Port dangerous cargo risk assessment method and system based on V-HAZOP model

Abstract

The invention provides a port dangerous cargo risk assessment method and system based on a V-HAZOP model, and relates to the technical field of risk assessment, and the method comprises the steps: obtaining the feature information and additional information of port dangerous cargos, and building a risk assessment basic data set; preprocessing the risk assessment basic data set based on an HAZOP method, and establishing a risk data index library; analyzing the risk data index library based on a correlation analysis algorithm, and updating the risk data index library; obtaining final risk index data on the basis of a Bayesian network conversion method based on the fault tree analysis model; performing visual data modeling according to the final risk index data, and constructing a V-HAZOP model; displaying the V-HAZOP model by using a VIEW-Glass multi-dimensional visualization technology so as to obtain data; according to the invention, the management difficulties of various types of dangerous goods, different physicochemical properties, high operation requirements and the like in the existing port can be solved, the safety production operation of the dangerous goods storage yard is optimized comprehensively, and the professional storage yard intelligent operation management of the dangerous goods in the port is realized.

Description

Technical Field

[0001] The present application relates to the technical field of risk assessment, and in particular to a method and system for risk assessment of dangerous goods at ports based on a V-HAZOP model. Background Art

[0002] With the continuous development of global trade, ports, as important hubs for the import and export of goods, are also increasing the throughput of dangerous goods. Dangerous goods involve a variety of complex situations in the yard, warehousing, loading and unloading, and transportation of ports. These dangerous goods may include explosives, flammable materials, toxic substances, etc. Once an accident occurs, it will not only cause serious damage to port facilities and staff, but also may have a catastrophic impact on the surrounding environment, residents' health, and the smooth progress of international trade. In the traditional risk assessment of dangerous goods at ports, data collection is often not comprehensive enough. For information on dangerous goods, only some characteristics, such as types and quantities, may be focused on, while other important additional information is ignored. Moreover, these data come from a wide range of sources and lack effective integration methods, making it difficult to ensure the accuracy of the data. Moreover, when analyzing risks, traditional models often do not deeply explore the correlation between data, and cannot accurately identify the mutual influence between risk factors, resulting in inaccurate risk assessment results. In terms of presenting risk assessment results, traditional methods lack effective visualization methods. It is difficult for decision makers to intuitively understand complex risk data and cannot quickly identify key information from many risk indicators. Therefore, a method is needed to solve the above problems.

[0003] In summary, the traditional risk assessment of dangerous goods at ports has problems such as incomplete data collection, low accuracy, inaccurate risk assessment results, and unclear structure, which seriously affect the safe management of cargo operations at ports. Summary of the invention

[0004] The present invention discloses a port dangerous goods risk assessment method and system based on the V-HAZOP model, which is used to solve the technical problems in the prior art such as vague risk assessment of dangerous goods at ports, unclear definition of dangerous goods risk status, difficulty in achieving dangerous goods prevention and control, and even potential safety hazards.

[0005] According to a first aspect of the present disclosure, a risk assessment method for dangerous goods at a port based on a V-HAZOP model is provided, comprising: Acquire characteristic information and additional information of dangerous goods at ports, and establish a basic data set for risk assessment, wherein the characteristic information includes the type, quantity, storage conditions, and transportation routes of dangerous goods, and the additional information includes monitoring data of storage yards, warehousing, meteorology, and the environment; preprocess the basic data set for risk assessment based on the HAZOP method, divide the HAZOP nodes, integrate the risk indicator data obtained by HAZOP analysis with the data in the MSDS file library, and establish a risk data indicator library, wherein the HAZOP nodes cover all key links in the operation of dangerous goods storage yards at ports; analyze the risk data indicator library based on the association analysis algorithm, mine the associations between item sets, filter out strong association rules according to the set minimum confidence, analyze the item sets in the strong association rules, filter out indicators with lower risk levels, and update the risk data indicator library, wherein the item sets include indicator data and dangerous results in the risk data indicator library, the minimum confidence is a preset minimum confidence threshold, and the strong association rules are association rules with confidence greater than or equal to the minimum confidence; transform the fault tree analysis model into the Bayesian network Based on the method, the relationship between the fault tree event and the Bayesian network node is obtained, the fault tree logical relationship is mapped to the Bayesian network, the Bayesian network model is constructed, and the final risk indicator data is obtained. The Bayesian network model screens the risk indicator data again through probability calculation; visual data modeling is performed according to the final risk indicator database, and on the basis of HAZOP analysis, a V-HAZOP model is constructed in combination with the visual data modeling results, and the indicator data after processing and analysis is connected with the V-HAZOP model. The construction of the V-HAZOP model is to determine the structure, nodes, guide words and parameter elements of the model, and to establish connections and relationships between nodes; VIEW-Glass multidimensional visualization technology is used to display the data obtained by the V-HAZOP model. The V-HAZOP model includes a decision-making VIEW-Glass model and a special management VIEW-Glass model. The decision-making VIEW-Glass model includes key indicators and dynamics, and the special management VIEW-Glass includes functional indicators and thematic indicators that are focused on.

[0006] According to a second aspect of the present disclosure, a port dangerous goods risk assessment system based on a V-HAZOP model is provided, comprising: A risk assessment basic data set establishment module, which is used to obtain characteristic information and additional information of dangerous goods at ports, and establish a risk assessment basic data set, wherein the characteristic information includes the type, quantity, storage conditions, and transportation routes of dangerous goods, and the additional information includes monitoring data of storage yards, warehousing, meteorology, and the environment; a risk data indicator library establishment module, which is used to pre-process the risk assessment basic data set based on the HAZOP method, divide the HAZOP nodes, integrate the risk indicator data obtained by HAZOP analysis with the data in the MSDS file library, and establish a risk data indicator library, wherein the HAZOP nodes cover all key links in the operation of the dangerous goods storage yard at ports; a risk data indicator library optimization module, which is used to optimize the risk data indicator library based on the association analysis algorithm The method analyzes the risk data indicator library, mines the association between item sets, selects strong association rules according to the set minimum confidence, analyzes the item sets in the strong association rules, filters out indicators with lower risk levels, and updates the risk data indicator library, wherein the item sets include indicator data and dangerous results in the risk data indicator library, the minimum confidence is a preset minimum confidence threshold, and the strong association rules are association rules with confidence greater than or equal to the minimum confidence; a Bayesian network risk assessment module, wherein the Bayesian network risk assessment module is used to obtain the relationship between the fault tree event and the Bayesian network node based on the fault tree analysis model to Bayesian network conversion method, map the fault tree logical relationship to the Bayesian network, complete the construction of the Bayesian network model, and obtain the final risk indicator data, and the Bayesian network model once again screens the risk indicator data through probability calculation; V - HAZOP model construction module, the V-HAZOP model construction module is used to perform visual data modeling according to the final risk indicator database, and on the basis of HAZOP analysis, the V-HAZOP model is constructed in combination with the visual data modeling results, and the indicator data after processing and analysis is connected with the V-HAZOP model. The construction of the V-HAZOP model is to determine the structure, nodes, guide words and parameter elements of the model, and establish connections and relationships between nodes; data display module, the data display module is used to use VIEW-Glass multi-dimensional visualization technology to display the data obtained by the V-HAZOP model, and the V-HAZOP model includes a decision-making VIEW-Glass model and a special management VIEW-Glass model. The decision-making VIEW-Glass model includes key indicators and dynamics, and the special management VIEW-Glass includes functional indicators and special indicators of key concern.

