.) or traffic conditions.

[0058] 5. Management risk (C5): Risks arising from organizational decision-making errors, poor information transmission or lack of systems.

[0059] Define the risk classification set C = {C1, C2, C3, C4, C5}. Establish a classification mapping f: R → C, and map each risk factor Rij to the corresponding risk category Ck.

[0060] The specific classification rules are as follows: if the risk factor involves the behavior, skills or status of the operator, then f(Rij) = C1; if the risk factor involves the performance or failure of the equipment, then f(Rij) = C2; if the risk factor involves the quality or storage of the materials themselves, then f(Rij) = C3; if the risk factor involves uncontrollable conditions of the external environment, then f(Rij) = C4; if the risk factor involves process management or information systems, then f(Rij) = C5.

[0061] For example, the risk factor “insufficient packaging materials” for processing and packaging (W1) is classified as a material risk (C3), while “operator error” is classified as a personnel risk (C1).

[0062] In this embodiment, step S2 of constructing the WBS-RBS decomposition model specifically includes the following sub-steps:

[0063] S2.1: Clarify the decomposition level of WBS. By decomposing the transportation process layer by layer, clarify the specific activities and responsibilities in each link and form a tree structure of tasks.

[0064] First layer: overall structure of the transport task

[0065] Define the main task modules in the transportation process, denoted as W = {W1, W2, W3, W4, W5}.

[0066] Second layer: subtasks of task modules

[0067] Each task module Wi is further decomposed into specific operation activities, denoted as Wi = {Wi.1, Wi.2, ..., Wi.n}.

[0068] Processing and packaging (W1): including goods and packaging disinfection W1.1, finished product packaging W1.2;

[0069] Receiving and warehousing (W2): includes goods shipment W2.1, receiving and inspecting goods W2.2, and unloading and warehousing W2.3;

[0070] Warehouse Management (W3): including goods storage and inventory W3.1, safety and health management W3.2;

[0071] Sorting and outbound (W4): includes order processing W4.1, picking and packing W4.2, and loading and outbound W4.3;

[0072] Transportation and Distribution (W5): including transportation plan formulation W5.1, transportation of important materials W5.2, and distribution and delivery W5.3;

[0073] The third layer: execution elements of operational activities

[0074] Clarify the specific execution elements involved in the operation activities, such as personnel, equipment, materials, and environment, recorded as E = {E1, E2, E3, E4}.

[0075] For example, W1.2 involves E1 (personnel), E2 (equipment), and E3 (materials).

[0076] S2.2: Construct a risk classification system for RBS, and clarify different categories of risks and their sources by classifying risk factors into hierarchical categories.

[0077] Level 1: Main categories of risk

[0078] Define the main categories of risk, denoted as C = {C1, C2, C3, C4, C5}.

[0079] C1 represents personnel risk, C2 represents equipment risk, C3 represents material risk, C4 represents environmental risk, and C5 represents management risk.

[0080] Level 2: Specific factors of risk categories

[0081] Each risk category Ck is further decomposed into specific risk factors, denoted as Ck = {Ck.1, Ck.2, ..., Ck.m}.

[0082] Human factors (C1): including lack of safety awareness (C1.1), irregular loading and unloading operations (C1.2), failure to carefully check material categories (C1.3), incomplete or irregular reinforcement operations (C1.4), failure to classify and pack materials according to regulations (C1.5), absence of on-site personnel (C1.6), and physical or psychological reasons (C1.7);

[0083] Equipment factors (C2): including damage to material packaging / containers (C2.1), improper maintenance of rolling stock (C2.2), abnormal opening of freight car doors (C2.3), train overturning or derailment (C2.4), failure of environmental control devices in the car (C2.5), and incomplete cleaning of vehicles or tank cars (C2.6);

[0084] Material factors (C3): including flammable and explosive C3.1, counterfeit and inferior products C3.2, perishable C3.3, easily damaged C3.4, and defective products C3.5;

[0085] Environmental factors (C4): including climate factors C4.1, disease transmission C4.2, and terrorist attacks C4.3;

[0086] Management factors (C5): including unreasonable operation plan (C5.1), unreasonable allocation of operation personnel (C5.2), incomplete safety responsibility system (C5.3), and incomplete operation procedures (C5.4);

[0087] The third layer: the manifestation of specific factors

[0088] Clarify the manifestation of specific risk factors, denoted as R = {R1, R2, ..., Rn}.

[0089] For example, C2.2 is represented by R1 (packaging machine failure) and R2 (forklift failure).

[0090] S2.3: Establish WBS-RBS mapping relationship

[0091] The execution elements of each task module Wi and subtask Wi.j are analyzed and their corresponding relationships are established with the risk categories and specific factors of RBS.

[0092] Construct a matrix M, where the rows represent task modules or subtasks Wi, the columns represent risk categories Ck, and the matrix elements Mij indicate whether Wi is associated with Ck.

[0093] If there is a risk of category Ck in task Wi, then Mik = 1, otherwise Mik = 0;

[0094] The matrix is ​​constructed as follows:

[0095]

[0096] For each Wi.j, further refine its corresponding specific risk factor Rm and record its occurrence conditions and impact scope.

[0097] The task-risk mapping function g(Wi.j) = {Rm1, Rm2, ..., Rmp} represents the set of risk factors involved in task Wi.j.

[0098] S2.4: Risk Prioritization Logic

[0099] After establishing the WBS-RBS mapping, risks need to be prioritized.

[0100] Determine the probability of risk occurrence (P): Based on historical data and expert evaluation, calculate the probability of occurrence P of each risk factor, with a value range of [0, 1].

[0101] Determine the risk impact level (I): According to the impact of the risk on the transportation task, set the value range of I, including "very weak", "weak", "weak", "average", "strong", "strong", and "very strong" seven-level evaluation language, corresponding to values ​​1-7;

[0102] Calculate the Risk Priority Index (RPI): RPI = P × I

[0103] Screen high-priority risks; set threshold RPI threshold , such as RPI threshold = 2;

[0104] If RPI ≥ RPI threshold , the risk is judged to be of high priority.

[0105] The priority index of each risk Ri is RPIi = Pi × Ii

[0106] High priority risk set H = {Ri | RPIi ≥ RPI threshold}

[0107] S2.5: Output the WBS-RBS decomposition model, including:

[0108] Work-Based System (WBS): A detailed breakdown of tasks and subtasks.

[0109] Risk Classification Structure (RBS): a detailed classification of risk categories and specific factors;

[0110] WBS-RBS mapping relationship: the correspondence between tasks and risks;

[0111] High-priority risk list: includes a specific list of high-priority risks and their RPI values.

[0112] In this embodiment, the step S3 of risk assessment and triangular intuitionistic fuzzy number specifically includes the following sub-steps:

[0113] The triangular intuitionistic fuzzy number consists of three parts, represented by a triple (a, b, c), where:

[0114] a: Membership degree, which indicates the degree to which an event or task belongs to a certain risk level, and its value range is [0, 1].

[0115] b: Non-membership degree, which indicates the degree to which an event or task does not belong to a certain risk level, and its value range is [0, 1].

[0116] c: Hesitation, which represents the ambiguity or uncertainty of the expert between membership and non-membership. The calculation formula is: c = 1 - a - b

[0117] In order to ensure the logical rigor, it must satisfy: a + b ≤ 1 and a, b, c ∈ [0, 1].

[0118] S3.1: Collect expert opinions. For each subtask, invite multiple experienced experts to evaluate the risk factors.

[0119] S3.2: For each subtask or risk factor, the language evaluation provided by the experts is converted into a predefined triangular intuitionistic fuzzy number table, including seven levels of evaluation language: "very weak", "weak", "weak", "average", "strong", "strong", and "very strong" to describe the importance of the risk; the triangular intuitionistic fuzzy numbers are:

[0120] 1. Very weak: T1 = (0.1, 0.9, 0.0)

[0121] 2. Weak: T2 = (0.2, 0.7, 0.1)

[0122] 3. Weaker: T3 = (0.3, 0.6, 0.1)

[0123] 4. General: T4 = (0.5, 0.5, 0.0)

[0124] 5. Stronger: T5 = (0.7, 0.3, 0.0)

[0125] 6. Strong: T6 = (0.8, 0.2, 0.0)

[0126] 7. Very strong: T7 = (0.9, 0.1, 0.0)

[0127] Assume that there are m subtasks, each task corresponds to the evaluation of n experts, and the experts' linguistic evaluation is mapped into triangular intuitionistic fuzzy numbers Tij = (aij, bij, cij), where i represents the subtask and j represents the expert.

[0128] S3.3: For each subtask risk assessment, combine the opinions of multiple experts into a unified fuzzy number. Assume each expert's weight is wj, where j = 1, 2, ..., n. Typically, the weight satisfies ∑(wj) = 1. Weights can be assigned based on expert experience or a weighting model (such as the AHP method or the entropy weight method).

[0129] The calculation formula of comprehensive fuzzy number Ti is: Ti = (ai, bi, ci)

[0130] ai = ∑(wj·aij) - bi = ∑(wj·bij) - ci = 1 - ai - bi

[0131] The comprehensive membership ai is the weighted average of the memberships of each expert, indicating the overall degree to which the task belongs to this risk level;

[0132] The comprehensive non-membership degree bi is the weighted average of the non-membership degrees of each expert, which indicates the overall degree to which the task does not belong to the risk level;

[0133] The comprehensive hesitation degree ci is directly calculated according to the formula 1 - ai - bi.

[0134] S3.4: After the comprehensive fuzzy number calculation is completed, the risks are quantified and ranked through the following steps:

[0135] The risk value R is based on the principle of maximizing membership and minimizing non-membership, and the formula is as follows: R = ai - bi; R represents the risk value of the subtask. The higher ai is, the higher the possibility that the task is high-risk; the lower bi is, the higher the possibility that the task is not high-risk.

[0136] By calculating the risk value R of each subtask, all subtasks are sorted from high to low according to the risk value.

[0137] S3.5: After completing the risk value calculation and ranking, formulate specific risk response strategies:

[0138] High-risk tasks should be given priority, medium-risk tasks require close attention, and low-risk tasks can be arranged as appropriate based on resource allocation.

[0139] For tasks whose risk values ​​are close to the boundaries of the interval, sensitivity analysis can be used to further verify whether their risk levels are reasonable. The sensitivity analysis process includes:

[0140] Adjust the distribution of expert weights wj and analyze the changes in risk value R.

[0141] Adjust the parameters of membership a and non-membership b, and observe the impact of hesitation c on the evaluation results.

[0142] After the risk assessment is completed, the risk value should be dynamically adjusted based on the actual situation during subsequent project execution. This can be done by reorganizing expert assessments and updating the membership a and non-membership b parameters to ensure that the risk assessment results are accurate and real-time.

[0143] A project consists of three subtasks (A, B, and C), and the risk factor is "high technical difficulty." The three experts' assessment results are as follows:

[0144] Subtask A:

[0145] Expert 1: T1 = (0.6, 0.3, 0.1), weight w1 = 0.4

[0146] Expert 2: T2 = (0.7, 0.2, 0.1), weight w2 = 0.3

[0147] Expert 3: T3 = (0.8, 0.1, 0.1), weight w3 = 0.3

[0148] Subtask B:

[0149] Expert 1: T1 = (0.4, 0.5, 0.1), weight w1 = 0.4

[0150] Expert 2: T2 = (0.5, 0.4, 0.1), weight w2 = 0.3

[0151] Expert 3: T3 = (0.6, 0.3, 0.1), weight w3 = 0.3

[0152] Subtask C:

[0153] Expert 1: T1 = (0.3, 0.6, 0.1), weight w1 = 0.4

[0154] Expert 2: T2 = (0.4, 0.5, 0.1), weight w2 = 0.3

[0155] Expert 3: T3 = (0.5, 0.4, 0.1), weight w3 = 0.3

[0156] Calculation process:

[0157] 1. Comprehensive evaluation:

[0158] Subtask A: a A = 0.4·0.6 + 0.3·0.7 + 0.3·0.8 = 0.68, b A = 0.4·0.3 +0.3·0.2 + 0.3·0.1 = 0.22, c A = 1 - 0.68 - 0.22 = 0.1, comprehensive fuzzy number T A = (0.68,0.22, 0.1).

[0159] Subtask B: a B = 0.4·0.4 + 0.3·0.5 + 0.3·0.6 = 0.48, b B = 0.4·0.5 +0.3·0.4 + 0.3·0.3 = 0.41, c B = 1 - 0.48 - 0.41 = 0.11, comprehensive fuzzy number T B = (0.48,0.41, 0.11).

[0160] Subtask C: a C = 0.4·0.3 + 0.3·0.4 + 0.3·0.5 = 0.38, b C = 0.4·0.6 +0.3·0.5 + 0.3·0.4 = 0.51, c C = 1 - 0.38 - 0.51 = 0.11, comprehensive fuzzy number TC = (0.38,0.51, 0.11).

[0161] Subtask A: R A = 0.68 - 0.22 = 0.46

[0162] Subtask B: R B = 0.48 - 0.41 = 0.07

[0163] Subtask C: R C = 0.38 - 0.51 = -0.13

[0164] Subtask A (risk value 0.46, high risk, priority).

[0165] Subtask B (risk value 0.07, medium risk, low priority).

[0166] Subtask C (risk value -0.13, low risk, no need to focus on it for now).

[0167] In this embodiment, the step S4 of optimizing the expert weight distribution specifically includes:

[0168] Assign initial weights based on the expert's area of ​​expertise, relevant experience, and role in the project. Assume there are n experts with initial weights wi, where Σ(wi) = 1, meaning the sum of the weights is 1.

[0169] The experts' credibility is then assessed. This can be based on a variety of factors, including historical accuracy, consistency analysis, and feedback ratings. Each expert is assigned a credibility score, Ui, which can be a quantitative indicator of their historical performance. For example, the accuracy of their past predictions or their agreement with other experts can be calculated.

[0170] After obtaining the credibility score, the adjusted weight is calculated. This step is achieved by combining the initial weight with the credibility score. The calculation formula for the adjusted weight w'i is: w'i = (Ui ·wi) / Σ(Ui ·wi)

[0171] This formula ensures that the adjusted weights also satisfy the requirement that the sum of the weights is 1. The adjusted weights reflect the relative credibility of the experts in the evaluation process, ensuring that more experienced or more accurate experts contribute more to the final evaluation results.

[0172] Next, the rationality of the weights needs to be verified. This is done through simulation analysis and expert feedback. Simulation analysis involves applying these weights in hypothetical scenarios to observe whether the decision results meet expectations. Expert feedback, on the other hand, involves gathering opinions from other experts on the weight assignments to ensure their rationality. Furthermore, sensitivity analysis is an important verification method, which involves slightly adjusting the weights to observe changes in the evaluation results, thereby ensuring the robustness of the decision.

[0173] Once validated, the optimized weights can be applied to the calculation of triangular intuitionistic fuzzy numbers in the risk assessment process. This will improve the accuracy and credibility of the assessment, thereby facilitating more scientific and effective decision-making.

[0174] Example 2

[0175] Clarify the context and needs for the transportation of critical materials during the transition period. As the socioeconomic landscape enters a transitional phase, demand for rail transportation shifts from large-scale, centralized transportation to smaller, more decentralized transportation. While demand for the transportation of critical materials is lower than during normal times, it must remain efficient and safe. Therefore, risk assessment and prevention methods must adapt to these changes.

[0176] S1: Define transportation scenarios and identify risks

[0177] When defining transportation scenarios, the first step is to clarify the transportation process for critical materials during the transition phase. This process typically includes five key steps: processing and packaging, receiving and warehousing, warehouse management, sorting and outbound delivery, and transportation and distribution. Each step requires detailed operational steps. For example, the processing and packaging phase requires consideration of specific tasks such as material sorting, packaging reinforcement, and quality inspection.

[0178] Risk identification is accomplished by categorizing risk factors into five broad categories: personnel, equipment, materials, environment, and management. Each risk category requires further refinement. For example, personnel risk might include specific factors such as operator error, fatigue, and staff shortages. Equipment risk might involve issues such as equipment failure, aging, and improper maintenance. This refinement creates a comprehensive risk hierarchy, ensuring that all possible risk factors are identified and considered.

[0179] S2: Build a WBS-RBS decomposition model

[0180] A work breakdown structure (WBS) details the entire transportation process, breaking down each major workflow into specific subtasks. For example, processing and packaging can be broken down into steps such as material sorting, packaging reinforcement, and quality inspection. This decomposition creates a hierarchical work structure, ensuring that the details of each task are clearly defined.

[0181] The Risk Decomposition Structure (RBS) combines identified risk factors with the work structure to form a WBS-RBS decomposition matrix. Each cell in this matrix represents the correlation between a subtask and a risk factor. This allows quantification of the risk exposure of each task.

[0182] S3: Introducing triangular intuitionistic fuzzy number theory for risk assessment

[0183] During the risk assessment process, experts were invited to verbally describe the risk importance of each subtask. This generated a seven-level evaluation language: "Very Weak," "Weak," "Weak," "Fair," "Strong," "Strong," and "Very Strong." To quantify these fuzzy expressions, triangular intuitionistic fuzzy numbers were used. These numbers, composed of membership, non-membership, and hesitation, more accurately reflect the expert's verbal information.

[0184] By filling the experts' fuzzy evaluation results into the improved 0-1 coupling matrix of WBS-RBS, a comprehensive risk assessment matrix can be formed. This matrix is ​​used to quantify the risk importance of each subtask.

[0185] S4: Optimize expert weight distribution

[0186] To improve the accuracy of the evaluation, a linear programming approach was used to optimize the expert weights. The goal was to minimize the variance in the comprehensive utility evaluation. The experts' fuzzy evaluation matrix was used to construct a linear programming model, and the objective function was to minimize the deviation of the expert evaluation results.

[0187] The weighting process considers the experts' emotional states, including extreme positivity, neutrality, and extreme negativity. A comprehensive score for each state is calculated using triangular intuitionistic fuzzy numbers, ultimately determining the expert's weight. This approach allows for a more scientific weighting and improves the rationality of risk assessment.

[0188] S5: Calculate the work-risk comprehensive ranking value

[0189] Based on the expert evaluation results and weights, a comprehensive risk ranking value is calculated for each subtask. This ranking value is used to identify tasks and links with higher risks. Using the comprehensive ranking formula, risks can be ranked from high to low, providing a basis for subsequent risk prevention measures.

[0190] S6: Develop targeted preventive measures

[0191] For tasks with higher risks in the ranking results, specific preventive measures are proposed. For example, improving personnel training to avoid operational errors, increasing equipment maintenance frequency to reduce the probability of equipment failure, and optimizing storage conditions to ensure material storage safety.

[0192] In addition, a dynamic adjustment mechanism is established to monitor changes in risks during transportation in real time and adjust preventive measures accordingly. To address unexpected risks, a targeted contingency plan is developed, including resource allocation plans, rapid response mechanisms, and communication and coordination processes.

[0193] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to the specific embodiments described. Obviously, many modifications and variations are possible based on the content of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for assessing and preventing risks in the transportation of important materials, characterized in that: The following steps are involved: S1: Clarify each link in the transportation process, identify possible risk factors, classify and refine the risk factors, and form a comprehensive risk structure; S2: Decompose the work structure of the transportation process and clarify the subtasks of each link; Associate risk factors with subtasks to form a WBS-RBS matrix; each cell in the matrix represents the correlation between a subtask and a risk factor; S3: Collect experts’ risk evaluation of each subtask, convert the language description into triangular intuitionistic fuzzy numbers, fill in the WBS-RBS matrix, and form a risk assessment matrix; S4: Construct a linear programming model to calculate the weight of each expert, consider the expert's emotional state, optimize the weight distribution, and reduce evaluation bias; S5: Calculate the comprehensive ranking value of each subtask and identify high-risk tasks based on the ranking value; S6: Propose improvement suggestions for high-risk tasks; Establish a dynamic adjustment mechanism to monitor risk changes in real time; Develop emergency plans to ensure rapid response to sudden risks.

2. The important material transportation risk assessment and prevention method according to claim 1, characterized in that: The work structure is decomposed into five main work processes: processing and packaging, receiving and warehousing, warehouse management, sorting and outbound delivery, and transportation and distribution, which correspond to 13 sub-tasks in total; the risk structure is decomposed into five main risk factors: personnel factors, equipment factors, material factors, environmental factors, and management factors, which correspond to 25 sub-risk factors in total.

3. The important material transportation risk assessment and prevention method according to claim 1, characterized in that: The step S1 specifically includes the following sub-steps: S1.1: Analyze and decompose the transportation process in detail, clarify the operational requirements, inputs and outputs, and key nodes of each link, and divide the transportation process into specific operational links through the work breakdown structure (WBS) and flow charts; including: W1: Processing and packaging: including packaging, labeling and sealing of materials; W2: Goods Receiving and Warehousing: Materials are unloaded from transport vehicles to the warehouse and registered for warehousing; W3: Warehouse management: monitoring of storage environment, stacking and protection of materials; W4: Sorting and outbound: sorting materials according to order requirements and loading them for transportation; W5: Transportation and distribution: Transport vehicles depart from the warehouse and complete terminal delivery; Define the set of transportation tasks as W = {W1, W2, W3, W4, W5}; each task Wi contains specific operational activities and is associated with inputs and outputs; S1.2: After clarifying each link in the process, identify the potential risk factors in each link one by one. The logic of risk identification is to match the tasks and operations in the transportation process with their potential risks one by one, ensuring that all possible risks are identified without omission. Define the risk factor set in task Wi as Ri = {Ri1, Ri2, ..., Rim}, and map the operational activities and risk points in each task one by one; S1.3: Systematically categorize all identified risk factors according to their source and nature; Define a risk classification set C = {C1, C2, C3, C4, C5}; establish a classification mapping f:R→C, and map each risk factor Rij to the corresponding risk category Ck.

4. The method for assessing and preventing risks in the transportation of important materials as claimed in claim 1, wherein: The step S2 specifically includes the following sub-steps: S2.1: Define the decomposition level of the WBS. By decomposing the transportation process layer by layer, clarify the specific activities and responsibilities in each link, and form a tree structure of tasks; First layer: overall structure of the transport task Define the main task modules in the transportation process, denoted as W = {W1, W2, W3, W4, W5}; Second layer: subtasks of task modules Each task module Wi is further decomposed into specific operation activities, recorded as Wi = {Wi.1, Wi.2, ..., Wi.n}; The third layer: execution elements of operational activities Identify the specific execution elements involved in the operation activities, such as personnel, equipment, materials, and environment, which are recorded as E = {E1, E2, E3, E4}; S2.2: Build a risk classification system for RBS, classify risk factors into hierarchical categories, and identify different types of risks and their sources; Level 1: Main categories of risk Define the main categories of risk, denoted as C = {C1, C2, C3, C4, C5}; C1 represents personnel risk, C2 represents equipment risk, C3 represents material risk, C4 represents environmental risk, and C5 represents management risk; Level 2: Specific factors of risk categories Each risk category Ck is further decomposed into specific risk factors, denoted as Ck = {Ck.1, Ck.2, ..., Ck.m}; The third layer: the manifestation of specific factors Identify the manifestation of specific risk factors, denoted as R = {R1, R2, ..., Rn}; S2.3: Establish WBS-RBS mapping relationship Analyze the execution elements of each task module Wi and subtask Wi.j, and establish corresponding relationships with the risk categories and specific factors of RBS; Construct a matrix M, where the rows represent task modules or subtasks Wi, the columns represent risk categories Ck, and the matrix elements Mij indicate whether Wi is associated with Ck; If there is a risk of category Ck in task Wi, then Mik = 1, otherwise Mik = 0; The matrix is ​​constructed as follows: For each Wi.j, further refine its corresponding specific risk factor Rm and record its occurrence conditions and impact scope; Task-risk mapping function g(Wi.j) = {Rm1, Rm2, ..., Rmp}, which represents the set of risk factors involved in task Wi.j; S2.4: Risk Prioritization Logic After establishing the WBS-RBS mapping, risks need to be prioritized; Determine the probability of risk occurrence P: Based on historical data and expert evaluation, calculate the probability of occurrence P of each risk factor, with a value range of [0, 1]; Determine the risk impact level I: According to the impact of the risk on the transportation task, set the value range of I, including "very weak", "weak", "weak", "average", "strong", "strong", "very strong" and seven-level evaluation language, corresponding to values ​​1-7; Calculate the risk priority index RPI: RPI = P × I Screen high-priority risks; set threshold RPI threshold , if RPI ≥ RPI threshold , then the risk is judged as high priority; The priority index of each risk Ri is RPIi=Pi×Ii High priority risk set H = {Ri|RPIi≥RPI threshold } S2.5: Output the WBS-RBS decomposition model, including: Task hierarchy structure WBS: detailed decomposition of task modules and subtasks; Risk Classification Structure (RBS): Detailed classification of risk categories and specific factors; WBS-RBS mapping relationship: the correspondence between tasks and risks; High-priority risk list: includes a specific list of high-priority risks and their RPI values.

5. The important material transportation risk assessment and prevention method according to claim 1, characterized in that: The step S3 specifically includes the following sub-steps: The triangular intuitionistic fuzzy number consists of three parts, represented by a triple (a, b, c), where: a: Membership degree, which indicates the degree to which an event or task belongs to a certain risk level, and its value range is [0, 1]; b: non-membership degree, which indicates the degree to which an event or task does not belong to a certain risk level, and its value range is [0, 1]; c: Hesitation, which indicates the ambiguity or uncertainty between the expert's membership and non-membership. The calculation formula is: c = 1-ab In order to ensure the rigor of logic, it must satisfy: a+b≤1 and a, b, c∈[0,1]; S3.1: Collect expert opinions. For each subtask, multiple experts will evaluate the risk factors. S3.2: For each subtask or risk factor, the language evaluation provided by the experts is converted into a predefined triangular intuitionistic fuzzy number table, including "very weak", "weak", "weak", "average", "strong", "strong", and "very strong" to describe the importance of the risk. The triangular intuitionistic fuzzy numbers are: Very weak: T1 = (0.1, 0.9, 0.0) Weak: T2 = (0.2, 0.7, 0.1) Weaker: T3 = (0.3, 0.6, 0.1) Generally: T4 = (0.5, 0.5, 0.0) Stronger: T5 = (0.7, 0.3, 0.0) Strong: T6 = (0.8, 0.2, 0.0) Very strong: T7 = (0.9, 0.1, 0.0) Assume that there are m subtasks, each task corresponds to the evaluation of n experts, and the expert's linguistic evaluation is mapped to a triangular intuitionistic fuzzy number Tij = (aij, bij, cij), where i represents the subtask and j represents the expert; S3.3: For each subtask risk assessment, combine the opinions of multiple experts into a unified fuzzy number. Set the weight of each expert to wj, where j = 1, 2, ..., n. Typically, the weight satisfies ∑(wj) = 1. Weights can be assigned based on expert experience or a weight model. The calculation formula of comprehensive fuzzy number Ti is: Ti=(ai,bi,ci) ai=∑(wj·aij)-bi=∑(wj·bij)-ci=1-ai-bi The comprehensive membership ai is the weighted average of the memberships of each expert, indicating the overall degree to which the task belongs to this risk level; The comprehensive non-membership degree bi is the weighted average of the non-membership degrees of each expert, which indicates the overall degree to which the task does not belong to the risk level; The comprehensive hesitation degree ci is directly calculated according to the formula 1-ai-bi; S3.4: After the comprehensive fuzzy number calculation is completed, the risks are quantified and ranked: The risk value R is based on the principle of maximizing membership and minimizing non-membership. The formula is as follows: R = ai - bi. R represents the risk value of the subtask. The higher ai is, the higher the possibility that the task is high-risk; the lower bi is, the higher the possibility that the task is not high-risk. By calculating the risk value R of each subtask, all subtasks are sorted from high to low according to the risk value; S3.5: After completing the risk value calculation and ranking, formulate specific risk response strategies: High-risk tasks should be prioritized, medium-risk tasks require close attention, and low-risk tasks can be arranged as appropriate based on resource allocation; For tasks with risk values ​​close to the interval boundary, sensitivity analysis can be used to further verify whether the risk level is reasonable. The sensitivity analysis process includes: Adjust the distribution of expert weights wj and analyze the changes in risk value R; Adjust the parameters of membership a and non-membership b, and observe the impact of hesitation c on the evaluation results; After the risk assessment is completed, the risk value should be dynamically adjusted based on the actual situation in the subsequent project execution; the risk assessment results can be ensured to be real-time and accurate by reorganizing expert evaluations and updating the membership a and non-membership b parameters.

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