Cross-system risk assessment method and system for urban safety

Abstract

The invention discloses a cross-system risk assessment method and system for urban safety, and relates to the technical field of urban risk assessment. The method comprises the steps of based on multi-source data fusion processing of various urban risk units, through accident case statistics, standard specification carding and mechanism model research, extracting safety operation sign indexes of the risk units, forming a risk unit sign index library, and further combining safety status, risk trend and prevention and control response analysis to establish a risk unit health file; based on the risk unit health file, through risk unit / risk event risk transfer analysis, taking the risk unit or the event as a node to construct a multi-risk unit / risk event causal map; through causal chain multi-dimensional analysis, risk unit / risk event cross-system coupling risks are evaluated, and causal atlas key node identification and regional comprehensive risk evaluation are realized based on a coupling risk analysis result; and finally, urban risk visual display is realized in combination with MIS and GIS technologies. According to the method, the urban emergency risk analysis, assessment and prediction capabilities can be effectively improved, the resource allocation and disposal decision can be effectively optimized in combination with the emergency disposal, and the urban emergency response capability is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of urban risk assessment, and in particular to a cross-system risk assessment method and system for urban safety. Background Art

[0002] In the context of the accelerated global urbanization process, cities, as complex systems of economic activities, social interactions and ecological environment, are facing unprecedented challenges in terms of security, stability and sustainable development. With the rapid development of information technology, the interconnection between various systems within cities, such as transportation, energy, and public safety, has become increasingly stronger, forming a highly coupled and interdependent complex network system. Although this high degree of integration has greatly improved the efficiency and service level of urban operations, it also means that risks or failures within any single system may quickly spread to other systems, triggering a chain reaction and causing significant losses and crises across systems.

[0003] Focusing on urban disasters, cross-system accidents, and coupled risks, the invention patent with the patent publication number CN114186772A discloses a method for predicting gas leakage risks under the coupling of multiple disasters. The patent refines the factor granularity of the multi-disaster coupling events of rainstorm-ground subsidence-gas pipeline leakage, and combines the coupling relationship analysis of multi-disaster dangerous factors to predict the leakage risk under the coupling of multiple disasters. The invention patent with the patent publication number CN118261425A discloses a multi-disaster risk assessment method based on multi-model coupling. The patent uses the SINMAP model and the HAND model to analyze the landslide and flood inundation areas, and through the superposition of risk zoning maps, combines the vulnerability and exposure of the disaster-bearing body to conduct a comprehensive assessment of multi-disaster risks. The invention patent with patent publication number CN118246745A discloses a data-driven natural disaster coupling risk assessment and mapping method. This patent obtains the geological conditions of the area to be assessed and predicts the weather conditions in the future, combines historical natural disaster information for comparative analysis, and assesses the types and risk levels of possible natural disasters. It also assesses the multi-disaster coupling risk by superimposing the results of different disaster risks.

[0004] However, although the above-mentioned existing technologies all take into account the impact of multiple factors on the risk of the analysis object, the actual assessment process tends to be single-system, and the analysis of multiple disasters is mostly focused on the superposition analysis of different disaster results, and does not involve factors such as mutual driving, amplification, and inhibition between different emergencies. It is difficult to assess the comprehensive risk under the coupling of cross-systems and multiple emergencies in complex scenarios within the city. Summary of the invention

[0005] The technical problem to be solved by the present invention is: in view of the difficulty and lack of methods in cross-system comprehensive risk assessment under the conditions of a large number of risk units in complex urban scenarios and frequent coupled cascade accidents, a new cross-system comprehensive risk assessment method for urban safety is proposed.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] A cross-system risk assessment approach for urban safety, including:

[0008] S10, through the aggregation, fusion and processing of multi-source data of various risk units in the city, a risk unit database based on multi-source data fusion is constructed;

[0009] S20, based on the investigation of accident cases corresponding to risk units, the sorting out of standards and specifications, and the research on mechanism models, extract various risk unit signs and indicators, and build a database of urban risk unit signs and indicators;

[0010] S30, based on the risk unit physical sign index library, through data matching of the risk database of the area / object to be analyzed, combined with the safety status, risk trend, and protection response analysis of physical sign indicators, a health profile of the risk unit is constructed;

[0011] S40, based on the health records of risk units, through the analysis of disaster-causing / disaster-bearing / response indicators, combined with risk transfer analysis, with risk units or events as nodes, to construct a causal map of multiple risk units / risk events;

[0012] S50, based on the risk unit / risk event causal map, conduct cross-system coupling risk assessment of risk units / risk events;

[0013] S60, based on the coupled risk assessment results, realize the identification of key nodes in the risk unit / risk event causal graph and regional comprehensive risk assessment.

[0014] In one embodiment of the present invention, the acquired risk unit multi-source data is first subjected to data standardization before being used to construct the risk unit database; and

[0015] Multi-source data types include: risk unit design, operation, monitoring, maintenance, and repair data, as well as economic, social, geographic, environmental, safety, and resource data;

[0016] The risk unit database is based on multi-source data of risk units and is constructed through data classification and hierarchical construction. The database content includes risk unit classification data, risk unit basic data, risk unit risk management data, and social / environmental dynamic / static data.

[0017] In one embodiment of the present invention, the risk unit classification includes but is not limited to: public infrastructure unit, densely populated area unit, social activity area unit, material storage area unit, industrial and mining enterprise unit, public safety key unit unit, natural disaster prone area unit, environmental protection sensitive area unit, construction activity area unit;

[0018] The basic data types of risk units include, but are not limited to: structural, functional, geographical, environmental, economic, social, safety, resource, and statistical information data;

[0019] Risk management data types of risk units include but are not limited to: hidden danger investigation data, hidden danger control data, patrol inspection data, monitoring and early warning data, inspection and testing data, maintenance data, historical maintenance records, and renovation plans;

[0020] Social / environmental dynamic / static data types include, but are not limited to: third-party construction data, social activity data, meteorological condition data, natural environment data, geological disaster data, building / structure data, and hazard protection target data.

[0021] In one embodiment of the present invention, the types of physical sign indicators include: risk unit design, operation, inspection, testing, monitoring, maintenance, repair, renewal, transformation and protection indicators;

[0022] The methods for extracting risk unit sign indicators include: extracting sign indicators based on emergency cause analysis, extracting sign indicators based on standard and specification hidden danger definitions, extracting sign indicators based on accident management experience, and extracting sign indicators based on accident mechanism research.

