CN116432880B - Intelligent selection and freight quotation system for shared cloud warehouse logistics city distribution route - Google Patents
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Abstract
The invention discloses an intelligent selection and freight quotation system for a shared cloud bin logistics city allocation route, which comprises the following steps: collecting information of vehicle owners and cargo owners related to truck delivery, uploading the information to a cloud data center of a shared cloud bin, and auditing the information in the cloud data center; matching the goods with the freight car by constructing a loading task according to the checked information, uploading the information of the goods which are not loaded to an edge end server of the shared cloud bin, and continuously completing the loading task by inquiring the vehicles which are not loaded fully until the goods are distributed; generating a delivery order after the goods are distributed, intelligently calculating an optimal path through analysis of the delivery order and according to the positions of a departure place and a destination, sending the optimal path to a vehicle owner for selection, and generating a decision order after the selection is completed; and formulating the freight policy, sending the freight policy to the shared cloud deck, and generating freight quotations by the sharing Yun Cang and sending the freight quotations to users in the system. Through path optimization and freight quotation, the transportation efficiency of urban distribution flows is improved.
Description
Technical Field
The invention relates to the technical field of logistics, in particular to an intelligent selection and freight quotation system for a logistics city distribution route of a shared cloud warehouse.
Background
With the deep development of emerging technologies such as cloud computing and edge computing, in the context of the internet +, the traditional logistics industry and logistics urban distribution have explored new modes of roads. The logistics city distribution is an important link in the whole logistics process, wherein 'distribution' mainly refers to matching of goods and trucks, the matching of the trucks and the goods is mainly divided into two types, one type is that the matching is uniformly carried out by a transportation center and the trucks and the owners select the goods by themselves, the distribution center mainly synthesizes goods information and truck information at a service end to carry out online matching, then the matching result is sent to the owners, the owners carry out transportation work, in the process, the owners do not care about other expenses generated by distribution and only pay attention to the benefits of distribution, in the distribution center, the owners select whether to suitably bear the goods transportation or not according to the weight, price, volume, benefits and the like of the comprehensive goods, in the process, the owners care about the benefits and comprehensively consider the time cost problem in the truck transportation process, on the other hand, after the matching of the trucks and the goods is completed, the owners transport the goods to customer demand points, in the process, the path selection relation to the transportation cost is the transportation cost, and therefore a road with the lowest path cost needs to be selected in the truck process.
However, in the selection of the current urban distribution route, how to combine the problems of "distribution" and "delivery" together and calculate the optimal urban distribution route is a always plagued problem, so that an intelligent selection route and a freight quotation system are required to be designed to perform route distribution of a logistics system in real time, thereby reducing the cost of additional paths and improving the benefits of the logistics industry.
Disclosure of Invention
The invention provides an intelligent selection and freight quotation system for a shared cloud bin logistics city allocation route, which aims to solve the problems in the prior art.
An intelligent selection and freight quotation system for a shared cloud warehouse logistics city allocation route, comprising:
and an information uploading module: collecting information of vehicle owners and cargo owners related to truck delivery, uploading the information to a cloud data center of a shared cloud bin, and auditing the information in the cloud data center;
and the vehicle cargo matching module is used for: matching the goods with the freight car by constructing a loading task according to the checked information, uploading the information of the goods which are not loaded to an edge end server of the shared cloud bin, and continuously completing the loading task by inquiring the vehicles which are not loaded fully until the goods are distributed;
and a path optimization module: generating a delivery order after the goods are distributed, intelligently calculating an optimal path through analysis of the delivery order and according to the positions of the departure place and the destination, sending the optimal path to a vehicle owner for selection, and generating a decision order after the selection is completed.
Preferably, an intelligent selection and freight quotation system for a shared cloud warehouse logistics urban distribution route further comprises:
and the freight decision module is used for: acquiring cargo information in a truck and the decision order, and implementing freight policies according to path information in the decision order, wherein the freight policies comprise a complete freight-free policy, a complete non-free policy and a conditional free policy;
and a freight quotation module: the freight policy formulated according to the goods information and the path information is sent to the shared cloud deck, and freight quotations are generated by the shared Yun Cang and sent to users in the system;
operation analysis module: and monitoring the freight quotation flow in real time, and calculating an optimal freight strategy to keep the logistics profit within the threshold range set by the system.
Preferably, the collecting owner and owner information related to truck delivery includes:
information acquisition unit: collecting owner information and goods owner information, and uploading the owner information to a shared cloud bin, wherein the owner information comprises personal information of an owner and goods information, the shared Yun Cang carries out auditing after receiving the owner information, and the owner information is extracted and forms goods information to be matched after the auditing is passed; the goods owner information comprises goods owner information and personal information, the shared cloud bin receives the goods owner information and then carries out auditing, and the audited information is extracted to form goods information to be matched;
an information classification unit: the sharing Yun Cang divides the delivery mode of the cargo owner into single-line delivery, individual delivery and priority delivery according to the type of the cargo owner after receiving the information of the vehicle owner and the information of the cargo owner; the single-line delivery takes the goods with special requirements as delivery standards, and the individual delivery takes the matching degree of the goods and the price as the delivery standards.
