A dangerous chemical transportation risk prediction system and method based on big data analysis

Abstract

The present application relates to the technical field of risk analysis, in particular to a dangerous chemical product transportation risk prediction system and method based on big data analysis, comprising a multi-dimensional data acquisition module, which collects four-dimensional data of "man-machine-environment-pipe" in the liquid ammonia water transfer process, and carries out standardized processing and space-time synchronization to obtain a multi-source heterogeneous data set; a risk level acquisition module, which constructs a "man-machine-environment-pipe" collaborative risk assessment model, analyzes the multi-source heterogeneous data set, and obtains a risk assessment result; the "man-machine-environment-pipe" collaborative risk assessment model comprises a man-machine interaction key node identification layer, a management behavior-equipment response correlation analysis layer, an environment-substance interaction dynamic risk assessment layer and a multi-dimensional factor risk cascade assessment layer; an emergency disposal scheme acquisition module, which constructs an emergency disposal scheme intelligent recommendation model to carry out hierarchical classification analysis on the risk assessment result, generates multi-level risk early warning and emergency disposal scheme, and optimizes the emergency disposal scheme.

Description

Technical Field

[0001] This invention relates to the field of risk analysis technology, specifically to a risk prediction system and method for hazardous chemical transportation based on big data analysis. Background Technology

[0002] In existing technologies, risk analysis for transportation is particularly important for ensuring safety in critical stages such as water and land transshipment. Currently, some monitoring and error detection systems based on industrial data processing are applied in the transportation sector to improve operational safety and emergency response capabilities. However, in specific scenarios like transshipment involving multiple modes of transport, complex operating environments, and dynamic operational processes, the risk prediction and safety monitoring capabilities of existing technologies still face many challenges, especially in terms of significant deficiencies in industrial data acquisition.

[0003] Specifically, existing technologies are insufficient in terms of the accuracy of risk identification and early warning. Traditional data acquisition methods often struggle to achieve accurate, real-time capture of status parameters and early warning, making it difficult to detect potential risks in a timely manner. Furthermore, existing technologies have difficulty effectively distinguishing between normal equipment operating fluctuations and abnormal signals indicating risks, and their intelligent identification capabilities for complex abnormal patterns such as aging connection points, potential equipment failures, and non-standard operator behavior are limited.

[0004] Secondly, existing technologies also have limitations in assessing the impact of dynamic and complex factors on risk. Current technologies often lack precise quantitative models of how environmental factors dynamically affect the leakage and diffusion of liquid ammonia, evaporation rates, and overall risk levels. Especially under extreme weather conditions, the amplification effect on the risk of liquid ammonia leakage and the complex interactions between these factors and the physicochemical properties of liquid ammonia are difficult for existing big data collection and processing technologies to accurately assess and make forward-looking predictions.

[0005] Meanwhile, existing industrial data processing technologies struggle to effectively assess the coupling risk between potential equipment failures and human error, lacking the technical means to conduct real-time, dynamic assessments of risks that may arise during human-machine interaction. Existing technologies also lag behind in effective tracking, prediction, and simulation analysis, lacking mature risk correlation analysis models and system-level risk transmission models, further limiting the effectiveness of existing technologies in predicting complex risks.

[0006] To address this, a risk prediction system and method for hazardous chemical transportation based on big data analysis is proposed. Summary of the Invention

[0007] The purpose of this invention is to provide a risk prediction system and method for hazardous chemical transportation based on big data analysis. This includes collecting four-dimensional data on "human-machine-environment-management" during the transfer of liquid ammonia to obtain a multi-source heterogeneous dataset; constructing a collaborative risk assessment model for "human-machine-environment-management" to analyze the multi-source heterogeneous dataset and obtain risk assessment results; the collaborative risk assessment model includes a key node identification layer for human-machine interaction, a management behavior-equipment response correlation analysis layer, a dynamic risk assessment layer for environment-material interaction, and a multi-dimensional factor risk cascade assessment layer; constructing an intelligent recommendation model for emergency response plans to perform hierarchical and categorized analysis of the risk assessment results, generating multi-level risk warnings and emergency response plans, and optimizing the emergency response plans; and dynamically simulating the spatiotemporal evolution process under the risk assessment results based on the multi-source heterogeneous dataset and emergency response plans to predict the outcome of the emergency response plans.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] A risk prediction system for hazardous chemical transportation based on big data analysis includes:

[0010] The multidimensional data acquisition module is used to collect four-dimensional data of "human-machine-environment-pipe" in the liquid ammonia water transfer process, and to perform standardization processing and spatiotemporal synchronization to obtain multi-source heterogeneous datasets;

[0011] The risk level acquisition module is used to construct a collaborative risk assessment model of "human-machine-environment-management" and analyze the multi-source heterogeneous dataset to obtain risk assessment results. The collaborative risk assessment model of "human-machine-environment-management" includes a human-machine interaction key node identification layer, a management behavior-equipment response correlation analysis layer, an environment-material interaction dynamic risk assessment layer, and a multi-dimensional factor risk cascade assessment layer.

[0012] The emergency response plan acquisition module is used to construct an intelligent recommendation model for emergency response plans, perform hierarchical and classification analysis on the risk assessment results, generate multi-level risk warnings and emergency response plans, and optimize the emergency response plans.

[0013] Preferably, the multi-source heterogeneous dataset includes real-time multi-source heterogeneous datasets and historical multi-source heterogeneous datasets; specifically, it includes personnel behavior monitoring data, equipment-item status monitoring data, environmental parameter sensing data, and management process digitization data.

