CN110599100A - Warehouse goods space recommendation method and device for commodities - Google Patents
Warehouse goods space recommendation method and device for commodities Download PDFInfo
- Publication number
- CN110599100A CN110599100A CN201910881384.5A CN201910881384A CN110599100A CN 110599100 A CN110599100 A CN 110599100A CN 201910881384 A CN201910881384 A CN 201910881384A CN 110599100 A CN110599100 A CN 110599100A
- Authority
- CN
- China
- Prior art keywords
- sales
- commodity
- commodities
- warehouse
- parameters
- 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.)
- Pending
Links
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
- G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
-
- 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
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G06Q30/0202—Market predictions or forecasting for commercial activities
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Development Economics (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- General Physics & Mathematics (AREA)
- Marketing (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- Human Resources & Organizations (AREA)
- Data Mining & Analysis (AREA)
- Game Theory and Decision Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a warehouse goods space recommendation method and device for commodities, wherein the method comprises the following steps: taking historical sales data and current sales data of the commodities, and personal purchasing power parameters, industry parameters and economic situation parameters as input parameters to carry out neural network model training; predicting sales heat values of the commodities in a future period and sales volumes of corresponding periods through the neural network model; recommending the warehouse goods location of the commodity according to the predicted sales heat value in the future period of the commodity and the sales volume in the corresponding period of time. According to historical sales data and current sales data of commodities, personal purchasing power parameters, industrial parameters and economic situation parameters, commodity popularity is predicted through artificial intelligence, dynamic goods positions of the commodities are set according to the commodity popularity, and in-warehouse production and sorting efficiency of the commodities is improved.
Description
Technical Field
The invention relates to the field of warehouse management, in particular to a commodity warehouse goods space recommendation method and device.
Background
Today, standardized Warehouse Management Systems (WMSs) are becoming mature, competition in systems is no longer limited to function standardization, and WMSs are developed in an intelligent and unmanned manner in the future. For an unmanned warehouse, how to improve the operation efficiency of the warehouse is important for an intelligent algorithm. The placing area and the placing sequence of the warehouse positions in the warehouse have important influence on the warehouse entering and exiting efficiency and the warehouse inventory efficiency.
The traditional warehouse layout generally judges the placing position of goods according to the experience of warehouse operators, and the goods positions are prefabricated in the system in advance. This form of fixed cargo space has a greater impact on the in-warehouse activity lines for knock-out type products or cold products.
The existing method is to statistically analyze the explosive money or hot sales products in a certain time period in the year according to the sales condition in the same period in the last year. The storage cargo space of the hot-sold products is maintained to a position convenient for picking or to an area beyond the warehouse for picking to be taken out of the warehouse at a certain time in advance. And recommending the commodities to an area convenient for picking when the commodities are put in storage.
However, if the past synchronization data is extracted for statistical analysis, the current situation is the same as the past synchronization situation, and the influence factors such as purchasing power, purchasing preference, economic situation, and industry dynamics of the customer cannot be reflected in the statistical analysis, so that the judgment is prone to deviation.
Disclosure of Invention
The embodiment of the invention provides a commodity warehouse goods space recommendation method and device, which are used for at least solving the problem that in the related technology, the future sales condition of a commodity is predicted only according to past contemporaneous data of the commodity, and the judgment is easy to deviate.
According to an embodiment of the present invention, there is provided a method for recommending a warehouse cargo space of a commodity, including: taking historical sales data and current sales data of the commodities, and personal purchasing power parameters, industry parameters and economic situation parameters as input parameters to carry out neural network model training; predicting sales heat values of the commodities in a future period and sales volumes of corresponding periods through the neural network model; recommending the warehouse goods location of the commodity according to the predicted sales heat value in the future period of the commodity and the sales volume in the corresponding period of time.
Preferably, before the neural network model training is performed by using the historical sales data and the current sales data of the commodity, and the personal purchasing power parameter, the industry parameter and the economic situation parameter as input parameters, the method further comprises the following steps: and collecting historical sales data of the commodity and current sales data of the commodity.
