CN116188066A

CN116188066A - Intelligent distribution method and system for store clients based on geographic position

Info

Publication number: CN116188066A
Application number: CN202310236494.2A
Authority: CN
Inventors: 高骥忠; 苑俊英; 王君
Original assignee: Guangzhou Nanfang College
Current assignee: Guangzhou Nanfang College
Priority date: 2023-03-10
Filing date: 2023-03-10
Publication date: 2023-05-30
Anticipated expiration: 2043-03-10
Also published as: CN116188066B

Abstract

The invention discloses a store customer intelligent distribution method and system based on geographic positions, wherein the method comprises the following steps: receiving a preset permission instruction, creating a drainage center and generating a corresponding drainage link/code; identifying a newly-added client entering the drainage center for the first time, and establishing a client file to record relevant online information; performing active analysis on the newly added clients for the first time to obtain IP and/or linked base station information of the clients, and recording the information as position association data; positioning based on the position association data to obtain the geographic position of the client; searching a preset offline store database based on the geographic position of the client to obtain a matched offline store; and searching a preset offline store client group database based on the matched offline store to obtain a matched offline store client group, and adding the corresponding newly-added client. The application has the effect of improving convenience and efficiency of offline store switching customer flow.

Description

Intelligent distribution method and system for store clients based on geographic position

Technical Field

The application relates to the technical field of electronic commerce, in particular to a store customer intelligent distribution method and system based on geographic positions.

Background

With the continuous expansion and perfection of mobile internet services, opportunities for offline store business are tightly combined with the internet, and particularly 020 mode is based on online marketing, online purchasing with offline operation and offline consumption, and the internet becomes a foreground of offline store business. Namely: through modes of advertisement putting, online platform residence, content marketing, campaign cooperation and the like, brand merchants acquire flow from online and feed back to the business and consumption of brand online stores.

In general, the acquisition of online traffic is not limited by the space-time of traditional offline store, but when traffic is converted to offline store allocation, the traffic conversion rate is affected by the online user's demand and the matching degree of store services.

Conventional off-line store diversion schemes are mainly implemented from two directions: firstly, channel division is carried out at a flow acquisition position, a plurality of drainage links (or regional group two-dimensional codes) are generally required to be configured, the configuration is troublesome, and the delivery management is complex; and the other is that the customer flow is uniformly acquired and then distributed by manpower, so that more manpower is required, and the customer flow cannot be met under certain timeliness requirement scenes.

The mode is low in efficiency and high in subsequent continuous operation cost, so that a new technical scheme is provided.

Disclosure of Invention

In order to improve convenience and efficiency of offline store switching customer flow, the application provides a store customer intelligent distribution method and system based on geographic positions.

In a first aspect, the present application provides a store customer intelligent distribution method based on a geographic location, which adopts the following technical scheme:

a store customer intelligent distribution method based on geographic location, comprising:

receiving a preset permission instruction, creating a drainage center and generating a corresponding drainage link/code;

identifying a newly-added client entering the drainage center for the first time, and establishing a client file to record relevant online information;

performing active analysis on the newly added clients for the first time to obtain IP and/or linked base station information of the clients, and recording the information as position association data;

positioning based on the position association data to obtain the geographic position of the client;

searching a preset offline store database based on the geographic position of the client to obtain a matched offline store; the method comprises the steps of,

searching a preset offline store client group database based on the matched offline store to obtain a matched offline store client group, and adding the corresponding newly-added client.

Optionally, if the geographic location of the located client meets the preset accuracy standard, then:

calculating a distance difference between the geographic location of the offline store and the geographic location of the customer;

judging whether the distance difference value is smaller than an error standard threshold value, if so, ending; if not, sending a theft and brushing abnormality prompt to the administrator of the corresponding offline store group, and stopping adding the newly added client to the matched offline store group.

