Multi-objective path optimization system and method for hazardous chemical substance road transportation

Abstract

The invention discloses a multi-objective path optimization system and method for hazardous chemical substance road transportation, and the system comprises a data obtaining module which is used for obtaining hazardous chemical substance transportation demand information and road network information, and generating a basic data set; the multi-objective optimization module is used for carrying out collaborative optimization of path planning and risk management and control based on the basic data set to generate an optimization scheme; and the path generation module is used for generating a final transportation path, a time arrangement and an emergency plan based on the optimization scheme, and performing dynamic adjustment according to the real-time road condition. The safety, reliability and efficiency of dangerous chemical substance road transportation are remarkably improved, potential risk exposure is reduced, and comprehensive technical support is provided for dangerous chemical substance transportation management.

Description

Technical Field

[0001] The present invention relates to the technical field of route optimization, and in particular to a multi-objective route optimization system and method for the road transportation of hazardous chemicals. Background Art

[0002] The intelligent planning system for road transportation of hazardous chemicals refers to a transportation guarantee system that uses a multi-objective optimization algorithm to plan the transportation routes of hazardous chemicals, comprehensively considering factors such as transportation distance, time, cost, safety risks and environmental impact, and forming a technologically advanced, safe and reliable transportation guarantee system.

[0003] Currently, hazardous chemical transportation systems generally suffer from modular fragmentation and single functionality. They lack systematic integration of data acquisition, multi-objective optimization, and route generation, making it difficult to effectively utilize transportation demand and road network information. Traditional systems often ignore differences in operating conditions under different scenarios and are unable to perform segmented optimization based on hazardous chemical type, time period, and weather conditions. Existing technologies often separate safety management and route planning, lacking collaborative optimization mechanisms, making it difficult to simultaneously address safety and route optimization, and unable to reduce transportation costs while minimizing risks. Furthermore, existing systems generally lack real-time dynamic adjustment capabilities and emergency plan generation functions, making them difficult to cope with complex and changing transportation environments.

[0004] Therefore, there is an urgent need for a multi-objective path optimization system and method for road transportation of hazardous chemicals. Summary of the Invention

[0005] The present invention provides a multi-objective path optimization system and method for road transportation of hazardous chemicals to solve the above-mentioned problems existing in the prior art.

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

[0007] A multi-objective route optimization system for hazardous chemicals road transportation, comprising:

[0008] Data acquisition module, used to obtain hazardous chemicals transportation demand information and road network information, and generate basic data sets;

[0009] Multi-objective optimization module, used to perform collaborative optimization of path planning and risk management based on basic data sets and generate optimization solutions;

[0010] The route generation module is used to generate the final transportation route, schedule and emergency plan based on the optimization plan, and dynamically adjust it according to real-time road conditions.

[0011] Among them, the multi-objective optimization module includes:

[0012] The scenario traversal submodule is used to sequentially traverse various hazardous chemicals transportation scenarios and divide them into multiple operating states according to different time periods and weather conditions;

[0013] The optimization training submodule is used to determine the optimization results of the optimization model in safety management and path planning for each operating state, and use all operating states and collaborative optimization basis as the training basis of the optimization model.

[0014] Among them, the optimization training submodule includes:

[0015] The safety optimization unit is used to interact with the simulation environment based on the preset safety control action space, generate risk assessment results, update safety control parameters, and determine the optimization plan to minimize safety risks and environmental impacts;

[0016] The path optimization unit is used to interact with the simulation environment based on the preset path selection action space, generate path planning results and update path planning parameters, and determine the optimization plan that minimizes transportation costs and safety risks;

[0017] The integration unit is used to integrate the optimization schemes of safety control parameters and path planning parameters as the basis for collaborative optimization under the current operating status.

[0018] Among them, the security optimization unit includes:

[0019] The risk analysis subunit is used to identify the key factors affecting safety risks and environmental impacts in the risk assessment results, determine risk clusters from them, and use the highest-level risk in each risk cluster as the management and optimization target;

[0020] The parameter adjustment subunit is used to treat the corresponding control optimization target as a risk point that needs further optimization and adjust the safety control parameters if the standard operating state representing that the control optimization target has been alleviated does not appear in the subsequent simulation environment.

[0021] The path generation module includes:

[0022] The path sorting submodule is used to sort all optional paths from low to high according to the safety risk and transportation cost according to the path planning results and risk control plan in the optimization plan to obtain the optimized path sequence;

[0023] The path selection submodule is used to divide the optimized path sequence into multiple local path groups, select the path with the highest safety and lowest cost from each local path group in turn, generate the final optimized transportation path and match the time schedule and emergency plan.

[0024] The path generation module also includes:

[0025] The dynamic adjustment submodule is used to generate a dynamic adjustment plan based on the changed road network information when real-time road condition changes or emergencies are detected;

[0026] The communication and early warning submodule is used to establish real-time communication between alternative routes and relevant personnel based on the dynamic adjustment plan, issue early warning information to relevant personnel and update the transportation plan.

[0027] The dynamic adjustment submodule includes:

[0028] A demand matching unit is used to pre-match and adjust demand templates based on real-time traffic changes or emergencies, and to determine multiple alternative adjustment demands and their priorities based on historical optimization results;

[0029] The solution generation unit is used to traverse the alternative adjustment requirements in sequence, generate a transportation scenario evolution sequence and display the evolution process, and generate a dynamic adjustment solution based on the transportation scenario selected by relevant personnel.

[0030] The demand matching unit includes:

[0031] The demand generation subunit is used to generate alternative adjustment demands based on key characteristic parameters input from real-time road condition changes or emergencies, combined with the adjustment demand generation template and historical optimization results;

[0032] The priority setting subunit is used to use the template weight of the adjustment requirement generation template based on which the alternative adjustment requirement is generated as the corresponding priority.

[0033] The data acquisition module includes:

[0034] The demand collection submodule is used to obtain the hazardous chemical transportation demand information including the type, quantity and transportation time requirements of hazardous chemicals;

[0035] The network collection submodule is used to collect road network information such as road nodes, road segment characteristics connecting nodes, real-time road conditions and environmental parameters;

[0036] The data integration submodule is used to integrate hazardous chemicals transportation demand information and road network information to generate a basic data set.

[0037] Among them, a multi-objective path optimization method for hazardous chemicals road transportation includes:

[0038] S1: Obtain hazardous chemicals transportation demand information and road network information to generate a basic data set;

[0039] S2: Perform collaborative optimization of path planning and risk management based on the basic data set to generate an optimization plan;

[0040] S3: Based on the optimization plan, the final transportation route, time schedule and emergency plan are generated, and dynamically adjusted according to real-time road conditions.