[0007] One or more technical solutions provided in the present disclosure have at least the following technical effects or advantages: obtaining characteristic information and additional information of dangerous goods at ports, establishing a basic data set for risk assessment, wherein the characteristic information includes the type, quantity, storage conditions, and transportation routes of dangerous goods, and the additional information includes monitoring data of storage yards, warehousing, meteorology, and the environment; preprocessing the basic data set for risk assessment based on the HAZOP method, dividing HAZOP nodes, integrating the risk indicator data obtained by HAZOP analysis with the data in the MSDS file library, and establishing a risk data indicator library, wherein the HAZOP nodes cover all key links in the operation of dangerous goods storage yards at ports; analyzing the risk data indicator library based on an association analysis algorithm, mining the associations between item sets, filtering out strong association rules according to a set minimum confidence, analyzing the item sets in the strong association rules, filtering out indicators with a lower risk level, and updating the risk data indicator library, wherein the item sets include indicator data and dangerous results in the risk data indicator library, wherein the minimum confidence is a preset minimum confidence threshold, and the strong association rules are association rules with a confidence greater than or equal to the minimum confidence; Based on the method of transforming the fault tree analysis model into the Bayesian network, the relationship between the fault tree event and the Bayesian network node is obtained, the fault tree logical relationship is mapped to the Bayesian network, the Bayesian network model is constructed, and the final risk indicator data is obtained. The Bayesian network model screens the risk indicator data again through probability calculation; visual data modeling is performed according to the final risk indicator database, and on the basis of HAZOP analysis, a V-HAZOP model is constructed in combination with the visual data modeling results, and the indicator data after processing and analysis is connected with the V-HAZOP model. The construction of the V-HAZOP model is to determine the structure, nodes, guide words and parameter elements of the model, and to establish connections and relationships between nodes; VIEW-Glass multidimensional visualization technology is used to display the data obtained by the V-HAZOP model. The V-HAZOP model includes a decision-making VIEW-Glass model and a special management VIEW-Glass model. The decision-making VIEW-Glass model includes key indicators and dynamics, and the special management VIEW-Glass includes functional indicators and thematic indicators that are focused on. It solves the technical problems in the existing technology, such as vague risk assessment of dangerous goods at ports, unclear definition of dangerous goods risk status, difficulty in preventing and controlling dangerous goods, and even the existence of safety hazards. It achieves the technical effect of improving the accuracy of risk indicator data, the level of safety management of dangerous goods at ports, and the accuracy of on-site operations.

[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the present disclosure or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0010] Figure 1 A schematic diagram of a process for risk assessment of dangerous goods at ports based on a V-HAZOP model provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of a port dangerous goods risk assessment system based on the V-HAZOP model provided in an embodiment of the present application.

[0011] Figure 3 A port flow chart of a dangerous goods risk assessment method and system at a port based on a V-HAZOP model provided in an embodiment of the present application; Explanation of the reference numerals: risk assessment basic data set establishment module 11, risk data indicator library establishment module 12, risk data indicator library optimization module 13, Bayesian network risk assessment module 14, V-HAZOP model construction module 15, data display module 16. DETAILED DESCRIPTION

[0012] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0013] Embodiment 1: The risk assessment method for dangerous goods at ports based on the V-HAZOP model provided in the embodiment of the present disclosure is referred to Figure 1 For illustration, the methods include: Obtain characteristic information and additional information of dangerous goods at ports and establish a basic data set for risk assessment. The characteristic information includes the type, quantity, storage conditions, and transportation routes of dangerous goods; the additional information includes monitoring data of storage yards, warehousing, meteorology, and the environment.

[0014] Specifically, systematic data collection includes characteristic information of the items themselves and additional information of the external environment. Characteristic information mainly includes data on the physical and chemical properties of items. For this category of data, it is necessary to collect data from multiple reliable sources. On the one hand, it is necessary to consult the information provided by the manufacturer of the goods, which usually contains detailed product specifications, such as the state, color, odor, density, melting point, boiling point and other basic physical properties of the substance, as well as chemical properties such as reactivity, oxidizability, reducibility and corrosivity. On the other hand, refer to relevant chemical databases, such as the chemical database of the American Chemical Society (ACS), which brings together a large amount of basic research data on chemical substances. Additional information mainly includes storage condition data. For the collection of storage condition data, it is necessary to check the warehouse facility documents of the port, including the type of warehouse, storage temperature and humidity requirements, ventilation conditions, etc.

[0015] Specifically, to ensure the accuracy of the data, the collected data must be cross-validated. Physical and chemical property data must be verified by comparing multiple chemical databases or data on the same product provided by different manufacturers. Storage condition data must be verified with the actual facilities at the port. At the same time, a data list must be established, classified according to the type, nature, storage and other categories of goods, to ensure that no data in any aspect is missed and the integrity of the data is guaranteed.

[0016] Based on the HAZOP method, the risk assessment basic data set is preprocessed, HAZOP nodes are divided, the risk indicator data obtained by HAZOP analysis is integrated with the data in the MSDS file library, and a risk data indicator library is established. The HAZOP nodes cover all key links of dangerous goods storage at ports.

[0017] Specifically, the established risk assessment basic data set is reviewed in detail to understand the various types of information on dangerous goods at ports contained in the data set, and to determine the appropriate HAZOP analysis direction. All key links in the storage of dangerous goods at ports are clarified, including the acceptance of dangerous goods upon arrival, the sorting and storage of different types of dangerous goods, and the operation of fire protection and safety facilities. Nodes are refined and boundaries are determined. Each key link is further refined into a specific HAZOP node, and sub-nodes under each node are set one by one. The input, output, and operating conditions of each node are determined. At the same time, for each HAZOP node, the corresponding HAZOP guide words are set to express the deviation, so as to statistically organize the indicator data involved in each node and the abnormal state expression method of the indicator data.

[0018] Specifically, the risk indicator data obtained from the HAZOP method needs to be matched with the data in the MSDS file library. For example, if the HAZOP analysis shows that a certain dangerous goods may increase the risk of leakage due to improper operation during storage, the physical and chemical properties of the goods, emergency treatment measures and other related data can be found in the MSDS file library through the name of the goods, UN number and other identification information. Based on the information obtained by both parties, the matched MSDS data and the HAZOP indicator data are integrated to jointly determine the comprehensive degree of harm to the surrounding environment and personnel in the event of a leak. Establish a unified data structure, store the integrated data, and form a risk data indicator library. This indicator library can be used for subsequent risk assessment, decision support and other work, such as formulating safety management strategies and emergency plans for dangerous goods storage yards at ports.

[0019] The risk data indicator library is analyzed based on an association analysis algorithm, associations between item sets are mined, strong association rules are screened out according to a set minimum confidence, item sets in the strong association rules are analyzed, indicators with lower risk levels are filtered out, and the risk data indicator library is updated. The item sets include indicator data and dangerous results in the risk data indicator library, the minimum confidence is a preset minimum confidence threshold, and the strong association rule is an association rule with a confidence greater than or equal to the minimum confidence.