[0014] The personnel behavior monitoring data includes the operator's behavioral characteristics, fatigue index, and qualification information; the equipment-item status monitoring data includes equipment vibration, pressure, temperature, sealing integrity, and the physical changes, volatility, and diffusion characteristics of liquid ammonia; the environmental parameter sensing data includes the temperature, humidity, wind speed, wind direction, water flow speed, wave height, and tides of the work area; and the digital management process data includes the digital representation of standard operating procedures, emergency response procedures, management requirements, safety inspection records, and operation logs.

[0015] Preferably, the key node identification layer for human-computer interaction includes: performing image segmentation and behavior recognition on the collected video data of operators, extracting the temporal features of the operators' actions, and obtaining a behavior pattern feature library; based on the operator's qualification information, historical operation records, and current fatigue index, combined with the vibration, pressure, temperature, and sealing integrity of the equipment, evaluating the safe operation capability of the human-computer system in real time; and performing time series analysis on historical human-computer interaction data, the behavior pattern feature library, and safe operation capability to predict human-computer interaction risk nodes and time windows, and generating human-computer interaction risk assessment indicators.

[0016] Preferably, the management behavior-equipment response correlation analysis layer includes: extracting management behavior features by performing text mining on the digital data of management processes; collecting equipment vibration, pressure, temperature and sealing integrity in real time, and extracting equipment state features through multi-scale entropy analysis; collecting and analyzing data on the physical changes, volatility and diffusion characteristics of liquid ammonia to obtain liquid ammonia state features; and analyzing the temporal correlation between management behavior features, equipment state features and liquid ammonia state features to obtain correlation risk assessment indicators.

[0017] Preferably, the dynamic risk assessment layer for environment-material interaction includes: fusing environmental parameter sensing data using a Kalman filter algorithm to obtain a dynamic environmental feature vector; analyzing the diffusion and volatilization of risky liquid ammonia under the dynamic environmental feature vector, simulating the interaction process between liquid ammonia and the environmental medium after leakage, and obtaining the diffusion trajectory; generating a concentration distribution cloud map from the diffusion trajectory, combining it with the predicted hazardous area range, applying a multi-objective decision-making method to assess the potential environmental risk impact, and generating an environmental risk assessment index that includes risk level, impact range, and duration.

[0018] Preferably, the multi-dimensional risk cascade assessment layer includes: analyzing the risk correlation and cascade propagation path among human-computer interaction risk assessment indicators, associated risk assessment indicators, and environmental risk assessment indicators; and obtaining risk assessment results by analyzing the risk correlation and cascade propagation path through a Bayesian network.

[0019] Preferably, the intelligent recommendation model for emergency response plans includes:

[0020] The system comprises four layers: a case library construction layer, which collects and structures historical case data of liquid ammonia leak accidents to establish a case knowledge base; a domain knowledge representation layer, which formally represents domain knowledge related to emergency response to liquid ammonia leaks to construct a domain knowledge graph that supports knowledge reasoning and querying; a scenario similarity calculation layer, which generates similarity scores by comparing risk assessment results with the case knowledge base to obtain the best similar case and its information; a solution generation layer, which generates emergency response solutions based on the best similar case information and risk assessment results; and a solution optimization layer, which analyzes emergency response solutions using knowledge reasoning and optimizes them from multiple dimensions, including response time, resource consumption, and risk control, based on genetic algorithms and multi-objective optimization methods, to output the optimal emergency response solution.

[0021] A method for predicting the risks of hazardous chemical transportation based on big data analysis includes:

[0022] Collect four-dimensional data on "human-machine-environment-pipeline" during the liquid ammonia water transfer process, and perform standardization processing and spatiotemporal synchronization to obtain a multi-source heterogeneous dataset;

[0023] A collaborative risk assessment model for "human-machine-environment-management" is constructed, and the multi-source heterogeneous dataset is analyzed to obtain risk assessment results. The collaborative risk assessment model for "human-machine-environment-management" includes a key node identification layer for human-machine interaction, a correlation analysis layer for management behavior and equipment response, a dynamic risk assessment layer for environment-material interaction, and a multi-dimensional factor risk cascade assessment layer.

[0024] An intelligent recommendation model for emergency response plans is constructed to perform hierarchical and categorized analysis on the risk assessment results, generate multi-level risk warnings and emergency response plans, and optimize the emergency response plans.

[0025] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0026] 1. This invention uses the four elements of "human-machine-environment-management" as the core perspective for risk modeling and analysis during the transportation of hazardous chemicals. It covers data elements across four dimensions: operators, transportation equipment, environmental conditions, and management processes, going beyond traditional equipment status monitoring or environmental sensing. Through standardized processing and spatiotemporal synchronization mechanisms, it achieves the fusion of real-time multi-source heterogeneous data with historical data. The data types encompass personnel behavior, equipment status, environmental parameters, and management processes, overcoming the problems of single data dimensions and severe information silos in existing technologies. It enables comprehensive perception of various potential risk sources throughout the entire liquid ammonia transportation process, providing high-quality basic data support for subsequent risk assessment and emergency response, significantly improving the intelligence level and perception granularity of safety management in hazardous chemical transportation.

[0027] 2. The "human-machine-environment-management" collaborative risk assessment model proposed in this invention comprises multiple logical layers, modeling risks from the perspectives of key human-machine interaction nodes, the temporal correlation between management behavior and equipment response, the dynamic interaction characteristics of the environment and materials, and the cascading propagation of risks from multiple factors. This not only enhances the dimensionality and granularity of risk identification but also achieves quantification and dynamism in risk assessment by introducing algorithms such as image recognition, temporal analysis, multi-scale entropy analysis, Kalman filtering, and Bayesian networks. Through in-depth analysis of risk propagation paths and risk triggering mechanisms, potential high-risk nodes and time windows can be identified in advance, enabling earlier and more accurate early warning responses and effectively improving the accuracy of risk prediction and control capabilities during the transportation of high-risk chemicals such as liquid ammonia.