Preferably, the construction and loading task matches the truck with the goods, including:
model construction unit: acquiring the information of the goods to be matched and the information of the goods to be matched, constructing a matching model for matching the goods and the vehicles, and feeding back a matching result to a user in the system;
an information transmission unit: recording a successfully matched vehicle-cargo matching order, calculating the loading rate of a truck according to the order, if the loading rate of the current truck is lower, constructing a task table by a shared cloud bin according to truck path information of the target address and historical real-time cargo information, and feeding back the task table to an edge node which is close to an edge server in road conditions and has more real-time cargoes in the truck driving process;
a path selection unit: and acquiring path information of the delivery target address of the truck, intelligently selecting an optimal path of the delivery route, and delivering.
Preferably, the construction matching model performs cargo matching, including:
cloud data processing subunit: the cloud data center calculates the loading rate of the trucks while carrying out first matching of the trucks, transmits the information of the trucks which are not fully loaded to an edge end server of the shared cloud bin, and carries out second matching of the trucks by the shared cloud bin;
edge service computation subunit: receiving real-time information of cargoes and real-time road condition information uploaded by a truck by the edge server, and calculating the loading rate of the second matching of the cargoes according to the real-time information of the cargoes and the real-time road condition information; the loading rate is used for inquiring whether the current truck has the use capacity for continuing loading and distribution.
Preferably, the intelligent selecting of the optimal path of the delivery route includes:
service function unit: managing truck information and path intelligent selection information, and recording the running route of the truck, combining the running route with the cargo information, and confirming the running preference of a truck owner and generating a delivery order;
order analysis unit: after the delivery order is generated, the system analyzes the information of the delivery order and is used for confirming the current position of the truck and predicting the income situation of the freight bill;
an intelligent path selection unit: and (3) carrying out path optimization by combining the initial condition and the real-time condition, taking the delivery route as a first assembly route, judging whether the current truck is in a full-load state or not by carrying out path optimization by combining the delivery information and the loading rate of the cargoes, and planning a second assembly route for the car owner according to the judgment result.
Preferably, the intelligent selecting the optimal path of the delivery route further includes:
an optimal path display unit: after optimizing the path of the truck, the system sends the optimal path to the truck owner and selects the optimal path according to the requirement of the truck owner;
truck information tracking unit: in the running process of the truck, the system acquires the current position of the truck and judges the current road condition of the truck in real time through GPS automatic navigation positioning, and judges whether a route needs to be re-planned according to the path condition;
real-time information feedback unit: in the running process of the trucks, the position sharing function is automatically started, running information of all trucks is counted by the shared cloud deck, real-time feedback from the trucks is received, and the running state of the vehicle is judged.
Preferably, the combining the initial and real-time conditions for path optimization includes:
an initial path optimization subunit: the cloud data center analyzes attribute information and loading rate of truck delivery, scores different orders according to the loading rate, calculates similarity of the attribute information of the historical orders and the attribute information of the current orders, and selects a path with highest similarity to recommend as an initial scheme;
real-time path optimization subunit: after receiving the initial scheme sent by the share Yun Cang, the edge server ranks the vehicle owners without full load in priority, receives real-time cargo information, ranks the cargoes according to emergency situations and yield of the cargoes, calculates matching schemes of the cargoes and the cargoes after ranking, and recommends the vehicle owners by combining real-time path situations.
Preferably, the sharing Yun Cang generates and sends a freight rate offer to the users in the system, including:
cargo classification unit: classifying cargoes to be transported by a car owner, and calculating freight charges of each kind of cargoes in a mode of changing unit price according to classification results;
model construction unit: constructing a freight quotation model for calculating freight of goods, removing fixed fees of the quality of the goods in the freight transportation process, wherein the residual freight grows along with the increase of the number of transport mileage and has a certain linear relation, and using a least square method to establish a functional relation to obtain a linear equation to obtain the freight; according to the classification of different properties of the goods, settlement formulas of different goods types are obtained, and a freight quotation model is constructed;
freight settlement unit: and carrying out freight rate summation on the transported freight car according to different kinds of cargoes through the freight rate quotation model to obtain freight rate of the whole freight car.
Preferably, the construction matching model performs cargo matching, including:
a data cooperation unit: sharing Yun Cang and an edge server together maintain data information in a system, setting a plurality of edge nodes in a shared cloud bin, setting data synchronization between the edge nodes and a center node, calculating task completion time of the edge nodes according to historical truck distribution amount, and distributing the data information to the edge nodes according to the task completion time;
resource coordination unit: the method comprises the steps of storing resource information of vehicle-cargo matching, carrying out collaborative task scheduling according to the freight car delivery quantity of the edge nodes by recording the resource information condition of the edge nodes, and combining the edge nodes with different resources;
an application coordination unit: and the cloud storage system is used for carrying out cargo matching deployment by combining the shared cloud storage with the edge server.
