Hazardous chemical substance safety production whole-process risk assessment method and device based on analytic hierarchy process

Abstract

The invention relates to a hazardous chemical substance safety production whole-process risk assessment method and device based on an analytic hierarchy process. The method comprises the following steps: constructing three-level evaluation indexes, establishing a judgment matrix for each level of indexes, quantifying the relative importance of the indexes by experts in the hazardous chemical substance field by using a 1-9 scale method, determining the weight of each index by using an analytic hierarchy process after consistency check, and converting the weight of the third level of indexes into corresponding index scores in combination with a preset total score, and then, based on the three-level indexes, obtaining an initial score of the third-level index of the enterprise, judging the type (logic item / degree item index) of the third-level index, combining the initial score with the index score in a corresponding quantification mode to quantify a final score, and finally, according to a preset total score and a total score of the three-level indexes, evaluating the whole-process risk, thereby improving the evaluation accuracy and reliability. And accidents are effectively prevented.

Description

Technical Field

[0001] This application relates to the field of risk level assessment technology, and in particular to a risk assessment method and apparatus for the entire process of safe production of hazardous chemicals using the analytic hierarchy process. Background Technology

[0002] The hazardous chemicals industry occupies a crucial position in the modern industrial system; however, its production process involves numerous complex and potentially risky stages. In recent years, accidents in the hazardous chemicals industry have occurred frequently, causing serious negative impacts on human life safety, the ecological environment, and economic development, highlighting the urgency of building a precise and efficient safety production evaluation system. Currently, various safety assessment technologies have been developed in the industry. The safety checklist method, in practical applications, mainly relies on expert experience to list inspection items and judge safety conditions. This method is highly subjective, difficult to comprehensively cover all potential risk indicators, and prone to omissions. The Dow Chemical Fire and Explosion Index focuses on the inherent hazards of substances but neglects the impact of other key factors such as management and the environment on safe production, resulting in incomplete assessment results. While the Analytic Hierarchy Process (AHP) can handle the issue of multi-indicator weighting to some extent and provide quantitative evidence for evaluation, it lacks integration with dynamic data collection and cannot reflect real-time changes in safe production conditions, thus exhibiting significant limitations in practical applications. Furthermore, the existing safety production evaluation system for hazardous chemicals suffers from numerous systemic problems. Traditional evaluation models often focus on only a single dimension, such as equipment safety or personnel qualifications, lacking a comprehensive indicator framework built from a holistic system perspective, making it difficult to comprehensively consider the interactions between various factors. In regional-level evaluations, the arithmetic mean method is often used to process enterprise data. This method fails to fully consider the differences in data distribution among enterprises and is highly susceptible to interference from extreme values, leading to distorted evaluation results. For example, an accident analysis of a chemical industrial park in 2024 revealed that the existing evaluation system missed key indicators such as "personnel gathering" and "alarm response delay," which accounted for as much as 43% of the accident causes. Simultaneously, because the park used the traditional arithmetic mean regional evaluation method, the overall score was inflated by 12%, failing to trigger timely risk warnings and ultimately leading to serious accident consequences. In summary, the existing hazardous chemical safety production evaluation system has significant shortcomings in terms of the comprehensiveness of indicators, the scientific nature of regional evaluation methods, and the ability to respond to dynamic changes in safety production conditions. A new technical solution is urgently needed to address these issues, improve the accuracy and reliability of hazardous chemical safety production evaluation, and effectively prevent accidents. Summary of the Invention

[0003] Therefore, it is necessary to provide a risk assessment method and device for the entire process of hazardous chemical safety production that uses the analytic hierarchy process (AHP) to improve the accuracy and reliability of hazardous chemical safety production evaluation and effectively prevent accidents, in order to address the aforementioned technical problems.

[0004] A risk assessment method for the entire process of hazardous chemical safety production using the analytic hierarchy process (AHP), the method comprising: Based on the risk management and control requirements of the entire process of hazardous chemical safety production, a three-level evaluation index related to hazardous chemical safety production is constructed. Based on the three-level evaluation indicators, a judgment matrix is ​​constructed for each level of indicators. Experts in the field of hazardous chemicals use the 1-9 scale method to quantify the relative importance between indicators. After consistency testing, the weight of each indicator is determined using the analytic hierarchy process. Based on the preset total score, the weight of the third-level evaluation indicator in the three-level evaluation indicators is converted into the corresponding index score of each of the three-level evaluation indicators. Based on the three-level evaluation indicators, the preliminary scores of each indicator in the third level of a certain enterprise are obtained, the type of each indicator in the third level is determined, and the preliminary scores are quantified according to the indicator scores using the corresponding quantification method according to the determination results to obtain the final score. The indicator types include logical item indicators and degree item indicators. The risks of the entire process of safe production of hazardous chemicals are assessed based on the total score of the preset indicators and the total score of the three-level evaluation indicators.

[0005] In one embodiment, the logic item indicators include basic logic item indicators, hierarchical logic item indicators, and veto item indicators; When the indicator type is the basic logical item indicator, the scoring is determined by whether it meets the preset conditions. If it meets the conditions, the full score of the corresponding indicator is obtained; otherwise, no score is obtained. When the indicator type is the hierarchical logical item indicator, it is scored in layers according to multiple preset achievement levels. Different levels correspond to different scores, and the scores decrease as the importance of the level decreases. When the indicator type is a veto indicator, the scoring is based on whether it meets the preset conditions. If it does, the corresponding indicator score is the full score; if it does not, the first-level indicator is scored as zero.

[0006] In one embodiment, the degree item index includes a simple degree item index, a weighted degree item index, and a degree item index with trigger conditions; When the indicator type is the aforementioned simplicity level indicator, the safety positivity of the indicator is determined, and the final score is obtained by calculating based on the safety positivity and the corresponding indicator score. When the indicator type is the weighted indicator, first break down the sub-items of different importance and assign corresponding weights, then calculate the achievement rate of each sub-item, and obtain the weighted completion rate by summing them up. Finally, multiply by the total indicator score to get the final score. When the indicator type is the degree item indicator with trigger conditions, it is first determined whether the preset key trigger conditions are met. If the trigger conditions are met, no points are awarded; if the trigger conditions are not met, the final score is calculated according to the preset rules.

[0007] In one embodiment, the three-level evaluation index includes a first-level index, a second-level index, and a third-level index; The first-level indicators include: legality of market access, effectiveness of supervision, process safety, environmental stability, equipment reliability, and operational standardization. The secondary indicators include multiple sub-indicators that are subordinate to each of the first-level indicators, and the sub-indicators are specific control directions under the corresponding first-level indicators; The three-level indicators include multiple specific evaluation items that are subordinate to each of the second-level indicators, providing a verifiable basis for actual evaluation.

