Intelligent logistics transportation carbon emission real-time tracking system and method

Abstract

The invention relates to the field of intelligent logistics transportation carbon emission real-time tracking, in particular to an intelligent logistics transportation carbon emission real-time tracking system and method. The method comprises the following steps: firstly, dividing a logistics transportation path into path sections, and generating a candidate path section set at the starting point of each path section to obtain candidate path sections; then, on the basis of the obtained traffic data, vehicle parameters and scheduling plans of the candidate path segments, the predicted carbon emission of the candidate path segments is calculated through a path segment carbon emission prediction algorithm; constructing an optimal path set based on the predicted carbon emissions of the candidate path segments; and finally, on the basis of the path segments in the optimal path set, obtaining actual operation data of the vehicle, and on the basis of the actual operation data of the vehicle, calculating the actual carbon emission. The technical problems that carbon emission prediction and optimization lack pertinence, obvious influences of speed change behaviors such as acceleration, deceleration and idling on energy consumption in actual driving are ignored, and comprehensive influences of vehicle load weight, driving speed and traffic conditions cannot be dynamically reflected are solved.

Description

Technical Field

[0001] This invention relates to the field of real-time carbon emission tracking in intelligent logistics transportation, and more particularly to a real-time carbon emission tracking system and method for intelligent logistics transportation. Background Technology

[0002] Logistics and transportation, as a vital component of the global economy, account for a significant proportion of global greenhouse gas emissions, particularly in urban delivery and long-haul freight. Traditional logistics and transportation management primarily focuses on optimizing transportation efficiency, cost, and time, with less consideration given to real-time monitoring and dynamic reduction of carbon emissions. However, as the concepts of green logistics and sustainable development gain wider acceptance, developing an intelligent real-time carbon emission tracking system and methodology for logistics and transportation is crucial. This system should achieve the goal of simultaneously tracking and reducing carbon emissions through real-time data collection, carbon emission prediction, dynamic route optimization, and actual carbon emission tracking.

[0003] However, existing technologies still suffer from high carbon emissions in logistics and transportation, a lack of dynamic optimization methods, complex traffic conditions, a lack of carbon emission guidance in route selection, and insufficient real-time performance in carbon emission data collection and tracking. Summary of the Invention

[0004] This invention provides a real-time carbon emission tracking system and method for intelligent logistics transportation, which addresses the technical problems of lack of specificity in carbon emission prediction and optimization, neglecting the significant impact of acceleration, deceleration, idling and other speed changes on energy consumption during actual driving, and failing to dynamically reflect the combined effects of vehicle load weight, driving speed and traffic conditions.

[0005] The present invention provides a real-time tracking system and method for carbon emissions in intelligent logistics transportation, specifically comprising the following technical solutions: A method for real-time tracking of carbon emissions in intelligent logistics transportation includes the following steps: S1. Divide the logistics transportation route into route segments, and generate a set of candidate route segments at the starting point of each route segment to obtain candidate route segments; S2. Based on the traffic data, vehicle parameters, and scheduling plans of the acquired candidate route segments, calculate the predicted carbon emissions of the candidate route segments using a route segment carbon emission prediction algorithm; and construct the optimal route set based on the predicted carbon emissions of the candidate route segments. S3. Based on the path segments in the optimal path set, obtain the actual vehicle operation data, and calculate the actual carbon emissions based on the actual vehicle operation data.

[0006] Preferably, S2 specifically includes: In the route segment carbon emission prediction algorithm, the predicted average speed of the candidate route segment is calculated based on the speed samples in the traffic data of the acquired candidate route segments.

[0007] Preferably, S2 specifically includes: The relative speed fluctuation coefficient is obtained based on the speed samples in the traffic data of the candidate path segments and the predicted average speed of the candidate path segments.

[0008] Preferably, S2 specifically includes: The comprehensive impact factor is calculated by integrating the vehicle load weight, predicted average speed, and relative speed fluctuation coefficient from the scheduling plans of the acquired candidate route segments with the predicted traffic congestion index from the traffic data of the acquired candidate route segments.

[0009] Preferably, S2 specifically includes: Based on the baseline energy consumption rate and comprehensive influence factor in the vehicle parameters of the acquired candidate path segments, the predicted energy consumption rate is obtained.

[0010] Preferably, S2 specifically includes: Based on the predicted energy consumption rate, the total energy consumption of the candidate path segment is obtained. Then, by introducing the emission factor, the predicted carbon emissions are obtained.