Optionally, if the first active analysis is performed to obtain the IP information of the client and the linked base station information, then:

positioning based on IP to obtain a geographic position I of the newly added client;

positioning the position of a base station, and calculating the geographic position II of the newly added client based on communication delays between different communication base stations and access equipment of the newly added client;

if the first geographic position meets the preset precision standard, calculating a distance difference value between the first geographic position and the second geographic position;

judging whether the distance difference value is smaller than an error standard threshold value, if so, ending; if not, sending a data hijack risk prompt to the administrator of the corresponding offline store group, and stopping adding the newly added client to the matched offline store group.

Optionally, acquiring user associated behavior data after pushing the message to the client;

if the user associated behavior data representation is active, locating the latest geographic position of the client;

and (3) carrying out preset travel analysis logic processing to determine whether to update the offline store client group to which the client belongs.

Optionally, the travel analysis logic processes, which includes:

calling a client file, counting the occurrence times of the same offline store in a specified period T, and defining resident stores exceeding a first threshold value and travel stores as other stores;

if the latest geographic position is matched with the resident store, ending;

if the latest geographic location matches a travel store and is different from the originally matched offline store, then:

searching a preset offline store client group database based on the latest geographic position to obtain a matched offline store client group, and enabling the client to join;

the offline store client group to which the client originally belongs is suspended from pushing active data to the client.

Optionally, if the latest geographic location of the client does not have a matched offline store, performing distance calculation based on an offline store database to obtain an offline store closest to the client, and matching the offline store.

Optionally, chat rooms are respectively built for each offline store group based on the drainage center;

the active data of the users in the same offline store group are transferred into the same chat room, and then the data distribution is carried out.

Optionally, the method further comprises:

receiving abnormal sample data and feature definition information uploaded by a background;

extracting features from the abnormal sample data, and generating abnormal feature behaviors by corresponding feature combinations;

performing feature recognition, extraction and feature record generation on active data of newly added clients entering a drainage center;

evaluating the abnormal probability of the feature record of the newly added client based on the abnormal feature behavior, and defining the client exceeding the risk threshold as a suspected client;

creating a safe chat room based on the drainage center, and stopping suspected clients from entering the chat room of the corresponding offline store group;

enabling active data of suspected clients to enter a safe chat room, and auditing the active data by preset auditing rules;

if the verification is passed, the active data is transferred into a chat room of the corresponding offline store group, and distribution data is received from the chat room and matched and distributed to suspected clients;

if the auditing is not passed, sending the suspected clients with preset error reporting data, and sending auditing prompts to the administrators of the corresponding offline store groups.

In a second aspect, the application provides a store customer intelligent distribution system based on geographic location, which adopts the following technical scheme:

a geographic location based store client intelligent distribution system comprising a memory and a processor, the memory having stored thereon a computer program capable of being loaded by the processor and performing any of the geographic location based store client intelligent distribution methods described above.

In summary, the present application includes at least one of the following beneficial technical effects: after the method is applied, the drainage inlet is normalized, a plurality of links are not required to be configured, or client shunt is carried out by relying on a manual shunt method; the intelligent flow calculation matching can be performed on the flow according to the geographic position of the entering flow, the position of the store and the like, and the flow is automatically split to the corresponding store for carrying out the connection, so that the convenience and the efficiency of switching the customer flow of the off-line store are effectively improved.

Drawings

FIG. 1 is a schematic of the main flow of the method of the present application.

Detailed Description

The present application is described in further detail below in conjunction with fig. 1.

The embodiment of the application discloses a store customer intelligent distribution method based on geographic positions.

Referring to fig. 1, a store customer intelligent distribution method based on geographic location includes:

s1, receiving a preset permission instruction, creating a drainage center and generating a corresponding drainage link/code.

It will be appreciated that the present method may be implemented by a corresponding computer program, and therefore the following explanation is made:

and the authority instruction is a trigger instruction created by a drainage center set by a developer.

The drainage code, i.e. such as a two-dimensional code, is in fact a url address.

The user pastes the drainage link/code print to the store, and the new client can access/enter the drainage center by inputting the address and the code through the mobile phone.

