CN112862517A - Advertisement pushing method and system based on real-time position and habit of client - Google Patents
Advertisement pushing method and system based on real-time position and habit of client Download PDFInfo
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Abstract
The invention discloses an advertisement pushing method and system based on real-time position and habit of a client, wherein the method comprises the following steps: s01, obtaining the action track of the customer in a preset time period, recording the stay time of the customer in different merchants, counting according to the business category of the merchant and the stay time of the customer, and constructing a customer habit database; s02 obtaining the real-time position of the client; s03, obtaining map data of the area corresponding to the real-time position of the client; s04, acquiring merchant information within the preset range of the real-time position of the customer; s05, calling a customer habit database, matching the customer habit database with merchant information in a customer real-time position preset range, pushing merchant advertisements to customers according to advertisement pushing priorities, and pushing travel route information reaching corresponding merchants; the scheme can realize the economic benefit of the commercial advertising investment of the merchant in a humanized and precise manner; and based on the trip cost consideration of the client, the client can achieve better balance of consumption experience and economic and time expenditure.
Description
Technical Field
The invention relates to the technical field of advertisement pushing, in particular to an advertisement pushing method and system based on real-time positions and habits of customers.
Background
The current merchant advertisement positioning and launching system usually launches in an area according to the radius range of the position and only pushes the advertisement to users positioned in a defined radius by a GPS, and the mode can cause the loss of a user group which can reach the merchant position in a short time through a vehicle, and the advertising effect of the merchant is weakened.
Because present people's consumption level and income average level are higher and higher, many consumers ' trip mode also becomes more diversified, and the traditional form of carrying out direct propelling movement advertisement according to the position radius, the effect bottleneck of acquireing is also more and more obvious, consequently, carry out more accurate propelling movement advertisement to customer's trip mode or custom, will have more positive realistic meaning, also be the important direction that improves marketing effect simultaneously.
Disclosure of Invention
In view of this, the present invention provides an advertisement delivery method and system based on the real-time location and habit of the client, which has the advantages of strong delivery pertinence, good publicity effect, humanization and low cost.
In order to achieve the technical purpose, the technical scheme adopted by the invention is as follows:
an advertisement pushing method based on real-time position and habit of customers comprises the following steps:
acquiring a customer action track in a preset time period, recording the stay time of the customer action track in different merchants, and carrying out statistics and constructing a customer habit database according to the business category of the merchant and the stay time of the customer;
acquiring a real-time position of a client;
obtaining map data of an area corresponding to the real-time position of a client;
acquiring merchant information within a preset range of the real-time position of a customer;
and calling a customer habit database, matching the customer habit database with merchant information in a customer real-time position preset range, pushing merchant advertisements to customers according to advertisement pushing priorities, and pushing travel route information reaching corresponding merchants.
As a possible implementation manner, further, the advertisement push priority is determined according to a route priority of the client from the real-time position to the position of the merchant, and the route priority is arranged in a short-to-long order according to the time consumed by the client from the real-time position to the position of the merchant, and pushed in a list form according to a preset time interval; for example, with 10 minutes, 20 minutes and 30 minutes as interval nodes, the advertisement information that the client can reach the merchant within the corresponding time is pushed in a list mode.
As a possible implementation manner, further, the travel route information reaching the merchant is pushed to be the route with the shortest distance, the shortest time or the highest preset priority weight value;
the client action track and/or the client real-time position are/is acquired in one of the following modes:
calling a GPS module built in the mobile terminal for positioning through an APP of the client mobile terminal;
calling a BDS module built in the mobile terminal to perform positioning through an APP of the client mobile terminal;
calling a GLONASS module built in the mobile terminal to perform positioning through the APP of the client mobile terminal;
calling a GALILEO module built in the mobile terminal for positioning through the APP of the client mobile terminal;
and positioning through the communication base station of the network service provider corresponding to the client mobile terminal.
As a possible implementation manner, further, the specific method for constructing the customer habit database includes:
acquiring a client action track in a preset time period, then extracting a stop point position with the stop time length larger than a preset value in the client action track, and setting the stop point position as a characteristic point position;
obtaining map data of an area corresponding to the action track of the client and associating the feature point position with the map data;
judging whether the area of the map data corresponding to the feature point location is a merchant, if so, calling merchant information, obtaining a merchant operation category, associating the merchant operation category with the stay time corresponding to the feature point location, and meanwhile, constructing a habit model of the operation category or performing habit model adjustment and updating on the constructed habit model;
and (4) carrying out habit weight sequencing on the habit models according to the habit models corresponding to different operation categories to obtain habit priorities under different operation categories.
