CN114493408B - Goods optimized loading method and device based on knowledge graph - Google Patents
Goods optimized loading method and device based on knowledge graph Download PDFInfo
- Publication number
- CN114493408B CN114493408B CN202111574296.4A CN202111574296A CN114493408B CN 114493408 B CN114493408 B CN 114493408B CN 202111574296 A CN202111574296 A CN 202111574296A CN 114493408 B CN114493408 B CN 114493408B
- Authority
- CN
- China
- Prior art keywords
- attribute information
- loaded
- compartment
- cargo
- goods
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000011068 loading method Methods 0.000 title claims abstract description 102
- 238000012216 screening Methods 0.000 claims abstract description 11
- 238000013507 mapping Methods 0.000 claims description 25
- 238000005259 measurement Methods 0.000 claims description 16
- 239000000126 substance Substances 0.000 claims description 15
- 238000000034 method Methods 0.000 claims description 10
- 238000005457 optimization Methods 0.000 abstract description 4
- 239000013598 vector Substances 0.000 description 6
- 239000003814 drug Substances 0.000 description 5
- 239000002184 metal Substances 0.000 description 5
- 229910052751 metal Inorganic materials 0.000 description 5
- 238000004364 calculation method Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 235000005911 diet Nutrition 0.000 description 3
- 230000037213 diet Effects 0.000 description 3
- 229940079593 drug Drugs 0.000 description 3
- 150000002739 metals Chemical class 0.000 description 3
- 239000000919 ceramic Substances 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 229920003023 plastic Polymers 0.000 description 2
- 239000004033 plastic Substances 0.000 description 2
- 239000000047 product Substances 0.000 description 2
- 230000003190 augmentative effect Effects 0.000 description 1
- 238000005260 corrosion Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000007717 exclusion Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/083—Shipping
- G06Q10/0832—Special goods or special handling procedures, e.g. handling of hazardous or fragile goods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/284—Relational databases
- G06F16/285—Clustering or classification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/284—Relational databases
- G06F16/288—Entity relationship models
Abstract
The invention discloses a cargo optimizing loading method based on a knowledge graph, which comprises the steps of obtaining the information of the attribute of cargo to be loaded and the information of the compartment attribute of the cargo to be loaded, obtaining the information of the compartment attribute of all vehicles and the information of the cargo attribute from a database, carrying out matching comparison on the information of the attribute of cargo to be loaded and the information of the cargo attribute, obtaining the candidate loading compartment of the cargo to be loaded, carrying out similarity matching between the information of the compartment attribute of the cargo to be loaded and the information of the compartment attribute of all vehicles, and the requirements of the candidate loading compartment, and obtaining a target loading compartment from the candidate loading compartment. Aiming at rich types of cargoes, the invention fully considers the association relations among cargoes, carriages and cargo carriages on the basis of a knowledge graph, and reasonably loads the carriages through two layers of screening and matching, thereby realizing the optimization strategy of cargo loading and reducing risks.
Description
Technical Field
The invention relates to the technical field of cargo transportation, in particular to a cargo optimal loading method and device based on a knowledge graph.
Background
With the development of internet technology, the logistics industry also develops, and more goods are distributed, and for the warehouse management of goods, the logistics industry becomes one of the matters of great concern in the industry. At present, a storage management mode of goods is generally adopted, and the storage shelf is placed by matching corresponding goods according to the volume data of the goods, the volume data of the storage shelf and the empty condition information.
However, as the matching factor of the goods placement storage shelf is single and the auxiliary performance of analyzing the goods placement strategy of the goods placement storage shelf is low, the matching accuracy of the goods placement strategy of the goods placement storage shelf is low under the condition of higher warehouse allocation efficiency.
In addition, only the matching relationship between the cargoes and the carriage is concerned in the current cargo loading process, but the clustering association relationship between the cargoes and the carriage in the freight carriage is ignored, and the prior art of the matching process is too simple and the loading vehicle cannot be reasonably analyzed according to the actual situation.
Disclosure of Invention
In order to solve the problem of inaccuracy of the optimal loading of the current goods, the invention discloses a goods optimal loading method and device based on a knowledge graph.
The invention firstly requests to protect a cargo optimizing loading method based on a knowledge graph, which is characterized by comprising the following steps:
acquiring attribute information of goods to be loaded;
acquiring compartment attribute information of a vehicle to be loaded;
acquiring a compartment attribute information knowledge graph and a cargo attribute information knowledge graph of all vehicles from a database;
matching and comparing the attribute information of the goods to be loaded with the goods attribute information knowledge graph to obtain candidate loading carriages of the goods to be loaded;
matching the compartment attribute information of the vehicle to be loaded with the compartment attribute information knowledge maps of all vehicles with the similarity of the candidate loading compartment requirements;
and acquiring a target loading carriage from the candidate loading carriages, and loading the cargo to be loaded into the target loading carriage.