[0023] In one embodiment of the present invention, based on the health records of risk units, the consequences of emergencies are analyzed by evaluating the mutual evolution and coupling of the abnormal and deviated physical indicators of each risk unit and combining the disaster-causing mechanism of the risk unit; wherein,

[0024] The abnormal and deviation of physical sign indicators of each risk unit are as follows: physical sign indicators exceed the normal safety threshold range;

[0025] The mutual evolution and coupling of physical sign indicators under abnormal and deviated states are: the process in which physical sign indicators under abnormal and deviated states lead to emergencies through their own evolution and the interaction with other physical sign indicators;

[0026] The disaster-causing mechanism of risk units is as follows: a risk unit disaster prediction model based on data-driven, the data is the mutually evolving and coupled data of the abnormal and deviated physical indicators of each risk unit;

[0027] The consequences of sudden incidents and accidents include the content, scope of impact, intensity of impact, predicted number of casualties, economic losses, level of social impact, type / number / density of protection targets, environmental impact, and impact on urban operation security.

[0028] In one embodiment of the present invention, risk cross-system transfer refers to the process in which a disaster-causing risk unit affects adjacent risk units through material transfer / contact, energy conduction / impact, and information disturbance / interruption after the disaster occurs;

[0029] The analysis of the risk transfer process across systems includes: risk unit disaster bearing analysis, risk unit disaster causing analysis, and emergency measures effectiveness analysis;

[0030] The risk unit disaster resistance analysis method is prioritized based on "quantitative, semi-quantitative, and qualitative", including quantitative judgment of disaster intensity and disaster resistance threshold based on mechanism model, semi-quantitative evaluation based on facility exposure, semi-quantitative and qualitative evaluation of disaster resistance damage based on disaster intensity, vulnerability, and hidden danger level, and semi-quantitative and qualitative evaluation based on experience / statistical data;

[0031] The risk unit disaster analysis method is prioritized based on "quantitative, semi-quantitative, and qualitative", including disaster intensity and impact field analysis based on mechanism models, semi-quantitative and qualitative evaluation of disaster intensity based on risk unit size and type, and semi-quantitative and qualitative evaluation based on experience / statistics;

[0032] The effectiveness analysis of emergency measures is to modify the risk transfer path and process. When emergency measures exist in the process of risk transfer across systems, the probability of risk transfer across systems is modified, including the situation of terminating the risk transfer across systems.

[0033] In one embodiment of the present invention, based on the "quantitative, semi-quantitative, and qualitative" risk cross-system transfer process analysis of risk units with multi-source disaster bearing, multiple disaster causes, and prevention and control responses, the risk transfer paths and evolution rules of many risk units within the regional scope are constructed, with risk units / risk events as nodes, rules and risk transfer probabilities as edges, and through node connection, a cross-system risk evolution causal map covering many risk units within the regional scope is formed; and the transfer rules are characterized by probability, and the probability range is normalized to [0,1].

[0034] In one embodiment of the present invention, the causal graph has an end point of evolution; and the transition rules of the end point include:

[0035] Set a probability threshold. When the node probability is less than the set probability threshold, it will no longer evolve downstream.

[0036] Set a specific event node. After a specific event is generated, it will no longer evolve downstream.

[0037] Set a time threshold. When the time exceeds a certain limit, the system will no longer evolve downstream.

[0038] Set the regional scope. When the node influence range / downstream affected facilities exceed the regional scope, they will no longer evolve downstream.

[0039] In one embodiment of the present invention, the coupling risk of each risk unit / risk event in the divided area is evaluated in the following manner:

[0040] Coupling risk R couple = {Coupling risk probability P couple , coupling risk consequences C couple};in,

[0041] Coupling risk probability P couple Calculation refers to the process of taking the risk unit / risk event to be evaluated as the end point, obtaining all evolution chains upstream of the risk unit / risk event, and evaluating the coupling risk probability of the risk unit / risk event based on the initial event and the entire causal chain of the risk unit / risk event;

[0042] Coupling risk consequences C couple It refers to the process of taking the risk unit / risk event to be assessed as the starting point, obtaining all the evolution chains of the risk unit / risk event downstream, and conducting a comprehensive assessment of the consequences of coupled risks based on the causal chain of direct disaster-causing events and secondary derivative events that may be caused by the risk unit / risk event.

[0043] In one embodiment of the present invention, the coupling risk probability P couple The acquisition process includes:

[0044] 1) For all upstream causal chains of risk units / risk events, if there are no common nodes between chains except the node of the risk unit / risk event, the coupling risk probability P of the risk unit / risk event is couple Comprehensive calculation based on the risk evolution probability results of each chain;

[0045] 1. If each link can cause the occurrence of node N of the risk unit / risk event independently, then considering the occurrence probability of the starting node, the occurrence coupling risk probability of node N of the risk unit / risk event is:

[0046] p N =1-(1-P x1 ×ΠP xi-xj )×(1-P y1 ×ΠP yi-yj )×(1-P z1 ×ΠP zi-zj )...(1-P M1×ΠP Mi-Mj ), (1);

[0047] In the formula, p N The occurrence coupling risk probability of node N represented as risk unit / risk event, P x1 , P y1 , P z1 , P M1 It is expressed as the coupling risk probability of the initial node in the link x, y, z...M, P xi-xj , P yi-yj , P zi-zj , ..., P Mi-Mj It is expressed as the probability between the connected nodes i and j in each link, ΠP xi-xj ,ΠP yi-yj ,ΠP zi-zj , ..., ΠP Mi-Mj It is expressed as the risk transfer probability from the initial node to the risk unit / risk event in each link;

[0048] And, the inter-node transfer probability under different links between two nodes is also calculated using formula (1);

[0049] Second, when there are multiple links acting together or a single link acting, resulting in the occurrence of node N, the occurrence probability of the starting node is considered, and the occurrence coupling risk probability of node N of the risk unit / risk event is:

[0050]

[0051] In the formula, 1-P z1 ×ΠP zi-zj ×P a1 ×ΠP ai-aj ×...×P A1 ×ΠP Ai-Aj It is expressed as the probability of node N occurring when link Z and link a work together until link A;

[0052] 2) For all upstream causal chains of the risk unit / risk event, if there are other common nodes between the chains except the node of the risk unit / risk event, the chains with common pointing nodes are aggregated from the starting point to the back, and the probability calculation method of the common nodes after aggregation is consistent with the method described in situation "1)"; through the aggregation of multiple chains, it is finally formed that multiple single chains point to the node of the risk unit / risk event to be risked, and the method described in situation "1)" is used again to calculate the coupling risk probability P of the risk unit / risk event. couple Comprehensive calculation.

