Hazardous chemical substance transportation path planning method and system

Abstract

The invention is suitable for the field of logistics transportation, and provides a hazardous chemical substance transportation path planning method and system, and the method comprises the steps: collecting road network information, road condition real-time data, weather data, population density distribution data, environment sensitive point distribution data, and hazardous chemical substance characteristic data; based on the collected data, establishing a risk assessment model, and performing risk assessment on each preset transportation path; constructing a multi-objective function, and distributing a corresponding weight for each objective; and searching and generating an optimal or suboptimal transportation path in the road network based on the multi-objective function, and when the change of the road condition data or the weather data exceeds a preset threshold value, performing risk assessment and path planning again, and regenerating the optimal or suboptimal transportation path. And the transportation data security is ensured. A path can be adjusted in real time to cope with road conditions and weather changes, the transportation risk is reduced, and the transportation cost, time and risk are balanced.

Description

Technical Field

[0001] The present invention belongs to the field of logistics and transportation, and in particular relates to a method and system for planning a hazardous chemicals transportation route. Background Art

[0002] The present invention belongs to the field of logistics and transportation, and in particular to a method and system for planning the transportation route of hazardous chemicals. With the rapid development of the chemical industry, the transportation demand for hazardous chemicals is increasing. Hazardous chemicals are flammable, explosive, toxic, corrosive, and other characteristics. Once leakage, explosion, and other accidents occur during transportation, they will cause serious harm to the safety of human life, the ecological environment, and the social economy.

[0003] The route planning of hazardous chemicals transportation needs to comprehensively consider multiple objective factors. However, it is difficult for existing methods to find the optimal balance between these objectives. They can only focus on one or a few objectives and cannot achieve multi-objective optimization in the true sense. Therefore, it is of great practical significance and application value to develop a hazardous chemicals transportation route planning method that can comprehensively consider multiple factors and achieve multi-objective optimization. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for planning a hazardous chemicals transportation route, aiming to solve the problems raised in the above-mentioned background technology.

[0005] The present invention is implemented in this way. On the one hand, a method for planning a hazardous chemicals transportation route comprises: Collect road network information, real-time road condition data, weather data, population density distribution data, environmental sensitive point distribution data, and hazardous chemical characteristics data; Based on the collected data, a risk assessment model is established to conduct risk assessment on each preset transportation route; Constructing a multi-objective function, the multi-objective function at least including a transportation cost target, a transportation time target and a transportation risk target, and assigning a corresponding weight to each target; Searching and generating an optimal or suboptimal transportation path in a road network based on the multi-objective function; During the transportation process, road condition data and weather data are monitored in real time. When the changes in road condition data or weather data exceed the preset threshold, risk assessment and route planning are re-performed to regenerate the optimal or suboptimal transportation route.

[0006] As a further solution of the present invention, establishing a risk assessment model based on the collected data specifically includes: Obtain road network information, real-time traffic data, weather data, population density distribution data, environmental sensitive point distribution data, and hazardous chemicals characteristics data, and perform standardized preprocessing on the above data; Use principal component analysis algorithm to extract key features from preprocessed data; According to the transportation scenario and data characteristics, select one or more combinations of hierarchical analysis method, fuzzy comprehensive evaluation method or neural network algorithm, initialize model parameters, and set the initial model weight and threshold; The model is trained using historical transportation data and simulated accident data, and the model parameters are adjusted through cross-validation methods; An independent test data set is used to verify the trained model and calculate model evaluation indicators, including accuracy, recall, and F1 value.

[0007] As a further solution of the present invention, the key features are extracted from the preprocessed data, and the key features include: Population density-related characteristics: including population density values ​​in different areas along the transportation route, as well as the changing trends of population density; Characteristics of environmentally sensitive points: type of environmentally sensitive points, distance between environmentally sensitive points and transportation routes, and importance of environmentally sensitive points; Road condition characteristics: road grade, road carrying capacity, whether it is a special road section; Weather condition characteristics: weather type, probability of occurrence of special weather; Characteristics of hazardous chemicals: types and hazard levels of hazardous chemicals.