Further, the acquiring attribute information of the goods to be loaded further includes:
acquiring category attributes of the goods to be loaded through a first sensor,
the measurement attribute data of the cargo to be loaded is acquired by a second sensor,
acquiring chemical attribute data of the goods to be loaded through a third sensor;
the obtaining the compartment attribute information of the vehicle to be loaded further includes:
acquiring compartment measurement attribute data of the vehicle to be loaded through a fourth sensor;
acquiring the internal environment attribute data of the vehicle to be loaded through a fifth sensor;
and acquiring chemical attribute data of the vehicle to be loaded through a sixth sensor.
Further, the acquiring the compartment attribute information knowledge graph and the cargo attribute information knowledge graph of all the vehicles from the database further includes:
extracting compartment attribute information of all vehicles;
clustering and dividing the compartment attribute information of all vehicles;
constructing a plurality of knowledge maps according to the compartment attribute information of all vehicles after clustering;
the number of the plurality of knowledge maps is the same as the number of the compartment attribute clusters, an association relationship is established among the plurality of knowledge maps, and each knowledge map also has a matching mapping relationship of vehicle attribute information and cargo attribute information;
the knowledge maps respectively comprise a plurality of subgraphs;
inputting all the goods attribute information potentially needing to be put in storage into a database;
clustering and dividing the cargo attribute information to be put in storage;
constructing a plurality of knowledge maps according to the goods attribute information to be put into storage after the clustering division;
the number of the plurality of knowledge maps is the same as the clustering number of the cargo attribute information, an association relation is established among the plurality of knowledge maps, and each knowledge map also has a matching mapping relation of the vehicle attribute information and the cargo attribute information;
the knowledge maps respectively comprise a plurality of subgraphs.
Further, the matching and comparing the attribute information of the cargo to be loaded with the cargo attribute information knowledge graph to obtain the candidate loading carriage of the cargo to be loaded specifically includes:
based on the attribute information of the goods to be loaded acquired by the sensor, matching the goods with the corresponding goods attribute information knowledge graph according to the category, and acquiring the matching degree of the goods attribute information knowledge graph;
when the matching degree of each cargo attribute information knowledge graph is larger than a first threshold value, acquiring the mapping relation between the cargo of the cargo attribute information knowledge graph and the vehicle attribute;
and taking the vehicle obtained by the mapping relation between the cargoes and the vehicle attributes of the cargo attribute information knowledge graph as a candidate loading carriage of the cargo to be loaded.
Further, the matching the compartment attribute information of the vehicle to be loaded with the compartment attribute information knowledge maps of all vehicles with the candidate loading compartment requirement in similarity includes:
based on the compartment attribute information of the vehicle to be loaded, which is acquired by the sensor, matching the compartment attribute information with the corresponding compartment attribute information knowledge graph according to the category to acquire the matching degree of each compartment attribute information knowledge graph;
when the matching degree of each carriage attribute information knowledge graph is larger than a first threshold value, acquiring a carriage of the carriage attribute information knowledge graph;
and matching the carriage information of the carriage attribute information knowledge graph with the candidate loading carriage in similarity, and acquiring the candidate loading carriage with the corresponding similarity larger than a second threshold value and the maximum similarity value as a target loading carriage.
The invention also claims a cargo optimizing and loading device based on the knowledge graph, which is characterized by comprising the following components:
the acquisition module is used for acquiring attribute information of cargoes to be loaded and acquiring compartment attribute information of the vehicles to be loaded;
the database module is used for acquiring compartment attribute information knowledge maps and cargo attribute information knowledge maps of all vehicles from the database;
the screening module is used for carrying out matching comparison on the attribute information of the goods to be loaded and the goods attribute information knowledge graph to obtain candidate loading carriages of the goods to be loaded;
the matching module is used for matching the compartment attribute information of the vehicles to be loaded with the compartment attribute information knowledge maps of all the vehicles with the similarity of the candidate loading compartment requirements;
and the output module acquires a target loading carriage from the candidate loading carriages and loads the cargo to be loaded into the target loading carriage.
Further, the acquisition module acquires attribute information of goods to be loaded, acquires compartment attribute information of a vehicle to be loaded, and further includes:
acquiring category attributes of the goods to be loaded through a first sensor,
the measurement attribute data of the cargo to be loaded is acquired by a second sensor,
acquiring chemical attribute data of the goods to be loaded through a third sensor;
the obtaining the compartment attribute information of the vehicle to be loaded further includes:
acquiring compartment measurement attribute data of the vehicle to be loaded through a fourth sensor;
acquiring the internal environment attribute data of the vehicle to be loaded through a fifth sensor;
and acquiring chemical attribute data of the vehicle to be loaded through a sixth sensor.
Further, the database module obtains the compartment attribute information knowledge graph and the cargo attribute information knowledge graph of all vehicles from the database, and further includes:
extracting compartment attribute information of all vehicles;
clustering and dividing the compartment attribute information of all vehicles;
constructing a plurality of knowledge maps according to the compartment attribute information of all vehicles after clustering;
the number of the plurality of knowledge maps is the same as the number of the compartment attribute clusters, an association relationship is established among the plurality of knowledge maps, and each knowledge map also has a matching mapping relationship of vehicle attribute information and cargo attribute information;
the knowledge maps respectively comprise a plurality of subgraphs;
inputting all the goods attribute information potentially needing to be put in storage into a database;
clustering and dividing the cargo attribute information to be put in storage;
constructing a plurality of knowledge maps according to the goods attribute information to be put into storage after the clustering division;
the number of the plurality of knowledge maps is the same as the clustering number of the cargo attribute information, an association relation is established among the plurality of knowledge maps, and each knowledge map also has a matching mapping relation of the vehicle attribute information and the cargo attribute information;
the knowledge maps respectively comprise a plurality of subgraphs.
