Method, system and device for assessing risk of security incident

Abstract

The application discloses a safety accident occurrence risk assessment method, system and device, relates to the technical field of risk assessment, and aims to solve the problem of inaccurate assessment results of the existing safety accident occurrence risk assessment method. The safety accident occurrence risk assessment method comprises the following steps: acquiring multi-source information of a tank area; constructing a risk probability model of safety accidents based on the multi-source information; determining a region range of predicted safety accidents based on the multi-source information; and determining a risk level of safety accidents in the tank area based on the risk probability model of safety accidents and the region range of predicted safety accidents.

Description

Technical Field

[0001] This application relates to the field of risk assessment technology, and in particular to methods, systems and equipment for assessing the risk of safety accidents. Background Technology

[0002] With the rapid development of the petrochemical industry, tank farms often house concentrated petrochemical storage tanks. Areas storing flammable petrochemical raw materials are particularly prone to safety accidents. Without appropriate methods for assessing the risk of such accidents, it is impossible to prevent potential incidents, which could lead to serious casualties and property damage.

[0003] Currently, the main methods for assessing the risk of safety accidents are to systematically and objectively evaluate the risk by analyzing historical data or constructing simulated scenarios. However, there are many factors that influence the assessment of the risk of safety accidents in large crude oil storage tank areas, and the above-mentioned methods often result in inaccurate assessment results. Summary of the Invention

[0004] The purpose of this application is to provide a method, system, and equipment for assessing the risk of safety accidents, in order to solve the problem of inaccurate assessment results in existing methods for assessing the risk of safety accidents.

[0005] To achieve the above objectives, this application adopts the following technical solution:

[0006] In a first aspect, this application provides a method for assessing the risk of a safety accident, comprising: acquiring multi-source information about the tank farm; constructing a risk probability model for the occurrence of a safety accident based on the multi-source information; determining the predicted area range for the occurrence of a safety accident based on the multi-source information; and determining the risk level of a safety accident occurring within the tank farm based on the risk probability model for the occurrence of a safety accident and the predicted area range for the occurrence of a safety accident.

[0007] The above technical solution brings at least the following beneficial effects: It constructs a risk probability model for safety accidents using multi-source information. Since multi-source information contains a wide variety and comprehensive range of information, the risk probability model based on multi-source information can accurately predict the probability of a safety accident occurring, providing an accurate data foundation for subsequently determining the risk level of the safety accident. The safety accident risk assessment method provided in this application also determines the predicted regional range of the safety accident using multi-source information, and determines the risk level of the safety accident by combining the risk probability and the regional range of the safety accident. By accurately determining the risk level of the safety accident based on both the risk probability and the geographical range of the safety accident, it can provide a relatively accurate assessment of the safety accident risk, thereby ensuring the accuracy of the assessment results.

[0008] In one possible implementation, a risk probability model for the occurrence of a safety accident is constructed based on multi-source information, including: determining a set of risk factors for the occurrence of a safety accident based on multi-source information; determining the target risk factors and the correlation between each risk factor based on the set of risk factors; and constructing a risk probability model for the occurrence of a safety accident based on the target risk factors and the correlation between each risk factor.

[0009] In one possible implementation, a risk probability model for the occurrence of a safety accident is constructed based on the target risk factors and the correlation between them. This includes: performing a cascading propagation mechanism analysis based on the target risk factors and the correlation between them to obtain target events, root node events, and intermediate node events; wherein, the target event is used to indicate the predicted safety accident occurring in the tank area, the root node event is used to indicate the initial risk factors for the predicted safety accident occurring in the tank area, and the intermediate node event is used to indicate the intermediate state of the tank area before triggering the safety accident; and a risk probability model for the occurrence of a safety accident is constructed based on the target event, root node event, and intermediate node events.

[0010] In one possible implementation, the predicted area range for a safety accident is determined based on multi-source information, including: determining the predicted area range for a safety accident based on multi-source information and the thermal radiation equation.

[0011] In one possible implementation, the thermal radiation equation satisfies the following formula:

[0012] Q = E × F

[0013] Where Q represents radiant flux, E represents radiant energy, and F represents the viewing angle coefficient.

[0014] In one possible implementation, the accident hazard level satisfies the following formula:

[0015]

[0016] Among them, R is the hazard level of the accident, and S is the hazard level of the accident. Ri C represents the area affected by the safety incident. cj For the degree of personal injury, C pi P represents the degree of property damage. li P represents the probability of a leakage safety accident occurring. igi P represents the probability of a safety accident caused by an ignition source. ig C represents the probability of ignition time delay occurring. sj For the success level of the j-th rescue method, P j Let be the probability of success for the j-th rescue method.

[0017] Secondly, this application provides a device for assessing the risk of a safety accident, the device comprising: an acquisition unit and a determination unit; the acquisition unit is used to acquire multi-source information of the tank area; the determination unit is used to construct a risk probability model for the occurrence of a safety accident based on the multi-source information; the determination unit is further used to determine the area range for predicting the occurrence of a safety accident based on the multi-source information; the determination unit is further used to determine the risk level of a safety accident occurring within the tank area based on the risk probability model for the occurrence of a safety accident and the area range for predicting the occurrence of a safety accident.