Compared with the prior art, the invention has the following advantages:
the invention provides an intelligent selection and freight quotation system for a shared cloud warehouse logistics city allocation route, which can effectively utilize resources through cloud computing vehicle-to-vehicle matching, improve vehicle-to-vehicle matching efficiency and realize maximization of platform benefits.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a block diagram of a shared cloud deck logistics city allocation route intelligent selection and freight quotation system in an embodiment of the invention;
FIG. 2 is a block diagram of a user in a system sending a freight quote generated by a shared cloud deck in an embodiment of the invention;
FIG. 3 is an internal block diagram of a matching model constructed to match vehicles in an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
Referring to fig. 1, an embodiment of the present invention provides an intelligent selection and freight quotation system for a shared cloud deck logistics city allocation route, including:
and an information uploading module: collecting information of vehicle owners and cargo owners related to truck delivery, uploading the information to a cloud data center of a shared cloud bin, and auditing the information in the cloud data center;
and the vehicle cargo matching module is used for: matching the goods with the freight car by constructing a loading task according to the checked information, uploading the information of the goods which are not loaded to an edge end server of the shared cloud bin, and continuously completing the loading task by inquiring the vehicles which are not loaded fully until the goods are distributed;
and a path optimization module: generating a delivery order after the goods are distributed, intelligently calculating an optimal path through analysis of the delivery order and according to the positions of the departure place and the destination, sending the optimal path to a vehicle owner for selection, and generating a decision order after the selection is completed.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the safety of cargo transportation is ensured by uploading the owner information and the cargo owner information, the optimal route is provided for delivery by matching the vehicles and the cargoes, the working efficiency is improved, and finally, the route is optimized, so that the owner can completely rely on a delivery system.
In another embodiment, a shared cloud deck logistics urban distribution route intelligent selection and freight quotation system further comprises:
and the freight decision module is used for: acquiring cargo information in a truck and the decision order, and implementing freight policies according to path information in the decision order, wherein the freight policies comprise a complete freight-free policy, a complete non-free policy and a conditional free policy;
and a freight quotation module: the freight policy formulated according to the goods information and the path information is sent to the shared cloud deck, and freight quotations are generated by the shared Yun Cang and sent to users in the system;
operation analysis module: and monitoring the freight quotation flow in real time, and calculating an optimal freight strategy to keep the logistics profit within the threshold range set by the system.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the freight policy is implemented, so that the transportation efficiency can be improved.
In another embodiment, the collecting owner and owner information related to truck delivery includes:
information acquisition unit: collecting owner information and goods owner information, and uploading the owner information to a shared cloud bin, wherein the owner information comprises personal information of an owner and goods truck information, the shared Yun Cang carries out auditing after receiving the owner information, and main information in the owner information is extracted and forms goods truck information to be matched after the auditing is passed; the goods owner information comprises goods owner information and personal information, the shared cloud bin receives the goods owner information and then carries out auditing, and the audited information is extracted to form goods information to be matched;
an information classification unit: the sharing Yun Cang divides the delivery mode of the cargo owner into single-line delivery, individual delivery and priority delivery according to the type of the cargo owner after receiving the information of the vehicle owner and the information of the cargo owner; the single-line delivery takes the goods with special requirements as delivery standards, and the individual delivery takes the matching degree of the goods and the price as the delivery standards.
The working principle of the technical scheme is as follows: the scheme adopted by the embodiment is that when route distribution is carried out on acquisition, an owner driver and a retrograde uploading is needed, the information uploading mainly comprises an owner, a cargo owner and a shared cloud bin, the shared Yun Cang is subjected to auditing after receiving owner information and cargo information, and main information after the auditing is passed is extracted, wherein the main information comprises the owner information and cargo information loaded by the cargo to form cargo information to be matched. The goods owner uploads goods information and personal monitoring information, the platform forms information audit after receiving, extracts and forms goods information to be matched, the shared cloud bin mainly comprises a path monitor, the information is automatically uploaded to the platform, and effective information of a road is formed through initial verification and recording.
After the share Yun Cang receives the owner information and the owner information, the owner may be classified into single-line owners according to the types of owners, and if the driver has special demands on the owner information and the road information, the driver does not accept other cargo dispatch. There are also individual drivers who distribute, which means drivers who have no special requirements on the type of goods and the special goods, mainly based on the price and matching degree of the goods. The cargo information can be divided into common cargo and special cargo, and the special cargo mainly refers to cargo with special requirements for the type of truck and the loading and unloading level. The information classification link is mainly to form cargo information and truck information to be matched by checking, cleaning and classifying the information, store the cargo information and the truck information in a platform, form the priority of the information according to the state of the information, upload the information according to the priority, and upload the information preferentially to urgent tasks.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the information of the vehicle owners and the vehicles and the cargoes is collected and classified, so that the sharing Yun Cang is facilitated to classify the cargoes.