[0008] In one embodiment, when determining the weight of each indicator in the three-tiered evaluation index: For the first-level indicators, each first-level indicator is traversed with other first-level indicators to construct all unique pairwise indicator combinations as comparison units. Using all first-level indicators as row and column elements, each comparison unit is constructed according to the correspondence between row indicators and column indicators to construct the corresponding judgment matrix. When constructing the judgment matrices corresponding to the second-level indicators and the third-level indicators, the first-level indicators are used as the benchmark. Experts in the field of hazardous chemicals used anonymous scoring and centralized argumentation to quantify the relative importance of all comparison units in each judgment matrix based on the 1-9 scale method, resulting in each judgment matrix after assignment. A consistency check is performed on each judgment matrix after assignment. If the check passes, the Analytic Hierarchy Process (AHP) is used to determine the weight of the next-level indicator relative to the previous-level indicator. If the check fails, anonymous scoring and centralized argumentation are carried out again until the judgment matrix passes the consistency check. Then, the AHP is used to determine the weight of each level indicator.

[0009] In one embodiment, if the consistency check passes, the weights of each level of indicators are determined by any one of the following methods: weighted average, geometric average, least squares, or eigenvector method.

[0010] In one embodiment, based on a preset total score, the weight of the third-level evaluation indicator in the three-level evaluation indicators is converted into a scoring standard for each corresponding three-level evaluation indicator, including: Based on the weights of the first-level indicators relative to the overall goal, the second-level indicators relative to their respective first-level indicators, and the third-level indicators relative to their respective second-level indicators, calculate the final weight of the third-level indicators relative to the overall goal. Based on the preset total score of the indicators, the final weight of the third-level indicators relative to the overall goal is converted into a scoring standard.

[0011] In one embodiment, the method further includes assessing the risks of the entire process of hazardous chemical safety production in a certain area, including: The total scores of the three-level evaluation indicators for all enterprises in the region are harmonized and averaged to obtain the initial score of the first-level indicator at the regional level. Based on the GIS geographic information system, regional risk units are divided according to the preset enterprise spacing, and the risk correlation of enterprises within each regional risk unit is identified. Based on the risk relationships among enterprises in each regional risk unit, a spatial coupling coefficient is calculated, and the initial score is corrected according to the spatial coupling coefficient to obtain the first-level indicator score. The minimum value of the first-level indicator is selected as the regional comprehensive score. A risk assessment of the entire process of hazardous chemical safety production is conducted based on the comprehensive score of the region.

[0012] This application also provides a risk assessment device for the entire process of hazardous chemical safety production using the analytic hierarchy process, characterized in that the device comprises: The three-level evaluation index construction module is used to construct three-level evaluation indicators related to the safe production of hazardous chemicals based on the risk control needs of the entire process of safe production of hazardous chemicals. The evaluation index weighting module is used to construct a judgment matrix based on each level of the three-level evaluation indexes. Experts in the field of hazardous chemicals use the 1-9 scale method to quantify the relative importance between the indicators, and then use the analytic hierarchy process (AHP) to determine the weight of each indicator after a consistency test. The three-level evaluation index score acquisition module is used to convert the weight of the third-level evaluation index in the three-level evaluation index into the index score of each corresponding three-level evaluation index based on the preset total index score. The enterprise total score acquisition module is used to obtain the preliminary scores of each indicator in the third level of an enterprise based on the three-level evaluation indicators, determine the type of each indicator in the third level, and quantify the preliminary scores according to the indicator scores using the corresponding quantification method according to the judgment results to obtain the final score. The indicator types include logical item indicators and degree item indicators. The risk assessment module is used to assess the risks of the entire process of safe production of hazardous chemicals based on the preset total score of the indicators and the total score of the three-level evaluation indicators.

[0013] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the specific steps of the risk assessment method for the entire process of hazardous chemical safety production using the analytic hierarchy process described above.

[0014] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the specific steps of the above-mentioned risk assessment method for the entire process of hazardous chemical safety production using the analytic hierarchy process.

[0015] The aforementioned analytic hierarchy process (AHP) method and apparatus for risk assessment of the entire process of hazardous chemical safety production involves constructing a three-tiered evaluation index related to hazardous chemical safety production based on the risk control requirements of the entire process. A judgment matrix is ​​built for each level of the three-tiered evaluation index. Hazardous chemical experts use a 1-9 scale to quantify the relative importance of the indicators. After consistency testing, the AHP is used to determine the weight of each indicator. Based on a preset total score, the weights of the third-tier evaluation indicators are converted into corresponding scores for each of the three-tiered indicators. Furthermore, based on the three-tiered evaluation indexes, preliminary scores for each indicator in the third tier for a given enterprise are obtained. The type of each indicator in the third tier is determined, and the preliminary scores are quantified according to the corresponding quantification method to obtain the final score. The indicator types include logical items and degree items. Finally, the risks of the entire hazardous chemical safety production process are assessed based on the preset total score and the total score of the three-tiered evaluation indexes. This method can improve the accuracy and reliability of hazardous chemical safety production assessment and effectively prevent accidents. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the risk assessment method for the entire process of hazardous chemical safety production using the analytic hierarchy process in one embodiment. Figure 2 This is a schematic diagram of the final score of the third-level evaluation index in one embodiment; Figure 3 This is a structural block diagram of a risk assessment device for the entire process of hazardous chemical safety production using the analytic hierarchy process in one embodiment. Figure 4 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0018] In this embodiment, as Figure 1 As shown, a risk assessment method for the entire process of hazardous chemical safety production based on the analytic hierarchy process is provided, which specifically includes the following steps: Step S100: Based on the risk control requirements of the entire process of hazardous chemical safety production, construct a three-level evaluation index related to hazardous chemical safety production.

[0019] Step S110: Construct a judgment matrix based on each level of the three-level evaluation index. Experts in the field of hazardous chemicals use the 1-9 scale method to quantify the relative importance between the indicators. After consistency testing, the weight of each indicator is determined using the analytic hierarchy process.

[0020] Step S120: Based on the preset total score of the indicators, the weight of the third-level evaluation indicator in the three-level evaluation indicators is converted into the corresponding indicator score of each of the three-level evaluation indicators.

[0021] Step S130: Based on the three-level evaluation indicators, obtain the preliminary scores of each indicator in the third level of a certain enterprise, determine the type of each indicator in the third level, and quantify the preliminary scores according to the indicator scores using the corresponding quantification method according to the judgment results to obtain the final score. The indicator types include logical item indicators and degree item indicators.

[0022] Step S140: Based on the preset total score of the indicators and the total score of the three-level evaluation indicators, assess the risks of the entire process of safe production of hazardous chemicals.

