CN113869591A - Logistics management system and method based on graph technology - Google Patents
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Abstract
The invention discloses a logistics management system and a method based on graph technology, wherein the system comprises the following steps: a dataset creation module: the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for establishing a data set according to logistics information and analyzing and processing the data set to obtain a plurality of point data and a plurality of side data; a model construction module: the system comprises a database, a plurality of point data and a plurality of edge data, wherein the database is used for storing a plurality of point data and a plurality of edge data; a model weighting module: and the system is used for performing weighting processing on the logistics model in the graph database according to the business requirements to generate a corresponding weighted model graph. According to the invention, the logistics model is constructed and stored in the graph database, and the intelligent selection function is realized by weighting the logistics model, so that a user can intuitively and quickly obtain the optimal logistics dispatching scheme, and the logistics management efficiency is improved.
Description
Technical Field
The invention relates to the technical field of logistics management, in particular to a logistics management system and method based on graph technology.
Background
Logistics Management (Logistics Management) refers to planning, organizing, commanding, coordinating, controlling and supervising Logistics activities according to the law of material data entity flow and applying the basic principle and scientific method of Management in the social reproduction process, so that the Logistics activities are optimally coordinated and matched to reduce Logistics cost and improve Logistics efficiency and economic benefit.
Logistics management requires scientific construction of transportation networks and the appropriate arrangement of group shipments and equitable inventory to reduce warehousing and freight expenses. The manager also needs to make a quick response to the demand change on the premise of ensuring the service quality and the logistics service stability. The biggest difficulty of logistics management is to effectively integrate complex information and make the most effective scheme at lower price. In addition, regardless of the size of the enterprise, inventory may be considered the lifeline of the enterprise, and managers need to balance consumer demand with inventory.
On the one hand, managers can always meet the needs of all customers by keeping a high inventory level, but then enterprises must bear high costs due to a large amount of inventory, including warehousing costs and clearing costs due to expired products. On the other hand, companies can avoid these potential costs by cutting inventory levels, but this drives off the risk of sales opportunities, resulting in customer disappointment and impacting future revenues.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a logistics management system and a logistics management method based on a graph technology, so as to improve the logistics management efficiency.
In a first aspect, a logistics management system based on graph technology includes:
a dataset creation module: the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for establishing a data set according to logistics information and analyzing and processing the data set to obtain a plurality of point data and a plurality of side data;
a model construction module: the system comprises a database, a plurality of point data and a plurality of edge data, wherein the database is used for storing a plurality of point data and a plurality of edge data;
a model weighting module: and the system is used for performing weighting processing on the logistics model in the graph database according to the business requirements to generate a corresponding weighted model graph.
Further, the air conditioner is provided with a fan,
the point data is position data and comprises a warehouse, a transfer center and a distribution point;
the edge data is the associated data between the two point data, and the type of the edge data is determined by the starting point data and the ending point data.
Further, the type of the side data includes a transportation mode and a distance, and specifically includes:
when the data of the starting point and the data of the ending point are different, the type of the side data is a transportation mode;
when the starting point data and the ending point data are the same and are not warehouses, the type of the side data is a transportation mode;
when the starting point data and the ending point data are the same and are warehouses, the side data type is distance;
wherein, the transportation mode includes but is not limited to road transportation, water transportation, air transportation and railway transportation, and the distance is the length of the route.
Further, the weighting processing specifically includes:
and according to the service requirements, carrying out attribute weighting on the multiple pieces of side data of the logistics model by adopting a graph technology to generate a weighted model graph with specific attributes.
Further, the attributes include time, cost, and route length.
In a second aspect, a method for logistics management based on graph technology includes the steps of:
creating a data set according to the logistics information, and analyzing and processing the data set to obtain a plurality of pieces of point data and a plurality of pieces of side data;
constructing a logistics model according to the plurality of point data and the plurality of edge data, and storing the logistics model in a graph database;
and performing weighting processing on the logistics model in the graph database according to the business requirements to generate a corresponding weighted model graph.
Further, the air conditioner is provided with a fan,
the point data is position data and comprises a warehouse, a transfer center and a distribution point;
the edge data is the associated data between the two point data, and the type of the edge data is determined by the starting point data and the ending point data.
Further, the type of the side data includes a transportation mode and a distance, and specifically includes:
when the data of the starting point and the data of the ending point are different, the type of the side data is a transportation mode;
when the starting point data and the ending point data are the same and are not warehouses, the type of the side data is a transportation mode;
when the starting point data and the ending point data are the same and are warehouses, the side data type is distance;
wherein, the transportation mode includes but is not limited to road transportation, water transportation, air transportation and railway transportation, and the distance is the length of the route.
