A logistics transportation scheme determination method and system

Abstract

This invention relates to the field of logistics management and provides a method and system for determining logistics transportation plans. The method includes the following steps: real-time collection of location data and ambient temperature data of refrigerated transport vehicles; incorporating meteorological data and predicting temperature changes in the areas refrigerated transport vehicles pass through to reach their destination to obtain a path temperature zone map; dynamically generating start / stop commands for refrigeration equipment and target temperature setpoints based on cargo preservation thresholds and ambient temperature data; planning cargo handover points according to the path temperature zone map and generating vehicle scheduling sequences based on real-time road conditions and handover point vehicle data; and calculating vehicle energy consumption, transfer costs, and time delay costs to generate an economic comparison report. This invention not only reduces the overall cost of refrigerated transport but also improves transportation efficiency and resource allocation flexibility, and is particularly suitable for cold chain logistics needs in complex scenarios such as high altitudes and long distances.

Description

Technical Field

[0001] This invention relates to the field of logistics management, specifically to a method and system for determining logistics transportation plans. Background Technology

[0002] Logistics and transportation refer to the activities of transferring goods from the supply location to the receiving location using transportation vehicles or equipment. By changing the spatial state of goods, the physical flow from production to consumption is realized.

[0003] Taking land logistics transportation as an example, it includes two modes: automobile and train. Among them, automobile transportation is the most common mode of transportation. However, with the continuous increase in demand, automobile transportation alone has also spawned several branches. For example, for some goods that need to be kept fresh, vehicles with refrigeration functions are often required for transportation. The maintenance cost of such vehicles is higher than that of conventional vehicles.

[0004] For long-distance transportation, goods are often transported to their destination in a single trip. Refrigerated trucks are used throughout the entire process for goods that require preservation. This process is more expensive than that of ordinary trucks in terms of both transportation costs and energy consumption. However, in some special circumstances, refrigeration conditions can be provided by the external environment. For example, when refrigerated trucks are transporting goods to high-altitude areas, it is essential to rationally allocate transportation plans to ensure the economic efficiency of the entire transportation process. Therefore, a method and system for determining logistics transportation plans are proposed. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a method and system for determining logistics transportation schemes, so as to solve the problems existing in the above-mentioned background technology.

[0006] This invention is implemented as follows: a method for determining a logistics transportation plan, the method comprising the following steps:

[0007] The location data and ambient temperature data of refrigerated transport vehicles are collected in real time. Meteorological data is introduced and the temperature changes in the areas through which the refrigerated transport vehicles pass to their destination are predicted to obtain a path temperature zone map. The location data includes the location data and altitude data of the refrigerated transport vehicles.

[0008] Based on cargo preservation threshold and ambient temperature data, start and stop commands for refrigeration equipment and target temperature settings are dynamically generated, so that when the ambient temperature is lower than the required temperature of the cargo and the altitude of the refrigerated transport vehicle is higher than the set value, the active refrigeration of the refrigerated transport vehicle is turned off.

[0009] The handover points for goods are planned based on the route temperature zone map, and a vehicle scheduling sequence is generated based on real-time road conditions and vehicle data at the handover points. The handover points are used to transfer goods from refrigerated vehicles to regular vehicles.

[0010] Calculate vehicle energy consumption, transfer costs, and time delay costs to generate an economic comparison report.

[0011] As a further aspect of the present invention: the step of introducing meteorological data and predicting temperature changes in the areas refrigerated transport vehicles pass through to reach their destination to obtain a path temperature map specifically includes:

[0012] Obtain the route information of the refrigerated transport vehicle for this transport through the vehicle management system;

[0013] The terrain distribution data is retrieved from the DEM data based on the route information. The terrain distribution data is the terrain distribution along the refrigerated transport route.

[0014] Generate a path terrain distribution model based on path information and terrain distribution data;

[0015] The acquired meteorological data is applied to the path terrain distribution model to obtain a path temperature zone map, which is used to simulate the temperature distribution along the refrigerated transport route.

[0016] As a further aspect of the present invention, the method further includes:

[0017] Based on DEM data and meteorological data, remote sensing data is introduced to construct a microclimate identification model;

[0018] Based on the microclimate identification model, microclimate characteristic regions in the path are identified, which are areas where the temperature changes abruptly compared to the surrounding areas.

[0019] Receive temperature fluctuation data inside tunnels and bridges via V2X vehicle-to-everything (V2X) network;

[0020] The temperature abrupt change data is compared with the path temperature zone map to correct the temperature zone prediction values ​​of the microclimate characteristic areas in the map.

[0021] As a further aspect of the present invention: the step of dynamically generating start / stop commands for the refrigeration equipment and the target temperature setpoint based on cargo preservation thresholds and ambient temperature data specifically includes:

[0022] Based on the cargo preservation threshold and real-time ambient temperature data, a dynamic temperature response model is constructed, and the start-up and shutdown trigger conditions of the refrigeration equipment are generated by combining the location data.

[0023] The refrigeration start-stop logic is dynamically adjusted by using an altitude-energy consumption mapping table and ambient temperature data. The altitude-energy consumption mapping table is obtained based on the non-linear relationship between the altitude of the refrigerated transport vehicle and the energy consumption of the refrigeration system.

