Chemical industry accident early warning grading method and device based on accident chain

Abstract

The present application relates to the technical field of chemical accident early warning, and provides a chemical accident early warning grading method and device based on an accident chain.According to the hidden danger development time and the hidden danger development degree, the present application constructs a chemical accident chain;monitoring data is compared and analyzed with the chemical accident chain to obtain a spatiotemporal matching depth; according to the spatiotemporal matching depth, the risk early warning level corresponding to the monitoring data is determined, so as to perform early warning and prediction according to the risk early warning level.In the present application, the current hidden danger development time and the hidden danger development degree are extracted from the monitoring data, which solves the problem that the early signs of an accident cannot be captured, resulting in a lag in early warning response and missing the best opportunity to take preventive measures, because the hidden danger development time and the hidden danger development degree of the accident cannot be determined.

Description

Technical Field

[0001] This invention relates to the field of chemical accident early warning technology, and in particular to a chemical accident early warning classification method and device based on accident chains. Background Technology

[0002] To effectively prevent and curb workplace accidents, various manufacturing enterprises currently monitor the production environment and equipment status in real time to promptly identify potential safety hazards. With the widespread application of information technologies such as the Internet of Things (IoT), modern production fields have generated a large amount of real-time monitoring data. This monitoring data includes multiple aspects such as equipment status, environmental monitoring, and personnel activities, containing information on potential safety risks.

[0003] However, these monitoring data do not directly reveal the specific details of the risks. In order to discover potential signs of safety risks, such as equipment failure, environmental changes, and operational abnormalities, it is necessary to conduct in-depth mining and processing of massive amounts of monitoring data. This will enable early identification of hidden dangers and abnormal situations, and provide early warnings, so as to take timely preventive measures to prevent the occurrence of safety accidents.

[0004] In existing technologies, solutions for monitoring and early warning of chemical accident risks often fail to determine the development time and extent of potential hazards, thus failing to capture early signs of an accident, resulting in delayed early warning responses and missing the best opportunity to take preventive measures.

[0005] Therefore, overcoming the shortcomings of the existing technology is an urgent problem to be solved in this technical field. Summary of the Invention

[0006] The technical problem to be solved by this invention is to provide a method and device for early warning and classification of chemical accidents based on accident chains. The purpose is to determine the development time and degree of potential hazards for various types of accidents by constructing chemical accident chains; to obtain the corresponding risk warning level by comparing and analyzing monitoring data according to the chemical accident chains; to capture the spatiotemporal characteristics of accident development contained in the monitoring data; and to extract the current development time and degree of potential hazards from the monitoring data. This solves the problem that the inability to determine the development time and degree of potential hazards leads to the inability to capture the initial signs of an accident, resulting in a delayed early warning response and missing the best opportunity to take preventive measures.

[0007] The present invention adopts the following technical solution:

[0008] In a first aspect, the present invention provides a chemical accident early warning and classification method based on an accident chain, comprising:

[0009] Construct a chemical accident chain based on the development time and degree of potential hazards;

[0010] The spatiotemporal matching depth was obtained by comparing and analyzing the monitoring data with the chemical accident chain.

[0011] Based on the spatiotemporal matching depth, the risk warning level corresponding to the monitoring data is determined, so as to make early warning and forecast based on the risk warning level.

[0012] Furthermore, the construction of a chemical accident chain based on the development time and degree of the hidden danger includes:

[0013] For different types of hidden dangers, a table of hidden danger development time values ​​is formulated based on the relationship between the speed of hidden danger development, the time range required to develop to the next stage, and the development time of hidden dangers.

[0014] Based on the causal sequence of various hidden dangers, at least one hidden danger node is constructed; according to the hidden danger development time value table, the hidden danger development time of the hidden danger node is determined to obtain the initial accident chain;

[0015] For different types of hazards, a hazard development degree value table is formulated based on the correlation between the hazard status description and the hazard development degree.

[0016] According to the table of potential hazard development levels, the potential hazard development level of the potential hazard nodes on the initial accident chain is determined to obtain the chemical accident chain.

[0017] Furthermore, the step of comparing and analyzing the monitoring data with the chemical accident chain to obtain the spatiotemporal matching depth includes:

[0018] From the table of potential hazard development levels, obtain the negligible impact value of the potential hazard development level; calculate the basic spatiotemporal characteristic value according to the negligible impact value and the potential hazard development time of the potential hazard node;

[0019] Based on the aforementioned chemical accident chain, determine the current spatiotemporal characteristic value corresponding to the monitoring data;

[0020] The spatiotemporal matching depth is calculated based on the current spatiotemporal feature value and the basic spatiotemporal feature value.

[0021] Furthermore, the chemical accident chain includes at least one branch, and the current spatiotemporal characteristic value is the branch spatiotemporal characteristic value of each branch;

[0022] The step of determining the current spatiotemporal characteristic value corresponding to the monitoring data according to the chemical accident chain includes:

[0023] Acquire at least one candidate data from the collected data; according to the table of potential hazard development levels, select the candidate data from the at least one candidate data whose potential hazard development level is not negligible and determine it as monitoring data;

[0024] According to the table of potential hazard development levels, determine the development level monitoring value corresponding to the monitoring data;

[0025] Based on the aforementioned chemical accident chain, the potential hazard nodes corresponding to the monitoring data are determined;

[0026] Based on the development level monitoring value, calculate the spatiotemporal characteristic value of the branch where the hidden danger node corresponding to the monitoring data is located; determine the spatiotemporal characteristic values ​​of other branches in the chemical accident chain to obtain the current spatiotemporal characteristic value.