Further, the weighting processing specifically includes:
and according to the service requirements, carrying out attribute weighting on the multiple pieces of side data of the logistics model by adopting a graph technology to generate a weighted model graph with specific attributes.
Further, the attributes include time, cost, and route length.
The invention has the beneficial effects that: by constructing the logistics model, storing the logistics model in the graph database and performing weighting processing on the logistics model, the intelligent selection function is realized, so that a user can intuitively and quickly obtain the optimal logistics dispatching scheme, and the logistics management efficiency is improved.
Drawings
In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.
Fig. 1 is a block diagram of a logistics management system based on graph technology according to an embodiment one;
FIG. 2 is a diagram of a logistics model of a logistics management system based on graph technology according to an embodiment;
fig. 3 is a flowchart of a logistics management method based on graph technology according to the second embodiment;
fig. 4 is a distribution cost model diagram of a logistics management method based on the graph technology according to a third embodiment;
fig. 5 is a model diagram of distribution time duration of a logistics management method based on graph technology according to a third embodiment;
fig. 6 is a warehouse distance model diagram of a logistics management method based on the graph technology according to the third embodiment.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and therefore are only examples, and the protection scope of the present invention is not limited thereby.
It is to be noted that, unless otherwise specified, technical or scientific terms used herein shall have the ordinary meaning as understood by those skilled in the art to which the invention pertains.
Example one
As shown in fig. 1, a logistics management system based on graph technology includes:
a dataset creation module: the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring logistics information of a user;
a model construction module: the system comprises a database, a plurality of point data and a plurality of edge data, wherein the database is used for storing the plurality of point data and the plurality of edge data;
a model weighting module: and the system is used for performing weighting processing on the logistics model in the graph database according to the business requirement to generate a corresponding weighted model graph.
Specifically, the data set creating module creates a data set according to the logistics information, wherein the data set comprises all the logistics information of the goods, such as: cargo distribution points, cargo warehouses, transfer centers, and the distance and mode of transportation of cargo from one geographic location to another.
Further, analyzing the data in the data set to obtain a plurality of point data and a plurality of edge data. And taking the position data such as the transfer center, the warehouse and the distribution point as point data, taking the associated data between the two point data as side data, and determining the type of the side data by the starting point data and the ending point data, wherein the type of the side data comprises a transportation mode and a distance. When the start point data and the end point data are different, or when the start point data and the end point data are the same and are not warehouses, the side data type is a transportation mode, such as: road transportation, water transportation, air transportation, railway transportation and the like. When the start point data and the end point data are the same and are warehouses, the type of the side data is distance, wherein the distance between the warehouses is the length of the route.
After obtaining the plurality of point data and the plurality of edge data, the model construction module constructs a logistics model according to the plurality of point data and the plurality of edge data, as shown in fig. 2, the logistics model includes a warehouse, a delivery point, a transfer center, and a relationship therebetween. And after the logistics model is built, the logistics model is stored in a graph database, the graph database integrates all relevant information on the supply chain model, the graph database is connected with the mobile terminal through a network, and at least one user in the supply chain can access and inquire the graph database through the network.
In the logistics management system, a user can perform weighting processing on the logistics model in the graph database through the model weighting module according to business requirements, namely, attribute weighting is performed on multiple pieces of side data of the logistics model by adopting a graph technology, and a weighting model graph with specific attributes is generated. Attributes include, among others, time, cost, and route length.
Example two
As shown in fig. 3, a logistics management method based on graph technology includes the steps of:
s1: creating a data set according to the logistics information, and analyzing and processing the data set to obtain a plurality of pieces of point data and a plurality of pieces of side data;
specifically, the data set creating module creates a data set according to the logistics information, wherein the data set comprises all the logistics information of the goods, such as: cargo distribution points, cargo warehouses, transfer centers, and the distance and mode of transportation of cargo from one location to another.
Further, analyzing the data in the data set to obtain a plurality of point data and a plurality of edge data. And taking the position data such as the transfer center, the warehouse and the distribution point as point data, taking the associated data between the two point data as side data, and determining the type of the side data by the starting point data and the ending point data, wherein the type of the side data comprises a transportation mode and a distance. When the start point data and the end point data are different, or when the start point data and the end point data are the same and are not warehouses, the side data type is a transportation mode, such as: road transportation, water transportation, air transportation, railway transportation and the like. When the start point data and the end point data are the same and are warehouses, the type of the side data is distance, wherein the distance between the warehouses is the length of the route.
