Local safety risk index construction method and system

Abstract

The invention discloses a local safety risk index construction method and system, and the method comprises the following steps: S1, setting a safety risk index which is divided into a first-level index and a second-level index; s2, calculating the index weight of each safety risk index in a combined manner, integrating subjective and objective information, and reducing expert experience deviation and data noise influence; s3, calculating each safety risk index according to the weight of the safety risk index and the collected data; s4, performing visual display on each safety risk index; according to the method, the local safety risk is quantitatively evaluated through scientific weight analysis and index calculation, a corresponding management mechanism is established on the basis of a quantitative result, risk early warning and problem searching can be achieved, and data support is provided for work of related departments.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and in particular relates to a method and system for constructing a local security risk index. Background Art

[0002] As society continues to progress and the economy develops rapidly, the importance of safety and stability of local areas as the core carrier of people's production and life has become increasingly prominent. In recent years, the industrial structure of various regions has been deeply adjusted, infrastructure construction has been steadily advanced, and economic exchanges and personnel exchanges between regions have become increasingly close. However, this development has also brought about some local security risk issues. Traditional safety hazards still exist, such as the risk of illegal operations in industrial production and the aging of fire protection facilities in old buildings; the rise of emerging industries such as artificial intelligence, big data, and new energy has brought new risks such as data leakage and new electrical fires. Various risks influence and interact with each other to form a complex risk network. At the same time, local safety management is also facing the dilemma of fragmented risk management and unified supervision. The various departments lack effective coordination, information flow is not smooth, management efficiency is low, and regulatory measures are difficult to adapt to the complex and changing security situation.

[0003] In view of the complex situation of local security risks, it is particularly important to build a scientific and reasonable local security risk index quantitative indicator system and carry out accurate risk assessment and analysis. By comprehensively and accurately grasping the local security risk index, we can clarify the responsibility for risk prevention and control, formulate and implement effective prevention and control measures for key industries, regions and places, and provide strong support for local security management, thereby improving the overall security management level and protecting people's lives and property safety and social stability.

[0004] However, the existing security risk assessment methods have many shortcomings. Some methods focus too much on abstract assessments at the macro level, and lack consideration of local specific conditions. The selected indicators are general and cannot accurately reflect the unique local security risk characteristics, resulting in a large deviation between the assessment results and the actual situation. Some methods only focus on a single field and cannot fully cover all aspects of local security risks, making it difficult to meet the needs of comprehensive management. In addition, due to the limitations of the multi-head management model, data sharing between departments is difficult, information flow is blocked, and assessment standards and methods are not unified, making it difficult to integrate and utilize risk quantification results, and it is difficult for relevant departments to carry out scientific and effective risk prevention and control work based on these results. Therefore, it is urgent to conduct in-depth research on the quantification method of the local security risk index and to build a scientific, comprehensive and effective method and system for building the local security risk index. Summary of the invention

[0005] To solve the above problems, the present invention proposes a method and system for constructing a local security risk index. The method quantitatively evaluates local security risks through scientific weight analysis and index calculation, and then establishes a corresponding management mechanism based on the quantitative results, which can achieve risk warning, problem finding and provide data support for the work of relevant departments.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] A method for constructing a local security risk index comprises the following steps:

[0008] S1. Set up security risk index indicators. The security risk index indicators are divided into primary indicators and secondary indicators;

[0009] S2. Combine and calculate the weights of various security risk index indicators, integrate subjective and objective information, and reduce the impact of expert experience bias and data noise;

[0010] S3. Calculate each security risk index indicator according to the security risk index indicator weight and the collected data;

[0011] S4. Visualize the indicators of each security risk index.