In another embodiment, the build loading task matches a truck with a cargo, comprising:
model construction unit: acquiring the information of the goods to be matched and the information of the goods to be matched, constructing a matching model for matching the goods and the vehicles, and feeding back a matching result to a user in the system;
an information transmission unit: recording a successfully matched vehicle-cargo matching order, calculating the loading rate of a truck according to the order, if the loading rate of the current truck is lower, constructing a task table by a shared cloud bin according to truck path information of the target address and historical real-time cargo information, and feeding back the task table to an edge node which is close to an edge server in road conditions and has more real-time cargoes in the truck driving process;
a path selection unit: and acquiring path information of the delivery target address of the truck, intelligently selecting an optimal path of the delivery route, and delivering.
In another embodiment, the construction matching model performs a cargo matching, comprising:
cloud data processing subunit: the cloud data center calculates the loading process of the truck while carrying out the first matching of the truck and the truck, transmits the information of the unloaded truck to an edge server of the shared cloud bin, and carries out the second matching of the truck and the truck by the shared cloud bin;
edge service computation subunit: receiving real-time information of cargoes and real-time road condition information uploaded by a truck by the edge server, and calculating the loading rate of the second matching of the cargoes according to the real-time information of the cargoes and the real-time road condition information; the loading rate is used for inquiring whether the current truck has the use capacity for continuing loading and distribution.
The working principle of the technical scheme is as follows: the scheme adopted by the embodiment is that the share Yun Cang builds a vehicle-cargo matching model after receiving the uploaded task, and feeds back the matching result to the system for user feedback. And simultaneously carrying out first matching, recording a scheme of successful matching by the shared cloud bin, calculating the loading rate of the truck, and judging whether the truck needs to be matched for the second time or not next according to the loading rate. And the shared cloud bin records the successfully matched vehicle-cargo matching orders, calculates the loading rate of the trucks according to the orders, collects truck information if the loading rate of the current trucks is low, forms a task table according to the truck path and the frequency of the historical real-time cargo information, and feeds back the task table to the edge nodes close to the road conditions in which the real-time cargoes are more in the truck driving process.
The uploading of the real-time cargo information is similar to the uploading of the initial cargo information, the cargo owner uploads the cargo and personal information to the platform, the platform carries out auditing and extraction, and reliable information is formed into the cargo information to be matched and uploaded to the edge server. After the edge server receives the second matching task, the edge server performs the second matching according to the truck information transmitted by the sharing Yun Cang and the received real-time cargo information, and compared with the initial matching process of the sharing cloud bin, the edge server needs to combine the second matching cost of the truck, the expiration time of the initial cargo and the like for matching when performing the matching.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the truck is matched with the truck owner, the most suitable distribution mode is selected, the matching model is constructed to facilitate the truck owner to select, and finally, the shortest path is calculated, so that the distribution process is simpler and faster.
In another embodiment, the intelligent selecting the optimal path of the delivery route includes:
service function unit: managing truck information and path intelligent selection information, and recording the running route of the truck, combining the running route with the cargo information, and confirming the running preference of a truck owner and generating a delivery order;
order analysis unit: after the delivery order is generated, the system analyzes the information of the delivery order and is used for confirming the current position of the truck and predicting the income situation of the freight bill;
an intelligent path selection unit: and (3) carrying out path optimization by combining the initial condition and the real-time condition, taking the delivery route as a first assembly route, judging whether the current truck is in a full-load state or not by carrying out path optimization by combining the delivery information and the loading rate of the cargoes, and planning a second assembly route for the car owner according to the judgment result.
The working principle of the technical scheme is as follows: the scheme adopted by the embodiment is that information in a truck is managed and relevant information of path optimization is saved, wherein the relevant information comprises the running time, the running route, the maintenance condition, the oiling condition, the maintenance condition and the like of the truck, historical path information of the truck is recorded, the truck, the running route and cargo information are combined, and running preference of a truck owner is confirmed. After the delivery order is generated, the system analyzes the order information, including a delivery point, a delivery receiving time, a receiving cut-off time, whether the delivery spelling is received, whether the delivery spelling is enough special requirements for the vehicle type, vehicle information and the like, and mainly analyzes the current position information of the order vehicle, the state information of the vehicle, the income condition of the order and other relevant information. When the initial path optimization is carried out, the path optimization is mainly carried out by combining the delivery information and the loading rate of the cargos, if the current cargos are in a full-load state, the loading condition of the cargos does not need to be considered, and the path optimization is carried out on the cargos in a targeted mode through the shortest path. If the loading rate of the current vehicle is low, when the path optimization of the vehicle is carried out, the path optimization should be carried out with the maximum benefit of the vehicle as a target, and when the vehicle is in the driving process, if the vehicle is in secondary matching, the path optimization at the moment should be combined with real-time path information to plan a proper secondary assembly route for the vehicle owner.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the optimal distribution route is quickly found out after the goods are matched with the car owners through planning the route, so that the operation efficiency is improved.