[0011] Preferably, S2 specifically includes: An optimal set of paths is constructed based on the candidate path segments with the lowest predicted carbon emissions.

[0012] A real-time carbon emission tracking system for intelligent logistics transportation includes the following components: The system includes a route segmentation module, a data acquisition module, a carbon emission prediction module, a route segment selection module, a vehicle navigation module, and a carbon emission tracking module. Path segmentation module: Divides the logistics transportation path into path segments, generates a set of candidate path segments at the starting point of each path segment, and outputs the set of candidate path segments to the data acquisition module; Data acquisition module: Based on the candidate path segments in the candidate path segment set, acquire traffic data, vehicle parameters and scheduling plans of the candidate path segments, and output them to the carbon emission prediction module; acquire actual vehicle operation data and output the acquired actual vehicle operation data to the carbon emission tracking module; Carbon emission prediction module: Based on the traffic data, vehicle parameters and scheduling plans of candidate route segments obtained by the data acquisition module, the predicted carbon emissions of the candidate route segments are calculated by the route segment carbon emission prediction algorithm and output to the route segment selection module. Route segment selection module: Based on the predicted carbon emissions of candidate route segments from the carbon emission prediction module, select the route segment with the lowest predicted carbon emissions from the candidate route set to construct the optimal route set, and push the route segments in the optimal route set to the vehicle navigation module and data acquisition module. Vehicle navigation module: Updates the driving route based on the route segment selection module; Carbon emission tracking module: Calculates actual carbon emissions based on the vehicle's actual operating data from the data acquisition module.

[0013] The beneficial effects of the technical solution of the present invention are: 1. By dividing the transportation route into independent route segments, modular management of the route is realized, the granularity of carbon emission prediction and optimization is refined, and the system can dynamically select low-carbon routes at each intersection, significantly reducing the carbon emissions in the overall transportation process and improving the flexibility and accuracy of logistics management.

[0014] 2. By accurately predicting the carbon emissions of each route segment, the system can identify low-carbon routes and avoid selecting high-emission route segments, thereby effectively reducing carbon emissions and optimizing transportation efficiency, providing data-driven low-carbon decision support for logistics and distribution management.

[0015] 3. Based on the predicted carbon emissions at each intersection, the candidate path segment with the lowest predicted carbon emissions is selected from the candidate path segment set to construct the overall optimal path set. This ensures that the selection of each path segment aims to minimize carbon emissions, achieving dynamic and real-time path selection. This significantly reduces carbon emissions in the logistics transportation process while taking into account transportation efficiency. It avoids the limitations of traditional path optimization based solely on distance or time, and improves the level of intelligence in logistics management.

[0016] 4. After each route segment is selected, the actual carbon emissions are calculated, enabling real-time tracking of carbon emissions. Attached Figure Description

[0017] Figure 1 This is a structural diagram of a real-time carbon emission tracking system for intelligent logistics transportation as described in this invention; Figure 2 This is a flowchart of a real-time carbon emission tracking method for intelligent logistics transportation as described in this invention. Detailed Implementation

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

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0020] The following description, in conjunction with the accompanying drawings, details the specific scheme of the intelligent logistics transportation carbon emission real-time tracking system and method provided by the present invention.

[0021] See attached document Figure 1 The diagram illustrates a real-time carbon emission tracking system for intelligent logistics transportation according to an embodiment of the present invention. The system includes the following components: The system includes a route segmentation module, a data acquisition module, a carbon emission prediction module, a route segment selection module, a vehicle navigation module, and a carbon emission tracking module. Path segment division module: Divides the entire logistics transportation path into multiple path segments according to intersections. At each intersection, which is the starting point of each path segment, a set of candidate path segments is generated and the set of candidate path segments is output to the data acquisition module. Data acquisition module: Based on the candidate path segments in the candidate path segment set, acquire traffic data, vehicle parameters and scheduling plans of the candidate path segments and output them to the carbon emission prediction module; acquire the actual vehicle operation data from the beginning to the end of the path segment, such as the actual energy consumption rate. The actual energy consumption rate is obtained by recording the fuel consumption of the path segment through the OBD-II interface and dividing it by the length of the path segment. The acquired actual vehicle operation data is output to the carbon emission tracking module. Carbon emission prediction module: Based on the traffic data, vehicle parameters and scheduling plans of candidate route segments obtained by the data acquisition module, the predicted carbon emissions of the candidate route segments are calculated by the route segment carbon emission prediction algorithm and output to the route segment selection module. Route segment selection module: Based on the predicted carbon emissions of candidate route segments from the carbon emission prediction module, select the route segment with the lowest predicted carbon emissions from the candidate route set to construct the optimal route set, and push the route segments in the optimal route set to the vehicle navigation module and data acquisition module. Vehicle navigation module: Updates the driving route in real time based on the route segment selection module; Carbon emission tracking module: Calculates actual carbon emissions based on the vehicle's actual operating data from the data acquisition module.

