Solid waste low-carbon transportation path planning system based on multi-objective optimization

Abstract

This invention relates to the field of solid waste transportation technology and discloses a multi-objective optimization-based low-carbon solid waste transportation route planning system, including a multi-dimensional data acquisition module, a task evaluation module, a route planning module, an adaptation evaluation module, and an optimization management module. The system acquires task, node, and transportation data for the entire solid waste collection and transportation chain through the multi-dimensional data acquisition module. The task evaluation module quantifies the execution difficulty of collection and transportation tasks through a transportation index, filling the blind spots in the control of hidden carbon emissions and achieving a scientific classification of transportation difficulty. The multi-dimensional evaluation has high accuracy. The route planning module, based on GIS road network topology, screens the optimal route combination from multiple dimensions. The adaptation evaluation module accurately quantifies the adaptability between the route and the collection and transportation task through a flow index. The optimization management module determines the readiness level of the collection and transportation task, scientifically classifies the primary execution route and backup routes, and solidifies mature routes through monitoring cycles, achieving multi-objective optimization and high intelligent planning adaptability.

Description

Technical Field

[0001] This invention relates to the field of solid waste transportation technology, specifically a solid waste low-carbon transportation route planning system based on multi-objective optimization. Background Technology

[0002] Solid waste refers to solid, semi-solid, and gaseous substances in containers that have lost their original utilization value or have not lost their utilization value but have been discarded or abandoned during production, daily life, and other activities. It also includes items and substances that are subject to solid waste management under laws and administrative regulations. Solid waste transportation refers to the spatial transfer of solid waste from its source, collection point, or transfer station via road, rail, waterway, or other means of transportation to treatment, utilization, or disposal sites. In the solid waste collection and transportation system, front-end collection is the process of transporting solid waste from its source to the collection station; short-distance collection uses medium-sized trucks to transfer solid waste from the collection station to the transfer station; and transshipment / long-distance transportation uses large trucks, railways, and ships to transport solid waste from the transfer station to the disposal or treatment plant. The core objectives of solid waste transportation optimization fall into two main categories: cost reduction and emission reduction. Cost reduction is achieved by minimizing transportation mileage, fuel consumption, toll fees, and time, while emission reduction is achieved by reducing CO2, PM2.5, and NO3. x The goal is to reduce pollutant emissions and achieve low-carbon environmental protection. Constraints for transportation optimization include load limits, route compliance, road restrictions, hazardous waste transportation route approval requirements, and leakage and spillage prevention standards during transportation. Multi-objective optimization requires combining real-time traffic conditions and congestion predictions to achieve dynamic route planning. For multi-source locations—from the point of origin to the transfer station to the disposal site—vehicle scheduling and loading scheme optimization must be carried out simultaneously. Furthermore, it is necessary to complete the quantitative calculation of fuel consumption and carbon emissions, as well as the precise matching of vehicle type, route, and load.

[0003] Currently, traditional solid waste low-carbon transportation route planning systems lack real-time perception of the dynamic status of docking node capacity. When faced with node overflow scenarios, they cannot provide early warnings or flow restriction scheduling, which can easily lead to gaps in transportation tasks and poor node capacity. In the target compatibility stage, it is difficult to achieve a balance between economic benefits and low-carbon goals, resulting in low route planning adaptability. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a solid waste low-carbon transportation route planning system based on multi-objective optimization. It has the advantages of high accuracy in multi-dimensional evaluation and high adaptability of intelligent planning, and solves the problems of poor node connectivity and low route planning adaptability in traditional solid waste low-carbon transportation route planning systems.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a solid waste low-carbon transportation route planning system based on multi-objective optimization, including a multi-dimensional data acquisition module, a task evaluation module, a route planning module, an adaptation evaluation module, and an optimization management module; The multi-dimensional acquisition module connects to the big data platform to acquire management data of all solid waste collection and transportation tasks, management data of all nodes, and management data of transportation, and classifies them into task datasets, node datasets, and transportation datasets. The task evaluation module analyzes the execution difficulty of each consolidation task based on the task dataset, node dataset, and transportation dataset, and generates a corresponding transportation index. ; The path planning module generates the optimal path combination based on the task dataset, node dataset, and transportation dataset through multi-dimensional filtering. ; The adaptation evaluation module evaluates the optimal path combination based on the task dataset and the transportation dataset. The compatibility between each path and the collection and transportation task is used to generate a corresponding flow index. ; The optimization management module is set with a fixed range of operation thresholds. Combined with the transportation index and movement index Determine the readiness level of the consolidation and transportation task and classify the optimal route combination. The system identifies the main execution path and the dynamic rerouting backup path, and outputs corresponding consolidation and shipping suggestions.

[0006] Preferably, the task dataset includes the solid waste category, solid waste weight, total transport load, total transport distance, time period type, organic component ratio of solid waste, methane production during solid waste degradation, and maximum allowable number of route changes for each collection and transportation task. The solid waste category includes domestic waste, industrial solid waste, hazardous waste, and construction waste, and the time period type includes daytime transport, nighttime transport, weekday transport, and holiday transport.

[0007] Preferably, the node dataset includes the solid waste transfer volume, transportation distance, connection carbon emission coefficient per unit turnover, connection economic cost coefficient per unit turnover, total designed storage capacity, and used storage capacity for each node.

[0008] Preferably, the transportation dataset includes the rated single-vehicle load of dedicated vehicles, single-vehicle travel speed, single-vehicle labor cost per unit time, single-vehicle energy consumption cost per unit time, average temperature of the vehicle compartment, total number of traffic lights, average number of starts and stops per vehicle, total length of continuous crossing of sensitive areas, total route length, regional average carbon emissions, actual carbon emissions under all operating conditions, historical average number of route changes, additional carbon emissions per unit mileage caused by historical route changes, and baseline carbon emissions per unit mileage.

