A safety risk quantification method and system for a chemical industrial park

Abstract

The application discloses a kind of chemical industry park safety risk quantification method and system, comprising: according to the total influencing factor of chemical industry park safety risk, multiple primary indicators and corresponding multiple secondary indicators are configured for the park combination to be quantified, and the secondary indicator data of each park to be quantified is obtained;Using secondary indicator data, a plurality of weights of each secondary indicator is obtained by using a variety of objective assignment method, to obtain the game theory combination weight of each secondary indicator, and then the first type game theory combination weight of each primary indicator is obtained;The relative importance degree of each primary indicator compared with safety risk is analyzed, and the weight of each primary indicator is obtained by using subjective weighting method, and then the second type game theory combination weight of each primary indicator is obtained by combining the first type game theory combination weight;The risk value of each park to be quantified is obtained, and further combined with the second type game theory combination weight, to generate safety risk quantification result.The application can effectively quantify the safety risk of chemical industry park.

Description

Technical Field

[0001] This invention belongs to the field of chemical production management technology, and in particular relates to a method and system for quantifying safety risks in chemical industrial parks. Background Technology

[0002] Currently, there is a clear trend towards large-scale and clustered development of chemical enterprises within chemical industrial parks. At the same time, these parks involve a wide variety and large quantity of hazardous chemicals, a relatively high concentration of major hazard sources, and complex hazardous chemical processes. Accidents occurring in these parks can not only easily result in personal and property losses but may also trigger a domino effect, leading to severe secondary disasters and other serious consequences. Therefore, quantitative analysis of the safety risks in chemical industrial parks is beneficial for implementing classified and graded management of these parks and for preventing and controlling major accidents.

[0003] Existing technologies disclose methods, systems, and devices for risk classification, control, assessment, and analysis in chemical industrial parks, relating to the field of safety control technology in chemical industrial parks. The key technical points are: selecting data for a regional risk assessment indicator system for chemical industrial parks; obtaining opinion data corresponding to each level of indicators based on the indicators at each level; using the analytic hierarchy process (AHP) to establish a judgment matrix for each indicator at each level and calculating the weight coefficients of each indicator at each level; after obtaining the weight coefficients of each indicator at each level, obtaining the risk parameter data for each indicator at the lowest level, substituting it into the risk assessment score calculation formula for each indicator at the lowest level, and calculating the risk assessment score for each indicator at the lowest level; substituting the risk assessment scores of each indicator at the lowest level and the weight coefficients of each factor at each level into the corresponding risk assessment score calculation formula for each indicator at each level to obtain the regional risk assessment score; and substituting the regional risk assessment score into the regional risk classification rules to obtain the regional risk level.

[0004] Existing technology also discloses a safety risk management and control system for chemical industrial parks, including a terminal platform, a data acquisition module, an analysis module, a processing module, a control module, and a prediction module. The terminal platform is used for the management and maintenance of the safety risk management and control system for chemical industrial parks, and the data acquisition module is used for collecting environmental data from the chemical industrial park. The safety risk management and control system for chemical industrial parks described in this invention can classify the risks and hazards in chemical industrial parks, analyze the safety status from the root cause of accidents, and implement risk classification and control in the investigation and treatment of hidden dangers. Hazard classification is an important follow-up measure for implementing risk classification and control, which improves the management and control efficiency of chemical industrial parks. A prediction model is established, and combined with prior probabilities, the probability of accidents is predicted. A node information database is designed, and the posterior distribution is obtained through data updates to calculate the probability of possible changes in consequences, thereby realizing dynamic risk classification management of chemical industrial parks.

[0005] In addition, to improve the overall safety of chemical industrial parks and promote their sustainable, healthy, and safe development, existing technologies have constructed a quantitative indicator system for the safety risks of chemical industrial parks, comprising five primary indicators and 20 secondary indicators, including layout, enterprise production status, supporting facilities, emergency rescue capabilities, and safety management. The analytic hierarchy process (AHP) was used to determine the weights of each level of indicators in the evaluation system, and the fuzzy comprehensive evaluation method was applied to assess the safety risks of a certain chemical industrial park. The results show that the safety risk level of this chemical industrial park is "relatively low risk." Based on the evaluation results, it is recommended to improve the park's risk prevention level by enhancing the safety level of enterprises within the park, improving supporting facilities, and enhancing emergency rescue capabilities.

[0006] Based on thorough research and in conjunction with the aforementioned existing technologies, the inventors discovered the following two main problems in quantifying the safety risks of chemical industrial parks during the development of this invention:

[0007] First, the existing standards and methods for assessing the safety risk level of chemical industrial parks are mainly based on subjective calculations through expert scoring. They are not objective or comprehensive enough in reflecting the characteristics of the original data and the actual situation, and lack objective weighting methods for assessing the safety risk level of chemical industrial parks.

[0008] Secondly, existing research on objective methods for assessing the safety risks of chemical industrial parks still lacks a scientific indicator system and risk quantification tools, and it is also impossible to compare the relative safety risks of different chemical industrial parks under a unified standard.

[0009] In summary, there is an urgent need for a method to quantify the safety risks of chemical industrial parks that can overcome the shortcomings of using a single type of weighting method and the inability to directly compare relative safety risks between different chemical industrial parks, and that is more widely applicable. Summary of the Invention

[0010] To address the aforementioned issues, this invention provides a method for quantifying the safety risks of chemical industrial parks. The method includes: configuring multiple primary indicators and multiple secondary indicators subordinate to each primary indicator for a combination of parks to be quantified, based on all influencing factors of safety risks in the chemical industrial park; obtaining secondary indicator data for each park; using the secondary indicator data, employing various objective assignment methods to obtain multiple weights for each secondary indicator, thereby obtaining a game-theoretic combination weight for each secondary indicator, and subsequently obtaining a first-type game-theoretic combination weight for each primary indicator; analyzing the relative importance of each primary indicator to safety risks, employing a subjective weighting method to obtain the weight of each primary indicator, and then combining this with the first-type game-theoretic combination weight to obtain a second-type game-theoretic combination weight for each primary indicator; obtaining the risk value for each park to be quantified, and further combining this with the second-type game-theoretic combination weight to generate a safety risk quantification result.