Further, the screening module performs matching comparison on the attribute information of the cargo to be loaded and the cargo attribute information knowledge graph to obtain candidate loading carriages of the cargo to be loaded, and the screening module further includes:
based on the attribute information of the goods to be loaded acquired by the sensor, matching the goods with the corresponding goods attribute information knowledge graph according to the category, and acquiring the matching degree of the goods attribute information knowledge graph;
when the matching degree of each cargo attribute information knowledge graph is larger than a first threshold value, acquiring the mapping relation between the cargo of the cargo attribute information knowledge graph and the vehicle attribute;
and taking the vehicle obtained by the mapping relation between the cargoes and the vehicle attributes of the cargo attribute information knowledge graph as a candidate loading carriage of the cargo to be loaded.
Further, the matching module performs similarity matching between the compartment attribute information of the vehicle to be loaded and the compartment attribute information knowledge maps of all vehicles and the candidate loading compartment requirements, and further includes:
based on the compartment attribute information of the vehicle to be loaded, which is acquired by the sensor, matching the compartment attribute information with the corresponding compartment attribute information knowledge graph according to the category to acquire the matching degree of each compartment attribute information knowledge graph;
when the matching degree of each carriage attribute information knowledge graph is larger than a first threshold value, acquiring a carriage of the carriage attribute information knowledge graph;
and matching the carriage information of the carriage attribute information knowledge graph with the candidate loading carriage in similarity, and acquiring the candidate loading carriage with the corresponding similarity larger than a second threshold value and the maximum similarity value as a target loading carriage.
The invention discloses a cargo optimizing loading method based on a knowledge graph, which comprises the steps of obtaining the information of the attribute of cargo to be loaded and the information of the compartment attribute of the cargo to be loaded, obtaining the information of the compartment attribute of all vehicles and the information of the cargo attribute from a database, carrying out matching comparison on the information of the attribute of cargo to be loaded and the information of the cargo attribute, obtaining the candidate loading compartment of the cargo to be loaded, carrying out similarity matching between the information of the compartment attribute of the cargo to be loaded and the information of the compartment attribute of all vehicles, and the requirements of the candidate loading compartment, and obtaining a target loading compartment from the candidate loading compartment. Aiming at rich types of cargoes, the invention fully considers the association relations among cargoes, carriages and cargo carriages on the basis of a knowledge graph, and reasonably loads the carriages through two layers of screening and matching, thereby realizing the optimization strategy of cargo loading and reducing risks.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive faculty for a person skilled in the art.
FIG. 1 is a workflow diagram of a knowledge-based cargo optimizing loading method in accordance with the present invention;
FIG. 2 is a workflow diagram of an embodiment one of a knowledge-based cargo optimizing loading method in accordance with the present invention;
fig. 3 is a structural block diagram of a cargo optimizing loading device based on a knowledge graph according to the present invention.
Detailed Description
Illustrative embodiments of the present application include, but are not limited to, a knowledge-graph-based cargo optimization loading method.
It will be understood that, as used herein, the term; a module; a unit; may refer to or include an Application Specific Integrated Circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or group) and/or memory that execute one or more software or firmware programs, a combinational logic circuit, and/or other suitable hardware components that provide the described functionality.
It is to be appreciated that in various embodiments of the present application, the processor may be a microprocessor, a digital signal processor, a microcontroller, or the like, and/or any combination thereof. According to another aspect, the processor may be a single core processor, a multi-core processor, or the like, and/or any combination thereof.
It is to be appreciated that a knowledge-graph based cargo optimization loading method provided herein can be implemented on a variety of electronic devices, including, but not limited to, servers, distributed server clusters of multiple servers, cell phones, tablet computers, laptop computers, desktop computers, wearable devices, head mounted displays, mobile email devices, portable gaming devices, portable music players, reader devices, personal digital assistants, virtual reality or augmented reality devices, televisions with one or more processors embedded or coupled therein, and the like.
Referring to fig. 1, the invention first claims a cargo optimizing loading method based on knowledge graph, which is characterized by comprising:
acquiring attribute information of goods to be loaded;
acquiring compartment attribute information of a vehicle to be loaded;
acquiring a compartment attribute information knowledge graph and a cargo attribute information knowledge graph of all vehicles from a database;
matching and comparing the attribute information of the goods to be loaded with the goods attribute information knowledge graph to obtain candidate loading carriages of the goods to be loaded;
matching the compartment attribute information of the vehicle to be loaded with the compartment attribute information knowledge maps of all vehicles with the similarity of the candidate loading compartment requirements;
and acquiring a target loading carriage from the candidate loading carriages, and loading the cargo to be loaded into the target loading carriage.
