CN113657812A - Method and system for intelligent decision-making of store operation based on big data and algorithm - Google Patents
Method and system for intelligent decision-making of store operation based on big data and algorithm Download PDFInfo
- Publication number
- CN113657812A CN113657812A CN202111028191.9A CN202111028191A CN113657812A CN 113657812 A CN113657812 A CN 113657812A CN 202111028191 A CN202111028191 A CN 202111028191A CN 113657812 A CN113657812 A CN 113657812A
- Authority
- CN
- China
- Prior art keywords
- algorithm
- store
- target store
- module
- intelligent decision
- 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
Images
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
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- 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
Abstract
The embodiment of the application discloses a method and a system for intelligent decision-making of store operation based on big data and algorithm, wherein the method comprises the following steps: acquiring relevant data X of a target store; inputting the relevant data X of the target store into an intelligent decision algorithm module M to obtain an operation suggestion Y of the target store, wherein the intelligent decision algorithm module M is a module generated after pre-training through a preset store operation big data sample and historical data based on an algorithm, and the adopted algorithm comprises one or more of a machine learning algorithm, a deep learning algorithm and a statistical algorithm; and outputting the target store operation suggestion Y to enable the user to make store operation decision. The method is accurate and efficient, greatly improves the store operation benefits, and is suitable for all store operation scenes including but not limited to hotels, residents, hotels, retail stores, exhibition halls, scenic spots, entertainment places and restaurants.
Description
Technical Field
The embodiment of the application relates to the technical field of big data and algorithms, in particular to a store operation intelligent decision method and system based on big data and algorithms.
Background
Decision making is always the most important ring in business operation and operation process, and can directly influence the change of later operation and operation condition.
At present, the mainstream of the operation decision of the store is judged by human experience, and the scientific operation decision judgment is lacked.
Disclosure of Invention
Therefore, the embodiment of the application provides a method and a system for intelligent decision-making of store operation based on big data and algorithm, a store operation scheme is made based on big data and algorithm modules, the method and the system are accurate and efficient, the store operation benefit is greatly improved, and the method and the system are suitable for all store operation scenes including but not limited to hotels, residents and residents, hotels, retail stores, exhibition halls, scenic spots, entertainment venues and restaurants.
In order to achieve the above object, the embodiments of the present application provide the following technical solutions:
according to a first aspect of the embodiments of the present application, there is provided a method for intelligent decision making of store operation based on big data and algorithm, the method including:
acquiring relevant data X of a target store;
inputting the relevant data X of the target store into an intelligent decision algorithm module M to obtain an operation suggestion Y of the target store, wherein the intelligent decision algorithm module M is a module obtained after pre-training is completed through presetting store-related big data samples and historical data on the basis of an algorithm, and the algorithm adopted by the intelligent decision algorithm module M comprises one or more of a machine learning algorithm, a deep learning algorithm and a statistical algorithm;
and outputting the target store operation suggestion Y to enable the user to make store operation decision.
Optionally, the target store-related data X includes target store-related information.
Optionally, the target store operation advice Y includes various expected operation decisions of the target store.
According to a second aspect of embodiments of the present application, there is provided a store operation intelligent decision system based on big data and algorithm, the system including:
the data acquisition module is used for acquiring related data X of the target store;
the intelligent decision-making module is used for inputting the relevant data X of the target store into an intelligent decision-making algorithm module M to obtain an operation suggestion Y of the target store, the intelligent decision-making algorithm module M is a module obtained after training is completed through preset store-related big data samples and historical data based on an algorithm, and the algorithm adopted by the intelligent decision-making algorithm module M comprises one or more of a machine learning algorithm, a deep learning algorithm and a statistical algorithm;
and the scheme output module is used for outputting the target store operation suggestion Y so as to enable the user to make store operation decision.
Optionally, the target store-related data X includes target store-related information.
Optionally, the target store operation advice Y includes various expected operation decisions of the target store.