[0011] Preferably, the step of obtaining the secondary indicator data of each park to be quantified includes: using multiple data processing methods to correct the basic data used to obtain the secondary indicator data, and using the corrected basic data to calculate the secondary indicator data, and then normalizing the calculated secondary indicator data to obtain the optimal secondary indicator data. The multiple data processing methods include, but are not limited to, Lagrange interpolation and KNN imputation.

[0012] Preferably, the normalization process for the calculated secondary indicator data includes: dividing all secondary indicators into positive indicators that are positively correlated with safety risks and negative indicators that are negatively correlated with safety risks; and then using the Min-Max normalization method to obtain the normalization results of the positive and negative indicators using different calculation methods, thereby completing the normalization process.

[0013] Preferably, the normalized results of the positive and negative indices are calculated using the following expressions:

[0014]

[0015] Among them, X 正向 X represents the normalized result of the positive index. ij X represents the j-th secondary indicator data in the i-th park to be quantified, n represents the total number of parks to be quantified, and X... 负向 This indicates the normalization result of the negative index.

[0016] Preferably, the multiple objective assignment methods include the entropy method and the Critic method. In the step of obtaining the weight of each secondary indicator using the entropy method, the method includes: obtaining the proportion of each secondary indicator data to the total of all secondary indicator data belonging to the same secondary indicator as the data of that secondary indicator, and calculating the information entropy value of each secondary indicator. Then, by obtaining the corresponding information entropy redundancy, the entropy weight of each secondary indicator is determined.

[0017] Preferably, the information entropy value of each secondary indicator is calculated using the following expression:

[0018]

[0019] Among them, e j p represents the information entropy value of the j-th secondary indicator. ij Let ln represent the proportion of the j-th secondary indicator data in the i-th quantified park to the total of all secondary indicator data belonging to the same secondary indicator category as the j-th secondary indicator data.

[0020] Preferably, the step of obtaining the weight of each secondary indicator using the Critic method includes: obtaining the standard deviation representing the variability of each secondary indicator and obtaining the correlation coefficient representing the correlation between secondary indicators, so as to obtain the conflict of each secondary indicator, and then determining the Critic method weight of each secondary indicator by calculating the information content of each secondary indicator.

[0021] Preferably, the step of obtaining the weight of each primary indicator using the subjective weighting method includes: obtaining a score result representing the relative importance of each primary indicator to the safety risk based on the Delphi method, and constructing a judgment matrix, thereby using the judgment matrix to determine the weight of each primary indicator.

[0022] Preferably, the process of obtaining a score representing the relative importance of each primary indicator to the safety risk includes: comparing the primary indicators pairwise, and using a 1-9 digit scale to score and measure the relative importance of the two compared primary indicators to the safety risk, thereby obtaining the corresponding score results.

[0023] Preferably, the process of determining the weight of each primary indicator using the judgment matrix includes: using the square root method to obtain the eigenvector of the judgment matrix, further obtaining the largest eigenvalue, then performing a consistency check on the current judgment matrix, and further reconstructing the judgment matrix when the current judgment matrix fails the consistency check, so as to obtain the optimal weight of each primary indicator using the judgment matrix that has passed the consistency check.

[0024] Preferably, the step of obtaining the game theory combination weights of each secondary indicator includes: constructing a first linear combination weight using multiple weights of each secondary indicator, and then constructing an objective function with the goal of minimizing the deviation between the combination weights and different weights to obtain the optimal linear combination coefficients, thereby determining the game theory combination weights of each secondary indicator.

[0025] Preferably, the step of obtaining the first type of game theory combination weight for each primary indicator includes: using the game theory combination weight for each secondary indicator, and employing the multiplication and normalization method to obtain the first type of game theory combination weight.

[0026] Preferably, the step of obtaining the second type of game theory combination weight for each primary indicator includes: constructing a second linear combination weight using the first type of game theory combination weight and the weight of each primary indicator to determine the second type of game theory combination weight.

[0027] Preferably, the step of generating the security risk quantification result includes: normalizing the risk value of each park to be quantified, and then using the Topsis method, combined with the second type of game theory combined weights, to calculate the corresponding security risk quantification result.

[0028] On the other hand, the present invention also provides a safety risk quantification system for chemical industrial parks. The safety risk quantification system includes the following modules: an indicator configuration module, used to configure multiple primary indicators and multiple secondary indicators subordinate to each primary indicator for a combination of parks to be quantified based on all influencing factors of safety risk in the chemical industrial park, and to obtain secondary indicator data for each park to be quantified; an objective weighting module, used to obtain multiple weights for each secondary indicator using the secondary indicator data and employing various objective weighting methods to obtain the game theory combination weights of each secondary indicator, thereby obtaining the first type of game theory combination weights for each primary indicator; a subjective weighting module, used to analyze the relative importance of each primary indicator to safety risk, use a subjective weighting method to obtain the weights of each primary indicator, and then combine the first type of game theory combination weights to obtain the second type of game theory combination weights for each primary indicator; and a safety risk quantification module, used to obtain the risk value of each park to be quantified, and further combine the second type of game theory combination weights to generate a safety risk quantification result.

[0029] Compared with the prior art, one or more embodiments of the above solutions may have the following advantages or beneficial effects:

[0030] This invention provides a method and system for quantifying the safety risks of chemical industrial parks. The method first constructs an indicator system for quantifying the safety risks of chemical industrial parks based on all influencing factors. Then, an objective weighting method is used to obtain multiple weights for each secondary indicator. Further, a game-theoretic weighting method is used to obtain the game-theoretic combination weights for each secondary indicator, thus obtaining the first type of game-theoretic combination weights for each primary indicator. Next, a subjective weighting method is used to obtain the weights for each primary indicator. This is further combined with the first type of game-theoretic combination weights, and then a game-theoretic weighting method is used again to fuse the data from the objective and subjective weighting methods. This process fully leverages the advantages of both methods to obtain the second type of game-theoretic combination weights for each primary indicator. Finally, the risk value of each park to be quantified is obtained, and the second type of game-theoretic combination weights are used to generate the quantified safety risk result. This invention enables the effective quantification of safety risks in chemical industrial parks, comprehensively and accurately reflecting the relative safety risk status between different chemical industrial parks. It overcomes the shortcomings of using a single-type weighting method and the inability to directly compare the relative safety risks between different chemical industrial parks. Furthermore, it has wider applicability and provides technical support for strengthening the supervision of hazardous chemicals and related enterprises in chemical industrial parks and preventing major accident risks in the parks.

