Urban sanitation vehicle cooperative scheduling and efficiency optimization method and system

Abstract

The present application relates to the technical field of vehicle scheduling, in particular to a city sanitation vehicle cooperative scheduling and efficiency optimization method and system, comprising: collecting vehicle operation state sequence and road environment state sequence, obtaining sanitation work task list; according to the vehicle operation state sequence and the road environment state sequence, analyzing the work demand to obtain the sanitation task demand information sequence, combining the sanitation work task list to analyze and obtain the task priority information; constructing the vehicle work path based on the first vehicle positioning data, and segmenting the vehicle work path to obtain a plurality of vehicle segmented work paths, wherein the first vehicle positioning data comprises the first two-dimensional coordinate data obtained by the vehicle at each sampling interval in the sanitation work process. Through real-time monitoring and analysis of the vehicle operation state and the road environment state, the present application can accurately judge the demand of each sanitation task and reasonably arrange the vehicle work, thereby improving the overall work efficiency.

Description

Technical Field

[0001] This invention relates to the field of vehicle dispatching technology, specifically to a method and system for collaborative dispatching and efficiency optimization of urban sanitation vehicles. Background Technology

[0002] Currently, traditional methods often rely on fixed work plans and experience-based judgments, lacking real-time monitoring of vehicle operating status and road conditions. This makes it difficult to accurately assess the needs of each sanitation task. Moreover, traditional scheduling usually adopts static route planning, without considering real-time traffic conditions or emergencies, which may result in long vehicle journeys, increased empty driving distances, and fuel consumption.

[0003] In addition, during peak periods or under special circumstances, traditional methods lack sufficient scheduling flexibility, making it difficult to quickly adjust work arrangements and affecting the timeliness and effectiveness of sanitation services. Moreover, due to the lack of systematic risk assessment, traditional scheduling methods may overlook potential safety hazards, increasing the probability of accidents. Summary of the Invention

[0004] To achieve the above objectives, the present invention provides the following technical solution: a method for collaborative scheduling and efficiency optimization of urban sanitation vehicles, comprising: Collect vehicle operation status sequences and road environment status sequences to obtain a list of sanitation operation tasks; conduct operation demand analysis based on vehicle operation status sequences and road environment status sequences to obtain a sanitation task demand information sequence; and analyze the task priority information in conjunction with the sanitation operation task list. Based on the first vehicle positioning data, a vehicle operation path is constructed and the vehicle operation path is segmented to obtain several vehicle segment operation paths. The first vehicle positioning data includes the first two-dimensional coordinate data obtained by the vehicle at each sampling interval during the sanitation operation. Based on the path characteristics of each vehicle segment operation path, calculate the vehicle fuel consumption and operation time cost of the corresponding vehicle segment operation path, and obtain the total operation cost of the vehicle operation path based on the vehicle fuel consumption and operation time cost of each vehicle segment operation path. The path characteristics include the road segment length and the change in road segment slope. The first vehicle dispatch cost is determined based on the total operating cost. The risk assessment of the vehicle operation path is carried out, and the first vehicle dispatch cost is adjusted according to the risk assessment results to obtain the second vehicle dispatch cost. The risk assessment includes risk assessment of the change in road slope of each vehicle segment operation path and risk assessment of the road surface damage corresponding to each vehicle segment operation path. A first vehicle scheduling strategy is determined with the goal of matching the second vehicle scheduling cost. The first vehicle scheduling strategy includes the number of vehicles and the vehicle load capacity corresponding to each vehicle segment operation path.

[0005] Preferably, after determining the first vehicle scheduling strategy with the goal of matching the second vehicle scheduling cost, the method further includes: When the task priority information is determined to be higher than the preset threshold and the vehicle operation status sequence shows that the vehicle is idle, the target operation area is obtained based on the task location and the service range. The first vehicle dispatching strategy is optimized based on the regional characteristics of each vehicle segment operation path to obtain the second vehicle dispatching strategy. The regional characteristics include the population density and garbage type of the sanitation operation area corresponding to the vehicle segment operation path. The second vehicle dispatching strategy includes the number of vehicles, vehicle load capacity, vehicle operation time period and vehicle operation priority corresponding to each vehicle segment operation path. Optimize vehicle dispatching routes within the target work area to obtain the optimal dispatching route for coordinated dispatching and control of sanitation vehicles. The aim is to reduce the increased empty driving distance of sanitation vehicle dispatching routes and reduce the deviation of vehicle speed from road speed limits within the dispatching route. The fitness of the dispatching route is calculated to optimize the dispatching route. Based on the second vehicle dispatching strategy and the optimal dispatching route, a final coordinated dispatching instruction is generated to control sanitation vehicles to perform tasks in the target work area according to the vehicle's work time and work priority.

[0006] Preferably, a vehicle operation path is constructed based on the first vehicle positioning data, and the vehicle operation path is segmented to obtain several segmented vehicle operation paths, including: The first vehicle positioning data is filtered to obtain the second vehicle positioning data, and the second vehicle positioning data is fitted to obtain the vehicle operation path. The second vehicle positioning data includes the second two-dimensional coordinate data obtained by the vehicle at each sampling interval exceeding the preset distance threshold during the sanitation operation. The segment point coordinate data of the vehicle operation path is obtained based on the preset curvature threshold. The segment point coordinate data includes the third two-dimensional coordinate data of each curvature change rate in the vehicle operation path that exceeds the preset curvature threshold. The vehicle operation path is divided based on the segmented point coordinate data, and the segmented vehicle operation path is subjected to segmented linear regression to obtain several vehicle segmented operation paths. Calculate the gradient difference of each vehicle segment operation path, and further divide the vehicle segment operation path for each gradient difference value that exceeds the preset gradient threshold.