S2, constructing a logistics model according to the plurality of point data and the plurality of edge data, and storing the logistics model in a database;
specifically, after obtaining the plurality of point data and the plurality of edge data, the model construction module constructs a logistics model according to the plurality of point data and the plurality of edge data, as shown in fig. 2, where the logistics model includes a warehouse, a distribution point, a transfer center, and a relationship therebetween. And after the logistics model is built, the logistics model is stored in a graph database, the graph database integrates all relevant information on the supply chain model, the graph database is connected with the mobile terminal through a network, and at least one user in the supply chain can access and inquire the graph database through the network.
S3, carrying out weighting processing on the logistics model in the graph database according to business requirements to generate a corresponding weighted model graph;
specifically, in the logistics management system, a user can perform weighting processing on the logistics model in the graph database through the model weighting module according to business requirements, that is, attribute weighting is performed on multiple pieces of side data of the logistics model by adopting a graph technology, so that a weighted model graph with specific attributes is generated. Specific attributes include, among others, time, cost and route length.
EXAMPLE III
A logistics management method based on graph technology comprises the following steps:
s1: creating a data set according to the logistics information, and analyzing and processing the data set to obtain a plurality of pieces of point data and a plurality of pieces of side data;
specifically, the data set creating module creates a data set according to the logistics information, wherein the data set comprises all the logistics information of the goods, such as: cargo distribution points, cargo warehouses, transfer centers, and the distance and mode of transportation of cargo from one location to another.
Further, analyzing the data in the data set to obtain a plurality of point data and a plurality of edge data. And taking the position data such as the transfer center, the warehouse and the distribution point as point data, taking the associated data between the two point data as side data, and determining the type of the side data by the starting point data and the ending point data, wherein the type of the side data comprises a transportation mode and a distance. When the start point data and the end point data are different, or when the start point data and the end point data are the same and are not warehouses, the side data type is a transportation mode, such as: road transportation, water transportation, air transportation, railway transportation and the like. When the start point data and the end point data are the same and are warehouses, the type of the side data is distance, wherein the distance between the warehouses is the length of the route.
S2, constructing a logistics model according to the plurality of point data and the plurality of edge data, and storing the logistics model in a database;
specifically, after obtaining the plurality of point data and the plurality of edge data, the model construction module constructs a logistics model according to the plurality of point data and the plurality of edge data, as shown in fig. 2, where the logistics model includes a warehouse, a distribution point, a transfer center, and a relationship therebetween. And after the logistics model is built, the logistics model is stored in a graph database, the graph database integrates all relevant information on the supply chain model, the graph database is connected with the mobile terminal through a network, and at least one user in the supply chain can access and inquire the graph database through the network.
S3, carrying out weighting processing on the logistics model in the graph database according to business requirements to generate a corresponding weighted model graph;
specifically, when the service requirement of the user is: when the distribution route cost is optimized in the road transportation mode, a model graph in the road transportation mode is selected from the graph database, then distribution expenses are selected as specific attributes, attribute weighting is carried out on the logistics model in the graph database, and a distribution expense model graph is generated, as shown in fig. 4, the side data of the model graph is the road distribution expenses. The distribution cost among various warehouses, operation centers and distribution points can be clearly shown through the distribution cost model diagram generated by weighting. If the goods are delivered from one position to another position, the user can intuitively select the delivery route with the lowest delivery cost according to the weighted delivery cost model diagram, so that the user can quickly select the delivery route with the lowest delivery cost. Such as: from warehouse 1 to delivery point 1, the shortest route is: warehouse 1 → transit center 2 → transit center 6 → distribution point 1, with the total cost: 1+7+8 ═ 16
When the service requirement of the user is as follows: when the distribution time length under the air transportation mode is the shortest, a model graph under the air transportation mode is selected from the graph database, then the distribution time length is selected as a specific attribute, the physical distribution model in the graph database is subjected to attribute weighting, a distribution time length model graph is generated, and as shown in fig. 5, the side data of the model graph is the air transportation distribution time length. The distribution time model diagram generated by weighting can clearly show the time consumption required by distribution among various warehouses, operation centers and distribution points. If the goods are delivered from one position to another position, the user can intuitively select the delivery route with the shortest delivery time according to the weighted delivery time model diagram, so that the user can quickly select the delivery route with the fastest delivery. If the warehouse is delivered from the delivery point 2, the warehouse with the shortest route from the delivery point 2 is selected as a delivery place, namely the warehouse 4 is selected as the delivery place, and then the fastest route is selected, namely: warehouse 4 → transit center 5 → delivery point 2, the delivery time is: 2+9 ═ 11.