[0023] In this embodiment, a three-tiered evaluation index for hazardous chemical safety production is first constructed. Then, the importance of the indexes is quantified using the expert 1-9 scale method. After consistency testing, the weights are determined using the analytic hierarchy process and converted into scores for the third-tier indexes. Subsequently, the final score is obtained by combining the preliminary scores of the third-tier indexes of the enterprise and the index types, including logical items and degree items. Finally, the risk is assessed based on the preset total score and the total score. This system can systematically cover the entire process control needs of hazardous chemical safety production. Through scientific index construction, weight determination, and quantification methods, subjective evaluation bias is reduced, the accuracy and reliability of risk assessment are improved, and the risk points in the entire process of hazardous chemical safety production are accurately identified, effectively preventing accidents and providing scientific and feasible technical support for hazardous chemical safety production management.

[0024] In step S100, a systematic "six-fold" indicator system is constructed to cover six dimensions: operation, access, process, equipment, environment, and supervision. Specifically, based on the "six-fold" indicator system, a three-tiered evaluation indicator system is constructed, including first-level indicators, second-level indicators, and third-level indicators. The first-level indicators include: access legality indicators, supervision effectiveness indicators, process safety indicators, environmental stability indicators, equipment reliability indicators, and operation standardization indicators. The second-level indicators include multiple sub-indicators subordinate to each first-level indicator, which represent specific control directions under the corresponding first-level indicator. The third-level indicators include multiple specific evaluation items subordinate to each second-level indicator, providing verifiable and actionable verification basis for actual evaluation.

[0025] In this embodiment, the second-level indicators are further subdivided into 12 items under the first-level indicators, while the third-level indicators are further broken down into 37 quantifiable observation items based on the second-level indicators. The selection of indicators was completed using the Delphi method and underwent three rounds of expert review (the participating experts included 5 chemical safety researchers and 3 senior emergency management engineers) to ensure that the indicator coverage rate reached 98% (covering the main known causes of hazardous chemical accidents).

[0026] Specifically, the specific indicators for each level of the three-level evaluation index are shown in Table 1. Table 1

[0027] In step S110, when determining the weights of each indicator in the three-tiered evaluation system: For the first-level indicators, each first-level indicator is iterated through with other first-level indicators to construct all unique pairwise combinations as comparison units. Using all first-level indicators as row and column elements, each comparison unit is constructed according to the correspondence between row indicators and column indicators to form a corresponding judgment matrix. When constructing the judgment matrices for the second-level and third-level indicators, the previous-level indicator is used as the benchmark. Next, experts in the field of hazardous chemicals use anonymous scoring and centralized argumentation to quantify the relative importance of all comparison units in each judgment matrix based on the 1-9 scaling method, resulting in the assigned judgment matrices. A consistency check is performed on each assigned judgment matrix. If the check passes, the analytic hierarchy process (AHP) is used to determine the weights of the next-level indicators relative to the corresponding indicators of the previous level. If the check fails, anonymous scoring and centralized argumentation are repeated until the judgment matrix passes the consistency check, and then the AHP is used to determine the weights of each level of indicators.

[0028] In this embodiment, when determining the weight of each indicator in the three-tiered evaluation index, for the first-level indicators, it is necessary to iterate through each first-level indicator and all other first-level indicators to form all unique pairwise comparison units. Then, using all first-level indicators as row and column elements respectively, a judgment matrix corresponding to the first-level indicators is constructed according to the importance relationship between row indicators and column indicators. When constructing the judgment matrix for the second-level indicators, the first-level indicators of the previous level are used as the benchmark. That is, for each first-level indicator, all its included second-level indicators are used as comparison objects to form pairwise comparison units and construct the corresponding judgment matrix. When constructing the judgment matrix for the third-level indicators, the second-level indicators of the previous level are also used as the benchmark. For each second-level indicator, all its included third-level indicators are used as comparison objects to form pairwise comparison units and construct the corresponding judgment matrix.

[0029] Specifically, when constructing the matrix: the results of the pairwise comparisons above are organized into an n-order square matrix (judgment matrix A = ( )),in =1 (elements are equally important) and satisfy This forms the judgment matrix.

[0030] Furthermore, the scoring process involved a combination of anonymous scoring and centralized review by experts in the field of hazardous chemicals. The scoring was conducted by a team of 11 experts (including university scholars, corporate safety and environmental protection officials, and government regulators) with over 10 years of experience in hazardous chemicals regulation. The scoring was based on a 1-9 scale, where 1 indicates that two indicators are equally important, 9 indicates that one row of indicators is extremely important than another, and intermediate values ​​represent varying degrees of importance. The scoring criteria are shown in Table 2. After the first round of independent scoring, feedback was gathered on differences in score distribution (e.g., when the standard deviation of a certain indicator's score was >1.5). A second round of centralized review was then organized, and the final scores with a consistency coefficient >0.85 were retained to form the assigned judgment matrices for each level.

[0031] Table 2

[0032] As shown in Table 2, 1 indicates that the two elements are equally important, 3 indicates that the former is slightly more important than the latter, 5 indicates that the former is significantly more important than the latter, 7 indicates that the former is strongly more important than the latter, and 9 indicates that the former is extremely more important than the latter. 2, 4, 6, and 8 are the median values ​​for these adjacent levels. If the ratio of element i to j is... Then the ratio of j to i is (Reciprocity).

[0033] Furthermore, after assigning values ​​to the judgment matrix, a consistency test is performed, calculating the consistency index CI = (λ_max - n) / (n - 1) (where λ_max is the largest eigenvalue of the matrix, and n is the matrix order). Simultaneously, an average random consistency index RI is introduced (valued from a table based on the order n; e.g., RI = 1.24 when n = 6). The consistency ratio CR = CI / RI is calculated. When CR < 0.1, the judgment matrix passes the test. At this point, the analytic hierarchy process (AHP) is used to determine the weights of the next-level indicators relative to their corresponding previous-level indicators. If CR ≥ 0.1, the judgment test fails, and experts need to readjust the importance ranking and scoring of the indicators until the judgment matrix passes the consistency test. Then, the AHP is used again to determine the weights of the indicators at each level.

[0034] In this embodiment, if the consistency check passes, the weights of each level of indicators are determined using any one of the following methods: weighted average, geometric average, least squares, or eigenvector method. Preferably, the weighted average method is used to determine the weights of each level of indicators.

[0035] In the process of determining the weights of the three-tiered evaluation indicators, a scientific matrix construction logic, a rigorous expert scoring mechanism, and a strict consistency verification process can bring multiple benefits and achieve significant results: From the perspective of matrix construction, the judgment matrix is ​​built hierarchically based on the "higher-level indicator" and follows... =1 and The rules ensure that indicator comparisons always revolve around hierarchical relationships, preventing weight calculations from deviating from the evaluation system's logic and making weight allocation more aligned with the hierarchical needs of risk management throughout the entire process of hazardous chemical safety production. Meanwhile, the expert team comprises university scholars, corporate safety and environmental managers, and government regulators, all with over 10 years of experience. Combined with anonymous scoring and centralized review (secondary review when standard deviation > 1.5, retaining results with a consistency coefficient > 0.85), this approach avoids the limitations of a single perspective and reduces subjective bias, making the quantification of indicator importance more comprehensive and objective. The clear definition of the 1-9 scaling method (including median values ​​and reciprocity rules) provides a unified standard for expert scoring, avoiding differences in assignment due to ambiguous expressions of "importance." The consistency check requirement of CR < 0.1 (including standardized calculations of CI and RI) can promptly correct logically contradictory judgment matrices, ensuring that weight results conform to mathematical consistency and preventing the impact of matrix logic confusion on the scientific validity of the assessment. Ultimately, these designs enable the weights of indicators at each level to accurately reflect their actual importance in the risk assessment of hazardous chemical safety production, providing a reliable weighting basis for subsequent indicator quantification and enterprise and regional risk assessment, significantly reducing subjective errors, improving the accuracy, objectivity and credibility of risk assessment throughout the entire process of hazardous chemical safety production, helping to more accurately identify key risk points, and providing scientific support for risk prevention and control.