[0053] In one embodiment of the present invention, based on the causal graph, the risk unit / risk event node N is taken as the analysis object, and the risk units / risk event nodes of a single chain m downstream, 1, 2, 3, 4...n, for the i-th chain node downstream of node N, the probability that the upstream node causes a certain risk to occur in the node, the accident-caused disaster consequence assessment result C i , from back to front, the risk consequences of the risk unit / risk event N coupling are evaluated, and the calculation rules are as follows:

[0054]

[0055] In the formula, C N,m,x1 , C N,m,x2 , C N,m,x3 , C N,m,x4 , C N,m,x5 They are respectively the evaluation results of personnel injury x1, economic loss x2, operation guarantee x3, ecological environment x4, and social impact x5 of node n under chain m. K1, K2, K3, K4, and K5 are the scores of direct consequence indicators. The sum of K1 to K5 is 1. C N,m It is expressed as the average coupled risk consequence of risk unit / risk event N on other risk units / risk events in the region;

[0056] If there are J risk transfer chains with node N as the starting point in the causal graph, then based on the consequence analysis of each single chain, the coupled risk consequence C of the risk unit / risk event node N is N Calculated as:

[0057]

[0058] Where H represents the number of risk units / risk events associated with the downstream chain of node N, and C N,x1 , C N,x2 , C N,x3 , C N,x4 , C N,x5 They are respectively represented as the coupled risk consequence assessment values ​​in the direct consequence indicators x1, x2, x3, x4, and x5 of the risk unit / risk event node N.

[0059] The present invention also provides a cross-system risk assessment system for urban safety, which uses the above-mentioned cross-system risk assessment method for urban safety, including:

[0060] The database module is used to aggregate, fuse and process multi-source data of various risk units in the city to build a risk unit database based on multi-source data fusion;

[0061] The indicator library module is used to extract various risk unit signs indicators based on the investigation of risk unit corresponding accident cases, the sorting of standards and specifications, and the research of mechanism models, and to build an urban risk unit sign indicator library;

[0062] The health record module is used to build a risk unit health record based on the risk unit physical sign index library, by matching the data in the risk database of the area / object to be analyzed, and combining the safety status, risk trend, and protection response analysis of physical sign indicators;

[0063] The causal map module is used to construct a causal map of multiple risk units / risk events based on the health records of risk units, through the analysis of disaster-causing / disaster-bearing / response indicators, combined with risk transfer analysis, with risk units or events as nodes;

[0064] The cross-system coupling risk assessment module is used to conduct cross-system coupling risk assessment of risk units based on the risk unit or the corresponding risk event causal map;

[0065] The key node and regional risk assessment module is used to identify key nodes in the risk unit / risk event causal map and conduct regional comprehensive risk assessment based on the coupled risk assessment results.

[0066] Compared with the prior art, the present invention has the following beneficial effects:

[0067] The present invention takes the urban risk unit as the analysis object, extracts the safe operation signs of the risk unit and constructs the health record, and combines the risk transfer analysis to construct the risk unit causal map; further, based on the risk unit / risk event coupling risk analysis, a comprehensive assessment of the cross-system risks within the region is conducted, and the regional

[0068] The key risk units of cross-system risks in the domain are analyzed and extracted; finally, the health records and risk assessment and response visualization are carried out in combination with MIS and GIS systems. The present invention can effectively improve the risk analysis, assessment and prediction capabilities of urban emergencies, and can effectively optimize resource allocation and disposal decisions in combination with emergency response, thereby improving the city's emergency response capabilities.

[0069] The risk unit coupling risk assessment and regional comprehensive risk assessment based on causal graphs fully consider the cross-system risk transfer process between risk units and combine with real-time information access to achieve comprehensive, dynamic analysis and assessment of cross-system risks of risk units and regions, providing theoretical support for regional risk prevention and control.

[0070] By extracting vital signs of safe operation of risk units and constructing health records, combined with risk monitoring and testing, we can gain an intuitive understanding of the risk status of risk units, which is conducive to carrying out targeted risk prevention and control of risk units.

[0071] The accident causality map constructed based on the analysis of risk unit disaster-causing, disaster-bearing and response can intuitively display the interaction between risk units within the region and the development and evolution of risk events.

[0072] The extraction of key nodes of coupled risks based on risk units and complex networks can provide support for regional risk governance. Through risk prevention and control of key nodes, the transfer and coupling of regional cross-system risks can be prevented / inhibited, effectively reducing the regional risk level. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 This is a flow chart of a cross-system risk assessment method for urban safety according to an embodiment of the present invention.

[0074] Figure 2 This is a diagram of an example of coupling probability calculation according to an embodiment of the present invention.

[0075] Figure 3 FIG. 4 is another example diagram of coupling probability calculation according to an embodiment of the present invention.

[0076] Figure 4 This is a probability aggregation calculation graph of the causal evolution chain according to an embodiment of the present invention.

[0077] Figure 5 This is another example diagram of coupling probability calculation according to an embodiment of the present invention.

[0078] Figure 6 This is another example diagram of coupling probability calculation according to an embodiment of the present invention.

[0079] Figure 7 The present invention is a block diagram of a cross-system risk assessment system for urban safety according to an embodiment of the present invention. DETAILED DESCRIPTION

[0080] In order to facilitate those skilled in the art to understand the technical solution of the present invention, the technical solution of the present invention is further described in conjunction with the accompanying drawings of the specification.

[0081] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0082] See also Figure 1 The present invention provides a cross-system risk assessment method for urban safety, comprising:

[0083] S10, through the aggregation, fusion and processing of multi-source data of various risk units in the city, a risk unit database based on multi-source data fusion is constructed.

[0084] In one embodiment of the present invention, a risk unit database based on multi-source data fusion is constructed by aggregating, merging and processing urban risk unit data. Before the acquired multi-source data is used to construct the risk unit database, data standardization is first performed.

[0085] Among them, multi-source data types include but are not limited to: risk unit design, operation, monitoring, maintenance, and repair data, as well as economic, social, geographical, environmental, safety, and resource data.

[0086] The division of risk units includes, but is not limited to: public infrastructure, densely populated areas, social activity areas, material storage areas, industrial and mining enterprises, key public safety units, areas prone to natural disasters, environmental protection sensitive areas, and construction activity areas.

[0087] The risk unit database is based on multi-source data of risk units and is constructed through data classification and hierarchical construction. The database content includes risk unit classification data, risk unit basic data, risk unit risk management data, and social / environmental dynamic / static data.

[0088] Among them, the risk unit database is based on multi-source data of risk units, and is constructed through data classification and hierarchical construction. The data content includes risk unit classification, risk unit basic data, risk unit risk management data, and social / environmental dynamic / static data; among them,

[0089] Risk unit classification data include but are not limited to: public infrastructure units, densely populated areas units, social activity areas units, material storage areas units, industrial and mining enterprise units, public safety key units units, natural disaster prone areas units, environmental protection sensitive areas units, construction activity areas units;

[0090] The basic data types of risk units include, but are not limited to: structural, functional, geographical, environmental, economic, social, safety, resource, and statistical information data;

[0091] Risk management data types of risk units include but are not limited to: hidden danger investigation data, hidden danger control data, patrol inspection data, monitoring and early warning data, inspection and testing data, maintenance data, historical maintenance records, and renovation plans;

[0092] Social / environmental dynamic / static data types include, but are not limited to: third-party construction data, social activity data, meteorological condition data, natural environment data, geological disaster data, building / structure data, and hazard protection target data.