[0009] Preferably, the transportation index The calculation process is as follows: S11. Based on the task dataset, node dataset, and transportation dataset, extract the management data of the current consolidation task to be executed, the management data of all nodes, and the management data of transportation. S12. Calculate the category complexity of the current consolidation task to be executed. ; S13. Calculate the vehicle complexity of the current pending container shipping task. ; S14. Calculate the capacity limitation coefficient for the current consolidation task to be executed. ; S15. Calculate the carbon emission coefficient of the current collection and transportation task to be performed. ; S16. Calculate the carbon emission degradation coefficient of the current collection and transportation task to be performed. ; S17. For the current pending consolidation and shipping tasks, normalize the category complexity. Vehicle complexity Capacity limitation factor Carbon emission coefficient of connection and degradation carbon emission coefficient ; S18. Based on S11-S17, calculate the transportation index of the current consolidation task to be executed using a weighted method. .

[0010] Preferably, the optimal path combination The planning process is as follows: S21. Based on the task dataset, node dataset, and transportation dataset, extract the management data of the current consolidation task to be executed, the management data of all nodes, and the management data of transportation. S22. Construct a directed road network topology based on map GIS, consisting of "starting node → connecting node → ending node", and generate a set of all passable path combinations. In this system, the starting node and the ending node do not coincide, the total designed storage capacity of each node is greater than 0 tons, each path is connected in series with nodes according to the road network topology, each path is bound to each node it passes through, and each path consists of several road segments. S23. Calculate the number of pending consolidation tasks. Storage capacity utilization rate of each node and the average storage capacity utilization rate of all nodes along the path ; S24. Combine all accessible paths into a set. The total number of paths included is denoted as Then, based on the node capacity dimension, path combinations are further filtered, with the following rules: (1) Forced exclusion: the set of all feasible path combinations Including storage capacity utilization rate Paths with a value of ≥0.9 or no overlap between the warehouse operation period and the current collection and transportation task to be executed, or with a degree of <30% overlap; (2) Candidate admission: the set of all possible path combinations In the middle, the average storage capacity utilization rate of all nodes along the path is satisfied. ≤0.8, and the path where the remaining warehouse capacity of a single node is greater than or equal to the single-transfer transport volume of the current consolidation task to be executed; (3) Dynamic sorting: based on the average storage capacity utilization rate of all nodes along the path. Sort in ascending order, total number of paths If there are ≤20 paths, take the first 50%; if there are less than 20 paths, take the first 50%. If there are ≤100 paths, take the top 30% of the total number of paths. >100 results, select the top 20%, and filter them into path combination one; S25. Calculate the set of all possible path combinations. In the middle, the first Passage impedance coefficient of the path ; S26. Based on the traffic efficiency dimension, path combination two is screened, and the rules are as follows: (1) Forced exclusion: the set of all feasible path combinations The route includes roads closed for construction, truck restrictions, and traffic control measures; (2) Candidate admission: the set of all possible path combinations In, the passing impedance coefficient The path must have an average slope of ≤0.8 and an average gradient of ≤6%, and must have at least two parallel emergency detour routes. (3) Dynamic sorting: based on the pass impedance coefficient Sort in ascending order, total number of paths If there are ≤20 paths, take the first 50%; if there are less than 20 paths, take the first 50%. If there are ≤100 paths, take the top 30% of the total number of paths. >100 results, the top 20% are selected and filtered into path combination two; S27. Generate the optimal path combination based on path combination one and path combination two. The rules are as follows: The optimal path combination is the intersection of path combination one and path combination two, which simultaneously satisfies the requirements of node capacity and traffic efficiency. ; If the intersection is empty, only the average storage capacity utilization rate of all nodes in path combination one and path combination two are retained. ≤0.9, Passing impedance coefficient For paths with an average slope of ≤0.8 and ≤6%, the optimal path combination is generated by prioritizing nodes with storage capacity > traffic efficiency, selecting the top 3 paths from path combination one and the top 2 paths from path combination two. .

[0011] Preferably, the movement index The evaluation process is as follows: S31. Based on the task dataset and transportation dataset, compile the management data and optimal route combinations for the current consolidation tasks to be executed. Transportation management data for the middle route; S32. Calculate the optimal path combination In the middle, the first Environmental Sensitive Area Avoidance of Each Path ; S33. Calculate the optimal path combination In the middle, the first Carbon emission compliance of the entire operating conditions of the route ; S34. Calculate the optimal path combination In the middle, the first Path rerouting adaptability ; S35. Based on S31-S34, calculate the first... The movement index of the path .

[0012] Preferably, the preliminary level assessment process is as follows: The upper limit of the transportation threshold range is denoted as... The lower limit of the transportation threshold range is denoted as ; If the current shipping index for the pending consolidation task is < This indicates that the current consolidation and transportation task is of low difficulty, with a readiness level of 1. Response measures include proceeding with preliminary preparations as planned, without requiring additional resource allocation or scheduling. ≤Current pending consolidation shipment index ≤ This indicates that the current consolidation task to be performed is of medium difficulty, with a preparedness level of 2. Response measures include prioritizing the deployment of new energy vehicles, optimizing route planning, and avoiding congested sections. If the current consolidation task's transportation index... > This indicates that the current consolidation and transportation task is highly challenging, with a preparedness level of 3. Response measures include activating temperature-controlled sealed vehicles, adjusting transportation schedules, and real-time monitoring throughout the entire process.

[0013] Preferably, the optimization management module optimizes the optimal path combination. All paths within, according to the movement index Sort in descending order, movement index The highest-ranking path is determined to be the primary execution path, while the paths ranked 2nd and 3rd are determined to be backup paths for dynamic rerouting.

[0014] Preferably, the optimization management module is set with a monitoring cycle of fixed duration. During the monitoring period If a single path is selected as the optimal path combination... If the cumulative number of occurrences is ≥3, the corresponding path is determined to be within a monitoring period. Mature benchmark routes within the country are additionally included in the monitoring cycle. Within, all consolidation and transportation tasks have dynamic rerouting backup routes.

