Tailing pond system safety risk cognitive mode reconstruction and evaluation method

Abstract

The invention relates to a tailing pond system security risk cognitive mode reconstruction and evaluation method, which comprises the following steps: constructing a comprehensive risk evaluation system integrating the inherent risk and dynamic risk of a risk point, and predicting the future security risk by utilizing the inherent attribute risk data of the system and the current timely state; a major risk assessment mode and a risk cognition path are redefined by taking the inherent risk of a safety risk point of a complex system as a principal line according to a conversion process of the risk point from the inherent risk to an initial risk and then to a real risk. A specific characterization method of inherent attributes of major risk points in a tailing pond safety system is studied, and the initial risk of dam break of the tailing pond is obtained by combining the possibility of occurrence of the risk points in a management state; and then through dynamic risk correction, establishing a static risk assessment model, a local dynamic risk assessment model and a real risk assessment model capable of timely correcting an initial risk, which accord with the actual state of the system. According to the invention, more accurate risk warning can be provided for a tailing pond system engineering manager in management, and the risk management efficiency is improved.

Description

Technical Field

[0001] The invention relates to a tailings dam system safety risk cognitive model reconstruction and assessment method, belonging to the technical field of tailings dam safety system engineering risk assessment. Background Art

[0002] Simple and practical tailings dam system safety risk assessment model and method are of great value for disaster prevention and control in key industries. The tailings dam safety system is complex, including equipment, processes, materials, sites and operations, and the information of these elements is mostly random and timely dynamic. In order to promote the transformation of the tailings dam safety supervision mechanism, the dual goals of risk classification and dynamic risk control of the tailings dam safety system are achieved.

[0003] Early accident control theories, represented by Heinrich's causal theory, energy theory, and trajectory intersection theory, effectively explained the logical relationship between accident causes and accident results, especially pointing out the role of "unsafe human behavior" and "unsafe state of objects" in the process of causing accidents. Traditional high-risk industries are more in line with the model of accident causation theory because of their dense personnel, dangerous materials, and complex processes. However, this traditional accident control model takes accidents as the research object, and has inherent "lag" and "passivity". The causes it finds and the measures it formulates are not universal, and it cannot effectively prevent major accidents of different types and industries.

[0004] Murphy's Law points out that risks are everywhere, and they are highly hidden and sporadic. In the production process, most of them are not perceived by people in the form of "unsafe behavior" or "unsafe state" in the short term. Therefore, enterprises invest a lot of manpower and material resources in screening hidden dangers, but still cannot control the occurrence of accidents. The "five highs" risk prevention and control model uses the principles of safety science to build a systematic accident prevention and control model.

[0005] However, investigations of major accidents have revealed that the causes of accidents are often accompanied by new unknown risks, which means that there is a scenario gap, that is, risks are constantly emerging with dynamic uncertainties, the unexpectedness of natural disasters, new technologies and other scenarios, and their risk consequences are dynamic. In other words, because risks are uncertain and their state is dynamic, the accompanying causes of industrial accidents are diversified, and there are widespread positions, links, and regions with leakage and loss of control, so decision-making based on risk assessment results is also uncertain. There is a gap between known risks and unknown risks in actual management, and this defect leads to inconsistencies with expected accident scenarios. Summary of the invention

[0006] In order to solve the problems existing in the above-mentioned prior art, the present invention provides a method for reconstructing and evaluating the safety risk cognition model of a tailings dam system. The present invention can provide more accurate risk warnings for tailings dam system engineering managers in management, so as to take corresponding risk control measures in time and improve risk management efficiency.

[0007] To achieve the above object, the present invention provides a technical solution: a method for reconstructing and evaluating the safety risk cognition model of a tailings dam system, comprising the following steps:

[0008] S1. According to the disaster-causing factors of the tailings dam system's inherent safety risk accidents, determine the major risk points of the tailings dam system's safety risk. The major risk points include dangerous places, dangerous processes, dangerous equipment, dangerous goods and dangerous operations, referred to as "five highs", and then set the quantitative characteristics of the "five highs" inherent risk indicators; inherent risk refers to the inherent energy of the equipment, facilities and places themselves;

[0009] S2. Determine the coordination between inherent risk, initial risk and actual risk characteristics and system attribute risk structure;

[0010] S3. Divide major risk indicators into inherent risk indicators, control risk indicators and dynamic risk indicators, and build a timely safety risk assessment system that combines dynamic and static aspects;

[0011] S4, quantify the characteristics of various major risk indicators in step S3, and set risk levels to build a realistic risk assessment model that integrates the inherent risk and dynamic risk of the "dam break" risk point;

[0012] S5. Perform risk assessment on the collected risk source data through the timely safety risk assessment system in step S3 and the actual risk assessment model in step S4 to determine the actual risk level.