[0007] Preferably, the vehicle fuel consumption and operation time cost for each vehicle segment operation path are calculated based on the path characteristics of each vehicle segment operation path, and the total operation cost of the vehicle operation path is obtained based on the vehicle fuel consumption and operation time cost of each vehicle segment operation path, including: Obtain the segment length and vehicle operating speed of each vehicle segment operation path, and perform regression on the segment length and vehicle operating speed to obtain the expected operation time data of the corresponding vehicle segment operation path. The segment length is calculated based on the change in road slope. Based on the expected operation time data, the real-time fuel consumption data of each vehicle segment operation path is accumulated and calculated to obtain the vehicle fuel consumption of the corresponding vehicle segment operation path. The fuel consumption and operation time cost of each vehicle segment operation route are summed to obtain the total operation cost of the vehicle operation route.

[0008] Preferably, a first vehicle dispatch cost is determined based on the total operating cost, a risk assessment is performed on the vehicle operation route, and the first vehicle dispatch cost is adjusted according to the obtained risk assessment results to obtain a second vehicle dispatch cost, including: The vehicle fuel consumption cost is calculated based on the total operating cost and the preset unit fuel consumption cost, and the first vehicle dispatch cost is obtained based on the vehicle fuel consumption cost and the vehicle depreciation cost. A risk assessment is conducted on the slope variation of each vehicle segment operation route to obtain the first risk coefficient of the corresponding vehicle segment operation route. A risk assessment is conducted on the road surface damage corresponding to each vehicle segment operation path to obtain the second risk coefficient of the corresponding vehicle segment operation path. The risk compensation amount for each vehicle segment operation path is determined based on the first risk coefficient and the second risk coefficient. The second vehicle scheduling cost is obtained by weighting each risk compensation amount with the first vehicle scheduling cost.

[0009] Preferably, a risk assessment is performed on the slope variation of each vehicle segment operation route to obtain the first risk coefficient for the corresponding vehicle segment operation route, including: The slope variation of each vehicle segment operation path is classified, and the first risk coefficient of the corresponding vehicle segment operation path is obtained based on the classification results.

[0010] Preferably, the first vehicle scheduling strategy is determined with the goal of matching the second vehicle scheduling cost, including: With the goal of matching the second vehicle scheduling cost, a multilayer perceptron is used to obtain the number of vehicles and the vehicle load capacity corresponding to each vehicle segment operation path.

[0011] Preferably, the first vehicle scheduling strategy is optimized based on the regional characteristics of each vehicle segment's work path to obtain a second vehicle scheduling strategy, including: The first vehicle dispatching strategy is optimized based on the pedestrian density in the sanitation operation area corresponding to each vehicle segment operation path to obtain the vehicle operation time period corresponding to each vehicle segment operation path. The pedestrian density in the sanitation operation area includes peak-hour pedestrian flow and off-peak-hour pedestrian flow. The first vehicle dispatching strategy is optimized based on the type of waste in the sanitation operation area corresponding to each vehicle segment operation path to obtain the vehicle operation priority corresponding to each vehicle segment operation path. The types of waste in the sanitation operation area include domestic waste and construction waste.

[0012] Preferably, vehicle dispatching routes are optimized within the target work area to obtain the optimal dispatching route, including: Within the target work area, randomly select the first dispatch path that does not conflict with the paths of other vehicles; Obtain the first scheduled empty driving distance of the first scheduling path and the first planned empty driving distance of the planned operation route within the target operation area. Calculate the difference between the first scheduled empty driving distance and the first planned empty driving distance as the first increased empty driving distance, and calculate the magnitude of the first increase in empty driving distance. Obtain the average vehicle speed within the planned operation route and the average vehicle speed within the first scheduling path, and calculate the first speed fluctuation amplitude. The first scheduling fitness of the first scheduling path is calculated based on the increase in the first empty driving distance and the first speed fluctuation amplitude. Continue optimizing the scheduling path within the target work area until the scheduling path optimization converges, and output the scheduling path with the highest scheduling fitness as the optimal scheduling path.

[0013] A collaborative scheduling and efficiency optimization system for urban sanitation vehicles, applicable to the aforementioned collaborative scheduling and efficiency optimization methods for urban sanitation vehicles, including: The demand analysis unit is used to collect vehicle operation status sequences and road environment status sequences to obtain a list of sanitation operation tasks; based on the vehicle operation status sequences and road environment status sequences, it performs operation demand analysis to obtain a sequence of sanitation task demand information; and combined with the sanitation operation task list, it analyzes to obtain task priority information. The path segmentation unit is used to construct a vehicle operation path based on the first vehicle positioning data and segment the vehicle operation path to obtain several vehicle segmented operation paths. The first vehicle positioning data includes the first two-dimensional coordinate data obtained by the vehicle at each sampling interval during the sanitation operation. The cost calculation unit is used to calculate the vehicle fuel consumption and operation time cost of the corresponding vehicle segment operation path based on the path characteristics of each vehicle segment operation path, and to obtain the total operation cost of the vehicle operation path based on the vehicle fuel consumption and operation time cost of each vehicle segment operation path. The path characteristics include the road segment length and the change in road segment slope. The cost adjustment unit is used to determine the first vehicle dispatch cost based on the total operating cost, conduct a risk assessment on the vehicle operation path, and adjust the first vehicle dispatch cost according to the obtained risk assessment results to obtain the second vehicle dispatch cost. The risk assessment includes risk assessment of the change in road slope of each vehicle segment operation path and risk assessment of the road surface damage corresponding to each vehicle segment operation path. The vehicle dispatching unit is used to determine a first vehicle dispatching strategy with the goal of matching the second vehicle dispatching cost. The first vehicle dispatching strategy includes the number of vehicles and the vehicle load capacity corresponding to each vehicle segment operation path.