[0024] Set three temperature thresholds to trigger the cooling equipment to shut down or enter low power mode, and use on-board sensors to provide real-time feedback on the cooling status to dynamically adjust the strategy.

[0025] By introducing a meteorological API to obtain the temperature forecast data for the next 24 hours along the route, and combining it with the current operating status of the refrigerated transport vehicles, a dynamic programming algorithm is used to calculate the optimal target temperature setpoint for the refrigeration equipment.

[0026] As a further aspect of the present invention: the step of planning the handover points of goods based on the path temperature zone map and generating a vehicle scheduling sequence based on real-time road conditions and handover point vehicle data specifically includes:

[0027] By combining the route temperature zone map and the cargo temperature resistance threshold, a candidate set of handover points that meet temperature compatibility and have transit capabilities is selected.

[0028] By integrating real-time traffic conditions and refrigerated transport vehicle location data, a multi-objective optimization algorithm is used to sort candidate handover points, and a scheduling sequence is generated based on the vehicle load at the handover points.

[0029] Develop a dynamic time window allocation plan, synchronize the arrival times of refrigerated transport vehicles and regular vehicles, and monitor the handover status through IoT devices;

[0030] Based on historical handover events and real-time traffic warning information, the selection of handover points and vehicle dispatch sequences are adjusted in real time.

[0031] As a further aspect of the present invention: the step of calculating vehicle energy consumption, transfer costs, and time delay costs to generate an economic comparison report specifically includes:

[0032] An energy consumption-cost correlation model is established based on the energy consumption data of refrigerated transport vehicles and the transfer costs at the handover points. A cost prediction model is used to predict the total cost under different handover point selections, and a comparison curve of energy consumption and cost is generated.

[0033] The timeliness of cargo transportation is transformed into a time delay cost function, and the timeliness risk of the scheduling scheme is evaluated by combining traffic congestion prediction and handover point queuing time simulation.

[0034] An economic comparison report is generated by taking into account the comprehensive cooling energy consumption, transfer costs, and time delay costs, and using a multi-attribute decision analysis method.

[0035] Collect actual energy consumption, costs, and time data throughout the transportation process, and dynamically update the cost prediction model through online learning algorithms.

[0036] Another object of the present invention is to provide a logistics transportation scheme determination system, the system comprising:

[0037] The environmental perception module is used to collect the location data and ambient temperature data of the refrigerated transport vehicle in real time, introduce meteorological data, and predict the temperature changes in the areas through which the refrigerated transport vehicle passes to reach its destination to obtain a path temperature zone map. The location data includes the location data and altitude data of the refrigerated transport vehicle.

[0038] The temperature control module is used to dynamically generate start / stop commands for the refrigeration equipment and target temperature setpoints based on the cargo preservation threshold and ambient temperature data, so that the active refrigeration of the refrigeration vehicle is turned off when the ambient temperature is lower than the required temperature of the cargo and the altitude of the refrigerated vehicle is higher than the set value.

[0039] The scheduling optimization module is used to plan the handover points of goods based on the route temperature zone map, and generate a vehicle scheduling sequence based on real-time road conditions and handover point vehicle data. The handover points are used to transfer goods from refrigerated vehicles to ordinary vehicles.

[0040] The dynamic evaluation module is used to calculate vehicle energy consumption, transfer costs, and time delay costs to generate an economic comparison report.

[0041] As a further aspect of the present invention: the environment sensing module includes:

[0042] The vehicle information retrieval unit is used to obtain the route information of the refrigerated transport vehicle for this transport through the vehicle management system;

[0043] The terrain data extraction unit is used to retrieve terrain distribution data from DEM data based on path information. The terrain distribution data is the terrain distribution along the refrigerated transport route.

[0044] Data construction unit, used to generate a path terrain distribution model based on path information and terrain distribution data;

[0045] The path temperature zone generation unit is used to apply the acquired meteorological data to the path terrain distribution model to obtain a path temperature zone map, which is used to simulate the temperature distribution on the refrigerated transport route.

[0046] As a further aspect of the present invention: the temperature control module includes:

[0047] The response model building unit is used to build a dynamic temperature response model based on the freshness threshold of goods and real-time ambient temperature data, and to generate the start-stop trigger conditions of refrigeration equipment by combining the location data.

[0048] The cold start-stop control unit is used to dynamically adjust the refrigeration start-stop logic through an altitude-energy consumption mapping table and ambient temperature data. The altitude-energy consumption mapping table is obtained based on the non-linear relationship between the altitude of the refrigerated transport vehicle and the energy consumption of the refrigeration system.

[0049] The multi-level control unit is used to set three temperature thresholds, trigger the cooling equipment to shut down or enter low-power mode, and dynamically adjust the strategy by providing real-time feedback on the cooling status through on-board sensors.

[0050] The temperature dynamic adjustment unit is used to obtain the temperature forecast data for the next 24 hours in the path by introducing meteorological API, and combine it with the current operating status of the refrigerated transport vehicle to calculate the optimal target temperature setpoint of the refrigeration equipment using dynamic programming algorithm.