[0013] Further improvements to the above technical solution are:

[0014] The quantitative characteristics of the "five highs" inherent risk indicators set in step S1 are specifically:

[0015] (1) The quantitative characteristic of the inherent risk index of dangerous equipment is the dangerous equipment hazard index h s The hazard index h of dangerous equipment is determined based on the tailings dam construction form, storage method and tailings type. s ;

[0016] (2) The quantitative characteristic of the inherent risk index of dangerous goods is the dangerous goods hazard index M, which is determined by the potential energy characteristics of the stored materials in the risk point reservoir. According to the classification method of tailings pond design level, the dangerous goods hazard index M is determined by the classification results of the current storage capacity and the current dam height;

[0017] (3) The quantitative characteristic of the inherent risk index of hazardous sites is the exposed personnel index E of hazardous sites, which is determined based on the number of exposed personnel within 1 km downstream of the tailings pond and the number of exposed personnel working in the tailings pond;

[0018] (4) The quantitative characteristic of the inherent risk index of hazardous processes is the monitoring failure rate correction coefficient K1, which is determined based on the tailings pond infiltration line, dam displacement, reservoir water level, precipitation and the failure rate of monitoring facilities of video surveillance;

[0019] (5) The quantitative characteristic of the inherent risk index of hazardous operations is the hazardous operation hazard correction coefficient K2, which is determined based on the total number of high-risk operation types in the tailings pond; high-risk operations include special operations, hazardous operations and special equipment operations.

[0020] The coordination method between risk structures in step S2 includes the following steps:

[0021] (1) Quantification of risk severity of inherent risk indicators of tailings dam system: Using the improved DOW method to determine the quantitative method of "five key parameters" in dangerous places, dangerous processes, dangerous equipment, dangerous goods and dangerous operations, the risk severity of inherent risk indicators of tailings dam system can be quantified;

[0022] (2) Quantification of initial risk assessment parameters: With risk control indicators as the center, obtain initial assessment parameters; construct a risk point accident tree through risk causal logic symbols, use fuzzy fault tree analysis (FFTA) to obtain the fuzzy language of complex system risk factors, predict the probability and causal relationship of basic events, provide the dynamic Bayesian network (DBN) with the root node prior probability, and update the probability of events with accompanying risk changes; combine the dynamic Bayesian network (DBN) with the fuzzy fault tree analysis (FFTA) model to realize the method of accident scenario simulation with graphical and digital display, use the DBN-FFTA probabilistic risk assessment method to determine the initial risk probability, and then determine the risk control frequency index based on the initial risk probability;

[0023] (3) Aggregate the inherent risk severity index and the risk control frequency index into the initial risk of the risk point: Determine the key dynamic risk indicator correction method based on the characteristic value of the monitoring project, dynamically correct the initial risk, and integrate the real risk assessment model after dynamic and timely correction to achieve timely risk assessment that couples system attributes with management status.

[0024] The construction of a dynamic and static combined timely safety risk assessment system in step S3 includes the following steps:

[0025] (1) Taking the dangerous places, dangerous processes, dangerous equipment, dangerous goods and dangerous operations in the tailings dam system as risk points, determine the inherent risks of the tailings dam system;

[0026] (2) Initial risk refers to the risk after the existing safety protection measures are established on the basis of the inherent risk of the tailings dam system. Based on the inherent risk of the tailings dam system, the risk control frequency index generated in the management and control process is introduced to correct the inherent risk, which is determined as the initial risk;

[0027] (3) Aggregate initial risk and dynamic risk to form real risk.