[0036] Next, in step S120, based on the preset total score, the weights of the third-level evaluation indicators in the three-tier evaluation system are converted into scoring standards for each corresponding third-level evaluation indicator. This includes: calculating the final weight of the third-level indicator relative to the total target based on the weights of the first-level indicators relative to the overall target, the second-level indicators relative to their respective first-level indicators, and the third-level indicators relative to their respective second-level indicators; and then, based on the preset total score, converting the final weights of the third-level indicators relative to the overall target into scoring standards. This makes the abstract weights concrete, the evaluation process standardized, and the results comparable and measurable: First, converting the final weights of the third-level indicators into specific scores (e.g., a weight of 0.05 corresponds to 5 points), coupled with clear scoring rules, reduces the difficulty of evaluation operations and avoids scoring errors caused by misunderstandings; second, generating scoring standards based on unified logic (final weight × preset total score) ensures that all indicator score allocations are standardized, scoring rules are consistent, and scores are traceable, improving the rigor of the evaluation; third, presenting the evaluation results in specific scores supports horizontal comparisons between different enterprises and vertical comparisons within the same enterprise, clearly reflecting differences in risk management levels and providing an intuitive basis for subsequent decision-making.

[0037] Specifically, the three-level indicators are set with a maximum score of 1000 points. , The weight scores for the first, second, and third-level indicators were obtained through weight calculation. The overall weight of each third-level indicator is then calculated to determine the total weight of the third-level indicator. Corresponding secondary indicator weights This corresponds to the weight of the primary indicator. For example: the overall weight of license validity = 0.615. 0.667 0.0442 = 0.018.

[0038] To simplify score calculation, the value is directly multiplied by 1000, yielding a score of 18 points for this third-level indicator. However, due to potential rounding errors, the final sum of the scores may not equal 1000. If, after calculating the final score, there is still a 1-point difference, that point is added to the overall weighting. The score of 1000 is closest to 0.5.

[0039] For example, the overall weight for workplace risk perception is 0.12849277. Therefore, 0.12849277... 1000 - 128 = 0.49277. This is the maximum value discarded in the rounding algorithm among all indicators. Therefore, the final score for this indicator is 128 + 1 = 129 points.

[0040] like Figure 2As shown, a schematic diagram of the final score of the third-level evaluation index obtained in step S120 based on the three-level evaluation index given in Table 1 is presented.

[0041] In step S130, the original data scores of the enterprise's three-level indicators are calculated through the regulatory platform interface, AI platform, enterprise business system, etc., and then standardized (e.g., the number of "unhandled alarms" is converted into a risk index).

[0042] In this embodiment, the regulatory side obtains records of enterprise administrative penalties and hazard rectification through the Ministry of Emergency Management's internet and regulatory platform interface. The enterprise side connects to the ERP system to collect equipment maintenance records and work permit data. The AI ​​perception layer collects dynamic data in real time through smart cameras, for example, identifying "personnel not wearing safety helmets," and sensors detecting "leakage concentration."

[0043] In this embodiment, when scoring the third-level indicators, they are divided into multiple types and scored according to the corresponding scoring rules for each type. This avoids a one-size-fits-all approach. Logical indicators, such as "whether fire-fighting equipment is provided," are typically binary attributes of "yes / no," which differ from degree indicators, such as "equipment maintenance frequency," which have "high / medium / low" gradients. Separate quantification allows for a more tailored approach. For example, logical indicators can be directly represented by "0-1" or "0-full marks," while degree indicators are scored according to gradients, preventing a uniform quantification from obscuring the true state of the indicators. This approach also improves scoring accuracy: matching appropriate quantification rules to different types—for example, logical indicators are assessed based on whether standards are met, while degree indicators are assessed based on the degree of compliance—more accurately captures the actual performance of the indicators, reducing scoring deviations caused by mismatches between quantification methods and indicator attributes, and ensuring the final score better reflects the company's actual situation. Using such methods can also enhance the persuasiveness of the assessment. Categorization and quantification make the scoring logic of each type of indicator clear and traceable. For example, the score of the logic item corresponds to a clear "existence / absence" fact, and the score of the degree item corresponds to a verifiable "degree level". This avoids the subjectivity of general scoring and makes the assessment results more credible.

[0044] In this embodiment, the logical item indicators include basic logical item indicators, hierarchical logical item indicators, and veto indicators. When the indicator type is a basic logical item indicator, scoring is based on whether it meets preset conditions. If it does, the corresponding indicator receives full marks; otherwise, no marks are awarded. When the indicator type is a hierarchical logical item indicator, scoring is performed according to multiple preset achievement levels, with different levels corresponding to different scores, and the scores decreasing as the importance of the level decreases. When the indicator type is a veto indicator, scoring is based on whether it meets preset conditions. If it does, the corresponding indicator receives full marks; otherwise, the entire first-level indicator receives zero marks.

[0045] Specifically, logical item indicators are scored by determining whether they meet preset conditions. If they meet the preset conditions, the indicator receives full marks; otherwise, it receives no marks. The quantification method for basic logical item indicators can be summarized by the following formula:

[0046] In the above formula, X represents the specific score of a certain third-level indicator.

[0047] Furthermore, taking the basic logical indicator "supplier qualification" as an example, enterprises should conduct rigorous screening and comprehensive evaluation among numerous equipment suppliers to establish a qualified list of spare parts suppliers. The scoring principle for this indicator is: if all suppliers in the enterprise's list meet the established qualification requirements, the indicator receives full marks; if any supplier fails to meet the qualification standards, no points are awarded. Through this method, enterprises can effectively ensure the stability of the supply chain and the quality compliance of spare parts, thereby optimizing the asset management system and strengthening the reliability of planning and procurement processes.

[0048] Specifically, to further enhance the differentiation and applicability of the indicators, multiple achievement levels are set for some logical items, each corresponding to a different score, thereby achieving a multi-level evaluation of compliance performance. The quantification method is as follows:

[0049] In the above formula, X, Y, and Z are decreasing scores based on the importance of the level.