Preferably, before recommending the warehouse cargo space of the commodity according to the predicted sales heat value in the future period of the commodity and the sales volume in the corresponding period of time, the method further comprises: docking the predicted sales heat value and sales volume of the commodity in a future period of time into a warehouse management system to bind with the commodity SKU, and unbinding the incidence relation between the commodity SKU (stock keeping Unit) and a cargo space.
Preferably, recommending a warehouse slot for the good according to the predicted sales heat value for the good in the future period and the sales volume of the corresponding period comprises: establishing an optimal stock area layout of the commodity according to the sales heat value and the sales volume of the commodity in a future period; setting a library crossing operation data dictionary and a production processing data dictionary of the commodity; and when the commodities are put in a warehouse, retrieving the warehouse-crossing operation data dictionary and the production processing data dictionary, and recommending the commodities to corresponding goods positions according to the sales heat value of the commodities calculated in real time.
According to another embodiment of the present invention, there is provided a warehouse cargo space recommendation device for goods, including: the training module is used for carrying out neural network model training by taking historical sales data and current sales data of commodities, and personal purchasing power parameters, industry parameters and economic situation parameters as input parameters; the prediction module is used for predicting the sales heat value of the commodity in the future time period and the sales volume of the corresponding time period through the neural network model; and the recommending module is used for recommending the warehouse goods space of the commodity according to the predicted sales heat value of the commodity in the future time period and the sales volume of the corresponding time period.
Preferably, the apparatus further comprises: and the acquisition module is used for acquiring historical sales data of the commodities and current sales data of the commodities.
Preferably, the apparatus comprises: and the binding module is used for butting the predicted sales heat value and the predicted sales volume of the commodity in the future time period into a warehouse management system to bind with the commodity SKU and unbinding the association relationship between the commodity SKU and the goods position.
Preferably, the recommendation module further comprises: the establishment unit is used for establishing the optimal layout of the commodity according to the sales heat value and the sales volume of the commodity in the future period; the setting unit is used for setting the library crossing operation data dictionary and the production processing data dictionary of the commodity; and the recommending unit is used for searching the cross-warehouse operation data dictionary and the production processing data dictionary when the commodities are warehoused, and recommending the commodities to corresponding goods positions according to the sales heat value of the commodities calculated in real time.
According to a further embodiment of the present invention, there is also provided a storage medium having a computer program stored therein, wherein the computer program is arranged to perform the steps of any of the above method embodiments when executed.
According to yet another embodiment of the present invention, there is also provided an electronic device, including a memory in which a computer program is stored and a processor configured to execute the computer program to perform the steps in any of the above method embodiments.
In the embodiment of the invention, according to historical sales data and current sales data of the commodities, and personal purchasing power parameters, industry parameters and economic situation parameters, the commodity popularity is predicted through artificial intelligence, and the dynamic goods position of the commodities is set according to the commodity popularity, so that the in-warehouse production and sorting efficiency of the commodities is improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
FIG. 1 is a flow chart of a method of warehouse cargo space recommendation for goods in accordance with an embodiment of the present invention;
FIG. 2 is a flow chart of a method for warehouse cargo space recommendation of merchandise in accordance with an alternative embodiment of the present invention;
fig. 3 is a block diagram showing the structure of a warehouse cargo space recommendation device for goods according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a warehouse cargo space recommendation device according to an alternative embodiment of the invention.
Detailed Description
The invention will be described in detail hereinafter with reference to the accompanying drawings in conjunction with embodiments. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
The present embodiment provides a method for recommending a goods warehouse goods location, and fig. 1 is a flowchart of a method for recommending a goods warehouse goods location according to an embodiment of the present invention, as shown in fig. 1, the flowchart includes the following steps:
step S102, taking historical sales data and current sales data of the commodities, and personal purchasing power parameters, industry parameters and economic situation parameters as input parameters to carry out neural network model training;
step S104, predicting the sales heat value of the commodity in the future time period and the sales volume of the corresponding time period through the neural network model;
and S106, recommending the warehouse goods space of the commodity according to the predicted sales heat value of the commodity in the future time period and the sales volume of the corresponding time period.
Before step S102 of the present embodiment, historical sales data of the commodity and current sales data of the commodity may be collected.
After step S104 in this embodiment, the method may further include: docking the predicted sales heat value and sales volume of the commodity in the future period of time into a warehouse management system to be bound with the commodity SKU, and unbinding the association relationship between the commodity SKU and a cargo space.