[0028] 3. This invention establishes a scenario-oriented intelligent recommendation mechanism for emergency response plans, enhancing the relevance and effectiveness of responses. By introducing a case-driven and knowledge graph-based emergency response recommendation mechanism, a structured historical case knowledge base is established. Through scenario similarity calculation and semantic matching, the optimal emergency response case is matched, and personalized response plans are automatically generated based on current risk assessment results, truly realizing a "scenario-driven" plan generation model. This not only improves the scientific rigor and timeliness of plan recommendations but also provides standardized, automated, and intelligent decision support for on-site response. Attached Figure Description

[0029] Figure 1 A schematic diagram of the structure of a hazardous chemical transportation risk prediction system based on big data analysis provided by the present invention;

[0030] Figure 2 A schematic diagram of a method for predicting the transportation risks of hazardous chemicals based on big data analysis provided by the present invention;

[0031] Figure 3 This is a schematic diagram of the "human-machine-environment-management" collaborative risk assessment model provided by the present invention. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] This invention provides a method for predicting the risks of hazardous chemical transportation based on big data analysis, which is applied to a hazardous chemical transportation risk prediction system based on big data analysis. See details below. Figure 1 System architecture diagram and Figure 2 Method flowchart.

[0034] Example 1

[0035] Please see Figures 1 to 3 This invention provides a risk prediction system and method for hazardous chemical transportation based on big data analysis. The technical solution is as follows:

[0036] The multidimensional data acquisition module is used to collect four-dimensional data of "human-machine-environment-pipe" in the liquid ammonia water transfer process, and to perform standardization processing and spatiotemporal synchronization to obtain a multi-source heterogeneous dataset containing spatiotemporal correlation attributes.

[0037] Furthermore, the multi-source heterogeneous dataset includes real-time collected multi-source heterogeneous data and historically accumulated multi-source heterogeneous data; specifically, it includes personnel behavior monitoring data, equipment-item status monitoring data, environmental parameter sensing data, and management process digitization data.

[0038] The personnel behavior monitoring data includes the operator's behavioral characteristic sequence, dynamic index of physiological fatigue, and professional qualification information database; the equipment-item status monitoring data includes the equipment's vibration spectrum, pressure change curve, temperature gradient distribution, seal integrity index, and liquid ammonia's physical state change parameters, volatility coefficient, and diffusion rate characteristics; the environmental parameter sensing data includes the temperature field, humidity field, wind speed vector, wind direction change trend, water flow velocity distribution, wave height spectrum, and tidal cycle data of the work area; and the management process digitization data includes structured digital representations of standard operating procedures, emergency response procedures, management requirements, safety inspection records, and operation logs.

[0039] In this embodiment, by introducing a four-dimensional multi-dimensional data acquisition mechanism encompassing "human-machine-environment-management," multiple key dimensions such as personnel behavior, equipment status, environmental changes, and management processes can be comprehensively covered, achieving refined perception and dynamic risk monitoring of the entire liquid ammonia water transfer process. Particularly in terms of multi-source heterogeneous data fusion, real-time and historical data are fully integrated to form a high-quality dataset with spatiotemporal correlation attributes, providing a solid data foundation for subsequent risk modeling and intelligent analysis.

[0040] The risk level acquisition module is used to construct a collaborative risk assessment model integrating "human-machine-environment-management" systems. It performs in-depth mining and analysis of the multi-source heterogeneous dataset to obtain quantitative risk assessment results; see details below. Figure 3 The “human-machine-environment-management” collaborative risk assessment model includes a human-machine interaction key node identification layer, a management behavior-equipment response correlation analysis layer, an environment-material interaction dynamic risk assessment layer, and a multi-dimensional factor risk cascade assessment layer. The layers exchange information through a risk factor transmission matrix.

[0041] Furthermore, the key node identification layer for human-computer interaction includes:

[0042] Deep learning networks are used to perform semantic segmentation and behavior recognition on the collected video data of operators, extract the temporal features of the operators' actions, and build an adaptive behavior pattern feature library.

[0043] Based on the operator's qualification information, historical operation records, and real-time fatigue index, combined with the equipment's dynamic response characteristic curve, a human-machine system safety operation capability assessment model is established.

[0044] Time series analysis and prediction are performed on historical human-computer interaction data, behavioral pattern feature database, and safe operation capability assessment results to identify human-computer interaction risk nodes and their time windows, and generate a human-computer interaction risk assessment index matrix that includes the probability and severity of risk occurrence.

[0045] In this embodiment, the present invention introduces a collaborative risk assessment model of "human-machine-environment-management" to construct a multi-level, multi-dimensional, and dynamically interactive risk level acquisition mechanism, which effectively improves the scientificity and accuracy of risk identification and assessment during the transportation of hazardous chemicals.

[0046] At the key node recognition layer of human-computer interaction, semantic segmentation and action recognition of operation videos are performed using deep learning networks. This not only improves the accuracy of behavior recognition but also constructs a dynamically updatable adaptive behavior pattern feature library, enabling the system to continuously learn and adapt to different operation scenarios. By combining operator qualifications, historical behavior, and fatigue indicators with equipment response curve features, the overall safe operation capability of the human-machine system is quantitatively analyzed, effectively avoiding potential risks caused by human error and equipment mismatch. Simultaneously, time series analysis and prediction techniques are used to deeply model interactive behavior data, identifying high-risk human-computer interaction nodes and their potential occurrence times, and generating a risk indicator matrix with probability and severity in advance, enhancing the proactive identification and early warning capabilities for risks. This not only improves the safety level of hazardous chemical transfer operations but also provides solid support for building an intelligent transportation safety system.

[0047] Furthermore, the management behavior-device response correlation analysis layer includes:

[0048] Deep text mining is performed on digital data of management processes using natural language processing and knowledge graph technologies to extract semantic feature vectors of management behaviors.