[0018] In one possible implementation, the determining unit is specifically used for: determining a set of risk factors for a safety accident based on multi-source information; determining the target risk factors and the relationships between the risk factors based on the set of risk factors; and constructing a risk probability model for a safety accident based on the target risk factors and the relationships between the risk factors.

[0019] In one possible implementation, the unit is specifically used for: performing cascading propagation mechanism analysis based on the target risk factors and the correlation between various risk factors to obtain the target event, the root node event, and the intermediate node event; wherein, the target event is used to indicate the predicted safety accident occurring in the tank area, the root node event is used to indicate the initial risk factors for the predicted safety accident occurring in the tank area, and the intermediate node event is used to indicate the intermediate state of the tank area before triggering the safety accident; and a risk probability model for the occurrence of the safety accident is constructed based on the target event, the root node event, and the intermediate node event.

[0020] In one possible implementation, the determining unit is also used to determine the predicted area range where a safety accident may occur based on multi-source information and the thermal radiation equation.

[0021] In one possible implementation, the thermal radiation equation satisfies the following formula:

[0022] Q = E × F

[0023] Where Q represents radiant flux, E represents radiant energy, and F represents the viewing angle coefficient.

[0024] In one possible implementation, the accident hazard level satisfies the following formula:

[0025]

[0026] Among them, R is the hazard level of the accident, and S is the hazard level of the accident. Ri C represents the area affected by the safety incident. cj For the degree of personal injury, C pi P represents the degree of property damage. li P represents the probability of a leakage safety accident occurring. igi P represents the probability of a safety accident caused by an ignition source. ig C represents the probability of ignition time delay occurring. sj For the success level of the j-th rescue method, P j Let be the probability of success for the j-th rescue method.

[0027] Thirdly, this application provides another apparatus for assessing the risk of a safety incident, the apparatus comprising: a processor and a communication interface; the communication interface and the processor are coupled, the processor being used to run computer programs or instructions to implement the method for assessing the risk of a safety incident as described in the first aspect and any possible implementation thereof.

[0028] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed on a terminal, cause the terminal to perform a method for assessing the risk of a security incident as described in the first aspect and any possible implementation thereof.

[0029] Fifthly, this application provides a computer program product containing instructions that, when run on a safety accident risk assessment device, causes the safety accident risk assessment device to perform the safety accident risk assessment method as described in the first aspect and any possible implementation thereof.

[0030] In a sixth aspect, this application provides a chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run computer programs or instructions to implement a method for assessing the risk of a security incident as described in the first aspect and any possible implementation thereof.

[0031] Specifically, the chip provided in this application also includes a memory for storing computer programs or instructions. Attached Figure Description

[0032] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 A schematic diagram of the structure of a Bayesian network provided in an embodiment of this application;

[0034] Figure 2 A schematic diagram of the structure of a safety accident risk assessment device provided in an embodiment of this application;

[0035] Figure 3 A flowchart illustrating a method for assessing the risk of a safety accident, as provided in an embodiment of this application.

[0036] Figure 4 This is a schematic diagram of another safety accident risk assessment device provided in an embodiment of this application. Detailed Implementation

[0037] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0038] In the description of this application, it should be understood that the terms "upper," "lower," "left," "right," "front," "rear," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or relative positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this application and for simplification, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application. Unless otherwise specified, the above-mentioned orientational descriptions can be flexibly set in practical applications, provided that the relative positional relationships shown in the accompanying drawings are satisfied.

[0039] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0040] With the rapid development of the petrochemical industry, tank farms often house concentrated petrochemical storage tanks. Areas storing flammable petrochemical raw materials are particularly prone to safety accidents. Without appropriate methods for assessing the risk of such accidents, it is impossible to prevent potential incidents, which could lead to serious casualties and property damage.

[0041] Currently, the main methods for assessing the risk of safety accidents include quantitative risk assessment, qualitative risk assessment, and probabilistic safety assessment. These methods primarily rely on risk assessment tools and software systems. The following provides a detailed explanation of these three risk assessment methods.

[0042] Quantitative risk assessment methods specifically include event tree analysis (ETA), fault tree analysis (FTA), risk-based detection (American Petroleum Institute, API) 581 method, failure mode and effects analysis (FMEA), and quantitative risk assessment (QRA). These methods can quantify potential safety events and the risk of such incidents using mathematical and / or statistical tools, and assess the impact on personnel, the environment, and property.

[0043] Traditional risk analysis methods, such as fault tree analysis, event tree analysis, and the API 581 method, focus on using historical data and empirical knowledge to statically assess the risk of safety incidents. These static assessment factors typically include equipment characteristics, operating conditions, and maintenance history.

[0044] Fault tree analysis (FMA) is a systematic approach used to analyze and understand the probability and influencing factors that lead to safety accidents within a system or process. It offers advantages such as systematicity and visualization. The system, also known as the framework, can represent the collective aspects of tank farm production and transportation, tank farm equipment, and tank farm personnel. The process refers to the development and occurrence of a safety accident. FMA can systematically analyze various possible paths and key events leading to safety accidents, identifying potential safety accident factors and risk sources. It also clearly displays the causal relationships and possible paths leading to safety accidents through graphical representations, aiding user understanding and communication. Furthermore, it can quantitatively assess the probability of each event and its impact on system safety, helping to optimize risk management strategies. Finally, FMA helps identify the root causes of safety accidents, rather than just surface phenomena, providing guidance for developing prevention and control measures.

[0045] However, fault tree analysis can require significant time and expertise to learn and use, especially in the analysis of complex systems or multi-factor safety incidents. When collecting system data and analyzing system failures, fault tree analysis relies on detailed and accurate data to support the analysis process; incomplete or inaccurate data can affect the effectiveness of the analysis and the reliability of the results. User subjective judgment can play a crucial role in constructing the fault tree and assessing the probability of events, thus requiring users to undergo professional training and gain experience to reduce subjective errors.

[0046] The API 581 methodology is a widely used static risk assessment standard, primarily used to evaluate the reliability and safety of industrial equipment (including storage tanks). Currently, the API 581 methodology is widely adopted globally in industries such as oil and chemical engineering to assess equipment health and risk levels. It can also be applied to static risk assessments, quantitatively evaluating equipment reliability using historical data and empirical knowledge. API 581 case studies typically aim to conduct detailed assessments of specific tank structures and operating conditions to develop effective maintenance strategies and risk control measures, optimize equipment maintenance strategies, maximize equipment safety and reliability, and thereby reduce the risk of production interruptions and safety accidents. Practical application cases of API 581 can provide valuable experience for other organizations within the industry to reference and learn from.

[0047] Determining inspection intervals and methods is primarily based on risk. The RBI method defines risk as the probability of failure (PoF) and consequences of failure (CoF) resulting from a combination of factors. The RBI method is a time-based inspection approach, which is unrelated to quality control. This is because each piece of equipment has different problems that change over time, making it impossible to accurately predict changes in equipment quality. The focus of inspection in the RBI method is on categorizing inspection processes, methods, locations, and intervals.

[0048] In summary, API 581, as an international standard, has gained widespread acceptance and application in the industry, possessing high authority and reliability. The API 581 method is relatively intuitive and simple, facilitating user understanding and application. It fully utilizes historical operating data and industry experience, enabling relatively accurate assessment of equipment health status. However, the API 581 method is primarily based on static data and historical experience, exhibiting weak responsiveness to dynamic changes in equipment. Its accuracy is affected by factors such as data quality, model parameter selection, and changes in the operating environment. Incomplete or highly erroneous data may impact the accuracy of the assessment results. In practical applications, the API 581 method may not fully consider certain specific operating environment factors or equipment characteristics. To improve the accuracy of the assessment, it is typically necessary to combine on-site inspections, regular monitoring, and professional judgment to correct and optimize the assessment results.

[0049] In summary, while traditional risk assessment methods such as fault trees and event trees can primarily focus on the logical relationships and probabilities of events, they cannot comprehensively consider all possible system variables and interactive effects, thus exhibiting limitations in certain complex situations. These limitations include: static nature, meaning they cannot dynamically update factors that change over time, such as corrosion and weld fatigue; and rule-based limitations, meaning they struggle to handle multi-factor coupled failures (such as the combined effects of earthquakes and corrosion), relying on manual experience for correction. On the other hand, while these methods comprehensively consider multiple factors such as the design, materials, operating environment, and operational management of storage tanks, the safety management of storage tanks in oil depot areas faces greater challenges due to the continuous increase in oil consumption. The storage of large quantities of explosive and volatile liquids in refineries or managed warehouses increases the risk of serious safety accidents. During long-term operation, storage tanks and their auxiliary equipment are affected by multiple sources, making their working conditions more complex than those of typical small mechanical equipment. The collected data may not only contain some misleading information but also, due to its large volume, is difficult to integrate and analyze in a simple manner. For multi-source factors, traditional processing methods cannot effectively obtain risk factors and accurately judge risk levels. At the same time, when the types and number of multi-source factors are large, it may lead to problems such as long calculation cycles and increased costs. In severe cases, it may even lead to incorrect judgment of accident risks of storage tanks, resulting in safety accidents. If there are no corresponding rescue measures, it may cause more serious casualties and property losses.

[0050] Failure mode and effects analysis (FMEA) is a systematic preventative measure designed to identify and assess potential failure modes in a system, product, or service, and the effects these failures may have on the system or users.

[0051] In probabilistic safety assessments, FMEA is often used in conjunction with methods such as event tree analysis and fault tree analysis to provide a comprehensive perspective and direction for improvement in system safety assessment. Examples include the analysis of safety incident scenarios during battery transportation and storage, and the analysis of fire or explosion incident scenarios in oil refineries. Qualitative risk assessment methods include Hazard and Operability Study (HAZOP) and Safety Case Assessment (SCA). These methods focus on identifying potential hazards and operational errors, assessing safety incident risks through expert judgment and system analysis.

[0052] Probabilistic safety assessment (PSA) is a systematic approach used to evaluate the safety of complex systems, such as nuclear power plants and chemical plants. The core idea of ​​PSA is to quantitatively assess a system's safety performance in the face of various possible events by combining probabilistic tools and / or system analysis tools. PSA comprehensively considers factors such as system failures, human error, and external events to assess the overall safety of a system or process. Multivariate risk assessment methods integrate multiple assessment techniques, such as combining quantitative and qualitative methods, or combining Bayesian networks and data-driven approaches, to improve the comprehensiveness and accuracy of risk assessment.

[0053] To overcome the problems of traditional risk assessment methods, Badreddine and Amor proposed the Bayesian network (BN) method. Figure 1 As shown, the Bayesian network structure consists of evidence nodes, directed edges, intermediate nodes, and a target node. The directed edges point from the evidence nodes to the intermediate nodes, and from the intermediate nodes to the target node.

[0054] Bayesian Networks (BN) is a probabilistic reasoning technique for uncertainty-based reasoning. It allows for a more dynamic study of possible combinations leading to the failure of target events and safety measures. Therefore, it is widely used in various aspects such as safety analysis, risk management, safety incident risk assessment, and the implementation of safety barriers, improving the implementation and optimization of reliability components. Due to its flexible structure, BN can easily represent causal relationships between a set of random variables, thus illustrating local dependencies. A key advantage of BN is that it updates the system's accident risk as new evidence becomes available. Several attempts have been made to develop process safety and accident risk analysis for complex systems using BN models, proving it to be a practical method for modeling uncertain accident risks. Accident risk models based on Bayesian networks have received widespread attention and development in recent years. They break away from traditional accident risk assessment methods by dynamically evaluating accident risk, offering a real-time, multi-source risk assessment method. Applying this method to the risk assessment of storage tanks and their auxiliary equipment has yielded satisfactory results.

[0055] Currently, probabilistic security assessments, due to their quantifiable nature, scientific methods and data analysis, relative objectivity, and ability to reduce the influence of subjective factors on security decisions, are frequently used as a primary method for assessing accident risks in various scenarios. This helps enterprises allocate resources rationally, focus on high-risk areas or processes, and improve the efficiency and effectiveness of security management. Through probabilistic security assessments, enterprises can identify potential security risks and vulnerabilities in advance, helping to prevent security incidents and ensure the safety of personnel and the environment.

[0056] Accident risk assessment tools and software systems primarily involve the development and application of various computer-aided tools and software systems, such as risk assessment model establishment and application platforms, facilitating systematic risk assessment and management for users. Currently, major risk assessment tools and software systems include Factor Analysis of Information Risk (FAIR), a quantitative risk assessment method focused on information security, helping organizations quantify risks and make decisions. On the other hand, commonly used enterprise software for calculating the consequences of safety accidents includes Process Hazard Analysis Software Tool (PHAST) / / SAFETI (or alternatively, quantitative risk analysis software), a software suite developed by DNV GL, used to assess the consequences of safety accidents in chemical processes, including fire accidents, explosion accidents, and toxic gas leak accidents. ANSYS FLUENT is a fluid dynamics simulation software widely used in engineering, supporting the simulation of various flow and heat transfer problems, and supporting the construction and consequence calculation of leak accidents, fire accidents, and explosion accidents. OpenFOAM is an open-source CFD software package that provides a wide range of numerical methods and models suitable for simulating various complex fluid flow and heat transfer problems, primarily applicable to the consequence calculation of gas leak accidents, fire accidents, etc. COMSOL Multiphysics software offers simulation software for multiphysics coupling, including fluid mechanics, heat transfer, and structural mechanics, suitable for multidisciplinary simulation analysis. These software programs provide a wide range of functions from basic flows to complex multiphysics couplings, allowing users to choose the appropriate software for fluid mechanics simulation and analysis based on specific application needs, and to build models and calculate consequences for safety accident scenarios. While this type of quantitative analysis method can quantify local risks (such as calculating the overpressure propagation range of fire and explosion shock waves), it is computationally expensive, requiring hours to days for a single simulation, making it unsuitable for real-time calculations and risk assessments. Furthermore, problems such as data fragmentation are frequently encountered during simulations; simulation results are usually independent of the management system and not linked to detection data. Commonly used simulation software by Chinese scholars includes FDS, FLACS, DNV-PHAST, and ALOHA, which have certain applicability and significant application value in tank failure consequence analysis.

[0057] However, safety accidents in large crude oil storage areas (such as large crude oil storage tank fires) are characterized by complex model construction and large computational load, often resulting in high simulation costs and long calculation times. Furthermore, large tank fires are subject to many uncontrollable factors, such as temperature and wind speed, which can significantly impact the accuracy of the simulation results.

[0058] Overall, existing risk assessment methods have been widely applied and advanced across various industries and application areas, continuously incorporating new technologies and methods to improve the scientific rigor, comprehensiveness, and practicality of assessments, thereby more effectively safeguarding industrial production and public safety. However, the application of this technology in assessing the risk of safety accidents (e.g., tank spill fires) still faces challenges, such as the need for further research and improvement in areas like data quality assurance, model accuracy optimization, and the applicability of real-time monitoring systems.

[0059] Overall, the methods for assessing the risk of safety accidents are developing towards intelligence, dynamism, and comprehensiveness, aiming to improve the accuracy of the assessment results.

[0060] In view of this, this application provides a method for assessing the risk of safety accidents. It constructs a probability model of safety accident risk by using multi-source information. Since multi-source information contains a wide variety and comprehensive range of information, the probability model can accurately predict the probability of a safety accident, providing an accurate data foundation for determining the risk level of the safety accident. The method also uses multi-source information to determine the predicted geographical area where a safety accident might occur, and combines this with the probability of the safety accident and the geographical area to determine the risk level. By accurately determining the risk level based on both the probability of the safety accident and its geographical scope, this method provides a relatively accurate assessment of the risk of safety accidents, thus ensuring the accuracy of the assessment results.

