CN111401967A - Big data-based digital business service analysis system and working method thereof - Google Patents

Big data-based digital business service analysis system and working method thereof Download PDF

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Publication number
CN111401967A
CN111401967A CN202010244712.3A CN202010244712A CN111401967A CN 111401967 A CN111401967 A CN 111401967A CN 202010244712 A CN202010244712 A CN 202010244712A CN 111401967 A CN111401967 A CN 111401967A
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module
user terminal
store
cloud server
real
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黄惠珠
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Suzhou Xishuo Data Technology Co ltd
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    • 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
    • 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
    • G06Q30/0203—Market surveys; Market polls
    • 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
    • G06Q30/0241—Advertisements
    • G06Q30/0251—Targeted advertisements
    • G06Q30/0261—Targeted advertisements based on user location
    • 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
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0631—Item recommendations

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  • Game Theory and Decision Science (AREA)
  • Data Mining & Analysis (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

A big data-based digital business service analysis system and a working method thereof comprise the following steps: cloud server, statistics terminal still include: a user terminal; the cloud server comprises a real-time data module, the statistical terminal is used for carrying out statistics on the real-time pedestrian volume of the store, and the statistical terminal is used for acquiring the business density of the store; the statistical terminal also obtains a single customer service index; the statistical unit outputs a single customer service index to the real-time data module; the cloud server also comprises a recommending module, the recommending module calculates the capacity of the current store according to the dynamic number of the persons who are allowed to enter and the single customer service index from the real-time data module; the method comprises the steps that a user terminal initiates a recommendation application to a cloud server, a recommendation module receives the application of the user terminal, and meanwhile, the recommendation module extracts the current position of the user terminal; and the recommending module is used for pushing recommended shops to the user terminal according to the current position of the user terminal and the reception capacity sequence of the shops.