Further, the acquiring attribute information of the goods to be loaded further includes:
acquiring category attributes of the goods to be loaded through a first sensor,
the measurement attribute data of the cargo to be loaded is acquired by a second sensor,
acquiring chemical attribute data of the goods to be loaded through a third sensor;
the obtaining the compartment attribute information of the vehicle to be loaded further includes:
acquiring compartment measurement attribute data of the vehicle to be loaded through a fourth sensor;
acquiring the internal environment attribute data of the vehicle to be loaded through a fifth sensor;
and acquiring chemical attribute data of the vehicle to be loaded through a sixth sensor.
Specifically, the category attribute of the goods to be loaded is obtained through a first sensor, wherein the category attribute of the goods to be loaded comprises diets, clothes, ceramics, metals, wooden products, plastics and medicines;
acquiring measurement attribute data of the goods to be loaded through a second sensor, wherein the measurement attribute data comprise length, width and height data, shape data and weight data of the goods to be loaded;
acquiring chemical attribute data of the goods to be loaded through a third sensor; including spoiled or damaged conditions or reaction conditions of the cargo to be loaded.
Acquiring carriage measurement attribute data of the vehicle to be loaded through a fourth sensor, wherein the carriage measurement attribute data comprise length, width and height data, shape data and loading capacity data of the vehicle to be loaded;
acquiring internal environment attribute data of the vehicle to be loaded through a fifth sensor, wherein the internal environment attribute data comprise internal temperature data, humidity data and pressure data of the vehicle to be loaded;
and acquiring chemical attribute data of the vehicle to be loaded through a sixth sensor, wherein the chemical attribute data comprise basic conditions of transport articles of the vehicle to be loaded.
Further, referring to fig. 2, the acquiring the compartment attribute information knowledge graph and the cargo attribute information knowledge graph of all vehicles from the database further includes:
extracting compartment attribute information of all vehicles;
clustering and dividing the compartment attribute information of all vehicles;
constructing a plurality of knowledge maps according to the compartment attribute information of all vehicles after clustering;
the number of the plurality of knowledge maps is the same as the number of the compartment attribute clusters, an association relationship is established among the plurality of knowledge maps, and each knowledge map also has a matching mapping relationship of vehicle attribute information and cargo attribute information;
the knowledge maps respectively comprise a plurality of subgraphs;
inputting all the goods attribute information potentially needing to be put in storage into a database;
clustering and dividing the cargo attribute information to be put in storage;
constructing a plurality of knowledge maps according to the goods attribute information to be put into storage after the clustering division;
the number of the plurality of knowledge maps is the same as the clustering number of the cargo attribute information, an association relation is established among the plurality of knowledge maps, and each knowledge map also has a matching mapping relation of the vehicle attribute information and the cargo attribute information;
the knowledge maps respectively comprise a plurality of subgraphs.
Specifically, the cargo attribute information to be put in storage is clustered and divided into patterns of diets, clothes, ceramics, metals, wooden products, plastics and medicines;
the multiple knowledge maps establish an association relationship, and each knowledge map also has a matching mapping relationship between vehicle attribute information and cargo attribute information, and specifically includes a diet storage low-temperature carriage, a metal storage low-humidity carriage, a woodwork storage anti-corrosion carriage, a clothing storage anti-static carriage, a medicine storage high-humidity carriage, and a mutual exclusion compatibility relationship between knowledge maps, for example, the metal carriage can store medicines, but the medicine carriage can not necessarily store metals.
The knowledge maps respectively comprise a plurality of subgraphs, and each level of the clustering content of the warehouse-in cargo attribute information is stored under each subgraph.
Further, the matching and comparing the attribute information of the cargo to be loaded with the cargo attribute information knowledge graph to obtain the candidate loading carriage of the cargo to be loaded specifically includes:
based on the attribute information of the goods to be loaded acquired by the sensor, matching the goods with the corresponding goods attribute information knowledge graph according to the category, and acquiring the matching degree of the goods attribute information knowledge graph;
when the matching degree of each cargo attribute information knowledge graph is larger than a first threshold value, acquiring the mapping relation between the cargo of the cargo attribute information knowledge graph and the vehicle attribute;
and taking the vehicle obtained by the mapping relation between the cargoes and the vehicle attributes of the cargo attribute information knowledge graph as a candidate loading carriage of the cargo to be loaded.
The attribute information of the goods to be loaded, which is acquired based on the sensor, is matched with the corresponding goods attribute information knowledge graph according to the category, so as to obtain the matching degree of each goods attribute information knowledge graph, the attribute information of the goods to be loaded and the information of the corresponding goods attribute information knowledge graph are subjected to vector conversion, the cosine similarity of the two vectors is used for calculation, and the cosine similarity of the two n-dimensional vectors X and Y is calculated:
the dimension n of the converted vector is the sum of the number of map nodes mapped to by two keyword sets and map nodes with one-hop relation, each dimension of the vector represents one map node, and the calculation formula of the value of the vector in the ith dimension is as follows:
wherein count () calculates the number of times a node appears in the graph node set, node is the graph node corresponding to the dimension, E is the set formed by all edges in the cargo attribute information knowledge graph, namely node j Is a node that has a one-hop relationship with the node, and weight takes a value between 0 and 1.
