CN-122022639-A - Logistics vehicle-cargo intelligent matching system and method based on carbon footprint

CN122022639ACN 122022639 ACN122022639 ACN 122022639ACN-122022639-A

Abstract

The invention discloses an intelligent logistics vehicle-cargo matching system and method based on carbon footprint, which relate to the technical field of logistics vehicle-cargo matching and comprise the steps of analyzing the adaptation degree between a vehicle and target cargoes to obtain candidate vehicles, obtaining a logistics track set, analyzing the energy consumption distribution condition of the candidate vehicles under different logistics tracks in the logistics track set, analyzing the carbon emission uncertainty degree of the candidate vehicles under different logistics tracks, evaluating the carbon emission steady condition of the candidate vehicles in the different logistics tracks, acquiring the target track carbon emission data of the different candidate vehicles, analyzing the logistics matching degree of the different candidate vehicles and the target cargoes, determining the target vehicles, acquiring the target logistics track of the target vehicles, carrying out logistics transportation on the target cargoes according to the target logistics track, not only greatly reducing the carbon emission in the target cargo transportation process, but also realizing logistics efficiency, and realizing true energy conservation and environmental protection in the transportation field.

Inventors

DU SONGLIN
ZHANG LIYANG
LEI TAO
Sadamu Shadik
WANG BINGQUAN
WANG XIAOFENG
YU JIONG
DU XUSHENG
ZHOU XIAOCHENG

Assignees

杭州骋风而来数字科技有限公司
新疆丝路融创网络科技有限公司

Dates

Publication Date: 20260512
Application Date: 20260414

Claims (10)