In step S106 of this embodiment, an optimal library area layout of the commodity may be established according to the sales heat value and the sales volume of the commodity in a future period; setting a library crossing operation data dictionary and a production processing data dictionary of the commodity; and when the commodities are put in a warehouse, retrieving the warehouse-crossing operation data dictionary and the production processing data dictionary, and recommending the commodities to corresponding goods positions according to the sales heat value of the commodities calculated in real time.
In order to facilitate an understanding of the technical solutions provided by the present invention, an embodiment of a specific application will be described in detail below.
In the embodiment, an artificial intelligence algorithm is combined with a warehouse management system, the commodity popularity is converted into numbers by predicting the sales popularity of commodities in a period of time in the future and is arranged in the system, and the commodities are dynamically distributed on different goods positions according to different popularity according to the priority of each parameter prefabricated by the system, so that the production efficiency and the goods picking operation efficiency in the warehouse are improved.
In this embodiment, as shown in fig. 2, the method includes the following main steps:
step S201, collecting sales data of the commodity in the same year, including but not limited to the following parameters: commodity SKU, commodity category (primary category, secondary category, tertiary category), commodity sales attribute (brand, model, color, specification parameters, length, width, and the like), whether a gift is attached, gift number, gift name, gift model, goodness, sales volume, and sales unit price.
Step S202, collecting the current sales data of the commodity, including but not limited to the following parameters: the method comprises the steps of browsing categories, commodity exposure rates, commodity sharing rates, commodity association degrees (spatial association and temporal association), commodity SKU, commodity categories (primary category, secondary category and tertiary category), commodity sales attributes (brand, model, color, specification parameters, length, width and the like), presence or absence of gifts, gift quantity, gift name, gift model, good appraisal, sales volume and sales unit price.
Step S203, the adopted data is washed, and the index irrelevant to the target is deleted.
And step S204, adding personal purchasing power parameters, industry parameters and economic situation parameters, establishing a neural network type intelligent model by applying the principle of a knowledge map, and predicting the sales heat value of the commodity and the sales volume of the corresponding time period.
And step S205, parameters such as sales heat value and sales volume are connected to the WMS system and bound with the commodity SKU.
And step S206, changing the method that the conventional warehouse binds the cargo space through the SKU, and unbinding the association relationship between the cargo space and the SKU.
Step S207, the warehouse operation establishes the optimal layout of the warehouse area through an intelligent algorithm layout or through past experience.
In step S208, a library-crossing operation data dictionary and a production processing data dictionary are set.
And S209, after warehousing and checking, the system searches the cross-warehouse dictionary and the production processing data dictionary, and recommends the commodity to a goods shelf most favorable for production and sorting according to the real-time calculated heat value and objective conditions such as physical inventory, expiration date and the like for shelving.
In the embodiment, the commodity heat is predicted through artificial intelligence, and the dynamic goods position of the commodity is set according to the commodity heat, so that the following technical effects are realized:
1) the fixed binding relationship between the cargo space and the SKU in the traditional warehouse is unbound, so that the system maintenance and operation cost is saved;
2) the dynamic cargo space management is realized, and the seamless butt joint with the WCS system can be realized in the unmanned warehouse project;
3) the goods level dynamically recommended according to the hot sales degree is beneficial to shortening the moving line in the warehouse and improving the production and sorting efficiency in the warehouse.
Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
In this embodiment, a device for recommending goods in a warehouse is further provided, and the device is used to implement the foregoing embodiments and preferred embodiments, which have already been described and will not be described again. As used below, the term "module" or "unit" may implement a combination of software and/or hardware of predetermined functions. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Fig. 3 is a block diagram illustrating a warehouse cargo space recommendation apparatus for goods according to an embodiment of the present invention, and as shown in fig. 3, the apparatus includes a training module 10, a prediction module 20, and a recommendation module 30.
The training module 10 is used for training the neural network model by taking the historical sales data and the current sales data of the commodities, and the personal purchasing power parameter, the industry parameter and the economic situation parameter as input parameters.
The prediction module 20 is used for predicting the sales heat value of the commodity in the future period and the sales volume of the corresponding period through the neural network model.