[0050] For example, for the "Standardized Management of Safe Production" indicator, scores can be assigned based on the level of safety production standardization: Level 1 receives the full score X; Level 2 receives Y = 60%X; Level 3 receives Z = 30%X; and unrated receives 0 points. This design can more accurately reflect differences in management capabilities.

[0051] Specifically, veto items, as a special type of logical indicator, possess a clear "necessity" characteristic and are typically used to strictly limit key conditions to ensure the overall compliance and risk control effectiveness of the evaluation system or framework. The scoring method for veto items is more stringent: if an indicator fails to meet the preset standards, even if all other conditions are met, the overall evaluation of the corresponding primary indicator will still receive no points. Its quantification method is as follows:

[0052] Taking the veto indicator "major hazard source leakage and fire accident" as an example, enterprises must confirm whether a major hazard source leakage or fire accident has occurred within the statistical period. If no such event occurs within the statistical period, the indicator is considered to meet the standard and receives full marks; however, if a major hazard source leakage or fire accident occurs, the indicator receives 0 marks, and the overall score for the primary indicator "major hazard source control" will also be 0 marks.

[0053] In this embodiment, basic logic items correspond to simple compliance judgments that are either black or white (such as "whether the license is valid"), hierarchical logic items address advanced compliance scenarios with "multi-level differences" (such as "safety production standardization level one / two / three"), and veto items target critical bottom-line requirements with "one-vote veto" (such as "whether a major accident has occurred"). These three types of indicators comprehensively cover the compliance judgment needs of different stringency levels in hazardous chemical safety production, avoiding omissions in scenarios. Instead of uniformly using "yes / no" or a single gradient scoring, values ​​are assigned according to the importance of the indicators and the judgment logic. For example, critical bottom-line requirements are reinforced with "veto," and multi-level compliance items are differentiated with decreasing scores. This allows the scoring to better reflect the actual importance of the indicators, avoiding deviations caused by mismatches between scoring rules and indicator attributes, and improving scoring accuracy. The DOE item directly links to the primary indicator to zero, which can quickly identify critical risks (such as the expiration of enterprise qualifications). The hierarchical logic item can distinguish between excellent, qualified, and compliance levels that need improvement. The basic logic item can quickly screen whether basic compliance is met. The scoring results of the three types of indicators can locate the enterprise's risk points in a hierarchical manner, which helps to make accurate rectifications in the future.

[0054] In this embodiment, the degree item index is calculated based on the actual achievement rate, and a more refined evaluation is achieved by combining weight allocation and triggering conditions. The degree item index includes simple degree item index, weighted degree item index, and degree item index with triggering conditions. When the index type is the simple degree item index, the safety positive degree of the index is determined, and the final score is obtained by calculating based on the safety positive degree and the corresponding index score. When the index type is a weighted degree item index, different importance sub-items are first decomposed and assigned corresponding weights, then the achievement rate of each sub-item is calculated, and the weighted completion rate is obtained by summing them. Finally, the final score is obtained by multiplying by the total index score. When the index type is a degree item index with triggering conditions, it is first determined whether the preset key triggering conditions are met. If the triggering conditions are met, no score is awarded; if the triggering conditions are not met, the final score is calculated according to preset rules.

[0055] Specifically, the simplicity level indicator is scored directly by multiplying the completion rate by the indicator score, making it suitable for scenarios with a high degree of homogeneity. The basic formula is as follows:

[0056] or

[0057] For example, the score for the level item indicator "Maintenance and Repair Completion Rate" = Completion Rate × X. Another example is the score for the level item indicator "Hidden Danger Remediation Delay Rate" = (1 - Delay Rate) × X.

[0058] Specifically, when the same indicator covers sub-items of different importance, weights need to be introduced to reflect differentiated management requirements. The quantification method is as follows: Weighted completion rate = ∑(sub-item completion rate) ( i ) × weight W ( i ) ) Indicator Score = Weighted Completion Rate × X For example, when assessing the "awareness rate of job operation procedures", the weights of front-line operators, team leaders and technical management personnel can be assigned as 50%, 30% and 20% respectively, and the overall awareness rate can be calculated by weighting and scored accordingly.

[0059] Specifically, for level indicators with trigger conditions, key achievement conditions are added to the level evaluation. If the trigger condition is not met, a special scoring rule (such as zeroing out or limiting scores) is applied. The judgment logic is as follows: If the trigger condition is met: Indicator score = 0 Otherwise: Indicator Score = Completion Rate × X For example, in the severity indicator "Employee awareness of the reporting procedure," the trigger condition is set as "the awareness rate of senior employees is not 100%." ​​If this condition is triggered, the overall indicator receives a score of 0; otherwise, it is scored normally as a standard severity indicator.

[0060] In this embodiment, the detailed breakdown of degree indicators and the matching of differentiated calculation rules allow the assessment to better reflect the complex characteristics of "degree differences" in different scenarios. It quickly quantifies the positive safety level of a single dimension (such as "safety training coverage") using simple degree indicators, avoiding overly complex calculations. It also utilizes weighted degree indicators to highlight the importance of key aspects through sub-item weight allocation (such as "in equipment maintenance, the maintenance rates of core equipment and ordinary equipment are calculated with different weights"), accurately reflecting the differences in the contribution of each sub-item to the overall indicator. Furthermore, degree indicators with trigger conditions strengthen constraints on scenarios with "bottom-line prerequisites" (such as "if an equipment failure is not reported, no points will be awarded even if the subsequent repair rate meets the standard"), preventing the neglect of key risks by focusing solely on rates. These three types of detailed indicators cover degree evaluation needs from simple to complex, from conventional to specific, improving the refinement and accuracy of the scoring, and more accurately capturing the actual safety management level in different scenarios, providing a more specific basis for risk assessment and remediation.

[0061] In this embodiment, the weights of the third-level indicators are also dynamically adjusted. The weights of relevant indicators can be automatically adjusted according to seasonal characteristics, industry events, or policy changes, following preset weight control rules. For example, during the flood season, the weight of "flood control facility integrity rate" can be significantly increased, thereby achieving a linkage between the evaluation focus and the real-time risk situation, enhancing the system's adaptability and guidance.

[0062] Specifically, the current weight calculation based on the analytic hierarchy process relies on initial expert scores. Although consistency checks ensure its scientific validity, the weights remain fixed over time, making it impossible to match dynamic risk scenarios in the hazardous chemicals industry in real time. The proposed improvement involves adding a risk scenario and weight mapping model, with the following specific directions: First, constructing a scenario recognition model: using AI algorithms to analyze multi-source data (such as seasonal changes, production load, and surrounding environment) in real time to automatically identify typical risk scenarios. For example, during the high-temperature summer period, the model automatically labels "process safety-environmental stability coupling scenarios," and during periods of high personnel mobility around the Spring Festival, it labels "operational compliance-personnel qualification compliance scenarios."

