Method for evaluating the safety protection resilience of personnel in the case of ammonia leakage in a food freezing plant

Abstract

The embodiment of the present application discloses a kind of food freezing plant liquid ammonia leakage scene personnel safety protection tenacity evaluation method, comprising: obtaining the evaluation index set of food freezing plant liquid ammonia leakage scene personnel safety protection tenacity, and multiple experts score each evaluation index of the food freezing plant to be evaluated;Based on fuzzy decision experiment analysis method, the weight of each secondary evaluation index is determined;Using floating cloud algorithm and comprehensive cloud algorithm, the weight of each secondary evaluation index and all scores are calculated respectively, and the floating evaluation cloud and the comprehensive evaluation cloud of each primary evaluation index are obtained respectively, wherein, evaluation cloud is used to reflect the data distribution of evaluation index score;Using relative entropy method, the evaluation results corresponding to the floating evaluation cloud and the comprehensive evaluation cloud of each primary evaluation index are combined and optimized respectively, and the evaluation grade belonging to each primary evaluation index is obtained, which considers both kinds of evaluation results. The embodiment improves the rationality of evaluation.

Description

Technical Field

[0001] This invention relates to the field of safety protection technology for food freezing plants, and in particular to a method for assessing the resilience of personnel safety protection in the event of a liquid ammonia leak in a food freezing plant. Background Technology

[0002] The widespread application of liquid ammonia refrigeration systems in the food freezing industry, coupled with rapid urban development, has brought about new regional safety risk management issues. Food freezing enterprises are the primary source of risk for liquid ammonia leaks, and urban development further accelerates the spatial and temporal spread of the consequences of such accidents.

[0003] Therefore, assessing and improving personnel safety resilience in the event of ammonia leaks at food freezing plants is particularly important. Accurate and reliable assessment results are beneficial for addressing personnel safety issues following ammonia leaks at ammonia-related food freezing plants in urban built-up areas, and can provide data support for improving urban disaster risk management and safety control measures.

[0004] Currently, research on personnel safety protection technologies in the event of ammonia leaks mainly focuses on emergency response, evacuation, and individual protective equipment (IPE), emphasizing post-accident measures while paying less attention to proactive risk identification and systematically improving the overall resilience of personnel safety. Resilience, in particular, refers to a system's ability to maintain or quickly recover to its intended function in the face of external shocks or internal disturbances. In the field of industrial safety, resilience is used to assess a system's ability to resist, absorb, recover from, and adapt to accidents.

[0005] Current resilience assessment methods are transitioning from qualitative description to quantitative modeling, but a specific method for assessing safety resilience in industrial accident scenarios has yet to be developed. Furthermore, traditional analytic hierarchy process (AHP) and TOPSIS models are susceptible to expert bias and lack the ability to handle dynamic risks and ambiguous information; the structured analysis of assessment results also needs improvement to provide more reasonable data support for real-world emergency preparedness and response. Summary of the Invention

[0006] This invention provides a method for assessing the resilience of personnel safety protection in the event of a liquid ammonia leak at a food freezing plant, in order to solve the aforementioned technical problems.

[0007] In a first aspect, embodiments of the present invention provide a method for assessing the resilience of personnel safety protection under a liquid ammonia leak scenario in a food freezing plant, including: S110. Obtain a set of assessment indicators for the resilience of personnel safety protection in the case of liquid ammonia leakage in a food freezing plant, and scores for each assessment indicator of the food freezing plant to be assessed by multiple experts. The set of assessment indicators includes multiple primary assessment indicators, each primary assessment indicator includes multiple secondary assessment indicators, and the scores of each expert are scores for the secondary assessment indicators. S120. Based on the fuzzy decision-making experimental analysis method, determine the weight of each secondary evaluation indicator, where each weight is used to characterize the importance of each secondary evaluation indicator. S130. Using the floating cloud algorithm and the comprehensive cloud algorithm, the weights and scores of each secondary evaluation indicator are calculated respectively, and the floating evaluation cloud and comprehensive evaluation cloud of each primary evaluation indicator are obtained respectively. The evaluation cloud is used to reflect the data distribution of the evaluation indicator scores. S140. Using the relative entropy method, the evaluation results corresponding to the floating evaluation cloud and the comprehensive evaluation cloud of each primary evaluation indicator are combined and optimized to obtain the evaluation level of each primary evaluation indicator that takes into account both evaluation results.

[0008] In a second aspect, embodiments of the present invention provide an electronic device, the electronic device comprising: One or more processors; Memory, used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the personnel safety protection resilience assessment method under the scenario of liquid ammonia leakage in a food freezing plant as described in any embodiment.

[0009] Thirdly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the personnel safety protection resilience assessment method under a liquid ammonia leakage scenario in a food freezing plant as described in any embodiment.

[0010] In summary, this embodiment provides a method for assessing the resilience of personnel safety protection in a liquid ammonia leak scenario at a food freezing plant. By integrating resilience theory, fuzzy DEMATEL weighting, and the principle of relative entropy to combine similarity, an improved cloud model of personnel safety protection resilience that follows data patterns is constructed. Specifically, the method in this embodiment uses fuzzy function theory to quantify the semantics of expert experience knowledge; and employs multiple data processing algorithms to mine data patterns. Based on the principle of relative entropy combined with similarity, the cloud model is improved, constructing a cloud model of personnel safety protection resilience that follows data patterns. Model calculation replaces subjective scoring, improving the accuracy and efficiency of the assessment. In particular, the floating cloud model can dynamically represent the randomness of leak diffusion, while the comprehensive cloud model can integrate fuzzy data. To balance the advantages of both, this embodiment uses fuzzy proximity calculation to assess the similarity between the cloud and the standard cloud, and uses the principle of relative entropy for optimal combination, achieving probabilistic determination and digital representation of resilience levels. This method of synergistic application of cloud models and relative entropy reduces errors compared to the traditional threshold method and has strong robustness to data gaps, enabling comparison of resilience levels under different conditions. Attached Figure Description

[0011] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0012] Figure 1 This is a flowchart of a method for assessing the resilience of personnel safety protection in the event of a liquid ammonia leak in a food freezing plant, provided by an embodiment of the present invention. Figure 2 This is a schematic diagram of a method for analyzing the resilience factors of personnel safety protection in the event of a liquid ammonia leak, provided by an embodiment of the present invention. Figure 3 This is a causal relationship diagram of the resilience factors for personnel safety protection after a liquid ammonia leak, provided in an embodiment of the present invention. Figure 4 This is a comprehensive evaluation cloud map of a regional improvement scheme provided in an embodiment of the present invention; Figure 5 This is a comprehensive cloud map of engineering resilience in different regions provided by an embodiment of the present invention; Figure 6 This is a comprehensive cloud map of system recovery resilience in different regions provided by an embodiment of the present invention; Figure 7 This is a comprehensive cloud map of preparation and response resilience in different regions provided in an embodiment of the present invention; Figure 8This is a comprehensive cloud map of different toughness factors provided in an embodiment of the present invention; Figure 9 This is a comprehensive cloud map of single toughness factor enhancement provided by an embodiment of the present invention; Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0014] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0015] In the description of this invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0016] To address the aforementioned problems described in the background art, this embodiment provides a method for assessing the resilience of personnel safety protection in the event of a liquid ammonia leak in a food freezing plant, and proposes the following improvements: First, the weighting method of the fuzzy DEMATEL (Decision Making Trial and Evaluation Laboratory) was improved. By integrating fuzzy set theory with the improved DEMATEL method, the interaction relationship of fuzzy indicators in the liquid ammonia leakage scenario was quantified, breaking through the subjectivity constraint in the traditional weight allocation.