[0049] Real-time acquisition of equipment vibration spectrum, pressure change curve, temperature gradient distribution and sealing integrity index; extraction of equipment state feature vector through multi-scale entropy analysis and anomaly detection algorithm.

[0050] Collect and analyze the phase change parameters, volatility coefficient, and diffusion rate characteristics of liquid ammonia to obtain the liquid ammonia state characteristic vector;

[0051] A temporal correlation deep neural network is constructed to analyze the temporal causal relationship between the feature vectors of management behavior, equipment status, and liquid ammonia status, and to obtain a correlation risk assessment index that includes correlation strength and lag effect.

[0052] In this embodiment, the present invention introduces various technologies such as natural language processing, knowledge graphs, multi-scale entropy analysis, and temporal neural networks into the "management behavior-equipment response correlation analysis layer" to achieve a precise characterization and risk quantification of the deep, multi-dimensional, and dynamic correlation between management behavior, equipment status, and the physical properties of liquid ammonia. Through real-time acquisition and multi-scale entropy analysis of multi-source equipment status data, it can not only comprehensively reflect the stability and abnormal trends of equipment operation but also dynamically extract key state parameters of liquid ammonia during transportation. The constructed temporal correlation deep neural network breaks through the limitations of traditional static analysis, enabling the identification of the time lag effect and causal strength between management behavior and equipment response, thereby providing more targeted risk assessment results. The modeling method based on temporal causality enhances the dynamic adaptability of risk identification and the forward-looking nature of decision-making, which is of great significance for improving the accuracy and timeliness of management decisions during the transportation of hazardous chemicals.

[0053] Furthermore, the dynamic risk assessment layer for environment-matter interaction includes:

[0054] The adaptive Kalman filter algorithm is used to fuse heterogeneous information from multi-source environmental parameter sensing data to obtain dynamic environmental feature vectors with spatiotemporal attributes.

[0055] Based on the principles of computational fluid dynamics, the diffusion and volatilization dynamics of liquid ammonia under specific dynamic environmental characteristic vector conditions are analyzed, and the multiphase interaction process between liquid ammonia and the environmental medium after leakage is simulated to obtain a three-dimensional diffusion trajectory model.

[0056] Based on a three-dimensional diffusion trajectory model, a time-varying concentration distribution cloud map is generated. Combined with population distribution and critical infrastructure location information, a multi-objective decision optimization method is applied to assess the potential environmental risk impact, generating a multi-dimensional environmental risk assessment index that includes risk level, impact range, duration, and exposure to sensitive targets.

[0057] In this embodiment, the present invention integrates an improved adaptive Kalman filter algorithm and a computational fluid dynamics model in the "environment-material interaction dynamic risk assessment layer," achieving a high degree of coupling modeling between environmental sensing data and the behavior of liquid ammonia, thereby improving the dynamic response capability and prediction accuracy of environmental risk assessment. By fusing multi-source heterogeneous environmental data, more spatiotemporally continuous dynamic environmental features are extracted, effectively compensating for the shortcomings of high noise and high latency in single-sensor data. Combined with computational fluid dynamics principles, the diffusion and volatilization paths of liquid ammonia under different environmental conditions can be realistically simulated, accurately depicting the complex multiphase interaction behavior between liquid ammonia and environmental media such as air, humidity, wind speed, and water flow after a leak, thus constructing a high-precision three-dimensional diffusion trajectory. The generated time-varying concentration cloud map not only has visualization capabilities but also utilizes multi-objective decision optimization methods to assess the comprehensive impact of leak accidents on environmental and personnel safety, enhancing early warning capabilities in complex environments.

[0058] Furthermore, the multidimensional factor risk cascade assessment layer includes:

[0059] Construct a Bayesian network model to analyze the risk correlation mechanism and cascading propagation path among the human-computer interaction risk assessment index matrix, related risk assessment indicators, and multidimensional environmental risk assessment indicators;

[0060] By using Monte Carlo simulation to randomly sample and infer risk association mechanisms and cascading propagation paths, a comprehensive risk assessment result is obtained, including the probability of risk occurrence, severity, scope of impact, and duration.

[0061] In this embodiment, by introducing a Bayesian network model, multi-source assessment results such as human-computer interaction risk, management-equipment response risk, and environment-material interaction risk are effectively integrated, constructing the logical dependencies and cascading evolution mechanisms among various risk factors. By dynamically characterizing the risk propagation paths among complex multidimensional factors, potential indirect influence chains and coupled failure modes are revealed, improving the ability to identify and predict complex risks. By introducing Monte Carlo simulation methods for high-frequency random sampling and inference on the Bayesian network, not only is the risk inference capability under uncertainty conditions enhanced, but also the quantitative output of multidimensional indicators such as the probability of risk occurrence, severity, scope of impact, and duration is achieved.

[0062] The emergency response plan acquisition module is used to construct an intelligent recommendation model for emergency response plans with knowledge reasoning capabilities, perform hierarchical and classification analysis on the risk assessment results, generate multi-level risk warning signals and targeted emergency response plans, and optimize the emergency response plans.

[0063] The specific process of optimizing the emergency response plan is as follows: based on the multi-source heterogeneous dataset and the emergency response plan, the spatiotemporal evolution process under specific risk assessment results is dynamically simulated using a spatiotemporal coupled numerical simulation method to predict the risk control effect and potential secondary disasters after the implementation of the emergency response plan.

[0064] Furthermore, the intelligent recommendation model for emergency response plans includes:

[0065] The case library construction layer collects and processes historical case data of liquid ammonia leakage accidents through structured semantic annotation, and establishes a multi-dimensional indexed case knowledge base;

[0066] The domain knowledge representation layer formally represents the domain knowledge of emergency response to liquid ammonia leakage using ontology engineering methods, and constructs a domain knowledge graph containing a concept layer, an instance layer, and a rule layer, supporting cross-level knowledge reasoning and semantic querying.