[0027] Further, based on the development level monitoring value, the calculation of the spatiotemporal characteristic value of the branch in which the hidden danger node corresponding to the monitoring data is located; determining the spatiotemporal characteristic values ​​of other branches in the chemical accident chain includes:

[0028] For each potential hazard node in each branch of the chemical accident chain, when the potential hazard node is the potential hazard node corresponding to the monitoring data, the product of the development degree monitoring value and the potential hazard development time of the potential hazard node is determined as the monitoring characteristic value of the potential hazard node.

[0029] When the hidden danger node is not the hidden danger node corresponding to the monitoring data, the product of the negligible impact value and the hidden danger development time of the hidden danger node is determined as the accident-free characteristic value of the hidden danger node.

[0030] The sum of the monitoring characteristic values ​​and / or accident-free characteristic values ​​of all potential hazard nodes on the branch is determined as the intermediate result of the branch; the intermediate result of the branch is divided by the total number of branches of the chemical accident chain to obtain the spatiotemporal characteristic value of the branch.

[0031] Furthermore, the spatiotemporal matching depth is the spatiotemporal matching depth of each branch;

[0032] The step of calculating the spatiotemporal matching depth based on the current spatiotemporal feature value and the basic spatiotemporal feature value includes:

[0033] The sum of the accident-free characteristic values ​​of all potential hazard nodes on the branch is determined as the branch baseline value.

[0034] The ratio of the spatiotemporal feature value of the branch to the baseline value of the branch is determined as the spatiotemporal matching depth of the branch.

[0035] Furthermore, the chemical accident chain includes at least one branch, and the spatiotemporal matching depth is the branch spatiotemporal matching depth of each branch; the level mapping relationship between the range of risk warning values ​​and the risk warning level is predetermined;

[0036] The step of determining the risk warning level corresponding to the monitoring data based on the spatiotemporal matching depth includes:

[0037] Calculate the risk warning value based on the spatiotemporal matching depth of each branch;

[0038] The risk warning level is obtained according to the level mapping relationship and the risk warning value.

[0039] Furthermore, the calculation of the risk warning value based on the spatiotemporal matching depth of each branch includes:

[0040] The difference between the preset unit value and the spatiotemporal matching depth of each branch is determined as the branch matching value of the branch.

[0041] The product of the branch matching values ​​of each branch is determined as the risk warning value.

[0042] Secondly, the present invention also provides a chemical accident early warning and classification device based on an accident chain, comprising:

[0043] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the processor for performing the chemical accident early warning and classification method based on the accident chain described in the first aspect.

[0044] Thirdly, the present invention also provides a non-volatile computer storage medium storing computer-executable instructions, which are executed by one or more processors to perform the chemical accident early warning and classification method based on accident chains described in the first aspect.

[0045] Fourthly, a computer program product containing instructions is provided, which, when executed on a computer or processor, causes the computer or processor to execute a chemical accident early warning and classification method based on an accident chain, as described in the first to third aspects and any one thereof.

[0046] Fifthly, the present invention also provides a chemical accident early warning and classification system based on an accident chain, including a chemical accident early warning and classification device based on an accident chain as described in the second aspect, and using the chemical accident early warning and classification method based on an accident chain as described in the first aspect to complete the interaction of the chemical accident early warning and classification device based on an accident chain as described in the second aspect.

[0047] Unlike existing technologies, the present invention has at least the following beneficial effects:

[0048] This invention constructs a chemical accident chain to determine the development time and degree of potential hazards for various accidents. It compares and analyzes monitoring data according to the chemical accident chain to determine the matching degree between the currently collected monitoring data and the development time and degree of hazards in the chain, capturing the spatiotemporal characteristics of accident development contained in the monitoring data and obtaining the spatiotemporal matching depth. Based on the spatiotemporal matching depth, it determines the risk warning level corresponding to the measured data and issues warnings and forecasts for the corresponding risk warning levels. This solves the problem that the inability to determine the development time and degree of hazards leads to the inability to capture the initial signs of accidents, resulting in delayed warning responses and missed opportunities to take preventative measures. Attached Figure Description

[0049] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments of the present invention will be briefly described below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0050] Figure 1 This is a flowchart illustrating a chemical accident early warning and classification method based on an accident chain, provided in an embodiment of the present invention.

[0051] Figure 2 This is a schematic diagram of a chemical accident early warning calculation process provided in an embodiment of the present invention;

[0052] Figure 3 This is a schematic diagram illustrating a specific example of a chemical accident chain provided in an embodiment of the present invention;

[0053] Figure 4 This is a flowchart illustrating step 10 provided in an embodiment of the present invention;

[0054] Figure 5 This is a schematic diagram illustrating a specific example of the spatiotemporal feature representation of a potential hazard node provided in an embodiment of the present invention;

[0055] Figure 6 This is a schematic diagram illustrating a specific example of the spatiotemporal characteristic representation of a chemical accident chain provided in an embodiment of the present invention;

[0056] Figure 7 This is a flowchart illustrating step 20 provided in an embodiment of the present invention;

[0057] Figure 8 This is a schematic diagram illustrating a specific example of the spatiotemporal feature representation of another potential hazard node provided in an embodiment of the present invention;

[0058] Figure 9This is a flowchart illustrating step 202 provided in an embodiment of the present invention;