[0017] Secondly, this method synergistically applies cloud models and relative entropy. Considering that floating cloud models can dynamically represent the randomness of leakage and diffusion, while integrated cloud models can integrate fuzzy data, to balance the advantages of both, fuzzy proximity calculations are used to evaluate the similarity between the cloud and the standard cloud for both floating cloud and integrated cloud algorithms. The optimal combination is then achieved by drawing on the principle of relative entropy, realizing probabilistic determination and digital representation of resilience levels. This method reduces errors compared to traditional threshold methods and is highly robust to missing data, enabling comparisons of resilience levels under different conditions.

[0018] Third, resilience enhancements were implemented for different hazard levels, both inside and outside the site, and the effectiveness of these enhancements was evaluated using an improved cloud model. Simultaneously, improvements were made to various factors, including spatial resilience, engineering resilience, preparedness and response resilience, and system recovery resilience, and the effectiveness of these enhancements was evaluated using the improved cloud model. Through these two comparative evaluations, the practical application value of the integrated personnel safety protection resilience enhancement strategy for a liquid ammonia leak scenario in a food freezing plant was strengthened.

[0019] Figure 1 This is a flowchart illustrating a method for assessing personnel safety resilience in the event of a liquid ammonia leak at a food freezing plant, as provided in an embodiment of the present invention. The method is executed by electronic equipment. Figure 1 As shown, the method specifically includes: S110. Obtain a set of assessment indicators for the resilience of personnel safety protection in the event of a liquid ammonia leak at a food freezing plant, and obtain scores from multiple experts for each assessment indicator of the food freezing plant to be assessed. The set of assessment indicators includes multiple primary assessment indicators, each primary assessment indicator includes multiple secondary assessment indicators, and the scores from each expert are scores for the secondary assessment indicators.

[0020] The resilience factors for personnel safety protection in the event of a liquid ammonia leak can be categorized according to the different stages of the emergency: accident prevention capability, monitoring and early warning capability, emergency support capability, emergency response capability, and recovery capability. Each resilience capability includes different elements, and each element belongs to one of four aspects of resilience capability: spatial resilience, engineering resilience, preparedness and response resilience, and system recovery resilience. Figure 1 As shown.

[0021] 1) Spatial Resilience. Spatial resilience refers to the dynamic adaptability of a liquid ammonia leak accident to achieve risk isolation, diffusion control, and personnel safety protection through optimized physical spatial layout and emergency facility configuration. Its core elements include: compliance with safety distances (C1), which physically isolates the risk of death and injury after a liquid ammonia leak by ensuring the legally required safety distance between the plant area and surrounding sensitive areas; suitability of refuge spaces (C2), which plans compliant emergency refuge sites to ensure the safety of personnel during emergency evacuation; and dynamic adjustment of evacuation routes based on wind vane coverage (C6).

[0022] 2) Engineering Resilience. Engineering resilience in the context of liquid ammonia leaks refers to the comprehensive capability to prevent liquid ammonia leaks through technical means and equipment system optimization, enabling risk prevention, rapid response, and functional recovery of personnel in the event of an accident. Its core elements include: on-site facility compliance (C3), ensuring the reliability of equipment materials and processes from an inherent safety perspective. As concluded in Chapter 5, pipeline rupture (X11) is the main cause of liquid ammonia leaks; compliance of pipeline materials and the prohibition of low-carbon steel in pressure vessels should be considered. Accuracy of on-site BPCS monitoring (C4), improving monitoring reliability and shortening leak response time through high-precision sensors and redundant systems. Monitoring and early warning capabilities are closely related to the accuracy and reliability of on-site BPCS monitoring; therefore, the accuracy of BPCS monitoring (C4) and the calibration status of on-site pressure pipelines, containers, safety accessories, and safety protection devices (C5) are included in the indicators. The wind vane coverage status (C6) guides personnel to evacuate against the wind, avoiding ammonia diffusion paths. Combined with air quality recovery capabilities (C7), the effectiveness of evacuation directions is dynamically calibrated to mitigate the escalation of casualties. Air quality recovery capabilities (C7) refer to the installation of dual pressure relief valves and a sprinkler system on site to automatically trigger pressure relief; the deployment of liquid nitrogen curtains or water mist barriers along the ammonia leak diffusion path to suppress ammonia diffusion speed; and the use of a negative pressure extraction system and dilute acetic acid neutralizing agent spraying devices to reduce ammonia concentration in the leak area.

[0023] 3) Preparedness and Response Resilience. Preparedness and response resilience refers to the organizational decision-making ability to achieve risk prevention, emergency coordination, and system recovery throughout the entire process of a liquid ammonia leak accident through system design, process optimization, and dynamic control. Regarding accident prevention, the on-site risk identification capability (C8) has a posterior probability of 40.9% in Chapter 5 (X3), indicating that insufficient personnel's ability to promptly detect abnormal conditions (such as micro-cracks in pipelines) may be a contributing factor to pipeline rupture. Personnel training and intelligent diagnostic systems can improve the probability of detecting micro-defects in pipelines. Closed-loop risk management is achieved through the company's rectification of hidden dangers (C9). The sharing and transmission of early warning information is a key link in regional integrated collaborative response, directly related to the sharing and transmission of early warning information within the site (C10), in high-risk areas outside the site (C11), and in non-high-risk areas outside the site (C12). Emergency response capability refers to the comprehensive ability to effectively control the accident site and minimize casualties and secondary disasters after a liquid ammonia leak accident through rapid response, scientific decision-making, and efficient execution. In the regional integrated emergency response capability, the on-site staff's emergency drills (C13), the on-site initial response capability (C15), and the on-site emergency material reserves (C14) are the core. The coordinated evacuation of high-risk areas outside the site (C16) and the coordinated evacuation of non-high-risk areas outside the site (C17) are important factors. Whether the nearest fire station has the capacity to handle liquid ammonia (C18) and the local district government's experience in emergency rescue (C19) are key factors for the success of regional integrated coordinated emergency response.

[0024] 4) System Resilience. System resilience in the context of ammonia leakage at a food freezing plant refers to the collective response capability achieved through community collaboration, public education, and multi-party coordination mechanisms to enhance risk awareness, integrate emergency resources, and restore order during an ammonia leakage incident. This includes: Improving the safety knowledge and skills of off-site personnel (C20 / C21) through knowledge dissemination and practical drills to enhance the self-rescue and mutual-aid capabilities of surrounding residents; Enhancing integrated emergency response coordination, as illustrated in Chapter 5, where effective off-site comprehensive emergency response can reduce the probability of kindergarten injuries and fatalities from 15% to below 5%, highlighting the importance of social collaboration; Enhancing personnel insurance coverage (C22) through compensation mechanisms to reduce the impact of accidents and accelerate functional recovery; and Improving injury and death recovery capabilities (C23) by constructing a tiered medical care network to minimize loss of life.

[0025] Table 1. Resilience Factors of Personnel Safety Protection under Liquid Ammonia Leakage Scenario

[0026] Based on the above indicator system, an expert scoring method can be adopted, inviting experts to participate. Each expert scored the various secondary evaluation indicators of the food freezing plant being evaluated, which served as the data source for the entire methodology.

[0027] S120. Based on the fuzzy decision-making experimental analysis method, determine the weight of each secondary evaluation indicator, where each weight is used to characterize the importance of each secondary evaluation indicator.

[0028] Decision laboratory analysis is a method that utilizes expert experience and knowledge to accurately identify and analyze factors in complex networks, determine the mutual influence relationships and degrees among these factors, systematically reveal complex causal relationships, quantitatively assess the importance of elements, and support the identification of key elements and decision optimization. However, the traditional Dematel method, due to its over-reliance on expert experience, may suffer from significant ambiguity in its judgments due to expert subjectivity and individual differences. The fuzzy Dematel method, by combining fuzzy set theory with the traditional Dematel framework, achieves semantic quantification, significantly reduces subjective judgment bias, and effectively solves the problems of ambiguity and dynamism in the weighting of indicators in complex systems.