[0061] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," "linking," and "communication" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection. They can refer to a direct connection or an indirect connection through an intermediate medium, or a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0062] In embodiments of this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, article, or apparatus that includes that element.

[0063] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0064] In the description of this specification, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0065] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0066] Figure 2 This is a schematic diagram of the structure of a safety accident risk assessment device 20 provided in an embodiment of this application. Figure 2 As shown, the safety accident risk assessment device 20 may include a processor 201, a bus 202, a communication interface 203, and a memory 204.

[0067] Furthermore, the safety incident risk assessment device 20 may also include a communication interface 203 and a memory 204. The processor 201, memory 204, and communication interface 203 can be connected via a bus 202.

[0068] The processor 201 can be a CPU, a general-purpose processor, a network processor (NP), a digital signal processor (DSP), a microprocessor, a microcontroller, a programmable logic device (PLD), or any combination thereof. The processor 201 can also be other devices with processing capabilities, such as circuits, devices, or software modules, without limitation.

[0069] Bus 202 is used to transmit information between the components included in the safety accident risk assessment device 20.

[0070] Communication interface 203 is used to communicate with other devices or other communication networks. These other communication networks can be Ethernet, radio access network (RAN), wireless local area network (WLAN), etc. Communication interface 203 can be a module, circuit, communication interface, or any device capable of enabling communication.

[0071] Memory 204 is used to store instructions. These instructions can be computer programs.

[0072] The memory 204 can be a read-only memory (ROM) or other type of static storage device that can store static information and / or instructions; it can also be a random access memory (RAM) or other type of dynamic storage device that can store information and / or instructions; it can also be an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, etc., without limitation.

[0073] It should be noted that the memory 204 can exist independently of the processor 201 or be integrated with the processor 201. The memory 204 can be used to store instructions, program code, or some data. The memory 204 can be located inside or outside the safety incident risk assessment device 20, without limitation. The processor 201 is used to execute the instructions stored in the memory 204 to implement the blind detection method provided in the following embodiments of this application.

[0074] In one example, processor 201 may include one or more CPUs.

[0075] As an optional implementation, the safety incident risk assessment device 20 includes multiple processors.

[0076] As an optional implementation, the safety accident risk assessment device 20 also includes output devices and input devices, which are not shown in the figure.

[0077] In this embodiment of the application, the chip system may be composed of chips or may include chips and other discrete devices.

[0078] Furthermore, the actions, terms, etc., involved in the various embodiments of this application can be referenced interchangeably without limitation. The message names or parameter names in the messages exchanged between the various devices in the embodiments of this application are merely examples, and other names may be used in specific implementations without limitation.

[0079] The following is combined with Figure 2 The apparatus for assessing the risk of safety accidents shown herein describes the method for assessing the risk of safety accidents provided in the embodiments of this application. The actions, terminology, etc., involved in the various embodiments of this application can be referred to mutually without limitation. The message names or parameter names in the messages exchanged between various devices in the embodiments of this application are merely examples; other names may be used in specific implementations without limitation. The actions involved in the various embodiments of this application are merely examples; other names may be used in specific implementations. For example, "included in" in the embodiments of this application can be replaced with "carried on" or "carried in," etc.

[0080] like Figure 3 As shown in the figure, this application embodiment provides a method for assessing the risk of a safety accident, the method including: S301-S304.

[0081] S301. Electronic equipment acquires multi-source information from the tank area.

[0082] Optionally, the multi-source information of the tank farm may include tank farm operation data, multi-device sensor monitoring data, meteorological station observation data, and other data (e.g., data of the storage tanks themselves), and this application embodiment does not limit this.

[0083] Furthermore, optionally, tank farm operation data may include tank farm inspection data, employee training record data, historical accident record data, tank farm layout and size data, crude oil storage data, and other data (e.g., tank quantity and specifications data). Tank farm multi-equipment sensor monitoring data may include temperature monitoring data, combustible gas detector monitoring data, combustible liquid detector monitoring data, leak monitoring data, tank farm auxiliary facility operation monitoring data, and other data (e.g., static electricity monitoring data). Meteorological station observation data includes precipitation data, temperature data, humidity data, wind speed data, and other data (e.g., ultraviolet radiation data), which are not limited in this embodiment.

[0084] Optionally, the multi-source information of the tank area can be obtained through manual recording, sensor detection, weather station detection, or other methods (e.g., drone detection). This application embodiment does not limit the methods used.

[0085] S302. Electronic devices construct a risk probability model for safety accidents based on multi-source information.

[0086] In one possible implementation, the above-mentioned S302 implementation process can be as follows: the electronic device determines the set of risk factors for the occurrence of a safety accident based on multi-source information; the electronic device determines the target risk factor and the correlation between each risk factor based on the set of risk factors; and the electronic device constructs a risk probability model for the occurrence of a safety accident based on the target risk factor and the correlation between each risk factor.

[0087] Furthermore, in an optional implementation, the process by which the electronic device determines the target risk factor can be as follows: the electronic device determines the target risk factor (or can be replaced by the main risk factor) affecting the normal operation of the tank farm based on tank farm condition data, historical tank farm data, and experience data. The target risk factor can be adjusted according to the actual situation of the tank farm. For example, when the tank farm is located in a high-temperature area, the high-temperature factor can be used as the target risk factor. This application embodiment does not limit this.