[0051] As a further aspect of the present invention: the scheduling optimization module includes:

[0052] The handover point candidate unit is used to combine the route temperature zone map and the cargo temperature resistance threshold to screen the candidate set of handover points that meet temperature compatibility and have transit capabilities.

[0053] The scheduling sequence generation unit integrates real-time road conditions and refrigerated transport vehicle location data, uses a multi-objective optimization algorithm to sort candidate handover points, and generates a scheduling sequence based on the vehicle load at the handover points.

[0054] The dynamic time control unit is used to formulate a dynamic time window allocation scheme, synchronize the arrival time of refrigerated transport vehicles and ordinary vehicles, and monitor the handover status through IoT devices;

[0055] The feedback adjustment unit is used to adjust the handover point selection and vehicle dispatch sequence in real time based on historical handover events and real-time traffic warning information.

[0056] Compared with the prior art, the beneficial effects of the present invention are:

[0057] This invention significantly improves the economic efficiency and resource utilization of refrigerated transportation by integrating real-time environmental data, weather forecasts, and intelligent decision-making algorithms. First, based on the location data of refrigerated vehicles and ambient temperature data, a route temperature zone map is generated using weather forecasts to accurately identify low-temperature areas during transportation, providing a scientific basis for dynamically adjusting refrigeration equipment. Second, by setting a refrigeration start-stop strategy linked to cargo preservation thresholds and altitude, unnecessary energy consumption is effectively reduced, lowering the operating costs of refrigerated vehicles. Third, based on the handover points planned using the temperature zone map and a vehicle scheduling sequence driven by real-time road conditions, collaborative transportation between refrigerated and regular vehicles is achieved, minimizing the usage time and coverage area of ​​refrigerated vehicles. Finally, by quantitatively calculating refrigeration energy consumption, transshipment costs, and time delay costs and generating an economic comparison report, data-driven decision support is provided for optimizing transportation plans. This solution not only reduces the overall cost of refrigerated transportation but also improves transportation efficiency and resource allocation flexibility through dynamic adaptation to environmental conditions and intelligent scheduling strategies. It is particularly suitable for cold chain logistics needs in complex scenarios such as high altitudes and long distances, demonstrating significant economic benefits and sustainable development value. Attached Figure Description

[0058] Figure 1 A flowchart illustrating a method for determining a logistics transportation plan.

[0059] Figure 2 This is a flowchart of a method for determining a logistics transportation plan to obtain a path temperature zone map.

[0060] Figure 3 This is a flowchart illustrating the generation of start / stop commands for refrigeration equipment and target temperature setpoints in a method for determining a logistics transportation plan.

[0061] Figure 4 This is a flowchart illustrating the generation of vehicle scheduling sequences in a method for determining a logistics transportation plan.

[0062] Figure 5 This is a flowchart for generating an economic comparison report in a method for determining a logistics transportation plan.

[0063] Figure 6 A schematic diagram of the system structure for determining a logistics transportation scheme.

[0064] Figure 7 This is a schematic diagram of the environmental perception module in a logistics transportation scheme determination system.

[0065] Figure 8 This is a schematic diagram of the temperature control module in a logistics transportation scheme determination system.

[0066] Figure 9 This is a schematic diagram of the scheduling optimization module in a logistics transportation scheme determination system. Detailed Implementation

[0067] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0068] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0069] like Figure 1 As shown in the figure, this embodiment of the invention provides a method for determining a logistics transportation plan, the method comprising the following steps:

[0070] S100: Real-time acquisition of location data and ambient temperature data of refrigerated transport vehicles; introduction of meteorological data; and prediction of temperature changes in the areas through which refrigerated transport vehicles pass to their destination to obtain a path temperature zone map. The location data includes the location data and altitude data of the refrigerated transport vehicles.

[0071] S200 dynamically generates start / stop commands for refrigeration equipment and target temperature settings based on cargo preservation thresholds and ambient temperature data, so that when the ambient temperature is lower than the required temperature of the cargo and the altitude of the refrigerated transport vehicle is greater than the set value, the active refrigeration of the refrigerated transport vehicle is turned off.

[0072] S300: Based on the route temperature zone map, the handover point of the goods is planned, and a vehicle scheduling sequence is generated based on real-time road conditions and vehicle data at the handover point. The handover point is used to transfer the goods from refrigerated vehicles to ordinary vehicles.

[0073] S400 calculates vehicle energy consumption, transfer costs, and time delay costs to generate an economic comparison report.