[0015] Compared with existing technologies, this invention provides a solid waste low-carbon transportation route planning system based on multi-objective optimization, which has the following beneficial effects: 1. This invention acquires task, node, and transportation data for the entire solid waste collection and transportation chain through a multi-dimensional acquisition module, providing complete data support for quantitative analysis and planning of the entire process. The task evaluation module uses a transportation index... The quantitative analysis of the difficulty of the collection and transportation task fills the blind spot in the management of hidden carbon emissions from anaerobic degradation during the transportation of organic solid waste, realizes the scientific classification of transportation difficulty, and provides a precise quantitative decision-making basis for resource scheduling and route planning. The multi-dimensional assessment has high accuracy.

[0016] 2. This invention, through a route planning module based on GIS road network topology, performs layer-by-layer screening from two dimensions: node capacity and road traffic efficiency. It considers both the feasibility of transfer operations and the timeliness of transportation, thus solving the pain points of traditional route planning's single dimension and insufficient practicality, and generating highly adaptable optimal route combinations. The adaptation assessment module generates a traffic flow index from three dimensions: environmental risk, low-carbon compliance, and emergency route rerouting flexibility. This system precisely quantifies the compatibility between routes and collection / transportation tasks, achieving a balance between low-carbon goals and emergency response capabilities. It optimizes the management module's assessment of the readiness level of collection / transportation tasks, scientifically classifying primary and backup routes, and simultaneously monitoring cycles. By solidifying mature routes, we achieve an optimal balance across multiple dimensions of solid waste transportation costs, efficiency, and low-carbon goals, comprehensively improving the low-carbon management level and execution stability of solid waste collection and transportation, and demonstrating high adaptability of intelligent planning. Attached Figure Description

