A method and device for calculating carbon emission reduction in bulk commodity transportation

Abstract

The present invention discloses a method and device for calculating carbon emission reduction in bulk commodity transportation, and relates to the technical field of transportation emission calculation. The method comprises: obtaining an initial transportation plan for a target bulk commodity; disassembling the initial transportation path to obtain an initial disassembled transportation path sequence; collecting carbon emission related data, calculating carbon emissions, and obtaining initial carbon emissions; performing serialized real-time monitoring to obtain an optimized disassembled transportation path sequence; performing mapping optimization adjustment to obtain an optimized vehicle iteration sequence, collecting carbon emission related data and mapping and integrating them, calculating carbon emissions, and obtaining the transportation carbon emission reduction of the target bulk commodity. The present invention solves the technical problems in the prior art of inaccurate carbon emission reduction calculation in bulk commodity transportation, delayed transportation path optimization, and difficulty in balancing environmental protection, thereby achieving the technical effect of improving the accuracy of carbon emission reduction calculation and realizing real-time optimization of transportation paths and environmental protection.

Description

Technical Field

[0001] The present invention relates to the technical field of transport displacement calculation, and in particular to a method and device for calculating carbon emission reduction in bulk commodity transportation. Background Art

[0002] With the continuous development of the global economy, the demand for the circulation of bulk commodities in various fields continues to grow. However, the large amount of carbon emissions generated during the transportation of bulk commodities has a serious impact on the environment. At present, the traditional mode of transporting bulk commodities has many shortcomings in carbon emission reduction. On the one hand, the traditional method of calculating carbon emission reduction is relatively simple and extensive, usually only considering limited factors such as the basic parameters of the vehicle and mileage, while ignoring key factors such as the complexity of the transportation task, the diversity of vehicle transportation configurations, and the dynamic changes of the transportation route. This makes the calculation results of carbon emission reduction not accurate enough and cannot provide a reliable basis for enterprises to formulate effective emission reduction strategies. On the other hand, in the planning and optimization of transportation routes, traditional methods often lack real-time performance. Usually, a one-time route planning is carried out before the start of the transportation task, and during the transportation process, it is impossible to respond to sudden traffic congestion, road construction, weather changes, etc. in a timely manner. This not only leads to low transportation efficiency, but also increases carbon emissions due to unreasonable route selection.

[0003] Existing technologies have technical problems such as inaccurate calculation of carbon emission reductions in bulk commodity transportation, lagging optimization of transportation routes, and difficulty in balancing environmental protection. Summary of the invention

[0004] The present application provides a method and device for calculating carbon emission reductions in bulk commodity transportation, which is used to solve the technical problems in the prior art of inaccurate calculation of carbon emission reductions in bulk commodity transportation, delayed optimization of transportation routes, and difficulty in balancing environmental protection.

[0005] In view of the above problems, the present application provides a method and device for calculating carbon emission reduction in bulk commodity transportation.

[0006] In a first aspect of the present application, a method for calculating carbon emission reductions in bulk commodity transportation is provided, the method comprising:

[0007] Obtain an initial transportation plan for the target bulk commodity, wherein the initial transportation plan includes K initial transportation routes, K transportation tasks, and K vehicle transportation configuration data; disassemble the K initial transportation routes based on the K transportation tasks and the K vehicle transportation configuration data to obtain K initial disassembled transportation route sequences, wherein the K initial disassembled transportation route sequences include K vehicle iteration sequences; traverse the K initial disassembled transportation route sequences and the K vehicle iteration sequences to collect carbon emission-related data to obtain K initial disassembled route related data sequences and K vehicle iteration related data sequences; map and integrate the K initial disassembled route related data sequences and the K vehicle iteration related data sequences, and combine them with The carbon emission factor is used to calculate the carbon emissions to obtain the initial carbon emissions; the K initial disassembly and transportation path sequences are serialized and monitored in real time, and the disassembly and transportation paths are optimized according to the monitoring results to obtain K optimized disassembly and transportation path sequences; the vehicle iteration sequence is mapped and optimized according to the K optimized disassembly and transportation path sequences to obtain K optimized vehicle iteration sequences; carbon emission related data are collected for the K optimized disassembly and transportation path sequences and the K optimized vehicle iteration sequences, and the collected data are mapped and integrated, and the carbon emissions are calculated in combination with the carbon emission factor to obtain the optimized carbon emissions; the carbon emission reduction is calculated based on the optimized carbon emissions and the initial carbon emissions to obtain the transportation carbon emission reduction of the target commodity.

[0008] A second aspect of the present application provides a device for calculating carbon emission reductions in bulk commodity transportation, the device comprising:

[0009] An initial transportation plan acquisition module, the initial transportation plan acquisition module is used to acquire an initial transportation plan for a target bulk commodity, wherein the initial transportation plan includes K initial transportation routes, K transportation tasks and K vehicle transportation configuration data; an initial disassembly transportation route sequence acquisition module, the initial disassembly transportation route sequence acquisition module disassembles the K initial transportation routes based on the K transportation tasks and the K vehicle transportation configuration data to obtain K initial disassembly transportation route sequences, wherein the K initial disassembly transportation route sequences include K vehicle iteration sequences; an associated data sequence acquisition module, the associated data sequence acquisition module is used to traverse the K initial disassembly transportation route sequences and the K vehicle iteration sequences to collect carbon emission associated data, and obtain K initial disassembly route associated data sequences and K vehicle iteration associated data sequences; an initial carbon emission acquisition module, the initial carbon emission acquisition module is used to map and integrate the K initial disassembly route associated data sequences and the K vehicle iteration associated data sequences, and combine the carbon emission a carbon emission factor is used to calculate the carbon emissions to obtain the initial carbon emissions; an optimized disassembly and transportation path sequence acquisition module, the optimized disassembly and transportation path sequence acquisition module is used to serialize and monitor the K initial disassembly and transportation path sequences in real time, optimize the disassembly and transportation paths according to the monitoring results, and obtain K optimized disassembly and transportation path sequences; an optimized vehicle iteration sequence acquisition module, the optimized vehicle iteration sequence acquisition module is used to map and optimize the vehicle iteration sequence according to the K optimized disassembly and transportation path sequences, and obtain K optimized vehicle iteration sequences; an optimized carbon emission acquisition module, the optimized carbon emission acquisition module is used to collect carbon emission-related data for the K optimized disassembly and transportation path sequences and the K optimized vehicle iteration sequences and map and integrate the collected data, calculate carbon emissions in combination with the carbon emission factor, and obtain optimized carbon emissions; a transportation carbon emission reduction acquisition module, the transportation carbon emission reduction acquisition module calculates carbon emission reduction based on the optimized carbon emissions and the initial carbon emissions, and obtains the transportation carbon emission reduction of the target commodity.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] Obtain the initial transportation plan for the target bulk commodity, disassemble the K initial transportation paths based on K transportation tasks and K vehicle transportation configuration data, and obtain K initial disassembled transportation path sequences; traverse the K initial disassembled transportation path sequences and K vehicle iteration sequences to collect carbon emission-related data, and obtain K initial disassembled path-related data sequences and K vehicle iteration-related data sequences; map and integrate the K initial disassembled path-related data sequences and the K vehicle iteration-related data sequences, calculate carbon emissions, and obtain initial carbon emissions; The path sequence is serialized and monitored in real time. The dismantling and transportation path is optimized according to the monitoring results to obtain K optimized dismantling and transportation path sequences; the vehicle iteration sequence is mapped and optimized according to the K optimized dismantling and transportation path sequences to obtain K optimized vehicle iteration sequences; the carbon emission related data of the K optimized dismantling and transportation path sequences and the K optimized vehicle iteration sequences are collected and mapped and integrated, and the carbon emissions are calculated to obtain the optimized carbon emissions; the carbon emission reduction is calculated based on the optimized carbon emissions and the initial carbon emissions to obtain the transportation carbon emission reduction of the target bulk commodity. The accuracy of carbon emission reduction calculation is improved, and the technical effect of real-time optimization of transportation paths and environmental protection is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0013] Figure 1 A schematic flow chart of a method for calculating carbon emission reductions in bulk commodity transportation provided in an embodiment of the present application;