[0067] The scene similarity calculation layer compares the current risk assessment results with historical cases in the case knowledge base using deep metric learning methods, calculates a multi-dimensional similarity matrix, and identifies the optimal set of similar cases and their key handling information.

[0068] The solution generation layer combines key handling information from the best similar case set with rule constraints from the domain knowledge graph to generate a multi-level emergency response plan that includes handling steps, resource requirements, personnel division of labor, and time arrangements based on the current risk assessment results.

[0069] The solution optimization layer uses knowledge reasoning to analyze emergency response plans. At the same time, based on genetic algorithms and multi-objective optimization methods, it optimizes emergency response plans from multiple dimensions such as response time, resource consumption, and risk control, and outputs the optimal emergency response plan.

[0070] In this embodiment, intelligent response and dynamic management of multi-level risks during the transportation of hazardous chemicals are realized. Based on risk assessment results, precise hierarchical classification analysis is performed, and multi-level emergency plans matching the current situation are generated, effectively improving risk response capabilities and efficiency. By constructing a structured case knowledge base and a formally represented domain knowledge graph, deep metric learning methods support multi-dimensional similarity calculations between the current risk scenario and historical cases, thereby accurately identifying optimal handling experience and transferring it for utilization. The integration of domain rules and evolutionary optimization algorithms not only ensures the rationality and feasibility of the plan but also generates the optimal emergency response plan by balancing multiple objectives such as response time, resource allocation, and control effectiveness. Simultaneously, through spatiotemporal coupled numerical simulation, the effects of the emergency plan implementation and potential secondary disasters are dynamically predicted, comprehensively improving emergency support capabilities and safety control levels throughout the entire hazardous chemical transportation process.

[0071] This invention delivers significant benefits by constructing a risk prediction system and method for hazardous chemical transportation based on big data analysis. First, its integrated "human-machine-environment-management" multi-dimensional data acquisition mechanism, through comprehensive collection and integration of real-time and historical data, achieves refined perception and dynamic risk monitoring of personnel behavior, equipment status, environmental changes, and management processes throughout the entire liquid ammonia transportation process. This forms a high-quality dataset with spatiotemporal correlation attributes, providing a solid data foundation for subsequent risk modeling and intelligent analysis. Second, the innovative "human-machine-environment-management" collaborative risk assessment model constructs a multi-level, multi-dimensional, and dynamically interactive risk level acquisition mechanism, effectively improving the scientific rigor and accuracy of risk identification and assessment during hazardous chemical transportation. Finally, the intelligent emergency response plan recommendation model with knowledge reasoning capabilities can perform hierarchical and categorized analysis based on risk assessment results, generating multi-level early warnings and targeted, optimized emergency response plans. Through spatiotemporal coupled numerical simulation, it predicts the effectiveness of the plans and secondary disasters, thereby comprehensively improving emergency response capabilities, risk response efficiency, and overall safety control levels throughout the entire hazardous chemical transportation process, providing strong technical support for building an intelligent transportation safety system.

[0072] Example 2

[0073] This embodiment demonstrates a specific application of big data analytics-based hazardous chemical transportation risk prediction in the liquid ammonia water-land intermodal transport scenario. This liquid ammonia water-land intermodal transport process involves transshipment operations from coastal chemical terminals to inland chemical industrial parks, encompassing three key stages: shipping, terminal loading and unloading, and land transportation.

[0074] In this embodiment, the collection of four-dimensional data on "human-machine-environment-pipeline" during the combined water-land transportation of liquid ammonia specifically includes:

[0075] Personnel behavior monitoring data is collected via cameras installed in the operating area to capture high-frequency video streams of operators' movements. Simultaneously, biometric monitoring wristbands are used to collect real-time data on operators' heart rate variability, skin conductance, and eye movement patterns, constructing a fatigue index database. Operator qualification information, including years of experience, professional qualification level, validity period of operating certificate, and past accident involvement, is integrated into the database.

[0076] Equipment and material status monitoring data is collected by installing strain and vibration sensors at key locations such as liquid ammonia storage tanks, pipelines, and valves to acquire vibration spectrum data; pressure sensors at key locations are used to monitor pressure change curves in real time; infrared thermal imaging sensors are configured to record temperature gradient distribution maps; and acoustic emission sensors are installed at pipeline interfaces and valves to calculate the seal integrity index. Simultaneously, a material property analysis module continuously monitors the physical state change parameters of liquid ammonia (such as density, viscosity, and surface tension), volatility coefficients (vapor pressure, heat of vaporization, and critical temperature), and diffusion characteristics (diffusion coefficient and interfacial mass transfer coefficient).

[0077] Environmental parameter sensing data is collected by meteorological stations deployed along the transportation route, which collect data on temperature field, humidity field, wind speed vector, and wind direction change trends; hydrological monitoring buoys configured in the water transportation section collect data on water flow velocity distribution, wave height spectrum, and tidal cycle.

[0078] The digitized data of management processes is structured through natural language processing technology and linked to a database with electronic security inspection records and operation logs.

[0079] All collected four-dimensional data are timestamped and geocoded to achieve spatiotemporal synchronization. To address the issue of inconsistent standards in multi-source heterogeneous data, an adaptive data preprocessing module is used to standardize data from different sources, mapping all data uniformly to the [0,1] interval. Missing value estimation algorithms are then used to fill in the gaps in the data, improving data integrity and consistency, ultimately forming a high-quality multi-source heterogeneous dataset.