[0028] Setting the risk level in step S4 to construct a realistic risk assessment model that integrates the inherent risk and dynamic risk of the "dam break" risk point includes the following steps:

[0029] (1) Calculation method for constructing the inherent risk index h of major risk points:

[0030] h=h s MEK1K2

[0031] In the formula, h s It represents the hazard index of dangerous equipment, with a value of 1 to 1.7; M represents the hazard index of dangerous goods, with a value of 1 to 9; E represents the index of exposed personnel in dangerous places, with a value of 1 to 9; K1 refers to the correction coefficient of monitoring failure rate, K1=1+p, p is the average value of the failure rate of monitoring facilities; K2 refers to the correction coefficient of the hazard of dangerous operations, K2=1+0.05q, q is the total number of high-risk operation types involved in the risk point;

[0032] Then, according to the early warning signal coefficient K3 of the online monitoring characteristic indicator, the inherent risk h of the major risk point is dynamically corrected to determine the dynamic correction value of the inherent hazard index of the major risk point:

[0033] H=hK3

[0034] (2) Establish an initial risk index and use the dynamic correction value of the inherent risk index of major risk points to describe the initial risk. The functional relationship between the initial risk index and the inherent risk index is:

[0035] R0=H×G

[0036] In the formula, R0 represents the initial risk index; H represents the dynamic correction value of the inherent risk index of major risk points; G is the risk control frequency index of major risk points;

[0037] (3) Determine the actual risk model of major risk points. The actual risk can be expressed by the function:

[0038] R=R0×K m

[0039] In the formula, R0 represents the initial risk index; K mrepresents the dynamic risk correction coefficient, and its value is m=3, 4, 5, ..., 8, where K3 is the early warning signal coefficient of the online monitoring characteristic index, K4 is the dynamic accident hazard index correction coefficient, K5 is the special period index correction coefficient, K6 is the natural environment index correction coefficient, K7 is the early warning and disposal index correction coefficient, and K8 is the comprehensive management index correction coefficient;

[0040] (4) Determine the risk control frequency index of major risk points, which can be expressed as:

[0041] G=1+P

[0042] Where P is the probability of major risk point accident events;

[0043] (5) Dynamic risk correction. The specific steps are as follows:

[0044] Step 1: Use the online monitoring characteristic indicator warning signal coefficient K3 to correct the inherent risk index of major risk points. The real-time alarm of the online monitoring project is divided into level 1 alarm (low alarm), level 2 alarm (medium alarm) and level 3 alarm (high alarm). When the online monitoring project reaches 3 level 1 alarms, it is recorded as 1 level 2 alarm; when the monitoring project reaches 2 level 2 alarms, it is recorded as 1 level 3 alarm; set the corresponding base of the online monitoring characteristic indicator warning signal coefficient K3 to 1.20, and the expression of the online monitoring characteristic indicator warning signal coefficient is:

[0045] K3=1.20 (a+2b+3c)

[0046] Where: a is the number of first-level alarms, with values ​​of a=0, 1, 2, 3; b is the number of second-level alarms, with values ​​of b=0, 1, 2; c is the number of third-level alarms, with values ​​of c=0, 1;

[0047] Step 2: Use other dynamic indicators to correct the initial risk index of major risk points, including dynamic accident hazard indicator correction, special period indicator correction, natural environment indicator correction, early warning and disposal indicator correction and comprehensive management indicator correction.

[0048] In step S5, risk assessment is performed on the collected risk source data to determine the actual risk level, including the following steps:

[0049] (1) The safety risk after risk control index adjustment and dynamic risk index adjustment is regarded as the actual risk of the final dam break event;

[0050] (2) The actual safety risk level of the tailings dam breach system is divided into Level I, Level II, Level III and Level IV;

[0051] (3) Obtain the risk values ​​of different states at each stage, and perform quantitative processing and inference calculations to determine the corresponding risk levels.

[0052] According to the above technical scheme, the method for reconstructing and evaluating the safety risk cognition model of a tailings dam system provided by the present invention can predict future safety risks by using the risk data of the inherent attributes of the system and the current timely status through constructing a comprehensive risk assessment system integrating the inherent risks and dynamic risks of risk points. The prediction results can present the risk status at different stages. Taking the inherent risks of safety risk points of complex systems as the main line, the major risk assessment model and risk cognition path are redefined according to the transformation process of risk points from inherent risks to initial risks and then to real risks. The specific characterization method of the inherent attributes of major risk points in the tailings dam safety system is studied, and the initial risk of tailings dam breach is obtained by combining the possibility of risk points occurring under the management state; with the update of informatization and abnormal information, dynamic risk correction is proposed, and a static risk assessment model that conforms to the actual state of the system, a local dynamic risk assessment, and a real risk assessment model that can correct the initial risk in a timely manner are established. Compared with the prior art, the present invention has the following advantages:

[0053] (1) The realistic risk assessment model constructed by the technical solution adopted in the present invention can highlight the key points, set dangerous equipment, dangerous goods, dangerous places, dangerous processes and dangerous operations as major risk points, highlight the dangers of key groups, equipment, processes and places, and can fully control the actual situation, further improving the accuracy of the model evaluation results.