[0041] Compared with the prior art, the present invention has the following advantages:

[0042] A multi-objective route optimization system for hazardous chemical road transport includes a data acquisition module for acquiring hazardous chemical transport demand information and road network information to generate a basic data set; a multi-objective optimization module for collaboratively optimizing route planning and risk management based on the basic data set to generate an optimization solution; and a route generation module for generating the final transport route, schedule, and emergency response plan based on the optimization solution, dynamically adjusting it based on real-time road conditions. This system significantly improves the safety, reliability, and efficiency of hazardous chemical road transport, reduces potential risk exposure, and provides comprehensive technical support for hazardous chemical transport management.

[0043] Other features and advantages of the present invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the present invention.

[0044] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0046] Figure 1 This is a structural diagram of a multi-objective path optimization system for hazardous chemicals road transportation according to an embodiment of the present invention;

[0047] Figure 2 This is a structural diagram of a multi-objective optimization module in an embodiment of the present invention;

[0048] Figure 3 This is a flowchart of a multi-objective path optimization method for hazardous chemicals road transportation in an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0050] An embodiment of the present invention provides a multi-objective path optimization system for road transportation of hazardous chemicals, including:

[0051] Data acquisition module, used to obtain hazardous chemicals transportation demand information and road network information, and generate basic data sets;

[0052] Multi-objective optimization module, used to perform collaborative optimization of path planning and risk management based on basic data sets and generate optimization solutions;

[0053] The route generation module is used to generate the final transportation route, schedule and emergency plan based on the optimization plan, and dynamically adjust it according to real-time road conditions.

[0054] The working principle of the above technical solution is as follows: when a hazardous chemical transportation request is received, the matching relationship between the hazardous chemical characteristics and the road network environmental parameters is analyzed to construct a basic data set suitable for the safe transportation of hazardous chemicals; in the data acquisition module, the hazardous chemical transportation request covers information such as the type of hazardous chemicals, physical and chemical characteristics, quantity, loading status, departure and destination, and arrival time limit; road network environmental parameters include road grade classification, number of lanes, road surface conditions, height and width restrictions, traffic capacity, distribution of sensitive areas along the road, meteorological conditions, and traffic flow variation patterns; the matching relationship analysis evaluates the transportation risk factors of different hazardous chemicals under various road conditions to establish quantitative indicators of the adaptability of hazardous chemical characteristics to the road environment; the basic data set is stored in a multi-level graph structure, in which nodes represent road intersections or key landmarks, and edges represent road segments connecting nodes. Each edge is accompanied by multi-dimensional attribute information, including distance, travel time, risk factor, traffic restriction regulations, etc.

[0055] Based on the constructed basic data set, the pre-trained multi-objective collaborative optimization algorithm is started to balance transportation time, cost and safety risks, and generate a multi-gradient trade-off optimization solution set; in the multi-objective optimization module, the multi-objective collaborative optimization algorithm adopts a hybrid strategy combining an improved genetic algorithm and simulated annealing to find the Pareto optimal solution set through iterative calculation; the optimization objective function includes three dimensions: time loss function, economic cost function and risk assessment function; the time loss function considers time factors such as road travel time, loading and unloading time, rest time, and waiting time; the economic cost function takes into account economic factors such as fuel consumption, tolls, labor costs, and equipment loss; the risk assessment function quantifies the safety risks that may be faced during transportation, such as leakage accidents, fire and explosion, and traffic accidents, and is weighted in combination with the population density and environmental sensitivity along the route; the multi-gradient trade-off optimization solution set provides alternative solutions under different decision preferences, allowing decision makers to choose the most suitable transportation strategy according to actual needs;

[0056] Based on the optimization plan set, real-time traffic conditions and weather change information are integrated to generate refined transportation route planning and dynamic response mechanisms to achieve fully controllable transportation of hazardous chemicals and rapid handling of emergencies. In the route generation module, refined transportation route planning includes main route design and alternative route preparation, and assigns recommended speed, stop location, and rest time arrangement to each section. The dynamic response mechanism monitors abnormal conditions during transportation in real time through data interaction between on-board sensors and the traffic management system. When encountering traffic congestion, bad weather, road construction, etc., the system automatically assesses the degree of impact and makes local route adjustments within the preset threshold range. If the threshold is exceeded, the alternative route switching is initiated. The rapid emergency handling process pre-plans evacuation channels in dangerous areas, emergency rescue resource allocation plans, and professional disposal team linkage mechanisms, and formulates targeted emergency disposal plans based on the types of hazardous chemicals.

[0057] The beneficial effects of the above technical solution are: fully controllable transportation enables information sharing and collaborative decision-making among regulatory authorities, transportation companies and vehicle drivers through positioning tracking, status monitoring and remote control, greatly improving the safety and reliability of road transportation of hazardous chemicals.

[0058] In another embodiment, the multi-objective optimization module includes:

[0059] The scenario traversal submodule is used to sequentially traverse various hazardous chemicals transportation scenarios and divide them into multiple operating states according to different time periods and weather conditions;

[0060] The optimization training submodule is used to determine the optimization results of the optimization model in safety management and path planning for each operating state, and use all operating states and collaborative optimization basis as the training basis of the optimization model.

[0061] The working principle of the above technical solution is as follows: the multi-objective optimization module systematically identifies and processes various hazardous chemical transportation scenarios through the scenario traversal sub-module, and subdivides each scenario into multiple operating states according to the spatiotemporal dimension characteristics; among them, hazardous chemical transportation scenarios refer to transportation activity scenarios involving flammable, explosive, toxic and harmful hazardous chemicals in different transportation networks such as roads, railways, and waterways, such as: alkane gas road transportation scenarios, strong acid substance railway transportation scenarios, flammable liquid water transportation scenarios, etc.; spatiotemporal dimension characteristics include time period characteristics and weather condition characteristics. Time period characteristics cover different traffic flow distribution periods such as morning peak, off-peak, evening peak, and night, and weather condition characteristics include different meteorological environments such as sunny, rainy, snowy, foggy, and extreme weather; operating state refers to a specific state of hazardous chemical transportation formed based on the combination of spatiotemporal dimension characteristics, such as: the operating state during the morning peak under sunny weather, the operating state at night under rainy and snowy weather, etc.

[0062] The optimization training submodule performs dual-objective optimization calculations for each operating state, dynamically balancing safety control requirements and path planning efficiency, and generating a collaborative optimization basis for a specific operating state. Dual-objective optimization calculations refer to the optimization calculation process that simultaneously considers safety control objectives and path planning objectives. Safety control requirements include safety factors such as compliance with relevant hazardous chemicals regulations, avoidance of risk-sensitive areas, and emergency response capability assurance. Path planning efficiency covers efficiency factors such as transportation time consumption, energy resource utilization, and economic cost control. The collaborative optimization basis refers to the decision-making reference foundation with the best safety-efficiency balance formed after dual-objective optimization calculations under a specific operating state.