[0014] Figure 2 A schematic diagram of the structure of a device for calculating carbon emission reduction in bulk commodity transportation provided in an embodiment of the present application.

[0015] Explanation of the accompanying drawings: initial transport plan acquisition module 10, initial disassembly and transport path sequence acquisition module 20, associated data sequence acquisition module 30, initial carbon emission acquisition module 40, optimized disassembly and transport path sequence acquisition module 50, optimized vehicle iteration sequence acquisition module 60, optimized carbon emission acquisition module 70, transport carbon emission reduction acquisition module 80. DETAILED DESCRIPTION

[0016] The present application provides a method and device for calculating carbon emission reductions in bulk commodity transportation, which is used to solve the technical problems in the prior art of inaccurate calculation of carbon emission reductions in bulk commodity transportation, delayed optimization of transportation routes, and difficulty in balancing environmental protection.

[0017] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0018] Embodiment 1, as Figure 1 As shown, the present application provides a method for calculating carbon emission reduction in bulk commodity transportation, the method comprising:

[0019] Step S100: Obtain an initial transportation plan for the target bulk commodity, wherein the initial transportation plan includes K initial transportation routes, K transportation tasks, and K vehicle transportation configuration data.

[0020] Specifically, first, the target bulk commodities are large quantities of goods of various types, such as coal, steel, grain, etc. For these bulk commodities, they need to be transported from the supply point to different destinations, so K initial transportation routes are designed. These initial transportation routes are planned based on a variety of factors, including geographical location, traffic conditions, transportation costs, etc. For example, if the target bulk commodity is coal, it will be transported to different power plants or industrial users. Different transportation modes such as railways, roads, and waterways will be considered to determine the most economical and efficient transportation route. The K transportation tasks clarify the specific transportation needs. Each transportation task corresponds to a destination, including the quantity of goods to be transported, delivery time requirements, etc. For example, a transportation task is to transport a certain amount of steel to a specific factory, requiring it to be delivered within a specific time. The K vehicle transportation configuration data details the vehicles used to perform these transportation tasks, including the type of vehicle, such as trucks, train cars, cargo ships, etc.; the number of vehicles; the load capacity of the vehicle; the fuel efficiency of the vehicle, etc. For example, for road transportation, there may be different models of trucks with different load capacities and fuel consumption. For rail transport, there are different types of train carriages with different capacities and operating costs. For water transport, the size, load capacity and sailing speed of cargo ships are also important configuration data. By obtaining information on these initial transport plans, specific basic data is provided for subsequent carbon emission reduction calculations. These data will be used to disassemble the transport route, collect related data, and calculate carbon emissions, thereby ultimately determining the carbon emission reduction potential of the target commodity during transportation.

[0021] Step S200: Disassembling the K initial transport paths based on the K transport tasks and the K vehicle transport configuration data to obtain K initial disassembled transport path sequences, wherein the K initial disassembled transport path sequences include K vehicle iteration sequences.

[0022] Specifically, consider K transportation tasks, which specify the specific requirements for transporting target bulk commodities to different destinations, including the quantity of commodities to be transported, delivery time requirements, etc. At the same time, combined with K vehicle transportation configuration data, including vehicle type, quantity, load capacity, fuel efficiency and other information. Based on these, the K initial transportation paths are disassembled. In the disassembly process, the commodity volume of each transportation task is first determined, and the most suitable vehicle type combination under full-load operation in the same period is analyzed according to the set of key indicators of vehicle transportation and the commodity volume. Then, the initial path feature sequence is fused by neighbor features, taking into full consideration various factors on the path, such as terrain, traffic conditions, etc., and the initial transportation path is split into multiple more specific sub-path segments in combination with the initial transportation path interval, thereby obtaining K initial disassembled transportation path sequences, and these K initial disassembled transportation path sequences also include K vehicle iteration sequences, which reflect the iteration of vehicles on each transportation path, that is, the order and quantity of vehicles in different sub-path segments change with the transportation process, such as using specific types of vehicles in some sections, and switching to other types or quantities of vehicles in other sections.

[0023] Step S300: traversing the K initial disassembly and transportation path sequences and the K vehicle iteration sequences to collect carbon emission related data, and obtaining K initial disassembly path related data sequences and K vehicle iteration related data sequences.

[0024] Specifically, we obtain carbon emission-related data, traverse K initial disassembly and transportation path sequences and K vehicle iteration sequences, and carefully collect various data related to each initial disassembly and transportation path, including transportation distance, driving speed, road condition information, energy consumption, etc., to form K initial disassembly path-related data sequences. At the same time, for each vehicle iteration sequence, that is, the different input order and quantity changes of vehicles on each transportation path, we collect relevant data such as vehicle type, fuel consumption rate, driving time, load change, etc., to obtain K vehicle iteration related data sequences. These related data will provide key basic information for the subsequent accurate calculation of carbon emissions. By comprehensively collecting various data under different transportation paths and vehicle iterations, we can more accurately evaluate the carbon emissions in the transportation of bulk commodities.