[0020] Specifically, the risk data indicator library is analyzed and processed based on the association analysis algorithm. The main steps include: data preparation, formatting the data in the risk data indicator library to make it suitable for the input requirements of the association analysis algorithm, and determining the item sets in the data set; calculating the support and confidence of the item sets, the support represents the frequency of the item set in the entire data set, and the confidence represents the probability of the occurrence of another item set when one item set appears.

[0021] Specifically, strong association rules are screened out according to the set minimum confidence, including: determining the minimum confidence threshold, and presetting the minimum confidence threshold according to actual needs and experience. The threshold can be determined by analyzing historical data, industry standards or expert opinions; screening strong association rules, traversing all calculated association rules, and screening out rules with confidence greater than or equal to the minimum confidence threshold. These strong association rules reflect the association relationship with high credibility between risk indicators in the risk data indicator library.

[0022] Specifically, the item sets in the strong association rules are analyzed, and the indicators with lower risk levels are excluded and filtered out. This step includes: risk level assessment, for each item set in the strong association rules, its risk level is assessed according to the pre-defined risk level assessment criteria. For example, if the frequency of occurrence of certain indicator data is low, it may be assessed as a low-risk indicator; while for some inherent dangerous characteristics of dangerous goods, it may be assessed as a high-risk indicator; indicator filtering, excluding those indicators with lower risk levels. This helps to simplify the risk data indicator library and focus on key indicators that have a greater impact on risks. If an item set contains multiple risk indicators, one of which is a low-risk protective measure, it can be removed from the item set.

[0023] Based on the method of transforming the fault tree analysis model into the Bayesian network, the relationship between the fault tree events and the Bayesian network nodes is obtained, the fault tree logical relationship is mapped to the Bayesian network, the Bayesian network model is constructed, and the final risk indicator data is obtained. The Bayesian network model once again screens the risk indicator data through probability calculation.

[0024] Specifically, the most basic event unit in the fault tree that cannot be further subdivided is directly mapped as the root node in the Bayesian network as the basic event. The root node is the starting node without a parent node in the Bayesian network, just like the basic event is the lowest level element of the fault tree analysis; at the same time, the intermediate events obtained by combining basic events or through certain logical relationship operations in the fault tree are converted into intermediate nodes of the Bayesian network. The intermediate node has a parent node in the Bayesian network, and its parent node may be the root node or other intermediate node, and its state is affected by the state of the parent node; and the top event in the fault tree, that is, the final result event of the entire fault tree analysis, needs to be converted into a leaf node in the Bayesian network.

[0025] Specifically, the logical relationship of the fault tree is mapped to the Bayesian network. The specific steps of converting the "logical and" relationship in the fault tree into the conditional probability of the Bayesian network include: S1: Analyze and extract the logical AND relationship in the fault tree. If events A and B jointly cause event C, then A, B and C are in a logical AND relationship. This logical relationship means that C will only occur when A and B occur at the same time. S2: Find the nodes corresponding to events A, B, and C in the Bayesian network and determine the connection relationship between the nodes. For the logical AND relationship, the conditional probability distribution of event C in the Bayesian network is expressed as: P (C | A, B); S3: When A=true and B=true, the value of P(C=true|A=true, B=true) is the value determined by the logical AND relationship in the fault tree, which is usually 1, because if A and B occur at the same time, C will inevitably occur. When A=false or B=false, the value of P(C=true|A,B) is 0, because the logical AND relationship requires A and B to occur at the same time.

[0026] Specifically, the logical relationship of the fault tree is mapped to the Bayesian network. The specific steps of converting the "logical or" relationship in the fault tree into the conditional probability of the Bayesian network include: S1: Analyze and extract the logic or relationship in the fault tree. In the fault tree, as long as event D or event E occurs, event F will occur. Then event D and event E lead to intermediate event F through the logic or relationship. S2: Find the nodes corresponding to events D, E, and F in the Bayesian network and determine the connection relationship between the nodes. For the logical OR relationship in the Bayesian network, the relationship between the corresponding nodes D, E, and F is expressed as: P (F|D, E); S3: When D=true or E=true, P(F=true|D=true, E); or P(F=true|D,E=true); The value of P is determined by the "logical or" relationship in the fault tree. This value is usually 1, because if D or E occurs, F will inevitably occur. When D=false and E=false, The value of P ( F=true|D=false, E=false ) is 0.

[0027] Specifically, according to the above-mentioned mapping transformation logic, a directed acyclic graph and a conditional probability table are formed to complete the modeling of the Bayesian network. The Bayesian network model is used for risk analysis, and the data in the risk data indicator library is input into the model. After obtaining the optimized risk data, the model calculates the probability value of each node based on the data, and classifies the data according to the probability relationship of each node, and then obtains the final risk assessment result through the importance of the root node, and obtains the final risk indicator data.

[0028] Visual data modeling is performed according to the final risk indicator database. On the basis of HAZOP analysis, a V-HAZOP model is constructed in combination with the visual data modeling results. The indicator data after processing and analysis is data-connected with the V-HAZOP model. The construction of the V-HAZOP model is to determine the structure, nodes, guide words and parameter elements of the model, and to establish connections and relationships between nodes.

[0029] Specifically, we first need to deeply understand the meaning and characteristics of risk indicator data categories, organize data of different categories, and ensure the integrity and accuracy of the data. Then we preprocess the data, including cleaning the data, removing outliers and duplicate data, and standardizing it, so as to convert data of different magnitudes into comparable forms for subsequent modeling work.

[0030] Specifically, select a visualization modeling method and build a visualization model. According to the nature of the risk indicator data, select an appropriate visualization modeling method. For multi-dimensional risk indicator data, it is necessary to use complex visualization methods such as radar charts and Sankey diagrams, combined with modeling techniques such as cluster analysis and principal component analysis. Use radar charts to show the performance of different devices in multiple risk indicator dimensions, classify devices with similar risk conditions into one category through cluster analysis, and build a visualization-based clustering model. When building a model using the selected visualization method and modeling technology, the relationship between different risk indicator data categories must be clearly expressed. For multi-dimensional visualization models, the coordinate axes or hierarchies must be accurately set to reflect the different dimensions of risk indicators and their interrelationships.

[0031] Specifically, determine the model structure, based on the HAZOP method and subsequent optimization processing, determine the basic structure of the V-HAZOP model. HAZOP analysis has already conducted risk assessments on various parts of the system, and the structure of the V-HAZOP model should reflect the relationship between these parts. Establish data connection, complete data mapping and conversion according to the determined V-HAZOP model, and establish actual data connection. Use database connection technology or file transfer protocol to achieve data transmission and connection from the indicator data storage location after processing and analysis to the V-HAZOP model.

[0032] The VIEW-Glass multi-dimensional visualization technology is used to display the data obtained by the V-HAZOP model. The V-HAZOP model includes a decision-making VIEW-Glass model and a special management VIEW-Glass model. The decision-making VIEW-Glass model includes key indicators and dynamics, and the special management VIEW-Glass includes key functional indicators and thematic indicators.

[0033] Specifically, design the interactive interface and interactive elements in the VIEW-Glass visualization interface to display the associations between models. Set up a navigation menu or sidebar that contains links to related models and indicators. Ensure that the interactive operations are intuitive and convenient. For example, use icons or prompts to guide users to perform interactive operations, and display the available operations when the mouse hovers over the relevant elements.