As a preferred implementation choice, preferably, the formula of the habit model is:
X＝X0+a，
wherein, X0Is the current weight value of the habit priority, a is the weight adjustment value, X is the adjusted habit priority weight value, and X0The initial value of (a) is 0.5, and a is 0.05.
As a preferred implementation choice, preferably, the scheme further includes updating the weight of the habit models corresponding to different operation categories in the customer habit database according to a preset time period, which specifically includes:
when the weight of the habit model corresponding to the operation category is adjusted in a preset period, skipping updating;
when the weight of the habit model corresponding to the operation category is not adjusted in the preset period, the habit model is adjusted according to the following formula:
X`＝X`0－b，
wherein, X ″, is0The current weight value of the habit priority, b is a weight adjustment value, X' is the weight value of the adjusted habit priority, and b is 0.05.
As a better implementation choice, preferably, before pushing the merchant advertisement to the customer, the scheme also judges in advance whether the customer has the current travel mode,
when the current travel mode is not set by the customer, the advertisements of the first N merchants with the advertisement push priorities are pushed in a list form, at least 1 travel route information reaching the corresponding merchant is pushed at the same time, and the travel routes are sequenced from short to long according to the expected use time;
when a current trip mode is set by a client, pushing N front merchant advertisements with the advertisement pushing priorities in a list form according to the current trip mode of the client, and simultaneously pushing at least 1 piece of trip route information containing information of a trip route to a corresponding merchant through the current trip mode of the client;
wherein N is a positive integer greater than 1;
the advertisement push priority is composed of a habit priority and a route priority, and a calculation formula of the advertisement push priority is as follows:
Z＝Y+X－Q，
wherein Z is the weighted value of the advertisement push priority, Y is the weighted value of the route priority, X is the weighted value of the habit priority, Q is the weighted value of the interference factor;
in addition, the weighted value Q of the interference factor is obtained by a camera inside a merchant according to the intensive situation of the current in-store personnel, then the image is digitally processed to obtain personnel density estimated values, Q values under different personnel densities are obtained according to a preset query table, and by the method, the advertisement push priority can be dynamically adjusted in real time.
As a better implementation choice, preferably, the weight value of the route priority is composed of a travel mode weight value of the customer, a time weight expected to arrive at the corresponding merchant, and an interference factor weight;
wherein, the trip mode includes: walking, riding, subway, bus or driving; in addition, the first and second substrates are,
when the travel mode is walking, the weight value of the route priority is obtained by the following formula:
Y＝Y1+c1－d1，
wherein Y is the weight value of the route priority, Y1A preset initial weight value for walking trip, c1Time-of-use weighting for walking trips，d1Is the interference factor weight;
when the travel mode is riding, the weight value of the route priority is obtained by the following formula:
Y＝Y2+c2－d2，
wherein Y is the weight value of the route priority, Y2For the preset initial weight value of riding trip, c2Weight of riding time, d2Is the interference factor weight;
when the travel mode is subway, the weight value of the route priority is obtained by the following formula:
Y＝Y3+c3－d3，
wherein Y is the weight value of the route priority, Y3Presetting initial weight value, c, for subway trip3Weight of time spent on subway trips, d3Is the interference factor weight;
when the travel mode is the bus, the weighted value of the route priority is obtained by the following formula:
Y＝Y4+c4－d4，
wherein Y is the weight value of the route priority, Y4For presetting an initial weight value, c, for bus trips4Time-of-use weight for bus travel, d4Is the interference factor weight;
when the travel mode is driving, the weight value of the route priority is obtained by the following formula:
Y＝Y5+c5－d5，
wherein Y is the weight value of the route priority, Y5A preset initial weight value, c, for driving5For driving journey, time-of-use weight, d5Is the interference factor weight;
in addition, the elapsed time weight c1、c2、c3、c4、c5Corresponding to preset values under different time consumption conditions, and forming a useful time weight lookup table;
when travel mode conversion exists in a planned route from a client to a merchant currently, the weighted value of the route priority is the sum of the weighted value of the route priority of different travel modes and the ratio of the travel mode to the total travel of the position of the client to the position of the corresponding merchant.
As a preferred implementation choice, it is preferred that the interference factor weight d1、d2、d3、d4Corresponding to preset values under different travel modes and travel conditions, and forming an interference factor weight query table;
in addition, the interference factor weight d5The reference factors comprise violation risk, parking risk and accident risk, and the formula is as follows:
d5＝d51+d52+d53，
wherein d is51For violation risk weight, d52As parking risk weight, d53Is the accident risk weight;
in addition, the first and second substrates are,
d51＝0.003×n，
n is the violation statistics number reported based on merchant return visit and/or customer active evaluation in a preset time period;
d52＝0.002×m，
m is the number of parking poor assessment statistics reported based on merchant return visits and/or customer active evaluations within a preset time period;
d53＝0.001×q，
and q is the statistical frequency of the journey accidents reported based on return visits of merchants and/or active evaluation of customers in a preset time period.