[0063] The optimization training submodule integrates all operating states and their corresponding collaborative optimization bases to form a training data set, and constructs an adaptive optimization model through iterative learning. The training data set consists of multiple operating state and collaborative optimization base mapping pairs. The iterative learning method adopts a step-by-step model training strategy, continuously adjusting the optimization model parameters to improve its prediction accuracy and generalization ability. The adaptive optimization model can quickly generate optimization results that meet the dual requirements of safety control and path planning for newly emerging hazardous chemical transportation scenarios and operating states.

[0064] The beneficial effects of the above technical solution are: through the scene traversal sub-module system, various hazardous chemical transportation scenarios are identified and multiple operating states are subdivided according to the characteristics of the time and space dimensions. The optimization training sub-module performs dual-objective optimization calculations for each operating state and generates collaborative optimization basis. Then, all operating states and collaborative optimization basis are integrated into a training data set to construct an adaptive optimization model, which achieves a dynamic balance between safety control and path planning during the transportation of hazardous chemicals, and significantly improves the safety, efficiency and reliability of hazardous chemical transportation.

[0065] In another embodiment, the optimization training submodule includes:

[0066] The safety optimization unit is used to interact with the simulation environment based on the preset safety control action space, generate risk assessment results, update safety control parameters, and determine the optimization plan to minimize safety risks and environmental impacts;

[0067] The path optimization unit is used to interact with the simulation environment based on the preset path selection action space, generate path planning results and update path planning parameters, and determine the optimization plan that minimizes transportation costs and safety risks;

[0068] The integration unit is used to integrate the optimization schemes of safety control parameters and path planning parameters as the basis for collaborative optimization under the current operating status.

[0069] The technical solution works as follows: The safety optimization unit interacts with the simulation environment based on a pre-defined safety control action space. The system generates comprehensive risk assessments and dynamically updates safety control parameters to determine an optimization plan that minimizes safety risks and environmental impacts. The safety optimization unit analyzes road conditions, traffic flow, weather conditions, and the characteristics of hazardous chemicals to establish a safety risk quantification model. The system then uses historical accident data to assess the risk of each road section.

[0070] The path optimization unit interacts with the simulation environment based on a preset path selection action space. By analyzing the road network topology, real-time traffic conditions, and the risk level of each road section, it generates path planning results and updates path planning parameters to determine the optimal solution that minimizes transportation costs and safety risks. The path optimization unit comprehensively considers fuel consumption, time costs, road tolls, and safety risk factors, and uses a multi-objective optimization algorithm to generate a Pareto optimal solution set.

[0071] The integration unit systematically integrates the optimization plans for safety control parameters and route planning parameters, serving as the basis for collaborative optimization within the current operational state. During this integration process, the balance between safety risk and cost-effectiveness is adaptively adjusted based on decision-making preferences and actual transportation needs, ultimately generating a final transportation execution plan.

[0072] The beneficial effects of the above technical solution are: the integration unit will also establish a feedback learning mechanism, and through the analysis of historical transportation data, continuously optimize the decision-making model to improve the system's adaptability to complex transportation environments.

[0073] In another embodiment, the security optimization unit includes:

[0074] The risk analysis subunit is used to identify the key factors affecting safety risks and environmental impacts in the risk assessment results, determine risk clusters from them, and use the highest-level risk in each risk cluster as the management and optimization target;

[0075] The parameter adjustment subunit is used to treat the corresponding control optimization target as a risk point that needs further optimization and adjust the safety control parameters if the standard operating state representing that the control optimization target has been alleviated does not appear in the subsequent simulation environment.

[0076] Among them, identify the key factors affecting safety risks and environmental impacts in the risk assessment results, including:

[0077] Obtain risk assessment results data for multiple target areas containing hazardous chemical transportation routes over a continuous period of time, including: determining transportation routes containing potential safety and environmental risks based on historical accident data and environmental monitoring data from multiple transportation areas; and determining target transportation routes containing significant risk characteristics based on risk assessment data corresponding to transportation routes containing potential safety and environmental risks;

[0078] For any target transport route, based on the risk assessment result data within a continuous period, identify the key influencing factors of safety risks and environmental impacts in the target transport route. The key influencing factors include road condition factors, weather change factors, transport vehicle performance factors, and population distribution factors along the route. Based on the risk assessment result data within a continuous period, identify the key influencing factors of safety risks and environmental impacts in the target transport route, including: uniformly gridding the risk assessment data corresponding to each period within the continuous period and the risk assessment data of the benchmark period; determining the risk contribution of road condition factors based on road traffic accident frequency and road surface quality indicators; determining the risk contribution of weather change factors based on meteorological condition changes and visibility levels; determining the risk contribution of transport vehicle performance factors based on vehicle technical status and load capacity; and determining the risk contribution of population distribution factors along the route based on population density distribution and sensitive area distribution.

[0079] Based on the spatial distribution characteristics of key influencing factors in the target transportation path, the risk set division criteria and the risk classification threshold corresponding to each key influencing factor are determined.

[0080] Based on the spatial distribution characteristics of key influencing factors in the target transportation route, the risk set division criteria are determined, including: dividing the transportation route into several risk sets based on the similarity characteristics of key influencing factors, each risk set representing a specific section or area with similar risk characteristics; if any section area in the transportation route simultaneously meets the conditions that the population density is higher than a set threshold and the road grade is lower than the set standard, the section area is classified into the urban density risk set; if any section area in the transportation route simultaneously meets the conditions that the terrain complexity is higher than a set threshold and the frequency of severe weather conditions exceeds a set ratio, the section area is classified into the terrain and climate risk set;

[0081] Conduct a risk level assessment on the key influencing factors of each risk cluster and determine the highest level risk. Calculate the degree of security threat and environmental impact corresponding to the highest level risk in each risk cluster. Specifically, this includes: calculating the risk level distribution of multiple key influencing factors in any risk cluster in each consecutive time period; identifying the influencing factor with the highest risk level in any risk cluster as the dominant risk factor of the risk cluster; assessing the degree of threat to transportation safety and the potential impact of the dominant risk factor on the surrounding environment; and determining the highest level risk of the risk cluster based on the comprehensive score of the threat level and impact level.

[0082] Based on the degree of security threat and environmental impact of the highest-level risk in each risk cluster, the management and control optimization objectives of the risk cluster are determined. This includes: if the highest-level risk in the risk cluster is mainly manifested in the risk of high incidence of traffic accidents, reducing the probability of accidents will be determined as the management and control optimization objective of the risk cluster; if the highest-level risk in the risk cluster is mainly manifested in the risk of pollution in environmentally sensitive areas, reducing the degree of environmental impact will be determined as the management and control optimization objective of the risk cluster; if the highest-level risk in the risk cluster includes both security threats and environmental impacts, comprehensive risk control will be determined as the management and control optimization objective of the risk cluster;

[0083] The management and control optimization objectives corresponding to different risk sets and the optimization strategies corresponding to different management and control optimization objectives are stored to obtain a multi-objective management and control optimization model for optimizing the transportation route of hazardous chemicals.