[0017] Figure 1 This is a system flowchart of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example Please see Figure 1 Table 1 shows the experimental data of the transportation index, Table 2 shows the experimental data of the optimal route combination, and Table 3 shows the experimental data of the flow index. This invention provides a solid waste low-carbon transportation route planning system based on multi-objective optimization, which includes a multi-dimensional data acquisition module, a task evaluation module, a route planning module, an adaptation evaluation module, and an optimization management module. The multi-dimensional data acquisition module connects to the big data platform to acquire management data for all solid waste collection and transportation tasks, management data for all nodes, and management data for transportation, and classifies them into task datasets, node datasets, and transportation datasets. The task dataset includes the solid waste category, solid waste weight, total transport load, total transport distance, time period type, organic component ratio of solid waste, methane production during solid waste degradation, and maximum allowable number of route changes for each collection and transportation task. The solid waste categories include municipal solid waste, industrial solid waste, hazardous waste, and construction waste, and the time period types include daytime transport, nighttime transport, weekday transport, and holiday transport. The node dataset includes the solid waste transfer volume, transportation distance, connection carbon emission coefficient per unit turnover, connection economic cost coefficient per unit turnover, total designed storage capacity, and used storage capacity for each node. The transportation dataset includes the rated single vehicle load of dedicated vehicles, single vehicle speed, single vehicle labor cost per unit time, single vehicle energy consumption cost per unit time, average temperature of the vehicle compartment, total number of traffic lights, average number of starts and stops per vehicle, total length of continuous crossing of sensitive areas, total route length, regional average carbon emissions, actual carbon emissions under all operating conditions, historical average number of route changes, additional carbon emissions per unit mileage caused by historical route changes, and baseline carbon emissions per unit mileage. The task evaluation module analyzes the execution difficulty of each consolidation task based on the task dataset, node dataset, and transportation dataset, and generates a corresponding transportation index. ; Transportation Index The calculation process is as follows: S11. Based on the task dataset, node dataset, and transportation dataset, extract the management data of the current consolidation task to be executed, the management data of all nodes, and the management data of transportation. S12. Calculate the category complexity of the current consolidation task to be executed. Its expression is as follows: In the formula, Indicating the first in the container shipping task The weight of each category of solid waste This indicates the total transport capacity of the consolidation and delivery mission. Indicating the first in the container shipping task The quality percentage of each category of solid waste; In the formula, This indicates the total quantity of different types of solid waste in the collection and transportation task. , This represents a correction factor used to avoid zero-value errors; Specifically, the more diverse and dispersed the types of solid waste, the more difficult it is to match special vehicles, manage loading and unloading, and calculate carbon emissions. S13. Calculate the vehicle complexity of the current pending container shipping task. Its expression is as follows: In the formula, This indicates the total transportation distance for the consolidation and distribution mission. Indicates the first Each category of solid waste corresponds to the rated single-vehicle load capacity of a dedicated vehicle. Specifically, by quantifying the difficulty of matching various types of solid waste with specialized vehicles, the more vehicle trips required, the higher the total transportation cost and total carbon emissions. S14. Calculate the capacity limitation coefficient for the current consolidation task to be executed. Its expression is as follows: In the formula, This indicates the vehicle speed corresponding to the time period type. Indicates the time period type. , Indicates the estimated transit time for the consolidation and shipping task; In the formula, This represents the labor cost per unit of time for a single vehicle. This represents the energy consumption cost per unit of time for a single vehicle. Specifically, by quantifying the constraints on transportation time based on time period, road conditions, and speed limits, the longer the transportation time, the higher the labor / energy costs and carbon emissions from low-speed idling. S15. Calculate the carbon emission coefficient of the current collection and transportation task to be performed. Its expression is as follows: In the formula, Indicating the first in the container shipping task Solid waste transfer volume at each node , This indicates the total number of nodes in the consolidation and shipping task. Indicating the first in the collection and transportation task The node and the first The transportation distance of each node, Indicating the first in the container shipping task Carbon emission coefficient per unit turnover of nodes Indicating the first in the container shipping task Economic cost coefficient of connection per unit turnover of each node; Specifically, by quantifying the hidden carbon emissions and economic costs of the connection links in the entire solid waste transportation process, a multi-dimensional and accurate assessment of the task difficulty can be achieved. S16. Calculate the carbon emission degradation coefficient of the current collection and transportation task to be performed. Its expression is as follows: In the formula, This indicates the average temperature of the carriage during transportation. The baseline value for representing the temperature inside the carriage is fixed at 298.15K (25℃). Indicates the first The percentage of organic components in each category of solid waste Indicates the first Methane production during the degradation of each type of solid waste The value representing the global warming potential of methane is fixed at 28. Specifically, driven by factors such as transportation time, ambient temperature, and solid waste weight, organic solid waste undergoes anaerobic degradation in enclosed transport compartments, releasing methane with high global warming potential. By quantifying the additional carbon emissions generated by this process, we can effectively fill the blind spots in low-carbon management and achieve a precise quantitative assessment of the difficulty of transport operations. S17. For the current pending consolidation and shipping tasks, normalize the category complexity. Vehicle complexity Capacity limitation factor Carbon emission coefficient of connection and degradation carbon emission coefficient Its expression is as follows: In the formula, This represents the maximum number of categories for the same type of solid waste collection and transportation tasks within a region, used to normalize the category complexity of each collection and transportation task. Mapping the values ​​to The interval facilitates horizontal comparison across multiple tasks. This represents the category complexity after normalization. In the formula, This represents the maximum value of the original vehicle complexity for similar solid waste collection and transportation tasks within the region. This represents the minimum complexity of the original vehicle for transporting similar types of solid waste within a region. This represents the vehicle complexity after normalization. In the formula, This represents the maximum value of the original transport capacity limitation coefficient for similar solid waste collection and transportation tasks within the region. This represents the minimum value of the original transport capacity limitation coefficient for similar solid waste collection and transportation tasks within the region. This represents the capacity limitation coefficient after normalization. In the formula, This represents the maximum value of the original connection carbon emission coefficient for similar solid waste collection and transportation tasks within the region. This represents the minimum value of the original connection carbon emission coefficient for similar solid waste collection and transportation tasks within the region. This represents the carbon emission coefficient of the connection after normalization. In the formula, This represents the maximum value of the original degradation carbon emission coefficient for the same type of solid waste collection and transportation task in the region. This represents the minimum original degradation carbon emission coefficient for similar solid waste collection and transportation tasks within the region. This represents the carbon emission coefficient after normalization. S18. Based on S11-S17, calculate the transportation index of the current consolidation task to be executed using a weighted method. Its expression is as follows: In the formula, Represents the weight, and satisfies ; Specifically, by integrating data from the entire transportation chain across four dimensions—task complexity, capacity constraints, cost pressure, and low-carbon pressure—the difficulty of task implementation can be accurately quantified, providing a scientific quantitative basis for subsequent multi-objective optimization scheduling based on real-time road conditions, vehicle status, and task requirements. The following is the experimental data for the transportation index, as shown in Table 1: Table 1: Experimental Data for the Transportation Index In Table 1, the experimental data of the transportation index were selected. The solid waste categories covered by the transportation task A included kitchen waste, garden waste and waste paper. The time period of the transportation task A was daytime transportation. The transportation task A covered three nodes: collection station, transfer and compression station and disposal site. Correction coefficient ; During normalization, the maximum number of categories for the same type of solid waste collection and transportation tasks within the region. Category complexity The value is 0.4472, representing the maximum complexity of the original vehicle for similar solid waste collection and transportation tasks within the region. minimum value Vehicle complexity The value is 0.564, representing the maximum original transport capacity limitation coefficient for similar solid waste collection and transportation tasks within the region. Yuan, minimum value Yuan, The value is 0.3684, representing the maximum original carbon emission coefficient for similar solid waste collection and transportation tasks in the region. minimum value Carbon emission coefficient of connection The value is 0.0463, representing the maximum original degradation carbon emission coefficient for similar solid waste collection and transportation tasks in the region. minimum value Degradation carbon emission coefficient The value is 0.1623; The weights are set as follows: , , , , ; The optimization management module is set with a fixed range of operation thresholds. Used to quickly determine the readiness level and transportation threshold range of a consolidation and transportation task. The calibration method is as follows: The solid waste collection and transportation task monitoring database was used to screen task samples at different preparatory levels, covering various situations such as preparatory level 1 (low difficulty), level 2 (medium difficulty), and level 3 (high difficulty). Core calculation data of the transportation index of the sample tasks were extracted (such as category complexity, vehicle complexity, capacity limitation coefficient, connection carbon emission coefficient, degradation carbon emission coefficient, etc.), scheduling configuration implementation records, and post-task execution monitoring results (such as transportation efficiency, carbon emission control, cost control, task completion rate, and transportation index). (Changes, etc.) Different candidate threshold ranges are set. In each calibration experiment, the sample tasks are preliminarily classified according to the candidate threshold range. The matching degree between the classification results and the actual collection and transportation task execution difficulty is recorded. Then, combined with regional collection and transportation dynamic scheduling data, the sample task operation index is simulated under different scheduling intervention intensities. The changing trend was analyzed, and the upper and lower limits of the candidate threshold intervals were adjusted. Multiple verification experiments were conducted to record the impact of threshold interval settings on the preliminary level assessment and subsequent scheduling configuration. For each candidate threshold interval, the collected sample calculation data and dynamic simulation results were used as inputs. The number of times low-difficulty tasks were misjudged as high-difficulty tasks due to improper interval range settings (counted as over-assessment), the number of times high-difficulty tasks were misjudged as low-difficulty tasks (counted as under-assessment), and the degree of fit between the preliminary level classification results and the subsequent scheduling configuration effectiveness (such as vehicle adaptability, route optimization, carbon emission control, transportation efficiency, task execution stability, etc.) were calculated. Finally, the interval range that minimizes both the over-assessment rate and the under-assessment rate and has the highest degree of fit with the subsequent scheduling configuration effectiveness was selected as the operation threshold interval. The preferred range; Table 1 shows the transportation threshold range in the experimental data of the transportation index. The preferred range is set to 0.3-0.7. Based on the judgment, <Transportation index of container shipping task A> < This indicates that the current collection and transportation task A to be performed is of medium difficulty, with a preparation level of 2. Response measures include prioritizing the allocation of new energy vehicles, optimizing route planning, and avoiding congested road sections. The route planning module generates the optimal route combination by filtering data from multiple dimensions based on the task dataset, node dataset, and transportation dataset. ; Optimal path combination The planning process is as follows: S21. Based on the task dataset, node dataset, and transportation dataset, extract the management data of the current consolidation task to be executed, the management data of all nodes, and the management data of transportation. S22. Construct a directed road network topology based on map GIS, consisting of "starting node → connecting node → ending node", and generate a set of all passable path combinations. In this system, the starting node and the ending node do not coincide, the total designed storage capacity of each node is greater than 0 tons, each path is connected in series with nodes according to the road network topology, each path is bound to each node it passes through, and each path consists of several road segments. S23. Calculate the number of pending consolidation tasks. Storage capacity utilization rate of each node and the average storage capacity utilization rate of all nodes along the path Its expression is as follows: In the formula, Indicates the first The used library capacity