Description

Big data-based digital business service analysis system and working method thereof
Technical Field
The invention relates to the field of digital communities, in particular to a digital commercial service analysis system based on big data and a working method thereof.
Background
The digital operation platform generally refers to a function of monitoring and scheduling from a client to a server by taking a core service as an entry point and by interface calling, application running, platform state monitoring and the like.
For the recommendation of the offline store, a map system is often used, and a user can only view the address of the store, a route to go, historical evaluation and the like, but cannot view information such as the traffic, service volume and the like of the actual store. The store traffic and the service inventory often affect the actual experience of the user in the store online, so a recommendation system for viewing the store service analysis is provided for the user.
Disclosure of Invention
The purpose of the invention is as follows:
in order to solve the technical problems mentioned in the background art, the invention provides a digital business service analysis system based on big data and a working method thereof.
The technical scheme is as follows:
a big-data based digital commerce service analysis system, comprising: cloud server, statistics terminal still include: a user terminal;
the cloud server is respectively in wireless connection with the statistical terminal and the user terminal;
the cloud server comprises a real-time data module, the statistical terminal is used for performing statistics on the real-time pedestrian volume of the store, the statistical terminal is used for acquiring the real-time pedestrian volume in the store and simultaneously acquiring the business area of the store, and the business density of the store is obtained according to the real-time pedestrian volume and the business area;
the statistical terminal outputs the business density to the real-time data module, the real-time data module is also provided with the optimal business density of different business states, and the real-time data module obtains the dynamic number of the entrances of the shop according to the optimal business density and the real-time business density of the business state of the shop;
the statistical terminal also obtains a single customer service index, wherein the single customer service index is the time for a single guest to obtain service; the statistical unit outputs a single customer service index to the real-time data module;
the cloud server further comprises a recommending module, the recommending module calculates the capacity of the current store according to the dynamic number of the persons who are allowed to enter and the single customer service index obtained from the real-time data module;
the recommendation module acquires the reception capabilities of a plurality of stores and sorts the recommendation priorities of the stores according to the reception capabilities;
the user terminal initiates a recommendation application to the cloud server, the recommendation module receives the application of the user terminal, and meanwhile, the recommendation module extracts the current position of the user terminal;
and the recommending module is used for pushing recommended shops to the user terminal according to the current position of the user terminal and the reception capacity sequence of the shops.
As a preferred mode of the present invention, the cloud server further classifies the categories of the stores, and the user terminal initiates the recommendation application including screening the categories.
As a preferred mode of the invention, the store is further provided with a duration monitoring module, and the duration monitoring module is used for monitoring the duration of the personnel entering the store; the time length monitoring module identifies and times the personnel when the personnel enter the store, identifies and stops timing the personnel when the personnel leave the store, and counts the stay time length of the personnel in the store;
the time length monitoring module uploads the stay time length of the personnel in the store to the cloud server;
the recommending module provides the average stay time of the staff in the shop to the user terminal.
As a preferred mode of the present invention, the statistical terminal further includes an inventory module, the inventory module is configured to record inventory records in the store, and the inventory module uploads the inventory records to the cloud server; the recommendation module acquires the inventory records and provides the inventory records to the user terminal.
A working method of a big data-based digital business service analysis system comprises the following steps:
the statistical terminal outputs the shop business density to the cloud server;
the cloud server receives the business density of the store and provides data to the real-time data module;
the real-time data module calculates the number of dynamic access persons according to the optimal business density and the real-time business density of the store;
the statistical terminal uploads the single customer service index to the cloud server;
the real-time data module acquires a single customer service index and calculates the reception capacity of the current store according to the single customer service index;
a user terminal initiates a recommendation application to a cloud server;
the recommendation module extracts the current position of the user terminal;
and the recommending module is used for pushing recommended shops to the user terminal according to the current position of the user terminal and the reception capacity sequence of the shops.
The method comprises the following steps:
the user terminal initiates a recommendation application to the cloud server;
the recommendation module extracts the required item class of the user terminal;
and the recommending module is used for pushing recommended shops to the user terminal according to the required categories of the user terminal and the capacity sequence of the corresponding shops.
The method comprises the following steps:
the time length monitoring module monitors the stay time length of the personnel in the shop;
the duration monitoring module uploads the stay duration of the personnel to the cloud server;
the recommendation module provides the recommended average person stay time of the store to the user terminal.