Based on the above method scheme, the present invention further provides an advertisement delivery system based on the real-time location and habit of the client, which comprises:
the storage unit is used for storing a customer habit database, map data, merchant information and merchant advertisements;
the position acquisition unit is used for acquiring the position of a client;
the matching unit is used for acquiring map data of an area corresponding to the real-time position of the customer and merchant information in a preset range of the real-time position of the customer, and matching the merchant information in the preset range of the real-time position of the customer with the customer habit database;
the route planning unit is used for planning a route from the client real-time position to the merchant;
the calculation unit is used for acquiring a customer habit database stored in the storage unit and a route from a customer real-time position planned by the route planning unit to a merchant, and outputting advertisement push priority;
and the pushing unit is used for pushing the merchant advertisements to the customers according to the advertisement pushing priorities and pushing the travel route information reaching the corresponding merchants at the same time.
Based on the above method scheme, the present invention further provides a computer-readable storage medium, where at least one instruction, at least one program, a code set, or an instruction set is stored in the storage medium, and the at least one instruction, the at least one program, the code set, or the instruction set is loaded by a processor and executed to implement the advertisement push method based on the client real-time location and the habit.
By adopting the technical scheme, compared with the prior art, the invention has the beneficial effects that: this scheme is through the trip mode of comprehensive consideration customer for the trade company advertisement can be according to customer's the mode of arriving at the shop, the shop time carries out the propelling movement, and this mode can break through the tradition and adopt to use the trade company as the center, carries out the limitation of propelling movement trade company advertisement according to apart from the radius scope, has introduced the trip mode and has considered the mechanism, makes some customers that can reach trade company's location fast through specific vehicle also can receive the advertisement propelling movement and obtain the trip route that arrives the trade company simultaneously. In addition, a customer habit database is also established, action tracks of customers are analyzed and counted, habits and preference degrees of individuals under different store operation categories are obtained, accordingly, merchant advertisements are pushed in a targeted manner, weights are configured according to customer habits and routes to merchants, and the priority of merchant advertisement pushing is considered from a comprehensive perspective, so that the economic benefits of merchant advertisement investment can be realized more humanizedly and accurately; in addition, economic and time risks of the customers arriving at the merchants are fully considered on the basis of the travel cost of the customers, so that the customers can achieve better balance between consumption experience and economic and time expenditure.
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 is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of an advertisement delivery method according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of the construction of a customer habit database of the advertisement delivery method according to the embodiment of the present invention;
FIG. 3 is a schematic flow chart of the advertisement delivery method of the present invention for periodically adjusting and updating the habit models;
fig. 4 is a schematic diagram of an advertisement delivery system according to an aspect of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be noted that the following examples are only illustrative of the present invention, and do not limit the scope of the present invention. Similarly, the following examples are only some but not all examples of the present invention, and all other examples obtained by those skilled in the art without any inventive work are within the scope of the present invention.
As shown in fig. 1, the present invention provides an advertisement delivery method based on real-time client location and habits, which includes:
s01, obtaining the action track of the customer in a preset time period, recording the stay time of the customer in different merchants, and counting and constructing a customer habit database according to the business category of the merchant and the stay time of the customer;
s02, acquiring the real-time position of the client;
s03, obtaining map data of the area corresponding to the real-time position of the client;
s04, acquiring merchant information within the preset range of the real-time position of the customer;
s05, calling a customer habit database, matching the customer habit database with merchant information in a customer real-time position preset range, pushing merchant advertisements to customers according to advertisement pushing priorities, and pushing travel route information reaching corresponding merchants.
In order to facilitate the customer to arrive at the merchant as soon as possible, as a possible implementation manner, further, the travel route information which is pushed to the merchant is the route with the shortest distance, the shortest time or the highest preset priority weight value.
The advertisement push priority is determined according to the route priority of the client reaching the position of the merchant in real time, the route priority is sequenced from short to long according to the time consumed by the trip mode of the client reaching the position of the merchant in real time, and the advertisement push priority is pushed in a list mode according to a preset time interval.
Based on the fact that the mobile terminal used by the client may have different built-in positioning system modules or different positioning strategies, the action track of the client and/or the real-time position of the client can be obtained in one of the following manners:
calling a GPS (global positioning system) module built in the mobile terminal for positioning through an APP of the client mobile terminal;
calling a BDS (China Beidou satellite navigation System) module built in the mobile terminal for positioning through the APP of the client mobile terminal;
calling a GLONASS (Global navigation satellite System) module built in the mobile terminal to perform positioning through the APP of the client mobile terminal;
calling a GALILEO (Galileo satellite navigation system) module built in the mobile terminal for positioning through the APP of the client mobile terminal;
and positioning through the communication base station of the network service provider corresponding to the client mobile terminal.