[0088] Optionally, the risk factor dataset may include internal risk factors, external risk factors, and other risk factors (e.g., human risk factors) of the tank farm, and this application embodiment does not limit this.

[0089] Furthermore, optionally, the method by which the electronic device determines the target risk factors and the correlation between the various risk factors can be sensitivity analysis, decision tree analysis, influence matrix analysis, or other methods (e.g., Monte Carlo simulation), and the embodiments of this application do not limit this.

[0090] Furthermore, in one possible implementation, the process by which the electronic device constructs a risk probability model for a safety accident based on the target risk factors and the relationships between these risk factors can be as follows: The electronic device performs a cascading propagation mechanism analysis (e.g., cascading propagation mechanism analysis can also be called causal relationship analysis before and after the event) based on the target risk factors and the relationships between these risk factors, obtaining the target event, the root node event, and the intermediate node events. The target event indicates the predicted safety accident occurring within the tank area. The root node event indicates the initial risk factors for the predicted safety accident occurring within the tank area. The intermediate node events indicate the intermediate state of the tank area before triggering the safety accident. The electronic device constructs a risk probability model for the safety accident based on the target event, the root node event, and the intermediate node events.

[0091] Optionally, the target event can be the endpoint of the safety accident risk analysis, representing a predicted safety accident occurring in the tank farm, such as a fire, a combustible material leak, or an accident involving the presence of an ignition source. A fire includes an accident caused by an ignition source igniting combustible materials. The root node event indicates that the root node event is the starting point of the safety accident risk analysis, representing the initial risk factors that may trigger a safety accident, such as equipment failure or operational errors; or, representing the initial triggering conditions of a safety accident. Relevant intermediate node events represent the intermediate state of the tank farm before the target event is triggered, such as delayed leak detection or failure of emergency measures.

[0092] For example, when the target event is a flammable material leak accident, the root node event can be equipment failure, operator error, or external factors, and the relevant intermediate node events can be leak detection delay, emergency measures failure, or environmental conditions, etc. This application embodiment does not limit these.

[0093] S303. Electronic equipment determines the predicted area range where a safety accident may occur based on multi-source information.

[0094] In one possible implementation, the above-mentioned S303 implementation process can be: the electronic device determines the predicted area range where a safety accident will occur based on multi-source information and the thermal radiation equation.

[0095] In one possible implementation, the thermal radiation equation satisfies the following formula 1:

[0096] Q=E×F Formula 1

[0097] Where Q represents radiant flux, E represents radiant energy, and F represents the viewing angle coefficient.

[0098] In one possible implementation, the calculation process of the thermal radiation equation can be as follows: When the initial velocity component u0 is greater than 0, the electronic device inputs the initial velocity component u0 into Formula 2 to obtain the dimensionless velocity u1, and inputs the dimensionless velocity u1 into Formulas 3 and 4 to obtain the flame height and the cosine value of the direction coefficient, respectively, and obtains the sine value of the direction coefficient based on Formula 5. When the initial velocity component u0 is less than or equal to 0, the electronic device can obtain the flame height based on Formula 6. The electronic device obtains the flame radiation flux based on Formula 7, and the flame surface radiation energy D in Formula 7 can be calculated using an exponential decay model. The electronic device defines the distance increment through Formula 8, and obtains the vertical and horizontal components of the distance through Formulas 9 and 10, and inputs the vertical and horizontal components of the distance into Formula 14 to obtain the total viewing angle coefficient. The intermediate variables A, B, and C in Formulas 9 and 10 can be obtained through Formulas 11, 12, and 13, respectively.

[0099] In one possible implementation, the dimensionless velocity satisfies the following formula 2:

[0100]

[0101] Where g is the acceleration due to gravity, BR is the combustion rate, D is the characteristic diameter, and ρ f Fuel density.

[0102] In one possible implementation, the flame height satisfies the following formula 3:

[0103]

[0104] Where, ρ a denoted as air density, g as gravitational acceleration, BR as combustion rate, and D as characteristic diameter.

[0105] In one possible implementation, the cosine value of the direction coefficient satisfies the following formula 4:

[0106]

[0107] In one possible implementation, the sine value of the direction coefficient satisfies the following formula 5:

[0108]

[0109] Where θ is the direction coefficient.

[0110] In one possible implementation, the flame height satisfies the following formula 6:

[0111] H = 0.235 × (BR × π × R) 2 ×H c ) 0.4 -1.02×D Formula 6

[0112] Where H is the flame height, BR is the combustion rate, R is the combustion pool radius, Hc is the fuel calorific value, D is the characteristic diameter, and π is pi.

[0113] In one possible implementation, the flame radiation flux satisfies the following formula 7:

[0114] E = 140 × e -0.12×D +20(1-e -0.12×D ) Formula 7

[0115] Where E is the flame radiation flux and D is the characteristic diameter.

[0116] In one possible implementation, the distance increment satisfies the following formula 8:

[0117] L = R + 0.1 (Formula 8)

[0118] Where R is the radius of the combustion pool.

[0119] In one possible implementation, the vertical component satisfies the following formula 9:

[0120]

[0121] Among them, F v θ is the vertical component, and θ is the direction coefficient.

[0122] In one possible implementation, the horizontal component satisfies the following formula 10:

[0123]

[0124] Among them, F H θ is the horizontal component, and θ is the direction coefficient.