[0059] Figure 10 This is a schematic diagram illustrating a specific example of the spatiotemporal feature representation of a potential accident node provided by an embodiment of the present invention;

[0060] Figure 11 This is a flowchart illustrating step 202 provided in an embodiment of the present invention;

[0061] Figure 12 This is a flowchart illustrating step 202 provided in an embodiment of the present invention;

[0062] Figure 13 This is a flowchart illustrating step 202 provided in an embodiment of the present invention;

[0063] Figure 14 This is a schematic diagram of another chemical accident early warning and classification device based on an accident chain provided in an embodiment of the present invention. Detailed Implementation

[0064] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0065] Unless the context otherwise requires, throughout the specification and claims, the term "comprising" is interpreted as openly inclusive, meaning "including, but not limited to." In the description of the specification, terms such as "one embodiment," "some embodiments," "exemplary embodiment," "example," "specific example," or "some examples" are intended to indicate that a particular feature, structure, material, or characteristic associated with that embodiment or example is included in at least one embodiment or example of this disclosure. The illustrative representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics mentioned may be included in any suitable manner in any one or more embodiments or examples; that is, although they may be incorporated into embodiments or examples using the above terms for reasons such as order and position, it does not limit them to be incorporated in combination by a single embodiment or example.

[0066] In the description of this invention, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this disclosure and simplifying the description, 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 disclosure.

[0067] In the description of this invention, 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 indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of embodiments of this disclosure, unless otherwise stated, "a plurality of" means two or more. Furthermore, for example, the description may use the prefix "A" or "B" to describe the same type of nouns as two independent entities. In this case, the corresponding features defined with "A" and "B" are used only to distinguish between similar entities and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features.

[0068] In describing some embodiments, the terms "coupled," "coupled," and "connected," and their derivative expressions, may be used. For example, the term "connected" may be used in describing some embodiments to indicate that two or more components have direct physical or electrical contact with each other. Similarly, the term "coupled" may be used in describing some embodiments to indicate that two or more components have direct physical or electrical contact. However, the terms "connected" or "coupled" may also refer to two or more components that do not have direct contact with each other but still cooperate or interact with each other, such as "optical coupling," "wireless connection," etc. The embodiments disclosed herein are not necessarily limited to the scope of this invention.

[0069] In the description of this invention, the expression “A and / or B” (where A and B are used to formally represent specific features) will be used. The corresponding expression includes the following three combinations: only A, only B, and a combination of A and B.

[0070] As used in this invention, “about,” “approximately,” or “approximately” includes the stated value and the average value within an acceptable range of deviation from a particular value, wherein the acceptable range of deviation is determined by a person skilled in the art taking into account the measurement under discussion and the error associated with the measurement of the particular quantity (i.e., the limitations of the measurement system).

[0071] Example 1:

[0072] Currently, existing chemical accident risk monitoring and early warning classification schemes have the following three problems:

[0073] (1) Insensitive to the time characteristics of accident development. Accidents usually occur as a process of gradual accumulation and transmission of various hidden dangers. However, traditional monitoring and early warning methods cannot distinguish the spatiotemporal characteristics of various hidden dangers that trigger accidents, that is, they do not differentiate between early warning and imminent expected indicators. This often leads to a lag in early warning response and failure to capture the initial signs of an accident in a timely manner. By the time key early signs of an accident appear, the accident is already imminent, and the best opportunity to take preventive measures has been missed.

[0074] (2) Insensitivity to the spatial characteristics of accident development. Traditional chemical accident monitoring and early warning schemes do not consider the spatial characteristics of accident development, i.e., the degree of development of potential hazards. The development of potential hazards also requires several state transitions, and the state of various potential hazards is constantly changing. Severe potential hazard states may accelerate the occurrence of accidents, while less severe potential hazards may require more time to develop. The monitoring and early warning process should fully consider the development state and stage of each node of the accident in order to improve the accuracy of early warning.

[0075] (3) Difficulty in comprehensive analysis and judgment of monitoring and early warning of multi-source data. Traditional chemical accident monitoring and early warning schemes usually rely on multiple different types of sensors and data sources (such as temperature sensors, pressure sensors and vibration sensors). These data come from different devices and sensors, but traditional schemes often cannot effectively integrate these data, resulting in the phenomenon of "information silos". Coordination and integration between various data sources are difficult to achieve.

[0076] Multi-source monitoring data collected by different sensors and monitoring equipment typically have different acquisition methods, formats, and units of measurement, making it difficult for existing technologies to fully explore the potential correlations between these data sources. When the causes of an accident involve multiple factors, only data from a single source can usually be processed, making it difficult to consider the complex interactions between different stages. This results in existing technologies being unable to effectively and comprehensively analyze and assess the hazard status at each stage of the development from a potential danger to an accident, neglecting the mutual influence and potential risks between monitoring data.

[0077] In summary, existing monitoring and early warning technologies have significant shortcomings in terms of spatiotemporal characteristics and multi-source data fusion, which poses a considerable challenge to accident prevention and emergency response.

[0078] To solve the above problems, such as Figure 1 As shown, this embodiment of the invention provides a chemical accident early warning and classification method based on an accident chain, including:

[0079] Step 10: Construct a chemical accident chain based on the development time and degree of the hidden danger.

[0080] Among them, the development time of the hidden danger is the time characteristic of the accident development, and the degree of development of the hidden danger is the spatial characteristic of the accident development.