[0031] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the description, claims and drawings. Attached Figure Description

[0032] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0033] Figure 1 This is a flowchart illustrating the steps of a method for quantifying safety risks in a chemical industrial park, as described in this application.

[0034] Figure 2 This is a block diagram of the safety risk quantification system for chemical industrial parks, as described in this application. Detailed Implementation

[0035] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings and examples, so that the process of how the present invention uses technical means to solve technical problems and achieve technical effects can be fully understood and implemented accordingly. It should be noted that, as long as there is no conflict, the various embodiments and features in the various embodiments of the present invention can be combined with each other, and the resulting technical solutions are all within the protection scope of the present invention.

[0036] Furthermore, the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0037] Currently, there is a clear trend towards large-scale and clustered development of chemical enterprises within chemical industrial parks. At the same time, these parks involve a wide variety and large quantity of hazardous chemicals, a relatively high concentration of major hazard sources, and complex hazardous chemical processes. Accidents occurring in these parks can not only easily result in personal and property losses but may also trigger a domino effect, leading to severe secondary disasters and other serious consequences. Therefore, quantitative analysis of the safety risks in chemical industrial parks is beneficial for implementing classified and graded management of these parks and for preventing and controlling major accidents.

[0038] Existing technologies disclose methods, systems, and devices for risk classification, control, assessment, and analysis in chemical industrial parks, relating to the field of safety control technology in chemical industrial parks. The key technical points are: selecting data for a regional risk assessment indicator system for chemical industrial parks; obtaining opinion data corresponding to each level of indicators based on the indicators at each level; using the analytic hierarchy process (AHP) to establish a judgment matrix for each indicator at each level and calculating the weight coefficients of each indicator at each level; after obtaining the weight coefficients of each indicator at each level, obtaining the risk parameter data for each indicator at the lowest level, substituting it into the risk assessment score calculation formula for each indicator at the lowest level, and calculating the risk assessment score for each indicator at the lowest level; substituting the risk assessment scores of each indicator at the lowest level and the weight coefficients of each factor at each level into the corresponding risk assessment score calculation formula for each indicator at each level to obtain the regional risk assessment score; and substituting the regional risk assessment score into the regional risk classification rules to obtain the regional risk level.

[0039] Existing technology also discloses a safety risk management and control system for chemical industrial parks, including a terminal platform, a data acquisition module, an analysis module, a processing module, a control module, and a prediction module. The terminal platform is used for the management and maintenance of the safety risk management and control system for chemical industrial parks, and the data acquisition module is used for collecting environmental data from the chemical industrial park. The safety risk management and control system for chemical industrial parks described in this invention can classify the risks and hazards in chemical industrial parks, analyze the safety status from the root cause of accidents, and implement risk classification and control in the investigation and treatment of hidden dangers. Hazard classification is an important follow-up measure for implementing risk classification and control, which improves the management and control efficiency of chemical industrial parks. A prediction model is established, and combined with prior probabilities, the probability of accidents is predicted. A node information database is designed, and the posterior distribution is obtained through data updates to calculate the probability of possible changes in consequences, thereby realizing dynamic risk classification management of chemical industrial parks.

[0040] In addition, to improve the overall safety of chemical industrial parks and promote their sustainable, healthy, and safe development, existing technologies have constructed a quantitative indicator system for the safety risks of chemical industrial parks, comprising five primary indicators and 20 secondary indicators, including layout, enterprise production status, supporting facilities, emergency rescue capabilities, and safety management. The analytic hierarchy process (AHP) was used to determine the weights of each level of indicators in the evaluation system, and the fuzzy comprehensive evaluation method was applied to assess the safety risks of a certain chemical industrial park. The results show that the safety risk level of this chemical industrial park is "relatively low risk." Based on the evaluation results, it is recommended to improve the park's risk prevention level by enhancing the safety level of enterprises within the park, improving supporting facilities, and enhancing emergency rescue capabilities.

[0041] Based on thorough research and in conjunction with the aforementioned existing technologies, the inventors discovered the following two main problems in quantifying the safety risks of chemical industrial parks during the development of this invention:

[0042] First, the existing standards and methods for assessing the safety risk level of chemical industrial parks are mainly based on subjective calculations through expert scoring. They are not objective or comprehensive enough in reflecting the characteristics of the original data and the actual situation, and lack objective weighting methods for assessing the safety risk level of chemical industrial parks.

[0043] Secondly, existing research on objective methods for assessing the safety risks of chemical industrial parks still lacks a scientific indicator system and risk quantification tools, and it is also impossible to compare the relative safety risks of different chemical industrial parks under a unified standard.

[0044] In summary, there is an urgent need for a method to quantify the safety risks of chemical industrial parks that can overcome the shortcomings of using a single type of weighting method and the inability to directly compare relative safety risks between different chemical industrial parks, and that is more widely applicable.