[0093] S20, based on the investigation of accident cases corresponding to risk units, the sorting out of standards and specifications, and the research on mechanism models, extract the vital signs indicators of various risk units and build a database of vital signs indicators of urban risk units.

[0094] In one embodiment of the present invention, the risk unit sign indicator refers to an indicator that can reflect whether the risk unit is currently operating in a healthy state, and the abnormality of the sign indicator is a precursor to an emergency. The types of sign indicators include: risk unit design, operation, inspection, detection, monitoring, maintenance, overhaul, update, transformation and protection indicators. The selection method of the sign indicator includes but is not limited to the extraction of sign indicators based on the cause analysis of the emergency, the extraction of sign indicators based on the definition of hidden dangers in standard specifications, the extraction of sign indicators based on accident management experience, and the extraction of sign indicators based on accident mechanism research.

[0095] S30, based on the risk unit physical sign index library, through data matching of the risk database of the area / object to be analyzed, combined with the safety status, risk trend, and protection response analysis of physical sign indicators, a risk unit health profile is constructed.

[0096] In one embodiment of the present invention, a risk unit health profile is constructed based on a risk unit vital sign index library in combination with historical maintenance records, historical hidden danger inspection records, historical detection / monitoring data and dynamic trend analysis.

[0097] The health file of a risk unit is a comprehensive record and management of the health status of the risk unit. When constructing a health file, the extracted physical signs and indicators need to be integrated and classified to form a systematic file structure. The content of the health file includes but is not limited to the basic information of the risk unit, historical maintenance records, historical hidden danger investigation records, historical detection / monitoring data, and dynamic change trends of physical signs and indicators.

[0098] S40, based on the health records of risk units, through disaster-causing / disaster-bearing / response analysis of physical signs and indicators, combined with risk transfer analysis, takes risk units or events as nodes to construct a causal map of multiple risk units / risk events.

[0099] In one embodiment of the present invention, based on the health records of risk units, the consequences of sudden incidents and accidents are analyzed through the mutual evolution and coupling analysis of abnormal and deviated vital signs of risk units, combined with the disaster-causing mechanism of risk units.

[0100] Through the analysis of the types, impact fields, and impact intensity of the consequences of emergencies, combined with the risk unit's damage mechanism and transformation mechanism, the cross-system transfer process and transfer rules of risk are constructed. Furthermore, through a comprehensive analysis of the causal relationship of risk transfer between various risk units, a multi-risk unit / risk event causal map is constructed with risk units or events as nodes.

[0101] The safety operation vital signs indicators of each risk unit are abnormal or deviated, which means that the safety operation vital signs indicators exceed the normal safety threshold range.

[0102] The abnormal and deviated physical signs and indicators evolve and couple with each other, forming a process in which sudden events occur through their own evolution and interaction with other physical signs and indicators.

[0103] The disaster-causing mechanism of risk units is: a data-driven risk unit disaster prediction model, in which the data are the mutually evolving and coupled data of the abnormal and deviated vital signs of each risk unit.

[0104] The consequences of sudden incidents and accidents include the content, scope of impact, intensity of impact, predicted number of casualties, economic losses, level of social impact, type / number / density of protection targets, environmental impact, and impact on urban operation security.

[0105] Risk transfer across systems refers to the process in which a disaster-causing risk unit affects adjacent risk units through material transfer / contact, energy conduction / impact, and information disturbance / interruption after a disaster occurs.

[0106] The analysis of the risk transfer process includes but is not limited to the risk unit disaster damage analysis, risk unit damage-causing disaster analysis, and emergency measures effectiveness analysis.

[0107] The methods for risk unit disaster damage analysis are prioritized according to "quantitative, semi-quantitative, and qualitative", including but not limited to quantitative judgment of disaster intensity and disaster tolerance threshold based on mechanism model, semi-quantitative evaluation based on facility exposure degree, semi-quantitative and qualitative assessment of disaster damage based on disaster intensity, vulnerability and hidden danger degree, and semi-quantitative and qualitative assessment based on experience / statistical data.

[0108] The methods for risk unit damage disaster analysis are also prioritized based on "quantitative, semi-quantitative, and qualitative", including but not limited to disaster intensity and impact field analysis based on mechanism models, semi-quantitative and qualitative evaluation of disaster intensity based on risk unit size and type, and semi-quantitative and qualitative evaluation based on experience / statistics;

[0109] The effectiveness of emergency measures is the correction of the risk transfer path and process. When emergency measures exist in the transfer process, the risk transfer probability is corrected or even the risk transfer process is terminated.

[0110] Based on the "quantitative, semi-quantitative, and qualitative" risk cross-system transfer process analysis of risk units with multiple sources of disasters, multiple causes of disasters, and prevention and control responses, the risk transfer paths and evolution rules of many risk units within the region are constructed. Risk units / risk events are used as nodes, and rules and risk transfer probabilities are used as edges. Through node connection, a cross-system risk evolution causal map covering many risk units within the region is formed; and the transfer rules are characterized by probability, and the probability range is normalized to [0,1].

[0111] In addition, to avoid an infinite loop of the causal chain, the causal graph has an end point for evolution. The rules for the end point include but are not limited to probability thresholds. When the node probability is less than the set threshold, it will no longer evolve downstream; specific event nodes. After a specific event occurs, it will no longer evolve downstream; set time thresholds. When a specific time is exceeded, it will no longer evolve downstream; regional scope. When the node impact range / downstream affected facilities exceed the analysis scope, it will no longer evolve downstream. The specific event nodes include but are not limited to casualties, property losses, and social impacts.

[0112] In one embodiment of the present invention, based on the risk unit data in the divided areas, a multi-risk unit / risk event causal graph is constructed through steps S10 and S40.

[0113] The division of the areas may be done manually based on the risk scope and data content, or may be based on existing regional divisions, and the existing division types include but are not limited to administrative divisions, responsibility grids, communities, and streets.

[0114] In the probability representation of the causal graph of multiple risk units / risk events, if there is external information indicating that an emergency event corresponding to a certain node has occurred (such as a gas alarm or a personnel alarm), then the probability of occurrence of this node is 1. The upstream chain pointing to this node can be handled separately or in combination in the following ways according to the display and functional needs: (1) All are cut off; (2) Through event cause analysis, the cause of the event is obtained, and the probabilities of the nodes involved in the cause event in the causal chain are all modified to 1, and the causal chain of the non-cause event is cut off; (3) The upstream chain is virtualized and there is no probability representation.

[0115] S50, based on the risk unit / risk event causal map, conduct cross-system coupling risk assessment of risk units / risk events.