The drainage center can be regarded as a WeChat group; the essence is a multi-person chat room established based on a server.

S2, identifying a new client entering the drainage center for the first time, and establishing a client file to record relevant online information.

Taking WeChat as an example, in this embodiment, the relevant online information refers to: weChat account number of the customer, active data of the drainage center, customer information supplemented by a merchant applying the method and the like.

S3, performing active analysis on the newly added clients for the first time to obtain IP and/or linked base station information of the clients, and recording the IP and/or linked base station information as position association data.

Regarding IP: considering that the current situation of each social App in China is that chat records are not disclosed externally, common IP detection and the like are not available when a mobile phone end only sends chat messages with an opposite party, and the IP acquisition needs to involve judicial and operator authorization and the like, so that the PC end is used as an example:

as known, any computer surfing net is assigned an IP; as is known, wechat is instant messaging, involving IP/TCP/UDP/Sockets, P2P, etc.

After the newly added client enters the drainage center, the newly added client actively transmits a message through the personal terminal or accesses a link appointed by a merchant to generate primary activity; at this point, its IP address may be resolved, such as based on IP/TCP.

Information about linked base stations:

conventional cell phone communications are known to be implemented by means of base stations, any of which have independent codes.

Therefore, after the newly added client enters the drainage center, the newly added client actively sends a message through a personal terminal (mobile phone) or accesses a link appointed by a merchant, and the newly added client generates activity once, namely, the base station information of the link of the newly added client can be acquired through TelePhony manager.

It can be understood that the above IP/TCP parsing and obtaining the base station according to the mobile phone signal are all in the prior art, so that the description is omitted.

It should be noted that the method of the embodiment does not involve access to the mobile phone GPS rights, and the related steps occur on the basis that the client is actively active in the drainage center, so that the user privacy is better; however, the method is not represented in that the position of the client can not be obtained directly by acquiring the GPS data of the mobile phone.

And S4, positioning based on the position association data to obtain the geographic position of the client.

Under the premise, the positioning comprises: IP positioning and base station positioning.

In this embodiment, IP positioning may be implemented through an interface of an API that is open to the hundred-degree map, through; base station positioning, then: calculating the distance between the base station and the base station based on communication delay in the three-way handshake and four-way waving which occur during the establishment of the TCP connection; and because the mobile phone is generally linked with a plurality of base stations for signal stability, the mobile phone can be positioned at the accurate position to finish positioning. The base station positioning and the triangular positioning algorithms are all in the prior art and are not repeated.

S5, searching a preset offline store database based on the geographic position of the client to obtain a matched offline store.

And the off-line store database records mapping relations of off-line stores, store positions and store jurisdictions.

When a customer's geographic location falls within the store jurisdiction of an off-line store, the off-line store matches the customer.

S6, searching a preset offline store client group database based on the matched offline store to obtain a matched offline store client group, and adding the corresponding newly-added client.

Client group, new client join group reference WeChat/QQ plus group.

According to the above, after the method is applied, the drainage inlet is normalized without configuring a plurality of links, or client shunt is performed by relying on a manual shunt method; the intelligent flow calculation matching can be performed on the flow according to the geographic position of the entering flow, the position of the store and the like, and the flow is automatically split to the corresponding store for carrying out the connection, so that the convenience and the efficiency of switching the customer flow of the off-line store are effectively improved.

In one embodiment of the present application, if the geographic location of the located client meets the preset accuracy standard, then:

judging whether the distance difference value is smaller than an error standard threshold value, if so, ending; if not, sending a theft and brushing abnormality prompt to the administrator (mobile phone end) of the corresponding offline store group, and stopping adding the newly added client into the matched offline store group.

The precision standard refers to: geographic location is accurate to the house number class, rather than a street, town. The distance difference value can be obtained by calculating the distance between two points on the GIS electronic map and referencing the distance between two points in the navigation App; if the data has longitude and latitude, longitude and latitude calculation can be performed.

The content occurs in the first active stage of the newly added client, and the effect is that: preventing others from maliciously stealing drainage links/codes of off-line stores.