[0045] Therefore, to address the aforementioned problems, this invention proposes a method and system for quantifying the safety risks of chemical industrial parks. The method first constructs an indicator system for quantifying the safety risks of chemical industrial parks based on all influencing factors. Then, an objective weighting method is used to obtain multiple weights for each secondary indicator, and a game-theoretic weighting method is further used to obtain the game-theoretic combination weights for each secondary indicator, thus obtaining the first type of game-theoretic combination weights for each primary indicator. Next, a subjective weighting method is used to obtain the weights for each primary indicator, and this is further combined with the first type of game-theoretic combination weights. The game-theoretic weighting method is then used again to fuse the data from the objective and subjective weighting methods, thereby obtaining the second type of game-theoretic combination weights for each primary indicator while fully leveraging the advantages of both methods. Finally, the risk value of each park to be quantified is obtained, and the second type of game-theoretic combination weights are further combined to generate the safety risk quantification result. This invention enables the effective quantification of safety risks in chemical industrial parks, comprehensively and accurately reflecting the relative safety risk status between different chemical industrial parks. It overcomes the shortcomings of using a single-type weighting method and the inability to directly compare the relative safety risks between different chemical industrial parks. Furthermore, it has wider applicability and provides technical support for strengthening the supervision of hazardous chemicals and related enterprises in chemical industrial parks and preventing major accident risks in the parks.

[0046] Example 1

[0047] Figure 1 This is a flowchart illustrating the steps of a method for quantifying safety risks in a chemical industrial park, as described in this application. (See below for reference.) Figure 1 This will explain the steps of this method.

[0048] like Figure 1As shown, in step S110, based on all influencing factors of safety risks in chemical industrial parks, multiple primary indicators and multiple secondary indicators subordinate to each primary indicator are configured for the combination of parks to be quantified, and the secondary indicator data for each park to be quantified are obtained. In a specific embodiment of this application, operational data of several chemical industrial parks under safety risks are first collected. By analyzing the changes in safety risks with operational data, all factors affecting the safety risks of chemical industrial parks are determined. Then, based on all influencing factors, an indicator system for quantifying the safety risks of chemical industrial parks is constructed. In the aforementioned indicator system, the overall situation of the chemical industrial park, the layout of the chemical industry within the park, the chemical safety supervision situation, the construction of safety equipment and facilities, and the occurrence of accidents are configured as corresponding primary indicators, forming a criterion layer. At the same time, according to the subordinate relationship shown in Table 1, secondary indicators adapted to each primary indicator are configured, forming an indicator layer. After the secondary indicators are configured, the secondary indicator data for each park to be quantified is obtained according to the indicator data acquisition method shown in Table 1.

[0049] Table 1 Evaluation Indicators and Data Acquisition Methods

[0050]

[0051]

[0052]

[0053]

[0054] Further, in step S120, using the secondary indicator data, multiple weights for each secondary indicator are obtained using various objective assignment methods to obtain the game theory combination weights for each secondary indicator, thereby obtaining the first type of game theory combination weights for each primary indicator. Specifically, this embodiment uses different objective weighting methods to calculate multiple weights for the same secondary indicator, thus obtaining multiple weights for each secondary indicator. Then, using the multiple weights for each secondary indicator, the Nash equilibrium point between the weights is found in a static game with complete information, thereby obtaining the game theory combination weights for each secondary indicator. After obtaining the game theory combination weights for each secondary indicator, the first type of game theory combination weights for each primary indicator are calculated using the game theory combination weights corresponding to the secondary indicators matched with each primary indicator.

[0055] By utilizing secondary indicator data, various data processing methods are employed to correct the underlying data used to obtain the secondary indicator data. The corrected underlying data is then used to calculate the secondary indicator data. Finally, the calculated secondary indicator data is normalized to obtain the optimal secondary indicator data.

[0056] Specifically, this embodiment uses a registration system to read operational data such as accident information and output value for each industrial park to be quantified as the basic data for obtaining secondary indicator data. To improve the accuracy and reliability of the safety risk quantification results, various data processing methods are used to preprocess the aforementioned basic data, such as cleaning and deduplication, thereby correcting the basic data. Then, the corrected basic data is used to calculate the secondary indicator data. To eliminate the original data dimensions, this embodiment normalizes the calculated secondary indicator data, thus using the normalized secondary indicator data as the optimal secondary indicator data.

[0057] In one specific embodiment of this application, various data processing methods include, but are not limited to, Lagrange interpolation and KNN imputation.

[0058] In the process of normalizing the calculated secondary indicator data, this invention divides all secondary indicators into positive indicators that are positively correlated with safety risks and negative indicators that are negatively correlated with safety risks. Then, the Min-Max normalization method is used to obtain the normalization results of the positive and negative indicators using different calculation methods, thereby completing the normalization process.

[0059] Specifically, this embodiment divides all secondary indicators into positive indicators (those that increase safety risk) and negative indicators (those that decrease safety risk) based on the actual impact of different factors on the safety risks of chemical industrial parks. Then, a Min-Max normalization method is used to obtain the normalized results for positive indicators by adapting the calculation method to the positive indicators, and the normalized results for negative indicators by adapting the calculation method to the negative indicators, thereby achieving the purpose of normalizing the calculated secondary indicator data. The normalized results for both positive and negative indicators are greater than or equal to zero and less than or equal to 1.

[0060] In this embodiment of the application, the normalization results of the positive and negative indices are calculated using the following expressions:

[0061]

[0062] Among them, X 正向 X represents the normalized result of the positive index. ij X represents the j-th secondary indicator data in the i-th park to be quantified, n represents the total number of parks to be quantified, and X... 负向 This indicates the normalization result of the negative index.

[0063] In one specific embodiment of this application, various objective assignment methods include the entropy method and the Critic method.

[0064] In the step of obtaining the weight of each secondary indicator using the entropy method, the proportion of each secondary indicator data to the total of all secondary indicator data belonging to the same secondary indicator is obtained, and the information entropy value of each secondary indicator is calculated. Then, by obtaining the corresponding information entropy redundancy, the entropy weight of each secondary indicator is determined.

[0065] Specifically, this embodiment first obtains the proportion of each secondary indicator data to the total amount of all secondary indicator data belonging to the same secondary indicator. Then, the information entropy value of each secondary indicator is calculated using the proportion data. Next, the information entropy redundancy of each secondary indicator is calculated using the information entropy value, thereby determining the entropy value method weight of each secondary indicator using the information entropy redundancy.