[0081] like Figure 2 As shown, this embodiment of the invention pre-determines the development time and corresponding degree of potential hazards for various typical chemical accidents in chemical enterprises (e.g., fires, explosions, poisoning, asphyxiation, etc.), and uses nodes to express the spatiotemporal characteristics of the chemical accident development process to construct the entire chain process of chemical accident occurrence, thus obtaining the corresponding chemical accident chain. Nodes include hazard nodes and accident nodes; accident nodes represent the final possible chemical accident, and hazard nodes represent the risk events at each stage leading to the occurrence of the chemical accident.

[0082] like Figure 3 The diagram illustrates a specific example of a chemical accident chain involving the leakage and explosion of a liquid ammonia storage tank at a chemical plant. Figure 3 The middle circle represents a potential hazard node, and the arrow indicates the time direction of the hazard occurrence. The corresponding chemical accident chain includes potential hazard node A1, potential hazard node A2, potential hazard node A3, potential hazard node A4, potential hazard node B1, potential hazard node B2, potential hazard node B3, and accident node C.

[0083] Step 20: Compare and analyze the monitoring data with the chemical accident chain to obtain the spatiotemporal matching depth.

[0084] This invention analyzes the degree of matching between monitoring data and corresponding chemical accident chains to calculate in real time the risk level of the chemical accident triggered by the chain, i.e., to obtain the spatiotemporal matching depth. For example, when the corresponding monitoring data can match such... Figure 3 When a risk event occurs at hidden danger nodes B1 and B2, it indicates that the risk event represented by hidden danger nodes B1 and B2 has already occurred. That is, the monitoring data has reflected the failure of the electrostatic piles, and it has been detected that personnel are working with static electricity.

[0085] In one alternative embodiment, such as Figure 2 As shown, the monitoring data can be IoT monitoring data, security risk monitoring data, and manual inspection information.

[0086] Step 30: Determine the risk warning level corresponding to the monitoring data based on the spatiotemporal matching depth, so as to make a warning and forecast based on the risk warning level.

[0087] This invention constructs a chemical accident chain to determine the development time and degree of potential hazards for various accidents. It compares and analyzes monitoring data according to the chemical accident chain to determine the matching degree between the currently collected monitoring data and the development time and degree of hazards in the chain, capturing the spatiotemporal characteristics of accident development contained in the monitoring data and obtaining the spatiotemporal matching depth. Based on the spatiotemporal matching depth, it determines the risk warning level corresponding to the measured data and issues warnings and forecasts for the corresponding risk warning levels. This solves the problem that the inability to determine the development time and degree of hazards leads to the inability to capture the initial signs of accidents, resulting in delayed warning responses and missed opportunities to take preventative measures.

[0088] The following is a further explanation of the chemical accident early warning and classification method based on accident chains according to an embodiment of the present invention:

[0089] To illustrate the process of constructing a chemical accident chain, such as Figure 4 As shown, step 10 includes:

[0090] Step 101: For different types of hazards, formulate a hazard development time value table based on the relationship between the hazard development speed, the time range required to develop to the next stage, and the hazard development time.

[0091] The spatiotemporal characteristics of accident hazards refer to the temporal and spatial features exhibited by the causal factors during the evolution of the accident. Temporally, this manifests as the causal sequence and the development time of the hazard. Spatially, it reflects the degree of hazard development. Accidents exhibit distinct spatiotemporal characteristics during their occurrence. Traditional monitoring and early warning analyses neglect these spatiotemporal characteristics, failing to consider the causal sequence of factors or their evolutionary process. Therefore, this invention, based on monitoring and early warning of chemical accident chains, characterizes spatiotemporal features through these chains. Since an accident may be triggered by multiple different risk events, one accident node may correspond to multiple branches (e.g., Figure 3 In the case of an accident node C, there are two branches. A branch is a necessary condition for the occurrence of an accident, and each branch consists of multiple potential hazard nodes. Considering the severity of the accident at the end, the spatiotemporal characteristics of the most vulnerable node at the very end of each branch are not considered. For example, Figure 3 The hidden danger nodes B3 and A4 have no spatiotemporal characteristics.

[0092] Among them, the development time of the hidden danger t f This refers to the time it takes for a potential hazard to develop from its current state to the next stage, such as the time from hazard node A2 "corrosion of liquid ammonia storage tank" to hazard node A3 "liquid ammonia leak alarm". In one optional embodiment, the unit for hazard development time can be the hazard investigation cycle unit or the sampling interval time unit of the monitoring sensor. Figure 3In the specific example shown, the investigation period for the potential hazard of "whether the liquid ammonia storage tank is corroded" is 1 week (7 days), then the corresponding hazard development time t f =4.5.

[0093] This invention provides a specific example of a table for determining the development time of potential hazards, as shown below:

[0094]

[0095]

[0096] The development time and corresponding description of potential hazards, as well as the specific value of the development speed of potential hazards, shall be determined by the relevant personnel based on the specific application scenario, and are not limited here.

[0097] Step 102: Based on the causal sequence of various hidden dangers, construct at least one hidden danger node; according to the hidden danger development time value table, determine the hidden danger development time of the hidden danger node to obtain the initial accident chain.

[0098] This invention uses chemical accidents as a scenario, comprehensively superimposing causal logic and the spatiotemporal dimension of accident evolution to reconstruct the entire process of an accident's initiation, gestation, development, occurrence, and disaster, in order to construct a chemical accident chain for a typical chemical accident.