[0012] Preferably, in step S1, the first-level indicators include production safety accidents, administrative law enforcement, fire, road traffic accidents and natural disasters. Five indicators in the first-level indicators directly or indirectly reflect the safety risk status of the region; the data of the second-level indicators come from the Emergency Management Department and consist of 17 items. The second-level indicators include the number of production safety accidents, the death rate of production safety accidents per 100 million yuan of GDP, the number of major production safety accidents in the three months before the index is released, the number of major production safety accidents in other months of the year, the number of deaths in major production safety accidents in the three months before the index is released, the number of deaths in major production safety accidents in other months of the year, the number of fires, the number of deaths in fires, the number of road traffic accidents, the number of deaths in road traffic accidents, the number of administrative law enforcement, the administrative penalty rate, the forest fire concealment rate, the forest fire cause unidentified rate, the productive fire use unreported rate, the number of deaths due to disasters in the three months before the index is released, and the number of deaths due to disasters in other months of the year.

[0013] Preferably, the specific process of step S2 is: combining the risk assessment methods of different disaster types, the applicability of technical requirements and regional characteristics, using a combined model combining information entropy, hierarchical analysis and grey correlation analysis to perform weight calculation, and determining the weights of the evaluation indicators at each level of the safety risk index indicator system. The specific calculation formula is:

[0014]

[0015] Among them, wj is the comprehensive weight of the j-th evaluation index; is the subjective weight of the j-th index obtained by the analytic hierarchy process; is the objective weight calculated based on the information entropy theory; is the corrected weight determined by grey relational analysis; α, β, and γ are combination coefficients, α + β + γ = 1, and are determined by least squares optimization.

[0016] Preferably, in step S3, the safety risk index of each district and county is calculated, and the specific process is as follows:

[0017] S31. Independent calculation of secondary indicators: For each secondary indicator, collect and process the data of each district and county respectively;

[0018] S32. Normalize all 17 secondary indicators:

[0019]

[0020] Among them, is the normalization result of the j-th index; is the original value of the j-th index; is the moving average of the j-th index in the rolling time window t, and t is set to 36 months; is the standard deviation of the corresponding time window; λ is the time decay coefficient, and λ is set to 0.05 / month; T is the current month; T0 is the data reference month;

[0021] S33. Calculate the basic risk index, and the calculation formula is:

[0022]

[0023] Among them, is the basic risk index;

[0024] S34. Risk coupling correction, quantify the interaction intensity between production safety accident categories, administrative law enforcement categories, fire accident categories, road traffic accident categories, and natural disaster categories in the first-level indicators, and the calculation formula is:

[0025]

[0026] Among them, is the risk index after risk coupling correction; δ is the coupling intensity coefficient, and δ is set to 0.2; R k is the value of the k-th first-level indicator; is the time series change rate of the k-th first-level indicator; C kl is the maximum coupling degree between the k-th first-level indicator and the l-th first-level indicator, calculated by Granger causality test, l ≠ k;

[0027] S35. Establish a management efficiency evaluation model for adjusting the risk index value. The calculation formula of the management efficiency evaluation model is:

[0028]

[0029] where m i is the management efficiency evaluation result; n is the number of management efficiency indicators; is the value of management efficiency indicator p; is the maximum value of management efficiency indicator p in each region; φ p is the weight of management efficiency indicator p, determined by the coefficient of variation method;

[0030] S36. Construct an efficiency decay function for the risk index value. The calculation formula of the efficiency decay function is:

[0031]

[0032] where is the risk index after considering management efficiency; χ is the maximum correction amplitude, and χ is set to 0.25; k M is the decay rate, determined by the non-linear relationship between management efficiency and accident rate, and k M is set to 2.5;

[0033] S37. Final score summary, and the index is normalized and output as:

[0034]

[0035] where Index i is the normalized result of the risk index.

[0036] Preferably, in step S3, the safety risk index of each city is also calculated. The specific process is as follows: Summarize the safety risk index indicators of each district and county, and calculate the safety risk index indicators of each city respectively; Sort the scores of the safety risk index indicators of each city to understand its own safety risk status within the provincial scope and formulate targeted risk prevention and response measures.