[0066] First, use the following expression to obtain the proportion of each secondary indicator data to the total of all secondary indicator data belonging to the same secondary indicator as that secondary indicator data:

[0067]

[0068] Where, p ij X represents the proportion of the j-th secondary indicator data in the i-th quantified park to the total sum of all secondary indicator data belonging to the same secondary indicator category as that data. ij ′ This indicates the normalization result of positive or negative indicators.

[0069] Secondly, because the normalization results of positive or negative indicators are used in the calculation of the aforementioned proportions, the calculated proportions may be equal to zero. Therefore, this embodiment considers this situation and uses the following expression to calculate the information entropy value of each secondary indicator:

[0070]

[0071] Among them, e j Let represent the information entropy value of the j-th secondary indicator, and ln represent the logarithmic function.

[0072] Next, the information entropy redundancy of each secondary indicator is calculated using the following expression:

[0073] d j =1-e j (5)

[0074] Where, d j This represents the information entropy redundancy of the j-th secondary indicator.

[0075] Finally, the entropy weight of each secondary indicator is determined using the following expression:

[0076]

[0077] Among them, wl j Let represent the entropy weight of the j-th secondary indicator, and m represent the total number of secondary indicators.

[0078] In the step of obtaining the weight of each secondary indicator using the Critic method, the standard deviation representing the variability of each secondary indicator and the correlation coefficient representing the correlation between secondary indicators are obtained to obtain the conflict of each secondary indicator. Then, by calculating the information content of each secondary indicator, the weight of each secondary indicator using the Critic method is determined.

[0079] Specifically, this embodiment first obtains the variability of secondary indicators in the form of standard deviation. That is, the standard deviation is used to represent the variation and fluctuation of the values ​​of each indicator. The larger the standard deviation, the greater the numerical difference of the indicator, the more information it reflects, and the stronger the evaluation strength of the indicator itself. Therefore, the indicator should be assigned more weight. Simultaneously, the correlation coefficient (e.g., Pearson correlation coefficient) representing the correlation between secondary indicators is obtained. Based on this, the conflict of each secondary indicator is further obtained in the form of correlation coefficient. That is, the stronger the correlation between indicators, the less conflict the indicator has with other indicators, the more identical information it reflects, and the more repetitive the evaluation content it embodies. This weakens the evaluation strength of the indicator to some extent, and the weight assigned to the indicator should be reduced. Next, the information content of each secondary indicator is calculated using the standard deviation and correlation coefficient of each secondary indicator. The information content is then used to determine the Critic weight of each secondary indicator. That is, the greater the information content, the greater the impact of the corresponding indicator on safety risk, and the more weight should be assigned to it.

[0080] First, obtain the standard deviation using the following expression:

[0081]

[0082] Among them, s j This represents the standard deviation of the j-th secondary indicator. This represents the average value of the j-th secondary indicator after normalization.

[0083] Secondly, the correlation coefficient and the conflict of each secondary indicator are obtained using the following expression:

[0084]

[0085] Where, r kl This represents the correlation coefficient between the k-th secondary indicator and the l-th secondary indicator. This represents the average value of the k-th secondary indicator after normalization. X represents the average value of the l-th secondary indicator after normalization. ik X represents the normalized k-th secondary indicator data in the i-th park to be quantified. il R represents the normalized l-th secondary indicator data in the i-th park to be quantified. j This indicates the conflict of the j-th secondary indicator.

[0086] Next, the information content of each secondary indicator is calculated using the following expression:

[0087] c j =s j *R j (10)

[0088] Among them, c j This represents the information content of the j-th secondary indicator.

[0089] Finally, the Critic weights for each secondary indicator are determined using the following expression:

[0090]

[0091] Among them, w2 j This represents the Critic weight of the j-th secondary indicator.

[0092] Furthermore, in step S130, the relative importance of each primary indicator to the safety risk is analyzed, and the weight of each primary indicator is obtained by subjective weighting method. Then, the weight of each primary indicator is obtained by combining the first type of game theory combination weights.

[0093] Specifically, this embodiment, based on the multiple primary indicators configured in step S110, compares each pair of primary indicators to determine the relative importance of one primary indicator to another in relation to security risk, thereby obtaining the relative importance of each primary indicator relative to security risk. Based on this, a subjective weighting method is used to determine the level of importance of each compared primary indicator and assign a value to each level of importance, thus obtaining the weight of each primary indicator. Next, using the weights of each primary indicator and the first type of game theory combined weights, a Nash equilibrium point between the weights is sought again in a static game of complete information, thereby obtaining the second type of game theory combined weights for each primary indicator.

[0094] In the step of obtaining the weight of each primary indicator using the subjective weighting method, the Delphi method is used to obtain the scoring results representing the relative importance of each primary indicator to the safety risk, and a judgment matrix is ​​constructed. The weight of each primary indicator is then determined using the judgment matrix.

[0095] Specifically, this embodiment is based on the Delphi method. For each primary indicator, two primary indicators are randomly selected for comparison at a time, and the importance level of one primary indicator relative to the other in relation to the security risk is determined as shown in Table 2. Further, following the matching relationship between the assignment results and the relative importance levels shown in Table 2, corresponding assignment results are set for the importance level of each primary indicator. After determining the importance level of each primary indicator, the corresponding assignment result is used as the corresponding relative importance score, thus obtaining a score representing the relative importance of each primary indicator relative to the security risk. Finally, a judgment matrix is ​​constructed using the score results to determine the weight of each primary indicator. After the judgment matrix is ​​constructed, the eigenvector of the judgment matrix is ​​obtained, and then, based on the eigenvector, a weight vector is obtained to evaluate the relative importance of each primary indicator, thus obtaining the weight of each primary indicator.

[0096] In obtaining the score results representing the relative importance of each primary indicator to the safety risk, the primary indicators are compared pairwise, and a 1-9 digit scaling method is used to score and measure the relative importance of the two compared primary indicators to the safety risk, thereby obtaining the corresponding score results.

[0097] Specifically, this embodiment uses a 1-9 digit scale as the scoring standard, and uses the first-level indicator assignment method shown in Table 2 to assign values ​​to the relative importance level of each first-level indicator being compared, thereby completing the scoring measurement of the relative importance level of each first-level indicator being compared, and thus obtaining the corresponding scoring results.