[0074] In this embodiment of the invention, by integrating real-time environmental data, meteorological forecasts, and intelligent decision-making algorithms, the economic benefits and resource utilization of refrigerated transportation are significantly improved. First, based on the location data (location, altitude) and ambient temperature data of refrigerated vehicles, a route temperature zone map is generated using meteorological forecasts to accurately identify low-temperature areas (such as high-altitude areas) during transportation, thus providing a scientific basis for dynamically adjusting refrigeration equipment. Second, by setting a refrigeration start-stop strategy linked to cargo preservation thresholds and altitude (e.g., shutting off active refrigeration when the ambient temperature is lower than demand and the altitude is higher than a preset value), unnecessary energy consumption is effectively reduced, lowering the operating costs of refrigerated vehicles. Third, based on the handover points planned using the temperature zone map and a vehicle scheduling sequence driven by real-time road conditions, collaborative transportation between refrigerated and ordinary vehicles is achieved (e.g., transferring goods to ordinary vehicles in low-temperature sections), minimizing the usage time and coverage area of ​​refrigerated vehicles. Finally, by quantitatively calculating refrigeration energy consumption, transfer costs, and time delay costs and generating an economic comparison report, data-driven decision support is provided for optimizing transportation plans. This solution not only reduces the overall cost of refrigerated transportation, but also improves transportation efficiency and resource allocation flexibility by dynamically adapting to environmental conditions and using intelligent scheduling strategies. It is particularly suitable for cold chain logistics needs in complex scenarios such as high altitude and long distances, and has significant economic benefits and sustainable development value.

[0075] like Figure 2 As shown, in a preferred embodiment of the present invention, the step of introducing meteorological data and predicting temperature changes in the areas refrigerated transport vehicles pass through to reach their destination to obtain a path temperature map specifically includes:

[0076] S101, obtain the route information of the refrigerated transport vehicle for this transport through the vehicle management system;

[0077] S102, retrieve terrain distribution data from DEM data based on route information, wherein the terrain distribution data is the terrain distribution along the refrigerated transport route;

[0078] S103, Generate a path terrain distribution model based on path information and terrain distribution data;

[0079] S104, The acquired meteorological data is applied to the path terrain distribution model to obtain the path temperature zone map, which is used to simulate the temperature distribution on the refrigerated transport route.

[0080] S105, based on DEM data and meteorological data, introduces remote sensing data to construct a microclimate identification model;

[0081] S106, Based on the microclimate identification model, identify the microclimate characteristic areas in the path, where the microclimate characteristic areas are areas where the temperature changes abruptly compared to the surrounding areas;

[0082] S107 receives temperature fluctuation data inside tunnels and bridges via V2X vehicle-to-everything (V2X) network;

[0083] S108, compare the temperature abrupt change data with the path temperature zone map to correct the temperature zone prediction values ​​of the microclimate characteristic areas in the map.

[0084] In this embodiment of the invention, transportation route information is first obtained based on the vehicle management system, and a route terrain distribution model is generated by combining terrain data from the DEM data. This model can quantitatively analyze the impact of altitude changes, slope undulations, etc., on temperature distribution (for example, high-altitude areas are usually colder, while valleys or basins may form local warming zones due to terrain closure). Subsequently, meteorological data (such as historical temperature, wind speed, and precipitation) are superimposed on the terrain model to generate a route temperature zone map, simulating the temperature gradient distribution of various areas along the transportation route (for example, the temperature in the plain section is stable at 20°C, while it may drop to -5°C in high-altitude mountainous areas). Based on this, remote sensing data is further introduced to construct a microclimate identification model, accurately capturing areas of local abrupt change (such as local high-temperature zones in river valleys, low-temperature zones or high-temperature zones formed in tunnels due to limited ventilation), and receiving real-time temperature abrupt change data in enclosed scenarios such as tunnels and bridges through V2X vehicle networking (for example, a sudden temperature rise in a tunnel due to differences in geological structure). By comparing the actual monitoring data with the temperature zone map, the microclimate region parameters in the prediction model are dynamically corrected. For example, when transporting perishable goods (such as vaccines), the system can identify low-temperature zones in mountainous areas caused by terrain obstruction in advance and automatically activate auxiliary heating before the vehicle enters the area. Simultaneously, in response to sudden high-temperature warnings within tunnels, the system can coordinate the dispatch of insulated containers or adjust vehicle speed to maintain cold chain integrity. This method not only overcomes the limitations of traditional single weather forecasts in covering the influence of terrain and microclimate, but also improves the dynamic adaptability of forecasts through real-time data closed-loop correction, ultimately achieving comprehensive benefits such as energy consumption optimization, improved transportation efficiency, and reduced cargo loss rates. It is particularly suitable for logistics scenarios involving high altitudes, complex terrain, and frequent extreme weather events.

[0085] like Figure 3 As shown in the preferred embodiment of the present invention, the step of dynamically generating refrigeration equipment start / stop commands and target temperature setpoints based on cargo preservation thresholds and ambient temperature data specifically includes:

[0086] S201, based on the cargo preservation threshold and real-time ambient temperature data, constructs a dynamic temperature response model, and combines positioning data to generate start-stop trigger conditions for refrigeration equipment;

[0087] S202, dynamically adjust the refrigeration start-stop logic through an altitude-energy consumption mapping table and ambient temperature data. The altitude-energy consumption mapping table is obtained based on the nonlinear relationship between the altitude of the refrigerated transport vehicle and the energy consumption of the refrigeration system.

[0088] S203 sets three temperature thresholds, triggers the cooling equipment to shut down or enter low power mode, and dynamically corrects the strategy by providing real-time feedback on the cooling status through on-board sensors.