The recommending module 30 is used for recommending the warehouse goods space of the commodity according to the predicted sales heat value in the future time period of the commodity and the sales volume in the corresponding time period.
Fig. 4 is a block diagram illustrating a structure of a warehouse goods location recommendation apparatus for goods according to an alternative embodiment of the present invention, which, as shown in fig. 4, includes an acquisition module 40 and a binding module 50 in addition to the training module 10, the prediction module 20 and the recommendation module 30 shown in fig. 3.
The collecting module 40 is used for collecting the historical sales data of the commodities and the current sales data of the commodities.
The binding module 50 is used for docking the predicted sales heat value and sales volume of the commodity in the future period of time into the warehouse management system to bind with the commodity SKU and unbinding the association relationship between the commodity SKU and the cargo space.
In this embodiment, the recommending module 30 further comprises a establishing unit 31, a setting unit 32 and a recommending unit 33.
The establishing unit 31 is configured to establish an optimal library area layout of the commodity according to the sales heat value and the sales volume of the commodity in a future period.
The setting unit 32 is configured to set the library crossing operation data dictionary and the production processing data dictionary for the commodity.
The recommending unit 33 is configured to retrieve the cross-warehouse operation data dictionary and the production processing data dictionary when the commodity is warehoused, and recommend the commodity to a corresponding goods space according to the sales heat value of the commodity calculated in real time.
It should be noted that, the above modules may be implemented by software or hardware, and for the latter, the following may be implemented, but not limited to: the modules are all positioned in the same processor; alternatively, the modules are respectively located in different processors in any combination.
Embodiments of the present invention also provide a storage medium having a computer program stored therein, wherein the computer program is arranged to perform the steps of any of the above method embodiments when executed.
Optionally, in this embodiment, the storage medium may include, but is not limited to: various media capable of storing computer programs, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.
Embodiments of the present invention also provide an electronic device comprising a memory having a computer program stored therein and a processor arranged to run the computer program to perform the steps of any of the above method embodiments.
Optionally, the electronic apparatus may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.
It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and alternatively, they may be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A warehouse goods space recommendation method of commodities is characterized by comprising the following steps:
taking historical sales data and current sales data of the commodities, and personal purchasing power parameters, industry parameters and economic situation parameters as input parameters to carry out neural network model training;
predicting sales heat values of the commodities in a future period and sales volumes of corresponding periods through the neural network model;
recommending the warehouse goods location of the commodity according to the predicted sales heat value in the future period of the commodity and the sales volume in the corresponding period of time.
2. The method of claim 1, wherein before performing neural network model training using historical sales data and current sales data of the goods, and the individual purchasing power parameter, the industry parameter, and the economic situation parameter as input parameters, further comprising:
and collecting historical sales data of the commodity and current sales data of the commodity.
3. The method of claim 1, wherein prior to recommending a warehouse location for the good based on the predicted sales heat value for the good over the future time period and the sales volume for the corresponding time period, further comprising:
docking the predicted sales heat value and sales volume of the commodity in the future period of time into a warehouse management system to be bound with the commodity SKU, and unbinding the association relationship between the commodity SKU and a cargo space.
4. The method of claim 3, wherein recommending a warehouse slot for the good based on the predicted sales heat value for the good over the future time period and the sales volume for the corresponding time period comprises:
establishing an optimal stock area layout of the commodity according to the sales heat value and the sales volume of the commodity in a future period;
setting a library crossing operation data dictionary and a production processing data dictionary of the commodity;
and when the commodities are put in a warehouse, retrieving the warehouse-crossing operation data dictionary and the production processing data dictionary, and recommending the commodities to corresponding goods positions according to the sales heat value of the commodities calculated in real time.
5. A warehouse cargo space recommendation device for merchandise, comprising:
the training module is used for carrying out neural network model training by taking historical sales data and current sales data of commodities, and personal purchasing power parameters, industry parameters and economic situation parameters as input parameters;
the prediction module is used for predicting the sales heat value of the commodity in the future time period and the sales volume of the corresponding time period through the neural network model;
and the recommending module is used for recommending the warehouse goods space of the commodity according to the predicted sales heat value of the commodity in the future time period and the sales volume of the corresponding time period.