[0014] Compared with the prior art, the beneficial effects of the present invention are: This invention, through real-time monitoring and analysis of vehicle operating status and road environment status, can accurately determine the needs of each sanitation task, rationally arrange vehicle operations, thereby improving overall operational efficiency. Moreover, by optimizing vehicle operation routes and scheduling strategies, unnecessary empty driving distances and fuel consumption can be reduced, directly lowering the operating costs of sanitation work. This invention adjusts the scheduling strategy in real time based on data, allowing for dynamic adjustments based on factors such as work priority, regional population density, and waste type. This ensures more flexible operational response during peak periods and special circumstances. Furthermore, by assessing the risk of vehicle operation routes, it can identify potential risks in advance and make corresponding adjustments, ensuring the safety of vehicles and personnel and reducing the accident rate. Attached Figure Description

[0015] Figure 1 This is a schematic flowchart of the overall method in one embodiment of the present invention; Figure 2 This is a schematic diagram of the overall system architecture in one embodiment of the present invention.

[0016] In the diagram: 1. Demand Analysis Unit; 2. Path Segmentation Unit; 3. Cost Calculation Unit; 4. Cost Adjustment Unit; 5. Vehicle Scheduling Unit. Detailed Implementation

[0017] 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.

[0018] Example 1, please refer to Figure 1 This invention provides a technical solution: a method for collaborative scheduling and efficiency optimization of urban sanitation vehicles, comprising: S1. Collect vehicle operation status sequence and road environment status sequence to obtain sanitation operation task list; perform operation demand analysis based on vehicle operation status sequence and road environment status sequence to obtain sanitation task demand information sequence, and analyze the task priority information in conjunction with sanitation operation task list. S2. Construct a vehicle operation path based on the first vehicle positioning data, and segment the vehicle operation path to obtain several vehicle segmented operation paths. The first vehicle positioning data includes the first two-dimensional coordinate data obtained by the vehicle at each sampling interval during the sanitation operation. S3. Calculate the vehicle fuel consumption and operation time cost of the corresponding vehicle segment operation path based on the path characteristics of each vehicle segment operation path, and obtain the total operation cost of the vehicle operation path based on the vehicle fuel consumption and operation time cost of each vehicle segment operation path. The path characteristics include the road segment length and the change in road segment slope. S4. Determine the first vehicle dispatch cost based on the total operating cost, conduct a risk assessment on the vehicle operation path, and adjust the first vehicle dispatch cost according to the obtained risk assessment results to obtain the second vehicle dispatch cost. The risk assessment includes risk assessment of the change in road slope of each vehicle segment operation path and risk assessment of the road surface damage corresponding to each vehicle segment operation path. S5. Determine the first vehicle scheduling strategy with the goal of matching the second vehicle scheduling cost, wherein the first vehicle scheduling strategy includes the number of vehicles and the vehicle load capacity corresponding to each vehicle segment operation path.

[0019] It should be noted that data on the operational status of sanitation vehicles and the road environment are collected; this includes the real-time location and speed of the vehicles, as well as information on road conditions; at the same time, a list of sanitation work tasks is obtained, which lists the specific sanitation work to be completed; next, by analyzing this data, the work requirements are determined and a sequence of requirements information is formed; combined with the task list, the priority of each task is further analyzed to provide a basis for subsequent scheduling decisions. Using the collected vehicle positioning data, the operation paths of sanitation vehicles are constructed. The operation paths of the vehicles are constructed based on the two-dimensional coordinates recorded at each sampling interval during the actual operation. Then, these paths are segmented to obtain segmented operation paths for multiple vehicles, which allows for more detailed analysis and optimization of each path segment. The fuel consumption and time cost of each work route are calculated based on its characteristics (such as route length and gradient variations). The purpose of this step is to evaluate the economy and efficiency of each segment route and ultimately determine the total work cost of each route. This helps to identify which route is the most cost-effective during the scheduling process. After calculating the total operating cost, the next step is to determine the dispatch cost of the first vehicle. At the same time, a risk assessment is conducted for each segment of the route, including assessing the risks brought about by changes in gradient and the impact of road surface damage. These assessment results will affect the dispatch cost, so adjustments need to be made based on the results of the risk assessment to arrive at the dispatch cost of the second vehicle, making it more reasonable and competitive. With the goal of matching the adjusted scheduling cost of the second vehicle, a scheduling strategy for the first vehicle is formulated. This strategy includes the number of vehicles allocated to each segment of the work path and the load capacity of each vehicle. This step ensures that while meeting the operational requirements, resource allocation is optimized and overall operational efficiency is improved.

[0020] In an optional embodiment, after determining the first vehicle scheduling strategy with the objective of matching the second vehicle scheduling cost, the method further includes: When the task priority information is determined to be higher than the preset threshold and the vehicle operation status sequence shows that the vehicle is idle, the target operation area is obtained based on the task location and the service range. The first vehicle dispatching strategy is optimized based on the regional characteristics of each vehicle segment operation path to obtain the second vehicle dispatching strategy. The regional characteristics include the population density and garbage type of the sanitation operation area corresponding to the vehicle segment operation path. The second vehicle dispatching strategy includes the number of vehicles, vehicle load capacity, vehicle operation time period and vehicle operation priority corresponding to each vehicle segment operation path. Optimize vehicle dispatching routes within the target work area to obtain the optimal dispatching route for coordinated dispatching and control of sanitation vehicles. The aim is to reduce the increased empty driving distance of sanitation vehicle dispatching routes and reduce the deviation of vehicle speed from road speed limits within the dispatching route. The fitness of the dispatching route is calculated to optimize the dispatching route. Based on the second vehicle dispatching strategy and the optimal dispatching route, a final coordinated dispatching instruction is generated to control sanitation vehicles to perform tasks in the target work area according to the vehicle's work time and work priority.