[0098] Table 2. Methods for Assigning Values ​​to Primary Indicators

[0099]

[0100]

[0101] In the process of determining the weight of each primary indicator using the judgment matrix, the square root method is used to obtain the eigenvector of the judgment matrix, and then the largest eigenvalue is obtained. Then, a consistency check is performed on the current judgment matrix. If the current judgment matrix fails the consistency check, the judgment matrix is ​​reconstructed so as to obtain the optimal weight of each primary indicator using the judgment matrix that has passed the consistency check.

[0102] Specifically, this embodiment employs the square root method, multiplying and normalizing each row of the judgment matrix to obtain corresponding eigenvectors. These eigenvectors are then used to calculate the largest eigenvalue of the judgment matrix. To ensure the accuracy of the final weights for each primary indicator, this embodiment performs a consistency check on the judgment matrix after construction, measuring the degree of deviation from consistency. If the judgment matrix fails the consistency check, each primary indicator needs to be re-compared pairwise to determine its importance level and obtain a score representing the relative importance of each primary indicator relative to the safety risk. A new judgment matrix is ​​then constructed based on this score. This new judgment matrix is ​​then subjected to another consistency check until it passes. Specifically, when CR < 0.1, the judgment matrix is ​​considered completely consistent and can effectively reflect the relative importance of each primary indicator. Finally, the optimal weights for each primary indicator are obtained using the new judgment matrix that has passed the consistency check.

[0103] In this embodiment of the application, the largest eigenvalue of the judgment matrix is ​​calculated using the following expression:

[0104]

[0105] Where, λ max Let A represent the largest eigenvalue, A represent the judgment matrix, and λ represent the weight of the first indicator. j ′ Let represent the feature vector of the j-th primary indicator.

[0106] In this embodiment of the application, the consistency check of the judgment matrix is ​​performed using the following expression:

[0107]

[0108]

[0109] Where CI represents the consistency index, CR represents the average consistency index, and RI represents the average random consistency index.

[0110] In the step of obtaining the game theory combination weights of each secondary indicator, the first linear combination weights are constructed using multiple weights of each secondary indicator. Then, with the goal of minimizing the deviation between the combination weights and different weights, an objective function is constructed to obtain the optimal linear combination coefficients, thereby determining the game theory combination weights of each secondary indicator.

[0111] This invention combines weights obtained from different objective weighting methods using a game-theoretic combinatorial weighting method to determine the optimal result of indicator weights, thereby more scientifically and effectively displaying changes in safety risks in chemical industrial parks. In a specific embodiment of this application, firstly, a first linear combination weight is constructed using the entropy method weight and the Critic method weight for each secondary indicator. Then, based on the idea of ​​a game-theoretic aggregation model, different weights are optimally combined to seek consistency and compromise among them, aiming to minimize the deviation between the combined weights and the entropy method weights and the Critic method weights, thus constructing an objective function. Further, the optimal first derivative of the objective function is obtained through the differential properties of matrices, and then the set of combination coefficients is obtained when the first derivative is 0. Finally, the optimal linear combination coefficients are obtained by normalizing the coefficients in the set of combination coefficients, and then the game-theoretic combination weights for each secondary indicator are calculated using the optimal linear combination coefficients.

[0112] In this embodiment of the application, the first linear combination weight is represented by the following expression:

[0113] w j =β1w 1j +β2w 2j (15)

[0114] β1+β2=1 (16)

[0115] Among them, w j Let w represent the weight vector set of the j-th secondary indicator, β1 and β2 represent the linear combination coefficients, and w 1，j w represents the entropy weight. 2，j This represents the weights in the Critic method.

[0116] In this embodiment, the objective function is represented by the following expression:

[0117]

[0118] Where w represents the combined weight, w k This represents the weight of the k-th objective assignment method.

[0119] In this embodiment, the first derivative of the optimization is expressed using the following expression:

[0120]

[0121] Where T represents transpose.

[0122] In this embodiment, the coefficients in the combination coefficient set are normalized using the following expression:

[0123]

[0124] in, Let β represent the optimal linear combination coefficient of the k-th objective assignment method. k Let represent the set of combination coefficients for the k-th objective assignment method.

[0125] In this embodiment of the application, the game-theoretic combination weight of each secondary indicator is calculated using the following expression:

[0126]

[0127] Among them, w j组合 This represents the game-theoretic combination weight of the secondary indicators for the j-th secondary indicator. and This represents the coefficients of the optimal linear combination.

[0128] In the step of obtaining the first-type game theory combination weights for each primary indicator, the game theory combination weights for each secondary indicator are used, and the multiplication and normalization method is employed to obtain the first-type game theory combination weights. Specifically, in this embodiment, the game theory combination weights for each secondary indicator are multiplied and normalized using the following expression to obtain the first-type game theory combination weights for each primary indicator:

[0129]

[0130] Among them, w j ′ 组合 Let m represent the first-type game theory combination weight of the j-th primary indicator. ′ This represents the total number of secondary indicators belonging to the j-th primary indicator.

[0131] In the step of obtaining the second type of game theory combination weights for each primary indicator, a second linear combination weight is constructed using the first type of game theory combination weights and the weights of each primary indicator to determine the second type of game theory combination weights. This invention also uses a game theory combination weighting method to combine the first type of game theory combination weights and the weights of each primary indicator to determine the optimal result for the corresponding indicator weights, thereby further demonstrating the changes in safety risks in chemical industrial parks more scientifically and effectively. In a specific embodiment of this application, following a similar approach to calculating the game theory combination weights for each secondary indicator, the second linear combination weights are first constructed, then a corresponding objective function is constructed to obtain the set of combination coefficients, and finally the optimal linear combination coefficients are obtained. These optimal linear combination coefficients are then used to calculate the second type of game theory combination weights.

[0132] Furthermore, in step S140, the risk value of each park to be quantified is obtained, and the weights are further combined with the second type of game theory to generate the safety risk quantification result.