[0008] As a further solution of the present invention, the construction of a multi-objective function and the allocation of a corresponding weight to each objective specifically include: Calculate vehicle driving costs; Acquiring real-time road condition data, wherein the real-time road condition data includes congested road section information and average vehicle speed information; Based on the real-time road condition data and the real-time vehicle speed, the path search algorithm is used to calculate and generate the estimated travel time for each preset path; Associate the risk assessment results of each preset transportation route with the transportation risk target to determine the risk values ​​corresponding to different risk levels; Integrate the transportation cost target, transportation time target and transportation risk target to form a complete multi-objective function; Based on the transportation task priority, a corresponding weight is assigned to each target.

[0009] As a further solution of the present invention, the searching and generating of the optimal or suboptimal transportation path in the road network based on the multi-objective function specifically includes: Randomly generate a set of initial paths as a population, each path represents a possible transportation solution, and set the population size and the encoding method of the initial paths; According to the multi-objective function, the fitness value of each path individual is calculated; The roulette wheel selection method is used to select path individuals with fitness greater than the fitness threshold from the population as the parents of the next generation of the population; Perform crossover operation on the selected parent path, exchange some path segments, generate new child paths, and at the same time, perform mutation operation on the remaining child paths with a preset probability, randomly changing the original nodes in the remaining child paths; Determine whether the termination condition of reaching the maximum number of iterations is met; If the termination condition is met, the optimal or suboptimal path is output.

[0010] As a further solution of the present invention, on the other hand, a hazardous chemicals transportation route planning system comprises: The collection module is used to collect road network information, real-time road condition data, weather data, population density distribution data, environmental sensitive point distribution data, and hazardous chemical characteristics data; The risk assessment model module is used to establish a risk assessment model based on the collected data and conduct risk assessment on each preset transportation route; Multi-objective function module, used to construct multi-objective functions and assign corresponding weights to each objective; A search and generation module, used for searching and generating an optimal or suboptimal transportation path in a road network based on the multi-objective function; Real-time monitoring module, used to monitor road condition data and weather data in real time during transportation; The secondary planning module re-performs risk assessment and path planning and regenerates the optimal or suboptimal transportation path when the changes in road condition data or weather data exceed the preset threshold.

[0011] As a further solution of the present invention, the multi-objective function module specifically includes: A first calculation unit, used for calculating the vehicle travel cost; An acquisition unit is used to acquire real-time road condition data. A calculation and generation unit, used to calculate and generate an estimated travel time for each preset route based on real-time road condition data and real-time vehicle travel speed using a route search algorithm; An association unit, used to associate the risk assessment result of each preset transportation route with the transportation risk target, and determine the risk value corresponding to different risk levels; Integration unit, used to integrate the transportation cost target, transportation time target and transportation risk target to form a complete multi-objective function; The allocation unit is used to allocate a corresponding weight to each target based on the transportation task priority.

[0012] As a further solution of the present invention, the search generation module specifically includes: Population unit, used to randomly generate a set of initial paths as a population, each path represents a possible transportation solution, and sets the population size and the encoding method of the initial paths; The second calculation unit is used to calculate the fitness value of each path individual according to the multi-objective function; A selection unit is used to select path individuals with fitness greater than a fitness threshold from the population using a roulette wheel selection method as the parents of the next generation population; A crossover operation unit is used to perform a crossover operation on the selected parent path, exchange some path segments, generate new child paths, and at the same time, perform a mutation operation on the remaining child paths with a preset probability, randomly changing the original nodes in the remaining child paths; A judgment unit, used to judge whether a termination condition of reaching a maximum number of iterations is met; The output unit is used to output the optimal or suboptimal path if the termination condition is met.