[0054] (2) Because the technical solution adopted by the present invention can conduct risk assessment on the collected risk source data and determine the actual risk level, targeted risk control measures can be formulated based on the risk level, such as stopping operation, immediately rectifying, and taking measures to reduce risks until the risks are reduced to an acceptable range.

[0055] (3) Because the technical solution adopted by the present invention can focus on inherent risks, the phased assessment is conducive to analyzing the changes in risk status, making it easier for personnel at various positions in the tailings pond to identify hidden dangers in a certain link and take precise risk control measures in a timely manner, which is helpful for the construction of a dual prevention mechanism for enterprises and remote supervision by the government, and lays a good foundation for controlling risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 Flowchart of the reconstruction of the safety risk cognition model and assessment method of the tailings dam system;

[0057] Figure 2 The main logic line of the risk structure. DETAILED DESCRIPTION

[0058] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments, but the protection scope of the present invention is not limited to the following embodiments.

[0059] In the technical solution provided by the present invention, a tailings dam system safety risk cognitive model reconstruction and assessment method is provided, such as Figure 1 As shown, the specific steps include:

[0060] S1. According to the disaster-causing factors of the tailings dam system's inherent safety risk accidents, determine the major risk points of the tailings dam system's safety risk. The major risk points include dangerous places, dangerous processes, dangerous equipment, dangerous goods and dangerous operations, referred to as "five highs", and then set the quantitative characteristics of the "five highs" inherent risk indicators; inherent risk refers to the inherent energy of the equipment, facilities and places themselves;

[0061] The quantitative characteristics of inherent risk indicators for major risk points are set as follows:

[0062] (1) The quantitative characteristic of the inherent risk index of dangerous equipment is the dangerous equipment hazard index h s The hazard index h of dangerous equipment is determined based on the tailings dam construction form, storage method and tailings type. s In this embodiment, the tailings pond is a valley-type tailings pond, the dam construction method is an upstream tailings dam, the tailings storage method is a wet storage, and the corresponding relationship between the inherent safety level of the tailings pond dam body and the hazard index is shown in Table 1.

[0063] Table 1 Correspondence between the intrinsic safety level of the tailings dam and the hazard index

[0064]

[0065] According to the three indicators of "dam construction method", "tailings storage method" and "tailings type", the three indicators are divided into grades and quantified. Each indicator selects a grade of characteristic value and multiplies them. The solution is the hazard index h of dangerous equipment. s is 1.70.

[0066] (2) The quantitative characteristic of the inherent risk index of dangerous goods is the dangerous goods hazard index M, which is determined by the potential energy characteristics of the stored materials in the risk point reservoir. According to the classification method of the tailings pond design grade, the dangerous goods hazard index M is determined by the classification results of the current storage capacity and the current dam height. In this embodiment, the tailings pond is graded as a third-grade pond. The corresponding relationship between the tailings pond design grade and the hazard index is shown in Table 2.

[0067] Table 2 Correspondence between tailings pond design level and hazard index

[0068]

[0069] (3) The quantitative characteristic of the inherent risk index of hazardous places is the exposed personnel index E of hazardous places, which is determined according to the number of exposed personnel within 1 km downstream of the tailings pond and the number of workers in the tailings pond; in this embodiment, the hazardous place is the downstream area of ​​the tailings pond, and the number of residents in the downstream area and the number of workers on the dam surface is 18. The corresponding relationship between the number of people affected in the downstream area of ​​the tailings pond and the exposed personnel index of hazardous places is shown in Table 3.

[0070] Table 3 Correspondence between the number of people affected and the index of people exposed to dangerous places

[0071]

[0072]

[0073] (4) The quantitative characteristic of the inherent risk index of the hazardous process is K1. The monitoring failure rate correction coefficient K1 is determined according to the tailings pond infiltration line, dam displacement, reservoir water level, precipitation and video monitoring monitoring facility failure rate. In this embodiment, the monitoring status of the seven types of monitoring facilities is intact. The corresponding relationship between the hazardous process and the monitoring failure rate correction coefficient is shown in Table 4.