[0025] Step S400: mapping and integrating the K initial disassembly path associated data sequences and the K vehicle iteration associated data sequences, and calculating carbon emissions in combination with a carbon emission factor to obtain initial carbon emissions.

[0026] Specifically, firstly, the K initial disassembly path-related data sequences and the K vehicle iteration-related data sequences are processed, and the two sets of data are mapped and integrated to establish a corresponding relationship between the two. The various data on the transportation path and the data on the iteration of the vehicle on different paths are organically combined. Then, the carbon emission factor is combined to calculate the carbon emissions. The carbon emission factor is a coefficient determined according to different modes of transportation, vehicle types, and energy consumption, and is used to convert the relevant data in the transportation activities into carbon emissions. By applying the carbon emission factor to the integrated data for calculation, the initial carbon emissions are finally obtained, which reflects the carbon emissions level generated during the transportation of the target bulk commodity under the current initial transportation plan.

[0027] Step S500: performing serialized real-time monitoring on the K initial disassembly and transportation path sequences, optimizing the disassembly and transportation paths according to the monitoring results, and obtaining K optimized disassembly and transportation path sequences.

[0028] Specifically, the K initial disassembly and transportation path sequences are serialized and monitored in real time. During the transportation of bulk commodities, each initial disassembly and transportation path is continuously tracked and observed. The monitoring content includes transportation progress, road condition changes, vehicle operation status and other information. When the transportation task begins, real-time data is collected through various monitoring methods to form K real-time monitoring path feature sequences. Then, these real-time monitoring path feature sequences are anomaly identified to find out abnormal conditions that affect transportation efficiency and carbon emissions, such as road congestion, vehicle failure, etc., so as to obtain K abnormal path feature sets. Then, according to these abnormal path feature sets, the K initial disassembly and transportation path sequences are matched, and the initial disassembly and transportation paths affected by the abnormalities are determined to obtain K abnormal initial disassembly and transportation path sets. With this abnormal set as the optimization source, the K initial disassembly and transportation path sequences are optimized. The optimization measures taken include re-planning routes, adjusting vehicle allocation, changing transportation methods, etc., and finally obtaining K optimized disassembly and transportation path sequences to improve transportation efficiency and reduce carbon emissions.

[0029] Step S600: mapping and optimizing the vehicle iteration sequence according to the K optimized disassembly and transportation path sequences to obtain K optimized vehicle iteration sequences.

[0030] Specifically, after obtaining K optimized disassembled transport path sequences, the mapping optimization adjustment of the vehicle iteration sequence is performed based on these optimized paths. Since the optimized transport paths have changed in length, road conditions, and transport task volume, it is necessary to determine the configuration of the corresponding vehicle combination on different paths based on these new path characteristics. First, carefully analyze the specific characteristics of each optimized disassembled transport path, such as road conditions such as road flatness, slope, and traffic flow, as well as task volume information such as the number of bulk commodities to be transported on the path. Then, adjust the type, quantity, and distribution of vehicles in different path segments based on these characteristics. For example, if the road conditions of an optimized path are good and the transport task volume is large, consider increasing the number of large and efficient transport vehicles; if a certain section of the path has narrow sections or special terrain restrictions, it is necessary to select more flexible small vehicles. By optimizing and adjusting the vehicle iteration sequence in this way, the configuration of the vehicle is more reasonable and can better adapt to the optimized transport path, and finally obtain K optimized vehicle iteration sequences to improve transportation efficiency and reduce costs and carbon emissions during transportation.

[0031] Step S700: collecting carbon emission related data for the K optimized disassembly transportation path sequences and the K optimized vehicle iteration sequences, mapping and integrating the collected data, calculating carbon emissions in combination with carbon emission factors, and obtaining optimized carbon emissions.

[0032] Specifically, firstly, carbon emission-related data collection is carried out for K optimized disassembled transport path sequences and K optimized vehicle iteration sequences. For each optimized transport path, detailed data including path length, road condition information, driving speed, etc. are collected. At the same time, for each optimized vehicle iteration, relevant information such as vehicle type, fuel consumption, driving time, and load change are collected. Then, the collected data are mapped and integrated, and the corresponding relationship between different data is established, so that the optimized disassembled transport path data and the optimized vehicle iteration data can be related to each other to form an organic whole. Then, combined with the carbon emission factors determined according to different transportation modes, vehicle characteristics, etc., the carbon emissions of the integrated carbon emission-related data are calculated. Through this process, the optimized carbon emissions are finally obtained. The optimized carbon emissions reflect the carbon emission level in the transportation process of bulk commodities after the transportation path optimization and vehicle iteration adjustment, which provides an important basis for evaluating and further improving the transportation plan.

[0033] Step S800: Calculate the carbon emission reduction based on the optimized carbon emission and the initial carbon emission to obtain the transportation carbon emission reduction of the target commodity.

[0034] Specifically, the carbon emission reduction is calculated based on the optimized carbon emissions and the initial carbon emissions. First, it is clear that the optimized carbon emissions are the carbon emissions generated during the transportation of bulk commodities after optimizing the transportation route and adjusting the vehicle iteration sequence, while the initial carbon emissions are the carbon emissions under the transportation plan before any optimization. The carbon emission reduction can be obtained by subtracting the optimized carbon emissions from the initial carbon emissions. This process accurately quantifies the carbon emission reduction effect achieved by optimizing the transportation plan, and the carbon emission reduction of the target bulk commodity obtained by transportation provides a specific numerical indicator for evaluating the improvement of the transportation plan, and also provides an important reference for further promoting the green and low-carbon development of bulk commodity transportation.

[0035] In a possible implementation, step S200 further includes:

[0036] Step S210: extracting indicators from the K vehicle transportation configuration data according to a preset transportation key indicator set to obtain K vehicle transportation key indicator sets.

[0037] Step S220: extracting path features of the K initial transport paths according to a preset interval step size to obtain K initial path feature sequences, wherein each initial path feature sequence includes an initial transport path interval.

[0038] Step S230: Taking the K vehicle transportation key indicator sets as constraints, the K initial transportation paths are disassembled according to the K transportation tasks and the K initial path feature sequences to obtain the K initial disassembled transportation path sequences.