[0013] The present invention provides a method and system for planning the transportation route of hazardous chemicals. The method and system significantly improve the calculation efficiency and path search accuracy through technical integration, and ensure the safety of transportation data. The route can be adjusted in real time to cope with changes in road conditions and weather, reduce transportation risks, balance transportation costs, time and risks, achieve optimal allocation of transportation resources, and improve transportation safety and economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a main flow chart of a hazardous chemicals transportation route planning method.

[0015] Figure 2 It is a specific flow chart for establishing a risk assessment model based on collected data in a hazardous chemicals transportation route planning method.

[0016] Figure 3 The present invention is a flowchart for constructing a multi-objective function and assigning a corresponding weight to each objective in a hazardous chemicals transportation route planning method.

[0017] Figure 4 The invention relates to a flow chart for searching and generating an optimal or suboptimal transportation route in a road network based on the multi-objective function in a hazardous chemicals transportation route planning method.

[0018] Figure 5 It is the main structure diagram of a hazardous chemicals transportation route planning system.

[0019] Figure 6 It is a structural block diagram of a multi-objective function module in a hazardous chemicals transportation path planning system.

[0020] Figure 7 It is a structural block diagram of the search and generation module in a hazardous chemicals transportation route planning system. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0022] The specific implementation of the present invention is described in detail below in conjunction with specific embodiments.

[0023] The present invention provides a hazardous chemicals transportation route planning method and system, which solves the technical problems in the background technology.

[0024] like Figure 1 FIG. 1 is a main flow chart of a hazardous chemicals transportation path planning method provided by an embodiment of the present invention, wherein the hazardous chemicals transportation path planning method comprises: Step S100: collecting road network information, real-time road condition data, weather data, population density distribution data, environmental sensitive point distribution data, and hazardous chemical characteristic data; Step S200: Based on the collected data, a risk assessment model is established to perform risk assessment on each preset transportation route; Step S300: construct a multi-objective function and assign a corresponding weight to each objective; The multi-objective function at least includes a transportation cost target, a transportation time target and a transportation risk target; Step S400: searching and generating an optimal or suboptimal transportation path in a road network based on the multi-objective function; Step S500: During the transportation process, the road condition data and weather data are monitored in real time. When the road condition data or weather data changes beyond a preset threshold, risk assessment and route planning are re-performed to regenerate an optimal or suboptimal transportation route; When this embodiment is applied, in the data collection link, the road network, road conditions, weather, population density, environmental sensitive points and hazardous chemicals characteristic data are widely collected, and the edge computing technology is used to preliminarily process the data in real time at the collection end, effectively reducing the transmission volume and delay, and providing an efficient and accurate data basis for subsequent analysis. During risk assessment, with the help of the transfer learning method of deep learning, a risk assessment model is established based on the collected data, and the impact of hazardous chemical leakage on the surrounding population and environment is comprehensively considered. The transportation risks under different scenarios are quickly and accurately assessed, and a multi-objective function covering transportation costs, time and risk targets is constructed. The weights are dynamically adjusted through reinforcement learning to meet the requirements of different transportation tasks and achieve comprehensive optimization of multiple objectives. The path planning adopts an intelligent optimization algorithm combined with a quantum annealing algorithm to search for paths in the road network based on the multi-objective function, and uses the parallel processing advantages of quantum computing to greatly improve the computing efficiency, quickly generate local optimal solutions, and accurately find the global optimal path. The 5G communication technology is used to connect the on-board sensors with the traffic and meteorological information platforms to collect data in real time. When the data changes exceed the threshold, the risk and path planning are re-evaluated, and adjustment instructions are issued in time to ensure the safety and efficiency of the entire transportation process.