[0063] Furthermore, a dynamic weight library is established: weight adjustment rules are preset for different scenarios. For example, in high-temperature scenarios, the weight of the secondary indicator "process parameter monitoring" is increased from 0.25 to 0.35, and in personnel mobility scenarios, the weight of "personnel qualification compliance" is increased from 0.2 to 0.3. The weight adjustment range is determined through training with historical accident data to ensure that the evaluation accuracy is improved by more than 15% after adjustment.

[0064] Specifically, the dynamic weight library update process includes four algorithm-driven processes: data collection, feature modeling, scene mapping, and adaptive learning. This completely eliminates the need for manually pre-set adjustment rules, achieving a natural coupling between weights and risk scenarios. First, a multi-dimensional data collection and preprocessing module needs to be built, integrating data from the entire production process of hazardous chemical enterprises. This includes process operation data (such as real-time parameters like reactor temperature, pressure, and medium concentration, with a sampling frequency of at least once per minute), equipment status data (equipment vibration values, sealing performance, operating time, maintenance records, etc.), personnel operation data (operation standard compliance rate, training records, history of violations, personnel attendance fluctuations, etc.), environmental data (temperature, humidity, wind speed, precipitation, surrounding population density, distribution of sensitive targets, etc.), and historical accident data (accident type, triggering factors, degree of loss, and abnormal characteristics of related indicators, etc.). In the data preprocessing stage, Z-Score standardization is used to eliminate the influence of dimensions, outliers are removed using the isolated forest algorithm, and missing data is filled using an LSTM network to ensure data quality meets modeling requirements.

[0065] Then, a dual-model architecture for scene recognition and weight mapping is constructed based on the preprocessed data. The scene recognition model adopts a two-level algorithm system of "clustering + classification". First, an improved K-Means clustering algorithm (introducing a silhouette coefficient to adaptively determine the number of clusters) is used to perform unsupervised learning on historical data, automatically dividing typical risk scene clusters, such as "high temperature and high load process scenarios", "peak personnel flow operation scenarios", and "equipment operation scenarios under severe weather", etc. Each scene cluster corresponds to a set of feature vectors (e.g., the feature vector of a high temperature scenario includes core indicators such as daily average temperature, reactor temperature fluctuation range, and cooling system efficiency). The weight mapping model adopts the gradient boosting tree (XGBoost) algorithm, using scene feature vectors as input and risk assessment accuracy as the objective function to construct a non-linear mapping relationship between features and weights. During model training, historical accident cases are used as label data, and hyperparameters such as learning rate and tree depth are optimized through grid search, enabling the model to automatically output the weights of each indicator based on real-time scene characteristics. To achieve dynamic iteration of weights, an online learning mechanism is introduced. Incremental model training is triggered every 100 new valid data points (including normal operation data and anomaly warning data). The training sample set is updated using a sliding window method to ensure the model can adapt to changes in risk characteristics brought about by industry technology upgrades and process improvements. Empirical testing shows that the matching degree between the weights output by this non-human-set model and the actual risk level is significantly improved compared to manually set weights. In the newly emerging "new energy material synthesis process scenario," the risk identification accuracy after automatic weight adjustment reaches over 90%, far exceeding manually preset rules.

[0066] Finally, a weight adjustment threshold is set: when a certain type of risk data fluctuates abnormally (such as the "leakage safety index" increasing by 20% week-on-week), a temporary weight adjustment is automatically triggered without manual intervention. For example, if a company's "gas cylinder not upright" data exceeds the standard for three consecutive days, the weight of the third-level indicator of "equipment reliability - major hazard source management" will be automatically increased by 0.1 to strengthen the focus on this potential hazard.

[0067] The real-time monitoring and adjustment execution phase relies on an industrial internet platform to build a millisecond-level data transmission channel for 24 / 7 uninterrupted monitoring of key risk indicators. The data monitoring module calculates the dynamic fluctuation values ​​of indicators in real time (such as week-on-week changes, daily averages, and duration of continuous exceedances) and compares them with preset thresholds in real time. When a threshold condition is triggered (such as "gas cylinder not upright" data exceeding the standard for 3 consecutive days, reaching an extremely high risk threshold), the system automatically initiates a weight adjustment process: First, the correlation between the abnormal indicator and other indicators is analyzed through association rule mining algorithms to determine the weight level to be adjusted (such as the third-level indicator of "equipment reliability - major hazard source management"); then, based on a pre-trained weight adjustment magnitude model (using the duration of the abnormality, the degree of deviation from the threshold, and the probability of similar historical abnormalities leading to accidents as input variables), the weight adjustment magnitude is calculated (such as an increase of 0.1); after adjustment, the weight parameters of the risk assessment model are immediately updated, and an adjustment report is generated and pushed to the safety management platform. The feedback optimization phase evaluates the effectiveness of the adjustment by comparing indicators such as the accuracy of risk identification and the completion rate of hazard rectification before and after the weight adjustment. If the delay time for hazard identification is reduced by more than 30% and the false judgment rate is less than 5% after the adjustment, the adjustment is deemed effective. If the risk assessment deviation increases after the adjustment, the system automatically triggers joint optimization of the threshold and weight mapping model, adjusting the model parameters through a genetic algorithm. Furthermore, a manual intervention mechanism is implemented. When the fluctuation of an indicator exceeds 1.5 times the threshold (e.g., a 50% surge in "reactor pressure" within one hour), the system automatically pushes an early warning to safety management personnel, supporting manual review of adjustment decisions and ensuring the rationality of weight adjustments in extreme scenarios. This closed-loop process achieves full automation from threshold setting to optimization, reducing the weight adjustment response time from 24 hours for manual setting to within 5 minutes, significantly improving the accuracy of risk warnings in extreme scenarios.

[0068] In this embodiment, the method further includes assessing the risks of the entire process of hazardous chemical safety production in a certain region, including: harmonizing and averaging the total scores of the three-level evaluation indicators of all enterprises in the region to obtain the initial score of the first-level indicator at the regional level; dividing the region into risk units based on a GIS geographic information system according to a preset enterprise spacing, identifying the risk correlation of enterprises within each divided risk unit, calculating the spatial coupling coefficient based on the risk correlation of enterprises in each risk unit, and correcting the initial score according to the spatial coupling coefficient to obtain the first-level indicator score; selecting the first-level indicator with the minimum value as the regional comprehensive score; and conducting a risk assessment of the entire process of hazardous chemical safety production based on the regional comprehensive score.

[0069] Specifically, existing regional assessments rely solely on the harmonic mean of enterprise scores, failing to consider the impact of spatial enterprise distribution on regional safety, such as the risk superposition effect of adjacent enterprises. Firstly, based on a GIS (Geographic Information System), regional risk units are divided according to enterprise spacing, for example, within a 500-meter radius, and the risk correlations between multiple enterprises within each unit are identified. For instance, if a unit contains both a liquid ammonia enterprise and a hot work operation enterprise, it is classified as a high-coupling risk unit.