[0089] S204 uses a dynamic programming algorithm to calculate the optimal target temperature setpoint for the refrigeration equipment by introducing a meteorological API to obtain the temperature forecast data for the next 24 hours along the route and combining it with the current operating status of the refrigerated transport vehicles.

[0090] In this embodiment of the invention, the energy efficiency optimization and cargo preservation capabilities of the refrigeration system in cold chain transportation are significantly improved through the integration of multi-dimensional dynamic control and intelligent algorithms. First, a dynamic temperature response model constructed based on cargo preservation thresholds and real-time ambient temperature data, combined with vehicle positioning data, can accurately generate start-stop trigger conditions for refrigeration equipment. For example, when a refrigerated transport vehicle enters a high-altitude area (such as the Qinghai-Tibet Plateau at altitudes above 4000 meters), the system identifies that the ambient temperature in this area is naturally lower than the required temperature for the cargo through an altitude-energy consumption mapping table, automatically shutting down active refrigeration and switching to a low-power maintenance mode, thereby avoiding redundant energy consumption. Second, a three-level temperature threshold mechanism (such as a safe zone, a warning zone, and a danger zone) achieves layered response and can also link with the feedback status of onboard sensors to dynamically correct strategies, ensuring that the cargo is always within a safe range. Furthermore, after introducing meteorological APIs to obtain 24-hour path temperature prediction data, the system combines the vehicle's current operating status with a dynamic programming algorithm to calculate the optimal target temperature setpoint. For example, if extreme high temperatures (e.g., 40℃) are predicted for a certain section of road in the afternoon, the system can adjust the target temperature setpoint from 5℃ to 3℃ in advance and increase the refrigeration power reserve accordingly, keeping temperature fluctuations within ±0.5℃ and effectively responding to sudden environmental changes. This solution, through real-time data closed-loop and forward-looking algorithm optimization, not only reduces the average energy consumption of refrigerated transport vehicles but also significantly improves the ability to control cargo loss rates. Furthermore, by dynamically adjusting the refrigeration strategy, it reduces mechanical losses caused by frequent compressor start-ups and shutdowns, ultimately achieving a triple improvement in economic efficiency, resource utilization, and cargo safety.

[0091] like Figure 4 As shown in the preferred embodiment of the present invention, the step of planning the handover points of goods based on the path temperature zone map and generating a vehicle scheduling sequence based on real-time traffic conditions and handover point vehicle data specifically includes:

[0092] S301, combining the route temperature zone map and cargo temperature resistance threshold, screen the candidate set of handover points that meet temperature compatibility and have transit capabilities;

[0093] S302 integrates real-time road conditions and refrigerated transport vehicle location data, uses a multi-objective optimization algorithm to sort candidate handover points, and generates a scheduling sequence based on the vehicle load at the handover points;

[0094] S303, formulate a dynamic time window allocation scheme, synchronize the arrival time of refrigerated transport vehicles and ordinary vehicles, and monitor the handover status through IoT devices;

[0095] S304 adjusts the selection of handover points and vehicle dispatch sequences in real time based on historical handover events and real-time traffic warning information.

[0096] In this embodiment of the invention, firstly, by combining the path temperature zone map (e.g., high-altitude low-temperature zone, urban heat island high-temperature zone) with the cargo temperature tolerance threshold (e.g., fresh produce requires 0-4℃), the system filters out a candidate set of handover points that meet both temperature compatibility (e.g., a high degree of matching between ambient temperature and cargo requirements, and also possess transshipment capabilities). For example, when transporting fresh produce on the Qinghai-Tibet Plateau, the system prioritizes handover points in natural low-temperature zones above 4000 meters (ambient temperature -5℃) to avoid refrigerated vehicles operating the entire route, thereby reducing energy consumption (mainly reducing the usage time of refrigerated vehicles). Secondly, by integrating real-time road conditions (e.g., sudden congestion on a certain road segment) with refrigerated vehicle location data, a multi-objective optimization algorithm (e.g., NSGA-II non-dominated sorting genetic algorithm) is used to perform multi-dimensional sorting of the candidate handover points, comprehensively considering distance, temperature adaptability, vehicle load, and transshipment costs to generate the optimal scheduling sequence. A dynamic time window allocation scheme is developed (e.g., refrigerated transport vehicles need to arrive at the handover point between 15:00 and 16:30, while regular vehicles are simultaneously scheduled to pick up between 16:00 and 17:00). The handover status is monitored in real time via IoT devices (such as vehicle-mounted GPS). If a refrigerated transport vehicle is detected to be 15 minutes late, the system automatically triggers an alert and adjusts the waiting strategy for subsequent vehicles (e.g., switching to an alternate handover point). Furthermore, based on historical handover events and real-time traffic alert information, the system dynamically adjusts the selection and scheduling sequence of handover points through online learning algorithms (such as reinforcement learning). In summary, by accurately matching temperature environments and transit resources, the overall operating costs of refrigerated transport vehicles are reduced, ultimately achieving a synergistic optimization of economic benefits and cargo safety.

[0097] like Figure 5As shown in the preferred embodiment of the present invention, the step of calculating vehicle energy consumption, transfer costs, and time delay costs to generate an economic comparison report specifically includes:

[0098] S401, based on the energy consumption data of refrigerated vehicles and the transfer costs at the handover points, establish an energy consumption-cost correlation model, use a cost prediction model to predict the total cost under different handover point selections, and generate a comparison curve of energy consumption and cost.