[0080] The "human-machine-environment-pipeline" collaborative risk assessment model constructed in the example of liquid ammonia water-land intermodal transport mainly includes the following implementation contents:

[0081] Human-Computer Interaction Key Node Recognition Layer: Employing a deep learning framework based on residual networks, this layer performs semantic segmentation and behavior recognition on operator video data. Through transfer learning techniques, it is pre-trained on a dataset of liquid ammonia operation video clips to identify standard and non-standard actions, including valve operation, pipeline connection, and parameter checks.

[0082] By extracting the temporal features of operators' actions, a feature library of typical behavioral patterns is constructed. This feature library employs an incremental learning strategy, continuously updating based on newly collected data to improve the model's adaptability to new behavioral patterns. Based on operators' qualification information, historical operation records, and real-time fatigue indicators, a multilayer perceptron network is constructed to score operators' operational capabilities, with scores ranging from 0 to 100. Simultaneously, by combining the equipment's vibration spectrum characteristics, pressure fluctuation rate, temperature gradient change rate, and seal integrity index, an equipment safety operation index is calculated.

[0083] By integrating personnel operational capability scores and equipment safety operation indices, a comprehensive evaluation model for the safe operation capability of human-machine systems is established, outputting a safety operation capability index. Based on long short-term memory networks, time series modeling and trend prediction are performed on historical human-machine interaction data, behavioral pattern feature databases, and safety operation capability evaluation results. This allows for the identification of potential human-machine interaction risk nodes 5-10 minutes in advance, prediction of their occurrence time windows, and the generation of a human-machine interaction risk assessment index matrix that includes the probability and severity of risk occurrence.

[0084] By combining visual behavior recognition, physiological fatigue monitoring, and equipment status feedback, a multimodal dynamic risk assessment framework for human-machine systems was constructed. Compared with traditional methods that solely rely on operational procedure compliance checks, this framework simultaneously considers changes in personnel status, equipment response characteristics, and the temporal characteristics of operational behaviors. Through mutual verification and complementary enhancement, it significantly improves the accuracy and foresight of human-machine interaction risk identification. Especially in high-risk operations such as liquid ammonia unloading, based on the operator's fatigue change curve and the completeness of specific operational sequences, combined with the real-time response status of the equipment, it accurately identifies risky operations that may lead to leaks, providing sufficient reaction time for risk intervention.

[0085] The management behavior-equipment response correlation analysis layer uses BERT-based natural language processing technology to perform deep text mining on the digital data of management processes, extracting semantic feature vectors of management behaviors, including operational requirements, safety inspection items, and emergency response steps.

[0086] The system collects vibration spectrum, pressure change curve, temperature gradient distribution, and sealing integrity index of the equipment in real time. After preprocessing with wavelet transform, a multi-scale entropy analysis algorithm is applied to calculate the complexity index of the equipment state. For identifying abnormal equipment states, a combination of unsupervised anomaly detection and LSTM-AE-based temporal anomaly detection methods is used, enabling the identification of subtle abnormal changes in the equipment at an early stage.

[0087] For monitoring the phase changes, volatility, and diffusion characteristics of liquid ammonia, a hybrid modeling method combining physical models and data-driven approaches was established. By using a neural network constrained by physical equations, the model's accuracy in predicting phase changes of liquid ammonia was improved while ensuring the physical plausibility of the predictions.

[0088] A deep learning model based on graph convolutional networks and temporal attention mechanisms is constructed to model and analyze the temporal causal relationships between management behavior feature vectors, equipment state feature vectors, and liquid ammonia state feature vectors. The model identifies the correlation strength (values ​​between 0 and 1) between management behaviors (such as safety inspection frequency and operating procedure execution quality) and equipment states (such as sealing performance degradation and pressure fluctuations) and time lag effects, and further correlates these correlations with changes in liquid ammonia state to generate risk warnings containing multi-dimensional correlation risk assessment indicators.

[0089] A risk correlation mining technique based on time-series causal inference was introduced to effectively identify complex causal chains and time-delay effects between management behavior, equipment status, and liquid ammonia properties. By constructing a time-series causal graph network, complex cascading causal chains such as "insufficient safety inspection - delayed valve maintenance - decreased sealing performance - minor leakage - changes in liquid ammonia properties" were tracked and analyzed, and the risk contribution and time window of each link were calculated.

[0090] The environment-material interaction dynamic risk assessment layer employs an adaptive Kalman filter algorithm to fuse heterogeneous information from collected multi-source environmental parameter sensing data. By dynamically adjusting the process noise and observation noise matrices, robustness under drastic environmental changes is improved. For the special environmental conditions in liquid ammonia water-land intermodal transport, such as special port meteorological conditions and wind field changes at the water-land interface, dynamic environmental feature vectors with spatiotemporal attributes are obtained.

[0091] Based on computational fluid dynamics principles, a diffusion and volatilization model of liquid ammonia in a multiphase environment was constructed. This model considers the physicochemical properties of liquid ammonia, environmental conditions, and topographic features. By solving the Navier-Stokes equations and the mass transfer equations, it simulates the diffusion behavior of liquid ammonia in air, water, and their interfaces after leakage, generating a three-dimensional diffusion trajectory model.

[0092] Based on a three-dimensional diffusion trajectory model, a continuously updated time-varying concentration distribution cloud map is generated. Combined with population density distribution maps near transportation routes and location information of critical infrastructure, a multi-objective decision-making method combining the analytic hierarchy process (AHP) and the approximation ideal solution ranking method is adopted.

[0093] A multi-dimensional risk cascade assessment layer is constructed, based on a dynamic Bayesian network. This model integrates a human-computer interaction risk assessment index matrix, related risk assessment indicators, and multi-dimensional environmental risk assessment indicators, characterizing the conditional probability relationships and temporal evolution characteristics among various risk factors.

[0094] During the combined water-land transport of liquid ammonia, key risk nodes and main cascading propagation paths were identified, and a probabilistic tree model of risk cascading propagation was constructed. Using Monte Carlo simulation, random sampling and probabilistic inference were performed on the risk propagation paths to obtain a comprehensive risk assessment result including the probability of risk occurrence, severity, scope of impact, and duration.