of each node. Indicates the first The total design capacity of each node; S24. Combine all accessible paths into a set. The total number of paths included is denoted as Then, based on the node capacity dimension, path combinations are further filtered, with the following rules: (1) Forced exclusion: the set of all feasible path combinations Including storage capacity utilization rate Paths with a value of ≥0.9 or no overlap between the warehouse operation period and the current collection and transportation task to be executed, or with a degree of <30% overlap; (2) Candidate admission: the set of all possible path combinations In the middle, the average storage capacity utilization rate of all nodes along the path is satisfied. ≤0.8, and the path where the remaining warehouse capacity of a single node is greater than or equal to the single-transfer transport volume of the current consolidation task to be executed; (3) Dynamic sorting: based on the average storage capacity utilization rate of all nodes along the path. Sort in ascending order, total number of paths If there are ≤20 paths, take the first 50%; if there are less than 20 paths, take the first 50%. If there are ≤100 paths, take the top 30% of the total number of paths. >100 results, select the top 20%, and filter them into path combination one; S25. Calculate the set of all possible path combinations. In the middle, the first Passage impedance coefficient of the path Its expression is as follows: In the formula, , Represents the set of all possible paths. The number of paths included. Indicates the first The total number of traffic lights along the route. Indicates the first The total length of the path, Indicates the first Traffic light density along the route; In the formula, Indicates the first The average number of starts and stops per vehicle on this route. Indicates the first The average number of starts and stops per vehicle per unit distance along the route; For the current pending consolidation and transportation tasks, the normalization process is performed on the first... Traffic light density along the route Average number of starts and stops per unit distance per bicycle Its expression is as follows: In the formula, This represents the maximum original traffic light density of the area. This represents the minimum original traffic light density along the route in the area. Represents the normalized th Traffic light density along the route; In the formula, This represents the maximum average number of starts and stops per unit mileage for a single vehicle within the original route in the region. This represents the minimum average number of starts and stops per unit mileage for a single vehicle along the original route within the region. Represents the normalized th The average number of starts and stops per vehicle per unit distance along the route; In the formula, Represents the weight, and satisfies ; S26. Based on the traffic efficiency dimension, path combination two is screened, and the rules are as follows: (1) Forced exclusion: the set of all feasible path combinations The route includes roads closed for construction, truck restrictions, and traffic control measures; (2) Candidate admission: the set of all possible path combinations In, the passing impedance coefficient The path must have an average slope of ≤0.8 and an average gradient of ≤6%, and must have at least two parallel emergency detour routes. (3) Dynamic sorting: based on the pass impedance coefficient Sort in ascending order, total number of paths If there are ≤20 paths, take the first 50%; if there are less than 20 paths, take the first 50%. If there are ≤100 paths, take the top 30% of the total number of paths. >100 results, the top 20% are selected and filtered into path combination two; S27. Generate the optimal path combination based on path combination one and path combination two. The rules are as follows: The optimal path combination is the intersection of path combination one and path combination two, which simultaneously satisfies the requirements of node capacity and traffic efficiency. ; If the intersection is empty, only the average storage capacity utilization rate of all nodes in path combination one and path combination two are retained. ≤0.9, Passing impedance coefficient For paths with an average slope of ≤0.8 and ≤6%, the optimal path combination is generated by prioritizing nodes with storage capacity > traffic efficiency, selecting the top 3 paths from path combination one and the top 2 paths from path combination two. ; The following is the experimental data for the optimal path combination, as shown in Table 2: Table 2: Experimental Data for Optimal Path Combination In Table 2, the experimental data of the transportation index were selected. The solid waste category of the transportation task B was kitchen waste. The single transfer transportation volume was set to 10t. The transfer time was from 8:00 to 12:00 on weekdays. The transportation task B had 1 starting node, 3 transfer nodes, and 1 ending node. The total storage capacity of the nodes was designed to be 40-100t. Based on map GIS, a directed road network topology of "starting node → connecting node → ending node" was constructed, generating a total of 5 walkable paths, as follows: Path 1: Starting node A (kitchen waste collection station) → Connecting node B (transfer and compression station 1) → Ending node C (final disposal site); Path 2: Starting node A → Connecting node D (transfer compression station 2) → Ending node C; Path 3: Starting node A → Connecting node B → Connecting node D → Ending node C; Path 4: Starting node A → Connecting node E (transfer compression station 3) → Ending node C; Path 5: Start node A → Connecting node E → Connecting node B → End node C; The node operation periods are as follows: Node A (7:00-18:00), Node B (7:00-18:00), Node C (24 hours a day), Node D (13:00-17:00), Node E (7:00-18:00). Based on the node capacity dimension, path combination is screened, and the process is as follows: (1) Forced exclusion: Path 2 and Path 3 pass through node D (node ​​D's storage capacity utilization rate) If the value is 0.9 and the overlap rate of work periods is 0%, it can be directly excluded; (2) Candidate admission: The remaining paths 1, 4 and 5 all meet the average storage capacity utilization rate of all nodes of the path. ≤0.8, and the remaining storage capacity of a single node is ≥10t (single connection volume), all are allowed to enter; (3) Dynamic sorting: based on the average storage capacity utilization rate of all nodes along the path. Sort in ascending order, the result is: Path 4 (0.2333) > Path 1 (0.2667) > Path 5 (0.3). Since the total number of paths... ≤20 items, take the first 50%, the final path combination is path 4 and path 1; For the current consolidation and transportation tasks to be executed, the traffic light density of each route is normalized. Average number of starts and stops per unit distance per bicycle Traffic light density along path 1 The value is 0.1667, representing the traffic light density of path 1. The value is 0.0513, representing the traffic light density along path 4. The value is 0.1098, representing the traffic light density of path 1. The value is 0.0268, representing the traffic light density of path 5. The value is 0.2083, representing the traffic light density of path 1. The value is 0.0667; The weights are set as follows: , ; Based on the traffic efficiency dimension, path combination two is screened, and its process is as follows: (1) Mandatory exclusion: There are no road constructions, truck restrictions, or traffic controls on any of the routes, and there are no mandatory exclusion items; (2) Candidate admission: Impedance coefficient of all paths All slopes are ≤0.8, the average slope is ≤6%, and all have ≥2 parallel emergency detour routes, all are permitted to enter; (3) Dynamic sorting: based on the pass impedance coefficient Sort in ascending order, the result is: Path 4 (0.0766) > Path 1 (0.1205) > Path 5 (0.1517). Since the total number of paths... ≤20 items, take the first 50%, the final path combination is path 4 and path 1; Based on the analysis, the intersection of path combination one and path combination two is path 1 and path 4, which is directly taken as the optimal path combination. ; The adaptation evaluation module evaluates the optimal path combination based on the task dataset and the transportation dataset. The compatibility between each path and the collection and transportation task is used to generate a corresponding flow index. ; Movement Index The evaluation process is as follows: S31. Based on the task dataset and transportation dataset, compile the management data and optimal route combinations for the current consolidation tasks to be executed. Transportation management data for the middle route; S32. Calculate the optimal path combination In the middle, the first Environmental Sensitive Area Avoidance of Each Path It is used to characterize the risk level of a path traversing an environmentally sensitive area, and its value range is fixed. The higher the value, the lower the environmental risk and the lower the probability of resident complaints. Its expression is as follows: In the formula, Indicates the first The route continuously traverses sensitive areas along its total length. Sensitive areas include residential areas, schools, hospitals, drinking water sources, and other similar environments. Indicates the first The total length of the path, if Calculate the environmental sensitivity zone avoidance degree according to the formula. ,like Environmentally sensitive area avoidance If you directly assign the value 0, Environmentally sensitive area avoidance Assign the value 1 directly; S33. Calculate the optimal path combination In the middle, the first Carbon emission compliance of the entire operating conditions of the route This value is used to characterize the level of low-carbon compliance of a pathway. The higher the value, the lower the carbon emissions and the stronger the low-carbon compliance. Its expression is as follows: In the formula, This represents the regional average carbon emissions. Indicates the first Actual carbon emissions of each route under all operating conditions; For the current pending consolidation and transportation tasks, the normalization process is performed on the first... Carbon emission compliance of the entire operating conditions of the route Its expression is as follows: In the formula, Represents the optimal path combination The maximum value of the original full-condition carbon emission compliance for all paths in the process. Represents the optimal path combination The minimum carbon emission compliance value under all original operating conditions for all paths. Indicates the number of digits after normalization. Carbon emission compliance of each route under all operating conditions; If after normalization, the th Carbon emission compliance of the entire operating conditions of the route , No. The movement index of the path If the value is directly assigned to 0, then after normalization... Carbon emission compliance of the entire operating conditions of the route Full-condition carbon emission compliance The value is 0, if After normalization Carbon emission compliance of the entire operating conditions of the route Retain valid values; if the normalized value is the first... Carbon emission compliance of the entire operating conditions of the route Full-condition carbon emission compliance The value is 1; S34. Calculate the optimal path combination In the middle, the first Path rerouting adaptability It is used to characterize the flexibility of dynamic route rerouting and the controllability of carbon emissions, and its value range is fixed. The higher the value, the stronger the line adaptability. Its expression is as follows: In the formula, Indicates the first The historical average number of route changes for this path. This indicates the maximum number of route changes allowed for the current pending consolidation and shipping task. Indicates the first The additional carbon emissions per unit mileage resulting from historical route rerouting. Indicates the first Baseline carbon emissions per unit mileage of the route Indicates the first The percentage of carbon emission increases from rerouting a particular route; like or , No. Path rerouting adaptability If you directly assign the value 0, Similarly, it is assigned the value 0; S35. Based on S31-S34, calculate the first... The movement index of the path Its expression is as follows: In the formula, Represents the weight, and satisfies ; The following is the experimental data of the movement line index, as shown in Table 3: Table 3: Experimental Data of Movement Line Index In Table 3, the experimental data for the mobility index were used. Task B was selected as the experimental objective, and the optimal path combination was known. For path 1 and path 4; Optimal path combination during normalization The maximum value of the original full-condition carbon emission compliance for all paths. The minimum carbon emission compliance of all paths under all operating conditions Path 1's carbon emission compliance under all operating conditions A value of 1 indicates the carbon emission compliance of Path 2 under all operating conditions. The value is 0; The weights are set as follows: , , ; Based on the judgment, according to the movement index Sort in descending order, the result is path 1 (0.8434) > path 4 (0.64). Path 1 is the main execution path, and path 4 is the backup path for dynamic rerouting. The optimization management module is set with a fixed range of operation thresholds. Combined with the transportation index and movement index Determine the readiness level of the consolidation and transportation task and classify the optimal route combination. The main execution path and the dynamically rerouted backup path are defined in the code, and corresponding consolidation and transportation suggestions are output. The preliminary assessment process is as follows: The upper limit of the transportation threshold range is denoted as... The lower limit of the transportation threshold range is denoted as ; If the current shipping index for the pending consolidation task is < This indicates that the current consolidation and transportation task is of low difficulty, with a readiness level of 1. Response measures include proceeding with preliminary preparations as planned, without requiring additional resource allocation or scheduling. ≤Current pending consolidation shipment index ≤ This indicates that the current consolidation task to be performed is of medium difficulty, with a preparedness level of 2. Response measures include prioritizing the deployment of new energy vehicles, optimizing route planning, and avoiding congested sections. If the current consolidation task's transportation index... > This indicates that the current consolidation and transportation task is highly difficult, with a preparedness level of 3. Response measures include activating temperature-controlled sealed vehicles, adjusting transportation time slots, and real-time monitoring throughout the entire process. The optimization management module optimizes the optimal path combination. All paths within, according to the movement index Sort in descending order, movement index The highest-ranking path is determined to be the primary execution path, while the paths ranked 2nd and 3rd are determined to be backup paths for dynamic rerouting. The optimization management module is set with a fixed monitoring cycle. During the monitoring period If a single path is selected as the optimal path combination... If the cumulative number of occurrences is ≥3, the corresponding path is determined to be within a monitoring period. Mature benchmark routes within the country are additionally included in the monitoring cycle. Within, all consolidation and transportation tasks have dynamic rerouting backup routes.