[0021] It should be noted that, based on the task priority information, if it is found that the priority of some tasks is higher than the preset threshold and the vehicle's operating status is displayed as idle, it means that the vehicle can be immediately put into a new task. At this time, the system will divide the service area according to the location of the task, thereby defining a target work area. This area is determined based on the location of the high-priority tasks that need to be completed, ensuring that resources can effectively meet these urgent needs. The regional characteristics of each work route are analyzed. These characteristics include the population density (i.e., the number of people in the area) and the type of waste (different types of waste may require different processing methods). By analyzing these regional characteristics, the scheduling strategy of the first vehicle can be optimized, thereby forming a new scheduling strategy, namely the second vehicle scheduling strategy. This strategy will list in detail the number of vehicles required for each segment of the work route, the load capacity of each vehicle, the time period of the operation, and the priority of the operation, so as to better adapt to the actual operation needs. After defining the target work area, the vehicle dispatch route is optimized. This process aims to find the optimal dispatch route to reduce the empty driving distance of sanitation vehicles during the execution of tasks, while minimizing the deviation between vehicle speed and road speed limits. Through such optimization, the vehicle's operating efficiency and work quality can be improved, enabling it to complete the task more smoothly within the specified time. By combining the second vehicle dispatch strategy and the optimized optimal dispatch path, the system will generate the final coordinated dispatch instructions; these instructions will clearly control the sanitation vehicles to perform specific tasks within the target work area according to their respective work periods and priorities.

[0022] In an optional embodiment, a vehicle operation path is constructed based on the first vehicle positioning data, and the vehicle operation path is segmented to obtain several segmented vehicle operation paths, including: The first vehicle positioning data is filtered to obtain the second vehicle positioning data, and the second vehicle positioning data is fitted to obtain the vehicle operation path. The second vehicle positioning data includes the second two-dimensional coordinate data obtained by the vehicle at each sampling interval exceeding the preset distance threshold during the sanitation operation. The segment point coordinate data of the vehicle operation path is obtained based on the preset curvature threshold. The segment point coordinate data includes the third two-dimensional coordinate data of each curvature change rate in the vehicle operation path that exceeds the preset curvature threshold. The vehicle operation path is divided based on the segmented point coordinate data, and the segmented vehicle operation path is subjected to segmented linear regression to obtain several vehicle segmented operation paths. Calculate the gradient difference of each vehicle segment operation path, and further divide the vehicle segment operation path for each gradient difference value that exceeds the preset gradient threshold.

[0023] It should be noted that the location data of the second vehicle is obtained by extracting useful information from the location data of the first vehicle. This process involves filtering out the two-dimensional coordinates recorded at each sampling interval that exceeds a certain preset distance threshold during the sanitation operation. This means that new location data will only be recorded when the distance traveled by the vehicle reaches a certain standard. Subsequently, the vehicle operation path is fitted based on these filtered data to form a relatively smooth operation path. By setting a preset curvature threshold, the curvature change of the vehicle's operating path is analyzed. When the curvature change rate of the path exceeds this threshold, it will be regarded as an important segmentation point. In other words, if the vehicle turns or changes direction significantly during driving, the path needs to be divided at this point. Those points that meet the conditions will be recorded as segmentation point coordinate data. After obtaining the coordinates of the segment points, the vehicle's operation path is divided based on these points; each segment can be seen as the vehicle's performance on different road sections; then, the linear regression method is used to analyze these divided paths to obtain several vehicle segment operation paths; this step can ensure that the characteristics of each segment can be effectively captured and provide a basis for subsequent analysis. Calculate the gradient difference for each vehicle segment operation route; the gradient difference reflects the degree of ascent or descent of the route; if the gradient difference of some routes exceeds the set gradient threshold, it indicates that the route may face significant gradient changes, and therefore further subdivision is required; for these special routes, secondary subdivision is performed to better manage and schedule vehicle operations in complex road conditions.

[0024] In an optional embodiment, the vehicle fuel consumption and operation time cost for each vehicle segment operation path are calculated based on the path characteristics of that path, and the total operation cost of the vehicle operation path is obtained based on the vehicle fuel consumption and operation time cost for each vehicle segment operation path, including: Obtain the segment length and vehicle operating speed of each vehicle segment operation path, and perform regression on the segment length and vehicle operating speed to obtain the expected operation time data of the corresponding vehicle segment operation path. The segment length is calculated based on the change in road slope. Based on the expected operation time data, the real-time fuel consumption data of each vehicle segment operation path is accumulated and calculated to obtain the vehicle fuel consumption of the corresponding vehicle segment operation path. The fuel consumption and operation time cost of each vehicle segment operation route are summed to obtain the total operation cost of the vehicle operation route.