[0099] In one embodiment, taking a chemical enterprise as an example, key information can be extracted based on the hazardous factor analysis results of the enterprise's risk pre-assessment report. This information can be further structured using phrases such as "...due to improper long-term maintenance or inadequate upkeep, the enterprise's liquid ammonia storage tank may corrode, potentially causing tank rupture or holes, leading to liquid ammonia leakage and diffusion into the space, reaching the explosion limit and exploding upon contact with an open flame...", "...the enterprise's liquid ammonia storage tank is equipped with a liquid ammonia gas leak monitoring sensor according to regulations to monitor the concentration of leaking liquid ammonia gas...", and "...the liquid ammonia storage tank is equipped with an electrostatic discharge device; personnel should release static electricity before entering the tank area, otherwise, static sparks may be generated, leading to an explosion upon contact with leaking gas...". Corresponding hazard nodes can then be constructed, thereby creating a system for identifying potential hazards. Figure 3 The chemical accident chain shown. Figure 3 The middle circle represents a potential hazard node. The occurrence and development of a series of hazards leads to the final accident. The arrows represent the temporal direction of the hazard occurrence and also the causal relationship between the hazard node and / or the accident node. For example, Figure 3 As shown, the potential hazard node can be a risk event characterized by textual data (e.g., monitoring data describing "personnel working with static electricity") or a risk event characterized by numerical data (e.g., monitoring data showing the concentration of liquid ammonia leak, with an alarm triggered when the monitoring data reaches a certain threshold).

[0100] Step 103: For different types of hazards, formulate a hazard development degree value table based on the correlation between the hazard status description and the hazard development degree.

[0101] Among them, the degree of development of hidden dangers d f This refers to the current state of a potential hazard, describing its current condition. For example, the state of hazard node A2, "corrosion of liquid ammonia storage tank," might be "severe." For instance, if the hazard state of "corrosion of liquid ammonia storage tank" is "severe," there is a very high probability of leakage or accelerated leakage, so the corresponding hazard development level d... f 0.9 is acceptable.

[0102] This invention provides a specific example of a table for evaluating the degree of hazard development, as shown below:

[0103]

[0104] Step 104: Determine the degree of hazard development of the hazard nodes on the initial accident chain according to the hazard development degree value table to obtain the chemical accident chain.

[0105] like Figure 5 The diagram illustrates the spatiotemporal characteristics of a chemical accident chain, from hazard node A2 ("liquid ammonia storage tank corrosion") to hazard node A3 ("liquid ammonia leak alarm"). The base width of the triangle between hazard nodes A2 and A3 represents the time it takes for the hazard "liquid ammonia storage tank corrosion" to develop into the "liquid ammonia leak alarm" stage, i.e., the hazard development time. The height of the triangle represents the current degree of development of the hazard "liquid ammonia storage tank corrosion". The larger the triangle area, the lower the degree of hazard deterioration, and the less likely the hazard is to evolve into an accident; conversely, the smaller the triangle area, the higher the degree of hazard deterioration, and the more likely the hazard is to evolve into an accident.

[0106] like Figure 3 The initial accident chain shown is calculated through spatiotemporal feature analysis, resulting in the following: Figure 6 The chemical accident chain is shown. In one embodiment, the initial state of the chemical accident chain has a hazard development level (i.e., hazard state) of 5.0 for all nodes.

[0107] This invention constructs a chemical accident chain to scientifically characterize the development time of potential hazards and express the impact of different development times on monitoring and early warning. At the same time, it can also analyze the transfer process of hazard development status and the impact of different development statuses of hazards on early warning and forecasting.

[0108] In order to analyze and monitor data according to the chemical accident chain, such as Figure 7 As shown, step 20 includes:

[0109] Step 201: Obtain the negligible impact value of the hazard development degree from the hazard development degree value table; calculate the basic spatiotemporal characteristic value according to the negligible impact value and the hazard development time of the hazard node.

[0110] In one embodiment, the negligible impact value can be 5.0.

[0111] Among them, the basic spatiotemporal characteristic value refers to the sum of the spatiotemporal characteristic values ​​of all nodes at the initial stage of the chemical accident chain. For example... Figure 3 As shown, the chemical accident chain includes at least one branch, and the current spatiotemporal characteristic value is the branch spatiotemporal characteristic value of each branch. Figure 6 The chemical accident chain in the text has two branches, and the corresponding basic spatiotemporal characteristic values ​​are: TS b1 and TS b2 In one embodiment, it can be calculated according to the following expression:

[0112]

[0113] Step 202: Determine the current spatiotemporal characteristic value corresponding to the monitoring data according to the chemical accident chain.

[0114] like Figure 8 As shown, the current spatiotemporal characteristic value refers to the sum of the spatiotemporal characteristic values ​​of all nodes in the chemical accident chain after updating the spatiotemporal characteristic values ​​of the corresponding hidden danger nodes in the chemical accident chain using monitoring data, based on the spatiotemporal characteristic values ​​of all nodes at the beginning of the construction of the chemical accident chain.

[0115] Step 203: Calculate the spatiotemporal matching depth based on the current spatiotemporal feature value and the basic spatiotemporal feature value.

[0116] Specifically, such as Figure 9 As shown, step 202 includes:

[0117] Step 2021: Obtain at least one candidate data from the collected data; according to the table of potential hazard development levels, select the candidate data from the at least one candidate data whose potential hazard development level is not a negligible value and determine it as monitoring data.