In another embodiment, the intelligent selecting the optimal path of the delivery route further includes:
an optimal path display unit: after optimizing the path of the truck, the system sends the optimal path to the truck owner and selects the optimal path according to the requirement of the truck owner;
truck information tracking unit: in the running process of the truck, the system acquires the current position of the truck and judges the current road condition of the truck in real time through GPS automatic navigation positioning, and judges whether a route needs to be re-planned according to the path condition;
real-time information feedback unit: in the running process of the trucks, the position sharing function is automatically started, running information of all trucks is counted by the shared cloud deck, real-time feedback from the trucks is received, and the running state of the vehicle is judged.
The working principle of the technical scheme is as follows: the scheme adopted by the embodiment is that after the system selects a proper path, the optimal path is sent to the vehicle owner and is freely selected by the vehicle owner, and after the vehicle owner selects the proper path, the system provides the optimal checking mode of the route for the vehicle owner by combining the actual situation and a third party platform. In the running process of the truck, the system acquires the current position of the truck through the GPS automatic positioning system and judges the current road condition by combining a large amount of truck information, so that a reference is provided for real-time path optimization. In the running process of the truck, through the position sharing function, the running information of the truck can be acquired in real time by the system, meanwhile, the system can judge the running condition of the current truck according to the real-time feedback of the truck, and planned gas stations, service stations, rest stations and the like are provided for the truck.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, better service is provided for the vehicle owners, and the transportation efficiency of the vehicle is effectively improved.
In another embodiment, the combining initial and real-time conditions for path optimization includes:
an initial path optimization subunit: the cloud data center analyzes attribute information and loading rate of truck delivery, scores different orders according to the loading rate, calculates similarity of the attribute information of the historical orders and the attribute information of the current orders, and selects a path with highest similarity to recommend as an initial scheme;
real-time path optimization subunit: after receiving the initial scheme sent by the share Yun Cang, the edge server ranks the vehicle owners without full load in priority, receives real-time cargo information, ranks the cargoes according to emergency situations and yield of the cargoes, calculates matching schemes of the cargoes and the cargoes after ranking, and recommends the vehicle owners by combining real-time path situations.
The working principle of the technical scheme is as follows: according to the scheme adopted by the embodiment, the cloud center analyzes the attribute information of delivery and delivery of the vehicle and the loading rate of the vehicle, scores different attributes of the order according to the loading rate of the vehicle, calculates the similarity according to the historical information and the attributes of the order, and selects a path with the highest similarity for recommendation, wherein the calculation formula of the overall similarity of the attributes of the order is as follows:
wherein,,similarity of attribute information representing each order, +.>Scoring representing optimal target properties, including time, cost, path, p 1 、p 2 、p 3 Respectively represent the scores of different degrees of time, cost and path according to the loading rate, l um Representing the loading rate of the departure place of the truck, l ux Attribute information of truck representing departure place, c um Representing truck destination truck load Rate, c ux Truck attribute information, q, representing truck destination 1 、q 2 Representing the order and the genus of the truck respectivelySex vector, x 1 、x 2 For randomly selecting attribute information of two users as scoring parameters, w 1 For order attribute similarity weight factor, w 2 And finally, calculating a weight factor for scoring the target attribute, and obtaining the overall similarity of sim as the order attribute.
After the edge server receives the initial scheme sent by the share Yun Cang, priority ranking is carried out on users which are not fully loaded, the edge server receives real-time goods information, ranks the goods according to the emergency degree and the yield of the goods, calculates a matching scheme of the goods van and the goods, sends the matching scheme to an owner of the goods van, and recommends the path again according to the current path condition.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the cargo distribution route is optimized according to the condition of the vehicle by calculating the similarity, so that the working efficiency of the vehicle is improved.
In another embodiment, referring to fig. 2, the sharing Yun Cang generates a freight rate offer to be sent to the in-system user, comprising:
cargo classification unit: classifying cargoes to be transported by a car owner, and calculating freight charges of each kind of cargoes in a mode of changing unit price according to classification results;
model construction unit: constructing a freight quotation model for calculating freight of goods, removing fixed fees of the quality of the goods in the freight transportation process, wherein the residual freight grows along with the increase of the number of transport mileage and has a certain linear relation, and using a least square method to establish a functional relation to obtain a linear equation to obtain the freight; according to the classification of different properties of the goods, settlement formulas of different goods types are obtained, and a freight quotation model is constructed;
freight settlement unit: and carrying out freight rate summation on the transported freight car according to different kinds of cargoes through the freight rate quotation model to obtain freight rate of the whole freight car.