[0037] A local safety risk index system includes:

[0038] An initial information setting module for setting safety risk index indicators. The setting of safety risk index indicators is divided into first-level indicators and second-level indicators;

[0039] A safety risk index indicator weight calculation module for combining and calculating the weights of each safety risk index indicator, integrating subjective and objective information, and reducing the influence of expert experience deviation and data noise;

[0040] A safety risk index indicator calculation module, which is used to calculate each safety risk index indicator according to the safety risk index indicator weights and the collected data;

[0041] A visualization display module, which is used to visually display each safety risk index indicator.

[0042] After adopting the above technical solutions, the present invention has the following beneficial effects: The improvements of the present invention over the prior art are mainly in the following aspects: First, in terms of indicator setting, it breaks through the traditional single or few-dimensional evaluation methods. In the setting of first-level indicators, it follows the five principles of comprehensiveness, representativeness, availability, scientificity, and dynamics, fully considering management factors, and integrating system risks and management risks. In the setting of second-level indicators, it comprehensively considers factors such as economic volume, administrative law enforcement, natural conditions, social characteristics, and policy formulation, etc., to construct a comprehensive indicator system in order to more accurately reflect the safety risk status; Second, in terms of weight setting, it adopts big data collection and scientific analysis methods, combines various factors such as the risk assessment methods of different disaster types, the applicability of technical requirements, and the regional characteristics of the applicable regions, etc., and considers the comprehensive application of expert investigation and analytic hierarchy process to more accurately and reasonably determine the weights of each level of evaluation units in the safety risk index indicator system; Third, it changes the construction of the traditional single index model. The present invention also integrates platform resources, inputs data, constructs corresponding models for independent calculation, and conducts visualization. It can sort and notify the safety risk index monthly, directly reflecting the safety risk status of each region within a statistical period. Description of the Drawings

[0043] Figure 1 It is a flowchart of a method for constructing a local safety risk index of the present invention;

[0044] Figure 2 It is a flowchart block diagram of a local safety risk index system of the present invention. Detailed Embodiments

[0045] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0046] As Figure 1 shown, a method for constructing a local safety risk index includes the following steps:

[0047] S1. Set safety risk index indicators. The safety risk index indicators are divided into first-level indicators and second-level indicators;

[0048] In step S1, the first-level indicators include production safety accident category, administrative law enforcement category, fire accident category, road traffic accident category, and natural disaster category. The five indicators in the first-level indicators directly or indirectly reflect the safety risk status of the region. The data of the second-level indicators comes from the Emergency Management Department and consists of 17 items. The second-level indicators include the number of production safety accidents, the death rate of production safety accidents per 100 million yuan of GDP, the number of relatively large production safety accidents in the 3 months before the index release month, the number of relatively large production safety accidents in other months of this year, the number of deaths in relatively large production safety accidents in the 3 months before the index release month, the number of deaths in relatively large production safety accidents in other months of this year, the number of fire accidents, the number of deaths in fire accidents, the number of road traffic accidents, the number of deaths in road traffic accidents, the number of administrative law enforcement times, the administrative penalty rate, the concealment rate of forest fire information, the rate of unidentified causes of forest fires, the rate of unreported productive fires, the number of deaths due to disasters in the 3 months before the index release month, and the number of deaths due to disasters in other months of this year.

[0049] S2. Combine and calculate the weights of each safety risk index indicator, integrate subjective and objective information, and reduce the influence of expert experience deviation and data noise.

[0050] The specific process of step S2 is as follows: Combining the risk assessment methods of different disaster types, the applicability of technical requirements, and regional characteristics, a combined model that combines information entropy, analytic hierarchy process, and grey relational analysis is used for weight calculation to determine the weights of each level of evaluation indicators in the safety risk index indicator system. The specific calculation formula is:

[0051]

[0052] where, w j is the comprehensive weight of the j-th evaluation indicator; is the subjective weight of the j-th indicator obtained through the analytic hierarchy process; is the objective weight calculated based on the information entropy theory; is the correction weight determined through grey relational analysis; α, β, and γ are combination coefficients, α + β + γ = 1, and are determined through least squares optimization;

[0053] S3. According to the weights of the safety risk index indicators and the collected data, calculate each safety risk index indicator.