[0084] The above technical solution works as follows: The risk analysis subunit first analyzes the risk assessment results of the transportation route and identifies corresponding key influencing factors. These key influencing factors include road conditions, weather changes, transportation vehicle performance, and population distribution along the route. These key influencing factors affect the safety hazards and environmental damage during transportation. The risk analysis subunit then divides the transportation route into several risk clusters based on these key influencing factors. Each risk cluster represents a specific section or area along the route with similar risk characteristics. For example, a hazardous chemical transportation route that passes through a densely populated urban area would be classified into one risk cluster due to its high population density, while a section that passes through rugged mountain roads would be classified into another risk cluster due to its complex terrain and narrow roads. This classification ensures targeted and accurate risk analysis. The risk analysis subunit then selects the highest-level risk from each risk cluster and determines it as the management and optimization target for that risk cluster. The highest-level risk represents the factor that poses the greatest threat to transportation safety or environmental impact. For example, a section of road might be designated as a high-risk area due to frequent traffic accidents, or an area might be environmentally sensitive due to its proximity to a water source protection area.

[0085] After the control and optimization target is determined, the parameter adjustment subunit optimizes the control and optimization target through the simulation environment. The simulation environment simulates the real-life scenario of road transportation of hazardous chemicals and provides a platform for dynamic testing and adjustment of safety control parameters. The parameter adjustment subunit monitors the operating status during the simulation process, paying particular attention to whether a standard operating state appears, indicating that the control and optimization target has been alleviated. The standard operating state refers to a state in which the risk for a certain control and optimization target has been effectively controlled or eliminated in the simulation. For example, by adjusting the transport speed or replacing a low-risk route, the probability of an accident on a high-risk road section is reduced, thereby achieving a standard operating state. If the standard operating state, indicating that the control and optimization target has been alleviated, does not appear in the simulation environment, the parameter adjustment subunit will identify the target as a risk point that requires further optimization. The system then adjusts the safety control parameters. Safety control parameters include: transport speed, vehicle spacing, and protective equipment configuration.

[0086] The continuous optimization mechanism can flexibly adapt to different transportation scenarios based on simulation results. For example, in rainy conditions, the system can mitigate the risk of slippery roads by reducing vehicle speeds and increasing emergency response measures.

[0087] The beneficial effects of this technical solution are as follows: the risk analysis subunit identifies key influencing factors and divides risk clusters, ensuring that the system focuses on the most threatening risk points. The parameter adjustment subunit uses dynamic optimization in a simulation environment to tailor safety and control strategies for each transportation route, avoiding the "one-size-fits-all" limitations of traditional methods.

[0088] In another embodiment, the path generation module includes:

[0089] The path sorting submodule is used to sort all optional paths from low to high according to the safety risk and transportation cost according to the path planning results and risk control plan in the optimization plan to obtain the optimized path sequence;

[0090] The path selection submodule is used to divide the optimized path sequence into multiple local path groups, select the path with the highest safety and lowest cost from each local path group in turn, generate the final optimized transportation path and match the time schedule and emergency plan.

[0091] The working principle of the above technical solution is as follows: Based on the path planning results and risk control plan from the optimization solution, the path sorting submodule ranks all available paths from low to high according to safety risk and transportation cost, thereby obtaining an optimized path sequence. In the path sorting submodule, the path sorting process first collects information on all feasible hazardous chemical transportation routes and then preliminarily screens these routes based on the existing path planning results. The system comprehensively considers safety risk factors for each route, such as road conditions, weather conditions, traffic density, and hazardous materials transport restricted areas. It also evaluates the transportation costs of each route, including economic indicators such as fuel consumption, tolls, and time costs. Through this multi-dimensional evaluation, the system ranks all paths from low to high according to the combined safety risk and transportation cost scores, forming an optimized path sequence. For example, in the scenario of transporting hazardous chemicals from point A to point B, the system will sort multiple routes combining different types of roads, such as expressways, national highways, and provincial highways, prioritizing routes that avoid densely populated areas, have dedicated roads for hazardous chemical transportation, and have lower transportation costs.

[0092] The path selection submodule divides the optimized path sequence into multiple local path groups, and selects the path with the highest safety and lowest cost from each local path group in the order of grouping. In the path selection submodule, the system first divides the entire transportation path into multiple local path groups based on geographical location, administrative divisions, or key nodes. This division method facilitates more refined path optimization within the local scope while taking into account the global optimal solution. For each local path group, the system comprehensively analyzes the safety risk index and transportation cost index of each path, and gives priority to the most cost-effective path while ensuring safety. For example, when hazardous chemicals need to pass through multiple cities, the system will divide the path into different groups such as internal city sections and inter-city connecting sections, and then select the optimal path for each group separately. Within the city, ring roads are preferred to avoid the city center, and dedicated hazardous chemical transportation channels are given priority for inter-city connections.

[0093] Furthermore, in the route selection submodule, the optimal routes selected from each local route group are connected to form a complete hazardous chemical transport route. At the same time, a corresponding time schedule is matched based on the route characteristics and the type of hazardous chemical, including the optimal departure time, estimated arrival time, and transit time at key nodes, to avoid peak traffic periods or adverse weather conditions. Contingency plans (including alternative routes, emergency contact information, and nearby emergency rescue points) are automatically generated for various emergencies that may arise during transportation (including traffic accidents, severe weather, and vehicle failures). For example, when the system generates a hazardous chemical transport route through mountainous areas, it will simultaneously match the time schedule to avoid travel during the rainy season and develop emergency response plans for situations such as landslides and collapses on mountain roads.

[0094] The beneficial effect of this technical solution is that it achieves refined management of hazardous chemical transportation routes through the two key links of route sorting and route selection. This not only improves the safety level of hazardous chemical transportation, but also reduces transportation costs and meets the special needs of hazardous chemical road transportation.

[0095] In another embodiment, the path generation module further includes:

[0096] The dynamic adjustment submodule is used to generate a dynamic adjustment plan based on the changed road network information when real-time road condition changes or emergencies are detected;

[0097] The communication and early warning submodule is used to establish real-time communication between alternative routes and relevant personnel based on the dynamic adjustment plan, issue early warning information to relevant personnel and update the transportation plan.