[0022] See attached document Figure 2 The diagram illustrates a flowchart of a real-time carbon emission tracking method for intelligent logistics transportation according to an embodiment of the present invention. The method includes the following steps: S1. Divide the logistics transportation route into route segments, and generate a set of candidate route segments at the starting point of each route segment to obtain candidate route segments.

[0023] The entire logistics transportation route is divided into multiple route segments according to intersections. Each route segment corresponds to the part from the current intersection to the next intersection. The total route is... A path set is composed of several path segments. ,in, Indicates the first Each path segment . No. The length of each path segment is The total path length constraint is satisfied:

[0024] in, Indicates the range from 1 to Sum of the lengths of all path segments; This indicates the total path length.

[0025] At each intersection, which is the starting point of each path segment, a set of candidate path segments is generated. ,in, Indicates the first The first path segment Candidate path segments, , It is the number of candidate path segments; the first The first path segment Candidate path segments Has the corresponding length and road type ,in, Indicates the first The first path segment The length of each candidate path segment Indicates time The The first path segment The candidate route segments are categorized by road type, such as highways or urban roads. The set of candidate route segments reflects the diversity of driving options at each intersection, and the length and road type of each candidate route segment determine the potential energy consumption and carbon emissions.

[0026] S2. Based on the traffic data, vehicle parameters, and scheduling plans of the acquired candidate path segments, calculate the predicted carbon emissions of the candidate path segments using a path segment carbon emission prediction algorithm; and construct the optimal path set based on the predicted carbon emissions of the candidate path segments.

[0027] Before each route segment begins, traffic data, vehicle parameters, and scheduling plans for the candidate route segment are obtained through navigation application programming interfaces, such as map APIs. The traffic data includes speed samples, predicted traffic congestion indices, and road types; vehicle parameters provide the vehicle's rated load weight, standard driving speed, and baseline energy consumption rate; and the scheduling plan provides the current vehicle load weight.

[0028] Based on the traffic data, vehicle parameters, and scheduling plans of the acquired candidate route segments, the predicted carbon emissions of the candidate route segments are obtained through a route segment carbon emission prediction algorithm.

[0029] The route segment carbon emission prediction algorithm integrates vehicle load weight, predicted average speed, relative speed fluctuation coefficient, and predicted traffic congestion index to calculate a comprehensive impact factor. This factor is used to adjust the predicted energy consumption rate of candidate route segments for each route segment, thereby predicting carbon emissions and generating a predicted carbon emission value for each candidate route segment.

[0030] The comprehensive influencing factor integrates the effects of vehicle load weight, predicted average speed, relative speed fluctuation, and traffic congestion. Specifically, the load ratio is calculated by dividing the current vehicle load weight by the vehicle's rated load weight, reflecting the impact of vehicle load weight on energy consumption. For example, heavy loads increase rolling resistance and engine load, while light loads reduce energy consumption. The speed ratio is calculated by dividing the predicted average speed by the standard driving speed, reflecting the impact of the predicted average speed deviating from the standard driving speed. If the vehicle travels at low speeds, such as in congested urban areas, engine efficiency decreases, which may lead to increased fuel consumption per kilometer due to frequent idling or low-speed operation, thereby increasing carbon emissions. The relative speed fluctuation coefficient and the predicted traffic congestion index are multiplied by adding one to each, used to characterize the gain effect of variable speed driving and congestion on energy consumption. Frequent variable speed driving will significantly increase energy consumption. For example, during acceleration, the engine needs additional output power to overcome inertia, while during braking, some energy is lost as heat.

[0031] Based on the speed samples in the traffic data of the acquired candidate path segments, the predicted average speed for each candidate path segment is calculated. The speed samples are a set of discrete instantaneous speed values, each corresponding to a time interval. The predicted average speed of the candidate path segment is obtained by averaging the data. The calculation formula is as follows:

[0032] in, Indicates time The The first path segment The predicted average speed of the candidate path segments; Indicates time The The first path segment The first candidate path segment The sum of the products of each velocity sample and its corresponding time interval; Indicates time The The first path segment The first candidate path segment One speed sample; Indicates the first The time interval between velocity samples; Indicates the number of velocity samples. ; This represents the sum of the time intervals for all velocity samples.