[0025] It should be noted that obtaining the segment length of each vehicle segment operation path and the vehicle's operating speed during task execution is essential; these data are the basis for evaluating operational efficiency and resource consumption. The calculation of segment length takes into account changes in road gradient, meaning that the distance the vehicle travels will vary at different inclines. By performing regression analysis on the road segment length and operating speed, the expected operating time data for each segment of the operating path is obtained; the purpose of this step is to predict the time required for the vehicle to complete each segment of the operation, so that the subsequent cost calculation can be more accurate. After obtaining the expected operation time, the system will perform cumulative calculations based on the real-time fuel consumption data of the vehicle on each segment of the operation path; this means that the system will record the fuel consumption of the vehicle during the driving process and combine this data with the expected operation time in order to calculate the actual fuel consumption of each segment of the operation path. The total operating cost of the entire vehicle operation route is obtained by summing the fuel consumption and time cost of each segment of the operation route. This total cost not only reflects the vehicle's fuel consumption but also includes the time cost required to complete the operation, allowing managers to comprehensively evaluate the economy and efficiency of the operation.

[0026] In an optional embodiment, a first vehicle dispatch cost is determined based on the total operating cost, a risk assessment is performed on the vehicle operation route, and the first vehicle dispatch cost is adjusted according to the obtained risk assessment results to obtain a second vehicle dispatch cost, including: The vehicle fuel consumption cost is calculated based on the total operating cost and the preset unit fuel consumption cost, and the first vehicle dispatch cost is obtained based on the vehicle fuel consumption cost and the vehicle depreciation cost. A risk assessment is conducted on the slope variation of each vehicle segment operation route to obtain the first risk coefficient of the corresponding vehicle segment operation route. A risk assessment is conducted on the road surface damage corresponding to each vehicle segment operation path to obtain the second risk coefficient of the corresponding vehicle segment operation path. The risk compensation amount for each vehicle segment operation path is determined based on the first risk coefficient and the second risk coefficient. The second vehicle scheduling cost is obtained by weighting each risk compensation amount with the first vehicle scheduling cost.

[0027] It should be noted that, based on the previously calculated total operating cost and the preset unit fuel consumption cost, the fuel cost consumed by the vehicle during operation is calculated; this cost is an important component of vehicle operating expenses and can reflect the economy of the vehicle when performing tasks; at the same time, the fuel consumption cost is combined with the vehicle depreciation cost (i.e. the value loss of the vehicle during use) to derive the first vehicle dispatch cost; this cost is intended to cover the basic cost of vehicle operation. A risk assessment is conducted on the slope variation of each vehicle segment's work path. Slope variation may affect vehicle driving stability and fuel efficiency, so assessing the risk of this factor can help predict potential operational difficulties and increased costs, thereby obtaining the corresponding first risk coefficient. For each vehicle segment's work route, a risk assessment of road surface damage is also required. Road surface conditions directly affect the vehicle's driving safety and comfort. Severely damaged road surfaces will increase vehicle wear and maintenance costs. Therefore, assessing the risk of road surface damage is crucial, as this will allow us to obtain the corresponding secondary risk coefficient. Based on the first risk coefficient and the second risk coefficient, the risk compensation amount for each vehicle segment operation route is determined; this compensation amount is to cope with potential risks and uncertainties, so as to ensure that a reasonable level of profitability can still be maintained in the event of unexpected situations. The second vehicle dispatch cost is derived by weighting each risk compensation amount with the first vehicle dispatch cost. This adjustment aims to make the cost more reasonable and flexible, so as to better cope with various risks that may arise in actual operations. Through this process, the dispatch cost not only takes into account the basic operating costs, but also fully incorporates the risk assessment, making the overall cost more competitive and sustainable.

[0028] In an optional embodiment, a risk assessment is performed on the slope variation of each vehicle segment operation path to obtain a first risk coefficient for the corresponding vehicle segment operation path, including: The slope variation of each vehicle segment operation path is classified, and the first risk coefficient of the corresponding vehicle segment operation path is obtained based on the classification results.

[0029] It should be noted that the slope change of each segment of the vehicle's work route needs to be classified. This means classifying different degrees of slope change according to their impact on vehicle operations. For example, slope changes may be divided into several levels, such as flat, slight slope, medium slope, and steep slope. This classification can more clearly identify which segments may pose greater risks during operations. Based on the above classification results, the first risk coefficient of the corresponding vehicle segment operation path is calculated. This risk coefficient reflects the challenges and potential cost increases that vehicles may face under different slope conditions. This coefficient helps to provide a reference for vehicle scheduling in actual operations, thereby reducing unexpected situations caused by slope changes.

[0030] In an optional embodiment, determining a first vehicle scheduling strategy with the objective of matching a second vehicle scheduling cost includes: With the goal of matching the second vehicle scheduling cost, a multilayer perceptron is used to obtain the number of vehicles and the vehicle load capacity corresponding to each vehicle segment operation path.

[0031] It's important to note that data related to vehicle operations needs to be collected. This data can include: road features such as gradient changes, road surface conditions, and traffic conditions; historical operational data including the performance of different types of vehicles under similar conditions, fuel consumption, and transport time; and historical pricing data and market conditions to understand current pricing levels. This data should be formatted suitable for training a multilayer perceptron, typically including input features and target output.

[0032] Feature engineering is performed on the collected data to extract features that influence the predicted number of vehicles and load capacity. For example, the following can be considered: the classification of road gradient changes; road length and speed limits; operational demand, such as cargo type and quantity; and vehicle performance parameters, such as maximum load capacity and fuel efficiency.

[0033] A multilayer perceptron model is constructed, with a basic structure including an input layer, hidden layers, and an output layer. The input layer receives the processed feature data. Multiple hidden layers are set up, and nonlinear transformations are performed using activation functions (such as ReLU) to learn the complex relationships between features. The output layer is used to output the number of vehicles and the vehicle load capacity required for each segment path, according to task requirements.

[0034] In an optional embodiment, the first vehicle scheduling strategy is optimized based on the regional characteristics of each vehicle segment operation path to obtain a second vehicle scheduling strategy, including: The first vehicle dispatching strategy is optimized based on the pedestrian density in the sanitation operation area corresponding to each vehicle segment operation path to obtain the vehicle operation time period corresponding to each vehicle segment operation path. The pedestrian density in the sanitation operation area includes peak-hour pedestrian flow and off-peak-hour pedestrian flow. The first vehicle dispatching strategy is optimized based on the type of waste in the sanitation operation area corresponding to each vehicle segment operation path to obtain the vehicle operation priority corresponding to each vehicle segment operation path. The types of waste in the sanitation operation area include domestic waste and construction waste.

[0035] It should be noted that the pedestrian density in the sanitation work area corresponding to each vehicle's segmented work route should be considered; this factor is an important basis for determining when to carry out sanitation work; based on pedestrian density, the work period can be divided into peak and off-peak periods: During these time periods, the area experiences high pedestrian traffic, which may disrupt sanitation operations and reduce efficiency. In such cases, vehicle dispatching strategies need to be more cautious, potentially requiring operations to be carried out during periods of less disruption. During these periods, pedestrian traffic is relatively low, improving operational efficiency. Therefore, the optimized dispatching strategy should prioritize operations during off-peak hours to minimize disruption to public activities and maximize efficiency. Based on this analysis of pedestrian density, optimizing the first vehicle dispatching strategy can yield more suitable operating times, ensuring sanitation operations are conducted at the most efficient time. Optimization also needs to consider the types of waste within the sanitation operation area; different types of waste have different treatment needs, thus significantly impacting the scheduling strategy; the main types of waste include: Household waste is usually generated in residential and commercial areas and needs to be disposed of promptly to avoid hygiene hazards caused by accumulation; construction waste mainly comes from construction sites and decoration activities and usually appears in concentrated areas and times. By analyzing the characteristics of these two types of waste, the priority of vehicle operations on each segmented operation route can be determined. For domestic waste that needs to be processed in a timely manner, vehicles should be prioritized for operation, while construction waste, although important, may not need to be cleaned up during peak hours. The optimized scheduling strategy should reasonably arrange the operation sequence according to the priority of different types of waste.

[0036] In an optional embodiment, optimizing vehicle dispatching routes within the target work area to obtain an optimal dispatching route includes: Within the target work area, randomly select the first dispatch path that does not conflict with the paths of other vehicles; Obtain the first scheduled empty driving distance of the first scheduling path and the first planned empty driving distance of the planned operation route within the target operation area. Calculate the difference between the first scheduled empty driving distance and the first planned empty driving distance as the first increased empty driving distance, and calculate the magnitude of the first increase in empty driving distance. Obtain the average vehicle speed within the planned operation route and the average vehicle speed within the first scheduling path, and calculate the first speed fluctuation amplitude. The first scheduling fitness of the first scheduling path is calculated based on the increase in the first empty driving distance and the first speed fluctuation amplitude. Continue optimizing the scheduling path within the target work area until the scheduling path optimization converges, and output the scheduling path with the highest scheduling fitness as the optimal scheduling path.

[0037] It should be noted that within the target work area, a preliminary dispatch route is randomly selected; this route is selected based on the principle of not conflicting with the routes of other vehicles, so as to avoid traffic congestion and potential accident risks. For the selected first scheduling route, calculate its empty driving distance; the empty driving distance refers to the distance the vehicle travels without a load; at the same time, it is also necessary to obtain the empty driving distance of the planned operation route; the difference between these two distances is used to evaluate the efficiency of the route, and the smaller the difference, the more reasonable the route design and the less empty driving the vehicle will have. By calculating the difference between the empty driving distance of the first scheduling path and the empty driving distance of the planned route, we can determine the first increase in empty driving distance; this metric helps to understand the efficiency loss of the currently selected path compared to the ideal situation. Assess the average speed of the vehicle on the planned operation route and the first dispatch path; this data is crucial for understanding the vehicle's operational efficiency on different paths; by comparing these two, the first speed fluctuation amplitude can be obtained, which reflects the speed stability and driving efficiency of the current path; Based on the calculated increase in empty driving distance and speed fluctuation, the first scheduling fitness can be derived. Fitness is a comprehensive evaluation index used to judge the rationality and effectiveness of the selected scheduling path. The higher the fitness, the better the path design and the higher the vehicle operating efficiency. After obtaining the initial scheduling fitness, the scheduling path continues to be optimized within the target work area; this process will be repeated until the path optimization reaches a convergence state, that is, no more significant improvements appear. Among all generated scheduling paths, the path with the highest fitness is selected as the optimal scheduling path. This path can not only effectively reduce empty driving distance and improve vehicle operating efficiency, but also ensure the rationality of the scheduling process.

[0038] Example 2, please refer to Figure 2 This invention provides a technical solution: a system for collaborative scheduling and efficiency optimization of urban sanitation vehicles, applicable to the aforementioned method for collaborative scheduling and efficiency optimization of urban sanitation vehicles, comprising: Demand Analysis Unit 1 is used to collect vehicle operation status sequences and road environment status sequences to obtain a list of sanitation operation tasks; based on the vehicle operation status sequences and road environment status sequences, it performs operation demand analysis to obtain a sequence of sanitation task demand information; and combined with the sanitation operation task list, it analyzes to obtain task priority information. The path segmentation unit 2 is used to construct a vehicle operation path based on the first vehicle positioning data and segment the vehicle operation path to obtain several vehicle segmented operation paths. The first vehicle positioning data includes the first two-dimensional coordinate data obtained by the vehicle at each sampling interval during the sanitation operation. Cost calculation unit 3 is used to calculate the vehicle fuel consumption and operation time cost of the corresponding vehicle segment operation path based on the path characteristics of each vehicle segment operation path, and to obtain the total operation cost of the vehicle operation path based on the vehicle fuel consumption and operation time cost of each vehicle segment operation path. The path characteristics include the road segment length and the change in road segment slope. Cost adjustment unit 4 is used to determine the first vehicle dispatch cost based on the total operating cost, conduct risk assessment on the vehicle operation path, and adjust the first vehicle dispatch cost according to the obtained risk assessment results to obtain the second vehicle dispatch cost. The risk assessment includes risk assessment of the change in road slope of each vehicle segment operation path and risk assessment of the road surface damage corresponding to each vehicle segment operation path. The vehicle scheduling unit 5 is used to determine a first vehicle scheduling strategy with the goal of matching the second vehicle scheduling cost. The first vehicle scheduling strategy includes the number of vehicles and the vehicle load capacity corresponding to each vehicle segment operation path.