[0025] like Figure 2 As shown, as a preferred embodiment of the present invention, the establishment of a risk assessment model based on the collected data specifically includes: Step S201: Obtain road network information, real-time road condition data, weather data, population density distribution data, environmental sensitive point distribution data, and hazardous chemicals characteristic data, and perform standardized preprocessing on the above data; Step S202: extract key features from the preprocessed data using a principal component analysis algorithm; Step S203: According to the transportation scenario and data characteristics, select one or more combinations of hierarchical analysis method, fuzzy comprehensive evaluation method or neural network algorithm, initialize model parameters, and set model initial weights and thresholds; Step S204: training the model using historical transportation data and simulated accident data, and adjusting the model parameters by cross-validation method; Step S205: Use an independent test data set to verify the trained model and calculate model evaluation indicators, which include accuracy, recall rate, and F1 value; First, comprehensively collect data such as road network, real-time road conditions, weather, population density distribution, distribution of environmental sensitive points, and characteristics of hazardous chemicals. Through standardized preprocessing, eliminate data dimension differences, ensure data consistency and comparability, and lay the foundation for subsequent analysis. Use the principal component analysis algorithm to process the preprocessed data, extract key features such as population density change trends, types and distances of environmental sensitive points, road conditions, weather conditions, and characteristics of hazardous chemicals, effectively reduce data dimensions, and reduce computational complexity. Then, according to the transportation scenario and data characteristics, flexibly select one or more combinations of hierarchical analysis method, fuzzy comprehensive evaluation method, or neural network algorithm to construct a risk assessment model. Initialize model parameters, set initial weights and thresholds, so that the model can better fit the actual transportation situation. Use rich historical transportation data and simulated accident data to train the model, continuously adjust model parameters through cross-validation, optimize model performance, and improve the accuracy of risk assessment. Finally, use an independent test data set to verify the trained model, calculate evaluation indicators such as accuracy, recall rate, and F1 value, and comprehensively evaluate the model effect to ensure that the model can accurately assess the risk of transportation routes and provide a reliable basis for hazardous chemicals transportation route planning.

[0026] As a preferred embodiment of the present invention, the key features are extracted from the preprocessed data, and the key features include: Population density-related characteristics: including population density values ​​in different areas along the transportation route, as well as the changing trends of population density; Characteristics of environmentally sensitive points: type of environmentally sensitive points, distance between environmentally sensitive points and transportation routes, and importance of environmentally sensitive points; Road condition characteristics: road grade, road carrying capacity, whether it is a special road section; Weather condition characteristics: weather type, probability of occurrence of special weather; Characteristics of hazardous chemicals: type and hazard level of hazardous chemicals; Characteristics related to population density can intuitively reflect the size of the population that may be affected and the degree of potential risk during the transportation process. Characteristics of environmental sensitive points can help determine the scope and severity of the impact on the surrounding environment once an accident occurs in the transportation of hazardous chemicals. Roads of different grades differ in safety and capacity. Special sections such as bridges and tunnels also have special risk factors. Weather conditions have a significant impact on the safety and timeliness of hazardous chemicals transportation. The characteristics of hazardous chemicals directly determine the risk level and response measures of hazardous chemicals during transportation.

[0027] like Figure 3 As shown, as a preferred embodiment of the present invention, the construction of a multi-objective function and the allocation of a corresponding weight to each objective specifically include: Step S301: Calculate vehicle travel cost; Step S302: Acquire real-time road condition data, wherein the real-time road condition data includes congested road section information and average vehicle speed information; Step S303: Based on the real-time road condition data and the real-time driving speed of the vehicle, the estimated driving time of each preset route is calculated and generated using a route search algorithm; Step S304: Associating the risk assessment result of each preset transportation route with the transportation risk target to determine the risk value corresponding to different risk levels; Step S305: Integrate the transportation cost target, transportation time target and transportation risk target to form a complete multi-objective function; Step S306: assigning a corresponding weight to each target based on the transportation task priority; It should be understood that the first step is to calculate the vehicle driving cost, obtain the real-time road condition data including congested road section information and average speed information, and combine the real-time vehicle driving speed to use the path search algorithm to calculate and generate the estimated driving time of each preset path, so as to provide an accurate basis for determining the transportation time target. The risk assessment results of each preset transportation path are associated with the transportation risk target. According to the risk assessment model, the risk value corresponding to different risk levels is clarified, and the population density and environmental sensitive points around the transportation path are fully considered to reflect the differentiation of transportation risks. The transportation cost target, transportation time target and transportation risk target are integrated to construct a complete multi-objective function, which comprehensively covers the key elements in the hazardous chemicals transportation path planning. Finally, according to the priority of the transportation task, the corresponding weight is reasonably assigned to each target, so that the transportation planning can find the optimal balance between cost, time and risk according to actual needs, realize the scientific planning of hazardous chemicals transportation path, and ensure the safety and efficiency of transportation.