The method comprises the following steps:
inventory module records inventory records within store
The inventory module uploads inventory records to a cloud server;
and the recommending module provides the inventory records to the user terminal according to the user demand degree.
The invention realizes the following beneficial effects:
the statistical terminal provides information including the flow of people, the business density and the like of the store to the cloud, the service volume of the store is measured and calculated, the capability of the current store for reception can be effectively judged, whether the user is recommended to go or not is convenient to determine, and whether the user goes or not is convenient to judge. The average stay time of the personnel in the current store is calculated, so that the user can conveniently judge the waiting time. And the inventory of the current store is counted, and the inventory consumption of the store is uploaded, so that the user can conveniently inquire and judge the commodities.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.
FIG. 1 is a system connection diagram of a big data-based digital business service analysis system according to the present invention;
FIG. 2 is a system connection diagram of a second big data-based digital commerce service analysis system provided by the present invention;
FIG. 3 is a system connection diagram of a third big-data-based digital commerce service analysis system provided by the present invention;
FIG. 4 is a flowchart illustrating a method for operating a big data based digital commerce service analysis system according to the present invention;
FIG. 5 is a flowchart of a class recommendation method for a big data based digital commerce service analysis system according to the present invention;
FIG. 6 is a flow chart of duration acquisition of a method of operation of a big data based digital commerce services analysis system according to the present invention;
fig. 7 is a flowchart illustrating the inventory acquisition process of the working method of the big data-based digital commerce service analysis system according to the present invention.
1. The system comprises a cloud server, 11, a real-time data module, 13, a recommendation module, 2, a statistic terminal, 21, an inventory module, 3, a user terminal and 4, a duration monitoring module.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
Example one
Reference is made to fig. 1 as an example.
A big-data based digital commerce service analysis system, comprising: cloud server 1, statistics terminal 2 still include: a user terminal 3.
The cloud server 1 is in wireless connection with the statistical terminal 2 and the user terminal 3 respectively. The statistical terminal 2 is installed in a shop, and operated by the shop to upload the number of people who enter the shop. The user terminal 3 is a user terminal of a mobile user.
The cloud server 1 comprises a real-time data module 11, the counting terminal 2 counts the real-time people flow of the store, the counting terminal 2 acquires the real-time people flow in the store and the business area of the store at the same time, and the business density of the store is obtained according to the real-time people flow and the business area.
The statistical terminal 2 obtains the current entrance traffic (x) of the store and the business area (n) of the store, wherein the business area is only the guest activity area and does not comprise the stock preparation area, the warehouse and other areas of the store. Business density(s) of the store1) Is s is1=x/n。
The statistical terminal 2 outputs the business density to the real-time data module 11, and the real-time data module 11 is further provided with the optimal business density(s) of different business states2) The real-time data module 11 obtains the dynamic number of entrances of the store according to the optimum business density and the real-time business density of the business state of the store, the dynamic number of entrances L =(s)2-s1）n。
The optimum operating density for different business states is set according to different business classifications, for example: the optimal business density of the catering and the entertainment are different. The degree of congestion of the current store and the customer experience can be judged according to the business density.
The statistical terminal 2 further obtains a single customer service index, wherein the single customer service index is the time when a single guest can obtain service. The statistical unit outputs a single customer service index to the real-time data module 11.
The single customer service index (p) is the probability that the same guest can be served in all hours in the store, and is derived from the number of people in the store (x) and the combination of the number of service people (y) and the hours in the store. If the number of passengers in the store is large, the single-customer service index is reduced, and the customer experience is influenced. The single customer service index p = x/y.
The cloud server 1 further comprises a recommending module 13, the recommending module 13 calculates the receptivity of the current store according to the dynamic number of the entrants (L) and the single customer service index (p), and the real-time data module 11, the obtained dynamic number of the entrants (L) and the single customer service index (p) are sent to the recommending module 13.
The recommendation module 2 acquires the reception capabilities of a plurality of stores and sorts the recommendation priorities of the stores according to the reception capabilities.
The store reception capability (T) is integrated data integrating the number of receivable visitors and the service capability, and X = p L.
The user terminal 3 initiates a recommendation application to the cloud server 1, the recommendation module 2 receives the application of the user terminal 3, and meanwhile, the recommendation module 2 extracts the current position of the user terminal 3.
The recommending module 2 pushes recommended stores to the user terminal 3 according to the current position of the user terminal 3 and the reception capacity of the stores.
Specifically, the statistical terminal 2 outputs the shop business density to the cloud server 1;
the cloud server 1 receives the business density of the store and provides data to the real-time data module 11;