[0039] Specifically, the preset set of key transport indicators plays a key guiding role. This set includes various types of transport vehicles, such as trucks of different specifications, etc.; it also includes the number of configurations of various types of vehicles, that is, the specific number of different vehicles under different transport tasks; it also covers a set of parameters of multiple transport vehicles, such as the maximum load weight of the vehicle, fuel consumption under different road conditions, and maximum driving speed. Based on this preset set, the K vehicle transport configuration data are deeply analyzed to extract the relevant key indicators. By screening, sorting and classifying the key information in these data, a set of K vehicle transport key indicators is finally obtained. This set provides accurate data support on vehicle characteristics for the subsequent disassembly of the initial transport route and the calculation of carbon emission reductions for the entire commodity transportation.

[0040] The preset interval step size determines the precision of the path feature extraction for the K initial transport paths. Each initial transport path is analyzed step by step with this specific step size as the unit. First, the vehicle transport configuration data is extracted. This step aims to obtain vehicle information related to each transport path, including vehicle type, quantity, load capacity, etc. These data will provide an important basis for subsequent path analysis and disassembly. Next, the initial transport path characteristics are analyzed, which includes considering factors such as the length of the path, road conditions, traffic flow, topography, etc. For example, for a highway transport path, attention will be paid to the flatness, slope, whether there are congested sections, etc. of the road; for a waterway transport path, water flow speed, channel depth, etc. will be considered. By analyzing these characteristics, the transportation conditions and potential risks of each path can be better understood. Then, path disassembly rules are established. According to the extracted vehicle transport configuration data and the analyzed path characteristics, reasonable path disassembly rules are formulated. These rules will determine how to split the initial transport path into more specific sub-path segments to better calculate carbon emission reductions and optimize transportation plans. Finally, K initial path feature sequences are obtained. Each sequence includes the corresponding initial transportation path interval, which covers the starting point, end point and key nodes in the middle of the path, providing a clear scope and basic information for subsequent path optimization and vehicle iterative adjustment.

[0041] The K vehicle transportation key indicator sets obtained in the previous step are used as important constraints. This key indicator set contains key information such as different vehicle types, vehicle configuration quantity, and vehicle parameters, which provides restrictions and guidance on vehicle characteristics for path decomposition. At the same time, the K initial transportation paths are decomposed by combining K transportation tasks and K initial path feature sequences. First, the key information in each transportation task is extracted, such as the number of goods transported, delivery time requirements, etc. Then, based on the characteristics of the path length, road conditions, traffic flow, etc. contained in the initial path feature sequence, and the constraints in the vehicle transportation key indicator set, how to split the initial transportation path into more specific sub-path segments is comprehensively considered. In the decomposition process, the initial transportation path is divided into different sections according to factors such as vehicle load, driving speed, and suitable road conditions, so that each section can better match a specific type of vehicle and transportation task. For example, if the road condition of a certain section of the path is poor, select the vehicle type suitable for this road condition and separate this section of the path. Through such a disassembly process, K initial disassembly transportation path sequences are finally obtained. These sequences not only reflect the specific division of the transportation paths, but also include the possible vehicle iterations on each sub-path, providing detailed basic data for subsequent carbon emissions calculations and transportation plan optimization.

[0042] In a possible implementation, step S210 further includes:

[0043] Step S211: The preset transport key indicator set includes multiple transport vehicle types, multiple types of vehicle configuration quantities and multiple transport vehicle parameter sets.

[0044] Specifically, the preset transportation key indicator set covers many important aspects, including multiple types of transportation vehicles, such as trucks of different specifications, and the number of vehicle configurations of multiple types, that is, the specific number of various vehicles for different transportation tasks. In addition, for each type of transportation vehicle, there are corresponding multiple transportation vehicle parameter sets. If the transportation vehicle type is a truck, then the transportation vehicle parameter set will include load capacity, that is, the weight of goods that the truck can carry; fuel efficiency, which reflects the efficiency of the truck's use of fuel during transportation; cruising range, which indicates the distance the truck can travel after a full tank of fuel; speed limit, which stipulates the maximum speed allowed for the truck under different road conditions and other parameters. These rich key indicators provide a comprehensive and specific basis for accurate analysis and planning of bulk commodity transportation, and help make more scientific and reasonable decisions in the subsequent transportation route disassembly and carbon emission reduction calculation processes.

[0045] In a possible implementation, step S230 further includes:

[0046] Step S231: extracting K transport task commodity quantities of the K transport tasks.

[0047] Step S232: Based on the K vehicle transportation key indicator sets and the K transportation task commodity quantities, a vehicle type combination analysis is performed under full-load operation in the same period to obtain K vehicle type combination sets.

[0048] Step S233: performing neighbor feature fusion on the K initial path feature sequences to obtain K fused initial path feature sequences, and disassembling the K initial transport paths in combination with the initial transport path interval of each initial path feature sequence to obtain the K initial disassembled transport path sequences.

[0049] Step S234: calling a vehicle matching identifier to perform matching analysis on the K fused initial path feature sequences and the K vehicle type combination sets to obtain the K vehicle iteration sequences.

[0050] Specifically, the K transport task commodity quantities corresponding to each of the K transport tasks are extracted. By carefully analyzing the specific requirements of each transport task, the quantity of bulk commodities to be transported is determined. This commodity quantity information is crucial for the subsequent determination of the appropriate vehicle type combination and the disassembly of the transport route.

[0051] Based on the K sets of key indicators of vehicle transportation and the K transport task commodity quantities, an in-depth analysis is conducted. The vehicle transportation key indicator set covers a variety of transport vehicle types, the number of configurations of different types of vehicles, and rich vehicle parameter information. The transport task commodity quantity clarifies the number of bulk commodities corresponding to each transport task. In order to efficiently complete the transport task under full load operation in the same period, it is necessary to reasonably combine different types of vehicles. Considering that the transport task must be completed within the specified time and the goal of minimizing carbon emissions must be achieved, it is necessary to comprehensively evaluate the performance of various vehicle types in terms of load capacity, fuel efficiency, and cruising range. Combined with the transport task commodity quantity, the combination of different vehicle types is analyzed to determine whether it can meet the transportation needs while minimizing carbon emissions. By comprehensively comparing and screening various vehicle type combinations, K vehicle type combination sets are finally obtained, which provides an important vehicle configuration solution basis for the subsequent transport route disassembly and optimization.