[0028] like Figure 4 As shown, as a preferred embodiment of the present invention, the searching and generating of the optimal or suboptimal transportation path in the road network based on the multi-objective function specifically includes: Step S401: randomly generate a group of initial paths as a population, each path represents a possible transportation solution, and set the population size and the encoding method of the initial paths; Step S402: Calculate the fitness value of each path individual according to the multi-objective function; Step S403: using the roulette wheel selection method, select path individuals with fitness greater than the fitness threshold from the population as the parent generation of the next generation population; Step S404: performing a crossover operation on the selected parent path, exchanging some path segments, generating a new child path, and at the same time, performing a mutation operation on the remaining child paths with a preset probability, randomly changing the original nodes in the remaining child paths; Step S405: Determine whether the termination condition of reaching the maximum number of iterations is met; Step S406: if the termination condition is met, output the optimal or suboptimal path; When this embodiment is applied, a group of initial paths are first randomly generated as a population. The population size can be reasonably set according to the actual transportation demand and computing resources, and can be set to 50 or 100. The initial path is encoded in binary or natural number, and each path represents a potential transportation plan. According to the multi-objective function including transportation cost, time and risk targets, the fitness value of each path individual is calculated, and the value comprehensively reflects the performance of the path on each target. Then, the roulette selection method is used to set a fitness threshold, and the path individuals with fitness greater than the threshold are selected from the population as the parents of the next generation population to achieve the survival of the fittest and retain the better transportation plan. For the selected parent path, a crossover operation is performed to randomly exchange some path segments to generate a new child path. At the same time, with a preset probability, which can be set to 0.1, a mutation operation is performed on the remaining child paths to randomly change the original nodes therein, so as to increase the diversity of the population and avoid the algorithm from falling into a local optimum. In the process of continuous iteration, it is continuously judged whether the preset maximum number of iterations is reached, which is the termination condition. If the conditions are met, the optimal or suboptimal path after multiple rounds of optimization will be output, providing a scientific and reasonable path planning solution for the transportation of hazardous chemicals, ensuring a good balance in cost, time and risk.

[0029] like Figure 5 As shown, as another preferred embodiment of the present invention, on the other hand, a hazardous chemicals transportation route planning system comprises: The collection module 100 is used to collect road network information, real-time road condition data, weather data, population density distribution data, environmental sensitive point distribution data, and hazardous chemical characteristic data; The risk assessment model module 200 is used to establish a risk assessment model based on the collected data and perform risk assessment on each preset transportation route; The multi-objective function module 300 is used to construct a multi-objective function and assign a corresponding weight to each objective; A search and generation module 400, for searching and generating an optimal or suboptimal transportation path in a road network based on the multi-objective function; A real-time monitoring module 500 is used to monitor the road condition data and weather data in real time during the transportation process; The secondary planning module 600 re-performs risk assessment and path planning to regenerate the optimal or suboptimal transportation path when the traffic data or weather data changes beyond a preset threshold.