[0116] In one embodiment of the present invention, the coupling risk of each risk unit / risk event in the divided area is evaluated in the following manner:

[0117] Coupling risk R couple = {Coupling risk probability P couple , coupling risk consequences C couple};

[0118] In one embodiment of the present invention, the coupling risk probability P couple Calculation refers to the process of taking the risk unit / risk event to be evaluated as the end point, obtaining all evolution chains upstream of the risk unit / risk event, and evaluating the coupled risk probability of the risk unit / risk event based on the initial event and the entire causal chain of the risk unit / risk event.

[0119] Coupling risk consequences C couple It refers to the process of taking the risk unit / risk event to be assessed as the starting point, obtaining all the evolution chains of the risk unit / risk event downstream, and conducting a comprehensive assessment of the consequences of coupled risks based on the causal chain of direct disaster-causing events and secondary derivative events that may be caused by the risk unit / risk event.

[0120] In one embodiment of the present invention, the coupling risk probability P couple The acquisition process includes:

[0121] 1) All upstream causal chains of risk units / risk events. If there are no common nodes between chains except the node of the risk unit / risk event, the coupling risk probability P of the risk unit / risk event is couple Comprehensive calculation based on the risk evolution probability results of each chain;

[0122] 1. If each link can cause the occurrence of node N of the risk unit / risk event independently, then considering the occurrence probability of the starting node, the occurrence coupling risk probability of node N of the risk unit / risk event is:

[0123] p N =1-(1-P x1 ×ΠP xi-xj )×(1-P y1 ×ΠP yi-yj )×(1-P z1 ×ΠP zi-zj )...(1-P M1 ×ΠP Mi-Mj ), (1);

[0124] In the formula, p N The occurrence coupling risk probability of node N represented as risk unit / risk event, P x1 , P y1 , P z1 , P M1 It is expressed as the coupling risk probability of the initial node in the link x, y, z...M, P xi-xj , P yi-yj , P zi-zj , ..., P Mi-Mj It is expressed as the probability between the connected nodes i and j in each link, ΠP xi-xj,ΠP yi-yj ,ΠP zi-zj , ..., ΠP Mi-Mj It is expressed as the risk transfer probability from the initial node to the risk unit / risk event in each link. And the node transfer probability between two nodes under different links is also calculated using formula (1).

[0125] In this example, see Figure 2 As shown, for example: if the risk unit N has a chain as follows:

[0126] Chain x: x1→x2→x3→x4→N, the probability of connecting node i and node j is P xi-xj ;

[0127] Chain y: y1→y2→y3→y4→y5→N, the probability of connecting node i and node j is P yi-yj ;

[0128] Chain z: z1→z2→z3→N, the probability of connecting node i and node j is P zi-zj ;

[0129] Then the probability of risk evolution from each chain to the risk unit is:

[0130] The risk transfer probability of chain x from the initial node to the risk unit is calculated as ∏P xi-xj ;

[0131] The risk transfer probability of chain y from the initial node to the risk unit is calculated as ∏P yi-yj ;

[0132] The risk transfer probability of chain z from the initial node to the risk unit is calculated as ∏P zi-zj ;

[0133] Among them, ∏ represents the multiplication symbol.

[0134] The coupling risk probability of the risk unit is: probability of occurrence = 1-probability of non-occurrence. Considering the probability of occurrence of the starting node, the probability of occurrence of node N is:

[0135] P N =1-(1-P x1 ×∏P xi-xj )×(1-P y1 ×∏P yi-yj )(1-P z1 ×∏P zi-zj );

[0136] Second, when there are multiple links acting together or a single link acting, resulting in the occurrence of node N, the occurrence probability of the starting node is considered, and the occurrence coupling risk probability of node N of the risk unit / risk event is:

[0137]

[0138] In the formula, 1-P z1 ×ΠP zi-zj ×P a1 ×ΠP ai-aj ×...×P A1 ×ΠP Ai-Aj It is expressed as the probability of node N occurring when link Z and link a work together until link A;

[0139] In the embodiment, for example, for the special case where multiple chains act together to cause node N, the calculation is: occurrence probability = chain x occurrence probability × chain y occurrence probability × ..., for example, z3 and y5 act together to cause node N to occur, then the probability of node N occurring is:

[0140] P N =1-(1-P x1 ×∏P xi-xj )×(1-P y1 ×∏P yi-yj ×P z1 ×∏P zi-zj );

[0141] 2) For all upstream causal chains of the risk unit / risk event, if there are other common nodes between the chains except the node of the risk unit / risk event, the chains with common pointing nodes are aggregated from the starting point to the back, and the probability calculation method of the common nodes after aggregation is consistent with the method described in situation "1)"; through the aggregation of multiple chains, it is finally formed that multiple single chains point to the node of the risk unit / risk event to be risked, and the method described in situation "1)" is used again to calculate the coupling risk probability P of the risk unit / risk event. couple Comprehensive calculation; among them,

[0142] The aggregation rules are:

[0143] 1. Starting from the initial node, multiple chains point to a common node, then the calculation method of the risk evolution probability of the node is the same as that of formula (1).

[0144] In this example, see Figure 3 As shown in Figure 2, starting from the initial node, the evolution chain points to a common node, then the risk evolution probability P of the node is x1’-x5 :

[0145] Px1’-x5 =1-(1-P x1 ×∏P xi-xj )×(1-P y1 ×∏P yi-yj );

[0146] Where: ∏P xi-xj and ∏P yi-yj Refers to the product of the transition probabilities of each connected node in the chain before the common node.

[0147] 2. Starting from the initial node, if there are multiple common nodes between two or more links, the risk evolution probability of the node is calculated as follows: first, the common node is taken as the starting node, and the calculation method of the risk evolution probability with the common node as the starting node is the same as the calculation method of formula (2); then the probability of the initial node occurring is considered, which is the same as the calculation method of formula (1).

[0148] See also Figure 4 As shown, in one embodiment of the present invention, starting from the initial node, two or more chains have multiple common nodes, then:

[0149] The probability is calculated as follows: probability of occurrence = 1-probability of non-occurrence, then P x2’-x4 The risk transfer probability is

[0150] P x2’-x4 =1-(1-P x2-x3 ×P x3-x4 )(1-P x2-y2 ×P y2-y3 ×P y3-x4 )

[0151] The calculation method of multiple chains is consistent.