The working principle of the technical scheme is as follows: the solution adopted in this embodiment is to divide the goods into common goods and special goods, wherein the common goods include daily-use department goods and various edible articles, and the special articles refer to goods having special requirements on the vehicle structure and the transportation mode in transportation, storage, loading and unloading, for example, refrigerated goods, fragile goods, and the like. Classifying the cargoes of the category, respectively establishing a relation diagram of freight charges and mileage of different cargoes, establishing a linear regression equation for solving a data set of freight charges and mileage to solve a linear regression equation of freight charges on mileage, and calculating the transport-related fee of any freight mileage by using the obtained linear regression equation, wherein the formula is as follows:
wherein x is i 、y i When a truck transports a certain kind of goods, the freight rate actually generated after arriving at a destination and the kilometer number of an actual driving path are respectively represented, n represents the kind number of all the goods transported by the current truck, i represents the kind of the current goods, and a is a corresponding relation linear equation of the calculated kind of the goods and the freight rate.
According to the calculated linear equation, a freight quotation model is built for different types of transport trucks, and when the vehicle owner inputs the vehicle type and the type of the transported goods in the system freight quotation model before transporting, the freight quotation can be automatically generated and uploaded to the shared cloud storage.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the goods are classified, the freight rate is calculated according to the types of the goods, and the distribution efficiency is improved.
In another embodiment, referring to fig. 3, the construction matching model performs cargo matching, comprising:
a data cooperation unit: sharing Yun Cang and an edge server together maintain data information in a system, setting a plurality of edge nodes in a shared cloud bin, setting data synchronization between the edge nodes and a center node, calculating task completion time of the edge nodes according to historical truck distribution amount, and distributing the data information to the edge nodes according to the task completion time;
resource coordination unit: the method comprises the steps of storing resource information of vehicle-cargo matching, carrying out collaborative task scheduling according to the freight car delivery quantity of the edge nodes by recording the resource information condition of the edge nodes, and combining the edge nodes with different resources;
an application coordination unit: and the cloud storage system is used for carrying out cargo matching deployment by combining the shared cloud storage with the edge server.
The working principle of the technical scheme is as follows: according to the scheme adopted by the embodiment, the shared cloud bin and the edge end server are combined together to jointly maintain the configuration of the data and the edge nodes in the system, the shared cloud bin and the edge end server are mainly used for carrying out overall data analysis by the cloud end when carrying out data processing, the edge end application carries out on-site processing on the data which cannot be uploaded to the cloud end for solving, loss of network transportation resources and storage resources is effectively reduced when the data cannot be uploaded to the center node, the service center is selectively deployed on the edge node for better service, equipment data are uploaded to the edge node, the center is used for realizing unified management of equipment and synchronizing the data, and in addition, in order to enable the unified management of the edge network to generate faults, the edge center can provide powerful support for the edge node and ensure usability of users.
When the resource coordination is carried out, the method mainly comprises the steps of dividing resources by a coordination shared cloud warehouse, dividing more tasks to edge nodes with rich resource quantity, simultaneously recording the resource condition of the edge nodes, carrying out coordination task scheduling according to the resource quantity of the edge nodes and the task quantity, combining the edge points with different resource quantities, and carrying out coordination task calculation when the tasks are urgent.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, data aggregation is performed through data collaboration, so that multi-dimensional space-time fusion of cross-region and cross-system is realized, and comprehensive management and control is realized. Through resource coordination, a plurality of data scheduling schemes are provided, user experience is improved, and accurate analysis of calculation is realized by application coordination.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (7)
1. An intelligent selection and freight quotation system for a shared cloud warehouse logistics city allocation route, comprising:
and an information uploading module: collecting information of vehicle owners and cargo owners related to truck delivery, uploading the information to a cloud data center of a shared cloud bin, and auditing the information in the cloud data center;
and the vehicle cargo matching module is used for: matching the goods with the freight car by constructing a loading task according to the checked information, uploading the information of the goods which are not loaded to an edge end server of the shared cloud bin, and continuously completing the loading task by inquiring the vehicles which are not loaded fully until the goods are distributed;
and a path optimization module: generating a delivery order after the goods are distributed, intelligently calculating an optimal path through analysis of the delivery order and according to the positions of a departure place and a destination, sending the optimal path to a vehicle owner for selection, and generating a decision order after the selection is completed;
the construction loading task matches a truck with goods, comprising:
model construction unit: acquiring information of a truck to be matched and information of the cargo to be matched, constructing a matching model for matching the truck and the cargo, and feeding back a matching result to a user in the system;
an information transmission unit: recording a successfully matched vehicle-cargo matching order, calculating the loading rate of a truck according to the order, if the loading rate of the current truck is lower, constructing a task table by a shared cloud bin according to truck path information and historical cargo information, and feeding back the task table to an edge node which is close to an edge server in road conditions and has more real-time cargoes in the running process of the truck;