[0030] When this embodiment is applied, the acquisition module 100 collects road network information, real-time road condition data, weather data, population density distribution data, environmental sensitive point distribution data and hazardous chemical characteristic data; the risk assessment model module 200 establishes a risk assessment model based on the collected data, and performs risk assessment on each preset transportation path; the multi-objective function module 300 constructs a multi-objective function and assigns a corresponding weight to each goal; the search generation module 400 searches for and generates an optimal or suboptimal transportation path in the road network based on the multi-objective function; during transportation, the real-time monitoring module 500 monitors the road condition data and weather data in real time; when the road condition data or weather data changes by more than a preset threshold, the secondary planning module 600 re-performs risk assessment and path planning, and regenerates the optimal or suboptimal transportation path.

[0031] like Figure 6 As shown, as another preferred embodiment of the present invention, the multi-objective function module 300 specifically includes: The first calculation unit 301 is used to calculate the vehicle travel cost; The acquisition unit 302 is used to acquire real-time road condition data. The calculation and generation unit 303 is used to calculate and generate the estimated travel time of each preset route based on the real-time road condition data and the real-time vehicle travel speed using a route search algorithm; An associating unit 304 is used to associate the risk assessment result of each preset transportation route with the transportation risk target to determine the risk value corresponding to different risk levels; An integration unit 305 is used to integrate the transportation cost target, the transportation time target and the transportation risk target to form a complete multi-objective function; The allocating unit 306 is used to allocate a corresponding weight to each target based on the transportation task priority.

[0032] When this embodiment is applied, the first calculation unit 301 calculates the vehicle driving cost, the acquisition unit 302 acquires the real-time road condition data, and based on the real-time road condition data and the real-time vehicle driving speed, the calculation generation unit 303 calculates and generates the estimated driving time of each preset path using the path search algorithm. The association unit 304 associates the risk assessment result of each preset transportation path with the transportation risk target, determines the risk value corresponding to different risk levels, and the integration unit 305 integrates the transportation cost target, the transportation time target and the transportation risk target to form a complete multi-objective function. Based on the transportation task priority, the allocation unit 306 assigns corresponding weights to each target.

[0033] like Figure 7 As shown, as another preferred embodiment of the present invention, the search generation module 400 specifically includes: A population unit 401 is used to randomly generate a group of initial paths as a population, each path represents a possible transportation solution, and set the population size and the encoding method of the initial paths; The second calculation unit 402 is used to calculate the fitness value of each path individual according to the multi-objective function; A selection unit 403 is used to select path individuals with fitness greater than a fitness threshold from the population using a roulette wheel selection method as the parent generation of the next generation population; The crossover operation unit 404 is used to perform a crossover operation on the selected parent path, exchange some path segments, generate new child paths, and at the same time, perform a mutation operation on the remaining child paths with a preset probability, randomly changing the original nodes in the remaining child paths; A judging unit 405 is used to judge whether a termination condition of reaching a maximum number of iterations is satisfied; An output unit 406 is used to output an optimal or suboptimal path if the termination condition is met; The population unit 401 randomly generates a group of initial paths as a population, each path represents a possible transportation plan, sets the population size and the encoding method of the initial path, and according to the multi-objective function, the second calculation unit 402 calculates the fitness value of each path individual, adopts the roulette wheel selection method, and the selection unit 403 selects the path individual whose fitness is greater than the fitness threshold from the population as the parent of the next generation population. The crossover operation unit 404 performs a crossover operation on the selected parent path, exchanges some path segments, and generates a new child path. At the same time, the remaining child paths are mutated with a preset probability, and the original nodes in the remaining child paths are randomly changed. The judgment unit 405 judges whether the termination condition of reaching the maximum number of iterations is met. If the termination condition is met, the output unit 406 outputs the optimal or suboptimal path.