[0152] Further aggregation, considering the probability of the initial event occurring P x2’ , P x4 The probability of occurrence is:

[0153] P x2’ =1-(1-P x1 ×P x1-x2 )(1-P y1 ×P y1-x2 );

[0154] P x4 =P x2’ ×P x2’-x4 ;

[0155] For further information, see Figure 5 As shown, the above polymerization method is illustrated as follows:

[0156] If the risk unit N exists in the following chain, the probability between nodes is described as Pi-j :

[0157] Chain 1: x1 → x2 → x3 → x4 → N

[0158] Chain 2: y1→y2→x3→y4→x4→N

[0159] Chain 3: z1→z2→x4→N

[0160] Chain 4: m1→m2→m4→N

[0161] The polymerized chain is Figure 6 As shown, the chain risk probability after aggregation is:

[0162] First step aggregation: Consider the probability of the initial event occurring P x1 , P y1 .

[0163] P x4 =[1-(1-P x1 ×P x1-x2 )×(1-P y1 ×P y1-y2 ×P y2-y3 )]×P x2-x3 ×[1-(1-P x3-x4 )×

[0164] (1-P x3-y4 ×P y4-x4 )];

[0165] Second step aggregation: Consider the probability of the initial event occurring P z1 , P m1 .

[0166] P x4’ =1-(1-P x4 )×(1-P z1 ×P z1-z2 ×P z2-x4 );

[0167] P N =1-(1-P x4’ )(1-P m1 ×P m1-m2 ×P m2-m3 ×P m3-m4 ×P m4-N ).

[0168] In one embodiment of the present invention, the coupling risk consequence C coupleIt refers to the process of taking the risk unit as the starting point, obtaining all the evolution chains of the risk unit / risk event downstream, and conducting a comprehensive assessment of the consequences of coupled risks based on the causal chain of direct disaster-causing events and secondary derivative events that may be caused by the risk unit / risk event.

[0169] Coupling risk consequence assessment methods include but are not limited to:

[0170] (1) Conduct quantitative, semi-quantitative, and qualitative assessments based on the types of disaster-causing accidents in risk units, possible impact ranges, impact intensity, number of surrounding hazard sources / protection targets, population density, property density, and buildings with high social impact, and conduct final coupling consequence assessments. The assessment methods include but are not limited to weighting, summation, multiplication, mean, and maximum values;

[0171] (2) Carry out quantitative, semi-quantitative and qualitative analysis of the consequences of each direct disaster-causing and secondary derivative event one by one, and conduct a comprehensive assessment of the coupled consequences by analyzing the contribution and proportion of each disaster-causing event to the risk consequences of the risk unit.

[0172] (3) Through the analysis of each disaster consequence, a coupled risk comprehensive consequence analysis is conducted by overlaying the GIS map / regional consequences.

[0173] In order to maintain the consistency of the benchmark for consequence assessment, the present invention divides the direct injury consequences of accidents into 5 categories, and the consequence levels are divided into 5 levels, with scores ranging from 1 to 5. Among them, there may be multiple evaluation indicators for one consequence level. In this case, the score of the consequence level is the maximum value of the scores of multiple evaluation indicators:

[0174] Direct consequence indicators illustrate Personal injury Number of deaths, serious injuries, minor injuries, evacuations, etc. Economic losses Direct and indirect economic losses caused by accidents, accident handling and accident repair Operation guarantee Impact on users caused by gas and heat supply interruptions and other lifeline and other facility failures Ecological Environment River pollution, air pollution, soil pollution, etc. Social Impact Types of units and enterprises affected by the accident and degree of public attention

[0175] In one embodiment of the present invention, in the causal graph, if the risk unit / risk event node N is taken as the analysis object, the risk unit / risk event nodes (1, 2, 3, 4...n) of a single chain m downstream, for the i-th chain node downstream of node N, the risk transfer probability P between the node and its upstream node is i-1,i , that is, the probability that the upstream node causes a certain risk to the node, and the accident-caused disaster consequence assessment result C i , from back to front, evaluate the risk consequences of the risk unit / risk event N coupling. The calculation rules are as follows:

[0176]

[0177] Where: n is the total number of nodes in the downstream chain; x1, x2, x3, x4, and x5 are the scores of the five direct consequence indicators. If a node does not have a direct consequence indicator, it is taken as 0. In this embodiment, the values ​​are 0.4, 0.2, 0.15, 0.15, and 0.1 respectively; P i-1,m,iis the risk transfer probability from node i-1 to node i under path m, C N,m,x1 , C N,m,x2 , C N,m,x3 , C N,m,x4 , C N,m,x5 They are respectively represented as the evaluation results of personnel damage x1, economic loss x2, operation guarantee x3, ecological environment x4, and social impact x5 of node n under chain m, C N,m It is expressed as the average coupled risk consequence of risk unit / risk event N to other risk units / risk events in the region.

[0178] For the feedback data of some nodes, the probability is corrected. If the nodes and node rules determine the scenario, the probability is 1, and if they do not exist, the probability is 0.

[0179] If there are J risk transfer chains with node N as the starting point in the causal graph, then based on the consequence analysis of each single chain, the coupled risk consequence C of the risk unit / risk event node N is N Calculated as:

[0180]

[0181] Where H represents the number of risk units / risk events associated with the downstream chain of node N, and C N,x1 , C N,x2 , C N,x3 , C N,x4 , C N,x5 They are respectively represented as the coupled risk consequence assessment values ​​in the direct consequence indicators x1, x2, x3, x4, and x5 of the risk unit / risk event node N.

[0182] In one embodiment of the present invention, the coupling risk of the risk unit / risk event is calculated as follows:

[0183] R couple =P couple ×C coupl ;

[0184] The corresponding risk levels are shown in the following table:

[0185] Coupling risk value Risk Level <![CDATA[R couple ≥4]]> Significant risks <![CDATA[3≤R couple <4]]> Greater risk <![CDATA[1≤R couple <3]]> General Risks <![CDATA[R couple <1]]> Low risk

[0186] S60, based on the coupled risk assessment results, realize the identification of key nodes in the risk unit causal graph and regional comprehensive risk assessment.

[0187] In one embodiment of the present invention, the regional comprehensive risk assessment method includes but is not limited to regional risk grading based on the proportion of risk units, risk index representation based on the sum of risk unit risk levels and corresponding scores, and risk level / index representation based on risk weight calculation. In this embodiment, the present invention provides a regional risk grading method based on the proportion of risk units, as shown in the following table:

[0188]

[0189]

[0190] The point risks mentioned in the table refer to the risks of each risk unit; the category risks mentioned refer to the risks of the industry to which the infrastructure belongs, including but not limited to the gas industry, drainage industry, water supply industry, electric power industry, communications industry, rail transit, and bridges; the key industries and fields include but are not limited to mining enterprises (coal mines, non-coal mines), fireworks and firecrackers production enterprises, civilian explosives production enterprises, hazardous chemicals, and construction enterprises.

[0191] In one embodiment of the present invention, in order to further control the risk of risk units within the designated area and across systems, the key nodes in the designated area are screened through coupled risk unit coupling risk assessment and causal graph analysis based on complex networks, and targeted prevention and control of key nodes and high-risk nodes are carried out to reduce regional risks.