According to a third aspect of embodiments herein, there is provided an apparatus comprising: the device comprises a data acquisition device, a processor and a memory; the data acquisition device is used for acquiring data; the memory is to store one or more program instructions; the processor is configured to execute one or more program instructions to perform the method of any of the first aspect.
According to a fourth aspect of embodiments herein, there is provided a computer-readable storage medium having one or more program instructions embodied therein for performing the method of any of the first aspects.
In summary, the embodiment of the present application provides a method and a system for intelligent decision making for store operation based on big data and algorithm, by acquiring relevant data X of a target store; inputting the relevant data X of the target store into an intelligent decision algorithm module M to obtain an operation suggestion Y of the target store, wherein the intelligent decision algorithm module M is a module obtained by pre-training through a preset store operation big data sample and historical data based on an algorithm, and the adopted algorithm comprises one or more of a machine learning algorithm, a deep learning algorithm and a statistical algorithm; and outputting the store target store operation suggestion Y to enable the user to make a target store operation decision. The method is accurate and efficient, greatly improves the store operating benefits, and is suitable for all store operation scenes including but not limited to hotels, residents, hotels, retail stores, exhibition halls, scenic spots, entertainment places, restaurants and the like.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It should be apparent that the drawings in the following description are merely exemplary, and that other embodiments can be derived from the drawings provided by those of ordinary skill in the art without inventive effort.
The structures, ratios, sizes, and the like shown in the present specification are only used for matching with the contents disclosed in the specification, so that those skilled in the art can understand and read the present invention, and do not limit the conditions for implementing the present invention, so that the present invention has no technical significance, and any structural modifications, changes in the ratio relationship, or adjustments of the sizes, without affecting the functions and purposes of the present invention, should still fall within the scope of the present invention.
FIG. 1 is a schematic flow chart of a method for store operation intelligent decision based on big data and algorithm according to an embodiment of the present application;
FIG. 2 is a schematic diagram of an embodiment of a method for store operation intelligent decision based on big data and algorithm provided by an embodiment of the present application;
fig. 3 is a block diagram of a system for implementing intelligent decision-making in store operation based on big data and algorithm according to an embodiment of the present disclosure.
Detailed Description
The present invention is described in terms of particular embodiments, other advantages and features of the invention will become apparent to those skilled in the art from the following disclosure, and it is to be understood that the described embodiments are merely exemplary of the invention and that it is not intended to limit the invention to the particular embodiments disclosed. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 illustrates a method for a store operation intelligent decision based on big data and an algorithm provided by an embodiment of the present application, as shown in fig. 1, the method includes:
step 101: acquiring relevant data X of a target store;
step 102: inputting the relevant data X of the target store into an intelligent decision algorithm module M to obtain an operation suggestion Y of the target store, wherein the intelligent decision algorithm module M is a module obtained after pre-training is completed through presetting store-related big data samples and historical data on the basis of an algorithm, and the algorithm adopted by the intelligent decision algorithm module M comprises one or more of a machine learning algorithm, a deep learning algorithm and a statistical algorithm;
step 103: and outputting the target store operation suggestion Y to enable the user to make store operation decision.
In a possible embodiment, the target store-related data X includes, but is not limited to, an attribute of a city to which the target store belongs, an attribute of a district to which the target store belongs, an attribute of a segment to which the target store belongs, peripheral supporting facilities of the target store, and a per-capita consumption level of the district to which the target store belongs. It should be noted that all store-related basic data are within the scope of the target store-related data protected by the embodiment of the present application.
In one possible embodiment, the target store operation advice Y includes, but is not limited to, decoration information of the target store, a person-to-store ratio, a commodity design, a choice decision, a price interval, a price setting, a commodity location, a passenger flow, a sales amount, a gross profit, and an expected profit. The intelligent decision algorithm module M stores need to be stated that all store-related operation data are within the scope of the target store operation suggested scheme protected by the embodiment of the present application.