[0034] Specifically, layout planning, planning the layout of the overall view, and reasonably placing the visualization results of the decision-making and special management VIEW-Glass models in one interface. Visualize the key indicators of the decision-making model at the top of the interface as an overview of the overall situation; visualize the functional indicators and thematic indicators of the special management model at the bottom of the interface, and distinguish them by columns or groups. Consider the spatial proportion and visual balance between different visualization elements to ensure the aesthetics and readability of the overall view. Avoid making a visualization element too large or too small, which affects the overall visual effect.

[0035] Specifically, integrate visualization elements, establish connections and interactive relationships between visualization elements, and use a unified visual style and color scheme to make the entire VIEW - Glass multi-dimensional visualization system consistent.

[0036] The method provided in the embodiment of the present disclosure also includes: Systematically collect data on dangerous goods at ports, including but not limited to the physical and chemical properties of the goods, storage conditions, and historical accident records; Divide the handling process of dangerous goods loading, unloading, storage and transportation at ports into HAZOP nodes to ensure comprehensive coverage of all possible dangerous scenarios during port transportation; Based on the data of dangerous goods at the port, according to the detailed chemical and safety properties of dangerous goods in the MSDS file library, the danger of dangerous goods operation at each node port is analyzed, and a dangerous goods risk data indicator library is constructed.

[0037] Furthermore, HAZOP nodes are divided to determine multiple specific HAZOP nodes, including loading and unloading nodes, storage nodes, etc. Loading and unloading nodes include the transfer process of goods from transport vehicles to loading and unloading platforms, the connection operation between loading and unloading equipment and goods, and the fixing and support of goods during loading and unloading. Storage nodes cover aspects such as warehouse warehousing operations, the stacking method of goods in the warehouse, monitoring during storage, and safety protection facilities in the warehouse. By dividing these nodes in detail, all possible dangerous scenarios in the transportation of dangerous goods at ports can be fully covered.

[0038] Furthermore, based on the nodes divided by HAZOP, a risk index library for dangerous goods is constructed in combination with the MSDS file library. The construction of the risk index library should comprehensively consider the dangerousness of the goods, the complexity of operations, and environmental factors. For the dangerousness of the goods, the corresponding indicators can be determined according to the dangerous classification in the MSDS file. The MSDS file library contains detailed chemical and safety property information of various dangerous goods. Aspects of operational complexity, such as the number of equipment involved in the loading and unloading process, and operating steps, can be used as indicators of operational complexity. Environmental factors, such as the population density around the storage warehouse, should also be included in the risk index library. Based on the above information, the dangerousness of each node of the dangerous goods operation at the export port can be analyzed, and a preliminary risk index database can be statistically compiled.

[0039] The method provided in the embodiment of the present disclosure also includes: Read the data set in the risk data indicator library, set each risk indicator data as a separate element, calculate the number of occurrences of each element, and sort them from high to low according to the number of occurrences; Starting from a single element, gradually generate item sets containing multiple elements; Calculate the support of each item set in the data set, which is used to measure the frequency of the item set in the entire data set; Filter out frequent item sets according to a preset minimum support, wherein the frequent item sets are item sets whose support is greater than or equal to the minimum support; For each filtered frequent item set, an association rule is generated and the confidence is calculated, where the confidence is the probability that the result part of the right element of the association rule appears under the condition that the condition part of the left element appears; Filter out strong association rules according to a preset minimum confidence, wherein the strong association rules are association rules whose confidence is greater than or equal to the minimum confidence; A strongly associated item set is obtained, low risk indicator data is filtered, and a risk data indicator library is updated, wherein the strongly associated item set is an item set with a strong association rule.

[0040] Specifically, first read the data set from the risk data indicator library, and perform data cleaning on the read data, including removing duplicate records, processing missing values, etc., to ensure the accuracy and consistency of the data. Set each indicator data as a separate element, that is, each different risk type is a separate element. Traverse the entire data set, calculate the number of occurrences of each element, and use a data structure to store each element and its corresponding number of occurrences. Finally, sort these elements from high to low according to the number of occurrences. This goal is achieved by sorting the database that stores elements and occurrences.

[0041] Furthermore, we generate itemsets containing multiple elements, starting from a single element, and gradually generate itemsets containing multiple elements. First, we consider an itemset containing two elements, and generate all possible two-element itemsets by looping through the set of single elements. Then, we generate three-element itemsets from the two-element itemsets in a similar way, and so on, to complete the construction of the itemsets, such as {a}, {a,b}, {a,b,c}.

[0042] Furthermore, for each item set, the support of its occurrence in the data set is calculated. The support of an association rule refers to the proportion of transactions that contain both the antecedent and the consequent in all transactions. The support reflects the universality of the pattern appearing in the rule and the frequency of the rule appearing in the data set. If the support of a rule is higher than the preset minimum support threshold, the rule is considered to be frequent and a meaningful candidate rule. The support is calculated by counting the number of times the item set appears in the entire data set and then dividing it by the total number of records in the data set. According to this method, the support is calculated for all generated item sets (from single elements to multi-element item sets). Frequent item sets are filtered out according to the set minimum support. For example, the minimum support is set to 0.03, and the item sets with support greater than or equal to 0.03 are filtered as frequent item sets. This process can be performed by traversing all item sets for which support has been calculated, comparing their support with the value of the minimum support, and retaining the item sets that meet the conditions.

[0043] Furthermore, for each filtered frequent item set, all possible association rules are generated, and the confidence of each association rule is calculated. Association rules are generally expressed in the form of X→Y, where X and Y are item sets. Confidence is a key metric, which indicates the proportion of transactions that contain the rule antecedent and also contain the rule consequent. The confidence calculation formula is: Confidence (X→Y) = Support (X∪Y) / Support (X), where support (X∪Y) indicates the proportion of transactions that contain both X and Y to the total number of transactions, and support (X) indicates the proportion of transactions that contain X to the total number of transactions.

[0044] Furthermore, strong association rules are screened out according to the set minimum confidence. For example, the minimum confidence is set to 0.3, and the association rules with confidence greater than or equal to 0.3 are screened out as strong association rules. Similarly, by traversing all association rules with calculated confidence, comparing their confidence with the value of the minimum confidence, the association rules that meet the conditions are retained. According to the strong correlation item set finally obtained, low-risk indicator data are identified. If the strong association rule indicates that certain indicator combinations are highly correlated with low-risk situations, then the indicators in these indicator combinations may be regarded as low-risk indicator data. The records corresponding to these low-risk indicator data are deleted from the data set, and the low-risk indicator data in the risk data indicator library are eliminated. Finally, the risk data indicator library is updated so that the data in the library reflects the results after filtering, so that subsequent risk analysis and decision-making operations can be based on the updated and more accurate risk indicator data.