[0099] S402 transforms the timeliness of cargo transportation into a time delay cost function, and combines traffic congestion prediction and handover point queuing time simulation to evaluate the timeliness risk of scheduling schemes.

[0100] S403, taking into account the comprehensive cooling energy consumption, transfer costs and time delay costs, uses a multi-attribute decision analysis method to generate an economic comparison report;

[0101] S404 collects actual energy consumption, cost, and time data throughout the transportation process and dynamically updates the cost prediction model through online learning algorithms.

[0102] In this embodiment of the invention, a closed-loop system for economic evaluation and optimization in cold chain logistics is constructed through multi-dimensional cost modeling and dynamic learning mechanisms, significantly improving the scientific nature of decision-making and the efficiency of resource utilization. First, an energy consumption-cost correlation model is established based on refrigerated vehicle energy consumption data and transfer costs at handover points. The total cost under different handover point selections is quantified through a cost prediction model, and a comparison curve of energy consumption and costs is generated (e.g., energy consumption shows a non-linear upward trend with increasing transfer frequency). Second, the timeliness of cargo transportation is transformed into a time delay cost function, and the timeliness risk of scheduling schemes is evaluated by combining traffic congestion prediction and handover point queuing time simulation. A comprehensive economic comparison report is generated using a multi-attribute decision analysis method (e.g., TOPSIS) that integrates refrigeration energy consumption, transfer costs, and time delay costs (e.g., customer penalties for delays), quantifying the comprehensive score of each scheme. Finally, by collecting actual energy consumption, transfer costs, and time data throughout the transportation process, an online learning algorithm (e.g., incremental random forest) is used to dynamically update the cost prediction model. For example, when historical data shows that the loading and unloading efficiency of a certain handover point has improved due to the addition of cold storage facilities, the model automatically adjusts its cost weight to make the subsequent scheduling plan more realistic.

[0103] like Figure 6 As shown in the figure, this embodiment of the invention also provides a logistics transportation scheme determination system, the system comprising:

[0104] The environmental perception module 100 is used to collect the location data and ambient temperature data of the refrigerated transport vehicle in real time, introduce meteorological data, and predict the temperature changes in the areas through which the refrigerated transport vehicle passes to reach its destination to obtain a path temperature zone map. The location data includes the location data and altitude data of the refrigerated transport vehicle.

[0105] The temperature control module 200 is used to dynamically generate start / stop commands and target temperature settings for refrigeration equipment based on cargo preservation threshold and ambient temperature data, so that the active refrigeration of the refrigeration vehicle is turned off when the ambient temperature is lower than the required temperature of the cargo and the altitude of the refrigerated vehicle is greater than the set value.

[0106] The scheduling optimization module 300 is used to plan the handover points of goods according to the route temperature zone map, and generate a vehicle scheduling sequence based on real-time road conditions and handover point vehicle data. The handover points are used to transfer goods from refrigerated vehicles to ordinary vehicles.

[0107] The dynamic evaluation module 400 is used to calculate vehicle energy consumption, transfer costs, and time delay costs to generate an economic comparison report.

[0108] In this embodiment of the invention, the system achieves intelligent and economic optimization of cold chain logistics through multi-module collaboration. The environmental perception module collects vehicle location and ambient temperature data in real time, and generates a route temperature zone map based on meteorological forecasts, providing a precise environmental benchmark for subsequent decision-making. The temperature control module dynamically adjusts the start and stop of refrigeration equipment based on cargo preservation thresholds and environmental data; for example, it automatically shuts off refrigeration to reduce energy consumption when the altitude is above 4000 meters and the ambient temperature is lower than the cargo's requirements. The scheduling optimization module uses the route temperature zone map to screen temperature-compatible junctions and generates efficient scheduling sequences based on real-time road conditions and vehicle load. Through dynamic time window allocation and IoT monitoring, the on-time delivery rate is improved. The dynamic evaluation module quantifies the economic differences of different solutions through an energy consumption-cost correlation model and multi-attribute decision analysis, and continuously optimizes the accuracy of cost prediction through online learning.

[0109] like Figure 7 As shown, in a preferred embodiment of the present invention, the environment sensing module 100 includes:

[0110] The vehicle information retrieval unit 101 is used to obtain the route information of the refrigerated transport vehicle for this transport through the vehicle management system.

[0111] The terrain data extraction unit 102 is used to retrieve terrain distribution data from DEM data based on path information. The terrain distribution data is the terrain distribution along the refrigerated transport route.

[0112] Data construction unit 103 is used to generate a path terrain distribution model based on path information and terrain distribution data;

[0113] The path temperature zone generation unit 104 is used to apply the acquired meteorological data to the path terrain distribution model to obtain a path temperature zone map, which is used to simulate the temperature distribution on the refrigerated transport route.

[0114] like Figure 8As shown, in a preferred embodiment of the present invention, the temperature control module 200 includes:

[0115] The response model construction unit 201 is used to construct a dynamic temperature response model based on the cargo preservation threshold and real-time ambient temperature data, and to generate start-stop trigger conditions for refrigeration equipment by combining positioning data.