[0118] Among them, the alternative data are the data collected by all monitoring devices. The alternative data may have different degrees of hazard development. The monitoring data are: the alternative data in which the degree of hazard development is not a negligible value.

[0119] Step 2022: Determine the development level monitoring value corresponding to the monitoring data according to the table of potential hazard development level values.

[0120] For example, when the development level of a potential hazard is "the degree of deterioration is average, slightly exceeding the normal level", the development level monitoring value is 1.0.

[0121] Step 2023: According to the chemical accident chain, determine the hidden danger node corresponding to the monitoring data.

[0122] The matching performed in this embodiment of the invention includes two aspects: first, matching of hidden danger nodes in the chemical accident chain, i.e., single hidden danger matching; second, matching of the entire chemical accident chain, i.e., branch chain matching.

[0123] Step 2024: Based on the development level monitoring value, calculate the spatiotemporal characteristic value of the branch where the hidden danger node corresponding to the monitoring data is located; determine the spatiotemporal characteristic value of other branches in the chemical accident chain to obtain the current spatiotemporal characteristic value.

[0124] Specifically, for each potential hazard node in each branch of the chemical accident chain, when the potential hazard node corresponds to the monitoring data, the product of the development level monitoring value and the potential hazard development time of the potential hazard node is determined as the monitoring characteristic value of the potential hazard node.

[0125] When the potential hazard node is not the same as the potential hazard node corresponding to the monitoring data, the product of the negligible impact value and the potential hazard development time of the potential hazard node is determined as the accident-free characteristic value of the potential hazard node.

[0126] The sum of the monitoring characteristic values ​​and / or accident-free characteristic values ​​of all potential hazard nodes on the branch is determined as the intermediate result of the branch; the intermediate result of the branch is divided by the total number of branches of the chemical accident chain to obtain the spatiotemporal characteristic value of the branch.

[0127] like Figure 10 As shown, after matching the monitoring data with the hidden danger nodes and branches, the spatiotemporal characteristic value TS of the branch is obtained. m This method characterizes the node matching status in a chemical accident chain and updates the status of corresponding potential hazard nodes in the basic spatiotemporal feature values ​​using monitoring data. It should be noted that the spatiotemporal feature values ​​of nodes before matching are not included in the calculation of the chemical accident chain.

[0128] For example, if on-site inspectors discover a potential hazard of "unsecured grounding of electrostatic discharge posts," they will use this as monitoring data and determine that the hazard's development level is moderate, i.e., hazard development level d. f =1.0. Simultaneously, the on-site monitoring equipment detected a high-level "liquid ammonia leak alarm," indicating a high concentration and a potential hazard development level (d). f =0.9. Calculate the branch spatiotemporal characteristic value TS of the monitoring data. m as follows:

[0129]

[0130] In this chemical accident chain, there are a total of 2 branches. Hazardous node B1 is the hazardous node corresponding to the monitoring data, and its corresponding development level monitoring value is set to 1.0. Hazardous node B2 is not a hazardous node corresponding to the monitoring data. For the branch "Hazardous node A1 - Hazardous node A2 - Hazardous node A3 - Hazardous node A4 - Accident node C", since the hazardous node corresponding to the monitoring data is hazardous node A3, the spatiotemporal characteristic values ​​of the nodes before matching (e.g., hazardous nodes A1 and A2) are not included in the calculation. Hazardous node A3 is the hazardous node corresponding to the monitoring data, and its corresponding development level monitoring value is set to 0.9. It should be noted that, if... Figure 10 As shown, the development level monitoring value, the negligible impact value, or the development time of each hidden danger node are: the development level monitoring value, the negligible impact value, or the development time of the hidden danger node from the development of the next hidden danger node; for example, for hidden danger node B1, the corresponding development level monitoring value is: the development level monitoring value corresponding to the development of hidden danger node B2 from hidden danger node B1.

[0131] For the branch "Hidden Danger Node B1 - Hidden Danger Node B2 - Hidden Danger Node B3 - Accident Node C": the development level monitoring value of hidden danger node B1 is 1.0, and the hidden danger development time of hidden danger node B1 is 4.5. The product of these two values ​​is determined as the monitoring characteristic value of hidden danger node B1. The negligible impact value of hidden danger node B2 is 5.0, and the hidden danger development time of hidden danger node B2 is 4.5. The product of these two values ​​is determined as the accident-free characteristic value of hidden danger node B2. Since this embodiment of the invention considers the urgency of the terminal accident and does not consider the spatiotemporal characteristics of the last hidden danger node in each branch, the sum of the monitoring characteristic value of hidden danger node B1 and the accident-free characteristic value of hidden danger node B2 is the intermediate result of the branch. Dividing this intermediate result by 2 yields the spatiotemporal characteristic value of the corresponding branch (i.e., TS). m1 ).

[0132] For the branch "Hidden Danger Node A3 - Hidden Danger Node A4 - Accident Node C": the development level monitoring value of hidden danger node A3 is 0.9, and the hidden danger development time of hidden danger node A3 is 0.9. The product of the two is determined as the monitoring characteristic value of hidden danger node A3. Since the spatiotemporal characteristic values ​​of nodes before matching (e.g., hidden danger nodes A1 and A2) are not included in the calculation, the monitoring characteristic value of hidden danger node A3 is the intermediate result of this branch. Dividing the intermediate result of this branch by 2 gives the spatiotemporal characteristic value of the corresponding branch (i.e., TS). m2 ).