[0054] In step S3, the safety risk index of each district and county is calculated. The specific process is as follows:

[0055] S31. Independent calculation of the second-level indicators: For each second-level indicator, collect and process the data of each district and county separately.

[0056] S32. Normalize all 17 second-level indicators:

[0057]

[0058] wherein, is the normalization result of the j-th index; is the original value of the j-th index; is the moving average of the j-th index in the rolling time window t, and t is set to 36 months; is the standard deviation of the corresponding time window; λ is the time decay coefficient, and λ is set to 0.05 / month; T is the current month; T0 is the data benchmark month;

[0059] S33. Calculation of the basic risk index, and the calculation formula is:

[0060]

[0061] wherein, is the basic risk index;

[0062] S34. Risk coupling correction, quantifying the interaction intensity among production safety accident category, administrative law enforcement category, fire accident category, road traffic accident category and natural disaster category in the first-level indicators, and the calculation formula is:

[0063]

[0064] wherein, is the risk index after risk coupling correction; δ is the coupling intensity coefficient, and δ is set to 0.2; R k is the value of the k-th first-level indicator; is the time series change rate of the k-th first-level indicator; C kl is the maximum coupling degree between the k-th first-level indicator and the l-th first-level indicator, calculated through Granger causality test, l≠k;

[0065] S35. Establish a management efficiency evaluation model for adjusting the risk index value; the calculation formula of the management efficiency evaluation model is:

[0066]

[0067] wherein, m i is the management efficiency evaluation result; n is the number of management efficiency indicators; is the value of the management efficiency indicator p; is the maximum value of the management efficiency indicator p in each region; φ p is the weight of the management efficiency indicator p, determined by the coefficient of variation method;

[0068] S36. Construct an efficiency decay function for the risk index value; the calculation formula of the efficiency decay function is:

[0069]

[0070] Among them, is the risk index after considering management efficiency; χ is the maximum correction amplitude, and χ is set to 0.25; k M is the attenuation rate, which is determined by the non-linear relationship between management efficiency and accident rate, and k M is set to 2.5;

[0071] S37. Final score summary, and the index is normalized and output as:

[0072]

[0073] Among them, Index i is the normalized result of the risk index.

[0074] In step S3, the safety risk index of each city is also calculated. The specific process is as follows: Summarize the safety risk index indicators of each district and county, and calculate the safety risk index indicators of each city respectively; Sort the scores of the safety risk index indicators of each city to understand its own safety risk status within the provincial scope and formulate targeted risk prevention and response measures;

[0075] S4. Visualize each safety risk index indicator.

[0076] As Figure 2 shown, a local safety risk index system includes:

[0077] An initial information setting module for setting safety risk index indicators, and the safety risk index indicators are set as first-level indicators and second-level indicators;

[0078] A safety risk index indicator weight calculation module for combining and calculating the weights of each safety risk index indicator, integrating subjective and objective information, and reducing the influence of expert experience deviation and data noise;

[0079] A safety risk index indicator calculation module for calculating each safety risk index indicator according to the weights of the safety risk index indicators and the collected data;

[0080] A visualization display module for visualizing each safety risk index indicator.

[0081] The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A method for constructing a local security risk index, characterized in that: The following steps are involved: S1. Set the safety risk index indicators. The safety risk index indicators are divided into primary indicators and secondary indicators; S2. Combine and calculate the weights of various security risk index indicators, integrate subjective and objective information, and reduce the impact of expert experience bias and data noise; S3. Calculate each security risk index indicator according to the security risk index indicator weight and the collected data; S4. Visualize the indicators of each security risk index.

2. The method for constructing a local security risk index according to claim 1, characterized in that: In step S1, the first-level indicators include production safety accidents, administrative law enforcement, fire, road traffic accidents and natural disasters. Five indicators in the first-level indicators directly or indirectly reflect the safety risk status of the region; the data of the second-level indicators come from the Emergency Management Department and consist of 17 items. The second-level indicators include the number of production safety accidents, the death rate of production safety accidents per 100 million yuan of GDP, the number of major production safety accidents in the three months before the index is released, the number of major production safety accidents in other months of the year, the number of deaths in major production safety accidents in the three months before the index is released, the number of deaths in major production safety accidents in other months of the year, the number of fires, the number of deaths in fires, the number of road traffic accidents, the number of deaths in road traffic accidents, the number of administrative law enforcement, the administrative penalty rate, the forest fire concealment rate, the forest fire cause unidentified rate, the productive fire use unreported rate, the number of deaths due to disasters in the three months before the index is released, and the number of deaths due to disasters in other months of the year.