a path selection unit: acquiring path information of a truck delivery target address, intelligently selecting an optimal path of a delivery route, and delivering;
the intelligent selection of the optimal path of the delivery route comprises the following steps:
service function unit: managing truck information and path intelligent selection information, and recording the running route of the truck, combining the running route with the cargo information, and confirming the running preference of a truck owner and generating a delivery order;
order analysis unit: after the delivery order is generated, the system analyzes the information of the delivery order and is used for confirming the current position of the truck and predicting the income situation of the freight bill;
an intelligent path selection unit: carrying out path optimization by combining initial and real-time conditions, taking a delivery route as a first assembly route, judging whether the current truck is in a full-load state or not by carrying out path optimization by combining delivery information and loading rate of cargoes, and if the current truck is in the full-load state, carrying out path optimization on the truck by taking the shortest path as a target; if the loading rate of the current vehicle is low, path optimization is carried out by taking the maximum benefit of the vehicle as a target, and if the vehicle is subjected to secondary matching, a second assembly route is planned by taking the maximum benefit of the vehicle as the target and combining real-time path information for a vehicle owner;
further comprises:
and the freight decision module is used for: acquiring cargo information in a truck and the decision order, and implementing freight policies according to path information in the decision order, wherein the freight policies comprise a complete freight-free policy, a complete non-free policy and a conditional free policy;
and a freight quotation module: the freight policy formulated according to the goods information and the path information is sent to the shared cloud deck, and freight quotations are generated by the shared Yun Cang and sent to users in the system;
operation analysis module: and monitoring the freight quotation flow in real time, and calculating an optimal freight strategy to keep the logistics profit within the threshold range set by the system.
2. The intelligent selection and freight quotation system for a shared cloud deck logistics city distribution route according to claim 1, wherein said collecting owner and owner information related to truck distribution comprises:
information acquisition unit: collecting owner information and goods owner information, and uploading the owner information to a shared cloud bin, wherein the owner information comprises personal information of an owner and goods information, the shared Yun Cang carries out auditing after receiving the owner information, and the owner information is extracted and forms goods information to be matched after the auditing is passed; the goods owner information comprises goods owner information and personal information, the shared cloud bin receives the goods owner information and then carries out auditing, and the audited information is extracted to form goods information to be matched;
an information classification unit: the sharing Yun Cang divides the delivery mode of the cargo owner into single-line delivery, individual delivery and priority delivery according to the type of the cargo owner after receiving the owner information and the cargo owner information; the single-line delivery takes the goods with special requirements as delivery standards, and the individual delivery takes the matching degree of the goods and the price as the delivery standards.
3. The intelligent selection and freight quotation system for a shared cloud deck logistics city allocation route according to claim 2, wherein said construction matching model performs a cargo-to-cargo matching, comprising:
cloud data processing subunit: the cloud data center calculates the loading rate of the trucks while carrying out first matching of the trucks, transmits the information of the trucks which are not fully loaded to an edge end server of the shared cloud bin, and carries out second matching of the trucks by the shared cloud bin;
edge service computation subunit: receiving real-time information of cargoes and real-time road condition information uploaded by a truck by the edge server, and calculating the loading rate of the second matching of the cargoes according to the real-time information of the cargoes and the real-time road condition information; the loading rate is used for inquiring whether the current truck has the use capacity for continuing loading and distribution.
4. The intelligent selection and freight quotation system for a shared cloud deck logistics city distribution route according to claim 1, wherein said intelligent selection of an optimal path of the distribution route further comprises:
an optimal path display unit: after optimizing the path of the truck, the system sends the optimal path to the truck owner and selects the optimal path according to the requirement of the truck owner;
truck information tracking unit: in the running process of the truck, the system acquires the current position of the truck and judges the current road condition of the truck in real time through GPS automatic navigation positioning, and judges whether a route needs to be re-planned according to the path condition;
real-time information feedback unit: in the running process of the trucks, the position sharing function is automatically started, running information of all trucks is counted by the shared cloud deck, real-time feedback from the trucks is received, and the running state of the vehicle is judged.
5. The intelligent selection and freight quotation system for a shared cloud deck logistics urban distribution route according to claim 1, wherein the path optimization combining initial and real-time conditions comprises:
an initial path optimization subunit: the cloud data center analyzes attribute information and loading rate of truck delivery, scores different delivery orders according to the loading rate, calculates similarity of the attribute information of the historical delivery orders and the attribute information of the current delivery orders, and selects a path of the historical delivery order with the highest similarity to recommend as an initial scheme;
real-time path optimization subunit: after receiving the initial scheme sent by the share Yun Cang, the edge server ranks the vehicle owners without full load in priority, receives real-time cargo information, ranks the cargoes according to emergency situations and yield of the cargoes, calculates matching schemes of the cargoes and the cargoes after the ranking, and recommends the matching schemes of the cargoes and the cargoes to the vehicle owners in combination with real-time path situations.