[0039] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited thereto. Various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention.

Claims

1. A method for collaborative scheduling and efficiency optimization of urban sanitation vehicles, characterized in that, include: Collect vehicle operation status sequences and road environment status sequences to obtain a list of sanitation operation tasks; Based on the vehicle operation status sequence and road environment status sequence, the operation demand analysis is carried out to obtain the sanitation task demand information sequence. Combined with the sanitation operation task list, the task priority information is obtained through analysis. Based on the first vehicle positioning data, a vehicle operation path is constructed and the vehicle operation path is segmented to obtain several vehicle segment operation paths. The first vehicle positioning data includes the first two-dimensional coordinate data obtained by the vehicle at each sampling interval during the sanitation operation. Based on the path characteristics of each vehicle segment operation path, calculate the vehicle fuel consumption and operation time cost of the corresponding vehicle segment operation path, and obtain the total operation cost of the vehicle operation path based on the vehicle fuel consumption and operation time cost of each vehicle segment operation path. The path characteristics include the road segment length and the change in road segment slope. The first vehicle dispatch cost is determined based on the total operating cost. The risk assessment of the vehicle operation path is carried out, and the first vehicle dispatch cost is adjusted according to the risk assessment results to obtain the second vehicle dispatch cost. The risk assessment includes risk assessment of the change in road slope of each vehicle segment operation path and risk assessment of the road surface damage corresponding to each vehicle segment operation path. A first vehicle scheduling strategy is determined with the goal of matching the second vehicle scheduling cost. The first vehicle scheduling strategy includes the number of vehicles and the vehicle load capacity corresponding to each vehicle segment operation path.

2. The method for collaborative scheduling and efficiency optimization of urban sanitation vehicles according to claim 1, characterized in that, After determining the first vehicle scheduling strategy with the goal of matching the second vehicle scheduling cost, the method further includes: When the task priority information is determined to be higher than the preset threshold and the vehicle operation status sequence shows that the vehicle is idle, the target operation area is obtained based on the task location and the service range. The first vehicle dispatching strategy is optimized based on the regional characteristics of each vehicle segment operation path to obtain the second vehicle dispatching strategy. The regional characteristics include the population density and garbage type of the sanitation operation area corresponding to the vehicle segment operation path. The second vehicle dispatching strategy includes the number of vehicles, vehicle load capacity, vehicle operation time period and vehicle operation priority corresponding to each vehicle segment operation path. Optimize vehicle dispatching routes within the target work area to obtain the optimal dispatching route for coordinated dispatching and control of sanitation vehicles. The aim is to reduce the increased empty driving distance of sanitation vehicle dispatching routes and reduce the deviation of vehicle speed from road speed limits within the dispatching route. The fitness of the dispatching route is calculated to optimize the dispatching route. Based on the second vehicle dispatching strategy and the optimal dispatching route, a final coordinated dispatching instruction is generated to control sanitation vehicles to perform tasks in the target work area according to the vehicle's work time and work priority.

3. The method for collaborative scheduling and efficiency optimization of urban sanitation vehicles according to claim 2, characterized in that, Based on the first vehicle positioning data, a vehicle operation path is constructed, and the vehicle operation path is segmented to obtain several segmented vehicle operation paths, including: The first vehicle positioning data is filtered to obtain the second vehicle positioning data, and the second vehicle positioning data is fitted to obtain the vehicle operation path. The second vehicle positioning data includes the second two-dimensional coordinate data obtained by the vehicle at each sampling interval exceeding the preset distance threshold during the sanitation operation. The segment point coordinate data of the vehicle operation path is obtained based on the preset curvature threshold. The segment point coordinate data includes the third two-dimensional coordinate data of each curvature change rate in the vehicle operation path that exceeds the preset curvature threshold. The vehicle operation path is divided based on the segmented point coordinate data, and the segmented vehicle operation path is subjected to segmented linear regression to obtain several vehicle segmented operation paths. Calculate the gradient difference of each vehicle segment operation path, and further divide the vehicle segment operation path for each gradient difference value that exceeds the preset gradient threshold.

4. The method for collaborative scheduling and efficiency optimization of urban sanitation vehicles according to claim 3, characterized in that, Based on the path characteristics of each vehicle segment's work path, the corresponding vehicle fuel consumption and work time cost are calculated for that work path. The total work cost for each vehicle segment's work path is then obtained based on these costs, including: Obtain the segment length and vehicle operating speed of each vehicle segment operation path, and perform regression on the segment length and vehicle operating speed to obtain the expected operation time data of the corresponding vehicle segment operation path. The segment length is calculated based on the change in road slope. Based on the expected operation time data, the real-time fuel consumption data of each vehicle segment operation path is accumulated and calculated to obtain the vehicle fuel consumption of the corresponding vehicle segment operation path. The fuel consumption and operation time cost of each vehicle segment operation route are summed to obtain the total operation cost of the vehicle operation route.