[0133] Specifically, this embodiment uses a second type of game theory combined weights to quantify the risk value of each industrial park to be quantified, thereby obtaining the safety risk quantification result of each industrial park. Then, by using each safety risk quantification result as a score for each chemical industrial park, the relative safety risk status between different chemical industrial parks can be obtained, thus achieving a ranking of the safety risks of the chemical industrial parks.

[0134] In the process of generating the quantification results of security risks, the risk value of each park to be quantified is first normalized, and then the Topsis method is used in combination with the weights of the second type of game theory to calculate the corresponding quantification results of security risks.

[0135] Specifically, this embodiment first normalizes the risk value of each industrial park to be quantified, and then selects the maximum and minimum normalized risk values. Next, using the Topsis method, the maximum normalized risk value is taken as the positive ideal solution, and the minimum normalized risk value is taken as the negative ideal solution. Using the combined weights of the positive and negative ideal solutions and the second type of game theory, the weighted distance between the risk value and the positive and negative ideal solutions is calculated for each industrial park to be quantified. The corresponding security risk quantification result is then calculated using the weighted distance.

[0136] In this embodiment of the application, the positive ideal solution and the negative ideal solution are represented by the following expressions:

[0137]

[0138] in, This represents the ideal solution. Let s represent the negative ideal solution. nJ This represents the risk value of the nth industrial park to be quantified.

[0139] In this embodiment of the application, the weighted distances between the risk value and the positive and negative ideal solutions are calculated using the following expressions:

[0140]

[0141] in, This represents the weighted distance to the ideal solution. W represents the weighted distance to the negative ideal solution. j Let represent the second-type game theory combination weight of the j-th primary indicator, and M represent the total number of primary indicators.

[0142] In this embodiment of the application, the corresponding security risk quantification result is calculated using the following expression:

[0143]

[0144] Among them, Si This represents the quantification result of the safety risk of the i-th park to be quantified.

[0145] Example 2

[0146] Based on the safety risk quantification method for chemical industrial parks described in Embodiment 1 above, this embodiment of the invention also provides a safety risk quantification system for chemical industrial parks (hereinafter referred to as the "safety risk quantification system").

[0147] Figure 2 This is a block diagram of the safety risk quantification system for chemical industrial parks, as described in an embodiment of this application. Figure 2 As shown, the security risk quantification system in this embodiment of the invention includes: an indicator configuration module 21, an objective weighting module 22, a subjective weighting module 23, and a security risk quantification module 24. Specifically, the indicator configuration module 21 is implemented according to the method described in step S110 above, configured to configure multiple primary indicators and multiple secondary indicators belonging to each primary indicator for the combination of parks to be quantified based on all influencing factors of safety risks in chemical industrial parks, and obtain the secondary indicator data of each park to be quantified; the objective weighting module 22 is implemented according to the method described in step S120 above, configured to use the secondary indicator data and adopt multiple objective weighting methods to obtain multiple weights for each secondary indicator, so as to obtain the game theory combination weight of each secondary indicator, and then obtain the first type of game theory combination weight of each primary indicator; the subjective weighting module 23 is implemented according to the method described in step S130 above, configured to analyze the relative importance of each primary indicator to safety risks, adopt subjective weighting methods to obtain the weight of each primary indicator, and then combine the first type of game theory combination weight to obtain the second type of game theory combination weight of each primary indicator; the safety risk quantification module 24 is implemented according to the method described in step S140 above, configured to obtain the risk value of each park to be quantified, and further combine the second type of game theory combination weight to generate the safety risk quantification result.

[0148] This invention discloses a method and system for quantifying safety risks in chemical industrial parks. The method first constructs an indicator system for quantifying safety risks in chemical industrial parks based on all influencing factors. Then, an objective weighting method is used to obtain multiple weights for each secondary indicator. Further, a game-theoretic weighting method is used to obtain the game-theoretic combination weights for each secondary indicator, thus obtaining the first type of game-theoretic combination weights for each primary indicator. Next, a subjective weighting method is used to obtain the weights for each primary indicator. This is further combined with the first type of game-theoretic combination weights, and then a game-theoretic weighting method is used again to fuse the data from the objective and subjective weighting methods. This process fully leverages the advantages of both methods to obtain the second type of game-theoretic combination weights for each primary indicator. Finally, the risk value for each park to be quantified is obtained, and the second type of game-theoretic combination weights are used to generate the safety risk quantification result. This invention enables the effective quantification of safety risks in chemical industrial parks, comprehensively and accurately reflecting the relative safety risk status between different chemical industrial parks. It overcomes the shortcomings of using a single-type weighting method and the inability to directly compare the relative safety risks between different chemical industrial parks. Furthermore, it has wider applicability and provides technical support for strengthening the supervision of hazardous chemicals and related enterprises in chemical industrial parks and preventing major accident risks in the parks.

[0149] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0150] Of course, the present invention may have other various embodiments. Without departing from the spirit and essence of the present invention, those skilled in the art can make various corresponding changes and modifications according to the present invention, but these corresponding changes and modifications should all fall within the protection scope of the claims of the present invention.

[0151] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device, or fabricating them separately as individual integrated circuit modules, or fabricating multiple modules or steps as a single integrated circuit module. Thus, the present invention is not limited to any particular hardware and software combination.

[0152] While the embodiments disclosed in this invention are as described above, the content is merely for the purpose of facilitating understanding of the invention and is not intended to limit the invention. Any person skilled in the art to which this invention pertains may make any modifications and variations in form and detail of the implementation without departing from the spirit and scope disclosed herein; however, the scope of patent protection for this invention shall still be determined by the scope defined in the appended claims.

Claims

1. A method for quantifying safety risks in chemical industrial parks, characterized in that, include: Based on all the factors influencing the safety risks of chemical industrial parks, multiple primary indicators and multiple secondary indicators belonging to each primary indicator are configured for the combination of parks to be quantified, and the secondary indicator data of each park to be quantified are obtained. Using the secondary indicator data, multiple weights for each secondary indicator are obtained by employing various objective assignment methods, thereby obtaining the game theory combination weights for each secondary indicator, and then obtaining the first type of game theory combination weights for each primary indicator. The relative importance of each primary indicator to the security risk is analyzed, and the weight of each primary indicator is obtained by subjective weighting method. Then, the weight of each primary indicator is obtained by combining the first type of game theory combination weight. Obtain the risk value of each park to be quantified, and further combine it with the second type of game theory combined weights to generate the safety risk quantification result.