Further, the matching the compartment attribute information of the vehicle to be loaded with the compartment attribute information knowledge maps of all vehicles with the candidate loading compartment requirement in similarity includes:
based on the compartment attribute information of the vehicle to be loaded, which is acquired by the sensor, matching the compartment attribute information with the corresponding compartment attribute information knowledge graph according to the category to acquire the matching degree of each compartment attribute information knowledge graph;
when the matching degree of each carriage attribute information knowledge graph is larger than a first threshold value, acquiring a carriage of the carriage attribute information knowledge graph;
and matching the carriage information of the carriage attribute information knowledge graph with the candidate loading carriage in similarity, and acquiring the candidate loading carriage with the corresponding similarity larger than a second threshold value and the maximum similarity value as a target loading carriage.
The specific matching calculation method is similar to the attribute information of the goods to be loaded, which is acquired based on the sensor, according to the category, with the corresponding goods attribute information knowledge graph.
Referring to fig. 3, the invention also claims a cargo optimizing loading device based on a knowledge graph, which is characterized by comprising:
the acquisition module is used for acquiring attribute information of cargoes to be loaded and acquiring compartment attribute information of the vehicles to be loaded;
the database module is used for acquiring compartment attribute information knowledge maps and cargo attribute information knowledge maps of all vehicles from the database;
the screening module is used for carrying out matching comparison on the attribute information of the goods to be loaded and the goods attribute information knowledge graph to obtain candidate loading carriages of the goods to be loaded;
the matching module is used for matching the compartment attribute information of the vehicles to be loaded with the compartment attribute information knowledge maps of all the vehicles with the similarity of the candidate loading compartment requirements;
and the output module acquires a target loading carriage from the candidate loading carriages and loads the cargo to be loaded into the target loading carriage.
Further, the acquisition module acquires attribute information of goods to be loaded, acquires compartment attribute information of a vehicle to be loaded, and further includes:
acquiring category attributes of the goods to be loaded through a first sensor,
the measurement attribute data of the cargo to be loaded is acquired by a second sensor,
acquiring chemical attribute data of the goods to be loaded through a third sensor;
the obtaining the compartment attribute information of the vehicle to be loaded further includes:
acquiring compartment measurement attribute data of the vehicle to be loaded through a fourth sensor;
acquiring the internal environment attribute data of the vehicle to be loaded through a fifth sensor;
and acquiring chemical attribute data of the vehicle to be loaded through a sixth sensor.
Further, the database module obtains the compartment attribute information knowledge graph and the cargo attribute information knowledge graph of all vehicles from the database, and further includes:
extracting compartment attribute information of all vehicles;
clustering and dividing the compartment attribute information of all vehicles;
constructing a plurality of knowledge maps according to the compartment attribute information of all vehicles after clustering;
the number of the plurality of knowledge maps is the same as the number of the compartment attribute clusters, an association relationship is established among the plurality of knowledge maps, and each knowledge map also has a matching mapping relationship of vehicle attribute information and cargo attribute information;
the knowledge maps respectively comprise a plurality of subgraphs;
inputting all the goods attribute information potentially needing to be put in storage into a database;
clustering and dividing the cargo attribute information to be put in storage;
constructing a plurality of knowledge maps according to the goods attribute information to be put into storage after the clustering division;
the number of the plurality of knowledge maps is the same as the clustering number of the cargo attribute information, an association relation is established among the plurality of knowledge maps, and each knowledge map also has a matching mapping relation of the vehicle attribute information and the cargo attribute information;
the knowledge maps respectively comprise a plurality of subgraphs.
Further, the screening module performs matching comparison on the attribute information of the cargo to be loaded and the cargo attribute information knowledge graph to obtain candidate loading carriages of the cargo to be loaded, and the screening module further includes:
based on the attribute information of the goods to be loaded acquired by the sensor, matching the goods with the corresponding goods attribute information knowledge graph according to the category, and acquiring the matching degree of the goods attribute information knowledge graph;
when the matching degree of each cargo attribute information knowledge graph is larger than a first threshold value, acquiring the mapping relation between the cargo of the cargo attribute information knowledge graph and the vehicle attribute;
and taking the vehicle obtained by the mapping relation between the cargoes and the vehicle attributes of the cargo attribute information knowledge graph as a candidate loading carriage of the cargo to be loaded.
Further, the matching module performs similarity matching between the compartment attribute information of the vehicle to be loaded and the compartment attribute information knowledge maps of all vehicles and the candidate loading compartment requirements, and further includes:
based on the compartment attribute information of the vehicle to be loaded, which is acquired by the sensor, matching the compartment attribute information with the corresponding compartment attribute information knowledge graph according to the category to acquire the matching degree of each compartment attribute information knowledge graph;
when the matching degree of each carriage attribute information knowledge graph is larger than a first threshold value, acquiring a carriage of the carriage attribute information knowledge graph;
and matching the carriage information of the carriage attribute information knowledge graph with the candidate loading carriage in similarity, and acquiring the candidate loading carriage with the corresponding similarity larger than a second threshold value and the maximum similarity value as a target loading carriage.