[0070] Furthermore, the coupling coefficient is calculated for different risk units using the following formula: Coupling coefficient = Number of high-risk enterprises within the unit × Risk type matching degree of adjacent enterprises Specifically, high-risk enterprises are defined by a score of <60 on the first-level indicator. The risk type matching degree is assigned a value according to a preset matching table such as "liquid ammonia enterprise - hot work enterprise" and "hazardous chemical storage - densely populated area". The matching degree is as high as 1 and as low as 0.3.

[0071] Next, the spatial coupling coefficient is incorporated into the calculation of the regional secondary indicator score during the correction region calculation. The correction formula is as follows: Corrected score = Harmonic mean score × (1 - Coupling coefficient × 0.2) For example, if the average harmonization score for a certain region is 80 and the coupling coefficient is 0.5, the corrected score is 80×(1-0.5×0.2)=72, which more accurately reflects the impact of the superposition of spatial risks on regional security.

[0072] The aforementioned Analytic Hierarchy Process (AHP) method for risk assessment of the entire process of hazardous chemical safety production comprehensively covers all elements of hazardous chemical safety production (including enterprise qualifications, personnel operation, equipment status, environmental risks, etc.) through a three-level indicator system (6 primary indicators, 12 secondary indicators, and 37 tertiary indicators). The indicator coverage rate reaches 90%, an improvement of over 60% compared to traditional single-dimensional evaluations (such as focusing only on equipment safety). It can effectively identify the superimposed risks of multiple factors, such as equipment failure combined with personnel violations. The AHP method, combined with anonymous expert scoring, ensures rigorous weight calculation logic through a consistency test (CR < 0.1), reducing errors by 40% compared to subjective experience-based assignment methods. Regional evaluation, based on the harmonic mean method, also considers the risk superposition effect of adjacent enterprises, more accurately reflecting the overall safety level of the region. Furthermore, real-time access to multi-source data achieves data acquisition latency of <1 hour, solving the data lag problem of traditional evaluations.

[0073] Furthermore, this method categorizes third-level indicators into multiple types and assigns scores according to the corresponding scoring rules for each type. This avoids a one-size-fits-all approach, effectively improving the accuracy of the final scores and making them more valuable and instructive. Additionally, the indicator system and weights can be dynamically adjusted according to regulations and standards, allowing this method to better adapt to changes in industry development and regulatory needs.

[0074] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0075] In one embodiment, such as Figure 3 As shown, a risk assessment device for the entire process of hazardous chemical safety production using the analytic hierarchy process is provided, including: a three-level evaluation index construction module 200, an evaluation index weighting module 210, a three-level evaluation index score generation module 220, an enterprise total score generation module 230, and a risk assessment module 240, wherein: The three-level evaluation index construction module 200 is used to construct three-level evaluation indicators related to the safe production of hazardous chemicals based on the risk control needs of the entire process of safe production of hazardous chemicals. The evaluation index weighting module 210 is used to construct a judgment matrix based on each level of the three-level evaluation indexes. Experts in the field of hazardous chemicals use the 1-9 scale method to quantify the relative importance between the indicators, and then use the analytic hierarchy process to determine the weight of each indicator after a consistency test. The three-level evaluation index score acquisition module 220 is used to convert the weight of the third-level evaluation index in the three-level evaluation index into the index score of each corresponding three-level evaluation index based on the preset total index score. The enterprise total score module 230 is used to obtain the preliminary scores of each indicator in the third level of an enterprise based on the three-level evaluation indicators, determine the type of each indicator in the third level, and quantify the preliminary scores according to the indicator scores using the corresponding quantification method according to the judgment results to obtain the final score. The indicator types include logical item indicators and degree item indicators. The risk assessment module 240 is used to assess the risks of the entire process of safe production of hazardous chemicals based on the preset total score of the indicators and the total score of the three-level evaluation indicators.

[0076] Specific limitations regarding the Analytic Hierarchy Process (AHP)-based risk assessment device for the entire process of hazardous chemical safety production can be found in the above-mentioned limitations of the AHP-based risk assessment method for the entire process of hazardous chemical safety production, and will not be repeated here. Each module in the aforementioned AHP-based risk assessment device for the entire process of hazardous chemical safety production can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0077] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 4 As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a risk assessment method for the entire process of hazardous chemical safety production using the analytic hierarchy process (AHP). The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0078] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0079] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps: Based on the risk management and control requirements of the entire process of hazardous chemical safety production, a three-level evaluation index related to hazardous chemical safety production is constructed. Based on the three-level evaluation indicators, a judgment matrix is ​​constructed for each level of indicators. Experts in the field of hazardous chemicals use the 1-9 scale method to quantify the relative importance between indicators. After consistency testing, the weight of each indicator is determined using the analytic hierarchy process. Based on the preset total score, the weight of the third-level evaluation indicator in the three-level evaluation indicators is converted into the corresponding index score of each of the three-level evaluation indicators. Based on the three-level evaluation indicators, the preliminary scores of each indicator in the third level of a certain enterprise are obtained, the type of each indicator in the third level is determined, and the preliminary scores are quantified according to the indicator scores using the corresponding quantification method according to the determination results to obtain the final score. The indicator types include logical item indicators and degree item indicators. The risks of the entire process of safe production of hazardous chemicals are assessed based on the total score of the preset indicators and the total score of the three-level evaluation indicators.

[0080] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor: Based on the risk management and control requirements of the entire process of hazardous chemical safety production, a three-level evaluation index related to hazardous chemical safety production is constructed. Based on the three-level evaluation indicators, a judgment matrix is ​​constructed for each level of indicators. Experts in the field of hazardous chemicals use the 1-9 scale method to quantify the relative importance between indicators. After consistency testing, the weight of each indicator is determined using the analytic hierarchy process. Based on the preset total score, the weight of the third-level evaluation indicator in the three-level evaluation indicators is converted into the corresponding index score of each of the three-level evaluation indicators. Based on the three-level evaluation indicators, the preliminary scores of each indicator in the third level of a certain enterprise are obtained, the type of each indicator in the third level is determined, and the preliminary scores are quantified according to the indicator scores using the corresponding quantification method according to the determination results to obtain the final score. The indicator types include logical item indicators and degree item indicators. The risks of the entire process of safe production of hazardous chemicals are assessed based on the total score of the preset indicators and the total score of the three-level evaluation indicators.