When the service requirement of the user is as follows: when the hub is addressed, a model graph with only warehouses as point data is selected from the graph database, then distance is selected as side data, attribute weighting is carried out on the logistics model in the graph database, and a warehouse distance model graph is generated, wherein the side data of the model graph is the route length between any two warehouses, as shown in fig. 6. The distance between each warehouse can be clearly shown through the warehouse distance model diagram. The user can quickly and intuitively select the warehouse with the shortest total distance to other warehouses according to the weighted warehouse distance model diagram, and the warehouse is used as a hub. For example, the minimum distance value in fig. 6 for each warehouse to the other warehouses is: and 6+5+5+9 is 25, namely, the warehouse 5 is selected as a hub.
According to the invention, through constructing the logistics model, storing the logistics model in the graph database and carrying out weighting processing on the logistics model, an intelligent selection function is realized, so that a user can intuitively and quickly obtain an optimal logistics dispatching scheme, and the logistics management efficiency is improved.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (10)
1. A logistics management system based on graph technology is characterized by comprising:
a dataset creation module: the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for establishing a data set according to logistics information and analyzing and processing the data set to obtain a plurality of point data and a plurality of side data;
a model construction module: the system comprises a database, a plurality of point data and a plurality of edge data, wherein the database is used for storing a plurality of point data and a plurality of edge data;
a model weighting module: and the system is used for performing weighting processing on the logistics model in the graph database according to the business requirements to generate a corresponding weighted model graph.
2. The logistics management system based on graph technology as claimed in claim 1,
the point data is position data and comprises a warehouse, a transfer center and a distribution point;
the edge data is the associated data between the two point data, and the type of the edge data is determined by the starting point data and the ending point data.
3. The logistics management system based on graph technology as claimed in claim 2, wherein the type of the side data includes transportation mode and distance, specifically:
when the data of the starting point and the data of the ending point are different, the type of the side data is a transportation mode;
when the starting point data and the ending point data are the same and are not warehouses, the type of the side data is a transportation mode;
when the starting point data and the ending point data are the same and are warehouses, the side data type is distance;
wherein, the transportation mode includes but is not limited to road transportation, water transportation, air transportation and railway transportation, and the distance is the length of the route.
4. The logistics management system based on graph technology as claimed in claim 1, wherein the weighting process specifically comprises:
and according to the service requirements, carrying out attribute weighting on the multiple pieces of side data of the logistics model by adopting a graph technology to generate a weighted model graph with specific attributes.
5. The logistics management system of claim 4, wherein the attributes comprise time, cost and route length.
6. A logistics management method based on graph technology is characterized by comprising the following steps:
creating a data set according to the logistics information, and analyzing and processing the data set to obtain a plurality of pieces of point data and a plurality of pieces of side data;
constructing a logistics model according to the plurality of point data and the plurality of edge data, and storing the logistics model in a graph database;
and performing weighting processing on the logistics model in the graph database according to the business requirements to generate a corresponding weighted model graph.
7. The logistics management method based on graph technology as claimed in claim 6,
the point data is position data and comprises a warehouse, a transfer center and a distribution point;
the edge data is the associated data between the two point data, and the type of the edge data is determined by the starting point data and the ending point data.
8. The logistics management method based on graph technology as claimed in claim 7, wherein the type of the side data includes transportation mode and distance, specifically:
when the data of the starting point and the data of the ending point are different, the type of the side data is a transportation mode;
when the starting point data and the ending point data are the same and are not warehouses, the type of the side data is a transportation mode;
when the starting point data and the ending point data are the same and are warehouses, the side data type is distance;
wherein, the transportation mode includes but is not limited to road transportation, water transportation, air transportation and railway transportation, and the distance is the length of the route.
9. The logistics management method based on graph technology as claimed in claim 6, wherein the weighting process specifically comprises:
and according to the service requirements, carrying out attribute weighting on the multiple pieces of side data of the logistics model by adopting a graph technology to generate a weighted model graph with specific attributes.
10. The logistics management method based on map technology of claim 9, wherein the attributes comprise time consumption, cost and route length.