[0133] like Figure 11 As shown, step 203 includes:

[0134] Step 2031: The sum of the accident-free characteristic values ​​of all potential hazard nodes on the branch is determined as the branch baseline value.

[0135] The spatiotemporal matching depth refers to the spatiotemporal matching depth of each branch.

[0136] Among them, the no-accident characteristic value refers to the value of the hazard development degree of a hazard node in the hazard development degree value table, which is the hazard development degree of "very light".

[0137] Step 2032: The ratio of the spatiotemporal feature value of the branch to the reference value of the branch is determined as the spatiotemporal matching depth of the branch.

[0138] Among them, the spatiotemporal matching depth D of the branch m This refers to the spatiotemporal feature value TS of the matched branch in the branch. m With fundamental spatiotemporal eigenvalues ​​TS b The ratio. D m ∝(0,1), and D m The larger the value of D, the deeper the node matching depth, but the farther the matched potential nodes are from the accident nodes, and the lower the probability of an accident occurring; conversely, the smaller the value of D... m The smaller the value, the shallower the node matching depth, but the closer the matched potential node is to the accident node, the greater the likelihood of the accident occurring. For example, ... Figure 10 The two accident branches in the data, and the corresponding spatiotemporal matching depth D of the branches. m1 D m2 :D m1 =TS m1 ÷TS b1 =13.5÷22.5=0.6

[0139] D m2 =TS m2 ÷TS b2 =0.405 ÷ 33.5 = 0.012

[0140] This invention follows the laws of accident evolution, reconstructing the entire risk evolution process through a chemical accident chain. It collects and analyzes monitoring data to comprehensively analyze the inherent early warning information of potential hazards, mines risk warning features contained in multi-source data, and scientifically calculates the risk warning level. Specifically, to determine the risk warning level of a chemical accident chain, such as... Figure 12 As shown, step 30 includes:

[0141] Step 301: Calculate the risk warning value according to the spatiotemporal matching depth of each branch.

[0142] The spatiotemporal matching depth of a branch represents the completeness of all aspects of the accident prevention process. The greater the spatiotemporal matching depth of a branch, the more complete the accident prevention process on that branch, the less likely the accident corresponding to the accident node is to occur, and the smaller the risk warning value should be. Conversely, the smaller the spatiotemporal matching depth of a branch, the less complete the accident prevention process on that branch, the more likely the corresponding accident is to occur, and the larger the risk warning value should be.

[0143] Step 302: Obtain the risk warning level according to the level mapping relationship and the risk warning value.

[0144] This invention combines historical industry hazard statistics to predetermine the mapping relationship between the range of risk warning values ​​and the risk warning level, and determines the risk warning level according to the corresponding range and risk warning value.

[0145] This invention provides a specific example of a level mapping relationship, as shown in the table below:

[0146]

[0147] In an optional embodiment, warnings and forecasts can also be provided using color.

[0148] For example, by combining on-site monitoring data such as "unsecured grounding of electrostatic piles" and "high alarm for liquid ammonia leakage", the risk warning level can be determined to be "Level 2", and the corresponding warning color is "orange". It also indicates that "the risk is close to the accident critical point", and a warning message should be issued immediately to take risk prevention and control measures in advance.

[0149] To illustrate the process of calculating the risk warning value, in one embodiment, such as Figure 13 As shown, step 301 includes:

[0150] Step 3011: The difference between the preset unit value and the spatiotemporal matching depth of each branch is determined as the branch matching value of the branch.

[0151] The preset unit value is selected by the skilled personnel according to the specific use scenario; in an optional embodiment, the preset unit value can be 1.

[0152] For example, the spatiotemporal matching depth of a branch is D. m1 The corresponding branch matching value is 1-D m1 The spatiotemporal matching depth of the other branch is D. m2 The corresponding branch matching value is 1-D m2 .

[0153] Step 3012: The product of the branch matching values ​​of each branch is determined as the risk warning value.

[0154] For example, the risk warning value E = (1-D) m1 )×(1-D m2 = (1-0.6)×(1-0.012)=0.3952.

[0155] The embodiments of the present invention can specifically improve the accuracy of chemical accident chain alarms, and the accuracy of risk warning levels will be greatly improved, thereby effectively preventing the occurrence of chemical accidents and reducing losses.

[0156] Example 2:

[0157] like Figure 14 The diagram shown is an architectural schematic of a chemical accident early warning and classification device based on an accident chain according to an embodiment of the present invention. This embodiment of the chemical accident early warning and classification device based on an accident chain includes one or more processors 21 and a memory 22. Wherein, Figure 14 Take a processor 21 as an example.

[0158] Processor 21 and memory 22 can be connected via a bus or other means. Figure 14 Taking the example of a connection between China and Israel via a bus.

[0159] The memory 22, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs and non-volatile computer-executable programs, such as the chemical accident early warning and classification method based on accident chains in this embodiment. The processor 21 executes the chemical accident early warning and classification method based on accident chains by running the non-volatile software programs and instructions stored in the memory 22.

[0160] Memory 22 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, memory 22 may optionally include memory remotely located relative to processor 21, which can be connected to processor 21 via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0161] The program instructions / modules are stored in the memory 22. When executed by one or more processors 21, they execute the chemical accident early warning and classification method based on accident chain in the above embodiments, for example, executing each step of the chemical accident early warning and classification method based on accident chain in the embodiments of the present invention described above.