It should be noted that, in the embodiments of the present application, each unit/module is a logic unit/module, and in physical aspect, one logic unit/module may be one physical unit/module, or may be a part of one physical unit/module, or may be implemented by a combination of multiple physical units/modules, where the physical implementation manner of the logic unit/module itself is not the most important, and the combination of functions implemented by the logic unit/module is the key to solve the technical problem posed by the present application. Furthermore, to highlight the innovative part of the present application, the above-described device embodiments of the present application do not introduce units/modules that are less closely related to solving the technical problems presented by the present application, which does not indicate that the above-described device embodiments do not have other units/modules.
It should be noted that in the examples and descriptions of the present application, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the term; comprises the following steps of; comprises the following steps of; or any other variation thereof, is intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further restrictions, by statement; comprising one; the definition of an element does not exclude the presence of other elements in a process, method, article or apparatus that comprises the element.
While the present application has been shown and described with reference to certain preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present application.
Claims (4)
1. The goods optimizing and loading method based on the knowledge graph is characterized by comprising the following steps of:
acquiring attribute information of goods to be loaded;
acquiring compartment attribute information of a vehicle to be loaded;
acquiring a compartment attribute information knowledge graph and a cargo attribute information knowledge graph of all vehicles from a database; matching and comparing the attribute information of the goods to be loaded with the goods attribute information knowledge graph to obtain candidate loading carriages of the goods to be loaded;
matching the compartment attribute information of the vehicle to be loaded with the compartment attribute information knowledge maps of all vehicles with the similarity of the candidate loading compartment requirements;
acquiring a target loading carriage from the candidate loading carriages, and loading the cargo to be loaded into the target loading carriage;
the method for acquiring the compartment attribute information knowledge graph and the cargo attribute information knowledge graph of all vehicles from the database further comprises the following steps:
extracting compartment attribute information of all vehicles;
clustering and dividing the compartment attribute information of all vehicles;
constructing a plurality of knowledge maps according to the compartment attribute information of all vehicles after clustering;
the number of the plurality of knowledge maps is the same as the number of the compartment attribute clusters, an association relationship is established among the plurality of knowledge maps, and each knowledge map also has a matching mapping relationship of vehicle attribute information and cargo attribute information;
the knowledge maps respectively comprise a plurality of subgraphs;
inputting all the goods attribute information potentially needing to be put in storage into a database;
clustering and dividing the cargo attribute information to be put in storage;
constructing a plurality of knowledge maps according to the goods attribute information to be put into storage after the clustering division;
the number of the plurality of knowledge maps is the same as the clustering number of the cargo attribute information, an association relation is established among the plurality of knowledge maps, and each knowledge map also has a matching mapping relation of the vehicle attribute information and the cargo attribute information;
the knowledge maps respectively comprise a plurality of subgraphs;
the matching and comparing the attribute information of the goods to be loaded with the goods attribute information knowledge graph to obtain candidate loading carriages of the goods to be loaded, which specifically comprises the following steps:
based on the attribute information of the goods to be loaded acquired by the sensor, matching the goods with the corresponding goods attribute information knowledge graph according to the category, and acquiring the matching degree of the goods attribute information knowledge graph;
when the matching degree of each cargo attribute information knowledge graph is larger than a first threshold value, acquiring the mapping relation between the cargo of the cargo attribute information knowledge graph and the vehicle attribute;
taking a vehicle obtained by the mapping relation between the cargoes of the cargo attribute information knowledge graph and the vehicle attributes as a candidate loading carriage of the cargo to be loaded;
the matching the compartment attribute information of the vehicle to be loaded with the compartment attribute information knowledge maps of all vehicles with the similarity to the candidate loading compartment requirement specifically includes:
based on the compartment attribute information of the vehicle to be loaded, which is acquired by the sensor, matching the compartment attribute information with the corresponding compartment attribute information knowledge graph according to the category to acquire the matching degree of each compartment attribute information knowledge graph;
when the matching degree of each carriage attribute information knowledge graph is larger than a first threshold value, acquiring a carriage of the carriage attribute information knowledge graph;
and matching the carriage information of the carriage attribute information knowledge graph with the candidate loading carriage in similarity, and acquiring the candidate loading carriage with the corresponding similarity larger than a second threshold value and the maximum similarity value as a target loading carriage.
2. The method for optimizing cargo loading based on knowledge graph as claimed in claim 1, wherein said obtaining attribute information of cargo to be loaded further comprises:
acquiring category attributes of the goods to be loaded through a first sensor,
the measurement attribute data of the cargo to be loaded is acquired by a second sensor,
acquiring chemical attribute data of the goods to be loaded through a third sensor;
the obtaining the compartment attribute information of the vehicle to be loaded further includes:
acquiring compartment measurement attribute data of the vehicle to be loaded through a fourth sensor;
acquiring the internal environment attribute data of the vehicle to be loaded through a fifth sensor;
and acquiring chemical attribute data of the vehicle to be loaded through a sixth sensor.