the real-time data module 11 calculates the number of dynamic access people according to the optimal business density and the real-time business density of the store;
the statistical terminal 2 uploads the single customer service index to the cloud server 1;
the real-time data module 11 acquires the single customer service index and calculates the reception capacity of the current store according to the single customer service index;
the user terminal 3 initiates a recommendation application to the cloud server 1;
the recommending module 2 extracts the current position of the user terminal 3;
and the recommending module 2 pushes recommended shops to the user terminal according to the current position of the user terminal 3 and the reception capacity of the shops.
Example two
Reference is made to fig. 1 as an example.
As a preferred mode of the present invention, the cloud server 1 further classifies the categories of the stores, and the user terminal 3 initiates a recommendation application including screening the categories.
The stores are classified, for example: and recommending the categories of catering, clothing, retail and the like according to the requirements of the user during recommendation.
Specifically, the user terminal 3 initiates a recommendation application to the cloud server 1;
the recommendation module 2 extracts the required item class of the user terminal 3;
the recommending module 2 pushes recommended stores to the user terminal 3 according to the required item class of the user terminal 3 and the capability sequence of the corresponding stores.
EXAMPLE III
Reference is made to fig. 2 as an example.
As a preferred mode of the present invention, the store is further provided with a duration monitoring module 4, and the duration monitoring module 4 is used for monitoring the duration of the person entering the store. The time length monitoring module 4 identifies and times the personnel when the personnel enter the store, identifies and stops timing the personnel when the personnel leave the store, and the time length monitoring module 4 counts the stay time length of the personnel in the store.
The duration monitoring module 4 uploads the stay duration of the staff in the store to the cloud server 1, and the recommending module 2 provides the average stay duration of the staff in the store to the user terminal 3.
The duration monitoring module 4 can identify and record the entering and exiting of personnel through biological identification, and the recommending module 2 calculates the average stay duration of the personnel in the same store. The average person stay length can be used as a reference for how long the user will spend at the store.
Specifically, the duration monitoring module 4 monitors the stay duration of the personnel in the shop;
the duration monitoring module 4 uploads the stay duration of the personnel to the cloud server 1;
the recommending module 2 provides the recommended average stay time of the store to the user terminal 3.
Example four
Reference is made to fig. 3 as an example.
As a preferred mode of the present invention, the statistical terminal 2 further includes an inventory module 21, the inventory module 21 is configured to record inventory records in the store, and the inventory module 21 uploads the inventory records to the cloud server 1. The recommendation module 2 obtains the inventory records and provides the inventory records to the user terminal 3.
The inventory records comprise commodity details in inventory and commodity export records. The recommending module 2 may recommend the most ordered goods to the user terminal 3. Or may provide the quantity of the goods and the ex-warehouse data required for the user query.
In particular, the inventory module 21 records inventory records within the store
The inventory module 21 uploads the inventory records to the cloud server 1;
the recommending module 2 provides the inventory records to the user terminal 3 according to the user demand degree.
EXAMPLE five
Reference is made to fig. 4-7 for an example.
A working method of a big data-based digital business service analysis system comprises the following steps:
the statistical terminal 2 outputs the access statistical data to the cloud server 1.
The cloud server 1 receives the access statistics and provides the data to the real-time data module 11.
The real-time data module 11 calculates the dynamic number of admissions based on the service inventory of the store.
And the statistical terminal 2 uploads the service stock to the cloud server 1.
The stock statistic module 12 acquires the service stock and calculates the service capacity of the current store according to the service stock.
The user terminal 3 initiates a recommendation application to the cloud server 1.
The recommending module 2 extracts the current position of the user terminal 3.
The recommending module 2 pushes recommended stores to the user terminal 3 according to the current position of the user terminal 3 and the reception capacity of the stores.
The method comprises the following steps:
and the user terminal 3 initiates a recommendation application to the cloud server 1.
The recommending module 2 extracts the required item class of the user terminal 3.
The recommending module 2 pushes recommended stores to the user terminal 3 according to the required item class of the user terminal 3 and the capability sequence of the corresponding stores.
The method comprises the following steps:
the length of stay monitoring module 4 monitors the length of stay of the person in the shop.
The duration monitoring module 4 uploads the stay duration of the personnel to the cloud server 1.
The recommending module 2 provides the recommended average stay time of the store to the user terminal 3.
The method comprises the following steps:
the inventory module 21 records inventory records within the store
The inventory module 21 uploads the inventory records to the cloud server 1.
The recommending module 2 provides the inventory records to the user terminal 3 according to the user demand degree.
The above embodiments are merely illustrative of the technical ideas and features of the present invention, and are intended to enable those skilled in the art to understand the contents of the present invention and implement the present invention, and not to limit the scope of the present invention. All equivalent changes or modifications made according to the spirit of the present invention should be covered within the protection scope of the present invention.