[0098] The dynamic adjustment plan is generated based on the changed road network information, including:

[0099] Acquire real-time monitoring information of the road network, identify road condition change events based on the real-time monitoring information, and obtain road condition change event information;

[0100] Obtaining pre-matching rational adjustment stage standard information, combining it with road condition change event information to make adjustment stage judgment, and obtaining rational adjustment stage information;

[0101] Obtain historical adjustment plan data, identify plan differences and generate adjustment support needs based on rational adjustment stage information, and obtain adjustment support demand information;

[0102] Perform multi-objective route optimization based on the adjustment support demand information and the changed road network information to generate a dynamic adjustment plan for hazardous chemicals transportation;

[0103] The process of obtaining real-time monitoring information of the road network and identifying road condition change events based on the real-time monitoring information specifically includes:

[0104] Obtain real-time monitoring information of the road network, perform data preprocessing on the real-time monitoring information, and obtain characteristic information of various traffic events, road conditions, and hazardous chemical transportation safety events based on big data retrieval to form an event feature comparison dataset;

[0105] Constructing a road condition change event recognition model, and constructing a training data set based on the event feature comparison data set to perform deep learning and training on the road condition change event recognition model. The road condition change event recognition model includes a data fusion layer, an event detection layer, and an impact assessment layer;

[0106] The data fusion layer performs multi-source data fusion on real-time monitoring information, integrating traffic flow data, road condition data, meteorological data, and emergency data. A fusion feature matrix is ​​constructed based on the weights of each data source to obtain fused monitoring feature information.

[0107] The fused monitoring feature information is input into the event detection layer for event detection. The temporal attention mechanism is introduced to calculate the event occurrence probability of each time node by combining the fused monitoring feature information with the temporal attention mechanism, and the event probability distribution graph is constructed to obtain the event probability distribution information.

[0108] An event classifier is constructed based on the improved CNN-LSTM network. The monitoring feature information and event probability distribution information are fused, and the event type is identified using a multi-scale convolution kernel to generate event classification results and obtain event type identification information.

[0109] Evaluate the impact of the detected event based on the event type identification information, calculate the degree and duration of the event's impact on the hazardous chemicals transportation route, and obtain event impact assessment information;

[0110] Input the event impact assessment information into the impact assessment layer for comprehensive assessment, evaluate the impact of road condition change events on the safety risks and timeliness of hazardous chemicals transportation, and obtain road condition change event information;

[0111] Among them, obtaining the standard information of the pre-matching rational adjustment stage and combining the road condition change event information to make the adjustment stage judgment specifically include:

[0112] Obtain standard information for the pre-matching rational adjustment phase, and build a phased adjustment standard library based on historical emergency response experience, including judgment criteria for the emergency response phase, path assessment phase, solution optimization phase, and execution monitoring phase;

[0113] Extract features from road condition change event information, including event type, impact, and urgency features, perform similarity matching with the phased adjustment standard library, and compare with the preset matching threshold to determine the rational adjustment phase corresponding to the current event and obtain phase matching information.

[0114] Combine the phase matching information and the road condition change event information to accurately locate the adjustment phase, analyze the phase transition conditions, sort the phase transition conditions according to the trigger priority, and obtain the phase transition sequence information;

[0115] Extract event development trend information and expected duration information based on road condition change event information, construct event development trend graph based on event intensity changes and impact range changes within unit time, predict event development direction and adjustment timing, and obtain adjustment timing prediction information;

[0116] Combining stage matching information, stage transition sequence information and adjustment timing prediction information to form rational adjustment stage information;

[0117] Among them, obtaining historical adjustment plan data, identifying plan differences and generating adjustment support needs based on rational adjustment stage information, specifically including:

[0118] Obtain historical adjustment plan data, retrieve historical processing plans for similar events based on the rational adjustment stage information, obtain historical plan collection information, and evaluate the effectiveness of the plans based on the execution results of the plans to obtain plan effectiveness evaluation information;

[0119] Obtain information from the rational adjustment phase, combine it with historical plan set information to perform plan difference analysis, identify decision divergence points in historical plans, and obtain plan divergence identification information;

[0120] Based on the solution divergence identification information, a divergence coordination model is constructed to analyze the advantages, disadvantages and applicable conditions of each divergent solution. Some solution differences are automatically coordinated, and the unreconcilable solution differences are retained as unresolved solution divergence to obtain unresolved solution divergence information.

[0121] Based on the information of the rational adjustment stage and the information of unresolved differences, an adjustment support demand generation model is constructed to analyze the role of real-time traffic information in promoting the entry into the next rational adjustment stage and obtain the stage advancement support demand information.

[0122] Construct a divergence coordination support demand analysis module to evaluate the degree of support for divergence coordination of unresolved solutions provided by real-time road conditions, calculate the feasibility and safety of each divergence solution under current road conditions, and obtain divergence coordination support demand information;

[0123] Combine the stage promotion support demand information and the divergence coordination support demand information to form the adjustment support demand information; among them, based on the adjustment support demand information and the changed road network information, multi-objective path optimization is performed to generate a dynamic adjustment plan for hazardous chemicals transportation, specifically including:

[0124] Acquire adjustment support demand information, determine path optimization constraints and objective functions based on the adjustment support demand information, and obtain optimization target setting information;

[0125] Obtain the changed road network information, combine it with the optimization target setting information to search for feasible paths, build a multi-objective optimization space that considers safety and timeliness, and obtain the candidate path set information;

[0126] A path optimization model is constructed based on an improved multi-objective particle swarm optimization algorithm, with minimizing safety risks, minimizing transportation time, and minimizing transportation costs as optimization goals, and a multi-objective optimization function is established.

[0127] According to the candidate path set information, the multi-objective optimization function is input to solve the problem, and the retention strategy and dynamic weight adjustment mechanism are used to perform iterative optimization to obtain the optimal solution set information;

[0128] Construct a solution evaluation decision model, combine the adjustment support demand information to comprehensively evaluate the Pareto optimal solution set, consider the current road conditions, transportation time requirements and safety risk tolerance, sort and screen the solutions, and obtain the optimal adjustment solution information;

[0129] Generate detailed route adjustment instructions based on the optimal adjustment plan information, including new route coordinates, estimated travel time, precautions and emergency plans, forming a dynamic adjustment plan for hazardous chemicals transportation.

[0130] The working principle of the above technical solution is as follows: The pre-matched rational adjustment stage criteria in the dynamic adjustment submodule are pre-set criteria for phased rational adjustments that match real-time road condition changes. These criteria are pre-set by technical personnel based on their experience handling various road emergencies in different phases. These rational adjustment stage criteria indicate how the system should determine the next phase for rationally adjusting hazardous chemical transportation routes after detecting a real-time road condition change, i.e., the next rational adjustment phase. The dynamic adjustment submodule then identifies unresolved solution differences from the adjustment phases of the system's previous handling of similar road condition changes. During this process, the system analyzes the various solutions generated in previous handling of similar road condition changes, automatically reconciling some of the differences between the solutions, while retaining any unreconciled differences as unresolved solution differences. The dynamic adjustment submodule then generates adjustment support requirements: real-time road condition information from the road network information source is beneficial for prompting the system to enter the next rational adjustment phase; and real-time road condition information from the road network information source is beneficial for prompting the system to reconcile any unresolved solution differences. Real-time road condition information refers to information currently available from the road network information source, such as road conditions, traffic flow, and emergencies. Adjustment support needs fall into two areas: First, real-time traffic information helps propel the system into the next rational adjustment phase. This means that after processing real-time traffic information, the system's adjustment strategy is guided into the next rational adjustment phase. For example, during a hazardous chemical transport, the system receives real-time traffic information indicating a traffic accident on the road ahead, resulting in severe congestion and an expected delay in resuming traffic. This information prompts the system to quickly analyze and determine the need for immediate route adjustment, thus entering the next rational adjustment phase. This includes calculating alternative routes and evaluating their safety and timeliness to avoid the additional risk of transport vehicles being stranded in congested sections. Second, real-time traffic information helps the system coordinate unresolved disagreements. This means that after processing real-time traffic information, the system's adjustment strategy is guided into reconciling unresolved disagreements. For example, when handling a traffic control emergency, the system may be divided over whether to detour via a longer highway with good traffic conditions or a shorter national highway with poor traffic conditions. At this time, real-time traffic information provides detailed data on the real-time traffic flow, road construction conditions, and weather conditions of the two routes, helping the system to fully understand the current situation and potential risks, and thus coordinate the differences between different plans under the guidance of information, and ultimately decide to choose a safer route to ensure the safety of hazardous chemicals transportation.

[0131] Based on the dynamic adjustment plan, the communication warning submodule establishes a real-time communication link between alternative routes and relevant personnel. Through this link, it sends warning information to drivers and transportation management personnel and updates the transportation plan. The communication warning submodule first assesses the warning priority, prioritizing information delivery based on the severity and urgency of the road condition change. The communication warning submodule then generates the warning content, including key information such as details of the road condition change, recommended alternative routes, adjusted estimated arrival times, and safety precautions. Using natural language processing technology, the warning content is converted into concise and clear text and images, ensuring that drivers can quickly understand it while driving. The communication warning submodule then selects the optimal communication channel. Based on current network coverage, the urgency of the information, and the type of the driver's receiving device, it automatically chooses the most reliable communication method, such as satellite communication, mobile network, or dedicated on-board communication equipment, to ensure timely and reliable delivery of the warning information.

[0132] The beneficial effects of the above technical solution are: in the process of dynamic adjustment and communication warning, the rational adjustment stage standard is introduced, based on the rapid determination of the next rational adjustment stage and the identification of unresolved solution differences, and finally the adjustment support demand is generated from the two aspects of real-time road condition information being beneficial to prompting the system to enter the next rational adjustment stage and being beneficial to prompting the system to coordinate unresolved solution differences. After establishing wireless communication between different road network information sources and transportation systems, the real-time road condition information of the road network information source can effectively guide the system to enter the next rational adjustment stage and coordinate unresolved solution differences, which greatly improves the accuracy, safety and efficiency of the optimization of hazardous chemicals road transportation routes, and improves the system's adaptability in complex traffic environments.

[0133] In another embodiment, the dynamic adjustment submodule includes:

[0134] A demand matching unit is used to pre-match and adjust demand templates based on real-time traffic changes or emergencies, and to determine multiple alternative adjustment demands and their priorities based on historical optimization results;

[0135] The solution generation unit is used to traverse the alternative adjustment requirements in sequence, generate a transportation scenario evolution sequence and display the evolution process, and generate a dynamic adjustment solution based on the transportation scenario selected by relevant personnel.

[0136] The working principle of the above technical solution is as follows: the demand matching unit first pre-matches the adjustment demand template based on the real-time road condition changes or emergencies. The pre-matched adjustment demand template refers to a pre-set adjustment template that matches the road condition changes or emergencies in the road transportation of hazardous chemicals. It can be pre-set by technical personnel based on previous experience in handling different road condition changes or emergencies. The demand matching unit determines multiple alternative adjustment demands and their priorities by analyzing historical optimization results, providing a basis for subsequent solution generation. In the process of determining alternative adjustment demands, the demand matching unit will identify unresolved adjustment conflicts in the historical transportation optimization process. In the historical optimization process, the various stakeholders in the transportation of hazardous chemicals will generate multi-objective conflicts, such as the conflict between safety and timeliness. Some conflicts will be resolved during the processing process, while those that are not resolved will be treated as unresolved adjustment conflicts.

[0137] The solution generation unit will traverse the alternative adjustment requirements in turn and generate a transportation scenario evolution sequence based on two goals: first, the evolution of the transportation scenario is conducive to prompting relevant personnel to accept the path adjustment plan for the next stage. This means that after viewing the evolution sequence, the decision-making ideas of the relevant personnel will be guided into the next path adjustment stage; for example: in the transportation of hazardous chemicals, the evolution sequence provided by the solution generation unit shows real-time data that the road ahead is slippery due to heavy rain and visibility is restricted. This information prompts relevant personnel to quickly identify the risk level of continuing the original route after viewing it, thereby guiding them to accept the adjustment plan of the alternative route. Second, the evolution of transportation scenarios is conducive to prompting relevant personnel to resolve unresolved adjustment conflicts. This means that after viewing the evolution sequence, the decision-making ideas of all relevant personnel will be guided to resolve unresolved adjustment conflicts. For example, when relevant personnel are dealing with a hazardous chemical transportation route adjustment, there is a disagreement on whether to choose a longer but safer detour route. One party believes that timeliness should be prioritized, while the other party believes that safety must be prioritized. At this time, the evolution sequence provided by the solution generation unit contains quantitative comparative data on the risk level of the original route and the time cost of the alternative route, which helps relevant personnel fully understand the current situation and potential risks, thereby prompting all parties to reach a consensus under the guidance of data and ultimately decide to adopt a safer route to avoid potential accident risks.

[0138] The beneficial effects of the above technical solution are: when generating a dynamic adjustment plan, the transportation scenario evolution sequence is introduced, based on which the implementation effect of the adjustment plan is quickly displayed, and unresolved adjustment conflicts are identified. Finally, the final dynamic adjustment plan is generated from two aspects: it is beneficial to encourage relevant personnel to accept the next stage path adjustment and it is beneficial to encourage relevant personnel to resolve unresolved adjustment conflicts. This enables the road transportation of hazardous chemicals to effectively respond to real-time road conditions or emergencies, greatly improving the safety, timeliness and economy of road transportation of hazardous chemicals, and enhancing the practical value of the system.

[0139] In another embodiment, the demand matching unit includes:

[0140] The demand generation subunit is used to generate alternative adjustment demands based on key characteristic parameters input from real-time road condition changes or emergencies, combined with the adjustment demand generation template and historical optimization results;

[0141] The priority setting subunit is used to use the template weight of the adjustment requirement generation template based on which the alternative adjustment requirement is generated as the corresponding priority.