[0192] The extraction of key nodes based on coupling risk assessment results is suitable for situations where the data is detailed, while the extraction of key nodes based on complex networks is suitable for situations where the data is not detailed.

[0193] In one embodiment of the present invention, the cross-system risk assessment method for urban safety further includes the following steps:

[0194] S70 visualizes cross-system risks for urban safety.

[0195] In one embodiment of the present invention, in step S70, based on the physical signs, health records, causal maps, and risk analysis results of the risk units in the designated area, the MIS system and the GIS system are combined to perform a visual display of the regional basic information, health records, causal maps, risk status, and control.

[0196] The content displayed in the dynamic health record includes but is not limited to the basic information of the infrastructure, historical maintenance records, and the trend of changes in physical indicators. Basic information may include the type of infrastructure, construction time, geographical location, etc. Historical maintenance records should record in detail the time, content, cause of failure, and other information of each maintenance. The trend of changes in physical indicators can be intuitively displayed by drawing charts, etc., so as to timely discover potential problems. At the same time, the health record should have the ability to be dynamically updated. With the continuous influx of new data, the content of the file should be updated in a timely manner to ensure the accuracy and timeliness of the file.

[0197] The risk unit causal map should be dynamically updated with the feedback of external information through data access and analysis, and interact in the GIS system.

[0198] The visual display of risk analysis results includes but is not limited to the risk level of risk units / risk areas, the display of risk units / risk areas in different colors, key risk units in risk areas, and targeted prevention and control measures / activities for key risk units. The targeted prevention and control measures / activity data include but are not limited to the types of prevention and control measures, materials required for prevention and control measures, departments responsible for risk prevention and control, and risk prevention and control supervision departments.

[0199] See also Figure 7 As shown, the present invention also provides a cross-system risk assessment system for urban safety, which applies the above-mentioned cross-system risk assessment method for urban safety, including:

[0200] The database module is used to build a risk unit database based on multi-source data fusion by aggregating, integrating and processing multi-source data of various risk units in the city.

[0201] The indicator library module is used to extract various risk unit sign indicators based on the risk unit corresponding accident case investigation, standard specification sorting, and mechanism model research, and to build an urban risk unit sign indicator library.

[0202] The health record module is used to build a risk unit health record based on the risk unit physical sign indicator library, through data matching with the risk database of the area / object to be analyzed, combined with the safety status, risk trend, and protection response analysis of physical sign indicators.

[0203] The causal map module is used to construct a causal map of multiple risk units / risk events based on the health records of risk units, through disaster-causing / disaster-bearing / response analysis of physical signs and indicators, combined with risk transfer analysis, with risk units or events as nodes.

[0204] The cross-system coupling risk assessment module is used to carry out cross-system coupling risk assessment of risk units based on the risk unit causal map.

[0205] The key node and regional risk assessment module is used to identify key nodes in the risk unit causal graph and conduct regional comprehensive risk assessment based on the coupled risk assessment results.

[0206] It is obvious to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential features of the present invention. Therefore, the embodiments should be regarded as exemplary and non-limiting from any point of view, and the scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention, and any reference numerals in the claims should not be regarded as limiting the claims involved.

[0207] The above-described embodiments merely represent implementation methods of the invention. The protection scope of the present invention is not limited to the above-described embodiments. For those skilled in the art, several modifications and improvements may be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. A cross-system risk assessment method for urban safety, characterized in that: include: S10, through the aggregation, fusion and processing of multi-source data of various risk units in the city, a risk unit database based on multi-source data fusion is constructed; S20, based on the investigation of accident cases corresponding to risk units, the sorting out of standards and specifications, and the research on mechanism models, extract various risk unit signs and indicators, and build a database of urban risk unit signs and indicators; S30, based on the risk unit physical sign index library, through data matching of the risk database of the area / object to be analyzed, combined with the safety status, risk trend, and protection response analysis of physical sign indicators, a health profile of the risk unit is constructed; S40, based on the health records of risk units, through the analysis of disaster-causing / disaster-bearing / response indicators, combined with risk transfer analysis, with risk units or events as nodes, to construct a causal map of multiple risk units / risk events; S50, based on the risk unit / risk event causal map, conduct cross-system coupling risk assessment of risk units / risk events; S60, based on the coupled risk assessment results, realize the identification of key nodes in the risk unit / risk event causal graph and regional comprehensive risk assessment.

2. The cross-system risk assessment method for urban safety according to claim 1 is characterized in that: The acquired multi-source data of risk units are first standardized before being used to construct the risk unit database; as well as, Multi-source data types include: risk unit design, operation, monitoring, maintenance, and overhaul data, as well as economic, social, geographic, environmental, safety, and resource data; The risk unit database is based on multi-source data of risk units and is constructed through data classification and hierarchical construction. The database content includes risk unit classification data, risk unit basic data, risk unit risk management data, and social / environmental dynamic / static data.

3. The cross-system risk assessment method for urban safety according to claim 1 is characterized in that: Types of physical indicators include: risk unit design, operation, inspection, testing, monitoring, maintenance, repair, renewal, transformation and protection indicators; The methods for extracting risk unit sign indicators include: extracting sign indicators based on emergency cause analysis, extracting sign indicators based on standard and specification hidden danger definitions, extracting sign indicators based on accident management experience, and extracting sign indicators based on accident mechanism research.

4. The cross-system risk assessment method for urban safety according to claim 1 is characterized in that: Risk cross-system transfer refers to the process in which a disaster-causing risk unit affects adjacent risk units through material transfer / contact, energy conduction / impact, and information disturbance / interruption after the disaster. The analysis of the risk transfer process across systems includes: risk unit disaster bearing analysis, risk unit disaster causing analysis, and emergency measures effectiveness analysis; The risk unit disaster resistance analysis method is prioritized based on "quantitative, semi-quantitative, and qualitative", including quantitative judgment of disaster intensity and disaster resistance threshold based on mechanism model, semi-quantitative evaluation based on facility exposure, semi-quantitative and qualitative evaluation of disaster resistance damage based on disaster intensity, vulnerability, and hidden danger level, and semi-quantitative and qualitative evaluation based on experience / statistical data; The risk unit disaster analysis method is prioritized based on "quantitative, semi-quantitative, and qualitative", including disaster intensity and impact field analysis based on mechanism models, semi-quantitative and qualitative evaluation of disaster intensity based on risk unit size and type, and semi-quantitative and qualitative evaluation based on experience / statistics; The effectiveness analysis of emergency measures is a correction to the risk transfer path and process. When emergency measures exist in the process of risk transfer across systems, the probability of risk transfer across systems is corrected, and this includes the situation of terminating the process of risk transfer across systems.