2. The method for quantifying security risks according to claim 1, characterized in that, The steps for obtaining secondary indicator data for each industrial park to be quantified include: Multiple data processing methods are used to correct the basic data used to obtain the secondary indicator data, and the corrected basic data is used to calculate the secondary indicator data. Then, the calculated secondary indicator data is normalized to obtain the optimal secondary indicator data. The multiple data processing methods include, but are not limited to, Lagrange interpolation and KNN imputation.

3. The method for quantifying security risks according to claim 2, characterized in that, The normalization process for the calculated secondary indicator data includes: All secondary indicators are divided into positive indicators that are positively correlated with safety risks and negative indicators that are negatively correlated with safety risks. Then, the Min-Max normalization method is used to obtain the normalization results of the positive and negative indicators using different calculation methods, thereby completing the normalization process.

4. The method for quantifying security risks according to claim 3, characterized in that, The normalized results of the positive and negative indices are calculated using the following expressions: Among them, X 正向 X represents the normalized result of the positive index. ij X represents the j-th secondary indicator data in the i-th park to be quantified, n represents the total number of parks to be quantified, and X... 负向 This indicates the normalization result of the negative index.

5. The method for quantifying security risks according to claim 3 or 4, characterized in that, The various objective assignment methods include the entropy method and the Critic method. The step of obtaining the weight of each secondary indicator using the entropy method includes: Obtain the proportion of each secondary indicator data to the total of all secondary indicator data belonging to the same secondary indicator, and calculate the information entropy value of each secondary indicator. Then, by obtaining the corresponding information entropy redundancy, determine the entropy value weight of each secondary indicator.

6. The method for quantifying security risks according to claim 5, characterized in that, The information entropy value of each secondary indicator is calculated using the following expression: Among them, e j p represents the information entropy value of the j-th secondary indicator. ij Let ln represent the proportion of the j-th secondary indicator data in the i-th quantified park to the total of all secondary indicator data belonging to the same secondary indicator category as the j-th secondary indicator data.

7. The method for quantifying security risks according to claim 5 or 6, characterized in that, The steps for obtaining the weight of each secondary indicator using the Critic method include: The standard deviation representing the variability of each secondary indicator and the correlation coefficient representing the correlation between secondary indicators are obtained to obtain the conflict of each secondary indicator. Then, by calculating the information content of each secondary indicator, the Critic weight of each secondary indicator is determined.

8. The method for quantifying safety risks according to any one of claims 1 to 7, characterized in that, The steps for obtaining the weights of each primary indicator using the subjective weighting method include: Based on the Delphi method, a score is obtained representing the relative importance of each primary indicator to the security risk, and a judgment matrix is ​​constructed. The weight of each primary indicator is then determined using the judgment matrix.

9. The method for quantifying security risks according to claim 8, characterized in that, The process of obtaining scores that represent the relative importance of each primary indicator relative to security risk includes: The primary indicators are compared pairwise, and a 1-9 digit scale is used to score and measure the relative importance of the two primary indicators compared to the safety risks, thereby obtaining the corresponding scoring results.

10. The method for quantifying security risks according to claim 8 or 9, characterized in that, The process of determining the weight of each primary indicator using the aforementioned judgment matrix includes: The eigenvectors of the judgment matrix are obtained by using the square root method, and the largest eigenvalue is obtained. Then, a consistency check is performed on the current judgment matrix. If the current judgment matrix fails the consistency check, the judgment matrix is ​​reconstructed so as to obtain the optimal weight of each first-level indicator using the judgment matrix that passes the consistency check.

11. The method for quantifying safety risks according to any one of claims 1 to 10, characterized in that, The steps for obtaining the game-theoretic combination weights of each secondary indicator include: By utilizing multiple weights for each secondary indicator, a first linear combination weight is constructed. Then, with the goal of minimizing the deviation between the combined weight and different weights, an objective function is constructed to obtain the optimal linear combination coefficient, thereby determining the game theory combination weight for each secondary indicator.

12. The method for quantifying security risks according to claim 11, characterized in that, The steps for obtaining the first-type game theory combination weights for each primary indicator include: The first type of game theory combination weights are obtained by using the game theory combination weights of each secondary indicator and the multiplication and normalization method.

13. The method for quantifying security risks according to claim 11 or 12, characterized in that, The steps for obtaining the second-type game theory combination weights for each primary indicator include: Using the first type of game theory combination weights and the weights of each primary index, a second linear combination weight is constructed to determine the second type of game theory combination weights.

14. The method for quantifying security risks according to any one of claims 1 to 13, characterized in that, The steps for generating quantitative security risk results include: The risk value of each park to be quantified is normalized, and then the Topsis method is used in combination with the second type of game theory weights to calculate the corresponding safety risk quantification results.

15. A safety risk quantification system for chemical industrial parks, characterized in that, The security risk quantification system includes the following modules: The indicator configuration module is used to configure multiple primary indicators and multiple secondary indicators belonging to each primary indicator for the combination of parks to be quantified based on all the influencing factors of safety risks in chemical industrial parks, and to obtain the secondary indicator data for each park to be quantified. The objective weighting module is used to obtain multiple weights for each secondary indicator by using the secondary indicator data and employing various objective weighting methods, so as to obtain the game theory combination weight of each secondary indicator, and then obtain the first type of game theory combination weight of each primary indicator. The subjective weighting module is used to analyze the relative importance of each primary indicator to the security risk. It uses the subjective weighting method to obtain the weight of each primary indicator, and then combines the first type of game theory combined weight to obtain the second type of game theory combined weight of each primary indicator. The safety risk quantification module is used to obtain the risk value of each park to be quantified, and further combine it with the second type of game theory combined weights to generate the safety risk quantification result.

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