[0125] In one possible implementation, the intermediate variable A satisfies the following formula 11:

[0126] A = a 2 +(b+1) 2 -2×a×(b+1)×sinθ Formula 11

[0127] In one possible implementation, the intermediate variable B satisfies the following formula 12:

[0128] B = a 2 +(b-1) 2 -2×a×(b-1)×sinθ Formula 12

[0129] In one possible implementation, the intermediate variable C satisfies the following formula 13:

[0130] C = 1 + (b) 2 -1)×cosθ Formula 13

[0131] In one possible implementation, the total viewing angle coefficient satisfies the following formula 14:

[0132]

[0133] Where F is the total viewing angle coefficient, F v For the vertical component, F H For horizontal components

[0134] Optionally, the multi-source information input to the thermal radiation equation can be the initial factors that trigger the target event. The initial factors of the target event may include the distance to the fire source, the leakage rate, the tank area, and other initial conditions (e.g., air humidity). This application embodiment does not limit this.

[0135] S304. Electronic equipment determines the risk level of a safety accident within the tank area based on a risk probability model of the occurrence of a safety accident and the predicted area range of the safety accident.

[0136] Among them, the fire accident risk level can be the flowing fire hazard risk level, which can include the flowing fire heat radiation range level, the degree of personal injury level, and the degree of property damage level.

[0137] Optionally, the accident hazard level of the total target event in the tank farm satisfies the following formula 15:

[0138]

[0139] Among them, R is the hazard level of the accident, and S is the hazard level of the accident. Ri C represents the area affected by the safety incident. cj For the degree of personal injury, C pi P represents the degree of property damage. li P represents the probability of a leakage safety accident occurring. igi P represents the probability of a safety accident caused by an ignition source. ig C represents the probability of ignition time delay occurring. sj For the success level of the j-th rescue method, P j Let be the probability of success for the j-th rescue method.

[0140] The safety accident risk assessment method provided in this application constructs a risk probability model for safety accidents using multi-source information. This multi-source information contains a wide variety of comprehensive data, thus enabling the risk probability model to accurately predict the probability of safety accidents occurring. This provides an accurate data foundation for determining the risk level of safety accidents. Furthermore, the method uses multi-source information to determine the predicted geographical area where safety accidents might occur, and combines this with the risk probability and geographical area of ​​the safety accident to determine its risk level. By accurately determining the risk level of safety accidents based on both the risk probability and the geographical area, the method provides a relatively accurate assessment of the risk of safety accidents, thereby ensuring the accuracy of the assessment results.

[0141] Furthermore, the safety accident risk assessment method described in this application combines theoretical analysis and formula derivation to obtain the assessment results, eliminating the need for complex numerical simulation processes. Numerical simulation, in particular, requires steps such as model data collection, model construction, and mesh generation. The safety accident risk assessment method described in this application has lower requirements for computer performance, thereby reducing the cost of safety accident risk assessment. Moreover, the method obtains the assessment results through a simple computer program, reducing computation time and thus improving the efficiency of safety accident risk assessment.

[0142] It is understood that the aforementioned method for assessing the risk of safety accidents can be implemented by a device for assessing the risk of safety accidents. To achieve the above functions, the device for assessing the risk of safety accidents includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, based on the modules and algorithm steps of the examples described in conjunction with the embodiments disclosed herein, the embodiments disclosed in this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments disclosed in this application.

[0143] The embodiments disclosed in this application can divide the safety accident risk assessment device generated by the above method examples into functional modules. For example, each function can be divided into its own functional module, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. It should be noted that the module division in the embodiments disclosed in this application is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0144] Figure 4 This is a schematic diagram of another safety accident risk assessment device 40 provided in an embodiment of the present invention. Figure 4 As shown, the safety accident risk assessment device 40 can be used to perform... Figure 3 The method for assessing the risk of a safety accident is shown. The safety accident risk assessment device 40 includes an acquisition unit 401 and a determination unit 402.

[0145] The acquisition unit 401 is used to acquire multi-source information of the tank area; the determination unit 402 is used to construct a risk probability model for the occurrence of a safety accident based on the multi-source information; the determination unit 402 is also used to determine the area range for the predicted occurrence of a safety accident based on the multi-source information; the determination unit 402 is also used to determine the risk level of a safety accident occurring in the tank area based on the risk probability model for the occurrence of a safety accident and the area range for the predicted occurrence of a safety accident.

[0146] In one possible implementation, the determining unit 402 is specifically used to: determine a set of risk factors for a safety accident based on multi-source information; determine the target risk factors and the relationships between the risk factors based on the set of risk factors; and construct a risk probability model for a safety accident based on the target risk factors and the relationships between the risk factors.

[0147] In one possible implementation, the determining unit 402 is specifically used to: perform cascading propagation mechanism analysis based on the target risk factors and the correlation between each risk factor to obtain the target event, the root node event, and the intermediate node event; wherein, the target event is used to indicate the predicted safety accident occurring in the tank area, the root node event is used to indicate the initial risk factor for the predicted safety accident occurring in the tank area, and the intermediate node event is used to indicate the intermediate state of the tank area before triggering the safety accident; and a risk probability model for the occurrence of a safety accident is constructed based on the target event, the root node event, and the intermediate node event.

[0148] In one possible implementation, the determining unit 402 is also used to determine the predicted area range where a safety accident may occur based on multi-source information and the thermal radiation equation.