[0142] The working principle of the above technical solution is as follows: the demand generation subunit receives key characteristic parameters of real-time road condition changes or emergency inputs, which include road condition information related to the transportation of hazardous chemicals, such as road congestion, weather conditions, and accident conditions. The demand generation subunit matches these parameters with the pre-set adjustment demand generation template, and combines historical optimization results to generate multiple alternative adjustment demands. The adjustment demand generation template is a standardized template pre-established by the system based on past experience in adjusting hazardous chemical transportation routes, and contains the optimal adjustment solutions under different road conditions. For example, when the system detects that the road ahead is slippery due to rain and snow, the demand generation subunit will generate alternative adjustment demands, including reducing driving speed and changing to an alternative route with a higher safety factor, based on the corresponding adjustment demand generation template.

[0143] The prioritization subunit is responsible for assigning appropriate priorities to each generated adjustment request candidate so that the system can process them in order of importance. The prioritization subunit uses the template weights of the adjustment request generation templates on which the adjustment requests are generated as the corresponding priorities. Template weights are pre-set by technical personnel based on hazardous chemical transportation safety standards, historical accident data, and expert experience, reflecting the importance of different adjustment requests in ensuring transportation safety and efficiency. For example, a template addressing the risk of hazardous chemical leaks may have the highest weight, while a route fine-tuning template addressing minor traffic congestion may have a relatively low weight.

[0144] The beneficial effects of this technical solution are: by introducing a demand-matching unit with real-time response capabilities, it can quickly generate adaptive adjustment requirements based on dynamic changes in road conditions and establish reasonable priorities based on professional knowledge, thus achieving multi-objective dynamic optimization of hazardous chemical transportation routes. This technology not only improves the system's adaptability and response speed to complex road conditions, but also enhances the standardization and consistency of adjustment decisions through template processing, significantly improving the safety, efficiency, and reliability of hazardous chemical road transportation, and has strong practical value.

[0145] In another embodiment, the data acquisition module includes:

[0146] The demand collection submodule is used to obtain the hazardous chemical transportation demand information including the type, quantity and transportation time requirements of hazardous chemicals;

[0147] The network collection submodule is used to collect road network information such as road nodes, road segment characteristics connecting nodes, real-time road conditions and environmental parameters;

[0148] The data integration submodule is used to integrate hazardous chemicals transportation demand information and road network information to generate a basic data set.

[0149] The working principle of the above technical solution is as follows: the basic dataset construction strategy refers to the pre-set data integration standards that match the hazardous chemical transportation. It can be pre-set by technical personnel based on previous experience in constructing datasets for different hazardous chemical transportation scenarios. The construction strategy is used to guide how the demand collection submodule and the network collection submodule work together to ensure that the collected information can meet the computational requirements of multi-objective path optimization.

[0150] The demand collection submodule obtains information on hazardous chemical transportation requirements, including parameters such as hazardous chemical type, quantity, and transportation time requirements. The demand collection submodule obtains specific hazardous chemical transportation requirements through an interface with the enterprise management system or through manual user input. The type of hazardous chemical determines the safety risk level during transportation, the quantity affects the selection of transportation vehicles and route planning, and the transportation time requirement provides a time constraint for route optimization. For example, a chemical company needs to transport a batch of flammable liquids with a total weight of 15 tons and must deliver it to the destination within 48 hours. This information, obtained by the demand collection submodule, will serve as the basis for route planning.

[0151] The network acquisition submodule acquires road network information, including characteristics of road nodes and road segments connecting these nodes, real-time road conditions, and environmental parameters. By interfacing with the traffic monitoring system, the network acquisition submodule obtains both static and dynamic information about the road network. Static information includes road segment characteristics such as road grade, speed limit, road width, slope, and curve radius. Dynamic information includes real-time traffic flow, accident information, weather conditions, and construction areas. For example, heavy rain on the K85-K120 section of a certain highway caused slippery road conditions, heavy traffic, and reduced speeds. The network acquisition submodule acquires this information in real time and transmits it to the data integration submodule.

[0152] The data integration submodule associates and matches the hazardous chemical transportation demand information and road network information to generate a basic data set; the data integration submodule associates the demand information with the network information according to the matching rules between the hazardous chemical type and the road safety level to form a multi-dimensional data structure; for example, for flammable and explosive hazardous chemicals, the system will automatically mark the risk level of special sections such as tunnels and bridges, and calculate the passing time based on the real-time traffic conditions, and finally form a network data structure containing nodes, edges, and weights as the input of the path optimization algorithm.

[0153] The beneficial effects of the above technical solution are as follows: when constructing the basic data set for hazardous chemical transportation, the embodiment of the present invention introduces a two-way association mechanism between demand information and network information, obtains hazardous chemical attribute parameters through the demand collection sub-module, and uses the network collection sub-module to capture road condition changes in real time. Finally, the data integration sub-module intelligently matches and fuses the two types of information, so that the basic data set can comprehensively reflect the safety risks and efficiency factors of hazardous chemical transportation, and provide accurate, dynamic and comprehensive data support for subsequent multi-objective path optimization, significantly improving the rationality and safety of path planning, and enhancing the system's adaptability to complex road environments.

[0154] In another embodiment, a multi-objective route optimization method for road transportation of hazardous chemicals includes:

[0155] S1: Obtain hazardous chemicals transportation demand information and road network information to generate a basic data set;

[0156] S2: Perform collaborative optimization of path planning and risk management based on the basic data set to generate an optimization plan;

[0157] S3: Based on the optimization plan, the final transportation route, time schedule and emergency plan are generated, and dynamically adjusted according to real-time road conditions.

[0158] The working principle of the above technical solution is as follows: when a hazardous chemical transportation request is received, the matching relationship between the hazardous chemical characteristics and the road network environmental parameters is analyzed to construct a basic data set suitable for the safe transportation of hazardous chemicals; in the data acquisition module, the hazardous chemical transportation request covers information such as the type of hazardous chemicals, physical and chemical characteristics, quantity, loading status, departure and destination, and arrival time limit; road network environmental parameters include road grade classification, number of lanes, road surface conditions, height and width restrictions, traffic capacity, distribution of sensitive areas along the road, meteorological conditions, and traffic flow variation patterns; the matching relationship analysis evaluates the transportation risk factors of different hazardous chemicals under various road conditions to establish quantitative indicators of the adaptability of hazardous chemical characteristics to the road environment; the basic data set is stored in a multi-level graph structure, in which nodes represent road intersections or key landmarks, and edges represent road segments connecting nodes. Each edge is accompanied by multi-dimensional attribute information, including distance, travel time, risk factor, traffic restriction regulations, etc.