6. The intelligent selection and freight quotation system for a shared cloud deck logistics city allocation route according to claim 1, wherein said sharing Yun Cang generates freight quotation and sends it to users in the system, comprising:
cargo classification unit: classifying cargoes to be transported by a car owner, and calculating freight charges of each kind of cargoes in a mode of changing unit price according to classification results;
model construction unit: constructing a freight quotation model for calculating freight of goods, removing fixed fees of the quality of the goods in the freight transportation process, wherein the residual freight grows along with the increase of the number of transport mileage and has a certain linear relation, and using a least square method to establish a functional relation to obtain a linear equation to obtain the freight; according to the classification of different properties of the goods, settlement formulas of different goods types are obtained, and a freight quotation model is constructed;
freight settlement unit: and carrying out freight rate summation on the transported freight car according to different kinds of cargoes through the freight rate quotation model to obtain freight rate of the whole freight car.
7. The intelligent selection and freight quotation system for a shared cloud deck logistics city allocation route according to claim 1, wherein said construction matching model performs a cargo-to-cargo matching, comprising:
a data cooperation unit: sharing Yun Cang and an edge server together maintain data information in a system, setting a plurality of edge nodes in a shared cloud bin, setting data synchronization between the edge nodes and a center node, calculating task completion time of the edge nodes according to historical truck distribution amount, and distributing the data information to the edge nodes according to the task completion time;
resource coordination unit: the method comprises the steps of storing resource information of vehicle-cargo matching, carrying out collaborative task scheduling according to the freight car delivery quantity of the edge nodes by recording the resource information condition of the edge nodes, and combining the edge nodes with different resources;
an application coordination unit: and the cloud storage system is used for carrying out cargo matching deployment by combining the shared cloud storage with the edge server.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106910038A (en) * | 2017-04-25 | 2017-06-30 | 物载天下网络科技（苏州）有限公司 | A kind of goods of same city dispatching and the system and method for vehicle match |
| CN107679646A (en) * | 2017-09-04 | 2018-02-09 | 安徽共生物流科技有限公司 | A kind of optimal transit route intelligent optimization method of benefit |
| CN109409613A (en) * | 2018-11-16 | 2019-03-01 | 南京大学 | One kind purchasing by group vehicle and goods matching optimization method based on logistics transport power |
| CN113627855A (en) * | 2021-08-17 | 2021-11-09 | 百安居信息技术(上海)有限公司 | Logistics distribution freight accounting method and system and electronic equipment |
| CN114331257A (en) * | 2021-12-06 | 2022-04-12 | 上海东普信息科技有限公司 | Logistics transportation loading management method, device, equipment and storage medium |
| CN114548723A (en) * | 2022-02-15 | 2022-05-27 | 南京邮电大学 | Real-time vehicle and goods matching and path recommendation method and system based on cloud edge cooperation |
| CN115700670A (en) * | 2021-07-22 | 2023-02-07 | 江西万佶物流有限公司 | Freight transportation goods splicing method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20230088950A1 (en) * | 2020-03-06 | 2023-03-23 | Braskem S.A. | Method and system for intelligent load optimization for vehicles |
-
2023
- 2023-03-29 CN CN202310363278.4A patent/CN116432880B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106910038A (en) * | 2017-04-25 | 2017-06-30 | 物载天下网络科技（苏州）有限公司 | A kind of goods of same city dispatching and the system and method for vehicle match |
| CN107679646A (en) * | 2017-09-04 | 2018-02-09 | 安徽共生物流科技有限公司 | A kind of optimal transit route intelligent optimization method of benefit |
| CN109409613A (en) * | 2018-11-16 | 2019-03-01 | 南京大学 | One kind purchasing by group vehicle and goods matching optimization method based on logistics transport power |
| CN115700670A (en) * | 2021-07-22 | 2023-02-07 | 江西万佶物流有限公司 | Freight transportation goods splicing method |
| CN113627855A (en) * | 2021-08-17 | 2021-11-09 | 百安居信息技术(上海)有限公司 | Logistics distribution freight accounting method and system and electronic equipment |
| CN114331257A (en) * | 2021-12-06 | 2022-04-12 | 上海东普信息科技有限公司 | Logistics transportation loading management method, device, equipment and storage medium |
| CN114548723A (en) * | 2022-02-15 | 2022-05-27 | 南京邮电大学 | Real-time vehicle and goods matching and path recommendation method and system based on cloud edge cooperation |
Non-Patent Citations (2)
| Title |
|---|
| Research and application of optimization algorithm based on two-stage in distribution routing planning;Minai He等;《2008 IEEE International Conference on Automation and Logistics》;2104-2109 * |
| 多温共配冷链物流车辆配送路径优化仿真;丁艳;《沈阳工业大学学报》;第43卷(第03期);311-316 * |
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