[0045] The method provided in the embodiment of the present disclosure also includes: Construct a fault tree model, determine the node corresponding to each event in the fault tree in the Bayesian network, convert the logical relationship of the fault tree into a conditional probability relationship in the Bayesian network, realize the complete mapping of the node relationship and conditional probability relationship of each event in the fault tree on the Bayesian network, form a directed acyclic graph and a conditional probability table, and complete the Bayesian network modeling. The directed acyclic graph is a structural representation of the Bayesian network. Collect data, calculate the probability value of the bottom event of the fault tree model, obtain the prior probability of the Bayesian network, and infer the probability of other nodes based on the prior probability of the root node and the conditional probability relationship. At the same time, train the basic data to obtain the importance of the root node of the Bayesian network, evaluate safety hazards based on the probability and importance of the node, identify key operational management risks, and integrate and summarize the final risk indicator data; The bottom event in the fault tree model is mapped to the root node of the Bayesian network model, the middle event in the fault tree model is mapped to the middle node of the Bayesian network model, and the top event in the fault tree model is mapped to the leaf node of the Bayesian network model; The logical relationship of the intermediate events in the fault tree model is converted into a conditional probability table by basic logic gate transformation; Through the mapping relationship, a directed acyclic graph and a conditional probability table are formed, which are combined into a Bayesian network for subsequent risk assessment; The root node probability importance and key importance are calculated based on the absolute value of the probability difference between the root node when the leaf node occurs and when the leaf node does not occur, and the ratio of the leaf node occurrence probability change rate to the root node occurrence probability change rate.

[0046] Specifically, each event unit in the fault tree is mapped to a node in the Bayesian network to form a directed acyclic graph. At the same time, the logical relationship of the fault tree is mapped to the Bayesian network, and a complete conditional probability table is constructed based on the logical relationship in the fault tree. The conditional probability table lists the probability of each node taking the value of 1 or 0 under different combinations of parent node values.

[0047] Furthermore, the bottom event probability value of the fault tree is determined by collecting data from multiple aspects, including historical records, equipment maintenance logs, etc., and counting the ratio of the number of specific faults to the total operating time to obtain the bottom event probability value. This probability value will be used as the prior probability of the Bayesian network.

[0048] Furthermore, the probabilities of other nodes are inferred based on the prior probability and conditional probability. After the fault tree is converted into a Bayesian network, the conditional probability relationship is constructed according to the logical relationship in the fault tree. For example, if event A and event B are connected to event C through an AND gate in the fault tree, then in the Bayesian network: P(C=1|A=1,B=1)=1 、 P(C=1|A=0,B=1)=0 、 P(C=1|A=1,B=0)=0 、 P(C=1|A=0,B=0)=0; Similarly, if event A and event B are connected to event C through an OR gate in the fault tree, then in the Bayesian network: P(C=1|A=1,B=1)=1, P ( C = 1 | A = 0, B = 1 ) = 1 , P ( C = 1 | A = 1, B = 0 ) = 1 , P(C=1|A=0,B=0)=0; For more complex logical relationships, a complete conditional probability table is gradually constructed by analyzing the logic gate combinations in the fault tree. Then, the Bayesian network inference algorithm, such as variable elimination or Gibbs sampling, is used to infer the probabilities of other nodes. The specific steps are as follows: S1: Confirm the prior probability and conditional probability relationship between the root nodes A, B and the intermediate node C.

[0049] like: P(A=1)=0.3 、 P(B=1)=0.4 、 P(C=1|A=0,B=1)=0.3 、 P(C=1|A=1,B=1)=0.8 、 P(C=1|A=1,B=0)=0.4 、 P(C=1|A=0,B=0)=0.1; S2: Calculate the value of P(C=1): P(C=1)=P(C=1|A=1,B=1)P(A=1)P(B=1)+ P(C=1|A=0,B=1)P(A=0)P(B=1)+P(C=1|A=1,B=0)P(A=1)P(B=0)+P(C=1|A=0,B=0)P(A=0)P(B=0).

[0050] S3: Calculate first: P(A=0)=1-P(A=1)=0.7, P(B=0)=1-P(B=1)=0.6; S4: Substituting into the above formula we get: P(C=1)=0.8×0.3×0.4+0.3×0.7×0.4+0.4×0.3×0.6+0.1×0.7×0.6; Furthermore, to calculate the probability importance, for the root node Xi and the leaf node T, first calculate the probability P(T=1) of the leaf node. Then, while fixing the probabilities of other root nodes, set the probability of Xi to 1 and recalculate P(T=1|Xi=1); Probability importance formula: Ip(Xi)=∣P(T=1∣Xi=1)-P(T=1)∣; Calculate the key importance, let ΔP(Xi) be the small change in the probability of the root node Xi, and ΔP(T) be the corresponding change in the probability of the leaf node T.

[0051] Key importance: Ic(Xi)=ΔP(Xi) / P(Xi)ΔP(T) / P(T); Furthermore, safety hazards are evaluated based on the probability and importance of the nodes. If the probability importance of a root node is very high, it may be an important safety hazard even if its prior probability is low. Based on the nodes judged to have high safety hazards, the indicator data involved in the event is compared with the existing risk data indicator library, and the same indicator data is retained to form the final risk indicator database. The final data is presented in a visual form so that decision makers can clearly understand the risk status of the system and formulate corresponding risk management strategies.

[0052] The method provided in the embodiment of the present disclosure also includes: Customize the visualization model and define the V-HAZOP model as a decision-making VIEW-Glass model and a special management VIEW-Glass. The decision-making VIEW-Glass model includes enterprise management VIEW-Glass, safety management VIEW-Glass, operation management VIEW-Glass, technical management VIEW-Glass, financial management VIEW-Glass, market management VIEW-Glass and comprehensive management VIEW-Glass; Integrate and analyze risk indicator data and map them to various VIEW-Glass models to achieve data visualization; The real-time status of the decision-making VIEW-Glass model and the special management VIEW-Glass is displayed in the form of multi-dimensional charts.

[0053] Specifically, a customized visual V-HAZOP model is developed, including a decision-making VIEW-Glass and a special management VIEW-Glass. The decision-making VIEW-Glass is further divided into enterprise management VIEW-Glass, safety management VIEW-Glass, operation management VIEW-Glass, technical management VIEW-Glass, financial management VIEW-Glass, market management VIEW-Glass, and comprehensive management VIEW-Glass. A classification algorithm is used to classify indicators in the existing risk database, and the risk indicator data that has undergone a series of screening, filtering, and classification processing is mapped to the VIEW- Glass model using database connection technology or file transfer protocols. Multidimensional charts are used to achieve data visualization, and visual charts such as bar charts, pie charts, line charts, radar charts, Gantt charts, and comprehensive scoring dashboards are constructed to display the data stored in various VIEW-Glass models.