[0116] The cold start-stop control unit 202 is used to dynamically adjust the refrigeration start-stop logic through an altitude-energy consumption mapping table and ambient temperature data. The altitude-energy consumption mapping table is obtained based on the nonlinear relationship between the altitude of the refrigerated transport vehicle and the energy consumption of the refrigeration system.

[0117] The multi-level control unit 203 is used to set three temperature thresholds, trigger the cooling equipment to shut down or enter a low-power mode, and dynamically correct the strategy by providing real-time feedback on the cooling status through on-board sensors.

[0118] The temperature dynamic adjustment unit 204 is used to obtain the temperature forecast data for the next 24 hours in the path by introducing the meteorological API, and combine it with the current operating status of the refrigerated transport vehicle to calculate the optimal target temperature setpoint of the refrigeration equipment using a dynamic programming algorithm.

[0119] like Figure 9 As shown, in a preferred embodiment of the present invention, the scheduling optimization module 300 includes:

[0120] The handover point candidate unit 301 is used to combine the path temperature zone map and the cargo temperature resistance threshold to screen the handover point candidate set that meets temperature compatibility and has transit capability.

[0121] The scheduling sequence generation unit 302 is used to integrate real-time road conditions and cold chain vehicle location data, sort candidate handover points using a multi-objective optimization algorithm, and generate a scheduling sequence based on the vehicle load of the handover points.

[0122] The dynamic time control unit 303 is used to formulate a dynamic time window allocation scheme, synchronize the arrival time of refrigerated transport vehicles and ordinary vehicles, and monitor the handover status through IoT devices.

[0123] The feedback adjustment unit 304 is used to adjust the handover point selection and vehicle dispatch sequence in real time based on historical handover events and real-time traffic warning information.

[0124] The above description only details the preferred embodiments of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0125] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0126] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0127] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the disclosure in the specification and embodiments. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

Claims

1. A logistics transportation plan determination method characterized by comprising: The method includes the following steps: The location data and ambient temperature data of refrigerated transport vehicles are collected in real time. Meteorological data is introduced and the temperature changes in the areas through which the refrigerated transport vehicles arrive at their destination are predicted to obtain a path temperature zone map. The location data includes the location data and altitude data of the refrigerated transport vehicles. Based on cargo preservation threshold and ambient temperature data, start and stop commands for refrigeration equipment and target temperature settings are dynamically generated, so that when the ambient temperature is lower than the required temperature of the cargo and the altitude of the refrigerated transport vehicle is higher than the set value, the active refrigeration of the refrigerated transport vehicle is turned off. The handover points for goods are planned based on the route temperature zone map, and a vehicle scheduling sequence is generated based on real-time road conditions and vehicle data at the handover points. The handover points are used to transfer goods from refrigerated vehicles to regular vehicles. Calculate vehicle energy consumption, transfer costs, and time delay costs to generate an economic comparison report; The step of introducing meteorological data and predicting temperature changes in the areas along the route of refrigerated transport vehicles to their destination to obtain a route temperature map specifically includes: Obtain the route information of the refrigerated transport vehicle for this transport through the vehicle management system; The terrain distribution data is retrieved from the DEM data based on the route information. The terrain distribution data is the terrain distribution along the refrigerated transport route. Generate a path terrain distribution model based on path information and terrain distribution data; The acquired meteorological data is applied to the path terrain distribution model to obtain a path temperature zone map, which is used to simulate the temperature distribution along the refrigerated transport route. The step of dynamically generating refrigeration equipment start / stop commands and target temperature setpoints based on cargo preservation thresholds and ambient temperature data specifically includes: Based on the cargo preservation threshold and real-time ambient temperature data, a dynamic temperature response model is constructed, and the start-up and shutdown trigger conditions of the refrigeration equipment are generated by combining the location data. The refrigeration start-stop logic is dynamically adjusted by using an altitude-energy consumption mapping table and ambient temperature data. The altitude-energy consumption mapping table is obtained based on the non-linear relationship between the altitude of the refrigerated transport vehicle and the energy consumption of the refrigeration system. Set three temperature thresholds to trigger the cooling equipment to shut down or enter low power mode, and use on-board sensors to provide real-time feedback on the cooling status to dynamically adjust the strategy. By introducing a meteorological API to obtain the temperature forecast data for the next 24 hours along the route, and combining it with the current operating status of the refrigerated transport vehicles, a dynamic programming algorithm is used to calculate the optimal target temperature setpoint for the refrigeration equipment.

2. The logistics transportation scheme determination method according to claim 1, characterized in that, The method further includes: Based on DEM data and meteorological data, remote sensing data is introduced to construct a microclimate identification model; Based on the microclimate identification model, microclimate characteristic regions in the path are identified, which are areas where the temperature changes abruptly compared to the surrounding areas. Receive temperature fluctuation data inside tunnels and bridges via V2X vehicle-to-everything (V2X) network; The temperature abrupt change data is compared with the path temperature map to correct the temperature prediction values ​​of the microclimate characteristic areas in the map.

3. The logistics transportation scheme determination method according to claim 1, characterized in that, The steps of planning cargo handover points based on the path temperature zone map and generating vehicle scheduling sequences based on real-time road conditions and handover point vehicle data specifically include: By combining the route temperature zone map and the cargo temperature resistance threshold, a candidate set of handover points that meet temperature compatibility and have transit capabilities is selected. By integrating real-time traffic conditions and refrigerated transport vehicle location data, a multi-objective optimization algorithm is used to sort candidate handover points, and a scheduling sequence is generated based on the vehicle load at the handover points. Develop a dynamic time window allocation plan, synchronize the arrival times of refrigerated transport vehicles and regular vehicles, and monitor the handover status through IoT devices; Based on historical handover events and real-time traffic warning information, the selection of handover points and vehicle dispatch sequences are adjusted in real time.

4. The method for determining a logistics transportation plan according to claim 1, characterized in that, The steps for calculating vehicle energy consumption, transfer costs, and time delay costs to generate an economic comparison report specifically include: An energy consumption-cost correlation model is established based on the energy consumption data of refrigerated transport vehicles and the transfer costs at the handover points. The total cost under different handover point selections is predicted using a cost prediction model, and a comparison curve of energy consumption and cost is generated. The timeliness of cargo transportation is transformed into a time delay cost function, and the timeliness risk of the scheduling scheme is evaluated by combining traffic congestion prediction and handover point queuing time simulation. An economic comparison report is generated by taking into account the comprehensive cooling energy consumption, transfer costs, and time delay costs, and using a multi-attribute decision analysis method. Collect actual energy consumption, costs, and time data throughout the transportation process, and dynamically update the cost prediction model through online learning algorithms.

5. A logistics transportation scheme determination system, characterized in that, The system includes: The environmental perception module is used to collect real-time location data and ambient temperature data of refrigerated transport vehicles, incorporate meteorological data, and predict temperature changes in the areas through which the refrigerated transport vehicles will reach their destination to obtain a path temperature zone map. The location data includes the location data and altitude data of the refrigerated transport vehicles. The temperature control module is used to dynamically generate start / stop commands for the refrigeration equipment and target temperature setpoints based on the cargo preservation threshold and ambient temperature data, so that the active refrigeration of the refrigeration vehicle is turned off when the ambient temperature is lower than the required temperature of the cargo and the altitude of the refrigerated vehicle is higher than the set value. The scheduling optimization module is used to plan the handover points of goods based on the route temperature zone map, and generate a vehicle scheduling sequence based on real-time road conditions and handover point vehicle data. The handover points are used to transfer goods from refrigerated vehicles to ordinary vehicles. The dynamic evaluation module is used to calculate vehicle energy consumption, transfer costs, and time delay costs to generate an economic comparison report. The environment sensing module includes: The vehicle information retrieval unit is used to obtain the route information of the refrigerated transport vehicle for this transport through the vehicle management system; The terrain data extraction unit is used to retrieve terrain distribution data from DEM data based on path information. The terrain distribution data is the terrain distribution along the refrigerated transport route. Data construction unit, used to generate a path terrain distribution model based on path information and terrain distribution data; The path temperature zone generation unit is used to apply the acquired meteorological data to the path terrain distribution model to obtain a path temperature zone map, which is used to simulate the temperature distribution on the refrigerated transport route. The temperature control module includes: The response model building unit is used to build a dynamic temperature response model based on the freshness threshold of goods and real-time ambient temperature data, and to generate the start-stop trigger conditions of refrigeration equipment by combining the location data. The cold start-stop control unit is used to dynamically adjust the refrigeration start-stop logic through an altitude-energy consumption mapping table and ambient temperature data. The altitude-energy consumption mapping table is obtained based on the non-linear relationship between the altitude of the refrigerated transport vehicle and the energy consumption of the refrigeration system. The multi-level control unit is used to set three temperature thresholds, trigger the cooling equipment to shut down or enter low-power mode, and dynamically adjust the strategy by providing real-time feedback on the cooling status through on-board sensors. The temperature dynamic adjustment unit is used to obtain the temperature forecast data for the next 24 hours in the path by introducing meteorological API, and combine it with the current operating status of the refrigerated transport vehicle to calculate the optimal target temperature setpoint of the refrigeration equipment using dynamic programming algorithm.

6. The logistics transportation scheme determination system according to claim 5, characterized in that, The scheduling optimization module includes: The handover point candidate unit is used to combine the route temperature zone map and the cargo temperature resistance threshold to screen the candidate set of handover points that meet temperature compatibility and have transit capabilities. The scheduling sequence generation unit integrates real-time road conditions and refrigerated transport vehicle location data, uses a multi-objective optimization algorithm to sort candidate handover points, and generates a scheduling sequence based on the vehicle load at the handover points. The dynamic time control unit is used to formulate a dynamic time window allocation scheme, synchronize the arrival time of refrigerated transport vehicles and ordinary vehicles, and monitor the handover status through IoT devices; The feedback adjustment unit is used to adjust the handover point selection and vehicle dispatch sequence in real time based on historical handover events and real-time traffic warning information.

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