[0034] The above embodiment of the present invention provides a method for planning a hazardous chemical transportation route, and provides a hazardous chemical transportation route planning system. In the data collection stage, road networks, road conditions, weather, population density, environmental sensitive points and hazardous chemical characteristic data are widely collected, and edge computing technology is used to preliminarily process data in real time at the collection end, effectively reducing transmission volume and delay, and providing an efficient and accurate data basis for subsequent analysis. During risk assessment, a risk assessment model is established based on the collected data with the help of a deep learning transfer learning method, comprehensively considering the impact of hazardous chemical leakage on the surrounding population and environment, quickly and accurately assessing transportation risks in different scenarios, and constructing a multi-objective function covering transportation cost, time and risk targets. Through reinforcement learning, the weights are dynamically adjusted to meet the needs of different transportation tasks and achieve comprehensive optimization of multiple objectives. The path planning adopts an intelligent optimization algorithm combined with a quantum annealing algorithm to search for paths in the road network based on multi-objective functions. The parallel processing advantages of quantum computing are used to greatly improve the computing efficiency, quickly generate local optimal solutions, and accurately find the global optimal path. 5G communication technology is used to connect vehicle sensors with traffic and meteorological information platforms to collect data in real time. When the data changes exceed the threshold, the risk and path planning are reassessed, and adjustment instructions are issued in time to ensure the safety and efficiency of the entire transportation process. This method and system significantly improve the computing efficiency and path search accuracy through technical integration, and ensure the security of transportation data. It can adjust the path in real time to respond to changes in road conditions and weather, reduce transportation risks, balance transportation costs, time and risks, and achieve optimal allocation of transportation resources, and improve transportation safety and economy.

[0035] In order to load the above-mentioned method and system and enable it to run smoothly, the system, in addition to the various modules mentioned above, may also include more or fewer components than described above, or a combination of certain components, or different components, for example, it may include input and output devices, network access devices, buses, processors and memories, etc.

[0036] The processor may be a central processing unit, or other general-purpose processors, digital signal processors, application-specific integrated circuits, off-the-shelf programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the system, and various interfaces and lines are used to connect various parts.

[0037] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0038] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0039] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for planning a hazardous chemicals transportation route, characterized in that: The method comprises: Collect road network information, real-time road condition data, weather data, population density distribution data, environmental sensitive point distribution data, and hazardous chemical characteristics data; Based on the collected data, a risk assessment model is established to conduct risk assessment on each preset transportation route; Constructing a multi-objective function, the multi-objective function at least including a transportation cost target, a transportation time target and a transportation risk target, and assigning a corresponding weight to each target; Searching and generating an optimal or suboptimal transportation path in a road network based on the multi-objective function; During the transportation process, road condition data and weather data are monitored in real time. When the changes in road condition data or weather data exceed the preset threshold, risk assessment and route planning are re-performed to regenerate the optimal or suboptimal transportation route.

2. The hazardous chemicals transportation route planning method according to claim 1 is characterized in that: The establishment of a risk assessment model based on the collected data specifically includes: Obtain road network information, real-time traffic data, weather data, population density distribution data, environmental sensitive point distribution data, and hazardous chemicals characteristics data, and perform standardized preprocessing on the above data; Use principal component analysis algorithm to extract key features from preprocessed data; According to the transportation scenario and data characteristics, select one or more combinations of hierarchical analysis method, fuzzy comprehensive evaluation method or neural network algorithm, initialize model parameters, and set the initial model weight and threshold; The model is trained using historical transportation data and simulated accident data, and the model parameters are adjusted through cross-validation methods; An independent test data set is used to verify the trained model and calculate model evaluation indicators, including accuracy, recall, and F1 value.

3. The hazardous chemicals transportation route planning method according to claim 2 is characterized in that: The key features are extracted from the preprocessed data, and the key features include: Population density-related characteristics: including population density values ​​in different areas along the transportation route, as well as the changing trends of population density; Characteristics of environmentally sensitive points: type of environmentally sensitive points, distance between environmentally sensitive points and transportation routes, and importance of environmentally sensitive points; Road condition characteristics: road grade, road carrying capacity, whether it is a special road section; Weather condition characteristics: weather type, probability of occurrence of special weather; Characteristics of hazardous chemicals: types and hazard levels of hazardous chemicals.