[0074] Table 4 Correspondence between dangerous processes and monitoring failure rate correction coefficients

[0075]

[0076] (5) The quantitative characteristic of the inherent risk index of dangerous operations is the dangerous operation hazard correction coefficient K2, which is determined according to the total number of high-risk operations in the tailings pond; high-risk operations include special operations, dangerous operations and special equipment operations. In this embodiment, high-risk operations are special operations, including safety inspection, tailings pond ore discharge, tailings pond damming and tailings pond drainage. Among them, the tailings pond has safety inspection and tailings pond drainage operations during the assessment. The corresponding relationship between dangerous operations and dangerous operation hazard correction coefficients is shown in Table 5.

[0077] Table 5 Correspondence between dangerous operations and dangerous operation hazard correction coefficients

[0078]

[0079] S2. Determine the coordination between inherent risk, initial risk, actual risk characteristics and system attribute risk structure; specifically, the following steps are included:

[0080] (1) Quantification of risk severity of inherent risk indicators of tailings dam system: Using the improved DOW method to determine the quantitative method of "five key parameters" in dangerous places, dangerous processes, dangerous equipment, dangerous goods and dangerous operations, the risk severity of inherent risk indicators of tailings dam system can be quantified;

[0081] (2) Quantification of initial risk assessment parameters: With risk control indicators as the center, obtain initial assessment parameters; construct a risk point accident tree through risk causal logic symbols, use fuzzy fault tree analysis (FFTA) to obtain the fuzzy language of complex system risk factors, predict the probability and causal relationship of basic events, provide the dynamic Bayesian network (DBN) with the root node prior probability, and update the probability of events with accompanying risk changes; combine the dynamic Bayesian network (DBN) with the fuzzy fault tree analysis (FFTA) model to realize the method of accident scenario simulation with graphical and digital display, use the DBN-FFTA probabilistic risk assessment method to determine the initial risk probability, and then determine the risk control frequency index based on the initial risk probability;

[0082] (3) Aggregate the inherent risk severity index and the risk control frequency index into the initial risk of the risk point: Determine the key dynamic risk indicator correction method based on the characteristic value of the monitoring project, dynamically correct the initial risk, and integrate the real risk assessment model after dynamic and timely correction to achieve timely risk assessment that couples system attributes with management status.

[0083] S3. Divide major risk indicators into inherent risk indicators, control risk indicators and dynamic risk indicators, and build a timely safety risk assessment system that combines dynamic and static aspects. The specific steps include:

[0084] (1) Taking the dangerous places, dangerous processes, dangerous equipment, dangerous goods and dangerous operations in the tailings dam system as risk points, determine the inherent risks of the tailings dam system;

[0085] (2) Initial risk refers to the risk after the existing safety protection measures are established on the basis of the inherent risk of the tailings dam system. Based on the inherent risk of the tailings dam system, the risk control frequency index generated in the management and control process is introduced to correct the inherent risk, which is determined as the initial risk;

[0086] (3) Aggregate initial risk and dynamic risk to form real risk.

[0087] S4. Quantify the characteristics of various major risk indicators in step S3, set risk levels, and build a realistic risk assessment model that integrates the inherent risk and dynamic risk of the "dam break" risk point; specifically, the following steps are included:

[0088] (1) Calculation method for constructing the inherent risk index h of major risk points:

[0089] h=h s MEK1K2

[0090] In the formula, h sIt represents the hazard index of dangerous equipment, with a value of 1 to 1.7; M represents the hazard index of dangerous goods, with a value of 1 to 9; E represents the index of exposed personnel in dangerous places, with a value of 1 to 9; K1 refers to the correction coefficient of monitoring failure rate, K1=1+p, p is the average value of the failure rate of monitoring facilities; K2 refers to the correction coefficient of the hazard of dangerous operations, K2=1+0.05q, q is the total number of high-risk operation types involved in the risk point;

[0091] Then, according to the early warning signal coefficient K3 of the online monitoring characteristic indicator, the inherent risk h of the major risk point is dynamically corrected to determine the dynamic correction value of the inherent hazard index of the major risk point:

[0092] H=hK3

[0093] (2) Establish an initial risk index and use the dynamic correction value of the inherent risk index of major risk points to describe the initial risk. The functional relationship between the initial risk index and the inherent risk index is:

[0094] R0=H×G

[0095] In the formula, R0 represents the initial risk index; H represents the dynamic correction value of the inherent risk index of major risk points; G is the risk control frequency index of major risk points;

[0096] (3) Determine the actual risk model of major risk points. The actual risk can be expressed by the function:

[0097] R=R0×K m

[0098] In the formula, R0 represents the initial risk index; K m represents the dynamic risk correction coefficient, and its value is m=3, 4, 5, ..., 8, where K3 is the early warning signal coefficient of the online monitoring characteristic index, K4 is the dynamic accident hazard index correction coefficient, K5 is the special period index correction coefficient, K6 is the natural environment index correction coefficient, K7 is the early warning and disposal index correction coefficient, and K8 is the comprehensive management index correction coefficient;

[0099] (4) Determine the risk control frequency index of major risk points, which can be expressed as:

[0100] G=1+P

[0101] Where P is the probability of major risk point accident events;

[0102] (5) Dynamic risk correction. The specific steps are as follows:

[0103] Step 1: Use the online monitoring characteristic indicator warning signal coefficient K3 to correct the inherent risk index of major risk points. The real-time alarm of the online monitoring project is divided into level 1 alarm (low alarm), level 2 alarm (medium alarm) and level 3 alarm (high alarm). When the online monitoring project reaches 3 level 1 alarms, it is recorded as 1 level 2 alarm; when the monitoring project reaches 2 level 2 alarms, it is recorded as 1 level 3 alarm; set the corresponding base of the online monitoring characteristic indicator warning signal coefficient K3 to 1.20, and the expression of the online monitoring characteristic indicator warning signal coefficient is:

[0104] K3=1.20 (a+2b+3c)

[0105] Where: a is the number of first-level alarms, with values ​​of a=0, 1, 2, 3; b is the number of second-level alarms, with values ​​of b=0, 1, 2; c is the number of third-level alarms, with values ​​of c=0, 1;

[0106] Step 2: Use other dynamic indicators to correct the initial risk index of the risk point, including dynamic accident hazard index correction, special period index correction, natural environment index correction, early warning and disposal index correction, and comprehensive management index correction. In this embodiment, other dynamic indicators are used to correct the initial risk index of the risk point as follows:

[0107] A. Correction of dynamic accident hazard indicators:

[0108] According to the accident hazard data reported by the enterprise, if there are only general accident hazards and no major hazards, the initial risk index R0 of the tailings pond system shall be corrected according to the 100 accident investigation standards and the number of hazards: if the number of hazards is between 1 and 5, the dynamic accident hazard index correction coefficient K4 = 20; if the number of hazards is between 6 and 20, the dynamic accident hazard index correction coefficient K4 = 1.40; if the number of hazards exceeds 20 times, the major accident hazard correction rules shall be followed.

[0109] As long as there is a major accident hazard, the dynamic index control coefficient k is directly used. i Modify the initial risk index R0 and the dynamic index control coefficient k i See Table 6 for the values.

[0110] B. Correction of indicators during special periods:

[0111] Special period indicators refer to statutory holidays, national or local important events, etc. Special period indicator correction coefficient K5 adopts dynamic indicator correction coefficient k i Value, k i See Table 6 for the values.

[0112] C. Natural environment index correction:

[0113] The natural environment index correction coefficient adopts the dynamic index correction coefficient ki Take the value, using K6=k i Modified initial risk index R0, see Table 6.

[0114] D. Revision of early warning and disposal indicators:

[0115] If a yellow warning message appears, the enterprise does not handle it within 24 hours; if an orange warning message appears, the enterprise does not handle it within 12 hours; the correction coefficient of the warning and disposal index adopts the dynamic index correction coefficient k i Take the value, using K7=k i Modified initial risk index R0,k i See Table 6 for the values.

[0116] E. Comprehensive governance index revision:

[0117] When enterprises adopt comprehensive management of tailings ponds by closing and disposing of them, the risk of tailings pond system will be significantly reduced. The correction coefficient of comprehensive management index adopts dynamic index correction coefficient k. c Take the value, using K7=k c Reduce the initial risk index R0, k c See Table 6 for the values.

[0118] Table 6 Dynamic risk index correction coefficient values

[0119]

[0120] Among them, i represents the number of times other dynamic indicators are adjusted. Direct adjustment more than three times is a major risk.

[0121] F. The direct adjustment of files when more than three indicators are revised at the same time is a major risk.

[0122] S5. Perform risk assessment on the collected risk source data through the timely safety risk assessment system in step S3 and the actual risk assessment model in step S4 to determine the actual risk level.

[0123] Determining the realistic risk level involves the following steps:

[0124] (1) The safety risk after risk control index adjustment and dynamic risk index adjustment is regarded as the actual risk of the final dam break event;

[0125] (2) The actual safety risk level of the tailings dam breach system is divided into Level I, Level II, Level III and Level IV;

[0126] (3) Obtain the risk values ​​of different states at each stage, and perform quantitative processing and inference calculations to determine the corresponding risk levels.