[0020] In this embodiment, a multi-dimensional acquisition module collects and standardizes task, node, and transportation data across the entire solid waste collection and transportation chain, providing comprehensive data support for quantitative analysis and planning throughout the entire process. The task evaluation module uses a transportation index... This quantitative approach to transporting and consolidating tasks fills a gap in the management of hidden carbon emissions from anaerobic degradation during the transportation of organic solid waste. It enables a scientific classification of transport difficulty, providing precise quantitative decision-making support for resource scheduling and route planning. The multi-dimensional assessment offers high accuracy. The route planning module, based on GIS road network topology, performs layer-by-layer screening from two dimensions: node capacity and road traffic efficiency. It considers both the feasibility of transshipment operations and the timeliness of transportation, addressing the pain points of traditional route planning's single dimension and insufficient practicality. This results in the generation of highly adaptable optimal route combinations. The adaptation assessment module generates a traffic flow index from three dimensions: environmental risk, low-carbon compliance, and emergency route rerouting flexibility. This system precisely quantifies the compatibility between routes and collection / transportation tasks, achieving a balance between low-carbon goals and emergency response capabilities. It optimizes the management module's assessment of the readiness level of collection / transportation tasks, scientifically classifying primary and backup routes, and simultaneously monitoring cycles. By solidifying mature routes, we achieve an optimal balance across multiple dimensions of solid waste transportation costs, efficiency, and low-carbon goals, comprehensively improving the low-carbon management level and execution stability of solid waste collection and transportation, and demonstrating high adaptability of intelligent planning.