3. Goods optimizing and loading device based on knowledge graph is characterized by comprising:
the acquisition module is used for acquiring attribute information of cargoes to be loaded and acquiring compartment attribute information of the vehicles to be loaded;
the database module is used for acquiring compartment attribute information knowledge maps and cargo attribute information knowledge maps of all vehicles from the database;
the screening module is used for carrying out matching comparison on the attribute information of the goods to be loaded and the goods attribute information knowledge graph to obtain candidate loading carriages of the goods to be loaded;
the matching module is used for matching the compartment attribute information of the vehicles to be loaded with the compartment attribute information knowledge maps of all the vehicles with the similarity of the candidate loading compartment requirements;
the output module acquires a target loading carriage from the candidate loading carriages and loads the cargo to be loaded into the target loading carriage;
the database module acquires the compartment attribute information knowledge graph and the cargo attribute information knowledge graph of all vehicles from the database, and further comprises:
extracting compartment attribute information of all vehicles;
clustering and dividing the compartment attribute information of all vehicles;
constructing a plurality of knowledge maps according to the compartment attribute information of all vehicles after clustering;
the number of the plurality of knowledge maps is the same as the number of the compartment attribute clusters, an association relationship is established among the plurality of knowledge maps, and each knowledge map also has a matching mapping relationship of vehicle attribute information and cargo attribute information;
the knowledge maps respectively comprise a plurality of subgraphs;
inputting all the goods attribute information potentially needing to be put in storage into a database;
clustering and dividing the cargo attribute information to be put in storage;
constructing a plurality of knowledge maps according to the goods attribute information to be put into storage after the clustering division;
the number of the plurality of knowledge maps is the same as the clustering number of the cargo attribute information, an association relation is established among the plurality of knowledge maps, and each knowledge map also has a matching mapping relation of the vehicle attribute information and the cargo attribute information;
the knowledge maps respectively comprise a plurality of subgraphs;
the screening module is used for carrying out matching comparison on the attribute information of the goods to be loaded and the goods attribute information knowledge graph to obtain candidate loading carriages of the goods to be loaded, and the screening module further comprises:
based on the attribute information of the goods to be loaded acquired by the sensor, matching the goods with the corresponding goods attribute information knowledge graph according to the category, and acquiring the matching degree of the goods attribute information knowledge graph;
when the matching degree of each cargo attribute information knowledge graph is larger than a first threshold value, acquiring the mapping relation between the cargo of the cargo attribute information knowledge graph and the vehicle attribute;
taking a vehicle obtained by the mapping relation between the cargoes of the cargo attribute information knowledge graph and the vehicle attributes as a candidate loading carriage of the cargo to be loaded;
the matching module performs similarity matching between the compartment attribute information of the vehicle to be loaded and the compartment attribute information knowledge maps of all vehicles and the candidate loading compartment requirements, and further includes:
based on the compartment attribute information of the vehicle to be loaded, which is acquired by the sensor, matching the compartment attribute information with the corresponding compartment attribute information knowledge graph according to the category to acquire the matching degree of each compartment attribute information knowledge graph;
when the matching degree of each carriage attribute information knowledge graph is larger than a first threshold value, acquiring a carriage of the carriage attribute information knowledge graph;
and matching the carriage information of the carriage attribute information knowledge graph with the candidate loading carriage in similarity, and acquiring the candidate loading carriage with the corresponding similarity larger than a second threshold value and the maximum similarity value as a target loading carriage.
4. The cargo optimizing loading device based on a knowledge graph according to claim 3, wherein the acquisition module acquires attribute information of cargo to be loaded, acquires compartment attribute information of a vehicle to be loaded, and further comprises:
acquiring category attributes of the goods to be loaded through a first sensor,
the measurement attribute data of the cargo to be loaded is acquired by a second sensor,
acquiring chemical attribute data of the goods to be loaded through a third sensor;
the obtaining the compartment attribute information of the vehicle to be loaded further includes:
acquiring compartment measurement attribute data of the vehicle to be loaded through a fourth sensor;
acquiring the internal environment attribute data of the vehicle to be loaded through a fifth sensor;
and acquiring chemical attribute data of the vehicle to be loaded through a sixth sensor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111574296.4A CN114493408B (en) | 2021-12-21 | 2021-12-21 | Goods optimized loading method and device based on knowledge graph |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111574296.4A CN114493408B (en) | 2021-12-21 | 2021-12-21 | Goods optimized loading method and device based on knowledge graph |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114493408A CN114493408A (en) | 2022-05-13 |