[0127] In this embodiment, the correspondence between the actual risk index and the actual safety risk level of the tailings dam system is shown in Table 7.

[0128] Table 7 Correspondence table of actual risk index and actual safety risk level

[0129]

Claims

1. A method for reconstructing and evaluating the safety risk cognitive model of a tailings dam system, characterized in that The following steps are involved: S1. According to the disaster-causing factors of the tailings dam system's inherent safety risk and major accidents, determine the major risk points of the tailings dam system's safety risk. Major risk points include dangerous places, dangerous processes, dangerous equipment, dangerous goods and dangerous operations, referred to as "five highs", and then set the quantitative characteristics of the "five highs" inherent risk indicators; Inherent risk refers to the energy inherent in the equipment, facilities and sites themselves; S2. Determine the coordination between inherent risk, initial risk and actual risk characteristics and system attribute risk structure; S3. Divide major risk indicators into inherent risk indicators, control risk indicators and dynamic risk indicators, and build a timely safety risk assessment system that combines dynamic and static aspects; S4, quantify the characteristics of various major risk indicators in step S3, and set risk levels to build a realistic risk assessment model that integrates the inherent risk and dynamic risk of the "dam break" risk point; S5. Perform risk assessment on the collected risk source data through the timely safety risk assessment system in step S3 and the actual risk assessment model in step S4 to determine the actual risk level.

2. The method for reconstructing and evaluating the safety risk cognitive model of the tailings dam system according to claim 1 is characterized in that The quantitative characteristics of the "five highs" inherent risk indicators set in step S1 are specifically: (1) The quantitative characteristic of the inherent risk index of dangerous equipment is the dangerous equipment hazard index h s The hazard index h of dangerous equipment is determined based on the tailings dam construction form, storage method and tailings type. s ; (2) The quantitative characteristic of the inherent risk index of dangerous goods is the dangerous goods hazard index M, which is determined by the potential energy characteristics of the stored materials in the risk point reservoir. According to the classification method of tailings pond design level, the dangerous goods hazard index M is determined by the classification results of the current storage capacity and the current dam height; (3) The quantitative characteristic of the inherent risk index of hazardous sites is the exposed personnel index E of hazardous sites, which is determined based on the number of exposed personnel within 1 km downstream of the tailings pond and the number of exposed personnel working in the tailings pond; (4) The quantitative characteristic of the inherent risk index of hazardous processes is the monitoring failure rate correction coefficient K1, which is determined based on the tailings pond infiltration line, dam displacement, reservoir water level, precipitation and the failure rate of monitoring facilities of video surveillance; (5) The quantitative characteristic of the inherent risk index of hazardous operations is the hazardous operation hazard correction coefficient K2, which is determined based on the total number of high-risk operation types in the tailings pond; high-risk operations include special operations, dangerous operations and special equipment operations.

3. The method for reconstructing and evaluating the safety risk cognitive model of the tailings dam system according to claim 1 is characterized in that The coordination method between risk structures in step S2 includes the following steps: (1) Quantification of risk severity of inherent risk indicators of tailings dam system: Using the improved DOW method to determine the quantitative characteristics of inherent risk indicators in hazardous places, hazardous processes, hazardous equipment, hazardous goods and hazardous operations, the risk severity of inherent risk indicators of tailings dam system can be quantified; (2) Quantification of initial risk assessment parameters: With risk control indicators as the center, obtain initial assessment parameters; construct a risk point accident tree through risk causal logic symbols, use fuzzy fault tree analysis (FFTA) to obtain the fuzzy language of complex system risk factors, predict the probability and causal relationship of basic events, provide the dynamic Bayesian network (DBN) with the root node prior probability, and update the probability of events with accompanying risk changes; combine the dynamic Bayesian network (DBN) with the fuzzy fault tree analysis (FFTA) model to realize the method of accident scenario simulation with graphical and digital display, use the DBN-FFTA probabilistic risk assessment method to determine the initial risk probability, and then determine the risk control frequency index based on the initial risk probability; (3) Aggregate the inherent risk severity index and the risk control frequency index into the initial risk of the risk point: Determine the key dynamic risk indicator correction method based on the characteristic value of the monitoring project, dynamically correct the initial risk, and integrate the real risk assessment model after dynamic and timely correction to achieve timely risk assessment that couples system attributes with management status.