1. The logistics vehicle-cargo intelligent matching method based on the carbon footprint is characterized by comprising the following steps of: Step S1, acquiring vehicle characteristic data of a vehicle, acquiring cargo characteristic data of target cargoes, and analyzing the adaptation degree between the vehicle and the target cargoes to obtain candidate vehicles; S2, generating a logistics track of a candidate vehicle for transporting target cargoes, obtaining a logistics track set, obtaining a history logistics record of the candidate vehicle, analyzing energy consumption distribution conditions of the candidate vehicle under different logistics tracks in the logistics track set, analyzing carbon emission uncertainty degree of the candidate vehicle under different logistics tracks, and evaluating carbon emission steady conditions of the candidate vehicle in different logistics tracks to obtain target track carbon emission data; s3, acquiring target track carbon emission data of different candidate vehicles, analyzing logistics matching degrees of the different candidate vehicles and target cargoes, and determining the target vehicles; and S4, acquiring a target logistics track of the target vehicle, and carrying out logistics transportation on target cargoes according to the target logistics track.
2. The method for intelligent matching of logistics vehicles and goods based on carbon footprint as set forth in claim 1, wherein the step S2 includes: the method comprises the steps of obtaining position coordinates of a target cargo and a unloading point of the target cargo from cargo characteristic data of the target cargo, obtaining position coordinates of a candidate vehicle in a current period from vehicle characteristic data, generating and collecting all logistics tracks of the candidate vehicle for transporting the target cargo, and obtaining a logistics track set of the candidate vehicle for transporting the target cargo; acquiring a logistics track from the logistics track in a concentrated way, acquiring each running change point in the logistics track, and marking a section between every two adjacent running change points in the logistics track as a logistics section of the logistics track; acquiring a pre-trained fractional energy consumption regression model, acquiring data of each road section attribute in a logistics road section in a logistics track, acquiring data of each vehicle index of a candidate vehicle in the running process of the logistics road section, acquiring predicted values of vehicle energy consumption of different fractional points of the candidate vehicle in the logistics road section by using the fractional energy consumption regression model, and constructing a road section energy consumption quantile matrix in the logistics track; Estimating the average value and standard deviation of the predicted value of the vehicle energy consumption in the logistics road section according to the road section energy consumption fractional number matrix to obtain the average value and standard deviation of the energy consumption of the logistics road section; Acquiring an adjacent road section adjacent to the logistics road section from the physical flow track, acquiring each characteristic history logistics record of the logistics road section and the adjacent road section, and calculating an energy consumption correlation coefficient rho of the candidate vehicle between the logistics road section and the adjacent road section; Obtaining carbon emission factors of all the logistics sections in the logistics track, calculating a carbon emission average mu R of the candidate vehicle in the logistics track, calculating a carbon emission variance S R of the candidate vehicle in the logistics track, and calculating a standard deviation of the carbon emission of the logistics track ; Calculating carbon emission robust value of candidate vehicle in logistics track Wherein λ represents a preset risk aversion coefficient; And acquiring a minimum value M min of carbon emission steady values of the candidate vehicle in each logistics track in the logistics track set, recording the logistics track corresponding to M min as a target logistics track of the candidate vehicle, acquiring the target logistics track and the carbon emission steady values of the candidate vehicle, and collecting to obtain target track carbon emission data.
3. The method for intelligent matching of logistics vehicles and goods based on carbon footprint as set forth in claim 1, wherein the step S1 includes: acquiring cargo characteristic data of target cargos to be subjected to logistics in a current period from a logistics platform, acquiring cargo types of the target cargos, and acquiring vehicles which are not transported by cargos and are compatible with the cargo types of the target cargos from the logistics platform; acquiring vehicle characteristic data of each vehicle which does not carry out cargo transportation in the current period from a logistics platform, and acquiring the maximum available transportation quality and the maximum available transportation volume of the vehicle from the vehicle characteristic data; Acquiring the total cargo mass G and the total cargo volume V of the target cargo from the cargo characteristic data, and reserving a certain vehicle when the maximum available transport mass and the maximum available transport volume of the certain vehicle are respectively larger than the total cargo mass and the total cargo volume of the target cargo; Calculating the load utilization rate of a certain vehicle G ́ is the maximum available transport mass of a certain vehicle over the current period; Calculating volume utilization of a vehicle Wherein V ́ is the maximum available transport volume of a vehicle over the current period; Obtaining a linear distance L ́ between a certain vehicle and the target cargo, obtaining a linear distance L between the target cargo and a unloading point of the target cargo, and calculating a linear total distance of the certain vehicle to the target cargo ; According to the load utilization rate f G of a certain vehicle, the volume utilization rate f V of a certain vehicle and the linear total distance L sum , calculating an adaptation degree score A between the certain vehicle and the target cargo, setting a scoring threshold value a, judging the adaptation between the certain vehicle and the target cargo when A > a, and marking the certain vehicle as a candidate vehicle of the target cargo.