In one possible embodiment, the intelligent decision algorithm module M calculates Y ═ f '(X), P' (Y | X) according to the following formula. All formula algorithms that achieve the same function are within the scope of the embodiments of the present application.
In one possible embodiment, the method further comprises: when the output value of the target store operation suggestion scheme meets a set condition, taking the current target store operation suggestion scheme as the output target store operation suggestion scheme; or when the output value of the target store operation proposal scheme does not meet the set condition, adjusting the target store operation proposal scheme according to the output value, and taking the adjusted target store operation proposal scheme as the current target store operation proposal scheme.
The system architecture described in the embodiment of the present application is for more clearly illustrating the technical solution of the embodiment of the present application, and does not constitute a limitation to the technical solution provided in the embodiment of the present application, and as a person having ordinary skill in the art knows along with the evolution of the network architecture, the technical solution provided in the embodiment of the present application is also applicable to similar technical problems.
Fig. 2 shows a schematic diagram of an embodiment of a method for store operation intelligent decision based on big data and an algorithm provided by an embodiment of the present application.
Based on the big data, taking information including but not limited to city attributes, district attributes, segment attributes, peripheral matching, per capita consumption capacity and the like of stores as x, x ═ { x1, x2, x3, … …, xn }, wherein x1, x2, x3, … … and xn belong to m-dimensional vectors, n > ═ 1, m > -1, each store in the preset stores is represented by one m-dimensional vector, and the number of the preset stores is n; the target information includes, but is not limited to, target information such as design and decoration style, human and store ratio, commodity design or selection, price interval, price setting, commodity positioning, passenger flow, sales volume, gross profit, expected income, and the like as y, y ═ { y1, y2, y3,.. once.. yn }, where y1, y2, y3,. once.. yn belongs to a k-dimensional vector, k ≧ 1, and the target information of each of the preset stores is represented by a k-dimensional vector.
Building a learning algorithm module or a learning system through an algorithm based on the data x and y, generating a key operation information intelligent decision module M with reasonable design and decoration style, man-store ratio, commodity design or selection, price interval, price setting, commodity positioning, passenger flow, sales volume, gross profit, expected income and the like of each store, and storing the module M; including but not limited to machine learning, deep learning, statistics.
For the target store, inputting target store related data X with the same attribute information as the X, calculating key operation information Y such as design and decoration style, people and store ratio, commodity design or selection, price interval, price setting, commodity positioning, passenger flow, sales amount, gross profit, expected income and the like expected by the target store by using a learned algorithm intelligent decision module M, and taking the key operation information as reference information and making related operation decisions for the target store based on the key operation information.
The intelligent decision-making module M is constructed based on big data and an algorithm, can capture high income or relevant decision-making suggestions according with expected target indexes of operators, can effectively give relevant store operation key decision-making suggestions when being applied to a new store or a target store, greatly improves store operation benefits, and is suitable for all store operation scenes including but not limited to hotels, residents, hotels, retail stores, exhibition halls, scenic spots, entertainment places, restaurants and the like.
In summary, the embodiment of the application provides a store operation intelligent decision method based on big data, and the relevant data X of a target store is obtained; inputting the relevant data X of the target store into an intelligent decision algorithm module M to obtain an operation suggestion Y of the target store, wherein the intelligent decision algorithm module M is a module obtained by pre-training through a preset store operation big data sample and historical data based on an algorithm, and the adopted algorithm comprises one or more of a machine learning algorithm, a deep learning algorithm and a statistical algorithm; and outputting the target store operation suggestion Y to enable the user to make store operation decision. The method is accurate and efficient, greatly improves the store operating benefits, and is suitable for all store operation scenes including but not limited to hotels, residents, hotels, retail stores, exhibition halls, scenic spots, entertainment places and restaurants.