[0095] Based on risk assessment results, the model divides risk levels into four levels: minor, moderate, severe, and catastrophic, and assigns corresponding warning signals for each level: blue, yellow, orange, and red. The response mechanisms include general attention, departmental response, regional response, and comprehensive response.

[0096] The case library construction layer collects historical cases of liquid ammonia water leakage accidents from home and abroad over the past 10 years. Through a combination of expert annotation and machine learning, the case data is processed with structured semantics to extract multi-dimensional information, including accident type, occurrence stage, leakage amount, environmental conditions, casualties, property damage, response measures and their effects, and to establish a case knowledge base with multi-level indexes.

[0097] In the domain knowledge representation layer, an ontology engineering approach is used to construct a domain knowledge graph for emergency response to liquid ammonia leaks, forming a three-layer knowledge architecture consisting of a concept layer, an instance layer, and a rule layer, which supports cross-domain and cross-level knowledge reasoning and semantic querying.

[0098] By combining case-based reasoning, ontology knowledge, and simulation optimization techniques, a comprehensive intelligent emergency response solution generation and optimization platform has been constructed. This platform achieves a closed-loop intelligent decision-making process from "scenario-case-knowledge-solution-optimization-verification." Through deep metric learning, the platform accurately calculates the similarity between the current risk scenario and historical cases in a multi-dimensional space, extracting valuable experience. Simultaneously, a reasoning mechanism based on domain ontology and knowledge graphs ensures the professionalism and compliance of the generated solutions. Finally, intelligent simulation and multi-objective optimization automatically evaluate the execution effectiveness of different solutions and continuously optimize and improve them.

[0099] The scene similarity calculation layer adopts a deep metric learning method based on Siamese neural network. It compares the current risk assessment results with historical cases in the case knowledge base using multi-dimensional features, calculates a similarity matrix including risk type similarity, scene condition similarity, impact scope similarity, and handling difficulty similarity, and then identifies the set of historical cases most similar to the current situation and their key handling information.

[0100] At the solution generation layer, based on key handling experience extracted from the best similar case set, combined with rule constraints in the domain knowledge graph and the current available resource status, a multi-level emergency response solution is generated for specific risk assessment results. This solution includes six stages: initial handling, leakage control, personnel evacuation, environmental monitoring, medical assistance, and post-treatment. It also includes clearly defined handling steps, a list of required resources, a personnel division matrix, and a time schedule.

[0101] The solution optimization layer first uses a semantic reasoning engine based on a resource description framework to analyze the compliance and effectiveness of the generated emergency response solutions, ensuring that the solutions conform to relevant regulations, standards, and operational procedures. Subsequently, a multi-objective optimization method based on a genetic algorithm is employed to iteratively optimize the solutions from three dimensions: response time, resource consumption, and risk control effectiveness. Through evolutionary computation, the solution maximizes response speed and minimizes resource consumption while ensuring control effectiveness, outputting a Pareto-optimal set of emergency response solutions.

[0102] Based on a multi-source heterogeneous dataset and an optimized emergency response plan, a spatiotemporally coupled numerical simulation method was used to dynamically simulate the spatiotemporal evolution of the emergency response under specific risk assessment results. The simulation process considered multiple factors such as liquid ammonia diffusion dynamics, emergency resource allocation logic, personnel movement paths, and changes in environmental conditions, and predicted the risk control effectiveness (such as leak source control time and hazardous area elimination rate) and potential secondary disasters (such as traffic congestion and medical resource strain) after the implementation of the plan.

[0103] To verify the effectiveness of the present invention in a real-world liquid ammonia water-land combined transport scenario, a series of tests and performance evaluations were conducted, and the results are shown in Table 1.

[0104] Table 1. Verification Results of Key Performance Indicators

[0105]

[0106] The above verification results show that the hazardous chemical transportation risk prediction system based on big data analysis constructed in this invention has significant technical advantages and application value in the liquid ammonia water-land intermodal transportation scenario.

[0107] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A hazardous chemical transportation risk prediction system based on big data analysis, characterized in that, include: The multidimensional data acquisition module is used to collect four-dimensional data of "human-machine-environment-pipe" in the liquid ammonia water transfer process, and to perform standardization processing and spatiotemporal synchronization to obtain multi-source heterogeneous datasets; The risk level acquisition module is used to construct a "human-machine-environment-management" collaborative risk assessment model, analyze the multi-source heterogeneous dataset, and obtain risk assessment results. The "human-machine-environment-management" collaborative risk assessment model includes a human-machine interaction key node identification layer, a management behavior-equipment response correlation analysis layer, an environment-material interaction dynamic risk assessment layer, and a multi-dimensional factor risk cascade assessment layer. The key node identification layer for human-computer interaction includes: performing image segmentation and behavior recognition on the collected video data of operators, extracting the temporal features of the operators' actions, and obtaining a behavior pattern feature library; based on the operator's qualification information, historical operation records, and current fatigue index, combined with the equipment's vibration, pressure, temperature, and sealing integrity, evaluating the safe operation capability of the human-computer system in real time; and performing time series analysis on historical human-computer interaction data, the behavior pattern feature library, and safe operation capability to predict human-computer interaction risk nodes and time windows, and generating human-computer interaction risk assessment indicators. The management behavior-equipment response correlation analysis layer includes: extracting management behavior features by text mining the digital data of management processes; collecting equipment vibration, pressure, temperature and sealing integrity data in real time and extracting equipment state features through multi-scale entropy analysis; collecting and analyzing liquid ammonia's phase change, volatility and diffusion characteristics data to obtain liquid ammonia state features; and analyzing the temporal correlation between management behavior features, equipment state features and liquid ammonia state features to obtain correlation risk assessment indicators. The dynamic risk assessment layer for environment-material interaction includes: fusing environmental parameter sensing data using a Kalman filter algorithm to obtain a dynamic environmental feature vector; analyzing the diffusion and volatilization of risky liquid ammonia under the dynamic environmental feature vector, simulating the interaction process between liquid ammonia and the environmental medium after leakage, and obtaining the diffusion trajectory; generating a concentration distribution cloud map from the diffusion trajectory, and combining it with the predicted hazardous area range, applying a multi-objective decision-making method to assess the potential environmental risk impact, and generating environmental risk assessment indicators that include risk level, impact range, and duration. The multidimensional risk cascade assessment layer includes: analyzing the risk correlation and cascade propagation path among human-computer interaction risk assessment indicators, related risk assessment indicators, and environmental risk assessment indicators; and obtaining risk assessment results by analyzing the risk correlation and cascade propagation path through Bayesian networks. The emergency response plan acquisition module is used to construct an intelligent recommendation model for emergency response plans, perform hierarchical and classification analysis on the risk assessment results, generate multi-level risk warnings and emergency response plans, and optimize the emergency response plans.