[0029] The risk of liquid ammonia leakage is influenced by a multi-dimensional dynamic interplay of factors, including equipment aging, environmental temperature and humidity, and personnel skill levels. Traditional methods struggle to accurately quantify this non-linear correlation. Fuzzy DEMATEL, however, constructs an elastic influence network between indicators using a fuzzy rule base, identifying key resilience factors. Furthermore, the vulnerability and redundancy of personnel safety protection systems significantly differ in their impact on leakage consequences. The asymmetric matrix characteristics of Fuzzy DEMATEL accurately characterize these path differences, supporting the development of targeted resilience enhancement strategies. By integrating fuzzy logic and an asymmetric weighting mechanism, Fuzzy DEMATEL provides a methodologically rigorous yet practically adaptable approach for the comprehensive assessment of personnel safety protection resilience in high-risk liquid ammonia leakage scenarios. The specific steps of Fuzzy DEMATEL are as follows: (1) The influence relationship between the secondary evaluation indicators is scored using an expert scoring method. Optionally, a 5-point scale expert scoring method is used to determine the mutual influence relationship between the factors.

[0030] (2) Fuzzy set theory is used to fuzzify the expert scoring results, quantifying the semantics of the expert experience knowledge represented by each score. Optionally, triangular fuzzy numbers can be used; the conversion between fuzzy numbers and interpretations is shown in Table 2. The table lists the triangular fuzzy numbers... l , m , r These represent the "most pessimistic value", "most likely value", and "most optimistic value", respectively.

[0031] Table 2 Fuzzy Numbers and Interpretations

[0032] Based on the above interpretation, for the first Based on the scores given by the experts, a direct influence matrix can be constructed. , elements in Indicates the first Experts believe the risk factors x i right x j The degree of direct impact. The scores in the matrix are then compared using the triangular fuzzy numbers in Table 2. By substitution, we obtain the transformed direct influence matrix. H k .

[0033] Defuzzification is performed using the method of converting fuzzy data into Crisp Scores (CFCS). The steps are as follows: Standardization process: (1) (2) (3) (4) Calculation of left and right standard values: (5) (6) Calculation of total standard value: (7) Calculation of sharpness value: (9) Then the first Experts reflected on the key elements right Impact value ,but The average value of the experts is (10) Obtain the average direct influence matrix This is also known as a clear matrix. All the above steps are existing techniques and are fundamental operations of CFCS.

[0034] (3) Calculate the matrix directly affected by normalization The formula is: (11) (4) Determine the comprehensive influence matrix : (12) (5) Finally, calculate the influence of the elements. And the degree of influence : (13) (14) in, For matrix The elements in.

[0035] (6) Calculate the centrality of each element. and causal degree .

[0036] (15) (16) Centrality The larger the value, the higher the importance of this evaluation indicator; centrality The smaller the value, the lower the importance of this evaluation indicator. If the degree of causation... A value greater than 0 indicates that the evaluation indicator is coupled with other evaluation indicators, and is called a causal factor; if the degree of causation is... A value less than 0 indicates that the evaluation indicator is coupled with other evaluation indicators, and is called an outcome factor. The centrality is used to calculate this. causal degree Using MATLAB, a causal relationship diagram of resilience assessment indicators is plotted with the x and y axes as the horizontal and vertical axes. The total centrality is obtained by summing the centrality values ​​of all indicators in the table. The weight value of each indicator is the ratio of its centrality to the total centrality. / Total centrality.

[0037] S130. Using the floating cloud algorithm and the comprehensive cloud algorithm, the weights and scores of each secondary evaluation indicator are calculated respectively, and the floating evaluation cloud and comprehensive evaluation cloud of each primary evaluation indicator are obtained respectively. The evaluation cloud is used to reflect the data distribution of the evaluation indicator scores.

[0038] First, we introduce the basic theory of the cloud model. The cloud model is an interdisciplinary mathematical model that integrates probability theory and fuzzy mathematics, aiming to construct a two-way mapping relationship between qualitative description and quantitative representation. The cloud model achieves fuzziness quantification through a three-parameter system: expectation... ,entropy and hyperentropy . It is the central positioning parameter that characterizes the core features of the concept, and its physical meaning corresponds to the benchmark reference value of the fuzzy evaluation system. It is a discrete measure that defines the fuzzy domain of concepts. This parameter reflects the degree of fuzziness of both the randomness of the system and the uncertainty of cognition. As a second-order uncertainty indicator, the fluctuation characteristics of entropy are quantified, and the entropy of the entropy value reflects the stability of cloud droplets. In this paper, , and This can represent the dual uncertainty of personnel safety protection levels. Corresponding to the standard value of protective effectiveness, Reflecting the robustness of the evaluation results, This quantifies the stability of the protection strategy.

[0039] The theoretical implementation of the cloud model relies on its bimodal generator mechanism, which can be divided into two core algorithm modules: the Backward Cloud Generator (BCG) and the Forward Cloud Generator (FCG). Specifically, the Backward Cloud Generator establishes a mapping relationship from quantitative samples to qualitative concepts through statistical analysis of cloud droplet data, outputting a set of digital feature triples representing the essence of the concepts. The Forward Cloud Generator, based on the input triplet parameters, generates a set of cloud droplets conforming to a probability-fuzzy joint distribution through random simulation, achieving a quantitative representation of qualitative concepts.

[0040] This embodiment addresses the problem of assessing personnel safety protection capabilities following a liquid ammonia leak at a food freezing plant. It employs a bidirectional coupling algorithm framework. First, a reverse cloud generator is used to extract features from the indicator scores, determining the core numerical features of personnel safety protection capabilities. Then, a forward cloud generator is used to form an assessment cloud map, establishing a probabilistic-fuzzy joint distribution model in a multi-dimensional state space. Specifically, the reverse cloud generator extracts features from all scores for each secondary assessment indicator, obtaining the mean, entropy, and hyperentropy values ​​for each secondary assessment indicator. The mean, entropy, and hyperentropy values ​​of the same secondary assessment indicator collectively constitute the assessment cloud for that indicator.

[0041] Optionally, feature extraction using a reverse cloud generator includes the following steps: 1) Calculate the mean : (17) In the formula: This represents the average value of the indicator data; The first in the index One data point; This refers to the number of data points contained in the indicator. In this embodiment... It is the first in a certain indicator Each person gets a score.

[0042] 2) Calculate the entropy value : (18) 3) Calculate the hyperentropy value : (19) (20) The three feature parameters mentioned above constitute a cloud model, which are also called the digital features of the cloud model.

[0043] The algorithm steps for generating an evaluation cloud map using a forward cloud generator are as follows: 1) Generate random numbers ,and .

[0044] 2) Generate random numbers ,and .

[0045] 3) Seeking cloud droplets ,and .

[0046] in, x It represents a specific numerical value (i.e., a generated random number), which represents one possible state of the indicator; , representing numerical values The degree of membership, i.e. The probability of belonging to a certain vague concept (such as "high security protection resilience").

[0047] It is a step in the positive cloud generator, used to extract numerical features (mean) ,entropy hyperentropy Generate cloud droplets. Specifically: From normal distribution Random numbers generated in the process, where It is the random entropy value (by...) generate); It is calculated using membership functions, and the formula is: .here, It is a Gaussian function, representing with the mean The higher the degree of deviation, the greater the membership degree, indicating... The more likely it is to belong to this concept. En' is the random entropy value, generated by the normal distribution N(En, He²), used to introduce uncertainty in entropy.

[0048] Optionally, MATLAB 2022b software can be used to perform the calculation of the forward cloud generator, and the results of the assessment of personnel safety protection capabilities after liquid ammonia leakage can be presented intuitively in the form of a cloud model diagram.