[0052] The nearest feature fusion operation is performed on the K initial path feature sequences. In this process, the features of the adjacent path segments in each initial path feature sequence are analyzed and integrated. Through fusion, the changes in the road conditions, traffic flow, topography and other aspects of the adjacent path segments are comprehensively considered, so as to obtain K fused initial path feature sequences. These fused sequences contain more comprehensive and accurate path information. Combined with the initial transportation path interval of each initial path feature sequence, the K initial transportation paths are disassembled, considering the starting point, end point and key nodes in the middle covered by the initial transportation path interval. At the same time, according to the path features reflected in the fused initial path feature sequence, the initial transportation path is split into multiple more specific sub-path segments. In the disassembly process, factors such as the length of the path, the complexity of the road conditions, and the changes in traffic flow are fully considered to ensure that each sub-path segment can better adapt to specific transportation needs and vehicle configurations. Through such operations, K initial disassembled transportation path sequences are finally obtained, which provides a detailed transportation path basis for subsequent vehicle matching and carbon emissions calculation.

[0053] The vehicle matching identifier is called to perform matching analysis on K fused initial path feature sequences and K vehicle type combination sets. This analysis process aims to determine which types of vehicles should be deployed and the changes in the number of vehicles on the sub-path segments represented by different fused initial path feature sequences. Through the precise analysis of the vehicle matching identifier, combined with the characteristics of the path features and vehicle type combinations, K vehicle iteration sequences are obtained. This sequence reflects the dynamic adjustment of the order and number of vehicles deployed as the path changes during the entire transportation process, providing a key decision-making basis for subsequent carbon emissions calculations and transportation plan optimization.

[0054] In a possible implementation, step S400 further includes:

[0055] Step S410: performing standardization processing on the K initial disassembly path association data sequences and the K vehicle iteration association data sequences to obtain K initial standardized path association data sequences and K standardized vehicle iteration association data sequences.

[0056] Step S420: performing a one-to-one mapping between the K initial standardized path association data sequences and the K standardized vehicle iteration association data sequences to obtain K initial standardized path-vehicle association data combination sequences.

[0057] Step S430: Calculate the carbon emissions based on the K initial standardized route-vehicle association data combination sequences and the carbon emission factor to obtain the initial carbon emissions.

[0058] Specifically, the K initial disassembled path-related data sequences and the K vehicle iteration-related data sequences are first standardized. Since the original data sequences have differences in numerical range, unit, and data distribution, direct use will affect the accuracy and effectiveness of subsequent calculations. The purpose of standardization is to make these data have a unified scale and comparability, adjust each data point, remove the dimension effect in the data, and enable different data sequences to be analyzed under the same standard. For the K initial disassembled path-related data sequences, including path length, road condition parameters, transportation time and other data, after standardization, these data will be in a unified numerical range, which is convenient for subsequent integration and analysis with other data. Similarly, for the K vehicle iteration-related data sequences, such as vehicle type, mileage, load change and other data, they are also standardized to have the same scale as the path-related data. Finally, K initial standardized path-related data sequences and K standardized vehicle iteration-related data sequences are obtained, providing a more standardized and comparable data basis for the subsequent one-to-one mapping and carbon emissions calculation.

[0059] The K initial standardized path association data sequences and K standardized vehicle iteration association data sequences that have been standardized are mapped one-to-one. This means that for each initial standardized path association data sequence, the corresponding standardized vehicle iteration association data sequence can be found and a clear association relationship can be established. In this process, considering that each transportation path has a specific vehicle running on it, through this one-to-one mapping, the characteristic data of the path can be closely combined with the corresponding vehicle operation data. For example, a specific initial standardized path association data sequence contains information such as the length and road conditions of the path, while the corresponding standardized vehicle iteration association data sequence contains data such as the type and speed of the vehicle traveling on the path. After mapping the two, an initial standardized path-vehicle association data combination sequence can be obtained. This sequence integrates the relevant information of the path and the vehicle and fully reflects the operation of the vehicle on a specific transportation path. By performing such operations on all K initial standardized path association data sequences and K standardized vehicle iteration association data sequences, K initial standardized path-vehicle association data combination sequences are finally obtained, which provides a more complete and detailed data combination for the subsequent accurate calculation of carbon emissions.

[0060] The initial carbon emissions are calculated based on K initial standardized path-vehicle association data combination sequences and carbon emission factors. This process includes multiple links. First, according to the mapping combination of paths and vehicles, the carbon emission factor of each path segment is calculated considering the vehicle fuel type, consumption efficiency and actual path operating conditions. The calculation formula is the product of fuel consumption and CO2 emissions per unit of fuel, and fuel consumption depends on the vehicle's driving efficiency on a specific path. Then, using the carbon emission factor and path length, the initial carbon emissions of each path segment are calculated by the formula that the carbon emissions of the path segment equals the path length multiplied by the carbon emission factor. Then, the carbon emissions of each path segment are adjusted in combination with vehicle operation data such as load changes and speed fluctuations, and dynamic corrections are made according to the real-time change data in the vehicle iteration sequence to make the calculation results closer to the actual situation. After that, the cumulative sum algorithm is used to integrate the carbon emissions of all path segments to calculate the total carbon emissions of each initial disassembly and transportation path. Finally, the carbon emissions of all K disassembly and transportation paths are added together to obtain the overall initial carbon emissions.

[0061] In a possible implementation, step S500 further includes:

[0062] Step S510: When the transportation task begins, the K initial disassembly and transportation path sequences are serialized and monitored in real time to obtain K real-time monitoring path feature sequences.

[0063] Step S520: performing abnormality identification on the K real-time monitoring path feature sequences to obtain K abnormal path feature sets.

[0064] Step S530: matching the K initial disassembly and transportation path sequences according to the K abnormal path feature sets to obtain K abnormal initial disassembly and transportation path sets.

[0065] Step S540: Taking the K abnormal initial disassembly and transportation path sets as optimization sources, optimizing the disassembly and transportation paths of the K initial disassembly and transportation path sequences to obtain the K optimized disassembly and transportation path sequences.

[0066] Specifically, when the transportation task begins, in order to better understand the situation during the transportation process, the K initial disassembly transportation path sequences are serialized and monitored in real time. In this process, various monitoring equipment and technical means are used to collect real-time data on each transportation path. These data include information such as traffic flow, vehicle speed, and road condition changes on the path, thereby obtaining K real-time monitoring path feature sequences. Through real-time monitoring, dynamic changes in the transportation process can be understood in a timely manner, providing data support for subsequent abnormal identification and path optimization.