6. The apparatus of claim 5, further comprising:
and the acquisition module is used for acquiring historical sales data of the commodities and current sales data of the commodities.
7. The apparatus of claim 5, further comprising:
and the binding module is used for butting the predicted sales heat value and the predicted sales volume of the commodity in the future time period into a warehouse management system to bind with the commodity SKU and unbinding the association relationship between the commodity SKU and the goods position.
8. The apparatus of claim 7, wherein the recommendation module further comprises:
the establishment unit is used for establishing the optimal layout of the commodity according to the sales heat value and the sales volume of the commodity in the future period;
the setting unit is used for setting the library crossing operation data dictionary and the production processing data dictionary of the commodity;
and the recommending unit is used for searching the cross-warehouse operation data dictionary and the production processing data dictionary when the commodities are warehoused, and recommending the commodities to corresponding goods positions according to the sales heat value of the commodities calculated in real time.
9. A computer-readable storage medium, in which a computer program is stored, wherein the computer program is arranged to perform the method of any of claims 1 to 4 when executed.
10. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, and wherein the processor is arranged to execute the computer program to perform the method of any of claims 1 to 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910881384.5A CN110599100A (en) | 2019-09-18 | 2019-09-18 | Warehouse goods space recommendation method and device for commodities |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910881384.5A CN110599100A (en) | 2019-09-18 | 2019-09-18 | Warehouse goods space recommendation method and device for commodities |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110599100A true CN110599100A (en) | 2019-12-20 |
Family
ID=68860520
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910881384.5A Pending CN110599100A (en) | 2019-09-18 | 2019-09-18 | Warehouse goods space recommendation method and device for commodities |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110599100A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111738648A (en) * | 2020-04-01 | 2020-10-02 | 北京京东乾石科技有限公司 | Product selection method, device, equipment and storage medium |
| CN112633793A (en) * | 2020-12-15 | 2021-04-09 | 嘉兴蓝匠仓储系统软件有限公司 | Method for optimizing warehouse entry allocation of goods space in automatic three-dimensional warehouse through big data analysis |
| CN113256223A (en) * | 2021-06-18 | 2021-08-13 | 深圳远荣智能制造股份有限公司 | Goods storage method, storage device, terminal equipment and storage medium |
| CN113537671A (en) * | 2020-04-17 | 2021-10-22 | 北京京东振世信息技术有限公司 | Sorting timeliness prediction method and device, storage medium and electronic equipment |
| US20230005044A1 (en) * | 2021-07-01 | 2023-01-05 | People Lens Inc. | Sales intelligence system and method for generating personalized recommendations from integrated datasets using explainable ai |
| CN116862561A (en) * | 2023-07-10 | 2023-10-10 | 深圳爱巧网络有限公司 | Product heat analysis method and system based on convolutional neural network |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105825354A (en) * | 2016-03-11 | 2016-08-03 | 北京小米移动软件有限公司 | Storage scheduling method and apparatus |
| CN107239924A (en) * | 2017-06-08 | 2017-10-10 | 北京惠赢天下网络技术有限公司 | It is a kind of that goods set meal generation method and system are matched somebody with somebody based on standard container |
| CN109509030A (en) * | 2018-11-15 | 2019-03-22 | 北京旷视科技有限公司 | Method for Sales Forecast method and its training method of model, device and electronic system |
| CN109784809A (en) * | 2019-01-10 | 2019-05-21 | 深圳市启海仓储有限公司 | Goods yard allocation management method and system |
| CN110046755A (en) * | 2019-04-03 | 2019-07-23 | 中国联合网络通信集团有限公司 | Allot the determination method and apparatus in warehouse |
-
2019
- 2019-09-18 CN CN201910881384.5A patent/CN110599100A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105825354A (en) * | 2016-03-11 | 2016-08-03 | 北京小米移动软件有限公司 | Storage scheduling method and apparatus |
| CN107239924A (en) * | 2017-06-08 | 2017-10-10 | 北京惠赢天下网络技术有限公司 | It is a kind of that goods set meal generation method and system are matched somebody with somebody based on standard container |
| CN109509030A (en) * | 2018-11-15 | 2019-03-22 | 北京旷视科技有限公司 | Method for Sales Forecast method and its training method of model, device and electronic system |
| CN109784809A (en) * | 2019-01-10 | 2019-05-21 | 深圳市启海仓储有限公司 | Goods yard allocation management method and system |
| CN110046755A (en) * | 2019-04-03 | 2019-07-23 | 中国联合网络通信集团有限公司 | Allot the determination method and apparatus in warehouse |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111738648A (en) * | 2020-04-01 | 2020-10-02 | 北京京东乾石科技有限公司 | Product selection method, device, equipment and storage medium |
| CN113537671A (en) * | 2020-04-17 | 2021-10-22 | 北京京东振世信息技术有限公司 | Sorting timeliness prediction method and device, storage medium and electronic equipment |
| CN112633793A (en) * | 2020-12-15 | 2021-04-09 | 嘉兴蓝匠仓储系统软件有限公司 | Method for optimizing warehouse entry allocation of goods space in automatic three-dimensional warehouse through big data analysis |
| CN112633793B (en) * | 2020-12-15 | 2024-05-14 | 嘉兴蓝匠仓储系统软件有限公司 | Method for optimizing warehouse entry allocation of goods space through big data analysis by automatic stereo warehouse |
| CN113256223A (en) * | 2021-06-18 | 2021-08-13 | 深圳远荣智能制造股份有限公司 | Goods storage method, storage device, terminal equipment and storage medium |
| US20230005044A1 (en) * | 2021-07-01 | 2023-01-05 | People Lens Inc. | Sales intelligence system and method for generating personalized recommendations from integrated datasets using explainable ai |
| CN116862561A (en) * | 2023-07-10 | 2023-10-10 | 深圳爱巧网络有限公司 | Product heat analysis method and system based on convolutional neural network |
| CN116862561B (en) * | 2023-07-10 | 2024-01-26 | 深圳爱巧网络有限公司 | Product heat analysis method and system based on convolutional neural network |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110599100A (en) | Warehouse goods space recommendation method and device for commodities | |
| Micale et al. | A combined interval-valued ELECTRE TRI and TOPSIS approach for solving the storage location assignment problem | |
| Altarazi et al. | Concurrent manual-order-picking warehouse design: a simulation-based design of experiments approach | |
| CN111667207B (en) | Supply chain inventory management method and device, storage medium and processor | |
| CN110400103A (en) | Replenishment quantity determines method, apparatus, computer installation and storage medium | |
| CN110135876A (en) | The method and device of Method for Sales Forecast | |
| Low et al. | Integrated scheduling of production and delivery with time windows | |
| US20210256443A1 (en) | Methods and systems for supply chain network optimization | |
| CN106408341A (en) | Goods sales volume prediction method and device, and electronic equipment | |
| CN116739217A (en) | Retail management method and system based on supply chain big data platform | |
| KR101441418B1 (en) | Automatic inventory management apparatus for daily necessaries | |
| CN107016587A (en) | A kind of pushing personalized pages method and device | |
| CN111325509A (en) | Data processing method and device, storage medium and electronic device | |
| KR20220045407A (en) | Ai-based cold chain logistics management system and method | |
| TWI684145B (en) | A system for configuring storage spaces of warehouse | |
| KR102392212B1 (en) | Method, device and system for managing inventory and analising sales for mart operation | |
| KR20200081050A (en) | Method for providing online to offline based imminent expiry date product sale service | |
| CN110135878A (en) | Method and device for firm sale price | |
| Jose et al. | On a retrial production inventory system with vacation and multiple servers | |
| CN107016006A (en) | A kind of page display method and system | |
| Yang | Selection Method of Cross‐Border e‐Commerce Export Logistics Mode Based on Collaborative Filtering Algorithm | |
| CN116258444A (en) | Inventory management method and system for short-shelf-life commodities | |
| Rahimi et al. | A robust optimization model for multi-objective multi-period supply chain planning under uncertainty considering quantity discounts | |
| CN112749928A (en) | Enterprise material information social management method and ecosystem | |
| CN116957648A (en) | Big data visual analysis method, system, electronic equipment and storage 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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20191220 |
|
| RJ01 | Rejection of invention patent application after publication |