2. The hazardous chemical transportation risk prediction system based on big data analysis according to claim 1, characterized in that: The multi-source heterogeneous dataset includes real-time multi-source heterogeneous datasets and historical multi-source heterogeneous datasets; specifically, it includes personnel behavior monitoring data, equipment-item status monitoring data, environmental parameter sensing data, and management process digitization data. The personnel behavior monitoring data includes the operator's behavioral characteristics, fatigue index, and qualification information; the equipment-item status monitoring data includes equipment vibration, pressure, temperature, sealing integrity, and the physical changes, volatility, and diffusion characteristics of liquid ammonia; the environmental parameter sensing data includes the temperature, humidity, wind speed, wind direction, water flow speed, wave height, and tides of the work area; and the digital management process data includes the digital representation of standard operating procedures, emergency response procedures, management requirements, safety inspection records, and operation logs.

3. The hazardous chemical transportation risk prediction system based on big data analysis according to claim 1, characterized in that: The intelligent recommendation model for emergency response plans includes: The system comprises the following layers: a case library construction layer, which collects and structures historical case data of liquid ammonia leak accidents to establish a case knowledge base; a domain knowledge representation layer, which formally represents domain knowledge related to emergency response to liquid ammonia leaks to construct a domain knowledge graph that supports knowledge reasoning and querying; a scenario similarity calculation layer, which generates similarity by comparing risk assessment results with the case knowledge base to obtain the best similar case and its information; and a solution generation layer, which generates emergency response solutions based on the risk assessment results, combining the information from the best similar case. The solution optimization layer uses knowledge reasoning to analyze emergency response plans. At the same time, based on genetic algorithms and multi-objective optimization methods, it optimizes emergency response plans from multiple dimensions such as response time, resource consumption, and risk control, and outputs the optimal emergency response plan.

4. A method for predicting the transportation risks of hazardous chemicals based on big data analysis, characterized in that, include: Collect four-dimensional data on "human-machine-environment-pipeline" during the liquid ammonia water transfer process, and perform standardization processing and spatiotemporal synchronization to obtain a multi-source heterogeneous dataset; A collaborative risk assessment model for "human-machine-environment-management" is constructed, and the multi-source heterogeneous dataset is analyzed to obtain risk assessment results. The collaborative risk assessment model for "human-machine-environment-management" includes a key node identification layer for human-machine interaction, a correlation analysis layer for management behavior and equipment response, a dynamic risk assessment layer for environment-material interaction, and a multi-dimensional factor risk cascade assessment layer. The key node identification layer for human-computer interaction includes: performing image segmentation and behavior recognition on the collected video data of operators, extracting the temporal features of the operators' actions, and obtaining a behavior pattern feature library; based on the operator's qualification information, historical operation records, and current fatigue index, combined with the equipment's vibration, pressure, temperature, and sealing integrity, evaluating the safe operation capability of the human-computer system in real time; and performing time series analysis on historical human-computer interaction data, the behavior pattern feature library, and safe operation capability to predict human-computer interaction risk nodes and time windows, and generating human-computer interaction risk assessment indicators. The management behavior-equipment response correlation analysis layer includes: extracting management behavior features by text mining the digital data of management processes; collecting equipment vibration, pressure, temperature and sealing integrity data in real time and extracting equipment state features through multi-scale entropy analysis; collecting and analyzing liquid ammonia's phase change, volatility and diffusion characteristics data to obtain liquid ammonia state features; and analyzing the temporal correlation between management behavior features, equipment state features and liquid ammonia state features to obtain correlation risk assessment indicators. The dynamic risk assessment layer for environment-material interaction includes: fusing environmental parameter sensing data using a Kalman filter algorithm to obtain a dynamic environmental feature vector; analyzing the diffusion and volatilization of risky liquid ammonia under the dynamic environmental feature vector, simulating the interaction process between liquid ammonia and the environmental medium after leakage, and obtaining the diffusion trajectory; generating a concentration distribution cloud map from the diffusion trajectory, and combining it with the predicted hazardous area range, applying a multi-objective decision-making method to assess the potential environmental risk impact, and generating environmental risk assessment indicators that include risk level, impact range, and duration. The multidimensional risk cascade assessment layer includes: analyzing the risk correlation and cascade propagation path among human-computer interaction risk assessment indicators, related risk assessment indicators, and environmental risk assessment indicators; and obtaining risk assessment results by analyzing the risk correlation and cascade propagation path through Bayesian networks. An intelligent recommendation model for emergency response plans is constructed to perform hierarchical and categorized analysis on the risk assessment results, generate multi-level risk warnings and emergency response plans, and optimize the emergency response plans.

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