[0054] Embodiment 2 is based on the same inventive concept as the risk assessment method for dangerous goods at ports based on the V-HAZOP model in the aforementioned embodiment. Figure 2 As shown, the present disclosure also provides a port dangerous goods risk assessment system based on the V-HAZOP model, the system comprising: The risk assessment basic data set establishment module 11 is used to obtain characteristic information and additional information of dangerous goods at the port and establish a risk assessment basic data set. The characteristic information includes the type, quantity, storage conditions, and transportation route of dangerous goods, and the additional information includes monitoring data of the yard, storage, meteorology, and environment; The risk data indicator library establishment module 12 is used to pre-process the risk assessment basic data set based on the HAZOP method, divide the HAZOP nodes, integrate the risk indicator data obtained by the HAZOP analysis with the data in the MSDS file library, and establish a risk data indicator library. The HAZOP nodes cover all key links of the operation of the port dangerous goods yard; The risk data indicator library optimization module 13 is used to analyze the risk data indicator library based on the association analysis algorithm, mine the association between item sets, filter out strong association rules according to the set minimum confidence, analyze the item sets in the strong association rules, filter out indicators with lower risk levels, and update the risk data indicator library. The item sets include indicator data and dangerous results in the risk data indicator library. The minimum confidence is a preset minimum confidence threshold, and the strong association rule is an association rule with a confidence greater than or equal to the minimum confidence. The Bayesian network risk assessment module 14 is used to obtain the relationship between the fault tree event and the Bayesian network node based on the fault tree analysis model to Bayesian network conversion method, map the fault tree logical relationship to the Bayesian network, complete the Bayesian network model construction, and obtain the final risk indicator data. The Bayesian network model once again screens the risk indicator data through probability calculation; V-HAZOP model construction module 15, the V-HAZOP model construction module 15 is used to perform visual data modeling according to the final risk index database, build a V-HAZOP model based on the HAZOP analysis and the visual data modeling results, and connect the index data after processing and analysis with the V-HAZOP model. The construction of the V-HAZOP model is to determine the structure, nodes, guide words and parameter elements of the model, and establish connections and relationships between nodes; Data display module, the data display module 16 is used to use VIEW-Glass multi-dimensional visualization technology to display the data obtained by the V-HAZOP model, the V-HAZOP model includes a decision-making VIEW-Glass model and a special management VIEW-Glass model, the decision-making VIEW-Glass model includes key indicators and dynamics, and the special management VIEW-Glass includes key functional indicators and thematic indicators.

[0055] Furthermore, the risk data indicator library establishment module 12 also includes a HAZOP application module: Systematically collect data on dangerous goods at ports, including but not limited to the physical and chemical properties of the goods, storage conditions, and historical accident records; Divide the handling process of dangerous goods loading, unloading, storage and transportation at ports into HAZOP nodes to ensure comprehensive coverage of all possible dangerous scenarios during port transportation; Based on the data of dangerous goods at the port, according to the detailed chemical and safety properties of dangerous goods in the MSDS file library, the danger of dangerous goods operation at each node port is analyzed, and a dangerous goods risk data indicator library is constructed.

[0056] Furthermore, the risk data indicator library optimization module 13 also includes an association rule mining module: Read the data set in the risk data indicator library, set each risk indicator data as a separate element, calculate the number of occurrences of each element, and sort them from high to low according to the number of occurrences; Starting from a single element, gradually generate item sets containing multiple elements; Calculate the support of each item set in the data set, which is used to measure the frequency of the item set in the entire data set; Filter out frequent item sets according to a preset minimum support, wherein the frequent item sets are item sets whose support is greater than or equal to the minimum support; For each filtered frequent item set, an association rule is generated and the confidence is calculated, where the confidence is the probability that the result part of the right element of the association rule appears under the condition that the condition part of the left element appears; Filter out strong association rules according to a preset minimum confidence, wherein the strong association rules are association rules whose confidence is greater than or equal to the minimum confidence; A strongly associated item set is obtained, low risk indicator data is filtered, and a risk data indicator library is updated, wherein the strongly associated item set is an item set with a strong association rule.

[0057] Furthermore, the Bayesian network risk assessment module 14 also includes a Bayesian network construction module based on the fault tree logical relationship: Construct a fault tree model, determine the node corresponding to each event in the fault tree in the Bayesian network, convert the logical relationship of the fault tree into a conditional probability relationship in the Bayesian network, realize the complete mapping of the node relationship and conditional probability relationship of each event in the fault tree on the Bayesian network, form a directed acyclic graph and a conditional probability table, and complete the Bayesian network modeling. The directed acyclic graph is a structural representation of the Bayesian network. Collect data, calculate the probability value of the bottom event of the fault tree model, obtain the prior probability of the Bayesian network, and infer the probabilities of other nodes based on the prior probability of the root node and the conditional probability relationship. At the same time, train the basic data to obtain the importance of the root node of the Bayesian network, evaluate safety hazards based on the probability and importance of the node, identify key operational management risks, and integrate and summarize the final risk indicator data.

[0058] Furthermore, the Bayesian network risk assessment module 14 also includes a fault tree mapping module: The bottom event in the fault tree model is mapped to the root node of the Bayesian network model, the middle event in the fault tree model is mapped to the middle node of the Bayesian network model, and the top event in the fault tree model is mapped to the leaf node of the Bayesian network model; The logical relationship of the intermediate events in the fault tree model is converted into a conditional probability table by basic logic gate transformation; Through the mapping relationship, a directed acyclic graph and a conditional probability table are formed and combined into a Bayesian network for subsequent risk assessment.

[0059] Furthermore, the Bayesian network risk assessment module 14 also includes an importance calculation module: The root node probability importance and key importance are calculated based on the absolute value of the probability difference between the root node when the leaf node occurs and when the leaf node does not occur, and the ratio of the leaf node occurrence probability change rate to the root node occurrence probability change rate.

[0060] Furthermore, the V-HAZOP model building module 15 also includes a VIEW-Glass building display module: Customize the visualization model and define the V-HAZOP model as a decision-making VIEW-Glass model and a special management VIEW-Glass. The decision-making VIEW-Glass model includes enterprise management VIEW-Glass, safety management VIEW-Glass, operation management VIEW-Glass, technical management VIEW-Glass, financial management VIEW-Glass, market management VIEW-Glass and comprehensive management VIEW-Glass; Integrate and analyze risk indicator data and map them to various VIEW-Glass models to achieve data visualization; The real-time status of the decision-making VIEW-Glass model and the special management VIEW-Glass is displayed in the form of multi-dimensional charts.