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Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1395198A (en) * | 2001-07-06 | 2003-02-05 | 英业达股份有限公司 | Storage management system by using graphic interface |
| JP2003058611A (en) * | 2001-08-10 | 2003-02-28 | Fuji Denki Reiki Co Ltd | Integrated physical distribution system |
| CN105404941A (en) * | 2015-12-07 | 2016-03-16 | 惠龙易通国际物流股份有限公司 | Intelligent optimization method and system for logistics transportation mode and path |
| CN106156897A (en) * | 2016-08-22 | 2016-11-23 | 武汉轻工大学 | Optimum path planning analog systems in logistics distribution |
| CN106780268A (en) * | 2016-12-28 | 2017-05-31 | 苏州商信宝信息科技有限公司 | A kind of outdoor scene modeling navigation optimization method and its system based on electric business platform |
| CN106919998A (en) * | 2015-12-28 | 2017-07-04 | 航天信息股份有限公司 | A kind of physical distribution model and method based on AOE networks |
| CN109271562A (en) * | 2018-09-30 | 2019-01-25 | 广东利通科技投资有限公司 | Current expense determines method, road-net node relational model construction method and device |
| CN111062530A (en) * | 2019-12-10 | 2020-04-24 | 江苏艾佳家居用品有限公司 | Weighted graph-based multi-place logistics distribution route recommendation method |
| CN111932030A (en) * | 2020-09-09 | 2020-11-13 | 江苏亿讯网络科技有限公司 | Method and system for logistics distribution path of self-built warehouse |
| US20210034795A1 (en) * | 2019-08-01 | 2021-02-04 | Hyperloop Technologies, Inc. | Generating a layout for a transportation system |
| CN112665601A (en) * | 2020-12-11 | 2021-04-16 | 杭州安恒信息技术股份有限公司 | Path planning method and device, electronic equipment and readable storage medium |
| CN112700177A (en) * | 2019-10-22 | 2021-04-23 | 北京京东振世信息技术有限公司 | Warehouse selection method and device for transporting transportation center and storage medium |
| CN113112285A (en) * | 2020-01-13 | 2021-07-13 | 北京京东振世信息技术有限公司 | Data analysis method and device |
-
2021
- 2021-09-30 CN CN202111165899.9A patent/CN113869591A/en active Pending
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1395198A (en) * | 2001-07-06 | 2003-02-05 | 英业达股份有限公司 | Storage management system by using graphic interface |
| JP2003058611A (en) * | 2001-08-10 | 2003-02-28 | Fuji Denki Reiki Co Ltd | Integrated physical distribution system |
| CN105404941A (en) * | 2015-12-07 | 2016-03-16 | 惠龙易通国际物流股份有限公司 | Intelligent optimization method and system for logistics transportation mode and path |
| CN106919998A (en) * | 2015-12-28 | 2017-07-04 | 航天信息股份有限公司 | A kind of physical distribution model and method based on AOE networks |
| CN106156897A (en) * | 2016-08-22 | 2016-11-23 | 武汉轻工大学 | Optimum path planning analog systems in logistics distribution |
| CN106780268A (en) * | 2016-12-28 | 2017-05-31 | 苏州商信宝信息科技有限公司 | A kind of outdoor scene modeling navigation optimization method and its system based on electric business platform |
| CN109271562A (en) * | 2018-09-30 | 2019-01-25 | 广东利通科技投资有限公司 | Current expense determines method, road-net node relational model construction method and device |
| US20210034795A1 (en) * | 2019-08-01 | 2021-02-04 | Hyperloop Technologies, Inc. | Generating a layout for a transportation system |
| CN112700177A (en) * | 2019-10-22 | 2021-04-23 | 北京京东振世信息技术有限公司 | Warehouse selection method and device for transporting transportation center and storage medium |
| CN111062530A (en) * | 2019-12-10 | 2020-04-24 | 江苏艾佳家居用品有限公司 | Weighted graph-based multi-place logistics distribution route recommendation method |
| CN113112285A (en) * | 2020-01-13 | 2021-07-13 | 北京京东振世信息技术有限公司 | Data analysis method and device |
| CN111932030A (en) * | 2020-09-09 | 2020-11-13 | 江苏亿讯网络科技有限公司 | Method and system for logistics distribution path of self-built warehouse |
| CN112665601A (en) * | 2020-12-11 | 2021-04-16 | 杭州安恒信息技术股份有限公司 | Path planning method and device, electronic equipment and readable storage medium |
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