[0049] Then, the floating cloud algorithm and the comprehensive cloud algorithm are used respectively to calculate the evaluation cloud and weight of each secondary evaluation indicator, thereby obtaining the floating cloud and comprehensive cloud of each primary indicator. In this embodiment, to accurately evaluate the regional integrated personnel comprehensive safety protection capability under the scenario of liquid ammonia leakage in a food freezing plant, the cloud feature value and comprehensive cloud digital feature value of the primary indicators need to be further calculated using the cloud synthesis algorithm. Currently, cloud synthesis algorithms mainly include two categories: floating cloud algorithms and comprehensive cloud algorithms, each with its own characteristics.

[0050] 1) The calculation formula for the floating cloud algorithm is shown in equation (23-25).

[0051] (twenty three) (twenty four) (25) The floating cloud algorithm is greatly affected by the weight of each indicator, and can sensitively characterize the randomness of leakage and diffusion.

[0052] 2) The calculation formula of the integrated cloud algorithm is shown in equation (26-28).

[0053] (26) (27) (28) In the formula: For the first The overall weight of each indicator; This represents the total number of evaluation metrics. The integrated cloud algorithm is significantly affected by entropy, thus enabling it to integrate data with high ambiguity.

[0054] S140. Using the relative entropy method, the evaluation results corresponding to the floating evaluation cloud and the comprehensive evaluation cloud of each primary evaluation indicator are combined and optimized to obtain the evaluation level of each primary evaluation indicator that takes into account both evaluation results.

[0055] This step utilizes a floating evaluation cloud and a comprehensive evaluation cloud to evaluate each primary evaluation indicator. Since both the floating cloud algorithm and the comprehensive cloud algorithm have certain advantages in the process of synthesizing the comprehensive cloud, relative entropy is used to modify and combine the evaluation results corresponding to the two evaluation clouds in order to effectively take into account their respective strengths.

[0056] The advantages of this approach are: firstly, its resistance to interference and robustness. The hyperentropy parameter of floating clouds ( This method effectively filters noise data from personnel safety resilience indicators in liquid ammonia leak scenarios, reducing the interference of outliers on assessment results by calculating the distribution difference of relative entropy. Secondly, it facilitates the scientific mapping of risk levels. By calculating the probability distribution difference between the assessment cloud and the standard cloud using relative entropy, it can quantitatively compare the hazards of liquid ammonia leaks with preset safety level ranges, overcoming the shortcomings of traditional threshold methods in terms of insufficient sensitivity to fuzzy boundaries. Thirdly, it supports the optimization of emergency decision-making. When using the assessment cloud for level assessment, the similarity between the assessment cloud and the standard cloud is calculated. The similarity calculation results can be directly mapped to the emergency preparedness and response action plans in personnel safety related emergency preparedness and emergency plans for liquid ammonia leak scenarios in food freezing plants, providing a practical basis for decision-making. In the personnel safety resilience assessment under liquid ammonia leak scenarios, relative entropy is introduced to measure the similarity between the floating cloud and the comprehensive assessment cloud, thereby achieving combined similarity calculation. The specific steps are as follows: First, determine the evaluation standard cloud. Optionally, obtain the score range corresponding to each evaluation level; based on each score range, determine the mean, entropy, and hyperentropy value for each evaluation level, and the mean, entropy, and hyperentropy value of the same evaluation level together constitute the standard cloud for the same evaluation level.

[0057] Specifically, the digital characteristics of the cloud This determines the fundamental characteristics of cloud images. When At that time, the cloud droplets in the cloud image completely follow a Gaussian curve and exhibit a normal distribution. As the cloud grows larger, the density of the cloud droplets decreases, the cloud droplet pattern disperses, and the cloud atomization process is observed.

[0058] The benchmark assessment cloud serves as a reference system for the comprehensive assessment of personnel safety protection resilience. Its core function is to establish a mapping relationship between multi-level fuzzy semantics and quantitative indicators. This study adopts a five-level classification system of "extremely low," "low," "medium," "high," and "extremely high," respectively representing the integrated progression of personnel safety protection resilience. By comparing and analyzing the cloud to be assessed with the benchmark cloud model, objective quantitative assessment conclusions can be obtained.

[0059] Due to the contribution of elements on the universe of discourse in the cloud model to qualitative concepts That is, for the domain of discourse Qualitative concepts The contributing quantitative values ​​mainly fall within the interval [ En-3He,En+3He That is, cloud droplets fall on two lines that follow a Gaussian distribution with an expected value of . variance is Between the curves, the domain of discourse The conditional probability density function is , ,but Therefore when At that time, the fogging requirements are met. In applications such as image segmentation, the 3En rule is used to identify the cloud kernel location (i.e., the region with the highest cloud droplet density). By excluding outlier data points outside the interval, the stability of standard cloud construction is improved, and the multi-threshold segmentation effect is optimized. Therefore, the standard cloud is set as follows: Let the rating interval for the i-th indicator corresponding to the j-th level be [ This semantic can be based on Rules, using a cloud model [ , , The characterization and parameter calculation process is as follows: 1) Expected value : Take the geometric center value of the level interval; this value reflects the core semantic features of that level. = ( ) / 2 (21) 2) Entropy : = ( ) / 6 (22) 3) Hyperentropy Considering the fuzzy and random characteristics of the decision-making object, a constant k is chosen (0.3 is chosen in this paper based on relevant literature).

[0060] Based on the above method, the standard cloud parameters for assessing personnel safety protection capabilities are shown in Table 3. The expected values ​​corresponding to the "Medium" level are... , , =0.3.

[0061] Table 3 Standard Cloud Model Parameters

[0062] After obtaining the standard cloud for each level, for each first-level evaluation indicator, calculate the fuzzy similarity between the evaluation cloud of that first-level evaluation indicator and the standard cloud of each evaluation level, and obtain the fuzzy similarity between each evaluation indicator and each evaluation level.

[0063] Optionally, this embodiment also improves the method for calculating cloud similarity: two evaluation clouds to be compared are set as follows: and Their fuzzy similarity V It can be calculated using the following formula: (29) (30) (31) Finally, the similarity is normalized using the following formula: (32) in, Indicates the first i The normalized similarity between each primary evaluation indicator and the j-th evaluation level standard cloud; C i Indicates the first i The evaluation cloud of each primary evaluation indicator; S j Indicates the first j Standard cloud for individual and assessment levels; m This indicates the total number of assessment levels.

[0064] Using the above method, the similarity between the floating evaluation cloud of the same first-level evaluation indicator and the standard cloud of each evaluation level can be calculated, and the evaluation results corresponding to the floating evaluation cloud are constituted by each similarity. Simultaneously, the similarity between the comprehensive evaluation cloud of the same first-level evaluation indicator and the standard cloud of each evaluation level can be calculated, and the evaluation results corresponding to the comprehensive evaluation cloud are constituted by each similarity. For ease of distinction and description, the above two evaluation results are referred to as the first evaluation result and the second evaluation result, respectively. Optionally, the similarities corresponding to the first evaluation result can be arranged sequentially according to the level to obtain a similarity vector, and the similarities corresponding to the second evaluation result can be arranged sequentially according to the level to obtain another similarity vector, which are then used in subsequent data processing.

[0065] The following section uses the relative entropy method to fuse the two evaluation results, resulting in a more reasonable evaluation level. First, we define relative entropy. ,in , where xi,yi>0, and This definition reveals the property that relative entropy is zero when two distributions are exactly the same. Therefore, relative entropy can measure the distance between cloud similarity vectors under two different synthesis algorithms. Based on the above principle, the aggregation weight of cloud similarity under each synthesis algorithm is determined. The solution can be found using the following mathematical programming model: (33) in, This represents the normalized similarity between the i-th indicator and the standard cloud under the j-th algorithm (j=1 corresponds to the floating evaluation cloud, j=2 corresponds to the comprehensive evaluation cloud); Denotes the optimal combination weights of the i-th indicator, satisfying ∑ = 1; p represents the number of algorithms, here p=2 (floating cloud algorithm and integrated cloud algorithm); a represents the number of primary evaluation indicators.