| CN114493408B true CN114493408B (en) | 2024-04-09 |
Family
ID=81494079
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111574296.4A Active CN114493408B (en) | 2021-12-21 | 2021-12-21 | Goods optimized loading method and device based on knowledge graph |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114493408B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109086347A (en) * | 2018-07-13 | 2018-12-25 | 武汉尼维智能科技有限公司 | A kind of construction method, device and the storage medium of international ocean shipping dangerous cargo knowledge mapping system |
| CN110705945A (en) * | 2019-10-15 | 2020-01-17 | 秒针信息技术有限公司 | Article transportation method and device, storage medium and electronic device |
| CN110705805A (en) * | 2019-10-15 | 2020-01-17 | 秒针信息技术有限公司 | Cargo assembling method and device, storage medium and electronic device |
| CN110738372A (en) * | 2019-10-15 | 2020-01-31 | 秒针信息技术有限公司 | Method and device for assembling goods |
| CN111325611A (en) * | 2020-03-05 | 2020-06-23 | 上海明略人工智能(集团)有限公司 | Order distribution method and device |
| CN112668815A (en) * | 2019-09-30 | 2021-04-16 | 北京国双科技有限公司 | Automobile data processing method and device |
| CN112860917A (en) * | 2021-03-09 | 2021-05-28 | 上海东普信息科技有限公司 | Method, device and equipment for processing data of goods to be warehoused and storage medium |
| CN112990551A (en) * | 2021-02-20 | 2021-06-18 | 国电南瑞科技股份有限公司 | Cascading failure evolution path tracing and predicting method and device based on knowledge graph |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10909494B2 (en) * | 2018-03-27 | 2021-02-02 | Accenture Global Solutions Limited | System for collaborative logistics using a collaborative logistics map and a knowledge graph |
-
2021
- 2021-12-21 CN CN202111574296.4A patent/CN114493408B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109086347A (en) * | 2018-07-13 | 2018-12-25 | 武汉尼维智能科技有限公司 | A kind of construction method, device and the storage medium of international ocean shipping dangerous cargo knowledge mapping system |
| CN112668815A (en) * | 2019-09-30 | 2021-04-16 | 北京国双科技有限公司 | Automobile data processing method and device |
| CN110705945A (en) * | 2019-10-15 | 2020-01-17 | 秒针信息技术有限公司 | Article transportation method and device, storage medium and electronic device |
| CN110705805A (en) * | 2019-10-15 | 2020-01-17 | 秒针信息技术有限公司 | Cargo assembling method and device, storage medium and electronic device |
| CN110738372A (en) * | 2019-10-15 | 2020-01-31 | 秒针信息技术有限公司 | Method and device for assembling goods |
| CN111325611A (en) * | 2020-03-05 | 2020-06-23 | 上海明略人工智能(集团)有限公司 | Order distribution method and device |
| CN112990551A (en) * | 2021-02-20 | 2021-06-18 | 国电南瑞科技股份有限公司 | Cascading failure evolution path tracing and predicting method and device based on knowledge graph |
| CN112860917A (en) * | 2021-03-09 | 2021-05-28 | 上海东普信息科技有限公司 | Method, device and equipment for processing data of goods to be warehoused and storage medium |
Non-Patent Citations (2)
| Title |
|---|
| Vehicle Routing with Shipment Consolidatio;Juan David Cortes 等;《International Journal of Production Economics》;20200117;第227卷;第1-13页 * |
| 我国智能化冷链物流信息数据采集与应用研究;赵恒;《中国优秀硕士学位论文全文数据库 经济与管理科学辑》;20191215(第12期);第J145-38页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114493408A (en) | 2022-05-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111798273A (en) | Training method of purchase probability prediction model of product and purchase probability prediction method | |
| CN106886601A (en) | A kind of Cross-modality searching algorithm based on the study of subspace vehicle mixing | |
| US20180053115A1 (en) | Spend Data Enrichment and Classification | |
| CN114912948B (en) | Cloud service-based cross-border e-commerce big data intelligent processing method, device and equipment | |
| CN113254354A (en) | Test case recommendation method and device, readable storage medium and electronic equipment | |
| Gonzalez-Diaz et al. | Neighborhood matching for image retrieval | |
| CN110059991B (en) | Warehouse item selection method, system, electronic device and computer readable medium | |
| CN110827112A (en) | Deep learning commodity recommendation method and device, computer equipment and storage medium | |
| CN110781203A (en) | Method and device for determining data width table | |
| CN108829707A (en) | Big data intelligent analysis system and method across business domains | |
| CN114386509A (en) | Data fusion method and device, electronic equipment and storage medium | |
| CN113360803A (en) | Data caching method, device and equipment based on user behavior and storage medium | |
| CN115018588A (en) | Product recommendation method and device, electronic equipment and readable storage medium | |
| CN114493408B (en) | Goods optimized loading method and device based on knowledge graph | |
| Zhao et al. | Monochromatic and bichromatic ranked reverse boolean spatial keyword nearest neighbors search | |
| CN115375385A (en) | Commodity information processing method and device, computer equipment and storage medium | |
| CN113569933A (en) | Trademark pattern matching method and corresponding device, equipment and medium | |
| CN107729523A (en) | Data service method, electronic installation and storage medium | |
| CN111651625A (en) | Image retrieval method, image retrieval device, electronic equipment and storage medium | |
| CN111428486B (en) | Article information data processing method, device, medium and electronic equipment | |
| CN111488479B (en) | Hypergraph construction method and device, computer system and medium | |
| CN112287140A (en) | Image retrieval method and system based on big data | |
| US7849038B2 (en) | Method for using the second homotopy group in assessing the similarity of sets of data | |
| CN114240560A (en) | Product ranking method, device, equipment and storage medium based on multidimensional analysis | |
| CN113343102A (en) | Data recommendation method and device based on feature screening, electronic equipment and medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |