CN115330320B - Product management method based on big data and industrial internet and related device - Google Patents
Product management method based on big data and industrial internet and related device Download PDFInfo
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- CN115330320B CN115330320B CN202211265389.3A CN202211265389A CN115330320B CN 115330320 B CN115330320 B CN 115330320B CN 202211265389 A CN202211265389 A CN 202211265389A CN 115330320 B CN115330320 B CN 115330320B
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Abstract
The application discloses a product management method and a related device based on big data and industrial internet, comprising the following steps: acquiring logistics warehouse information and acquiring product shipment data and a position information table corresponding to each warehouse in the logistics warehouse information; the method comprises the steps of generating a shipment demand portrait of each warehouse according to product shipment data, generating a second logistics warehouse set by combining the shipment demand portrait with preset conditions, and generating a product distribution demand table in the second logistics warehouse set according to the shipment demand portrait and a position information table. The product shipment tendency of each warehouse is obtained by generating a shipment demand portrait, and a product delivery demand table is generated by combining the position information of each warehouse, so that the effect of intelligently managing product delivery by utilizing big data and the Internet is further realized.
Description
Technical Field
The present application relates to the field of product management, and in particular, to a product management method and related apparatus based on big data and industrial internet.
Background
With the development of science and technology, the internet becomes an indispensable part of people's life. For a large-scale production and manufacturing company or an e-commerce platform, there are many logistics warehouses in China, and the logistics warehouses are used for receiving products which are produced and manufactured and sending the products to order destinations according to order demands. However, currently, product management for logistics warehouses still remains in the stage of manually configuring product delivery, and a case that products need to be called from a remote logistics warehouse often occurs. Therefore, how to carry out reasonable planning by using big data and the internet in the product management process becomes a technical problem to be solved urgently.
The above is only for the purpose of assisting understanding of the technical solution of the present invention, and does not represent an admission that the above is the prior art.
Disclosure of Invention
In order to carry out reasonable planning and management on product distribution, the application provides a product management method and a related device based on big data and industrial Internet.
In a first aspect, the product management method based on big data and industrial internet provided by the application adopts the following technical scheme:
a big data and industrial Internet based product management method comprises a big data and industrial Internet based product management method, and comprises the following steps:
acquiring logistics warehouse information, and acquiring product shipment data and a position information table of each warehouse in the logistics warehouse information;
generating a shipment demand portrait according to the product shipment data of each warehouse;
generating a second logistics warehouse set according to the logistics warehouse corresponding to the shipment demand portrait meeting the preset conditions;
and generating a product distribution demand table in the second logistics warehouse set according to the shipment demand portrait and the position information table.
Optionally, the generating a shipment demand representation according to the product shipment data of each warehouse includes:
acquiring product shipment data corresponding to each warehouse;
determining weight information of a preset label in each warehouse according to the shipment data;
and generating a shipment demand portrait of each warehouse according to the weight information of the preset labels in each warehouse.
Optionally, the generating the shipment demand representation of each warehouse according to the weight information of the preset tag in each warehouse includes:
acquiring weight information of a preset label in each warehouse;
determining the product flowing tendency according to the weight information of the preset label;
judging the effectiveness of the product flowing tendency;
if the product flow tendency is valid, a shipment demand image is generated according to the product flow tendency.
Optionally, the generating a second logistics warehouse set according to the logistics warehouse corresponding to the shipment demand portrait meeting the preset condition includes:
acquiring preset conditions, and screening the shipment demand portrait according to the preset conditions;
adding identification information to the logistics warehouse in the logistics warehouse information according to the screening result;
and acquiring a historical identification record, and generating a second logistics warehouse set in the logistics warehouse information according to the identification information and the historical identification record.
Optionally, the generating a product delivery demand table according to the shipment demand sketch and the location information table in the second logistics warehouse set includes:
generating a predicted cargo shipment table according to the shipment demand picture in the second logistics warehouse;
acquiring order information, and generating a current actual goods delivery table according to destination information in the order information and the position information;
and generating a product distribution demand table according to the estimated cargo shipment table and the actual cargo shipment table.
Optionally, the generating a product delivery demand table according to the predicted cargo shipment table and the actual cargo shipment table comprises:
acquiring the expected shipment information of each product in the expected shipment list;
acquiring actual shipment information of each product in the actual shipment table;
and generating a product distribution demand table according to the estimated shipment information and the actual shipment information.
Optionally, after the generating a product delivery demand table according to the shipment demand representation and the location information table in the second logistics warehouse set, the method further includes:
generating a shortest distribution path scheme according to the product distribution demand table;
acquiring the distribution cost corresponding to the shortest distribution path scheme;
and obtaining historical distribution cost, and comparing the historical distribution cost with the distribution cost corresponding to the scheme with the shortest distribution path to generate scheme result feedback.
In a second aspect, the present application provides a big data and industrial internet based product management apparatus, comprising:
the warehouse information acquisition module is used for acquiring logistics warehouse information and acquiring product shipment data and a position information table of each warehouse in the logistics warehouse information;
the shipment portrait generation module is used for generating shipment demand portraits according to the product shipment data of each warehouse;
the second logistics warehouse generation module is used for generating a second logistics warehouse set according to the logistics warehouse corresponding to the shipment demand portrait meeting the preset conditions;
and the demand table generating module is used for generating a product delivery demand table in the second logistics warehouse set according to the shipment demand portrait and the position information table.
In a third aspect, the present application provides a computer apparatus, the apparatus comprising: a memory, a processor, which when executing computer instructions stored by the memory performs a method as described above.
In a fourth aspect, the present application provides a computer-readable storage medium comprising instructions which, when executed on a computer, cause the computer to perform the method as described above.
In summary, the present application includes the following beneficial technical effects:
the method comprises the steps of obtaining logistics warehouse information and obtaining product shipment data and a position information table corresponding to each warehouse in the logistics warehouse information; the method comprises the steps of generating a shipment demand sketch of each warehouse according to product shipment data, generating a second logistics warehouse set by combining the shipment demand sketch through preset conditions, and generating a product delivery demand table in the second logistics warehouse set according to the shipment demand sketch and a position information table. The product shipment tendency of each warehouse is obtained by generating the shipment demand portrait, and the product delivery demand table is generated by combining the position information of each warehouse, so that the effect of intelligently managing product delivery by utilizing big data and the Internet is further realized.
Drawings
FIG. 1 is a schematic diagram of a computer device architecture of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart diagram of a first embodiment of the big data and industrial Internet based product management method of the present invention;
FIG. 3 is a flow chart of a second embodiment of the big data and industrial Internet based product management method of the present invention;
FIG. 4 is a flow chart of a third embodiment of the big data and industrial Internet based product management method of the present invention;
fig. 5 is a block diagram showing the construction of a first embodiment of the big data and industrial internet based product management apparatus according to the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a computer device in a hardware operating environment according to an embodiment of the present invention.
As shown in fig. 1, the computer device may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a Wireless interface (e.g., a Wireless-Fidelity (Wi-Fi) interface). The Memory 1005 may be a high-speed Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the configuration shown in FIG. 1 is not intended to be limiting of computer devices and may include more or fewer components than shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 1, the memory 1005, which is a storage medium, may include therein an operating system, a network communication module, a user interface module, and a product management program based on big data and industrial internet.
In the computer device shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 of the computer device of the present invention may be provided in a computer device that calls the product management program based on big data and industrial internet stored in the memory 1005 through the processor 1001 and performs the product management method based on big data and industrial internet provided by the embodiment of the present invention.
An embodiment of the present invention provides a product management method based on big data and an industrial internet, and referring to fig. 2, fig. 2 is a schematic flow diagram of a first embodiment of the product management method based on big data and an industrial internet according to the present invention.
In this embodiment, the product management method based on big data and industrial internet includes the following steps:
step S10: and acquiring logistics warehouse information, and acquiring product shipment data and a position information table of each warehouse in the logistics warehouse information.
It should be noted that the execution subject of this embodiment may be a computing service device with data processing, network communication, and program running functions, such as a smart phone, a tablet computer, a personal computer, or other devices that can implement the above functions, which is not limited in this embodiment.
It can be understood that, in this embodiment, the logistics warehouse information, that is, the logistics warehouse information set according to the specific use requirement, may include all logistics warehouses having a storage function in the use situation, or may be a logistics warehouse set in a certain area, and is preset according to a specific use scenario. The present embodiment is not limited thereto. For example: all logistics warehouses in province A are specified to be used as data sources in the logistics warehouse information in the embodiment.
It should be noted that, in this embodiment, the logistics warehouse information includes: the system comprises position information of a logistics warehouse, basic scale information of the logistics warehouse, intermediate commodity information of the logistics warehouse, historical shipment information of the logistics warehouse, special setting information of the logistics warehouse, other information and the like.
It is understood that the product shipment data is information of the shipment process of the logistics warehouse within a preset time period, such as: and in the previous 24 hours, the order shipment data of all the commodities in the logistics warehouse through a certain preset channel can be regarded as corresponding product shipment data.
The location information table is location information corresponding to the logistics warehouse.
In specific implementation, the logistics warehouse information is acquired, and the corresponding product shipment data is acquired from the logistics warehouse information, wherein the product shipment data is the position information of each logistics warehouse according to the product shipment data set under the use condition within the specified time.
Step S20: and generating a shipment demand portrait according to the product shipment data of each warehouse.
In this embodiment, the shipment-required image is an image of each warehouse that is shipped within a predetermined time. The image may be expressed as a shipping tendency, consisting of a product structure. The representation may be presented in a cylindrical graph or other statistical manner, and the embodiment is not limited herein.
In specific implementation, the step of generating the shipment demand representation according to the product shipment data of each warehouse is to acquire the product data and preset classification labels at the same time, and determine specific numerical values of the shipment data under each label and the proportion of the specific numerical values to the total data according to the preset classification labels. For example: four products A, B, C and D are arranged in a warehouse, wherein after the products A and B are classified through a preset classification label, the products A and B are arranged in the label 1, the products C and D are arranged in the label 2, and the current shipping tendency of the label 1 and the label 2 in the warehouse can be obtained through analyzing specific product shipping data so as to finish shipping demand portrayal.
Step S30: and generating a second logistics warehouse set according to the logistics warehouse corresponding to the shipment demand portrait meeting the preset conditions.
The preset conditions are conditions set for further sorting the warehouse after the shipment image is generated.
It is understood that the second logistics warehouse set represents the logistics warehouse set generated after the screening, and the second logistics warehouse set must exist in the logistics warehouse information, which means a smaller range of logistics warehouse numbers.
In this embodiment, the preset condition is to filter the warehouse that does not reach the basic shipment data in the generated shipment demand image. For example: and according to the preset condition corresponding to the actual use condition, 6000 basic shipment data of the warehouse can be regarded as effective shipment after being acquired within 50 hours. When the judgment is carried out according to the preset conditions, the shipment demand portrait generated by the warehouse of which the basic shipment data does not reach 6000 pieces is an invalid portrait, and the invalid portrait is filtered.
In specific implementation, the step of generating the second logistics warehouse set according to the logistics warehouse corresponding to the shipment demand portrait under the preset condition refers to the step of screening the shipment demand portrait through the preset condition to obtain a target shipment demand portrait, the step of obtaining the corresponding logistics warehouse according to the target shipment demand portrait, and the step of taking the logistics warehouse as the second logistics warehouse set.
Further, in order to select a logistics warehouse with a reasonable position as a calculation object, the generating of the second logistics warehouse set according to the logistics warehouse corresponding to the shipment demand portrait meeting the preset condition comprises: acquiring preset conditions, and screening the shipment demand portrait according to the preset conditions; adding identification information to the logistics warehouse in the logistics warehouse information according to the screening result; and acquiring a historical identification record, and generating a second logistics warehouse set in the logistics warehouse information according to the identification information and the historical identification record.
Step S40: and generating a product distribution demand table in the second logistics warehouse set according to the shipment demand portrait and the position information table.
It should be noted that the product delivery requirement table represents the product list generated according to the embodiment for delivering to the warehouse, wherein the product list includes the label of the product, the number of the product, the corresponding shipment route, and the expected arrival time.
In a specific implementation, the product delivery demand table is generated according to the shipment demand representation and the position information table by analyzing the shipment demand representation and predicting the total number of products required by the warehouse in a future preset time period and the shipment tendency. And on the basis, the route time is estimated according to the position information and the original delivery place so as to generate a product delivery demand table.
Further, in order to verify whether the product delivery requirement table is valid after the product management method is generated, after the generating of the product delivery requirement table according to the shipment requirement representation and the location information table in the second logistics warehouse set, the method further includes: generating a shortest distribution path scheme according to the product distribution demand table; acquiring the distribution cost corresponding to the shortest distribution path scheme; and obtaining historical distribution cost, and comparing the historical distribution cost with the distribution cost corresponding to the scheme with the shortest distribution path to generate scheme result feedback.
It should be noted that, in order to check whether the product distribution demand table generated in the present embodiment can improve the management capability for the product, further verification is performed by comparing the historical data with the data of the warehouse in which the present solution is adopted. If the data comparison is not as good as the historical data, product label information is obtained, and the product label information is adjusted to regenerate the shipment demand portrait.
In the embodiment, logistics warehouse information is acquired, and product shipment data and a position information table corresponding to each warehouse are acquired from the logistics warehouse information; the method comprises the steps of generating a shipment demand sketch of each warehouse according to product shipment data, generating a second logistics warehouse set by combining the shipment demand sketch through preset conditions, and generating a product delivery demand table in the second logistics warehouse set according to the shipment demand sketch and a position information table. The product shipment tendency of each warehouse is obtained by generating the shipment demand portrait, and the product delivery demand table is generated by combining the position information of each warehouse, so that the effect of intelligently managing product delivery by utilizing big data and the Internet is further realized.
Referring to fig. 3, fig. 3 is a schematic flowchart of a second embodiment of the big data and industrial internet based product management method according to the present invention.
Based on the first embodiment, the step S20 of the product management method based on big data and industrial internet in this embodiment further includes:
step S201: acquiring product shipment data corresponding to each warehouse;
step S202: determining weight information of a preset label in each warehouse according to the shipment data;
in specific implementation, different labels are given different weights according to actual use conditions, and the effect of effectively generating the shipment demand portrait according to specific actual conditions can be effectively realized.
In specific implementation, before determining the preset label weight information in each warehouse, the method further includes obtaining the weight setting and the label information corresponding to the warehouse. And if the label information is detected to exist and the corresponding weight is not set, acquiring default weight setting, and endowing the default weight setting to the label. If the labels with the weight exceeding the preset number do not set the weight, feedback is carried out to the management terminal, and the shipment requirement portrait generated at the time is also considered to be invalid.
Step S203: and generating a shipment demand portrait of each warehouse according to the weight information of the preset labels in each warehouse.
In specific implementation, the step of generating the shipment demand sketch of each warehouse according to the weight information of the preset labels in each warehouse means that the total number of the shipment products and the total number of the labels in the warehouse are obtained, the shipment tendency is analyzed according to the proportion value generated by the label weight, and the shipment demand sketch is further generated.
Further, in order to be more reasonable in the process of generating the shipment demand representation, the generating the shipment demand representation of each warehouse according to the weight information of the preset tags in each warehouse comprises: acquiring weight information of a preset label in each warehouse; determining the product flowing tendency according to the weight information of the preset label; judging the effectiveness of the product flowing tendency; if the product flow tendency is valid, a shipment demand image is generated according to the product flow tendency.
In the embodiment, the shipment data corresponding to each warehouse is obtained; determining weight information of a preset label in each warehouse according to the shipment data; generating a shipment demand portrait of each warehouse according to the weight information of the preset labels in each warehouse; the management and the classification of the products with different importance degrees are realized by adjusting the weight, and the generated delivery requirement portrait is more in line with the use requirement.
Referring to fig. 4, fig. 4 is a flowchart illustrating a third embodiment of the big data and industrial internet based product management method according to the present invention.
Based on the first embodiment, the step S40 of the product management method based on big data and industrial internet in this embodiment further includes:
step S401: and generating a predicted cargo shipment table in the second logistics warehouse according to the shipment demand sketch.
It should be noted that the expected shipment table is a shipment preset table after a preset time period, for example: according to the actual use requirement, the setting time is 20 days in the embodiment, and the corresponding shipment preset table, namely the shipment quantity of the two products of the labels A and B, is estimated.
Step S402: and acquiring order information, and generating a current actual goods delivery table according to destination information in the order information and the position information.
It will be appreciated that the order information is the information that is targeted for delivery of the order to the warehouse. The products in the warehouse can be delivered to individuals by users and delivered by express companies or other logistics units, or can be used for goods calling between warehouses and used for supporting the situation that the quantity of the products in other warehouses is insufficient.
It should be noted that the actual cargo shipment table may be the current day or the week, and the statistics of the number of shipments in the warehouse in the preset channel may be performed. The shipment mode, shipment purpose and shipment property can all be set according to actual conditions, and this embodiment is not limited herein.
Step S403: and generating a product distribution demand list according to the estimated goods delivery list and the actual goods delivery list.
Further, in order to generate a product distribution demand table more reasonably, the generating a product distribution demand table according to the predicted shipment table and the actual shipment table includes: acquiring the expected shipment information of each product in the expected shipment list; acquiring actual shipment information of each product in the actual shipment table; and generating a product distribution demand table according to the estimated shipment information and the actual shipment information.
This embodiment passes. Generating a predicted cargo shipment table according to the shipment demand picture in the second logistics warehouse; acquiring order information, and generating a current actual goods delivery table according to destination information in the order information and the position information; and generating a product distribution demand table according to the estimated cargo shipment table and the actual cargo shipment table. The product delivery demand table is generated by combining the predicted goods delivery table and the actual goods delivery table, and the product delivery demand table is generated by comparing the numerical values in the predicted goods delivery table and the actual goods delivery table, so that the accuracy of product management is further realized.
Furthermore, an embodiment of the present invention further provides a computer-readable storage medium, on which a program for big data and industrial internet based product management is stored, and the program for big data and industrial internet based product management, when executed by a processor, implements the steps of the method for big data and industrial internet based product management as described above.
Referring to fig. 5, fig. 5 is a block diagram illustrating a first embodiment of the apparatus for product management based on big data and industrial internet according to the present invention.
As shown in fig. 5, the apparatus for product management based on big data and industrial internet according to the embodiment of the present invention includes:
the warehouse information acquisition module 10 is configured to acquire logistics warehouse information, and acquire product shipment data and a position information table of each warehouse in the logistics warehouse information;
a shipment image generation module 20, configured to generate a shipment demand image according to the product shipment data of each warehouse;
the second logistics warehouse generation module 30 is configured to generate a second logistics warehouse set according to the logistics warehouse corresponding to the shipment demand representation meeting the preset condition;
and the demand table generating module 40 is configured to generate a product delivery demand table according to the shipment demand representation and the location information table in the second logistics warehouse set.
It should be understood that the above is only an example, and the technical solution of the present invention is not limited in any way, and in a specific application, a person skilled in the art may set the technical solution as needed, and the present invention is not limited thereto.
In the embodiment, logistics warehouse information is acquired, and product shipment data and a position information table corresponding to each warehouse are acquired from the logistics warehouse information; the method comprises the steps of generating a shipment demand sketch of each warehouse according to product shipment data, generating a second logistics warehouse set by combining the shipment demand sketch through preset conditions, and generating a product delivery demand table in the second logistics warehouse set according to the shipment demand sketch and a position information table. The product shipment tendency of each warehouse is obtained by generating a shipment demand portrait, and a product delivery demand table is generated by combining the position information of each warehouse, so that the effect of intelligently managing product delivery by utilizing big data and the Internet is further realized.
In an embodiment, the shipment image generating module 20 is further configured to obtain shipment data of products corresponding to each warehouse; determining weight information of a preset label in each warehouse according to the shipment data; and generating a shipment demand portrait of each warehouse according to the weight information of the preset labels in each warehouse.
In an embodiment, the shipment image generation module 20 is further configured to obtain weight information of a preset tag in each warehouse; determining the product flowing tendency according to the weight information of the preset label; judging the effectiveness of the product flowing tendency; if the product flow tendency is valid, a shipment demand image is generated according to the product flow tendency.
In an embodiment, the second logistics warehouse generation module 30 is further configured to obtain a preset condition, and screen the shipment demand image according to the preset condition; adding identification information to the logistics warehouse in the logistics warehouse information according to the screening result; and acquiring a historical identification record, and generating a second logistics warehouse set in the logistics warehouse information according to the identification information and the historical identification record.
In an embodiment, the demand table generating module 40 is further configured to generate a predicted shipment table according to the shipment demand representation in the second logistics warehouse; acquiring order information, and generating a current actual goods delivery table according to destination information in the order information and the position information; and generating a product distribution demand table according to the estimated goods delivery table and the actual goods delivery table.
In an embodiment, the demand table generating module 40 is further configured to obtain expected shipment information of each product in the expected shipment table; acquiring actual shipment information of each product in the actual shipment table; and generating a product distribution demand table according to the estimated shipment information and the actual shipment information.
In an embodiment, the requirement table generating module 40 is further configured to generate a shortest delivery path scheme according to the product delivery requirement table; acquiring the distribution cost corresponding to the shortest distribution path scheme; and obtaining historical distribution cost, and comparing the historical distribution cost with the distribution cost corresponding to the scheme with the shortest distribution path to generate scheme result feedback.
It should be noted that the above-described work flows are only exemplary, and do not limit the scope of the present invention, and in practical applications, a person skilled in the art may select some or all of them to achieve the purpose of the solution of the embodiment according to actual needs, and the present invention is not limited herein.
In addition, the technical details that are not described in detail in this embodiment may refer to a method for product management based on big data and industrial internet provided in any embodiment of the present invention, and are not described herein again.
Furthermore, it should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system 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 system. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of other like elements in a process, method, article, or system comprising the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention or portions thereof that contribute to the prior art may be embodied in the form of a software product, where the computer software product is stored in a storage medium (e.g. a Read Only Memory (ROM)/RAM, a magnetic disk, and an optical disk), and includes several instructions for enabling a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (8)
1. A product management method based on big data and industrial Internet is characterized by comprising the following steps:
acquiring logistics warehouse information, and acquiring product shipment data and a position information table of each warehouse in the logistics warehouse information;
generating a shipment demand portrait according to the product shipment data of each warehouse;
generating a second logistics warehouse set according to the logistics warehouse corresponding to the shipment demand portrait meeting the preset conditions;
generating a product delivery demand table according to the shipment demand portrait and the position information table in the second logistics warehouse set;
the generating of the shipment demand sketch according to the product shipment data of each warehouse comprises:
acquiring product shipment data corresponding to each warehouse;
determining weight information of a preset label in each warehouse according to the shipment data;
generating a shipment demand portrait of each warehouse according to the weight information of the preset labels in each warehouse; the generating of the shipment demand sketch of each warehouse according to the weight information of the preset label in each warehouse comprises:
acquiring weight information of a preset label in each warehouse;
determining the product flowing tendency according to the weight information of the preset label;
judging the effectiveness of the product flowing tendency;
if the product flow tendency is valid, a shipment demand image is generated according to the product flow tendency.
2. The big data and industrial internet based product management method according to claim 1, wherein the generating of the second logistics warehouse set according to the logistics warehouse corresponding to the shipment demand representation satisfying the preset condition comprises:
acquiring preset conditions, and screening the shipment demand images according to the preset conditions;
adding identification information to the logistics warehouse in the logistics warehouse information according to the screening result;
and acquiring a historical identification record, and generating a second logistics warehouse set in the logistics warehouse information according to the identification information and the historical identification record.
3. The big data and industrial internet based product management method according to claim 1, wherein the generating of the product delivery requirement table in the second logistics warehouse set according to the shipment requirement representation and the location information table comprises:
generating a predicted cargo shipment table according to the shipment demand picture in the second logistics warehouse;
acquiring order information, and generating a current actual goods delivery table according to destination information in the order information and the position information;
and generating a product distribution demand table according to the estimated cargo shipment table and the actual cargo shipment table.
4. The big data and industrial internet based product management method according to claim 3, wherein the generating of the product delivery demand table from the projected shipment table and the actual shipment table comprises:
acquiring the expected shipment information of each product in the expected shipment list;
acquiring actual shipment information of each product in the actual shipment table;
and generating a product distribution demand table according to the estimated shipment information and the actual shipment information.
5. The big data and industrial internet based product management method according to any one of claims 1 to 4, wherein after the generating of the product delivery requirement table according to the shipment requirement representation and the location information table in the second logistics warehouse set, further comprising:
generating a shortest distribution path scheme according to the product distribution demand table;
acquiring the distribution cost corresponding to the shortest distribution path scheme;
and obtaining historical distribution cost, and comparing the historical distribution cost with the distribution cost corresponding to the shortest distribution path scheme to generate scheme result feedback.
6. A big data and industrial internet based product management apparatus, comprising:
the warehouse information acquisition module is used for acquiring logistics warehouse information and acquiring product shipment data and a position information table of each warehouse in the logistics warehouse information;
the shipment portrait generation module is used for generating shipment demand portraits according to the product shipment data of each warehouse;
the second logistics warehouse generation module is used for generating a second logistics warehouse set according to the logistics warehouse corresponding to the shipment demand portrait meeting the preset conditions;
the demand table generating module is used for generating a product distribution demand table in the second logistics warehouse set according to the shipment demand sketch and the position information table;
the method for generating the shipment demand portrait by the shipment portrait generation module comprises the following steps:
acquiring product shipment data corresponding to each warehouse;
determining weight information of a preset label in each warehouse according to the shipment data;
generating a shipment demand portrait of each warehouse according to the weight information of the preset labels in each warehouse; the generating of the shipment demand sketch of each warehouse according to the weight information of the preset label in each warehouse comprises:
acquiring weight information of a preset label in each warehouse;
determining the product flowing tendency according to the weight information of the preset label;
judging the effectiveness of the product flowing tendency;
if the product flow tendency is valid, a shipment demand image is generated according to the product flow tendency.
7. A computer device, characterized in that the device comprises: a memory, a processor that, when executing computer instructions stored by the memory, performs the method of any of claims 1-5.
8. A computer-readable storage medium comprising instructions that, when executed on a computer, cause the computer to perform the method of any of claims 1 to 5.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB0213632D0 (en) * | 2001-07-03 | 2002-07-24 | Inventec Corp | Computer system for goods management in a stock company |
| CN110880084A (en) * | 2018-09-06 | 2020-03-13 | 北京京东振世信息技术有限公司 | Warehouse replenishment method and device |
| CN112613751A (en) * | 2020-12-25 | 2021-04-06 | 北京航天云路有限公司 | Wisdom commodity circulation integrated system based on industry internet |
| CN113159694A (en) * | 2021-05-14 | 2021-07-23 | 中国建设银行股份有限公司 | Material storage management method and device |
| CN114240295A (en) * | 2021-12-20 | 2022-03-25 | 平安壹钱包电子商务有限公司 | Information processing method, device, equipment and medium for warehousing goods in warehouse |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11939162B2 (en) * | 2020-02-10 | 2024-03-26 | Staples, Inc. | System-directed robotic cart picking |
| CN111476520A (en) * | 2020-04-03 | 2020-07-31 | 上海明略人工智能(集团)有限公司 | Method and device for determining placement position, storage medium and electronic device |
-
2022
- 2022-10-17 CN CN202211265389.3A patent/CN115330320B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB0213632D0 (en) * | 2001-07-03 | 2002-07-24 | Inventec Corp | Computer system for goods management in a stock company |
| CN110880084A (en) * | 2018-09-06 | 2020-03-13 | 北京京东振世信息技术有限公司 | Warehouse replenishment method and device |
| CN112613751A (en) * | 2020-12-25 | 2021-04-06 | 北京航天云路有限公司 | Wisdom commodity circulation integrated system based on industry internet |
| CN113159694A (en) * | 2021-05-14 | 2021-07-23 | 中国建设银行股份有限公司 | Material storage management method and device |
| CN114240295A (en) * | 2021-12-20 | 2022-03-25 | 平安壹钱包电子商务有限公司 | Information processing method, device, equipment and medium for warehousing goods in warehouse |
Non-Patent Citations (2)
| Title |
|---|
| 基于物联网技术的物流仓储配送系统设计;李婷;《物流工程与管理》;20180615(第06期);全文 * |
| 基于计算机三维建模技术的物流仓储管理仿真研究;蒋爱先;《物流技术》;20141225(第24期);全文 * |
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