[0066] (34) Linear programming models have a global optimal solution ,in: (35) The above method calculates the relative entropy of the similarity vectors corresponding to the two evaluation results based on the current weight combination; and iteratively optimizes the weight combination until the relative entropy is minimized. The final weight combination is the optimal weight combination. A new similarity vector is obtained by weighted averaging the similarity vectors corresponding to the two evaluation results based on this optimal weight combination. This new vector can effectively integrate the evaluation information of the floating cloud for personnel safety protection and the comprehensive evaluation cloud in a liquid ammonia leak scenario, thereby improving the accuracy and scientific rigor of the evaluation. Furthermore, it is worth mentioning that, due to the large number of formulas and the limited number of variable symbols in this embodiment, some formulas may contain the same variable symbol corresponding to different variables, or different variable symbols corresponding to the same variable. In such cases, the interpretation should be based on the meaning of each variable symbol in the specific formula, and the same applies below.

[0067] By weighting the characteristic values ​​(i.e., a level identifier) ​​of each assessment level according to the new similarity vector, the level characteristic value of the same level assessment indicator can be determined, thereby determining the assessment level to which the assessment indicator belongs. Specifically, considering the inapplicability of the maximum membership principle under fuzzy concepts, this paper uses the level characteristic value K to quantify the assessment results and determine the assessment level of personnel safety protection resilience in the scenario of liquid ammonia leakage in a food freezing plant. The calculation formula is as follows: (36) in, Let m be the similarity between the current evaluation index and the j-th evaluation level after fusion, and m be the number of evaluation levels. For example, if the feature values ​​of the 5 evaluation levels are 1, 2, 3, 4, and 5 respectively, and are substituted into equation (36) as j to calculate 2.1, then the current index belongs to the second evaluation level.

[0068] Furthermore, the above assessment method can be used for the following practical applications: The food freezing plant and its surrounding area can be divided into on-site, off-site high-risk areas, and off-site non-high-risk areas, and various resilience enhancement strategies (such as control measures) can be implemented for different areas. Using methods S110-S140, the personnel safety protection resilience of each area before and after the implementation of each resilience enhancement strategy is assessed. Based on the change in assessment level before and after implementation, the optimal enhancement strategy is determined for each area to improve its safety protection resilience.

[0069] In addition, different resilience enhancement strategies can be implemented for the food freezing plant based on the resilience of each primary assessment indicator. The S110-S140 method can be used to assess the personnel safety protection resilience before and after implementation. Based on the change in assessment level before and after implementation, the strongest or best-improved influencing factor (i.e., primary assessment indicator) can be determined to improve the safety protection resilience of the area.

[0070] The following is an application example of the above method. This example uses a food freezing plant in northern China as an example. The plant uses liquid ammonia as a refrigerant in its production, with a maximum usage of 15 tons, exceeding the specified critical limit of 10 tons (GB18218-2018, 2018). This constitutes a Level III major hazard source for hazardous chemicals and is one of the major liquid ammonia hazard sources in the area. The plant has implemented Level III safety production standardization. The surrounding area is flat and densely built, with kindergartens and residential buildings nearby (a subway line is within 50 meters, a kindergarten within 200 meters; a university and residential area within 300 meters; and a shopping mall within 500 meters). A liquid ammonia leak could spread to the surrounding area, causing large-scale casualties. The liquid ammonia leak accident scenario constructed in this investigation and research is based on a mid-August scenario, with daytime temperatures set at 32-35℃, wind speed of 3.5 m / s, and a southwest wind direction. The company established a "one-to-one emergency plan" for liquid ammonia leaks. However, the following unfavorable factors exist after a liquid ammonia leak: First, the nearest fire brigade cannot meet the needs if the leak expands. A team with professional handling capabilities is 8.5 km away. Second, the hospital is 3 km away, with only 6 ambulances; traffic congestion hinders treatment of the injured, and the emergency room is full. Third, the district environmental protection bureau is 3.7 km away from the refrigeration plant, with a current emergency team of 5 people, ammonia concentration testing reagents, and the location of ammonia emergency supplies. Fourth, there is no emergency coordination between the incident site and surrounding areas. The subway station on this line is above ground, and there is no communication and coordination method with the municipal rail transit command center; evacuation of people in the surrounding area faces problems such as difficulty in delivering notices, lack of evacuation guarantees, and unclear resettlement locations. Fifth, after the accident, the area to be notified for evacuation or shelter lacks early warning terminals, even loudspeakers, and there is a lack of sufficient manpower to ensure evacuation order. The evacuation location in the plan—a certain square—does not meet the conditions for a personnel evacuation site.

[0071] First, the weights of the indicators were calculated. Relevant information on personnel safety protection after a liquid ammonia leak at a food freezing plant was provided to 10 experts. The five-level scoring rules were introduced, and the influence relationship of the resilience factors in personnel safety protection after a liquid ammonia leak was scored. The semantics of the experts' experience and knowledge were quantified using a triangular fuzzy function. Based on formulas (13)-(16), the influence degree, affected degree, centrality, and causal degree of the resilience indicators for personnel safety protection after a liquid ammonia leak at a food freezing plant were calculated. The results are shown in Table 4. The corresponding causal relationship diagram is shown in Table 4. Figure 3 .

[0072] Table 4. Influence and Weights of Comprehensive Factors on Resilience of Personnel Safety Protection After Liquid Ammonia Leakage at a Food Freezing Plant

[0073] Identification of causal dominant factors. C8 has a causality degree of 1.441 and a weight of 0.053, while C1 has a causality degree of 1.388 and a weight of 0.048, exhibiting high causality and medium-high centrality, making them core driving factors of the system with a significant promoting effect on other factors. C9 has a causality degree of 0.863, which is slightly lower, but its centrality is 2.689 and its weight is 0.049, indicating that it is still a critical node. In addition, C16 and C23 have the highest weight values ​​(0.059 and 0.053 respectively), and their global impact on system resilience should be given priority.

[0074] Identification of passive response elements. C23 has a causality of -1.664 and an influence of 2.294; C16 has a causality of -1.436 and an influence of 2.341, exhibiting extremely low causality and high influence, making them vulnerable nodes in the system, easily affected by external factors. C17 has a causality of -1.089, and C15 has a causality of -0.726, also showing significantly negative causality, potentially becoming a cause of system failure.

[0075] Centrality distribution characteristics. C16 has a centrality of 3.246, C23 has a centrality of 2.925, and C8 has a centrality of 2.887, constituting the core hubs of the system. Their dynamic changes will directly affect network stability. C13 has a centrality of 2.744, and C7 has a centrality of 2.565. Their central status, combined with their negative causation degree, can be used to assess their risk transmission path.

[0076] Influence network hierarchy: High-influence-low-affected groups, such as C1, C8, and C19, dominate the direction of system evolution, while low-influence-high-affected groups, such as C23, C16, and C11, constitute the risk-bearing layer.

[0077] Therefore, for core driving factors such as C8 and C1, resource allocation can be optimized to enhance system anti-interference capabilities. However, C1 is a fait accompli and difficult to change. For passive elements such as C23 and C16, effective control of vulnerable nodes should be implemented, and real-time monitoring mechanisms (such as leakage diffusion simulation and personnel evacuation effectiveness assessment) should be established to reduce their sensitivity to external shocks. Given the high weight values ​​of C16 and C23 (weight > 0.05), the protective effectiveness of high-weight indicators should be prioritized for improvement.