[0061] Embodiment 3: Another embodiment of the present invention provides a method and system for risk assessment of dangerous goods at ports based on a V-HAZOP model, including: The user end is used to obtain the characteristic information and additional information of dangerous goods at the port, and send the basic data set of risk assessment composed of the characteristic information and additional information to the processing end; The processing end is used to receive the risk assessment basic data set and perform subsequent data processing, including: pre-processing the risk assessment basic data set based on the HAZOP method to establish a risk data indicator library; analyzing the risk data indicator library based on the association analysis algorithm, excluding and filtering out indicators with low risk levels, and updating the risk data indicator library; obtaining the final risk indicator data based on the fault tree analysis model to Bayesian network transformation method; performing visual data modeling based on the final risk indicator data, and connecting the processed and analyzed indicator data with the V-HAZOP model; and finally sending the data obtained by the V-HAZOP model to the display end; The display end is used to display the data obtained by the V-HAZOP model using VIEW-Glass multi-dimensional visualization technology; Specifically, the characteristic information obtained by the user end includes the type, quantity, and storage conditions of dangerous goods, and the additional information includes the monitoring data of the yard, storage, weather, and environment; Specifically, the processing end pre-processes the risk assessment basic data set based on the HAZOP method, divides the HAZOP nodes, integrates the risk indicator data obtained by the HAZOP analysis with the data in the MSDS file library, and establishes a risk data indicator library. The HAZOP nodes cover all key links in the operation of the port dangerous goods yard; Specifically, the processing end analyzes the risk data indicator library based on the association analysis algorithm, mines the association between item sets, screens out strong association rules according to the set minimum confidence, analyzes the item sets in the strong association rules, filters out indicators with lower risk levels, and updates the risk data indicator library, wherein the item sets include indicator data and dangerous results in the risk data indicator library, the minimum confidence is a preset minimum confidence threshold, and the strong association rule is an association rule with a confidence greater than or equal to the minimum confidence; Specifically, the processing end obtains the relationship between the fault tree event and the Bayesian network node based on the fault tree analysis model to Bayesian network conversion method, maps the fault tree logical relationship to the Bayesian network, completes the Bayesian network model construction, and obtains the final risk indicator data. The Bayesian network model once again screens the risk indicator data through probability calculation; Specifically, the processing end performs visual data modeling according to the final risk indicator database, builds a V-HAZOP model based on the HAZOP analysis and the visual data modeling results, and connects the indicator data after processing and analysis with the V-HAZOP model. The construction of the V-HAZOP model is to determine the structure, nodes, guide words and parameter elements of the model, and establish connections and relationships between nodes; Specifically, the display end uses VIEW-Glass multi-dimensional visualization technology to display the data obtained by the V-HAZOP model. The V-HAZOP model includes a decision-making VIEW-Glass model and a special management VIEW-Glass model. The decision-making VIEW-Glass model includes key indicators and dynamics, and the special management VIEW-Glass includes key functional indicators and thematic indicators.

Claims

1. The risk assessment method for dangerous goods at ports based on the V-HAZOP model is characterized by: The method comprises: Obtain characteristic information and additional information of dangerous goods at ports and establish a basic data set for risk assessment. The characteristic information includes the type, quantity, storage conditions, and transportation routes of dangerous goods, and the additional information includes monitoring data of storage yards, warehousing, meteorology, and the environment; Based on the HAZOP method, the risk assessment basic data set is preprocessed, HAZOP nodes are divided, and the risk indicator data obtained by HAZOP analysis is integrated with the data in the MSDS file library to establish a risk data indicator library. The HAZOP nodes cover all key links of the operation of the port dangerous goods yard; Analyze the risk data indicator library based on the association analysis algorithm, mine the association between item sets, filter out strong association rules according to the set minimum confidence, analyze the item sets in the strong association rules, filter out indicators with lower risk levels, and update the risk data indicator library, wherein the item sets include indicator data and dangerous results in the risk data indicator library, the minimum confidence is a preset minimum confidence threshold, and the strong association rule is an association rule with a confidence greater than or equal to the minimum confidence; Based on the method of transforming the fault tree analysis model into the Bayesian network, the relationship between the fault tree event and the Bayesian network node is obtained, the fault tree logical relationship is mapped to the Bayesian network, the Bayesian network model is constructed, and the final risk indicator data is obtained. The Bayesian network model once again screens the risk indicator data through probability calculation; Visual data modeling is performed according to the final risk indicator database. On the basis of HAZOP analysis, a V-HAZOP model is constructed in combination with the visual data modeling results, and the processed and analyzed indicator data is data-connected with the V-HAZOP model. The construction of the V-HAZOP model is to determine the structure, nodes, guide words and parameter elements of the model, and to establish connections and relationships between nodes; The VIEW-Glass multi-dimensional visualization technology is used to display the data obtained by the V-HAZOP model. The V-HAZOP model includes a decision-making VIEW-Glass model and a special management VIEW-Glass model. The decision-making VIEW-Glass model includes key indicators and dynamics, and the special management VIEW-Glass includes key functional indicators and thematic indicators.

2. The method according to claim 1, characterized in that The HAZOP method also includes: Systematically collect data on dangerous goods at ports, including but not limited to the physical and chemical properties of the goods, storage conditions, and historical accident records; Divide the handling process of dangerous goods loading, unloading, storage and transportation at ports into HAZOP nodes to ensure comprehensive coverage of all possible dangerous scenarios during port transportation; Based on the data of dangerous goods at the port, according to the detailed chemical and safety properties of dangerous goods in the MSDS file library, the danger of dangerous goods operation at each node port is analyzed, and a dangerous goods risk data indicator library is constructed.

3. The method according to claim 1, characterized in that The association analysis algorithm also includes: Read the data set in the risk data indicator library, set each risk indicator data as a separate element, calculate the number of occurrences of each element, and sort them from high to low according to the number of occurrences; Starting from a single element, gradually generate item sets containing multiple elements; Calculate the support of each item set in the data set, which is used to measure the frequency of the item set in the entire data set; Filter out frequent item sets according to a preset minimum support, wherein the frequent item sets are item sets whose support is greater than or equal to the minimum support; For each filtered frequent item set, an association rule is generated and the confidence is calculated, where the confidence is the probability that the result part of the right element of the association rule appears under the condition that the condition part of the left element appears; Filter out strong association rules according to a preset minimum confidence, wherein the strong association rules are association rules whose confidence is greater than or equal to the minimum confidence; A strongly associated item set is obtained, low risk indicator data is filtered, and a risk data indicator library is updated, wherein the strongly associated item set is an item set with a strong association rule.

4. The method according to claim 1, characterized in that The method for converting the fault tree analysis model into a Bayesian network also includes: Construct a fault tree model, determine the node corresponding to each event in the fault tree in the Bayesian network, convert the logical relationship of the fault tree into a conditional probability relationship in the Bayesian network, realize the complete mapping of the node relationship and conditional probability relationship of each event in the fault tree on the Bayesian network, form a directed acyclic graph and a conditional probability table, and complete the Bayesian network modeling. The directed acyclic graph is a structural representation of the Bayesian network. Collect data, calculate the probability value of the bottom event of the fault tree model, obtain the prior probability of the Bayesian network, and infer the probabilities of other nodes based on the prior probability of the root node and the conditional probability relationship. At the same time, train the basic data to obtain the importance of the root node of the Bayesian network, evaluate safety hazards based on the probability and importance of the node, identify key operational management risks, and integrate and summarize the final risk indicator data.

5. The method according to claim 4, characterized in that The Bayesian network modeling also includes: The bottom event in the fault tree model is mapped to the root node of the Bayesian network model, the middle event in the fault tree model is mapped to the middle node of the Bayesian network model, and the top event in the fault tree model is mapped to the leaf node of the Bayesian network model; The logical relationship of the intermediate events in the fault tree model is converted into a conditional probability table by basic logic gate transformation; Through the mapping relationship, a directed acyclic graph and a conditional probability table are formed and combined into a Bayesian network for subsequent risk assessment.

6. The method according to claim 4, characterized in that The obtaining of the importance of the Bayesian network root node also includes: The root node probability importance and key importance are calculated based on the absolute value of the probability difference between the root node when the leaf node occurs and when the leaf node does not occur, and the ratio of the leaf node occurrence probability change rate to the root node occurrence probability change rate.

7. The method according to claim 1, characterized in that The V-HAZOP model also includes: Customize the visualization model and define the V-HAZOP model as a decision-making VIEW-Glass model and a special management VIEW-Glass. The decision-making VIEW-Glass model includes enterprise management VIEW-Glass, safety management VIEW-Glass, operation management VIEW-Glass, technical management VIEW-Glass, financial management VIEW-Glass, market management VIEW-Glass and comprehensive management VIEW-Glass; Integrate and analyze risk indicator data and map them to various VIEW-Glass models to achieve data visualization; The real-time status of the decision-making VIEW-Glass model and the special management VIEW-Glass is displayed in the form of multi-dimensional charts.