Based on the same technical concept, an embodiment of the present application further provides a store operation intelligent decision system based on big data, as shown in fig. 3, the system includes:
the message receiving module 301 is configured to obtain target store related data X;
the intelligent decision module 302 is configured to input the target store-related data X into an intelligent decision algorithm module M to obtain a target store operation suggestion Y, where the intelligent decision algorithm module M is a module obtained after training is completed through preset store-related big data samples and historical data based on an algorithm, and the algorithm used for constructing the intelligent decision algorithm module M includes one or more of a machine learning algorithm, a deep learning algorithm and a statistical algorithm;
and the scheme output module 303 is configured to output the target store operation suggestion Y so that the user makes a store operation decision.
In one possible embodiment, the target store-related data X includes target store-related information.
In one possible embodiment, the target store operation advice Y includes various expected operation decisions of the target store.
In one possible embodiment, the system further comprises: the correction module is used for taking the current standard store operation proposal as the output target store operation proposal when the output value of the target store operation proposal meets the set condition; or when the output value of the target store operation proposal scheme does not meet the set condition, adjusting the target store operation proposal scheme according to the output value, and taking the adjusted target store operation proposal scheme as the current target store operation proposal scheme.
Based on the same technical concept, an embodiment of the present application further provides an apparatus, including: the device comprises a data acquisition device, a processor and a memory; the data acquisition device is used for acquiring data; the memory is to store one or more program instructions; the processor is configured to execute one or more program instructions to perform the method.
Based on the same technical concept, the embodiment of the present application also provides a computer-readable storage medium, wherein the computer-readable storage medium contains one or more program instructions, and the one or more program instructions are used for executing the method.
In the present specification, each embodiment of the method is described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. Reference is made to the description of the method embodiments.
It is noted that while the operations of the methods of the present invention are depicted in the drawings in a particular order, this is not a requirement or suggestion that the operations must be performed in this particular order or that all of the illustrated operations must be performed to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions.
Although the present application provides method steps as in embodiments or flowcharts, additional or fewer steps may be included based on conventional or non-inventive approaches. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When an apparatus or client product in practice executes, it may execute sequentially or in parallel (e.g., in a parallel processor or multithreaded processing environment, or even in a distributed data processing environment) according to the embodiments or methods shown in the figures. The terms "comprises," "comprising," or any other variation thereof, are 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 limitation, the presence of additional identical or equivalent elements in a process, method, article, or apparatus that comprises the recited elements is not excluded.
The units, devices, modules, etc. set forth in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. Of course, in implementing the present application, the functions of each module may be implemented in one or more software and/or hardware, or a module implementing the same function may be implemented by a combination of a plurality of sub-modules or sub-units, and the like. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may therefore be considered as a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.
The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, classes, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
From the above description of the embodiments, it is clear to those skilled in the art that the present application can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, or the like, and includes several instructions for enabling a computer device (which may be a personal computer, a mobile terminal, a server, or a network device) to execute the method according to the embodiments or some parts of the embodiments of the present application.
The embodiments in the present specification are described in a progressive manner, and the same or similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. The application is operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.
The above-mentioned embodiments are further described in detail for the purpose of illustrating the invention, and it should be understood that the above-mentioned embodiments are only illustrative of the present invention and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (8)
1. A method for store operation intelligent decision based on big data and algorithm, the method comprising:
acquiring relevant data X of a target store;
inputting the relevant data X of the target store into an intelligent decision algorithm module M to obtain an operation suggestion Y of the target store, wherein the intelligent decision algorithm module M is a module obtained after pre-training is completed through presetting store-related big data samples and historical data on the basis of an algorithm, and the algorithm adopted by the intelligent decision algorithm module M comprises one or more of a machine learning algorithm, a deep learning algorithm and a statistical algorithm;
and outputting the target store operation suggestion Y to enable the user to make store operation decision.
2. The method of claim 1, wherein the target store-related data X comprises target store-related information.
3. The method of claim 1, wherein the target store operation recommendation Y comprises expected operation decisions for a target store.