[0078] Then, an improved cloud model assessment was conducted. The improved cloud model was used to assess the resilience level of personnel safety protection under a liquid ammonia leak scenario at a food freezing plant. Based on the opinions of 10 experienced experts, scores for each indicator were obtained. Feature extraction was performed using a reverse cloud generator, and according to equations (17)-(20), the digital features of the cloud for each secondary indicator assessment were obtained, as shown in Table 5.

[0079] Table 5 Cloud Digital Characteristics of Secondary Indicators

[0080] Substituting the cloud digital features of the secondary indicators into the floating cloud algorithm and the comprehensive cloud algorithm synthesis algorithm, the cloud feature values ​​of the primary indicators and the comprehensive cloud digital features under the two cloud models are calculated by equations (23)-(25) and (26-28), respectively. The calculation results are shown in Table 6.

[0081] Table 6 Cloud Digital Characteristics of Primary Indicators

[0082] Advantages identified. Through comparison of cloud digital features, it was found that the engineering resilience dimension had the highest expected value among the primary indicators, serving as the core support of the protection system; its entropy value was significantly higher than other dimensions, indicating substantial differences in expert perception regarding engineering resilience. Among the secondary indicators, C3 and C22 had expected values ​​far exceeding other indicators, indicating that the company's facilities were compliant, standardized equipment maintenance processes were effectively implemented, and insurance coverage for surrounding personnel was high.

[0083] Weaknesses identified: The expected values ​​for the spatial and preparedness / response dimensions are relatively low, with minimal fluctuations, reflecting the current unsatisfactory state of spatial elements such as safety distance settings and the suitability of refuge spaces; cross-departmental coordination mechanisms and emergency preparedness need further improvement. Among the secondary indicators, C11, C16, and C17 are the lowest-scoring indicators in the system, reflecting significant deficiencies in the current status of monitoring and early warning, the completeness of emergency plans, and evacuation coordination. The high entropy value of C17 reveals that the dynamic adjustment capability of evacuation routes is constrained by the interaction of factors such as poor information sharing.

[0084] Uncertainty characteristics diagnosis. The relatively low hyperentropy value of C7 (He=0.912) indicates that its uncertainty mainly stems from the interaction effect between the intensity of vertical administrative intervention and the execution of horizontal cooperation agreements, rather than the bias of the assessment method itself.

[0085] In order to quantify the evaluation results, the above cloud feature values ​​were substituted into the similarity calculation formula (29)-(32) between the evaluation cloud and the standard cloud. The evaluation cloud and the evaluation standard cloud corresponding to the overall and each level indicators were normalized to obtain the final result of the comprehensive evaluation of the personnel safety protection resilience under the scenario of liquid ammonia leakage in the food freezing plant, as shown in Table 7.

[0086] Table 7 Similarity of Primary Indicators

[0087] In order to take into account the advantages of both floating cloud and integrated cloud, relative entropy is used to modify and combine the two similarities. The similarity after combination is calculated according to equations (33)-(35), and the grade feature value is calculated according to equation (36), as shown in Table 8.

[0088] Table 8. Characteristic values ​​of first-level indicators for relative entropy weighting

[0089] High-risk exposure areas. The level characteristics of preparedness and response resilience and spatial resilience are low, mainly due to unreasonable safety distances between enterprises and their surroundings, and insufficient coordination in emergency preparedness and response with the surrounding areas.

[0090] To verify the accuracy of the cloud model (improved cloud model) based on relative entropy combined similarity, this study uses Multi-Level Root Mean Square Deviation (ML-RMSD) as the evaluation metric. Unlike traditional RMSD, ML-RMSD calculates the similarity dispersion within each risk level hierarchically and integrates the overall deviation, avoiding interference from cross-level data distribution differences on the verification results. The specific definition is as follows: Single-level RMSD calculation: For the k-th risk level (k∈{very low, low, medium, high, very high}), its similarity set is as follows: The calculation formula is: ,in, This represents the average similarity score for that level.

[0091] Overall ML-RMSD calculation: By aggregating all level deviations through equal-weighted averaging, a comprehensive evaluation index is obtained. In the formula, K=5, corresponding to five risk levels. This indicator reflects the consistency of the model's similarity discrimination under multi-level resilience scenarios; the smaller the value, the higher the model accuracy. The calculation results are shown in Table 9.

[0092] Table 9. Root Mean Square Deviation of First-Level Indicator Similarity for the Three Cloud Models

[0093] Model accuracy comparison. The combined algorithm shows significant advantages. Among all dimensions, the improved cloud model based on relative entropy combined similarity has the lowest ML-RMSD value, indicating that its similarity calculation results deviate the least from the actual risk level. The comprehensive dimension exhibits outstanding stability, with the ML-RMSD value of the comprehensive dimension generally lower than that of other dimensions, demonstrating that the fusion of multiple indicators effectively improves the robustness of the model.

[0094] Algorithm Defect Analysis. The integrated cloud algorithm exhibits anomaly in the preparedness and response dimensions: its ML-RMSD value (0.109) is significantly higher than other models in the "preparedness and response" dimension, possibly due to the ambiguity and dynamism of management indicators (such as emergency plans and personnel training), leading to insufficient adaptability of traditional cloud models. The floating cloud algorithm suffers from local adaptability limitations: its deviation in the "spatial" dimension (0.077) is higher than that of the combined algorithm (0.042), reflecting its higher sensitivity to spatial layout parameters, requiring further optimization of the weight allocation strategy.

[0095] Conclusion Verification. The improved cloud model based on relative entropy combined similarity exhibits the best accuracy in cross-dimensional risk discrimination (average ML-RMSD=0.043), verifying the effectiveness of integrating multi-source data through the entropy weight method. It demonstrates outstanding comprehensive dimensional stability and improves the robustness of the model.

[0096] Similarly, the resilience level of personnel safety protection under the scenario of liquid ammonia leakage in a food freezing plant after different improvement schemes can be obtained by following the above calculation process, and the corresponding cloud digital features, similarity, and level feature values ​​can be obtained. The resilience level improvement effects of different schemes can be compared and analyzed.

[0097] Finally, the results of this application's method can be used to enhance the comparative analysis of different regions. Ten experts were invited to score the personnel safety protection resilience level of the food freezing plant under the liquid ammonia leak scenario set in this paper on a 100-point scale, divided into five states: the current status of the enterprise (S1-1), improving the resilience level only within the plant (S1-2), improving the resilience level only in high-risk areas outside the plant (S1-3), improving the resilience level in both high-risk areas inside and outside the plant (S1-4), and comprehensively improving the resilience level both inside and outside the plant (S1-5). Based on the above steps, the comprehensive evaluation cloud after improving the resilience level of different areas inside and outside the plant under the liquid ammonia leak scenario of the food freezing plant can be calculated, as shown in Table 10. The specific comprehensive cloud is as follows: Figure 4 As shown.

[0098] Table 10 Calculation results of improved cloud models for different regional enhancement schemes

[0099] Comparative analysis revealed that improving the personnel safety resilience level S1-2 within the facility or improving the high-risk area S1-3 outside the facility alone was significantly less effective than a coordinated improvement of S1-4, confirming the "barrel effect"—local improvements are insufficient to overcome system bottlenecks. A comprehensive upgrade to the personnel safety resilience level S1-5, reaching "high," suggests that further improvements require breaking through existing technological frameworks, such as upgrading ammonia refrigeration systems to green and environmentally friendly systems, which would require substantial investment. The change in entropy in S1-4 indicates that collaborative efforts between the facility and government should focus on: the risks associated with the integration of the company's self-control systems with government regulations, and the balancing of responsibility among various parties in emergency plans.

[0100] A comparative analysis was conducted on the primary indicator characteristic values ​​of the improved cloud model for personnel safety protection resilience assessment under different regional enhancement schemes. Specific data are shown in Table 11, and the comprehensive cloud map is shown below. Figure 5 As shown.