8. The port dangerous goods risk assessment system based on the V-HAZOP model is characterized by: A method for risk assessment of dangerous goods at ports based on a V-HAZOP model for implementing any one of claims 1 to 7, the system comprising: A risk assessment basic data set establishment module, which is used to obtain characteristic information and additional information of dangerous goods at ports and establish a risk assessment basic data set, wherein the characteristic information includes the type, quantity, storage conditions, and transportation routes of dangerous goods, and the additional information includes monitoring data of storage yards, warehousing, meteorology, and the environment; A risk data indicator library establishment module, which is used to pre-process the risk assessment basic data set based on the HAZOP method, divide the HAZOP nodes, integrate the risk indicator data obtained by the HAZOP analysis with the data in the MSDS file library, and establish a risk data indicator library, wherein the HAZOP nodes cover all key links of the operation of the port dangerous goods yard; A risk data indicator library optimization module, the risk data indicator library optimization module is used to analyze the risk data indicator library based on an association analysis algorithm, mine the associations between item sets, filter out strong association rules according to a set minimum confidence, analyze the item sets in the strong association rules, filter out indicators with a lower risk level, and update the risk data indicator library, the item sets include indicator data and dangerous results in the risk data indicator library, the minimum confidence is a preset minimum confidence threshold, and the strong association rule is an association rule with a confidence greater than or equal to the minimum confidence; A Bayesian network risk assessment module, which is used to obtain the relationship between the fault tree event and the Bayesian network node based on the fault tree analysis model to Bayesian network conversion method, map the fault tree logical relationship to the Bayesian network, complete the Bayesian network model construction, and obtain the final risk indicator data. The Bayesian network model once again screens the risk indicator data through probability calculation; A V-HAZOP model building module, wherein the V-HAZOP model building module is used to perform visual data modeling according to the final risk indicator database, to build a V-HAZOP model based on the HAZOP analysis and in combination with the visual data modeling results, and to connect the processed and analyzed indicator data with the V-HAZOP model. The V-HAZOP model building module is used to determine the structure, nodes, guide words and parameter elements of the model, and to establish connections and relationships between nodes; A data display module is used to display data obtained by a V-HAZOP model using VIEW-Glass multi-dimensional visualization technology. The V-HAZOP model includes a decision-making VIEW-Glass model and a special management VIEW-Glass model. The decision-making VIEW-Glass model includes key indicators and dynamics, and the special management VIEW-Glass includes functional indicators and thematic indicators of key concern.

9. The port dangerous goods risk assessment system based on the V-HAZOP model as claimed in claim 8, characterized in that: Also included is a HAZOP application module to perform the following steps: Systematically collect data on dangerous goods at ports, including but not limited to the physical and chemical properties of the goods, storage conditions, and historical accident records; Divide the handling process of dangerous goods loading, unloading, storage and transportation at ports into HAZOP nodes to ensure comprehensive coverage of all possible dangerous scenarios during port transportation; Based on the data of dangerous goods at the port, according to the detailed chemical and safety properties of dangerous goods in the MSDS file library, the danger of dangerous goods operation at each node port is analyzed, and a dangerous goods risk data indicator library is constructed.

10. The port dangerous goods risk assessment system based on the V-HAZOP model as claimed in claim 8, characterized in that: It also includes an association rule mining module to perform the following steps: Read the data set in the risk data indicator library, set each risk indicator data as a separate element, calculate the number of occurrences of each element, and sort them from high to low according to the number of occurrences; Starting from a single element, gradually generate item sets containing multiple elements; Calculate the support of each item set in the data set, which is used to measure the frequency of the item set in the entire data set; Filter out frequent item sets according to a preset minimum support, wherein the frequent item sets are item sets whose support is greater than or equal to the minimum support; For each filtered frequent item set, an association rule is generated and the confidence is calculated, where the confidence is the probability that the result part of the right element of the association rule appears under the condition that the condition part of the left element appears; Filter out strong association rules according to a preset minimum confidence, wherein the strong association rules are association rules whose confidence is greater than or equal to the minimum confidence; A strongly associated item set is obtained, low risk indicator data is filtered, and a risk data indicator library is updated, wherein the strongly associated item set is an item set with a strong association rule.

11. The port dangerous goods risk assessment system based on the V-HAZOP model as claimed in claim 8, characterized in that: It also includes a Bayesian network building module based on the fault tree logic relationship to perform the following steps: Construct a fault tree model, determine the node corresponding to each event in the fault tree in the Bayesian network, convert the logical relationship of the fault tree into a conditional probability relationship in the Bayesian network, realize the complete mapping of the node relationship and conditional probability relationship of each event in the fault tree on the Bayesian network, form a directed acyclic graph and a conditional probability table, and complete the Bayesian network modeling. The directed acyclic graph is a structural representation of the Bayesian network. Collect data, calculate the probability value of the bottom event of the fault tree model, obtain the prior probability of the Bayesian network, and infer the probabilities of other nodes based on the prior probability of the root node and the conditional probability relationship. At the same time, train the basic data to obtain the importance of the root node of the Bayesian network, evaluate safety hazards based on the probability and importance of the node, identify key operational management risks, and integrate and summarize the final risk indicator data.

12. The port dangerous goods risk assessment system based on the V-HAZOP model as claimed in claim 8, characterized in that: A Fault Tree Mapping module is also included to perform the following steps: The bottom event in the fault tree model is mapped to the root node of the Bayesian network model, the middle event in the fault tree model is mapped to the middle node of the Bayesian network model, and the top event in the fault tree model is mapped to the leaf node of the Bayesian network model; The logical relationship of the intermediate events in the fault tree model is converted into a conditional probability table by basic logic gate transformation; Through the mapping relationship, a directed acyclic graph and a conditional probability table are formed and combined into a Bayesian network for subsequent risk assessment.

13. The port dangerous goods risk assessment system based on the V-HAZOP model as claimed in claim 8, characterized in that: It also includes an importance calculation module to perform the following steps: The root node probability importance and key importance are calculated based on the absolute value of the probability difference between the root node when the leaf node occurs and when the leaf node does not occur, and the ratio of the leaf node occurrence probability change rate to the root node occurrence probability change rate.

14. The port dangerous goods risk assessment system based on the V-HAZOP model as claimed in claim 8, characterized in that: Also included is the VIEW - Glass build display module to perform the following steps: Customize the visualization model and define the V-HAZOP model as a decision-making VIEW-Glass model and a special management VIEW-Glass. The decision-making VIEW-Glass model includes enterprise management VIEW-Glass, safety management VIEW-Glass, operation management VIEW-Glass, technical management VIEW-Glass, financial management VIEW-Glass, market management VIEW-Glass and comprehensive management VIEW-Glass; Integrate and analyze risk indicator data and map them to various VIEW-Glass models to achieve data visualization; The real-time status of the decision-making VIEW-Glass model and the special management VIEW-Glass is displayed in the form of multi-dimensional charts.

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