5. The cross-system risk assessment method for urban safety according to claim 4 is characterized in that: Based on the "quantitative, semi-quantitative, and qualitative" risk cross-system transfer process analysis of risk units with multiple sources of disasters, multiple causes of disasters, and prevention and control responses, the risk transfer paths and evolution rules of many risk units within the region are constructed. Risk units / risk events are used as nodes, and rules and risk transfer probabilities are used as edges. Through node connection, a cross-system risk evolution causal map covering many risk units within the region is formed; and the transfer rules are characterized by probability, and the probability range is normalized to [0,1].

6. The cross-system risk assessment method for urban safety according to claim 5 is characterized in that: The causal graph has an end point of evolution; and the transition rules of the end point include: Set a probability threshold. When the node probability is less than the set probability threshold, it will no longer evolve downstream. Set a specific event node. After a specific event is generated, it will no longer evolve downstream. Set a time threshold. When the time exceeds a certain limit, the system will no longer evolve downstream. Set the regional scope. When the node influence range / downstream affected facilities exceed the regional scope, they will no longer evolve downstream.

7. The cross-system risk assessment method for urban safety according to claim 1 is characterized in that: The coupling risk of each risk unit / risk event in the divided area is assessed in the following way: Coupling risk R couple = {Coupling risk probability P couple , coupling risk consequences C couple };in, Coupling risk probability P couple Calculation refers to the process of taking the risk unit / risk event to be evaluated as the end point, obtaining all evolution chains upstream of the risk unit / risk event, and evaluating the coupling risk probability of the risk unit / risk event based on the initial event and the entire causal chain of the risk unit / risk event; Coupling risk consequences C couple It refers to the process of taking the risk unit / risk event to be assessed as the starting point, obtaining all the evolution chains of the risk unit / risk event downstream, and conducting a comprehensive assessment of the consequences of coupled risks based on the causal chain of direct disaster-causing events and secondary derivative events that may be caused by the risk unit / risk event.

8. The cross-system risk assessment method for urban safety according to claim 7 is characterized in that: Coupling risk probability P couple The acquisition process includes: 1) For all upstream causal chains of risk units / risk events, if there are no common nodes between chains except the node of the risk unit / risk event, the coupling risk probability P of the risk unit / risk event is couple Comprehensive calculation based on the risk evolution probability results of each chain; 1. If each link can cause the occurrence of node N of the risk unit / risk event independently, then considering the occurrence probability of the starting node, the occurrence coupling risk probability of node N of the risk unit / risk event is: p N =1-(1-P x1 ×ΠP xi-xj )×(1-P y1 ×ΠP yi-yj )×(1-P z1 ×ΠP zi-zj )...(1-P M1 ×ΠP Mi-Mj ),(1); In the formula, p N The occurrence coupling risk probability of node N represented as risk unit / risk event, P x1 , P y1 , P z1 , P M1 It is expressed as the coupling risk probability of the initial node in the link x, y, z...M, P xi-xj , P yi-yj , P zi-zj , ..., P Mi-Mj It is expressed as the probability between the connected nodes i and j in each link, ΠP xi-xj ,ΠP yi-yj ,ΠP zi-zj , ..., ΠP Mi-Mj It is expressed as the risk transfer probability from the initial node to the risk unit / risk event in each link; And, the inter-node transfer probability under different links between two nodes is also calculated using formula (1); Second, when there are multiple links acting together or a single link acting, resulting in the occurrence of node N, the occurrence probability of the starting node is considered, and the occurrence coupling risk probability of node N of the risk unit / risk event is: In the formula, 1-P z1 ×ΠP zi-zj ×P a1 ×ΠP ai-aj ×...×P A1 ×ΠP Ai-Aj It is expressed as the probability of node N occurring when link Z and link a work together until link A; 2) For all upstream causal chains of the risk unit / risk event, if there are other common nodes between the chains except the node of the risk unit / risk event, the chains with common pointing nodes are aggregated from the starting point to the back, and the probability calculation method of the common nodes after aggregation is consistent with the method described in situation "1)"; through the aggregation of multiple chains, it is finally formed that multiple single chains point to the node of the risk unit / risk event to be risked, and the method described in situation "1)" is used again to calculate the coupling risk probability P of the risk unit / risk event. couple Comprehensive calculation.

9. The cross-system risk assessment method for urban safety according to claim 7 is characterized in that: Based on the causal graph, the risk unit / risk event node N is taken as the analysis object, and the risk units / risk event nodes of a single chain m downstream, 1, 2, 3, 4...n, for the i-th chain node downstream of node N, the probability that the upstream node causes a certain risk to occur in this node, and the accident-caused disaster consequence assessment result C i , from back to front, the risk consequences of the risk unit / risk event N coupling are evaluated, and the calculation rules are as follows: In the formula, C N,m,x1 , C N,m,x2 , C N,m,x3 , C N,m,x4 , C N,m,x5 They are respectively the evaluation results of personnel injury x1, economic loss x2, operation guarantee x3, ecological environment x4, and social impact x5 of node n under chain m. K1, K2, K3, K4, and K5 are the scores of direct consequence indicators. The sum of K1 to K5 is 1. C N,m It is expressed as the average coupled risk consequence of risk unit / risk event N on other risk units / risk events in the region; If there are J risk transfer chains with node N as the starting point in the causal graph, then based on the consequence analysis of each single chain, the coupled risk consequence C of the risk unit / risk event node N is N Calculated as: Where H represents the number of risk units / risk events associated with the downstream chain of node N, and C N,x1 , C N,x2 , C N,x3 , C N,x4 , C N,x5 They are respectively represented as the coupled risk consequence assessment values ​​in the direct consequence indicators x1, x2, x3, x4, and x5 of the risk unit / risk event node N.

10. A cross-system risk assessment system for urban safety, characterized in that: The cross-system risk assessment method for urban safety according to any one of claims 1 to 9 is applied, comprising: The database module is used to aggregate, fuse and process multi-source data of various risk units in the city to build a risk unit database based on multi-source data fusion; The indicator library module is used to extract various risk unit signs indicators based on the investigation of risk unit corresponding accident cases, the sorting of standards and specifications, and the research of mechanism models, and to build an urban risk unit sign indicator library; The health record module is used to build a risk unit health record based on the risk unit physical sign index library, by matching the data in the risk database of the area / object to be analyzed, and combining the safety status, risk trend, and protection response analysis of physical sign indicators; The causal map module is used to construct a causal map of multiple risk units / risk events based on the health records of risk units, through the analysis of disaster-causing / disaster-bearing / response indicators, combined with risk transfer analysis, with risk units or events as nodes; Cross-system coupling risk assessment module, which is used to conduct cross-system coupling risk assessment of risk units / risk events based on the risk unit / risk event causal map; The key node and regional risk assessment module is used to identify key nodes in the risk unit causal graph and conduct regional comprehensive risk assessment based on the coupled risk assessment results.

Images