4. A store operation intelligent decision system based on big data and algorithm, the system comprising:
the data acquisition module is used for acquiring related data X of the target store;
the intelligent decision-making module is used for inputting the relevant data X of the target store into an intelligent decision-making algorithm module M to obtain an operation suggestion Y of the target store, the intelligent decision-making algorithm module M is a module obtained after pre-training is completed through preset store-related big data samples and historical data on the basis of an algorithm, and the algorithm adopted by the intelligent decision-making algorithm module M comprises one or more of a machine learning algorithm, a deep learning algorithm and a statistical algorithm;
and the scheme output module is used for outputting the target store operation suggestion Y so as to enable the user to make store operation decision.
5. The system of claim 4, wherein the target store-related data X comprises target store-related information.
6. The system of claim 4, wherein the target store operation recommendation Y comprises expected operation decisions for a target store.
7. An apparatus, characterized in that the apparatus comprises: the device comprises a data acquisition device, a processor and a memory;
the data acquisition device is used for acquiring data; the memory is to store one or more program instructions; the processor, configured to execute one or more program instructions to perform the method of any of claims 1-3.
8. A computer-readable storage medium having one or more program instructions embodied therein for performing the method of any of claims 1-3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111028191.9A CN113657812A (en) | 2021-09-02 | 2021-09-02 | Method and system for intelligent decision-making of store operation based on big data and algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111028191.9A CN113657812A (en) | 2021-09-02 | 2021-09-02 | Method and system for intelligent decision-making of store operation based on big data and algorithm |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113657812A true CN113657812A (en) | 2021-11-16 |
Family
ID=78482739
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111028191.9A Pending CN113657812A (en) | 2021-09-02 | 2021-09-02 | Method and system for intelligent decision-making of store operation based on big data and algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113657812A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114819469A (en) * | 2022-02-24 | 2022-07-29 | 北京清华同衡规划设计研究院有限公司 | Intelligent tourism planning and designing method and system based on big data |
| CN116579634A (en) * | 2023-07-14 | 2023-08-11 | 深圳市科脉技术股份有限公司 | Store operation intelligent decision method and system based on big data |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106408181A (en) * | 2016-09-09 | 2017-02-15 | 广州速鸿信息科技有限公司 | Smart store system and method based on big data analysis |
| CN107122464A (en) * | 2017-04-27 | 2017-09-01 | 国信优易数据有限公司 | A kind of aid decision-making system and method |
| CN108932585A (en) * | 2018-06-19 | 2018-12-04 | 腾讯科技（深圳）有限公司 | A kind of trade company's operation management method and its equipment, storage medium, electronic equipment |
| CN109658141A (en) * | 2018-12-12 | 2019-04-19 | 中智关爱通（上海）科技股份有限公司 | Merchandise sales prediction technique, server and storage medium |
| CN111861073A (en) * | 2019-04-30 | 2020-10-30 | 北京嘀嘀无限科技发展有限公司 | Operation strategy prediction method and device, electronic equipment and readable storage medium |
| CN112419729A (en) * | 2020-11-23 | 2021-02-26 | 北京嘀嘀无限科技发展有限公司 | Method and system for determining decision strategy according to predicted traffic flow |
| CN112734276A (en) * | 2021-01-20 | 2021-04-30 | 杭州盈兴科技有限公司 | Smart hotel operation management method and device and electronic equipment |
| CN112884590A (en) * | 2021-01-26 | 2021-06-01 | 浙江工业大学 | Power grid enterprise financing decision method based on machine learning algorithm |
| CN113011618A (en) * | 2019-12-21 | 2021-06-22 | 上海鸿鹊信息科技有限公司 | Hotel income management system and method |
| CN113205282A (en) * | 2021-05-31 | 2021-08-03 | 广州大学 | New retail commodity operation system and device based on big data analysis |
-
2021