[0067] For the abnormal identification of K real-time monitoring path feature sequences, first, a set of abnormal identification standards and methods are established. These standards include traffic flow on the path exceeding a specific range, vehicle speed significantly lower or higher than expected, and serious damage to the road conditions. Then, for each real-time monitoring path feature sequence, each feature data is analyzed one by one. For example, check whether the traffic flow is within the normal fluctuation range. If the flow suddenly increases or decreases significantly, it means that an abnormal situation has occurred. At the same time, evaluate whether the vehicle speed conforms to the usual speed distribution of the path. If the speed fluctuates abnormally, it may also be an abnormal signal. In addition, attention will be paid to changes in road condition information, such as road construction, accidents, etc. that cause the road conditions to deteriorate. Through a detailed analysis of each feature sequence, the features that do not conform to the normal situation are identified. Finally, these abnormal features are summarized to obtain K abnormal path feature sets, which provide a basis for subsequent determination of abnormal paths and path optimization.

[0068] The K initial disassembly and transportation path sequences are matched according to the K abnormal path feature sets. First, the features in each abnormal path feature set are analyzed one by one. These features include traffic congestion in a specific section of road, road damage, abnormal vehicle speed, etc. Then, these features are compared with each path in the K initial disassembly and transportation path sequence. If a certain initial disassembly and transportation path has a situation that matches the abnormal path feature set, for example, a certain section on a certain path has traffic congestion or road damage consistent with the description in the abnormal feature set, then this initial disassembly and transportation path is considered abnormal. By performing such matching analysis on all initial disassembly and transportation paths, K abnormal initial disassembly and transportation path sets are finally determined. This set contains all the initial disassembly and transportation paths that are determined to be abnormal, providing a clear target object for subsequent optimization of these abnormal paths.

[0069] Taking K abnormal initial disassembly and transportation path sets as the source of optimization, the specific abnormal conditions in each abnormal initial disassembly and transportation path set are analyzed. These abnormalities include sections with severe traffic congestion, areas where traffic is blocked due to road construction or accidents, and sections with low vehicle driving efficiency. In response to these abnormal conditions, the optimization strategy is used to adjust the K initial disassembly and transportation path sequences. For example, if there is a traffic congestion problem on a certain path, find an alternative route, or adjust the driving time and sequence of the vehicle on the path. For sections blocked by road construction, re-plan the vehicle's driving route to avoid the construction area. At the same time, combined with the vehicle's transportation capacity and actual needs, the paths are more reasonably disassembled and combined. By carefully optimizing and adjusting each initial disassembly and transportation path, K optimized disassembly and transportation path sequences are finally obtained. These optimized path sequences can better adapt to actual transportation conditions, improve transportation efficiency, and reduce transportation costs and carbon emissions.

[0070] In a possible implementation, step S540 further includes:

[0071] Step S541: input the K abnormal path feature sets and the K abnormal initial disassembly and transportation path sets into a path optimization identifier to obtain K abnormal optimized disassembly and transportation path sets.

[0072] Step S542: Based on the K abnormal optimized disassembly and transportation path sets, the K initial disassembly and transportation path sequences are adaptively adjusted in terms of forward and backward paths to obtain the K optimized disassembly and transportation path sequences.

[0073] Specifically, the path optimization identifier is a tool specifically used to handle abnormal transportation paths and generate optimized path plans. Its main function is to receive two sets of input data: K abnormal path feature sets and K abnormal initial disassembly transportation path sets. For the abnormal path feature set, the path optimization identifier will analyze the various abnormal features in it, such as the specific degree of traffic congestion, the type and severity of road damage, and the specific manifestations of vehicle driving abnormalities (such as abnormal speed, abnormal fuel consumption, etc.). Through a deep understanding of these features, the identifier can determine how and to what extent the abnormal situation affects the transportation path. For the abnormal initial disassembly transportation path set, the identifier will study in detail each specific path where the abnormality occurs, including information such as the length of the path, the geographical area passed through, and the type of vehicle involved. Combining these two sets of data, the path optimization identifier comprehensively analyzes multiple factors, such as the current traffic conditions, the transportation capacity and efficiency of different vehicles, and the time requirements of the transportation task. By weighing and optimizing these factors, the identifier generates an optimized disassembly transportation path plan for each abnormal situation, and finally forms a set of K abnormal optimized disassembly transportation paths. Each element in this set is the optimal transportation route solution for a specific abnormal situation, providing specific guidance for subsequent route adjustments.

[0074] Based on the K abnormal optimized disassembly and transportation path sets, the K initial disassembly and transportation path sequences are adaptively adjusted to the front and rear paths. First, each optimized path scheme in the abnormal optimized disassembly and transportation path set is analyzed. These schemes are specially designed for abnormal situations, involving path changes, vehicle reallocation or adjustment of driving strategies. Then, for each path in the K initial disassembly and transportation path sequences, the part related to the abnormal optimized path is checked. If an initial path contains a path segment in the abnormal optimized path set, it is necessary to adaptively adjust the paths before and after it. This adjustment includes adjusting the driving speed of the vehicle in the front and rear path segments, replanning the driving order of the vehicle, and selecting a more suitable vehicle type to adapt to the new path conditions. By making detailed adjustments to each affected path segment, it is ensured that the front and rear paths can be seamlessly connected, and the overall transportation process is more efficient and stable. Finally, after comprehensive adjustments, K optimized disassembly and transportation path sequences are obtained. These optimized path sequences can better cope with various abnormal situations, improve the efficiency and reliability of bulk commodity transportation, and also help reduce carbon emissions.

[0075] Embodiment 2 is based on the same inventive concept as the method for calculating carbon emission reduction in bulk commodity transportation in the above embodiment. Figure 2 As shown, the present application provides a device for calculating carbon emission reduction in bulk commodity transportation, and the device and method embodiments in the present application embodiments are based on the same inventive concept. The device includes:

[0076] The initial transportation plan acquisition module 10 is used to obtain an initial transportation plan for the target bulk commodity, wherein the initial transportation plan includes K initial transportation routes, K transportation tasks and K vehicle transportation configuration data.

[0077] The initial disassembly and transportation path sequence acquisition module 20 disassembles the K initial transportation paths based on the K transportation tasks and the K vehicle transportation configuration data to obtain K initial disassembly and transportation path sequences, wherein the K initial disassembly and transportation path sequences include K vehicle iteration sequences.