[0021] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by those skilled in the art for each set of sample data; as long as it does not affect the ratio between the parameter and the quantized value, it is acceptable.

[0022] The above formulas are all derived from software simulation using a large amount of data and are selected to be close to the actual values. The coefficients in the formulas are set by those skilled in the art according to the actual situation. The above description is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any equivalent substitutions or changes made by those skilled in the art within the technical scope disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the protection scope of the present invention.

Claims

1. A solid waste low-carbon transportation route planning system based on multi-objective optimization, characterized in that: It includes a multi-dimensional data acquisition module, a task evaluation module, a path planning module, an adaptation evaluation module, and an optimization management module; The multi-dimensional acquisition module connects to the big data platform to acquire management data of all solid waste collection and transportation tasks, management data of all nodes, and management data of transportation, and classifies them into task datasets, node datasets, and transportation datasets. The task evaluation module analyzes the execution difficulty of each consolidation task based on the task dataset, node dataset, and transportation dataset, and generates a corresponding transportation index. ; The path planning module generates the optimal path combination based on the task dataset, node dataset, and transportation dataset through multi-dimensional filtering. ; The adaptation evaluation module evaluates the optimal path combination based on the task dataset and the transportation dataset. The compatibility between each path and the collection and transportation task is used to generate a corresponding flow index. ; The optimization management module is set with a fixed range of operation thresholds. Combined with the transportation index and movement index Determine the readiness level of the consolidation and transportation task and classify the optimal route combination. The system identifies the main execution path and the dynamic rerouting backup path, and outputs corresponding consolidation and shipping suggestions.

2. The solid waste low-carbon transportation route planning system based on multi-objective optimization according to claim 1, characterized in that: The task dataset includes the solid waste category, solid waste weight, total transport load, total transport distance, time period type, organic component ratio of solid waste, methane production during solid waste degradation, and maximum allowable number of route changes for each collection and transportation task. The solid waste categories include domestic waste, industrial solid waste, hazardous waste, and construction waste, and the time period types include daytime transport, nighttime transport, weekday transport, and holiday transport.

3. The solid waste low-carbon transportation route planning system based on multi-objective optimization according to claim 2, characterized in that: The node dataset includes the solid waste transfer volume, transportation distance, connection carbon emission coefficient per unit turnover, connection economic cost coefficient per unit turnover, total designed storage capacity, and used storage capacity for each node.

4. The solid waste low-carbon transportation route planning system based on multi-objective optimization according to claim 3, characterized in that: The transportation dataset includes the rated single-vehicle load of dedicated vehicles, single-vehicle travel speed, single-vehicle labor cost per unit time, single-vehicle energy consumption cost per unit time, average temperature of the vehicle compartment, total number of traffic lights, average number of starts and stops per vehicle, total length of continuous crossing of sensitive areas, total route length, regional average carbon emissions, actual carbon emissions under all operating conditions, historical average number of route changes, additional carbon emissions per unit mileage caused by historical route changes, and baseline carbon emissions per unit mileage.

5. The solid waste low-carbon transportation route planning system based on multi-objective optimization according to claim 4, characterized in that: The transportation index The calculation process is as follows: S11. Based on the task dataset, node dataset, and transportation dataset, extract the management data of the current consolidation task to be executed, the management data of all nodes, and the management data of transportation. S12. Calculate the category complexity of the current consolidation task to be executed. ; S13. Calculate the vehicle complexity of the current pending container shipping task. ; S14. Calculate the capacity limitation coefficient for the current consolidation task to be executed. ; S15. Calculate the carbon emission coefficient of the current collection and transportation task to be performed. ; S16. Calculate the carbon emission degradation coefficient of the current collection and transportation task to be performed. ; S17. For the current pending consolidation and shipping tasks, normalize the category complexity. Vehicle complexity Capacity limitation factor Carbon emission coefficient of connection and degradation carbon emission coefficient ; S18. Based on S11-S17, calculate the transportation index of the current consolidation task to be executed using a weighted method. .