[0081] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0082] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0083] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A risk assessment method for the entire process of hazardous chemical safety production using the analytic hierarchy process (AHP), characterized in that, The method includes: Based on the risk management and control requirements of the entire process of hazardous chemical safety production, a three-level evaluation index related to hazardous chemical safety production is constructed. Based on the three-level evaluation indicators, a judgment matrix is ​​constructed for each level of indicators. Experts in the field of hazardous chemicals use the 1-9 scale method to quantify the relative importance between indicators. After consistency testing, the weight of each indicator is determined using the analytic hierarchy process. Based on the preset total score, the weight of the third-level evaluation indicator in the three-level evaluation indicators is converted into the corresponding index score of each of the three-level evaluation indicators. Based on the three-level evaluation indicators, the preliminary scores of each indicator in the third level of a certain enterprise are obtained, the type of each indicator in the third level is determined, and the preliminary scores are quantified according to the indicator scores using the corresponding quantification method according to the determination results to obtain the final score. The indicator types include logical item indicators and degree item indicators. The risks of the entire process of safe production of hazardous chemicals are assessed based on the total score of the preset indicators and the total score of the three-level evaluation indicators.

2. The risk assessment method for the entire process of hazardous chemical safety production using the analytic hierarchy process as described in claim 1, characterized in that, The logic item indicators include basic logic item indicators, hierarchical logic item indicators, and veto item indicators; When the indicator type is the basic logical item indicator, the scoring is determined by whether it meets the preset conditions. If it meets the conditions, the full score of the corresponding indicator is obtained; otherwise, no score is obtained. When the indicator type is the hierarchical logical item indicator, it is scored in layers according to multiple preset achievement levels. Different levels correspond to different scores, and the scores decrease as the importance of the level decreases. When the indicator type is a veto indicator, the scoring is based on whether it meets the preset conditions. If it does, the corresponding indicator score is the full score; if it does not, the first-level indicator is scored as zero.

3. The risk assessment method for the entire process of hazardous chemical safety production using the analytic hierarchy process as described in claim 2, characterized in that, The degree items include simple degree items, weighted degree items, and degree items with trigger conditions; When the indicator type is the aforementioned simplicity level indicator, the safety positivity of the indicator is determined, and the final score is obtained by calculating based on the safety positivity and the corresponding indicator score. When the indicator type is the weighted indicator, first break down the sub-items of different importance and assign corresponding weights, then calculate the achievement rate of each sub-item, and obtain the weighted completion rate by summing them up. Finally, multiply by the total indicator score to get the final score. When the indicator type is the degree item indicator with triggering conditions, it is first determined whether the preset key triggering conditions are met. If the triggering conditions are met, no points are awarded. If the triggering condition is not met, the final score will be calculated according to the preset rules.

4. The risk assessment method for the entire process of hazardous chemical safety production using the analytic hierarchy process according to claim 3, characterized in that, The three-tiered evaluation indicators include first-level indicators, second-level indicators, and third-level indicators; The first-level indicators include: legality of market access, effectiveness of supervision, process safety, environmental stability, equipment reliability, and operational standardization. The secondary indicators include multiple sub-indicators that are subordinate to each of the first-level indicators, and the sub-indicators are specific control directions under the corresponding first-level indicators; The three-level indicators include multiple specific evaluation items that are subordinate to each of the second-level indicators, providing a verifiable basis for actual evaluation.

5. The risk assessment method for the entire process of hazardous chemical safety production using the analytic hierarchy process according to claim 4, characterized in that, When determining the weight of each indicator in the three-tiered evaluation index: For the first-level indicators, each first-level indicator is traversed with other first-level indicators to construct all unique pairwise indicator combinations as comparison units. Using all first-level indicators as row and column elements, each comparison unit is constructed according to the correspondence between row indicators and column indicators to construct the corresponding judgment matrix. When constructing the judgment matrices corresponding to the second-level indicators and the third-level indicators, the first-level indicators are used as the benchmark. Experts in the field of hazardous chemicals used anonymous scoring and centralized argumentation to quantify the relative importance of all comparison units in each judgment matrix based on the 1-9 scale method, resulting in each judgment matrix after assignment. A consistency check is performed on each judgment matrix after assignment. If the check passes, the weight of the next-level indicator corresponding to the matrix relative to the corresponding indicator of the previous level is determined by the analytic hierarchy process. If the test fails, anonymous scoring and centralized argumentation will be carried out again until the judgment matrix passes the consistency test. Then, the weight of each level of indicators will be determined by the analytic hierarchy process.

6. The risk assessment method for the entire process of hazardous chemical safety production using the analytic hierarchy process according to claim 4, characterized in that, If the consistency test passes, the weights of each level of indicators shall be determined by any one of the following methods: weighted average, geometric average, least squares, or eigenvector method.

7. The risk assessment method for the entire process of hazardous chemical safety production using the analytic hierarchy process according to claim 5, characterized in that, Based on the preset total score, the weights of the third-level evaluation indicators in the three-level evaluation system are converted into scoring criteria for each of the three-level evaluation indicators, including: Based on the weights of the first-level indicators relative to the overall goal, the second-level indicators relative to their respective first-level indicators, and the third-level indicators relative to their respective second-level indicators, calculate the final weight of the third-level indicators relative to the overall goal. Based on the preset total score of the indicators, the final weight of the third-level indicators relative to the overall goal is converted into a scoring standard.

8. The risk assessment method for the entire process of hazardous chemical safety production using the analytic hierarchy process according to any one of claims 1-7, characterized in that, The method also includes assessing the risks of the entire process of hazardous chemical safety production in a certain region, including: The total scores of the three-level evaluation indicators for all enterprises in the region are harmonized and averaged to obtain the initial score of the first-level indicator at the regional level. Based on the GIS geographic information system, regional risk units are divided according to the preset enterprise spacing, and the risk correlation of enterprises within each regional risk unit is identified. Based on the risk relationships among enterprises in each regional risk unit, a spatial coupling coefficient is calculated, and the initial score is corrected according to the spatial coupling coefficient to obtain the first-level indicator score. The minimum value of the first-level indicator is selected as the regional comprehensive score. A risk assessment of the entire process of hazardous chemical safety production is conducted based on the comprehensive score of the region.

9. A risk assessment device for the entire process of hazardous chemical safety production using the analytic hierarchy process, characterized in that, The device includes: The three-level evaluation index construction module is used to construct three-level evaluation indicators related to the safe production of hazardous chemicals based on the risk control needs of the entire process of safe production of hazardous chemicals. The evaluation index weighting module is used to construct a judgment matrix based on each level of the three-level evaluation indexes. Experts in the field of hazardous chemicals use the 1-9 scale method to quantify the relative importance between the indicators, and then use the analytic hierarchy process (AHP) to determine the weight of each indicator after a consistency test. The three-level evaluation index score acquisition module is used to convert the weight of the third-level evaluation index in the three-level evaluation index into the index score of each corresponding three-level evaluation index based on the preset total index score. The enterprise total score acquisition module is used to obtain the preliminary scores of each indicator in the third level of an enterprise based on the three-level evaluation indicators, determine the type of each indicator in the third level, and quantify the preliminary scores according to the indicator scores using the corresponding quantification method according to the judgment results to obtain the final score. The indicator types include logical item indicators and degree item indicators. The risk assessment module is used to assess the risks of the entire process of safe production of hazardous chemicals based on the preset total score of the indicators and the total score of the three-level evaluation indicators.

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