[0078] The associated data sequence acquisition module 30 is used to traverse the K initial disassembly and transportation path sequences and the K vehicle iteration sequences to collect carbon emission associated data, and obtain K initial disassembly path associated data sequences and K vehicle iteration associated data sequences.

[0079] The initial carbon emission acquisition module 40 is used to map and integrate the K initial disassembly path associated data sequences and the K vehicle iteration associated data sequences, and calculate the carbon emissions in combination with the carbon emission factor to obtain the initial carbon emissions.

[0080] The optimized disassembly and transportation path sequence acquisition module 50 is used to perform serialized real-time monitoring on the K initial disassembly and transportation path sequences, optimize the disassembly and transportation paths according to the monitoring results, and obtain K optimized disassembly and transportation path sequences.

[0081] The optimized vehicle iterative sequence acquisition module 60 is used to perform mapping optimization adjustment on the vehicle iterative sequence according to the K optimized disassembly and transportation path sequences to obtain K optimized vehicle iterative sequences.

[0082] The optimized carbon emission acquisition module 70 is used to collect carbon emission related data for the K optimized disassembly and transportation path sequences and the K optimized vehicle iteration sequences and map and integrate the collected data, calculate the carbon emissions in combination with the carbon emission factors, and obtain the optimized carbon emissions.

[0083] The transport carbon emission reduction acquisition module 80 calculates the carbon emission reduction based on the optimized carbon emissions and the initial carbon emissions to obtain the transport carbon emission reduction of the target commodity.

[0084] Furthermore, the initial disassembly and transportation path sequence acquisition module 20 further includes:

[0085] The vehicle transportation key indicator set acquisition unit is used to extract indicators from the K vehicle transportation configuration data according to a preset transportation key indicator set to obtain K vehicle transportation key indicator sets.

[0086] An initial path feature sequence acquisition unit is used to extract path features from the K initial transport paths according to a preset interval step to obtain K initial path feature sequences, wherein each initial path feature sequence includes an initial transport path interval.

[0087] The initial transport path disassembly unit is used to disassemble the K initial transport paths according to the K transport tasks and the K initial path feature sequences based on the K vehicle transport key indicator sets as constraints to obtain the K initial disassembled transport path sequences.

[0088] Furthermore, the vehicle transportation key indicator set acquisition unit further includes:

[0089] The preset transport key indicator set includes multiple transport vehicle types, multiple types of vehicle configuration quantities and multiple transport vehicle parameter sets.

[0090] Furthermore, the initial transport path disassembly unit further includes:

[0091] A transport task commodity quantity extraction unit is used to extract K transport task commodity quantities of the K transport tasks.

[0092] A vehicle type combination set acquisition unit is configured to obtain K vehicle type combination sets by performing vehicle type combination analysis under full-load operation in the same period based on the K vehicle transportation key indicator sets and the K transportation task commodity quantities.

[0093] A fused initial path feature sequence acquisition unit is used to perform neighbor feature fusion on the K initial path feature sequences to obtain K fused initial path feature sequences, and to disassemble the K initial transport paths in combination with the initial transport path interval of each initial path feature sequence to obtain the K initial disassembled transport path sequences.

[0094] A vehicle iterative sequence acquisition unit is used to call a vehicle matching identifier to perform matching analysis on the K fused initial path feature sequences and the K vehicle type combination sets to obtain the K vehicle iterative sequences.

[0095] Furthermore, the initial carbon emission acquisition module 40 further includes:

[0096] The associated data sequence standardization processing unit is used to perform standardization processing on the K initial disassembly path associated data sequences and the K vehicle iterative associated data sequences to obtain K initial standardized path associated data sequences and K standardized vehicle iterative associated data sequences.

[0097] An initial standardized path-vehicle association data combination sequence acquisition unit is used to perform a one-to-one mapping between the K initial standardized path association data sequences and the K standardized vehicle iterative association data sequences to obtain K initial standardized path-vehicle association data combination sequences.

[0098] An initial carbon emission calculation unit is configured to calculate carbon emissions based on the K initial standardized route-vehicle association data combination sequences and the carbon emission factor to obtain the initial carbon emissions.

[0099] Furthermore, the optimized disassembly and transportation path sequence acquisition module 50 further includes:

[0100] A real-time monitoring path feature sequence acquisition unit is used to perform serialized real-time monitoring on the K initial disassembly and transportation path sequences when the transportation task begins, so as to obtain K real-time monitoring path feature sequences.

[0101] The abnormal path feature set acquisition unit is used to perform abnormality identification on the K real-time monitoring path feature sequences to obtain K abnormal path feature sets.

[0102] The abnormal initial disassembly and transportation path set acquisition unit is used to match the K initial disassembly and transportation path sequences according to the K abnormal path feature sets to obtain K abnormal initial disassembly and transportation path sets.

[0103] The disassembly and transportation path optimization unit is used to optimize the disassembly and transportation paths of the K initial disassembly and transportation paths with the K abnormal initial disassembly and transportation paths set as the optimization source, so as to obtain the K optimized disassembly and transportation paths sequence.

[0104] Furthermore, the disassembly and transportation path optimization unit also includes:

[0105] The abnormal optimized disassembly and transportation path set acquisition unit is used to input the K abnormal path feature sets and the K abnormal initial disassembly and transportation path sets into the path optimization identifier to obtain K abnormal optimized disassembly and transportation path sets.

[0106] A front-and-back path adaptive adjustment unit is configured to adaptively adjust the front-and-back paths of the K initial disassembly and transportation path sequences based on the K abnormal optimized disassembly and transportation path sets to obtain the K optimized disassembly and transportation path sequences.