[0159] Based on the constructed basic data set, the pre-trained multi-objective collaborative optimization algorithm is started to balance transportation time, cost and safety risks, and generate a multi-gradient trade-off optimization solution set; in the multi-objective optimization module, the multi-objective collaborative optimization algorithm adopts a hybrid strategy combining an improved genetic algorithm and simulated annealing to find the Pareto optimal solution set through iterative calculation; the optimization objective function includes three dimensions: time loss function, economic cost function and risk assessment function; the time loss function considers time factors such as road travel time, loading and unloading time, rest time, and waiting time; the economic cost function takes into account economic factors such as fuel consumption, tolls, labor costs, and equipment loss; the risk assessment function quantifies the safety risks that may be faced during transportation, such as leakage accidents, fire and explosion, and traffic accidents, and is weighted in combination with the population density and environmental sensitivity along the route; the multi-gradient trade-off optimization solution set provides alternative solutions under different decision preferences, allowing decision makers to choose the most suitable transportation strategy according to actual needs;

[0160] Based on the optimization plan set, real-time traffic conditions and weather change information are integrated to generate refined transportation route planning and dynamic response mechanisms, realizing fully controllable transportation of hazardous chemicals and rapid handling of emergencies. In the route generation module, refined transportation route planning includes main route design and alternative route preparation, assigning recommended speeds, stop locations, and rest time arrangements to each section. The dynamic response mechanism monitors abnormal conditions during transportation in real time through data exchange between on-board sensors and the traffic management system. When encountering traffic congestion, bad weather, road construction, etc., the system automatically assesses the degree of impact and makes local route adjustments within the preset threshold range. If the threshold is exceeded, the alternative route switch is initiated. The rapid emergency response process pre-plans evacuation channels for dangerous areas, emergency rescue resource allocation plans, and professional disposal team linkage mechanisms, and formulates targeted emergency response plans based on the type of hazardous chemicals. Fully controllable transportation realizes information sharing and collaborative decision-making among regulatory departments, transportation companies, and vehicle drivers through positioning tracking, status monitoring, and remote control, significantly improving the safety and reliability of road transportation of hazardous chemicals.

[0161] The beneficial effects of the above technical solution are: by introducing a collaborative optimization mechanism of risk management and route planning, the optimal route that balances transportation efficiency and safety is quickly generated based on a multi-objective optimization algorithm, and dynamic adjustments are made in combination with real-time road conditions information. This effectively solves the problem of insufficient single-objective optimization in traditional hazardous chemicals transportation route planning, significantly improves the safety, reliability and efficiency of hazardous chemicals road transportation, reduces potential risk exposure, and provides comprehensive technical support for hazardous chemicals transportation management.

[0162] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the invention.

Claims

1. A multi-objective path optimization system for hazardous chemicals road transportation, characterized by: include: Data acquisition module, used to obtain hazardous chemicals transportation demand information and road network information, and generate basic data sets; Multi-objective optimization module, used to perform collaborative optimization of path planning and risk management based on basic data sets and generate optimization solutions; The route generation module is used to generate the final transportation route, schedule and emergency plan based on the optimization plan, and dynamically adjust it according to real-time road conditions.

2. The multi-objective path optimization system for hazardous chemicals road transportation according to claim 1 is characterized in that: The multi-objective optimization module includes: The scenario traversal submodule is used to sequentially traverse various hazardous chemicals transportation scenarios and divide them into multiple operating states according to different time periods and weather conditions; The optimization training submodule is used to determine the optimization results of the optimization model in safety management and path planning for each operating state, and use all operating states and collaborative optimization basis as the training basis of the optimization model.

3. The multi-objective path optimization system for hazardous chemicals road transportation according to claim 2 is characterized in that: The optimization training submodules include: The safety optimization unit is used to interact with the simulation environment based on the preset safety control action space, generate risk assessment results, update safety control parameters, and determine the optimization plan to minimize safety risks and environmental impacts; The path optimization unit is used to interact with the simulation environment based on the preset path selection action space, generate path planning results and update path planning parameters, and determine the optimization plan that minimizes transportation costs and safety risks; The integration unit is used to integrate the optimization schemes of safety control parameters and path planning parameters as the basis for collaborative optimization under the current operating status.

4. The multi-objective path optimization system for hazardous chemicals road transportation according to claim 3 is characterized in that: The safety optimization unit includes: The risk analysis subunit is used to identify the key factors affecting safety risks and environmental impacts in the risk assessment results, determine risk clusters from them, and use the highest-level risk in each risk cluster as the management and optimization target; The parameter adjustment subunit is used to treat the corresponding control optimization target as a risk point that needs further optimization and adjust the safety control parameters if the standard operating state representing that the control optimization target has been alleviated does not appear in the subsequent simulation environment.

5. The multi-objective path optimization system for hazardous chemicals road transportation according to claim 1 is characterized in that: The path generation module includes: The path sorting submodule is used to sort all optional paths from low to high according to the safety risk and transportation cost according to the path planning results and risk control plan in the optimization plan to obtain the optimized path sequence; The path selection submodule is used to divide the optimized path sequence into multiple local path groups, select the path with the highest safety and lowest cost from each local path group in turn, generate the final optimized transportation path and match the time schedule and emergency plan.

6. The multi-objective path optimization system for hazardous chemicals road transportation according to claim 1 is characterized in that: The path generation module also includes: The dynamic adjustment submodule is used to generate a dynamic adjustment plan based on the changed road network information when real-time road condition changes or emergencies are detected; The communication and early warning submodule is used to establish real-time communication between alternative routes and relevant personnel based on the dynamic adjustment plan, issue early warning information to relevant personnel and update the transportation plan.

7. The multi-objective path optimization system for hazardous chemicals road transportation according to claim 6 is characterized in that: The dynamic adjustment submodule includes: A demand matching unit is used to pre-match and adjust demand templates based on real-time traffic changes or emergencies, and to determine multiple alternative adjustment demands and their priorities based on historical optimization results; The solution generation unit is used to traverse the alternative adjustment requirements in sequence, generate a transportation scenario evolution sequence and display the evolution process, and generate a dynamic adjustment solution based on the transportation scenario selected by relevant personnel.

8. The multi-objective path optimization system for hazardous chemicals road transportation according to claim 7 is characterized in that: The demand matching unit includes: The demand generation subunit is used to generate alternative adjustment demands based on key characteristic parameters input from real-time road condition changes or emergencies, combined with the adjustment demand generation template and historical optimization results; The priority setting subunit is used to use the template weight of the adjustment requirement generation template based on which the alternative adjustment requirement is generated as the corresponding priority.

9. The multi-objective path optimization system for hazardous chemicals road transportation according to claim 1 is characterized in that: The data acquisition module includes: The demand collection submodule is used to obtain the hazardous chemical transportation demand information including the type, quantity and transportation time requirements of hazardous chemicals; The network collection submodule is used to collect road network information such as road nodes, road segment characteristics connecting nodes, real-time road conditions and environmental parameters; The data integration submodule is used to integrate hazardous chemicals transportation demand information and road network information to generate a basic data set.

10. A multi-objective path optimization method for hazardous chemicals road transportation applied to the system of claim 1, characterized in that: include: S1: Obtain hazardous chemicals transportation demand information and road network information to generate a basic data set; S2: Perform collaborative optimization of path planning and risk management based on the basic data set to generate an optimization plan; S3: Based on the optimization plan, the final transportation route, time schedule and emergency plan are generated, and dynamically adjusted according to real-time road conditions.

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