[0162] This invention also provides a non-volatile computer storage medium storing computer-executable instructions that are executed by one or more processors, for example... Figure 14A processor 21 can enable one or more of the processors to execute the chemical accident early warning and classification method based on accident chain in the specific embodiments of the present invention, for example, to execute the various steps of the chemical accident early warning and classification method based on accident chain in the embodiments of the present invention described above; it can also implement Figure 14 The various modules and units described above; or the chemical accident early warning and classification method based on accident chain in the specific embodiments of the present invention, for example, executing the various steps of the chemical accident early warning and classification method based on accident chain in the embodiments of the present invention described above; can also be implemented. Figure 14 The various modules and units mentioned above.

[0163] It is worth noting that the information interaction and execution process between the modules and units in the above-mentioned device and system are based on the same concept as the processing method embodiment of the present invention. For details, please refer to the description in the method embodiment of the present invention, and will not be repeated here.

[0164] Those skilled in the art will understand that all or part of the steps in the various methods of the embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc.

[0165] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A chemical accident early warning and classification method based on accident chains, characterized in that, include: Based on the development time and degree of potential hazards, a chemical accident chain is constructed, including: For different types of hazards, a hazard development time value table is developed based on the relationship between the hazard development speed, the time range required to develop to the next stage, and the hazard development time; based on the causal sequence of various hazards, at least one hazard node is constructed; according to the hazard development time value table, the hazard development time of the hazard node is determined to obtain the initial accident chain; For different types of hazards, a hazard development degree value table is developed based on the relationship between the hazard status description and the hazard development degree; according to the hazard development degree value table, the hazard development degree of the hazard nodes on the initial accident chain is determined to obtain the chemical accident chain. The monitoring data is compared and analyzed with the chemical accident chain to obtain the spatiotemporal matching depth, including: obtaining the negligible impact value of the hazard development degree from the hazard development degree value table; summing the negligible impact value with the product of the hazard development time of all hazard nodes in the chemical accident chain to obtain the basic spatiotemporal characteristic value; determining the current spatiotemporal characteristic value corresponding to the monitoring data according to the chemical accident chain; and determining the spatiotemporal matching depth based on the ratio of the current spatiotemporal characteristic value to the corresponding basic spatiotemporal characteristic value. Based on the spatiotemporal matching depth, the risk warning level corresponding to the monitoring data is determined, so as to make early warning and forecast based on the risk warning level.

2. The chemical accident early warning and classification method based on accident chain as described in claim 1, characterized in that, The chemical accident chain includes at least one branch, and the current spatiotemporal characteristic value is the branch spatiotemporal characteristic value of each branch. The step of determining the current spatiotemporal characteristic value corresponding to the monitoring data according to the chemical accident chain includes: Acquire at least one candidate data from the collected data; according to the table of potential hazard development levels, select the candidate data from the at least one candidate data whose potential hazard development level is not negligible and determine it as monitoring data; According to the table of potential hazard development levels, determine the development level monitoring value corresponding to the monitoring data; Based on the aforementioned chemical accident chain, the potential hazard nodes corresponding to the monitoring data are determined; For each potential hazard node in each branch of the chemical accident chain, when the potential hazard node is the potential hazard node corresponding to the monitoring data, the product of the development degree monitoring value and the potential hazard development time of the potential hazard node is determined as the monitoring characteristic value of the potential hazard node. When the hidden danger node is not the hidden danger node corresponding to the monitoring data, the product of the negligible impact value and the hidden danger development time of the hidden danger node is determined as the accident-free characteristic value of the hidden danger node. The sum of the monitoring characteristic values ​​and / or accident-free characteristic values ​​of all potential hazard nodes on the branch is determined as the intermediate result of the branch; the intermediate result of the branch is divided by the total number of branches of the chemical accident chain to obtain the spatiotemporal characteristic value of the branch.

3. The chemical accident early warning and classification method based on accident chain as described in claim 2, characterized in that, The spatiotemporal matching depth is the spatiotemporal matching depth of each branch; The step of calculating the spatiotemporal matching depth based on the current spatiotemporal feature value and the basic spatiotemporal feature value includes: The sum of the accident-free characteristic values ​​of all potential hazard nodes on the branch is determined as the branch baseline value. The ratio of the spatiotemporal feature value of the branch to the baseline value of the branch is determined as the spatiotemporal matching depth of the branch.

4. The chemical accident early warning and classification method based on accident chain as described in claim 1, characterized in that, The chemical accident chain includes at least one branch chain, and the spatiotemporal matching depth is the branch spatiotemporal matching depth of each branch chain; the level mapping relationship between the range of risk warning values ​​and the risk warning level is predetermined; The step of determining the risk warning level corresponding to the monitoring data based on the spatiotemporal matching depth includes: The difference between the preset unit value and the spatiotemporal matching depth of each branch is determined as the branch matching value of the branch; the product of the branch matching values ​​of each branch is determined as the risk warning value. The risk warning level is obtained according to the level mapping relationship and the risk warning value.

5. A chemical accident early warning and classification device based on an accident chain, characterized in that, The chemical accident early warning and classification device based on the accident chain includes at least one processor and a memory, which are connected via a data bus. The memory stores instructions that can be executed by the at least one processor. After being executed by the processor, the instructions are used to implement the chemical accident early warning and classification method based on the accident chain as described in any one of claims 1-4.

6. A non-volatile computer storage medium, characterized in that, The computer storage medium stores computer-executable instructions, which are executed by one or more processors to perform the chemical accident early warning and classification method based on accident chains as described in any one of claims 1-4.

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