4. The method for intelligent matching of logistics vehicles and goods based on carbon footprint as set forth in claim 1, wherein the step S3 includes: Acquiring target track carbon emission data of each candidate vehicle of the target cargo, acquiring a target logistics track of the candidate vehicle from the target track carbon emission data, and acquiring expected logistics time of the candidate vehicle for carrying out logistics transportation on the target cargo according to the target logistics track; Acquiring a time threshold value and a carbon emission threshold value of a target cargo logistics, when the estimated logistics time of a certain candidate vehicle of the target cargo passing through a target logistics track is smaller than the time threshold value and the carbon emission steady value of the certain candidate vehicle in the target logistics track is smaller than the carbon emission threshold value, reserving the certain candidate vehicle, otherwise, removing the certain candidate vehicle from each candidate vehicle of the target cargo; respectively carrying out normalization processing on the carbon emission steady value and the expected logistics time of each candidate vehicle of the target cargo in the target logistics track, and calculating the characteristic logistics cost U of transporting the target cargo by a certain candidate vehicle logistics: , Wherein phi t represents a preset time weight, phi M represents a preset carbon emission weight, phi t >0,φ M >0, M △ is the carbon emission steady value of a certain candidate vehicle in the target logistics track; And acquiring a minimum value U min of the characteristic logistics cost of the object goods transported by the object goods by each candidate vehicle logistics, acquiring a candidate vehicle corresponding to the minimum value U min , matching the candidate vehicle corresponding to the minimum value U min into the object vehicle for carrying out logistics transportation on the object goods, and acquiring the object logistics track of the object vehicle.
5. The method for intelligent matching of logistics vehicles and goods based on carbon footprint as set forth in claim 1, wherein the step S4 includes: obtaining each candidate package of the target goods from the logistics platform, obtaining the residual use times of each candidate package in the current period, and taking the candidate package corresponding to the maximum value of the residual use times as the target package of the target goods; the method comprises the steps of obtaining a target vehicle and a target logistics track of target goods, sending the target logistics track to a driver of the target vehicle, packaging the target goods by using target packages, and prompting the driver of the target vehicle to carry out logistics transportation on the target goods according to the target logistics track through a logistics platform.
6. A logistics vehicle-cargo intelligent matching system based on carbon footprint, which is used for executing the logistics vehicle-cargo intelligent matching method based on carbon footprint in any one of claims 1-5, and is characterized in that the system comprises an adaptation degree analysis module, a logistics track carbon emission evaluation module, a logistics matching analysis module and a logistics transportation module; the adaptation degree analysis module is used for acquiring vehicle characteristic data of the vehicle, acquiring cargo characteristic data of target cargoes, and analyzing the adaptation degree between the vehicle and the target cargoes to obtain candidate vehicles; the logistics track carbon emission evaluation module is used for evaluating the carbon emission steady state of the candidate vehicle in different logistics tracks to obtain target track carbon emission data; the logistics matching analysis module is used for acquiring the carbon emission data of the target track of different candidate vehicles, analyzing the logistics matching degree of the different candidate vehicles and the target goods and determining the target vehicles; and the logistics transportation module is used for carrying out logistics transportation on the target goods according to the target logistics track of the target vehicle.
7. The intelligent matching system for logistics vehicles and goods based on carbon footprint as claimed in claim 6, wherein the adaptation degree analysis module comprises a vehicle screening unit and an adaptation degree analysis unit; the vehicle screening unit is used for screening each vehicle which does not carry out cargo transportation in the current period according to the total cargo mass and the total cargo volume of the target cargo; The adaptation degree analysis unit is used for calculating the adaptation degree scores between the vehicles and the target goods which are not transported in the current period, and analyzing the adaptation degree between the vehicles and the target goods according to the adaptation degree scores to obtain candidate vehicles.
8. The intelligent logistics vehicular cargo matching system based on the carbon footprint of claim 6, wherein the logistics track carbon emission assessment module comprises an energy consumption distribution analysis unit and a logistics track carbon emission assessment unit; the energy consumption distribution analysis unit is used for acquiring the generated logistics track set, acquiring the history logistics records of the candidate vehicles and analyzing the energy consumption distribution conditions of the candidate vehicles under different logistics tracks in the logistics track set; the logistics track carbon emission evaluation unit is used for analyzing the carbon emission uncertainty degree of the candidate vehicle under different logistics tracks, evaluating the carbon emission steady state of the candidate vehicle in different logistics tracks and obtaining target track carbon emission data.
9. The intelligent matching system for logistics vehicles and goods based on carbon footprint as claimed in claim 6, wherein the logistics matching analysis module comprises a data acquisition unit and a logistics matching analysis unit; The data acquisition unit is used for acquiring target track carbon emission data of each candidate vehicle of the target goods; And the logistics matching analysis unit is used for analyzing the logistics matching degree of different candidate vehicles and target cargoes according to the target track carbon emission data of each candidate vehicle and determining the target vehicle.
10. The carbon footprint-based logistics vehicular cargo intelligent matching system of claim 6, wherein the logistics transportation module comprises a logistics transportation unit; The logistics transportation unit is used for acquiring target packages of logistics transportation of target goods, acquiring target vehicles and target logistics tracks of the target goods, packaging the target goods by using the target packages, and prompting a driver of the target vehicles to carry out logistics transportation on the target goods according to the target logistics tracks through the logistics platform.