6. The solid waste low-carbon transportation route planning system based on multi-objective optimization according to claim 5, characterized in that: The optimal path combination The planning process is as follows: S21. Based on the task dataset, node dataset, and transportation dataset, extract the management data of the current consolidation task to be executed, the management data of all nodes, and the management data of transportation. S22. Construct a directed road network topology based on map GIS, consisting of "starting node → connecting node → ending node", and generate a set of all passable path combinations. In this system, the starting node and the ending node do not coincide, the total designed storage capacity of each node is greater than 0 tons, each path is connected in series with nodes according to the road network topology, each path is bound to each node it passes through, and each path consists of several road segments. S23. Calculate the number of pending consolidation tasks. Storage capacity utilization rate of each node and the average storage capacity utilization rate of all nodes along the path ; S24. Combine all accessible paths into a set. The total number of paths included is denoted as Then, based on the node capacity dimension, path combinations are further filtered, with the following rules: (1) Forced exclusion: the set of all feasible path combinations Including storage capacity utilization rate Paths with a value of ≥0.9 or no overlap between the warehouse operation period and the current collection and transportation task to be executed, or with a degree of <30% overlap; (2) Candidate admission: the set of all possible path combinations In the middle, the average storage capacity utilization rate of all nodes along the path is satisfied. ≤0.8, and the path where the remaining warehouse capacity of a single node is greater than or equal to the single-transfer transport volume of the current consolidation task to be executed; (3) Dynamic sorting: based on the average storage capacity utilization rate of all nodes along the path. Sort in ascending order, total number of paths If there are ≤20 paths, take the first 50%; if there are less than 20 paths, take the first 50%. If there are ≤100 paths, take the top 30% of the total number of paths. >100 results, select the top 20%, and filter them into path combination one; S25. Calculate the set of all possible path combinations. In the middle, the first Passage impedance coefficient of the path ; S26. Based on the traffic efficiency dimension, path combination two is screened, and the rules are as follows: (1) Forced exclusion: the set of all feasible path combinations The route includes roads closed for construction, truck restrictions, and traffic control measures; (2) Candidate admission: the set of all possible path combinations In, the passing impedance coefficient The path must have an average slope of ≤0.8 and an average gradient of ≤6%, and must have at least two parallel emergency detour routes. (3) Dynamic sorting: based on the pass impedance coefficient Sort in ascending order, total number of paths If there are ≤20 paths, take the first 50%; if there are less than 20 paths, take the first 50%. If there are ≤100 paths, take the top 30% of the total number of paths. >100 results, the top 20% are selected and filtered into path combination two; S27. Generate the optimal path combination based on path combination one and path combination two. The rules are as follows: The optimal path combination is the intersection of path combination one and path combination two, which simultaneously satisfies the requirements of node capacity and traffic efficiency. ; If the intersection is empty, only the average storage capacity utilization rate of all nodes in path combination one and path combination two are retained. ≤0.9, Passing impedance coefficient For paths with an average slope of ≤0.8 and ≤6%, the optimal path combination is generated by prioritizing nodes with storage capacity > traffic efficiency, selecting the top 3 paths from path combination one and the top 2 paths from path combination two. .

7. The solid waste low-carbon transportation route planning system based on multi-objective optimization according to claim 6, characterized in that: The movement index The evaluation process is as follows: S31. Based on the task dataset and transportation dataset, compile the management data and optimal route combinations for the current consolidation tasks to be executed. Transportation management data for the middle route; S32. Calculate the optimal path combination In the middle, the first Environmental Sensitive Area Avoidance of Each Path ; S33. Calculate the optimal path combination In the middle, the first Carbon emission compliance of the entire operating conditions of the route ; S34. Calculate the optimal path combination In the middle, the first Path rerouting adaptability ; S35. Based on S31-S34, calculate the first... The movement index of the path .

8. The solid waste low-carbon transportation route planning system based on multi-objective optimization according to claim 7, characterized in that: The preliminary level assessment process is as follows: The upper limit of the transportation threshold range is denoted as The lower limit of the transportation threshold range is denoted as ; If the current shipping index for the pending consolidation task is < This indicates that the current consolidation and transportation task is of low difficulty, with a readiness level of 1. Response measures include proceeding with preliminary preparations as planned, without requiring additional resource allocation or scheduling. ≤Current pending consolidation shipment index ≤ This indicates that the current consolidation task to be performed is of medium difficulty, with a preparedness level of 2. Response measures include prioritizing the deployment of new energy vehicles, optimizing route planning, and avoiding congested sections. If the current consolidation task's transportation index... > This indicates that the current consolidation and transportation task is highly challenging, with a preparedness level of 3. Response measures include activating temperature-controlled sealed vehicles, adjusting transportation schedules, and real-time monitoring throughout the entire process.

9. The solid waste low-carbon transportation route planning system based on multi-objective optimization according to claim 8, characterized in that: The optimization management module optimizes the optimal path combination. All paths within, according to the movement index Sort in descending order, movement index The highest-ranking path is determined to be the primary execution path, while the paths ranked 2nd and 3rd are determined to be backup paths for dynamic rerouting.

10. The solid waste low-carbon transportation route planning system based on multi-objective optimization according to claim 9, characterized in that: The optimization management module is set with a fixed monitoring cycle. During the monitoring period If a single path is selected as the optimal path combination... If the cumulative number of occurrences is ≥3, the corresponding path is determined to be within a monitoring period. Mature benchmark routes within the country are additionally included in the monitoring cycle. Within, all consolidation and transportation tasks have dynamic rerouting backup routes.

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