[0101] Table 11. Primary Indicator Characteristic Values ​​of Improved Cloud Models for Different Regional Enhancement Schemes

[0102] The dominance of engineering resilience. The engineering resilience eigenvalue of the S1-5 scenario reached 4.009, an increase of 57.1% compared to S1-1, indicating that the personnel safety protection resilience level has a significant impact on the engineering facility resilience level under the liquid ammonia leakage scenario. The eigenvalue jumped by 8.5% from the S1-4 to S1-5 stage, verifying the increasing marginal benefits of engineering measures under high concentration leakage scenarios.

[0103] The system's resilience exhibits nonlinear growth. The resilience eigenvalue of system S1-5 (3.910) increases sharply by 95.1% compared to S1-4 (2.004), indicating that community emergency response capability becomes a key constraint under extreme scenarios. A detailed comprehensive cloud map is shown below. Figure 6 As shown.

[0104] The preparedness and response resilience gradient was released. The preparedness and response resilience eigenvalues ​​increased by 202.6% from S1-1 to S1-5, confirming the continuous optimization effect of effective emergency management measures on personnel safety protection and risk control in the liquid ammonia leak scenario. The specific comprehensive cloud map is shown below. Figure 7 As shown.

[0105] Furthermore, it can enhance the comparative analysis of different resilience factors. The calculation results of the personnel safety protection resilience level of a food freezing plant under a liquid ammonia leak scenario are compared between the current situation (S1-1) and the comprehensive improvement of the on-site and off-site resilience level (S1-5). The results are structurally decomposed from four dimensions: spatial resilience, engineering resilience, preparedness and response resilience, and system recovery resilience. Different combinations of improvement schemes are constructed to calculate and compare the resilience levels of different factor combinations using an improved cloud model. The current state is denoted as State S2-1. The improvements in single-factor spatial resilience, engineering resilience, preparedness and response resilience, and system recovery resilience are denoted as S2-2, S2-3, S2-4, and S2-5, respectively. The improvements in two-factor spatial-engineering, spatial-preparation and response, spatial-system recovery, engineering-preparation and response, engineering-system recovery, and preparedness and response-system recovery are denoted as S2-6, S2-7, S2-8, S2-9, S2-10, and S2-11, respectively. The improvements in spatial-engineering-preparation and response, spatial-engineering-system recovery, spatial-preparation and response-system recovery, and engineering-preparation and response-system recovery are denoted as S2-12, S2-13, S2-14, and S2-15, respectively. The overall improvement is denoted as S2-16. Cloud feature values, similarity, and registration features are calculated using an improved cloud model. The calculation results are shown in Table 12, and the comprehensive cloud map is shown below. Figure 8 As shown.

[0106] Table 12

[0107] When improving a single resilience factor, the preparedness and response dimension has a dominant effect. The improvement in the preparedness and response dimension (S2-4) with a 53.4% ​​increase far exceeds other single-factor improvements (space / engineering / system recovery are all ≤2.6%), revealing that optimized preparedness and response measures have a leverage effect. Improving emergency plans can shorten emergency response time, and establishing cross-departmental coordination mechanisms can improve the efficiency of personnel safety protection. For example... Figure 9 As shown, a magnified effect occurs when two factors work together. The improvement rate of the two-factor engineering-preparation and response (S2-9) reaches 57.7%, exceeding the sum of the improvement rates of the engineering factor (2.6%) and the preparation and response factor (53.4%) (56%). Three-factor critical breakthrough characteristics are observed. When the three-factor engineering-preparation and response-system recovery (S2-15) is coupled, the improvement rate is 80%, approaching the 94.5% improvement rate of the all-factor improvement.

[0108] In summary, this embodiment provides a method for assessing the resilience of personnel safety protection in a liquid ammonia leak scenario at a food freezing plant. By integrating resilience theory, fuzzy DEMATEL weighting, and the principle of relative entropy combined with similarity, an improved cloud model is constructed that follows data patterns to assess the resilience of personnel safety protection. The method of this embodiment can achieve the following beneficial effects: 1) Using the “scenario-task-factor” analysis framework, we analyzed the personnel safety protection resilience factors under the scenario of liquid ammonia leakage in a food freezing plant. We established four criterion factors and 23 indicator factors, which accurately reflected the hierarchical structure of personnel safety protection resilience indicators under the scenario of liquid ammonia leakage, and provided a reliable input design for the application of the assessment method.

[0109] 2) By employing fuzzy function theory, the semantics of expert experience and knowledge are quantified. Multiple data processing algorithms are used to mine data patterns, and the cloud model is improved based on relative entropy combined similarity. This constructs a cloud model for personnel safety protection resilience that follows data patterns. Model calculations replace subjective scoring, improving the accuracy and efficiency of the assessment. Specifically, considering that the floating cloud model can dynamically represent the randomness of leakage and diffusion, while the comprehensive cloud model can integrate fuzzy data, to balance the advantages of both, fuzzy proximity calculations are used to assess the similarity between the cloud and the standard cloud. The principle of relative entropy is used for optimal combination, achieving probabilistic determination and digital representation of resilience levels. This method of synergistic application of cloud models and relative entropy reduces errors compared to traditional threshold methods and is highly robust to data gaps, enabling comparisons of resilience levels under different conditions.

[0110] 3) Based on data distribution, the accuracy of various cloud models is verified. The improved cloud model in this embodiment shows the best accuracy in cross-dimensional risk discrimination (average ML-RMSD=0.043), which verifies the effectiveness of integrating multi-source data through entropy weight method. The overall dimensional stability is outstanding, which improves the robustness of the model. 4) Utilizing the improved cloud model, it is possible to compare and select personnel safety resilience enhancement schemes for ammonia leak scenarios in food freezing plants, providing decision support for such schemes. Specifically, this embodiment compares and contrasts resilience enhancement strategies for different areas, such as on-site and off-site high-risk areas, and for different factors, including spatial resilience, engineering resilience, preparedness and response resilience, and system recovery resilience, using the improved cloud model. This structured comparative analysis of resilience enhancement strategies strengthens the practical application value of an integrated regional personnel safety resilience enhancement strategy for ammonia leak scenarios in food freezing plants. On the one hand, given that the food freezing plant is temporarily unable to relocate the liquid ammonia hazard source, five combinations of personnel safety protection resilience enhancement schemes were developed for high-risk areas inside and outside the plant, as well as high-risk areas outside the plant. Through calculation and comparative analysis using an improved cloud model based on relative entropy combination similarity, it was found that the comprehensive enhancement scheme S1-5 performed best, but it needs to be combined with a dynamic budget mechanism to balance cost sensitivity. In the short term, the enhancement of both inside and outside high-risk areas, through cross-regional collaboration and improvement of weak links, can raise the level of personnel safety protection resilience to a medium level. In the early stages of a liquid ammonia accident, proactive adjustments can be made through a regional integrated solution, but as the situation expands, some reliance on external intervention is required.

[0111] On the other hand, given that the liquid ammonia hazard source could not be moved, a structured decomposition analysis was conducted on the investigation results of the comprehensive improvement of resilience level. Combinations of improvement schemes were proposed based on single-factor, dual-factor, and triple-factor resilience. Through calculations using an improved cloud model based on relative entropy combination similarity, and comparative analysis with the current situation, it was found that when improving single-factor resilience, the dominant effect of the preparedness and response dimensions far exceeded other single-factor improvement schemes with an improvement rate of 53.4%. When dual-factor synergy was used, the effects of engineering-preparation and response dual factors were amplified. The improvement rate of the triple-factor engineering-preparation and response-system recovery was 80%, close to the improvement rate of 94.5% of the full-factor improvement.

[0112] Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention, such as... Figure 10 As shown, the device includes a processor 60, a memory 61, an input device 62, and an output device 63; the number of processors 60 in the device can be one or more. Figure 10 Taking a processor 60 as an example; the processor 60, memory 61, input device 62, and output device 63 in the device can be connected via a bus or other means. Figure 10 Taking the example of a connection between China and Israel via a bus.