Claims (8)

1. A big-data based digital commerce service analysis system, comprising: high in the clouds server, statistics terminal, its characterized in that still includes: a user terminal;
the cloud server is respectively in wireless connection with the statistical terminal and the user terminal;
the cloud server comprises a real-time data module, the statistical terminal is used for performing statistics on the real-time pedestrian volume of the store, the statistical terminal is used for acquiring the real-time pedestrian volume in the store and simultaneously acquiring the business area of the store, and the business density of the store is obtained according to the real-time pedestrian volume and the business area;
the statistical terminal outputs the business density to the real-time data module, the real-time data module is also provided with the optimal business density of different business states, and the real-time data module obtains the dynamic number of the entrances of the shop according to the optimal business density and the real-time business density of the business state of the shop;
the statistical terminal also obtains a single customer service index, wherein the single customer service index is the time for a single guest to obtain service; the statistical unit outputs a single customer service index to the real-time data module;
the cloud server further comprises a recommending module, the recommending module calculates the capacity of the current store according to the dynamic number of the persons who are allowed to enter and the single customer service index obtained from the real-time data module;
the recommendation module acquires the reception capabilities of a plurality of stores and sorts the recommendation priorities of the stores according to the reception capabilities;
the user terminal initiates a recommendation application to the cloud server, the recommendation module receives the application of the user terminal, and meanwhile, the recommendation module extracts the current position of the user terminal;
and the recommending module is used for pushing recommended shops to the user terminal according to the current position of the user terminal and the reception capacity sequence of the shops.
2. The big-data-based digital business service analysis system as claimed in claim 1, wherein the cloud server further classifies the categories of the stores, and the user terminal initiates the recommendation application including screening the categories.
3. The big-data-based digital business service analysis system according to claim 1, wherein the shop is further provided with a duration monitoring module for monitoring the duration of the personnel entering the shop; the time length monitoring module identifies and times the personnel when the personnel enter the store, identifies and stops timing the personnel when the personnel leave the store, and counts the stay time length of the personnel in the store;
the time length monitoring module uploads the stay time length of the personnel in the store to the cloud server;
the recommending module provides the average stay time of the staff in the shop to the user terminal.
4. The big-data-based digital business service analysis system according to claim 1, wherein the statistical terminal further comprises an inventory module, the inventory module is used for recording inventory records in the store, and the inventory module uploads the inventory records to the cloud server; the recommendation module acquires the inventory records and provides the inventory records to the user terminal.
5. The working method of big data based digitalized commercial service analysis system according to any of claims 1-4, characterized by comprising the following steps:
the statistical terminal outputs the shop business density to the cloud server;
the cloud server receives the business density of the store and provides data to the real-time data module;
the real-time data module calculates the number of dynamic access persons according to the optimal business density and the real-time business density of the store;
the statistical terminal uploads the single customer service index to the cloud server;
the real-time data module acquires a single customer service index and calculates the reception capacity of the current store according to the single customer service index;
a user terminal initiates a recommendation application to a cloud server;
the recommendation module extracts the current position of the user terminal;
and the recommending module is used for pushing recommended shops to the user terminal according to the current position of the user terminal and the reception capacity sequence of the shops.
6. The method of claim 5, comprising the steps of:
the user terminal initiates a recommendation application to the cloud server;
the recommendation module extracts the required item class of the user terminal;
and the recommending module is used for pushing recommended shops to the user terminal according to the required categories of the user terminal and the capacity sequence of the corresponding shops.
7. The method of claim 5, comprising the steps of:
the time length monitoring module monitors the stay time length of the personnel in the shop;
the duration monitoring module uploads the stay duration of the personnel to the cloud server;
the recommendation module provides the recommended average person stay time of the store to the user terminal.
8. The method of claim 7, comprising the steps of:
the inventory module records inventory records in the store;
the inventory module uploads inventory records to a cloud server;
and the recommending module provides the inventory records to the user terminal according to the user demand degree.
CN202010244712.3A 2020-03-31 2020-03-31 Big data-based digital business service analysis system and working method thereof Withdrawn CN111401967A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111915820A (en) * 2020-09-03 2020-11-10 安徽上尚电子科技股份有限公司 Self-service business terminal guide service providing system

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111915820A (en) * 2020-09-03 2020-11-10 安徽上尚电子科技股份有限公司 Self-service business terminal guide service providing system

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