[0149] In one possible implementation, the thermal radiation equation satisfies the following formula:

[0150] Q = E × F

[0151] Where Q represents radiant flux, E represents radiant energy, and F represents the viewing angle coefficient.

[0152] In one possible implementation, the accident hazard level satisfies the following formula:

[0153]

[0154] Among them, R is the hazard level of the accident, and S is the hazard level of the accident. Ri C represents the area affected by the safety incident. cj For the degree of personal injury, C pi P represents the degree of property damage. li P represents the probability of a leakage safety accident occurring. igi P represents the probability of a safety accident caused by an ignition source. igC represents the probability of ignition time delay occurring. sj For the success level of the j-th rescue method, P j Let be the probability of success for the j-th rescue method.

[0155] Through the above description of the embodiments, those skilled in the art will clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0156] This disclosure also provides a computer-readable storage medium storing instructions that, when executed by a processor of an electronic device, enable the electronic device to perform the safety incident risk assessment method provided in the embodiments of this disclosure described above.

[0157] This disclosure also provides a computer program product containing instructions that, when run on an electronic device, cause the electronic device to execute the risk assessment method for security incidents provided in the above-described embodiments of this disclosure.

[0158] The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: electrical connections having one or more wires; portable computer disks; hard disks; random access memory (RAM); read-only memory (ROM); erasable programmable read-only memory (EPROM); registers; hard disks; optical fibers; portable compact disc read-only memory (CD-ROM); optical storage devices; magnetic storage devices; or any suitable combination thereof; or any other form of computer-readable storage medium known in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium may also be a component of the processor. The processor and the storage medium may reside in an application-specific integrated circuit (ASIC). In the embodiments of this application, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0159] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for assessing the risk of a safety accident, characterized in that, include: Obtain multi-source information about the tank farm; A risk probability model for the occurrence of a safety accident is constructed based on the multi-source information. Based on the multi-source information, the predicted area range where the safety accident will occur is determined; The risk level of the safety accident within the tank farm is determined based on the risk probability model of the safety accident and the predicted area where the safety accident will occur.

2. The method according to claim 1, characterized in that, The risk probability model for the occurrence of the safety incident, constructed based on the multi-source information, includes: Based on the multi-source information, determine the set of risk factors that led to the safety accident; Based on the set of risk factors, the target risk factors and the relationships between the various risk factors are determined. A risk probability model for the occurrence of the safety accident is constructed based on the target risk factors and the correlation between the various risk factors.

3. The method according to claim 2, characterized in that, The risk probability model for the occurrence of the safety accident, constructed based on the target risk factors and the correlation between the various risk factors, includes: Based on the target risk factors and the correlation between them, a cascading propagation mechanism analysis is performed to obtain the target event, the root node event, and the intermediate node event. The target event is used to indicate the predicted safety accident occurring in the tank area, the root node event is used to indicate the initial risk factor for the predicted safety accident occurring in the tank area, and the intermediate node event is used to indicate the intermediate state of the tank area before the safety accident is triggered. Based on the target event, the root node event, and the intermediate node event, a risk probability model for the occurrence of the security incident is constructed.

4. The method according to claim 1, characterized in that, Determining the predicted area range for the occurrence of the safety incident based on the multi-source information includes: Based on the multi-source information and the thermal radiation equation, the predicted area range where the safety accident will occur is determined.

5. The method according to claim 4, characterized in that, The thermal radiation equation satisfies the following formula: Q = E × F Where Q represents radiant flux, E represents radiant energy, and F represents the viewing angle coefficient.

6. The method according to claim 1, characterized in that, The hazard level of the accident satisfies the following formula: Wherein, R refers to the accident hazard level, S Ri C represents the area of ​​the safety incident. cj C represents the degree of injury to the person in question. pi P represents the extent of the property damage. li P represents the probability of a leakage safety accident occurring. igi P represents the probability of a safety accident caused by an ignition source. ig C represents the probability of ignition time delay occurring. sj For the success level of the j-th rescue method, P j Let be the probability of success for the j-th rescue method.

7. A risk assessment device, characterized in that, The device includes: an acquisition unit and a determination unit; The acquisition unit is used to acquire multi-source information about the tank farm; The determining unit is used to construct a risk probability model for the occurrence of a safety accident based on the multi-source information. The determining unit is further configured to determine the predicted area range where the safety accident will occur based on the multi-source information; The determining unit is further configured to determine the risk level of the safety accident occurring in the tank area based on the risk probability model of the safety accident and the predicted area range of the safety accident.

8. The apparatus according to claim 7, characterized in that, The determining unit is specifically used for: Based on the multi-source information, determine the set of risk factors that led to the safety accident; Based on the set of risk factors, the target risk factors and the relationships between the various risk factors are determined. A risk probability model for the occurrence of the safety accident is constructed based on the target risk factors and the correlation between the various risk factors.

9. A device for assessing the risk of a safety accident, characterized in that, include: A processor and a communication interface; the communication interface is coupled to the processor, the processor being used to run computer programs or instructions to implement the risk assessment method for the occurrence of a safety incident as described in any one of claims 1-6.

10. A computer-readable storage medium storing instructions, characterized in that, When the computer executes the instruction, the computer performs the risk assessment method for the occurrence of a safety incident as described in any one of claims 1-6.

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