3. The method for constructing a local security risk index according to claim 1, characterized in that: The specific process of step S2 is: combining the risk assessment methods of different disaster types, the applicability of technical requirements and regional characteristics, using a combined model combining information entropy, hierarchical analysis and grey correlation analysis to calculate weights, and determining the weights of the evaluation indicators at each level of the safety risk index indicator system. The specific calculation formula is: Among them, w j is the comprehensive weight of the j-th evaluation indicator; is the subjective weight of the jth indicator obtained through the analytic hierarchy process; is the objective weight calculated based on information entropy theory; is the correction weight determined by grey relational analysis; α, β and γ are combination coefficients, α+β+γ=1, which are determined by least squares optimization.

4. The method for constructing a local security risk index according to claim 1, characterized in that: In step S3, the safety risk index of each district and county is calculated. The specific process is as follows: S31. Independent calculation of secondary indicators: For each secondary indicator, data from each district and county are collected and processed separately; S32. Normalize all 17 secondary indicators: in, is the normalized result of the j-th indicator; is the original value of the j-th indicator; is the moving average of the jth indicator in the rolling time window t, where t is set to 36 months; is the standard deviation of the corresponding time window; λ is the time decay coefficient, and λ is set to 0.05 / month; T is the current month; T0 is the data base month; S33. Basic risk index calculation, the calculation formula is: in, is the underlying risk index; S34, risk coupling correction, quantify the interaction intensity between the production safety accident category, administrative law enforcement category, fire category, road traffic accident category and natural disaster category in the first-level indicators, and the calculation formula is: in, is the risk index after risk coupling correction; δ is the coupling strength coefficient, δ is set to 0.2; R k is the value of the kth first-level indicator; is the time series change rate of the kth primary indicator; C kl is the maximum coupling degree between the kth first-level indicator and the lth first-level indicator, calculated by Granger causality test, l≠k; S35. Establish a management effectiveness evaluation model to adjust the risk index value; the calculation formula of the management effectiveness evaluation model is: Among them, m i is the management effectiveness evaluation result; n is the number of management effectiveness indicators; is the value of the management effectiveness index p; is the maximum value of the management effectiveness index p in each place; φ p is the weight of the management effectiveness index p, determined by the coefficient of variation method; S36. Construct an efficacy attenuation function and take the risk index as the value; the calculation formula of the efficacy attenuation function is: in, is the risk index after considering management effectiveness; χ is the maximum correction range, χ is set to 0.25; k M is the decay rate, which is determined by the nonlinear relationship between management effectiveness and accident rate, k M Set to 2.5; S37, the final score summary, index normalization output is: Among them, Index i is the normalized result of the risk index.

5. The method for constructing a local security risk index as claimed in claim 4, characterized in that: In step S3, the safety risk index of each city is also calculated. The specific process is: summarize the safety risk index indicators of each district and county, and calculate the safety risk index indicators of each city respectively; sort the safety risk index indicator scores of each city to understand the safety risk status of itself within the province, and formulate targeted risk prevention and response measures.

6. A local security risk index system, characterized in that: include: The initial information setting module is used to set the security risk index indicator. The security risk index indicator setting is divided into primary indicators and secondary indicators; The safety risk index indicator weight calculation module is used to combine and calculate the weights of various safety risk index indicators, integrate subjective and objective information, and reduce the impact of expert experience bias and data noise; The safety risk index indicator calculation module is used to calculate various safety risk index indicators according to the safety risk index indicator weights and the collected data; The visualization module is used to visualize various security risk index indicators.

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