Description

Logistics vehicle-cargo intelligent matching system and method based on carbon footprint Technical Field The invention relates to the technical field of logistics vehicle-cargo matching, in particular to a logistics vehicle-cargo intelligent matching system and method based on carbon footprint. Background In the conventional matching method of logistics vehicles and goods in the transportation industry, only whether the vehicle type is matched with the tonnage of goods, whether the oil cost is too much or not, and the transportation distance length are considered, so that the consideration of the quantity of carbon emission of the vehicles in the process of transporting the goods by the vehicles is lacked, the fuel is consumed inefficiently in the process of transporting the goods by the vehicles, and a large amount of extra emission which can be avoided is generated, therefore, in order to solve the problem of carbon emission in the process of transporting the goods by the logistics, the logistics vehicles and the logistics goods are mainly matched through a digital platform, thereby realizing logistics transportation by matching the logistics goods with the logistics vehicles, greatly reducing the carbon emission of the goods in the logistics process by the conventional logistics vehicles and goods matching method, but only simply considering the carbon emission of the logistics vehicles in the logistics process of transporting the logistics goods is fixed, and the uncertainty of carbon emission of the same logistics vehicles in the logistics process of transporting the logistics goods is lacked, namely, the uncertainty of carbon emission of the same logistics vehicles in the logistics process of the logistics goods is often different in terms of carbon emission of the same path, the uncertainty of the carbon emission of the same logistics vehicles in the same path is different, the carbon emission of the logistics vehicles in the same path is likely, and the carbon emission of the same path is very high, and the carbon emission of the physical vehicles and the physical carbon emission is very high in the same condition is very high, and even the optimal carbon emission is very high along with the actual carbon emission. Disclosure of Invention The invention aims to provide a logistics vehicle-cargo intelligent matching system and method based on carbon footprint, which are used for solving the problems in the prior art. In order to achieve the purpose, the invention provides the technical scheme that the logistics vehicle-cargo intelligent matching method based on the carbon footprint comprises the following steps: Step S1, acquiring vehicle characteristic data of a vehicle, acquiring cargo characteristic data of target cargoes, and analyzing the adaptation degree between the vehicle and the target cargoes to obtain candidate vehicles; S2, generating a logistics track of a candidate vehicle for transporting target cargoes, obtaining a logistics track set, obtaining a history logistics record of the candidate vehicle, analyzing energy consumption distribution conditions of the candidate vehicle under different logistics tracks in the logistics track set, analyzing carbon emission uncertainty degree of the candidate vehicle under different logistics tracks, and evaluating carbon emission steady conditions of the candidate vehicle in different logistics tracks to obtain target track carbon emission data; s3, acquiring target track carbon emission data of different candidate vehicles, analyzing logistics matching degrees of the different candidate vehicles and target cargoes, and determining the target vehicles; and S4, acquiring a target logistics track of the target vehicle, and carrying out logistics transportation on target cargoes according to the target logistics track. Further, step S2 includes: the method comprises the steps of obtaining position coordinates of a target cargo and a unloading point of the target cargo from cargo characteristic data of the target cargo, obtaining position coordinates of a candidate vehicle in a current period from vehicle characteristic data, generating and collecting all logistics tracks of the candidate vehicle for transporting the target cargo, and obtaining a logistics track set of the candidate vehicle for transporting the target cargo; acquiring a logistics track from the logistics track in a concentrated way, acquiring each running change point in the logistics track, and marking a section between every two adjacent running change points in the logistics track as a logistics section of the logistics track; acquiring a pre-trained fractional energy consumption regression model, acquiring data of each road section attribute in a logistics road section in a logistics track, acquiring data of each vehicle index of a candidate vehicle in the running process of the logistics road section, acquiring predicted values of vehicle energy consumption of different fractional