5. The method for collaborative scheduling and efficiency optimization of urban sanitation vehicles according to claim 4, characterized in that, The first vehicle dispatch cost is determined based on the total operating cost. A risk assessment is then performed on the vehicle operation routes. Based on the risk assessment results, the first vehicle dispatch cost is adjusted to obtain the second vehicle dispatch cost, which includes: The vehicle fuel consumption cost is calculated based on the total operating cost and the preset unit fuel consumption cost, and the first vehicle dispatch cost is obtained based on the vehicle fuel consumption cost and the vehicle depreciation cost. A risk assessment is conducted on the slope variation of each vehicle segment operation route to obtain the first risk coefficient of the corresponding vehicle segment operation route. A risk assessment is conducted on the road surface damage corresponding to each vehicle segment operation path to obtain the second risk coefficient of the corresponding vehicle segment operation path. The risk compensation amount for each vehicle segment operation path is determined based on the first risk coefficient and the second risk coefficient. The second vehicle scheduling cost is obtained by weighting each risk compensation amount with the first vehicle scheduling cost.

6. The method for collaborative scheduling and efficiency optimization of urban sanitation vehicles according to claim 5, characterized in that, A risk assessment is performed on the slope variation of each vehicle segment's work route to obtain the first risk coefficient for that route, including: The slope variation of each vehicle segment operation path is classified, and the first risk coefficient of the corresponding vehicle segment operation path is obtained based on the classification results.

7. The method for collaborative scheduling and efficiency optimization of urban sanitation vehicles according to claim 6, characterized in that, Determining the first vehicle dispatching strategy with the goal of matching the second vehicle dispatching cost includes: With the goal of matching the second vehicle scheduling cost, a multilayer perceptron is used to obtain the number of vehicles and the vehicle load capacity corresponding to each vehicle segment operation path.

8. The method for collaborative scheduling and efficiency optimization of urban sanitation vehicles according to claim 7, characterized in that, The first vehicle scheduling strategy is optimized based on the regional characteristics of each vehicle segment's work path to obtain a second vehicle scheduling strategy, including: The first vehicle dispatching strategy is optimized based on the pedestrian density in the sanitation operation area corresponding to each vehicle segment operation path to obtain the vehicle operation time period corresponding to each vehicle segment operation path. The pedestrian density in the sanitation operation area includes peak-hour pedestrian flow and off-peak-hour pedestrian flow. The first vehicle dispatching strategy is optimized based on the type of waste in the sanitation operation area corresponding to each vehicle segment operation path to obtain the vehicle operation priority corresponding to each vehicle segment operation path. The types of waste in the sanitation operation area include domestic waste and construction waste.

9. The method for collaborative scheduling and efficiency optimization of urban sanitation vehicles according to claim 8, characterized in that, Optimize vehicle dispatching routes within the target work area to obtain the optimal dispatching route, including: Within the target work area, randomly select the first dispatch path that does not conflict with the paths of other vehicles; Obtain the first scheduled empty driving distance of the first scheduling path and the first planned empty driving distance of the planned operation route within the target operation area. Calculate the difference between the first scheduled empty driving distance and the first planned empty driving distance as the first increased empty driving distance, and calculate the magnitude of the first increase in empty driving distance. Obtain the average vehicle speed within the planned operation route and the average vehicle speed within the first scheduling path, and calculate the first speed fluctuation amplitude. The first scheduling fitness of the first scheduling path is calculated based on the increase in the first empty driving distance and the first speed fluctuation amplitude. Continue optimizing the scheduling path within the target work area until the scheduling path optimization converges, and output the scheduling path with the highest scheduling fitness as the optimal scheduling path.

10. A system for collaborative scheduling and efficiency optimization of urban sanitation vehicles, applicable to the method for collaborative scheduling and efficiency optimization of urban sanitation vehicles as described in any one of claims 1-9, characterized in that, include: The demand analysis unit is used to collect vehicle operation status sequences and road environment status sequences to obtain a list of sanitation operation tasks. Based on the vehicle operation status sequence and road environment status sequence, the operation demand analysis is carried out to obtain the sanitation task demand information sequence. Combined with the sanitation operation task list, the task priority information is obtained through analysis. The path segmentation unit is used to construct a vehicle operation path based on the first vehicle positioning data and segment the vehicle operation path to obtain several vehicle segmented operation paths. The first vehicle positioning data includes the first two-dimensional coordinate data obtained by the vehicle at each sampling interval during the sanitation operation. The cost calculation unit is used to calculate the vehicle fuel consumption and operation time cost of the corresponding vehicle segment operation path based on the path characteristics of each vehicle segment operation path, and to obtain the total operation cost of the vehicle operation path based on the vehicle fuel consumption and operation time cost of each vehicle segment operation path. The path characteristics include the road segment length and the change in road segment slope. The cost adjustment unit is used to determine the first vehicle dispatch cost based on the total operating cost, conduct a risk assessment on the vehicle operation path, and adjust the first vehicle dispatch cost according to the obtained risk assessment results to obtain the second vehicle dispatch cost. The risk assessment includes risk assessment of the change in road slope of each vehicle segment operation path and risk assessment of the road surface damage corresponding to each vehicle segment operation path. The vehicle dispatching unit is used to determine a first vehicle dispatching strategy with the goal of matching the second vehicle dispatching cost. The first vehicle dispatching strategy includes the number of vehicles and the vehicle load capacity corresponding to each vehicle segment operation path.

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