- 2021-09-02 CN CN202111028191.9A patent/CN113657812A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106408181A (en) * | 2016-09-09 | 2017-02-15 | 广州速鸿信息科技有限公司 | Smart store system and method based on big data analysis |
| CN107122464A (en) * | 2017-04-27 | 2017-09-01 | 国信优易数据有限公司 | A kind of aid decision-making system and method |
| CN108932585A (en) * | 2018-06-19 | 2018-12-04 | 腾讯科技（深圳）有限公司 | A kind of trade company's operation management method and its equipment, storage medium, electronic equipment |
| CN109658141A (en) * | 2018-12-12 | 2019-04-19 | 中智关爱通（上海）科技股份有限公司 | Merchandise sales prediction technique, server and storage medium |
| CN111861073A (en) * | 2019-04-30 | 2020-10-30 | 北京嘀嘀无限科技发展有限公司 | Operation strategy prediction method and device, electronic equipment and readable storage medium |
| CN113011618A (en) * | 2019-12-21 | 2021-06-22 | 上海鸿鹊信息科技有限公司 | Hotel income management system and method |
| CN112419729A (en) * | 2020-11-23 | 2021-02-26 | 北京嘀嘀无限科技发展有限公司 | Method and system for determining decision strategy according to predicted traffic flow |
| CN112734276A (en) * | 2021-01-20 | 2021-04-30 | 杭州盈兴科技有限公司 | Smart hotel operation management method and device and electronic equipment |
| CN112884590A (en) * | 2021-01-26 | 2021-06-01 | 浙江工业大学 | Power grid enterprise financing decision method based on machine learning algorithm |
| CN113205282A (en) * | 2021-05-31 | 2021-08-03 | 广州大学 | New retail commodity operation system and device based on big data analysis |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114819469A (en) * | 2022-02-24 | 2022-07-29 | 北京清华同衡规划设计研究院有限公司 | Intelligent tourism planning and designing method and system based on big data |
| CN116579634A (en) * | 2023-07-14 | 2023-08-11 | 深圳市科脉技术股份有限公司 | Store operation intelligent decision method and system based on big data |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110008409A (en) | Based on the sequence of recommendation method, device and equipment from attention mechanism | |
| US20200057918A1 (en) | Systems and methods for training artificial intelligence to predict utilization of resources | |
| CN111079015B (en) | Recommendation method and device, computer equipment and storage medium | |
| CN108205768A (en) | Database building method and data recommendation method and device, equipment and storage medium | |
| CN109583904A (en) | Training method, impaired operation detection method and the device of abnormal operation detection model | |
| CN109299994A (en) | Recommended method, device, equipment and readable storage medium storing program for executing | |
| CN113657812A (en) | Method and system for intelligent decision-making of store operation based on big data and algorithm | |
| CN110019568A (en) | Site selecting method, device, computer equipment and storage medium based on space clustering | |
| Xue et al. | Computational experiment-based evaluation on context-aware O2O service recommendation | |
| EP4036821A1 (en) | System for the matrix-digital transformation of a variable data set | |
| CN110276012A (en) | The generation method of visual user portrait a kind of, device and calculate equipment | |
| CN108255706A (en) | Edit methods, device, terminal device and the storage medium of automatic test script | |
| CN109690581A (en) | User guided system and method | |
| CN110363485A (en) | Intelligent social activity creation method and device | |
| CN108900619A (en) | A kind of independent Statistics of accessing population method and device | |
| CN112862525A (en) | Shop site selection data determination method and system and electronic equipment | |
| CN109816157A (en) | Project plan optimization method, device, computer equipment and storage medium | |
| CN111538852B (en) | Multimedia resource processing method, device, storage medium and equipment | |
| CN109978575A (en) | A kind of method and device excavated customer flow and manage scene | |
| CN115130811A (en) | Method and device for establishing power user portrait and electronic equipment | |
| Subramanian et al. | Systems dynamics-based modeling of data warehouse quality | |
| CN116862454B (en) | Indoor building design management method and system | |
| CN109783381A (en) | A kind of test data generating method, apparatus and system | |
| CN108875032A (en) | Area type determines method and device | |
| CN109272351A (en) | Passenger flow moving-wire and passenger flow hot-zone determine method and device |
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 |