[0033] Based on the predicted average speed of the candidate path segment, a relative speed fluctuation coefficient is further calculated to characterize the relative intensity of speed fluctuations in the candidate path segment. The relative speed fluctuation coefficient is obtained by dividing the standard deviation of the speed sample relative to the predicted average speed of the candidate path segment by the predicted average speed of the candidate path segment. It is used to quantify the impact of speed fluctuations, such as frequent acceleration, deceleration, or idling, which can lead to additional energy consumption.

[0034] The formula for calculating the relative velocity fluctuation coefficient is:

[0035] in, Indicates time The The first path segment The relative speed fluctuation coefficient of the candidate path segments; Indicates time The The first path segment The first candidate path segment The variance of each velocity sample; Indicates time The The first path segment The first candidate path segment The standard deviation of the velocity samples.

[0036] The formula for calculating the comprehensive impact factor is:

[0037] in, Indicates time The The first path segment The comprehensive impact factor of the candidate path segments; This indicates the load ratio, which reflects the impact of the actual vehicle load weight on energy consumption. Indicates time The vehicle's load weight; Indicates the vehicle's rated load weight; The speed ratio is used to reflect the impact of predicted average speed on energy consumption, such as high speed increasing wind resistance and low speed reducing engine efficiency. Indicates standard driving speed; Indicates time The The first path segment The predicted traffic congestion index for each candidate path segment ranges from [value range missing]. .

[0038] Furthermore, the baseline energy consumption rate is multiplied by the comprehensive impact factor to obtain the predicted energy consumption rate. The predicted energy consumption rate is then multiplied by the length of the candidate path segment to obtain the total energy consumption of the candidate path segment. Finally, it is multiplied by the emission factor to obtain the predicted carbon emissions.

[0039] The formula for predicting carbon emissions is:

[0040] in, Indicates the first The first path segment Candidate path segments in time The predicted carbon emissions, in units of ; Indicates the first The first path segment The emission factor for each candidate route segment is determined based on the vehicle's energy type, such as 2-3 for gasoline-powered vehicles. Specifically, for example, gasoline 2.3 is Diesel fuel is 2.7. ; Indicates the first The first path segment The length of each candidate path segment, in units of ; Indicates the first The first path segment Candidate path segments in time Total energy consumption; Indicates the first The first path segment Candidate path segments in time The predicted energy consumption rate is calculated using the following formula: ; The baseline energy consumption rate is the energy consumption rate of a vehicle under its rated load weight and standard driving speed, and the unit is 1000 kJ / m². The benchmark energy consumption rate typically relies on standard data provided by vehicle manufacturers.

[0041] At each intersection, from the set of candidate paths Select the candidate path segment with the lowest predicted carbon emissions as the first... Path segment Construct the optimal path set The selected number Each route segment is pushed to the vehicle navigation module for real-time route updates. The selection of each route segment is done independently to ensure that a low-carbon route is dynamically selected at each intersection.

[0042] No. The formula for selecting each path segment is:

[0043] in, The candidate path segment with the lowest predicted carbon emissions is designated as the first... One path segment; Indicates the first A set of candidate paths for each path segment; This indicates the operation of selecting the minimum value; Indicates the first The first path segment Candidate path segments.

[0044] S3. Based on the path segments in the optimal path set, obtain the actual vehicle operation data, and calculate the actual carbon emissions based on the actual vehicle operation data.

[0045] After selecting each path segment from the optimal path set, the actual vehicle operating data from the start to the end of the path segment is obtained, such as the actual energy consumption rate. The actual energy consumption rate is obtained by recording the fuel consumption of the path segment through the OBD-II interface and dividing it by the length of the path segment. Based on the actual vehicle operating data, the actual carbon emissions are calculated using the following formula:

[0046] in, Indicates the first Each path segment at the end time of the path segment Actual carbon emissions, in units of ; Indicates the first Each path segment at the end time of the path segment The actual energy consumption rate is recorded via the OBD-II interface. The fuel consumption of the first route segment divided by the first... The length of each path segment is obtained, in units of ; Indicates the first The emission factor for each route segment is determined based on the vehicle's energy type; for example, it is 2-3 for gasoline-powered vehicles. Specifically, for example, gasoline 2.3 is Diesel fuel is 2.7. ; Indicates the first The length of each path segment, in units of .

