Distributed truck logistics collaborative scheduling system based on edge computing

Abstract

This invention relates to the field of data analysis technology, and in particular to a distributed truck logistics collaborative scheduling system based on edge computing. The loss assessment module integrates multi-dimensional data and calculates the total loss risk score for each truck using a weighted scoring method, thereby determining whether loss reduction scheduling needs to be initiated. The path analysis module identifies overlapping road segments for high-risk trucks. The platooning constraint module filters out platoons that meet the conditions based on time and space constraints. The collaborative driving module formulates the optimal platooning driving order and unified speed strategy based on the ranking and volatility of loss risk within the platoon. This invention combines individual vehicle loss risk assessment with group path collaborative optimization. Through intelligent platooning driving strategies, it effectively reduces fuel consumption and mechanical wear of trucks during transportation, thereby significantly reducing logistics transportation costs and improving overall transportation efficiency and economy.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, and in particular to a distributed truck logistics collaborative scheduling system based on edge computing. Background Technology

[0002] With the intelligent development of the logistics industry, the requirements for real-time performance and accuracy in truck transportation scheduling are increasing. Existing truck logistics scheduling systems mostly adopt a centralized architecture, relying on cloud-based centralized data processing. This results in high information transmission latency, slow decision-making response, and difficulty adapting to dynamically changing road conditions and vehicle status. Furthermore, traditional scheduling strategies primarily focus on route planning and capacity matching, lacking comprehensive analysis of multi-dimensional data such as real-time vehicle energy consumption, load, weather, and road conditions. This makes it impossible to accurately assess vehicle wear and tear risks and formulate dynamic collaborative solutions, leading to high energy consumption and accelerated vehicle wear during transportation, ultimately hindering overall transportation efficiency and economic viability. The rise of edge computing technology provides a new technical path to solve these problems. By performing real-time data processing and decision-making at edge nodes closer to the data source, the pressure on the cloud can be effectively reduced, and the scheduling response speed can be improved.

[0003] Chinese Patent Publication No. CN117649116B discloses a big data logistics management system, belonging to the field of logistics management. It includes an order management module, a risk management and planning module, a warehousing and transportation management module, a cargo tracking module, a route planning module, a user feedback module, a customized packaging module, a capacity coordination module, and an optimization decision support module. The order management module is used to receive, process, and track received order information. This invention provides a big data logistics management system that can realize order management, risk warning, cargo tracking, route planning, customized packaging, capacity coordination, and key node optimization. It helps to manage and optimize the logistics process, provides customized packaging for valuable and complex-shaped goods to reduce damage risk and improve customer satisfaction, and schedules idle return freight vehicles to maximize the utilization efficiency of logistics resources and meet the concepts of energy conservation, environmental protection, and sustainable development. Therefore, it is evident that existing technologies have the following problems:

[0004] Existing truck logistics dispatching systems suffer from high information processing latency and poor real-time decision-making. Furthermore, dispatching strategies lack comprehensive consideration of real-time vehicle wear and tear, environmental conditions, and road conditions, making it difficult to achieve dynamic collaborative optimization based on individual vehicle status. This results in high overall transportation energy consumption and vehicle wear and tear. Summary of the Invention

[0005] To address this, the present invention provides a distributed truck logistics collaborative scheduling system based on edge computing, which overcomes the problems of high information processing latency and poor real-time decision-making in existing truck logistics scheduling systems. Furthermore, the scheduling strategies lack comprehensive consideration of the real-time wear and tear of vehicles, the environment, and road conditions, making it difficult to achieve dynamic collaborative optimization based on the individual vehicle status, resulting in high overall transportation energy consumption and vehicle wear and tear.

[0006] To achieve the above objectives, the present invention provides a distributed truck logistics collaborative scheduling system based on edge computing, comprising:

[0007] The edge acquisition module is used to collect and store real-time location data, real-time loss data, remaining route data, weather data and road condition data for the remaining distance of each truck. The real-time loss data includes real-time fuel consumption data and truck load data. The remaining route data includes the remaining driving segment, estimated driving time and timeliness requirements.

[0008] The edge assessment module is used to integrate the weather data, road condition data, real-time fuel consumption data and truck load data to generate a power consumption assessment dataset. Based on the loss assessment dataset, it determines the loss risk level of each truck for the subsequent journey, and determines whether the corresponding truck needs to start loss reduction scheduling based on the loss risk level.

[0009] The cloud analysis module is used to identify the freight cars to be dispatched with reduced losses and obtain the remaining route data of each freight car to be dispatched. Based on the remaining route data, several overlapping road segments are filtered to determine whether they are overlapping routes based on the length of each overlapping road segment and to determine the overlapping train groups corresponding to each overlapping route.

[0010] The cloud-based platooning module is used to perform constraint checks on the overlapping train groups on the route based on the real-time positioning data of each freight car to be dispatched, and to determine the train groups that meet the platooning conditions based on the constraint check results.

[0011] The cloud-based collaboration module is used to determine the platooning order and uniform driving speed based on the total loss risk score of each freight car to be dispatched in the platoon.

[0012] As a preferred technical solution for a distributed truck logistics collaborative scheduling system based on edge computing, the edge evaluation module compares each evaluation factor in the power consumption evaluation dataset with the corresponding preset scoring threshold to obtain the score of each factor, and calculates the total loss risk score by weighted summation according to preset weights to determine the loss risk level.

[0013] As a preferred technical solution for a distributed truck logistics collaborative scheduling system based on edge computing, the edge assessment module determines that the corresponding truck needs to be scheduled to reduce losses and records the truck as a truck to be dispatched based on the judgment result that the loss risk level is high risk level.

[0014] As a preferred technical solution for a distributed truck logistics collaborative scheduling system based on edge computing, the cloud analysis module determines several overlapping road segments based on the remaining path data, and determines whether the corresponding overlapping road segments are overlapping routes based on the comparison between the distance length of each overlapping road segment and a preset length, wherein:

[0015] Based on the determination result that the length of the overlapping road segment is greater than or equal to the preset length, the corresponding overlapping road segment is determined to be an overlapping route;

[0016] Conversely, determine that the corresponding overlapping road segments are not overlapping routes.

[0017] As a preferred technical solution for a distributed truck logistics collaborative scheduling system based on edge computing, the cloud analysis module determines all trucks to be dispatched for any overlapping route based on the remaining route data of each truck to be dispatched and records them as a route overlapping vehicle group for that overlapping route. The cloud formation module performs constraint checks on the route overlapping vehicle group based on the real-time positioning data of each truck to be dispatched in a single route overlapping vehicle group and the corresponding starting point of the overlapping route, and determines the formation vehicle group that meets the formation conditions based on the constraint check results.

[0018] As a preferred technical solution for a distributed truck logistics collaborative scheduling system based on edge computing, the cloud-based platooning module performs the following operations during constraint checks:

[0019] The driving distance between the truck to be dispatched and the starting point of the overlapping route is determined based on the real-time positioning data of each truck to be dispatched, and the corresponding truck to be dispatched is determined to meet the platooning conditions based on the driving distance.

[0020] Furthermore, the cloud-based platooning module identifies all freight cars that meet the platooning conditions within a single overlapping path as platooning groups.

[0021] As a preferred technical solution for a distributed truck logistics collaborative scheduling system based on edge computing, the cloud platooning module determines that the corresponding trucks to be dispatched meet the platooning conditions based on the determination result that the driving distance is less than or equal to the preset distance.

[0022] Conversely, it is determined that the corresponding freight car to be dispatched does not meet the platooning requirements.

[0023] As a preferred technical solution for a distributed truck logistics collaborative scheduling system based on edge computing, the cloud collaboration module determines the driving order of the platoon based on the total loss risk score of each truck to be dispatched in a single platoon.

[0024] The total loss risk score is positively correlated with the driving sequence.

[0025] As a preferred technical solution for a distributed truck logistics collaborative scheduling system based on edge computing, the cloud collaboration module calculates the average risk score and the average risk score deviation of each truck to be dispatched in a single platoon based on the total loss risk score of each truck to be dispatched. Furthermore, it determines the loss fluctuation of each truck to be dispatched in the corresponding platoon based on the ratio of the average risk score deviation to the average risk score.

[0026] The loss fluctuation is determined to be large based on the judgment result that the ratio of the average deviation of the risk score to the average value of the risk score is greater than a preset ratio;

[0027] The loss fluctuation is determined to be small based on the determination result that the ratio of the average deviation of the risk score to the average value of the risk score is less than or equal to a preset ratio.

[0028] As a preferred technical solution for a distributed truck logistics collaborative scheduling system based on edge computing, the cloud-based collaborative module determines the uniform driving speed of each platoon of vehicles based on the aforementioned loss fluctuation, including:

[0029] Based on the judgment of large fluctuations in wear and tear, the range of values ​​for a uniform driving speed is determined as the minimum economic speed.

[0030] Based on the determination of small fluctuations in wear and tear, the range of values ​​for a uniform driving speed is determined to be the maximum economic speed.

[0031] Compared with existing technologies, the beneficial effects of this invention are as follows: The distributed truck logistics collaborative scheduling system based on edge computing provided by this invention achieves precise truck logistics scheduling and loss reduction optimization through a distributed architecture of edge computing and cloud collaboration: The edge acquisition module comprehensively collects multi-dimensional data on real-time vehicle positioning, loss, route, and environment, ensuring data integrity and timeliness, and providing a reliable foundation for subsequent decision-making; The edge assessment module integrates multi-dimensional data to construct an assessment system, accurately determining the truck loss risk level and loss reduction scheduling needs, ensuring targeted scheduling; The cloud analysis module identifies high-risk trucks, filters effective overlapping road segments and corresponding overlapping vehicle groups, ensuring collaborative feasibility; The cloud platooning module improves platooning efficiency and success rate based on real-time positioning constraint checks; The cloud collaboration module optimizes platooning order and uniform speed based on loss risk scores, adapts to individual vehicle differences, maximizes the reduction of vehicle group loss, and ensures driving stability; The invention as a whole forms a closed loop integrating data acquisition, risk assessment, route selection, platooning formation, and strategy optimization, solving the problems of traditional scheduling data lag, single consideration, and inefficient collaboration, significantly improving scheduling accuracy, collaboration, and loss reduction effects;

[0032] In particular, the edge assessment module provides accurate and reliable decision support for the system's loss reduction scheduling, realizing the scientific nature of loss risk assessment and the targeted nature of scheduling initiation, effectively avoiding the problems of one-sided assessment and blind scheduling in traditional methods. Based on the objective law that truck loss is affected by multiple factors, this module compares each assessment factor in the power consumption assessment dataset with the corresponding preset scoring threshold, and then calculates the total loss risk score according to preset weights to determine the loss risk level. This avoids the limitations of single-factor assessment and reflects the differences in the impact of each factor on loss through weight allocation, so that the assessment results can comprehensively and realistically reflect the loss status of trucks in the subsequent journey. At the same time, this module only initiates loss reduction scheduling for trucks judged to be at high risk and marks them as trucks to be dispatched, following the core logic of on-demand scheduling. This avoids the waste of resources caused by indiscriminate scheduling and ensures that high-risk vehicles receive timely intervention, guaranteeing the necessity and efficiency of loss reduction scheduling, and laying a precise object foundation for subsequent cloud-based collaborative scheduling.

[0033] In particular, the cloud-based analysis module provides precise support for collaborative objects and road segments in platooning coordination, comprehensively solving the problems of unfounded selection of overlapping road segments and blind formation of vehicle groups in traditional collaborative scheduling, and ensuring the feasibility and effectiveness of subsequent platooning scheduling: Based on the core premise that platooning coordination must have actual driving value, this module first selects overlapping road segments based on the remaining route data of the trucks to be dispatched, and then determines the overlapping routes by comparing the route length with the preset length. This design can accurately select road segments with collaborative value and avoid the waste of resources caused by ineffective coordination. On this basis, this module identifies all trucks to be dispatched on the same overlapping route as a route overlapping vehicle group, ensuring that the vehicles in the group have a common basis for collaborative driving, avoiding platooning chaos caused by inconsistent road segments, and providing vehicle group objects with clear objectives and collaborative conditions for the constraint checks and coordination strategy formulation of the subsequent cloud-based platooning module, thereby improving the overall coordination and rationality of scheduling;

[0034] In particular, the cloud-based platooning module lays a solid and feasible foundation for collaborative driving, comprehensively solving the problems of assembly failure and inefficient collaboration caused by neglecting the differences in vehicle spatial positions in traditional platooning. This ensures the operability and stability of platooning: Based on the objective requirement that platooning vehicles need to be within a reasonable spatial range to efficiently assemble, this module calculates the travel distance based on the real-time positioning data of the trucks to be dispatched and the starting point of overlapping routes. Spatial adaptability is used as the core dimension of constraint checking to ensure that the selected vehicles have the basic conditions for timely assembly. By using a preset distance threshold to determine whether the trucks to be dispatched meet the platooning requirements, vehicles that cannot participate in the platooning synchronously due to excessive distance are excluded from the source, avoiding invalid waiting or platooning chaos. Finally, all trucks to be dispatched that meet the conditions are assembled into a platoon, so that all vehicles in the platoon have the spatial adaptability for collaborative driving. This provides a structurally stable and efficiently collaborative platoon object for the subsequent cloud-based collaborative module to formulate driving strategies, further improving the overall coherence and reliability of scheduling.

[0035] In particular, the cloud-based collaborative module enables precise adaptation and deep optimization of platooning strategies, comprehensively solving the problems of rigidity and inability to adapt to individual vehicle wear differences in traditional platooning strategies. This maximizes the advantages of platooning in reducing wear while ensuring safety. Based on the aerodynamic principle that the wind resistance is lower in the middle and rear of the platoon, this module determines the order according to the rule that the total wear risk score is positively correlated with the driving order, placing high-wear-risk vehicles in a better driving position to specifically reduce their additional wear. Considering that the wear risk differences of vehicles within the group will affect the speed adaptation effect, the module determines the wear fluctuation by calculating the mean risk score, average deviation, and the ratio of the two, accurately quantifying the consistency of wear within the group. Based on the fluctuation differences, a unified driving speed is adapted. When the fluctuation is large, the lowest economic speed is used to balance the wear of vehicles with different risks, and when the fluctuation is small, the highest economic speed is used to improve transportation efficiency. At the same time, combined with the adjustment of road section speed limits, the speed is ensured to meet safety and compliance requirements, making the driving strategy deeply integrated with the group status and road rules, significantly improving the accuracy and practicality of collaborative wear reduction. Attached Figure Description

[0036] Figure 1 This is a connection diagram of a distributed truck logistics collaborative scheduling system based on edge computing, according to an embodiment of the present invention.

[0037] Figure 2 This is a flowchart illustrating the workflow of the cloud-based analysis module in an embodiment of the present invention.

[0038] Figure 3 This is a flowchart illustrating the workflow of the cloud-based formation module in an embodiment of the present invention. Detailed Implementation

[0039] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0040] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0041] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0042] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0043] Please see Figure 1 - Figure 3 The diagrams shown are, respectively, a connection diagram of the distributed truck logistics collaborative scheduling system based on edge computing according to an embodiment of the present invention, a workflow diagram of the cloud analysis module according to an embodiment of the present invention, and a workflow diagram of the cloud platooning module according to an embodiment of the present invention. An embodiment of the present invention provides a distributed truck logistics collaborative scheduling system based on edge computing, comprising:

[0044] The edge acquisition module is used to collect and store real-time location data, real-time loss data, remaining route data, weather data and road condition data for the remaining distance of each truck. The real-time loss data includes real-time fuel consumption data and truck load data. The remaining route data includes the remaining driving segment, estimated driving time and timeliness requirements.

[0045] An edge assessment module, which is connected to the edge acquisition module, is used to integrate the weather data, the road condition data, the real-time fuel consumption data and the truck load data to generate a power consumption assessment dataset. Based on the loss assessment dataset, the loss risk level of each truck for the subsequent journey is determined, and based on the loss risk level, it is determined whether the corresponding truck needs to start loss reduction scheduling.

[0046] The cloud analysis module is connected to the edge assessment module and the edge acquisition module respectively. It is used to identify the freight cars to be dispatched with reduced losses and to obtain the remaining path data of each freight car to be dispatched. Based on the remaining path data, it filters several overlapping road segments to determine whether they are overlapping routes based on the length of each overlapping road segment and to determine the overlapping vehicle groups corresponding to each overlapping route.

[0047] The cloud-based platooning module is connected to the cloud-based analysis module and the edge acquisition module, respectively, and is used to perform constraint checks on the overlapping vehicle groups based on the real-time positioning data of each freight car to be dispatched, and to determine the platooning vehicle groups that meet the platooning conditions based on the constraint check results.

[0048] The cloud-based collaboration module is connected to both the edge assessment module and the cloud-based platooning module, and is used to determine the platooning order and uniform driving speed based on the total loss risk score of each freight car to be dispatched in the platooning group.

[0049] In implementation, the preset weights for each evaluation factor are as follows: weather data 0.2, road condition data 0.25, real-time fuel consumption data 0.3, and truck load data 0.25. It is understandable that weather data (heavy rain, strong winds, high temperatures) indirectly increases losses by affecting driving resistance / engine load, and its impact on power consumption is moderate, hence its weight is set at 0.2. Road condition data (congestion, uphill driving, potholes) directly increases vehicle braking and acceleration frequency and driving resistance, significantly impacting power consumption, therefore its weight should be slightly higher than weather data, set at 0.25. Real-time fuel consumption directly reflects the current vehicle wear status; high fuel consumption is usually accompanied by high wear and is a core indicator of wear risk, therefore its weight should be the highest, set at 0.3. Load directly determines vehicle driving resistance (generally, the greater the load, the greater the resistance), and is a fundamental factor affecting wear, with a similar impact to road conditions, hence its weight is also set at 0.25.

[0050] In implementation, each assessment factor is divided into 3 to 4 levels according to the degree of impact and corresponds to different scores between 1 and 5. The higher the score, the greater the risk of loss. In one implementation, the preset score thresholds for each assessment factor are: (1) The levels of weather data are divided into sunny / cloudy (at this time there is no precipitation and the wind force is ≤3, which has no obvious impact on driving and the risk of loss is the lowest), cloudy / light rain / light wind (at this time there is a small amount of precipitation or the wind force is 4 to 5, the driving resistance is slightly increased and the risk of loss is low), and moderate rain / strong wind (at this time there is a daily precipitation of 10mm to 25m). (1) Moderate rain or wind force of level 6 to 7, driving resistance increases significantly and frequent braking is required, resulting in moderate risk of loss. (2) Heavy rain / storm / snowstorm / typhoon (at this time, there is heavy precipitation with a daily rainfall of more than 25 mm or wind force ≥ 8, low visibility and slippery road surface will seriously affect driving safety and efficiency, resulting in extremely high risk of loss), the scores are 1 point, 2 points, 3 points and 5 points respectively; (3) The road condition data is divided into smooth (at this time there is no congestion and the vehicle speed is ≥ 60 km / h, the risk of loss is the lowest) and slow (at this time there is slight congestion and the vehicle speed is mostly within 60 km / h, the risk of loss is the lowest). 30km / h~60km / h, occasional acceleration / braking, low risk of loss), congestion (at this time, the vehicle speed is <30km / h, frequent start-stop is required and the engine idle time is long, fuel consumption will increase by 10%~15%, medium risk of loss), severe congestion / construction section (at this time, the vehicle speed is <10km / h or detour is required, the start-stop frequency is extremely high, fuel consumption will increase by more than 20%, extremely high risk of loss), their scores are 1 point, 2 points, 3 points, 5 points respectively; (3) The real-time fuel consumption level can be divided into more than 10% below the rated fuel consumption (the vehicle is in The ratings are 1 point, 2 points, 3 points, and 5 points respectively for the following conditions: (4) The load capacity of the truck is classified according to the load ratio between the actual load and the rated load, including 4 levels: load ratio. (Under light load conditions, driving resistance is low, and the risk of wear and tear is minimal) Load ratio (Under medium load conditions, driving resistance is moderate, and the risk of wear and tear is low) Load ratio (Under heavy load conditions, driving resistance increases significantly, fuel consumption increases by 5% to 10%, and the risk of wear and tear is moderate) and load ratio > 1 (under overload conditions, driving resistance is extremely high, engine load is too high, fuel consumption increases by more than 20%, and tire and braking system wear is accelerated, resulting in an extremely high risk of wear and tear), with scores of 1, 2, 3, and 5 points respectively.

[0051] In implementation, the loss risk level is divided into three risk levels: low risk, medium risk and high risk. Specifically, the total loss risk score for the low risk level should be ≤2 points, the total loss risk score for the medium risk level should be higher than 2 points and less than or equal to 3.5 points, and the total loss risk score for the high risk level should be higher than 3.5 points.

[0052] Understandably, at a low-risk level, all factors are in a low-risk state, and vehicle wear and tear is within a normal and controllable range, so there is no need to initiate wear and tear reduction scheduling. At a medium-risk level, some assessment factors may be at a medium-risk score, resulting in a slight increase in wear and tear on the corresponding trucks. At a high-risk level, at least one assessment factor is at a high-risk score or multiple factors are at a medium-risk score, resulting in a significant increase in current truck wear and requiring mandatory initiation of wear and tear reduction scheduling.

[0053] Specifically, the cloud-based analysis module extracts the remaining travel segments (including the coordinates of the starting point, ending point, and passing nodes) from the remaining path data of each freight car to be dispatched. It then uses a built-in path space matching algorithm (this is existing technology and will not be elaborated further) to compare the remaining travel segments of the freight cars to be dispatched to identify segments that are completely overlapping or continuously connected in spatial location and have the same travel direction. These segments are considered overlapping segments. When the actual length of the overlapping segment is greater than or equal to a preset length, it indicates that the segment has sufficient length to support convoy travel (significant losses can be reduced by reducing wind resistance and minimizing rapid acceleration and braking), and the scheduling costs of convoy assembly and coordination can be covered by the benefits of loss reduction. Therefore, it is determined to be an overlapping route. Conversely, if the length of the overlapping segment is less than the preset length threshold, the benefits of convoy loss reduction are insufficient to offset the scheduling costs of assembly and disbandment, and there is no actual coordination value. Therefore, it is not determined to be an overlapping route.

[0054] In implementation, the preset length threshold ranges from 5km to 30km, determined according to the transportation scenario: (1) for intra-city freight or short-distance transportation around the city, the value is 5km to 15km, preferably set to 10km; (2) for cross-regional long-distance transportation / highway / national trunk road transportation, the value is 10km to 30km, preferably set to 20km. It is understandable that, based on the actual test data of platooning in the logistics industry, when the length of overlapping road sections is ≥5km, platooning can reduce fuel consumption by more than 8%, and the loss reduction benefit exceeds the platooning assembly time cost and communication scheduling cost; when the length is ≥30km, the road condition complexity increases, the platooning stability decreases, and the loss reduction benefit diminishes marginally.

[0055] In practice, for trunk line transportation scenarios such as highways / national roads, due to the high speed and relatively simple road conditions, vehicles can start preparing for platooning from a greater distance without significantly affecting the overall transportation efficiency. Therefore, the preset distance is 10km to 20km, preferably 15km. For urban roads / short-distance delivery scenarios, due to the complex roads, numerous traffic lights, and slow speeds, if the preset distance is too far, the uncertainty of the time it takes for vehicles to reach the assembly point will greatly increase, which may lead to frequent changes in the platooning plan. Therefore, the preset distance should be smaller, usually 3km to 10km, preferably 5km.

[0056] According to aerodynamic principles, in a stable convoy, the leading vehicle breaks the wind for the following vehicles, and the following vehicles, traveling in the wake of the leading vehicle, experience significantly reduced air resistance. Therefore, placing the vehicle with the highest total loss risk score in the middle or rear of the convoy allows it to maximize the use of the wake effect of the leading vehicle, thereby effectively reducing its driving resistance, achieving fuel savings and reducing mechanical wear. Conversely, vehicles with lower total loss risk scores, which are in better condition or face less environmental challenges, have a relatively lower need for wind resistance optimization and can therefore be placed at the front of the convoy to assume a leading role. Through this differentiated position allocation, the overall energy consumption and loss level of the entire convoy are systematically optimized, realizing a transformation from individual risk control to overall benefit improvement.

[0057] Understandably, the average total loss risk score reflects the overall loss level of the train crew, while the average deviation reflects individual risk differences. The ratio of the average deviation of the risk score to the average risk score (i.e., the loss fluctuation) quantifies the risk consistency of the train crew: the larger the ratio, the more significant the difference between high and low risk freight cars within the train crew, and the weaker the compatibility of driving conditions; the smaller the ratio, the more uniform the risk distribution within the train crew and the stronger the compatibility of driving conditions.

[0058] Therefore, for train sets with large fluctuations in wear and tear, the minimum speed limit to the minimum economic speed range is selected for operation. By driving at low speeds and maintaining stability, the additional wear and tear on high-risk trucks is reduced, and the instability of the platoon caused by speed differences is avoided. For train sets with small fluctuations in wear and tear, the maximum economic speed is adopted to give full play to the advantage of the consistency of the train set status. This improves transportation efficiency without increasing wear and tear, and achieves the synergistic goal of matching risk with speed and efficiency with status.

[0059] In practice, the preset ratio is usually set between 0.15 and 0.25. When the ratio is lower than the preset ratio, the risk difference of freight cars in the platoon is within a controllable range and can be adapted to efficient driving. When the ratio is higher than the preset ratio, the risk difference exceeds the collaborative compatibility and needs to be balanced by low-speed steady driving. The preset ratio avoids both the situation where the ratio is too strict and the situation where most platoons are forced to drive at low speeds, and the situation where the ratio is too loose and the real risk difference cannot be distinguished. Preferably, it is set to 0.2.

[0060] In practice, the economical speed range for heavy-duty freight trucks is 60 km / h to 80 km / h, with the minimum economical speed being 60 km / h and the maximum economical speed being 80 km / h. Based on the power characteristics and aerodynamic principles of freight trucks, it is known that: above 60 km / h, engine thermal efficiency enters the high-efficiency range, and fuel consumption tends to stabilize; below 80 km / h, air resistance does not increase exponentially, and losses are controllable; 60 km / h as the minimum economical speed can balance losses and driving stability in vehicle groups with large fluctuations; 80 km / h as the maximum economical speed can maximize efficiency in vehicle groups with small fluctuations, balancing safety and practicality.

[0061] It should be understood that when determining a uniform driving speed, the speed limit range of each road segment should also be considered. Generally:

[0062] If the minimum speed limit is higher than the minimum economic speed, then the uniform driving speed when the wear and tear is large shall be the minimum speed limit.

[0063] If the maximum speed limit is lower than the maximum economic speed, then the uniform driving speed when the wear and tear is large shall be the maximum speed limit.

[0064] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0065] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A distributed truck logistics collaborative scheduling system based on edge computing, characterized in that, include: The edge acquisition module is used to collect and store real-time location data, real-time loss data, remaining route data, weather data and road condition data for the remaining distance of each truck. The real-time loss data includes real-time fuel consumption data and truck load data. The remaining route data includes the remaining driving segment, estimated driving time and timeliness requirements. The edge assessment module is used to integrate the weather data, road condition data, real-time fuel consumption data and truck load data to generate a power consumption assessment dataset. Based on the loss assessment dataset, it determines the loss risk level of each truck for the subsequent journey, and determines whether the corresponding truck needs to start loss reduction scheduling based on the loss risk level. The cloud analysis module is used to identify the freight cars to be dispatched with reduced losses and obtain the remaining route data of each freight car to be dispatched. Based on the remaining route data, several overlapping road segments are filtered to determine whether they are overlapping routes based on the length of each overlapping road segment and to determine the overlapping train groups corresponding to each overlapping route. The cloud-based platooning module is used to perform constraint checks on the overlapping train groups on the route based on the real-time positioning data of each freight car to be dispatched, and to determine the train groups that meet the platooning conditions based on the constraint check results. The cloud-based collaboration module is used to determine the platooning order and uniform driving speed based on the total loss risk score of each freight car to be dispatched in the platoon.

2. The distributed truck logistics collaborative scheduling system based on edge computing according to claim 1, characterized in that, The edge assessment module compares each assessment factor in the power consumption assessment dataset with the corresponding preset scoring threshold to obtain the score of each factor, and calculates the total loss risk score by weighted summation according to preset weights to determine the loss risk level.

3. The distributed truck logistics collaborative scheduling system based on edge computing according to claim 2, characterized in that, Based on the determination that the loss risk level is high, the edge assessment module determines that the corresponding truck needs to be dispatched to reduce losses and records the truck as a truck to be dispatched.

4. The distributed truck logistics collaborative scheduling system based on edge computing according to claim 1, characterized in that, The cloud-based analysis module determines several overlapping road segments based on the remaining path data, and determines whether the corresponding overlapping road segments are overlapping routes based on the comparison between the distance length of each overlapping road segment and a preset length, wherein: Based on the determination result that the length of the overlapping road segment is greater than or equal to the preset length, the corresponding overlapping road segment is determined to be an overlapping route; Conversely, determine that the corresponding overlapping road segments are not overlapping routes.

5. The distributed truck logistics collaborative scheduling system based on edge computing according to claim 1, characterized in that, The cloud analysis module determines all the trucks to be dispatched for any overlapping route based on the remaining route data of each truck to be dispatched and records them as the overlapping route vehicle group. The cloud formation module performs constraint checks on the overlapping route vehicle group based on the real-time positioning data of each truck to be dispatched in a single overlapping route vehicle group and the corresponding overlapping route starting point, and determines the formation vehicle group that meets the formation conditions based on the constraint check results.

6. The distributed truck logistics collaborative scheduling system based on edge computing according to claim 5, characterized in that, The cloud-based formation module performs the following operations during constraint checks: The driving distance between the truck to be dispatched and the starting point of the overlapping route is determined based on the real-time positioning data of each truck to be dispatched, and the corresponding truck to be dispatched is determined to meet the platooning conditions based on the driving distance. Furthermore, the cloud-based platooning module identifies all freight cars that meet the platooning conditions within a single overlapping path as platooning groups.

7. The distributed truck logistics collaborative scheduling system based on edge computing according to claim 6, characterized in that, The cloud-based platooning module determines that the corresponding freight car to be dispatched meets the platooning conditions based on the determination result that the driving distance is less than or equal to the preset distance; Conversely, it is determined that the corresponding freight car to be dispatched does not meet the platooning requirements.

8. The distributed truck logistics collaborative scheduling system based on edge computing according to claim 1, characterized in that, The cloud-based collaborative module determines the driving order of the convoy based on the total loss risk score of each freight car to be dispatched in a single convoy. The total loss risk score is positively correlated with the driving sequence.

9. The distributed truck logistics collaborative scheduling system based on edge computing according to claim 1, characterized in that, The cloud-based collaboration module calculates the average risk score and the average risk score deviation based on the total loss risk score of each freight car to be dispatched in a single platoon, and determines the loss fluctuation of each freight car to be dispatched in the corresponding platoon based on the ratio of the average risk score deviation to the average risk score, wherein: The loss fluctuation is determined to be large based on the judgment result that the ratio of the average deviation of the risk score to the average value of the risk score is greater than a preset ratio; The loss fluctuation is determined to be small based on the determination result that the ratio of the average deviation of the risk score to the average value of the risk score is less than or equal to a preset ratio.

10. The distributed truck logistics collaborative scheduling system based on edge computing according to claim 9, characterized in that, The cloud-based collaborative module determines the uniform driving speed of each convoy vehicle group based on the wear fluctuation, including: Based on the judgment of large fluctuations in wear and tear, the range of values ​​for a uniform driving speed is determined as the minimum economic speed. Based on the determination of small fluctuations in wear and tear, the range of values ​​for a uniform driving speed is determined to be the maximum economic speed.

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