4. The method for planning a hazardous chemicals transportation route according to claim 1, characterized in that: The construction of the multi-objective function and the allocation of corresponding weights to each objective specifically include: Calculate vehicle driving costs; Acquiring real-time road condition data, wherein the real-time road condition data includes congested road section information and average vehicle speed information; Based on the real-time road condition data and the real-time vehicle speed, the path search algorithm is used to calculate and generate the estimated travel time for each preset path; Associate the risk assessment results of each preset transportation route with the transportation risk target to determine the risk values ​​corresponding to different risk levels; Integrate the transportation cost target, transportation time target and transportation risk target to form a complete multi-objective function; Based on the transportation task priority, a corresponding weight is assigned to each target.

5. The method for planning the hazardous chemicals transportation route according to claim 1, characterized in that: The searching and generating of the optimal or suboptimal transportation path in the road network based on the multi-objective function specifically includes: Randomly generate a set of initial paths as a population, each path represents a possible transportation solution, and set the population size and the encoding method of the initial paths; According to the multi-objective function, the fitness value of each path individual is calculated; The roulette wheel selection method is used to select path individuals with fitness greater than the fitness threshold from the population as the parents of the next generation of the population; Perform crossover operation on the selected parent path, exchange some path segments, generate new child paths, and at the same time, perform mutation operation on the remaining child paths with a preset probability, randomly changing the original nodes in the remaining child paths; Determine whether the termination condition of reaching the maximum number of iterations is met; If the termination condition is met, the optimal or suboptimal path is output.

6. A hazardous chemicals transportation route planning system, characterized in that: The method for planning a hazardous chemicals transportation route according to any one of claims 1 to 5 is applied, wherein the system comprises: The collection module is used to collect road network information, real-time road condition data, weather data, population density distribution data, environmental sensitive point distribution data, and hazardous chemical characteristics data; The risk assessment model module is used to establish a risk assessment model based on the collected data and conduct risk assessment on each preset transportation route; Multi-objective function module, used to construct multi-objective functions and assign corresponding weights to each objective; A search and generation module, used for searching and generating an optimal or suboptimal transportation path in a road network based on the multi-objective function; Real-time monitoring module, used to monitor road condition data and weather data in real time during transportation; The secondary planning module re-performs risk assessment and path planning and regenerates the optimal or suboptimal transportation path when the changes in road condition data or weather data exceed the preset threshold.

7. The hazardous chemicals transportation route planning system according to claim 6, characterized in that: The multi-objective function module specifically includes: A first calculation unit, used for calculating the vehicle travel cost; An acquisition unit is used to acquire real-time road condition data. A calculation and generation unit, used to calculate and generate an estimated travel time for each preset route based on real-time road condition data and real-time vehicle travel speed using a route search algorithm; An association unit, used to associate the risk assessment result of each preset transportation route with the transportation risk target, and determine the risk value corresponding to different risk levels; Integration unit, used to integrate the transportation cost target, transportation time target and transportation risk target to form a complete multi-objective function; The allocation unit is used to allocate a corresponding weight to each target based on the transportation task priority.

8. The hazardous chemicals transportation route planning system according to claim 6, characterized in that: The search generation module specifically includes: Population unit, used to randomly generate a set of initial paths as a population, each path represents a possible transportation solution, and sets the population size and the encoding method of the initial paths; The second calculation unit is used to calculate the fitness value of each path individual according to the multi-objective function; A selection unit is used to select path individuals with fitness greater than a fitness threshold from the population using a roulette wheel selection method as the parents of the next generation population; A crossover operation unit is used to perform a crossover operation on the selected parent path, exchange some path segments, generate new child paths, and at the same time, perform a mutation operation on the remaining child paths with a preset probability, randomly changing the original nodes in the remaining child paths; A judgment unit, used to judge whether a termination condition of reaching a maximum number of iterations is met; The output unit is used to output the optimal or suboptimal path if the termination condition is met.

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