4. The method for reconstructing and evaluating the safety risk cognitive model of the tailings dam system according to claim 1 is characterized in that The construction of a dynamic and static combined timely safety risk assessment system in step S3 includes the following steps: (1) Taking the dangerous places, dangerous processes, dangerous equipment, dangerous goods and dangerous operations in the tailings dam system as risk points, determine the inherent risks of the tailings dam system; (2) Initial risk refers to the risk after the existing safety protection measures are established on the basis of the inherent risk of the tailings dam system. Based on the inherent risk of the tailings dam system, the risk control frequency index generated in the management and control process is introduced to correct the inherent risk, which is determined as the initial risk; (3) Aggregate initial risk and dynamic risk to form real risk.

5. The method for reconstructing and evaluating the safety risk cognitive model of the tailings dam system according to claim 1 is characterized in that Setting the risk level in step S4 to construct a realistic risk assessment model that integrates the inherent risk and dynamic risk of the "dam break" risk point includes the following steps: (1) Calculation method for constructing the inherent risk index h of major risk points: h=h s MEK1K2 In the formula, h s It represents the hazard index of dangerous equipment, with a value of 1 to 1.7; M represents the hazard index of dangerous goods, with a value of 1 to 9; E represents the index of exposed personnel in dangerous places, with a value of 1 to 9; K1 refers to the correction coefficient of monitoring failure rate, K1=1+p, p is the average value of the failure rate of monitoring facilities; K2 refers to the correction coefficient of the hazard of dangerous operations, K2=1+0.05q, q is the total number of high-risk operation types involved in the risk point; Then, according to the early warning signal coefficient K3 of the online monitoring characteristic indicator, the inherent risk h of the major risk point is dynamically corrected to determine the dynamic correction value of the inherent hazard index of the major risk point: H=hK3 (2) Establish an initial risk index and use the dynamic correction value of the inherent risk index of major risk points to describe the initial risk. The functional relationship between the initial risk index and the inherent risk index is: R0=H×G In the formula, R0 represents the initial risk index; H represents the dynamic correction value of the inherent risk index of major risk points; G is the risk control frequency index of major risk points; (3) Determine the actual risk model of major risk points. The actual risk can be expressed by the function: R=R0×K m In the formula, R0 represents the initial risk index; K m represents the dynamic risk correction coefficient, and its value is m=3, 4, 5, ..., 8, where K3 is the early warning signal coefficient of the online monitoring characteristic index, K4 is the dynamic accident hazard index correction coefficient, K5 is the special period index correction coefficient, K6 is the natural environment index correction coefficient, K7 is the early warning and disposal index correction coefficient, and K8 is the comprehensive management index correction coefficient; (4) Determine the risk control frequency index of major risk points, which can be expressed as: G=1+P Where P is the probability of major risk point accident events; (5) Dynamic risk correction. The specific steps are as follows: Step 1: Use the online monitoring characteristic indicator warning signal coefficient K3 to correct the inherent risk index of major risk points. The real-time alarm of the online monitoring project is divided into level 1 alarm (low alarm), level 2 alarm (medium alarm) and level 3 alarm (high alarm). When the online monitoring project reaches 3 level 1 alarms, it is recorded as 1 level 2 alarm; when the monitoring project reaches 2 level 2 alarms, it is recorded as 1 level 3 alarm; set the corresponding base of the online monitoring characteristic indicator warning signal coefficient K3 to 1.20, and the expression of the online monitoring characteristic indicator warning signal coefficient is: K3＝1.20 (a+2b+3c) Where: a is the number of first-level alarms, with values ​​of a=0, 1, 2, 3; b is the number of second-level alarms, with values ​​of b=0, 1, 2; c is the number of third-level alarms, with values ​​of c=0, 1; Step 2: Use other dynamic indicators to correct the initial risk index of major risk points, including dynamic accident hazard indicator correction, special period indicator correction, natural environment indicator correction, early warning and disposal indicator correction and comprehensive management indicator correction.

6. The method for reconstructing and evaluating the safety risk cognitive model of the tailings dam system according to claim 1 is characterized in that In step S5, risk assessment is performed on the collected risk source data to determine the actual risk level, including the following steps: (1) The safety risk after risk control index adjustment and dynamic risk index adjustment is regarded as the actual risk of the final dam break event; (2) The actual safety risk level of the tailings dam breach system is divided into Level I, Level II, Level III and Level IV; (3) Obtain the risk values ​​of different states at each stage, and perform quantitative processing and inference calculations to determine the corresponding risk levels.

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