[0047] The actual vehicle operating data provides a reliable input for calculating actual carbon emissions, supports real-time capture of dynamic driving conditions, and enables real-time tracking of carbon emissions in intelligent logistics transportation.

[0048] In summary, a real-time tracking system and method for carbon emissions in intelligent logistics transportation have been developed.

[0049] The order of the embodiments is for illustrative purposes only and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0050] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0051] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for real-time tracking of carbon emissions in intelligent logistics transportation, characterized in that, Includes the following steps: S1. Divide the logistics transportation route into route segments, and generate a set of candidate route segments at the starting point of each route segment to obtain candidate route segments; S2. Based on the traffic data, vehicle parameters, and scheduling plans of the acquired candidate route segments, calculate the predicted carbon emissions of the candidate route segments using a route segment carbon emission prediction algorithm; and construct the optimal route set based on the predicted carbon emissions of the candidate route segments. S3. Based on the path segments in the optimal path set, obtain the actual vehicle operation data, and calculate the actual carbon emissions based on the actual vehicle operation data.

2. The method for real-time tracking of carbon emissions in intelligent logistics transportation according to claim 1, characterized in that, S2 specifically includes: In the route segment carbon emission prediction algorithm, the predicted average speed of the candidate route segment is calculated based on the speed samples in the traffic data of the acquired candidate route segments.

3. The method for real-time tracking of carbon emissions in intelligent logistics transportation according to claim 2, characterized in that, S2 specifically includes: The relative speed fluctuation coefficient is obtained based on the speed samples in the traffic data of the candidate path segments and the predicted average speed of the candidate path segments.

4. The method for real-time tracking of carbon emissions in intelligent logistics transportation according to claim 3, characterized in that, S2 specifically includes: The comprehensive impact factor is calculated by integrating the vehicle load weight, predicted average speed, and relative speed fluctuation coefficient from the scheduling plans of the acquired candidate route segments with the predicted traffic congestion index from the traffic data of the acquired candidate route segments.

5. The method for real-time tracking of carbon emissions in intelligent logistics transportation according to claim 4, characterized in that, S2 specifically includes: Based on the baseline energy consumption rate and comprehensive influence factor in the vehicle parameters of the acquired candidate path segments, the predicted energy consumption rate is obtained.

6. The method for real-time tracking of carbon emissions in intelligent logistics transportation according to claim 5, characterized in that, S2 specifically includes: Based on the predicted energy consumption rate, the total energy consumption of the candidate path segment is obtained. Then, by introducing the emission factor, the predicted carbon emissions are obtained.

7. The method for real-time tracking of carbon emissions in intelligent logistics transportation according to claim 6, characterized in that, S2 specifically includes: An optimal set of paths is constructed based on the candidate path segments with the lowest predicted carbon emissions.

8. A real-time carbon emission tracking system for intelligent logistics transportation, applied to the real-time carbon emission tracking method for intelligent logistics transportation as described in claim 1, characterized in that, Includes the following parts: The system includes a route segmentation module, a data acquisition module, a carbon emission prediction module, a route segment selection module, a vehicle navigation module, and a carbon emission tracking module. Path segmentation module: Divides the logistics transportation path into path segments, generates a set of candidate path segments at the starting point of each path segment, and outputs the set of candidate path segments to the data acquisition module; Data acquisition module: Based on the candidate path segments in the candidate path segment set, acquire traffic data, vehicle parameters and scheduling plans of the candidate path segments, and output them to the carbon emission prediction module; Acquire actual vehicle operating data and output the acquired actual vehicle operating data to the carbon emission tracking module; Carbon emission prediction module: Based on the traffic data, vehicle parameters and scheduling plans of candidate route segments obtained by the data acquisition module, the predicted carbon emissions of the candidate route segments are calculated by the route segment carbon emission prediction algorithm and output to the route segment selection module. Route segment selection module: Based on the predicted carbon emissions of candidate route segments from the carbon emission prediction module, select the route segment with the lowest predicted carbon emissions from the candidate route set to construct the optimal route set, and push the route segments in the optimal route set to the vehicle navigation module and data acquisition module. Vehicle navigation module: Updates the driving route based on the route segment selection module; Carbon emission tracking module: Calculates actual carbon emissions based on the vehicle's actual operating data from the data acquisition module.

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