It is to be noted that some merchants adopt a member system, and the application of the content can directly skip the traditional member registration and authentication, so long as the customers who are allowed to enter the store scan the group in the store to be members; and, other people copy the drainage link/code steal brush, and the drainage center cannot be accessed because the place where the first activity occurs is not in a store and the geographic position is not right, so that people such as a wool party, a card merchant and the like are effectively restrained.

Further, if the first active analysis is performed, the IP information of the client and the linked base station information are obtained, that is, both data are obtained, and if one of the two data is not obtained, the mutual cross-validation is performed:

According to the content, the method can prevent other people from maliciously performing network hijacking and falsifying the IP to a certain extent. Because, according to the above, the base station positioning position is compared with the IP positioning position; meanwhile, because the positions obtained by the two positioning modes are compared, cross verification is achieved to a certain extent, any abnormal positioning position can be found, the illegal action implementation cost is increased, and the safety is improved.

In one embodiment of the present application, the method further comprises:

acquiring user associated behavior data after pushing information to a client (through a drainage center/a corresponding group);

The user associated behavior data are as follows: the user responds to the push message; the user does not respond to the push message. Active, i.e., responsive, such as: and the client browses and accesses the pushed information.

From the above, the method divides the customer to a certain off-line store, and updates the customer according to the actual location of the customer.

Regarding travel analysis logic processing, specifically:

calling a client file, counting the occurrence times of the same offline store in a specified period T (7 days), and defining resident stores exceeding a preset first threshold value, and other resident stores being travel stores;

if the latest geographic position is matched with the resident store, ending;

According to the above, when the customer moves from the resident city A to the city B, the method can match the customer group of the city B for the customer so as to ensure that the customer can enjoy the relevant service of the offline store of the city B during the city activity of the city B, thereby effectively improving the customer experience.

Because the original group does not exit after the client joins a new group, the method does not have the condition that a client account repeatedly joins and exits the group for a plurality of times, thereby avoiding the loss and the like caused by exiting the client from the original group.

Meanwhile, the active data pushed to the client by the original group is stopped, so that the following situations can be avoided: the merchant headquarters push out a sales promotion, each store under the line has to push to the corresponding customer, a plurality of groups that the customer joins, repeated push is many times, causes the puzzlement.

Note that the above is built in an offline store where the customer's latest geographic location has a match, but in reality: the influence of the volume of a merchant and the like often occurs, and the latest geographic position is not matched with an offline store, so that the method is not applicable, and comprises the following steps:

and calculating the distance based on the offline store database, obtaining the offline store closest to the offline store, and matching.

In one embodiment of the method, for the repeated group adding and removing problems, the method is further configured to:

establishing chat rooms for each off-line store group based on the drainage center;

Regarding the creation of chat rooms, it is state of the art; the present embodiment is presented in an example of a university course presentation level:

the server side instantiates a ServerSocket object and waits for the connection of the client side by using an accept () method in the ServerSocket object.

The server establishes the client and applies for connection to the server while waiting all the time. In the client code, we shall instantiate a Socket object, which should have the same port number as the server-side ServreSocket, to ensure that an accurate connection is made to the server.

After the client side successfully puts forward the connection application, the method returns to the server side accept () method, and after the connection application is successfully obtained, the returned value is a Socket object, and the Socket object is the connection to the client side.

After TCP connection is successfully established between the client and the server through the Socket object and the Serversocket object;

the input stream and the output stream in the corresponding directions are obtained through a getInputStream () method and a getOutputStrea () method in the Socket class.

The server side can write information into the client side through the OutputStream stream created by using the getoutputstream () method on the Socket object obtained by the Socket () and can read information from the client side by using the InputStream obtained by using the getInputStream () method on the Socket object. And vice versa.

The method in this embodiment is mainly for creating a chat room, but it should be noted that, unlike the conventional WeChat group, the method is: each chat room is in a drainage center.