[0107] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0108] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0109] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A method for calculating carbon emission reduction in bulk commodity transportation, characterized in that: The method comprises: Obtaining an initial transportation plan for the target bulk commodity, wherein the initial transportation plan includes K initial transportation routes, K transportation tasks, and K vehicle transportation configuration data; Disassembling the K initial transport paths based on the K transport tasks and the K vehicle transport configuration data to obtain K initial disassembled transport path sequences, wherein the K initial disassembled transport path sequences include K vehicle iteration sequences; Traversing the K initial disassembly and transportation path sequences and the K vehicle iteration sequences to collect carbon emission related data, and obtaining K initial disassembly path related data sequences and K vehicle iteration related data sequences; Mapping and integrating the K initial disassembly path associated data sequences and the K vehicle iteration associated data sequences, and calculating carbon emissions in combination with a carbon emission factor to obtain initial carbon emissions; Performing serialized real-time monitoring on the K initial disassembly and transportation path sequences, optimizing the disassembly and transportation paths according to the monitoring results, and obtaining K optimized disassembly and transportation path sequences; According to the K optimized disassembly and transportation path sequences, mapping optimization adjustment is performed on the vehicle iteration sequence to obtain K optimized vehicle iteration sequences; Collecting carbon emission-related data for the K optimized disassembly and transportation path sequences and the K optimized vehicle iteration sequences, mapping and integrating the collected data, and calculating carbon emissions in combination with carbon emission factors to obtain optimized carbon emissions; Calculating carbon emission reduction based on the optimized carbon emission and the initial carbon emission to obtain the transportation carbon emission reduction of the target commodity; The method for calculating carbon emission reductions in bulk commodity transportation also includes: Extracting indicators from the K vehicle transportation configuration data according to a preset transportation key indicator set to obtain a K vehicle transportation key indicator set; Extracting path features from the K initial transport paths according to a preset interval step length to obtain K initial path feature sequences, wherein each initial path feature sequence includes an initial transport path interval; Taking the K vehicle transportation key indicator sets as constraints, the K initial transportation paths are disassembled according to the K transportation tasks and the K initial path feature sequences to obtain the K initial disassembled transportation path sequences; The preset transport key indicator set includes a plurality of transport vehicle types, a plurality of vehicle configuration quantities of a plurality of types, and a plurality of transport vehicle parameter sets; The K initial transport paths are disassembled according to the K transport tasks and the K initial path feature sequences with the K vehicle transport key indicator sets as constraints to obtain the K initial disassembled transport path sequences, including: Extracting K transport task commodity quantities of the K transport tasks; Based on the K vehicle transportation key indicator sets and the K transportation task commodity quantities, a vehicle type combination analysis is performed under a full-load operation state during the same period to obtain a K vehicle type combination set; Performing neighbor feature fusion on the K initial path feature sequences to obtain K fused initial path feature sequences, and disassembling the K initial transport paths in combination with the initial transport path interval of each initial path feature sequence to obtain the K initial disassembled transport path sequences; A vehicle matching identifier is called to perform matching analysis on the K fused initial path feature sequences and the K vehicle type combination sets to obtain the K vehicle iteration sequences.

2. A method for calculating carbon emission reductions in bulk commodity transportation as claimed in claim 1, characterized in that: include: Standardizing the K initial disassembly path associated data sequences and the K vehicle iterative associated data sequences to obtain K initial standardized path associated data sequences and K standardized vehicle iterative associated data sequences; Performing a one-to-one mapping between the K initial standardized path association data sequences and the K standardized vehicle iteration association data sequences to obtain K initial standardized path-vehicle association data combination sequences; Carbon emissions are calculated based on the K initial standardized route-vehicle association data combination sequences and the carbon emission factor to obtain the initial carbon emissions.

3. A method for calculating carbon emission reductions in bulk commodity transportation as claimed in claim 1, characterized in that: The K initial disassembly and transportation path sequences are serially monitored in real time, and the disassembly and transportation paths are optimized according to the monitoring results to obtain K optimized disassembly and transportation path sequences, including: When the transportation task begins, the K initial disassembly and transportation path sequences are serialized and monitored in real time to obtain K real-time monitoring path feature sequences; Performing abnormality identification on the K real-time monitoring path feature sequences to obtain K abnormal path feature sets; Matching the K initial disassembly and transportation path sequences according to the K abnormal path feature sets to obtain K abnormal initial disassembly and transportation path sets; The K abnormal initial disassembly and transportation path sets are used as optimization sources, and the K initial disassembly and transportation path sequences are optimized to obtain the K optimized disassembly and transportation path sequences.

4. A method for calculating carbon emission reductions in bulk commodity transportation as claimed in claim 3, characterized in that: include: Inputting the K abnormal path feature sets and the K abnormal initial disassembly and transportation path sets into a path optimization identifier to obtain K abnormal optimized disassembly and transportation path sets; Based on the K abnormal optimized disassembly and transportation path sets, the K initial disassembly and transportation path sequences are adaptively adjusted in terms of forward and backward paths to obtain the K optimized disassembly and transportation path sequences.

5. A device for calculating carbon emission reduction in bulk commodity transportation, characterized in that: The device is used to execute the method for calculating carbon emission reduction in bulk commodity transportation according to any one of claims 1 to 4, comprising: An initial transportation plan acquisition module, which is used to acquire an initial transportation plan for the target bulk commodity, wherein the initial transportation plan includes K initial transportation routes, K transportation tasks, and K vehicle transportation configuration data; an initial disassembly and transportation path sequence acquisition module, wherein the initial disassembly and transportation path sequence acquisition module disassembles the K initial transportation paths based on the K transportation tasks and the K vehicle transportation configuration data to obtain K initial disassembly and transportation path sequences, wherein the K initial disassembly and transportation path sequences include K vehicle iteration sequences; An associated data sequence acquisition module, the associated data sequence acquisition module is used to traverse the K initial disassembly and transportation path sequences and the K vehicle iteration sequences to collect carbon emission associated data, and obtain K initial disassembly path associated data sequences and K vehicle iteration associated data sequences; An initial carbon emission acquisition module, the initial carbon emission acquisition module is used to map and integrate the K initial disassembly path associated data sequences and the K vehicle iteration associated data sequences, and calculate the carbon emissions in combination with the carbon emission factor to obtain the initial carbon emissions; An optimized disassembly and transportation path sequence acquisition module, which is used to perform serialized real-time monitoring of the K initial disassembly and transportation path sequences, optimize the disassembly and transportation paths according to the monitoring results, and obtain K optimized disassembly and transportation path sequences; An optimized vehicle iterative sequence acquisition module, wherein the optimized vehicle iterative sequence acquisition module is used to perform mapping optimization adjustment on the vehicle iterative sequence according to the K optimized disassembly and transportation path sequences to obtain K optimized vehicle iterative sequences; An optimized carbon emission acquisition module, which is used to collect carbon emission related data for the K optimized disassembly transportation path sequences and the K optimized vehicle iteration sequences, map and integrate the collected data, calculate carbon emissions in combination with carbon emission factors, and obtain optimized carbon emissions; A transport carbon emission reduction acquisition module is used to calculate the carbon emission reduction based on the optimized carbon emissions and the initial carbon emissions to obtain the transport carbon emission reduction of the target commodity.

Images