[0113] The memory 61, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the personnel safety protection resilience assessment method under the scenario of liquid ammonia leakage in a food freezing plant in this embodiment of the invention. The processor 60 executes various functional applications and data processing of the device by running the software programs, instructions, and modules stored in the memory 61, thereby realizing the aforementioned personnel safety protection resilience assessment method under the scenario of liquid ammonia leakage in a food freezing plant. The input device 62 can be used to receive input digital or character information and generate key signal inputs related to user settings and function control of the device. The output device 63 may include a display screen or other display device.

[0114] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the personnel safety protection resilience assessment method under a liquid ammonia leak scenario in a food freezing plant according to any embodiment.

[0115] The computer storage medium of this invention can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0116] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A method for assessing the resilience of personnel safety protection under a liquid ammonia leak scenario in a food freezing plant, characterized in that, include: S110. Obtain a set of assessment indicators for the resilience of personnel safety protection in the event of a liquid ammonia leak at a food freezing plant, and scores for each assessment indicator of the food freezing plant to be assessed by multiple experts. The set of assessment indicators includes multiple primary assessment indicators; each primary assessment indicator includes multiple secondary assessment indicators, and the scores of each expert are scores for the secondary assessment indicators. S120. Based on the fuzzy decision-making experimental analysis method, determine the weight of each secondary evaluation indicator, where each weight is used to characterize the importance of each secondary evaluation indicator. S130. Using the floating cloud algorithm and the comprehensive cloud algorithm, the weights and scores of each secondary evaluation indicator are calculated respectively, and the floating evaluation cloud and comprehensive evaluation cloud of each primary evaluation indicator are obtained respectively. The evaluation cloud is used to reflect the data distribution of the evaluation indicator scores. S140. Using the relative entropy method, the evaluation results corresponding to the floating evaluation cloud and comprehensive evaluation cloud of each primary evaluation indicator are combined and optimized to obtain the evaluation level of each primary evaluation indicator that takes into account both evaluation results. Specifically, the similarity between the floating evaluation cloud of the same primary evaluation indicator and the standard cloud of each evaluation level is calculated, and the similarity constitutes the first evaluation result; the similarity between the comprehensive evaluation cloud of the same primary evaluation indicator and the standard cloud of each evaluation level is calculated, and the similarity constitutes the second evaluation result; the relative entropy between the first evaluation result and the second evaluation result is calculated according to the current weight combination; the weight combination is iteratively optimized by minimizing the relative entropy; and the evaluation level of the same primary evaluation indicator is determined according to the optimal weight combination. The floating cloud model can dynamically characterize the randomness of leakage and diffusion, while the integrated cloud model can integrate fuzzy data. This method of synergistic application of cloud model and relative entropy can reduce errors and has strong robustness to data missing, enabling comparison of resilience levels under different states. The primary evaluation indicators in the set of evaluation indicators include spatial resilience, engineering resilience, preparedness and response resilience, and system recovery resilience. The secondary assessment indicators for spatial resilience include compliance with safety distances and the suitability of refuge spaces; The secondary assessment indicators for project resilience include the compliance of on-site facilities, the accuracy of on-site BPCS monitoring, the condition of on-site pressure pipelines, containers and safety accessories and safety protection devices, the coverage of wind vanes, and the ability to restore and meet air quality standards. The secondary indicators of preparedness and response resilience include the ability to identify risks within the site, the company's rectification of potential hazards, the sharing and transmission of early warning information within the site, the sharing and transmission of early warning information in high-risk areas outside the site, the sharing and transmission of early warning information in non-high-risk areas outside the site, the status of emergency drills conducted by employees within the site, the status of emergency supplies reserves within the site, the initial response capabilities within the site, the coordinated evacuation in high-risk areas outside the site, the coordinated evacuation in non-high-risk areas outside the site, whether the nearest fire station has the capability to handle liquid ammonia, and the experience of the district government in emergency rescue over the past three years. System resilience includes the safety knowledge and skills level of personnel in high-risk areas outside the venue, the safety knowledge and skills level of personnel in non-high-risk areas outside the venue, the personnel insurance coverage, and the ability to treat and recover injured or killed personnel.

2. The method for assessing personnel safety resilience under a liquid ammonia leak scenario in a food freezing plant according to claim 1, characterized in that, The fuzzy decision-making experimental analysis method determines the weights of each secondary evaluation indicator, including: The influence relationships between the various secondary evaluation indicators were scored using an expert scoring method. The semantics of the expert experience knowledge represented by each scorer are quantified using a triangular fuzzy function. Based on the quantitative results, the influence, affectedness, centrality, causality, and weight values ​​of each secondary evaluation indicator are calculated.

3. The method for assessing personnel safety resilience under a liquid ammonia leak scenario in a food freezing plant according to claim 1, characterized in that, The floating cloud algorithm and the comprehensive cloud algorithm are used to calculate the weights and scores of each secondary evaluation indicator, respectively, to obtain the floating evaluation cloud and comprehensive evaluation cloud for each primary evaluation indicator, including: Using a reverse cloud generator, features are extracted from all scores of each secondary evaluation indicator to obtain the mean, entropy, and hyperentropy of each secondary evaluation indicator. The mean, entropy, and hyperentropy of the same secondary evaluation indicator together constitute the evaluation cloud of the same secondary evaluation indicator. The floating cloud algorithm and the comprehensive cloud algorithm are used to calculate the evaluation cloud and weight of each secondary evaluation indicator, and the floating cloud and comprehensive cloud of each primary indicator are obtained respectively.

4. The method for assessing personnel safety resilience under a liquid ammonia leak scenario in a food freezing plant according to claim 1, characterized in that, The calculation of the similarity between the floating evaluation cloud for the same level of evaluation index and the standard cloud for each evaluation level includes: Obtain the score range corresponding to each assessment level; Based on each score range, the mean, entropy, and super-entropy values ​​for each evaluation level are determined. The mean, entropy, and super-entropy values ​​for the same evaluation level together constitute the standard cloud for that same evaluation level.

5. The method for assessing personnel safety resilience under a liquid ammonia leak scenario in a food freezing plant according to claim 1, characterized in that, The calculation of the similarity between the floating evaluation cloud for the same level of evaluation index and the standard cloud for each evaluation level includes: Calculate the fuzzy similarity between the evaluation cloud for the same evaluation index and the standard cloud for each evaluation level.

6. The method for assessing personnel safety resilience under a liquid ammonia leak scenario in a food freezing plant according to claim 1, characterized in that, The step of determining the evaluation level of the same level evaluation index based on the optimal weight combination includes: Based on the similarity between the same primary evaluation indicator and the standard cloud of each evaluation level, a weighted average is performed on the identifiers of each evaluation level to obtain the evaluation level identifier to which the same primary evaluation indicator belongs.

7. The method for assessing personnel safety resilience under a liquid ammonia leak scenario in a food freezing plant according to claim 1, characterized in that, Also includes: Using methods S110-S140, the personnel safety protection resilience of the food freezing plant and its surrounding area before and after the implementation of various resilience enhancement strategies is assessed, and the optimal enhancement strategy for different areas is determined based on the changes in assessment levels before and after implementation; or Using methods S110-S140, the personnel safety protection resilience of the food freezing plant before and after the implementation of resilience improvement strategies for each primary assessment indicator is assessed, and the strongest influencing factor is determined based on the change in assessment level before and after implementation.

8. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the personnel safety protection resilience assessment method under the scenario of liquid ammonia leakage in a food freezing plant as described in any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the personnel safety protection resilience assessment method under the scenario of liquid ammonia leakage in a food freezing plant as described in any one of claims 1-7.

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