I.e. the chat room of each off-line store is below a normalized total chat room. Data is advanced to the chat room and the chat rooms of each child are redistributed. This arrangement facilitates a further subsequent secure chat mechanism implementation of the method.

An intuitive application of the above-mentioned contents, without providing secure chat remembering, can see differences from the traditional ones, such as:

and covering a client side display interface of the drainage center by using surface layer information of the offline store group to which the client belongs, and receiving and displaying distribution data of a corresponding chat room by the client side.

That is, the client is in the group which is added from beginning to end, only the group name, introduction, personnel and the like which can be seen by the client are of the offline store group, and the chat message which is seen is also after shielding other offline store groups; therefore, the problems of repeated group adding and group dropping can be avoided to a certain extent, the group is unchanged, and only the information of the client is selectively covered, shielded and received.

In one embodiment of the method, the method further comprises:

receiving exception sample data uploaded by a background (such as IP is changed for a plurality of times in a short time and the same equipment ID is a newly added client) and feature definition information (such as definition data A is the number of IP changes and definition data B is the number of clients corresponding to the same equipment ID);

extracting features from the abnormal sample data, and combining the corresponding features (such as establishing an and, or relationship among a plurality of features) to generate abnormal feature behaviors;

based on abnormal feature behavior evaluation (such as defining a score and a weight value for each feature, and calculating the weight), the abnormal probability of the feature record of the newly added client (such as calculating the score by the weight/presetting a standard score), and defining the client exceeding the risk threshold as a suspected client;

According to the above, the IP positioning and abnormal base station positioning reminding can be further improved, even if other people maliciously tamper with the IP, forge base station signals and the like, certain discovery probability exists after related operations are executed for many times, malicious cost is further increased, and the safety of the method is improved; meanwhile, it should be noted that:

abnormal sample data, which may also be: a set of non-civilized chat text/graphs;

feature definition information, which may also be: defining words a, pictures b and the like as non-civilized words, and defining words c and d as sensitive words;

at this time, the content can also be used for timely eliminating sensitive words when the clients do not make civilized speaking, so that negative effects caused by the sensitive words are reduced.

In summary, the method can help the off-line store to transfer the on-line traffic of the clients to off-line and manage the traffic.

The embodiment of the application also discloses a store customer intelligent distribution system based on the geographic position.

The geographic location based store client intelligent distribution system includes a memory and a processor, the memory having stored thereon a computer program capable of being loaded by the processor and performing any of the geographic location based store client intelligent distribution methods described above.

The foregoing are all preferred embodiments of the present application, and are not intended to limit the scope of the present application in any way, therefore: all equivalent changes in structure, shape and principle of this application should be covered in the protection scope of this application.

Claims

1. The intelligent distribution method for the store clients based on the geographic positions is characterized by comprising the following steps of:

2. The intelligent distribution method for store clients based on geographic locations according to claim 1, wherein: if the geographic position of the client obtained by positioning accords with a preset precision standard, the geographic position of the client obtained by positioning is:

3. The intelligent distribution method for store clients based on geographic locations according to claim 2, wherein if the client IP information and the linked base station information are obtained by first active analysis, then:

4. The intelligent distribution method for store clients based on geographic locations according to claim 1, comprising:

acquiring user associated behavior data after pushing information to a client;

5. The intelligent diversion method for store clients based on geographical locations according to claim 4, wherein the travel analysis logic process comprises:

if the latest geographic position is matched with the resident store, ending;

6. The intelligent distribution method for store clients based on geographic locations according to claim 5, wherein: if the latest geographic position of the client does not have the matched offline store, calculating the distance based on the offline store database, obtaining the offline store closest to the client, and matching the offline store.

7. The intelligent distribution method for store clients based on geographic locations according to claim 1, wherein:

8. The geographic location based store customer intelligent distribution method according to claim 3, further comprising:

9. A geolocation-based store client intelligent distribution system comprising a memory and a processor, the memory having stored thereon a computer program capable of being loaded by the processor and performing the geolocation-based store client intelligent distribution method of any one of claims 1 to 8.