In addition, as a possible implementation manner, with reference to fig. 2, further, the specific method for constructing the customer habit database includes:
s011, obtaining a customer action track in a preset time period, then extracting a stop point position with the stop time length being larger than a preset value in the customer action track, and setting the stop point position as a feature point position;
s012, obtaining map data of an area corresponding to the action track of the client and associating the characteristic point with the map data;
s013, judging whether the area of the map data corresponding to the feature point location is a merchant;
s014, if yes, calling merchant information, obtaining merchant operation categories, and associating the merchant operation categories with the stay time corresponding to the feature points;
s015, constructing a habit model of the business category or adjusting and updating the constructed habit model;
and S016, performing habit weight sorting according to habit models corresponding to different operation categories to obtain habit priorities under different operation categories.
As a preferred implementation choice, preferably, the formula of the habit model is:
X＝X0+a，
wherein, X0Is the current weight value of the habit priority, a is the weight adjustment value, X is the adjusted habit priority weight value, and X0Is 0.5, a is 0.05, as an example, 5 minutes is used as a preset value, more than 5 minutes is used as a condition decision point, when the customer stays in the business for more than 5 minutes, the customer updates the weight value of the habit model under the habit model of the business category, that is, X is X0+0.05。
Based on the custom of the customer can change with time to a certain extent, if the weight value of the custom priority adjusted for many times in a certain period is not adjusted regularly, the new custom priority is difficult to be put in quickly, preferentially and accurately, so that the method is preferably implemented and selected, preferably, referring to fig. 3, and the method further comprises the following steps of updating the weight value of the custom model corresponding to different business categories in the custom database according to a preset time period, wherein the method specifically comprises the following steps:
when the weight of the habit model corresponding to the operation category is adjusted in a preset period, skipping updating;
when the weight of the habit model corresponding to the operation category is not adjusted in the preset period, the habit model is adjusted according to the following formula:
X`＝X`0－b，
wherein, X ″, is0B is a weight value of the current habit priority, b is a weight adjustment value, X' is a weight value of the adjusted habit priority, and b is 0.05; as an example, the weight value X' of the habit priority of a certain business category habit model0The habit priority is 0.6, 15 days are used as a preset value, more than 15 days are used as a conditional decision point, when a certain habit model is adjusted within 15 days, the weight of the habit priority is not adjusted downwards, otherwise, the weight of the habit priority is adjusted downwards by 0.05 every node time, namely, the weight X' of the adjusted habit priority is 0.55.
In the case of pushing the merchant advertisement to the customer, it is also very critical how to guide the customer to quickly, conveniently and safely reach the address of the merchant, and as a better implementation choice, preferably, before pushing the merchant advertisement to the customer, the scheme also judges in advance whether the customer has the current travel mode,
when the current travel mode is not set by the customer, the advertisements of the first N merchants with the advertisement push priorities are pushed in a list form, at least 1 travel route information reaching the corresponding merchant is pushed at the same time, and the travel routes are sequenced from short to long according to the expected use time;
when a current trip mode is set by a client, pushing N front merchant advertisements with advertisement pushing priorities in a list form according to the current trip mode of the client (namely, filtering a scheme of adopting other trip modes as main travel modes), and simultaneously pushing at least 1 piece of trip route information which comprises information of a trip route reaching a corresponding merchant through the current trip mode of the client;
wherein, N is a positive integer greater than 1, for example, 3 to 10, or the number of N can be set by the customer, so as to improve the human-based push.
Based on consideration of travel modes, the advertisement push priority in the scheme consists of a habit priority and a route priority, and a calculation formula of the advertisement push priority is as follows:
Z＝Y+X－Q，
wherein Z is the weighted value of the advertisement push priority, Y is the weighted value of the route priority, X is the weighted value of the habit priority, Q is the weighted value of the interference factor;
in addition, the weighted value Q of the interference factor is obtained by a camera inside the merchant to obtain the current situation of density of people in the store, then the image is digitally processed to obtain a people density estimated value, and a Q value under different people densities is obtained according to a preset lookup table, and it is the prior art to determine the people density in a fixed place by using a video, so the principle of the method is not repeated, and the method uses the people density as a reference factor for pushing the advertisement of the merchant. As an example, with the density of people in the corresponding merchant store as a main factor, the weight value Q of the interference factor may be matched according to the following lookup table to determine a specific value,
TABLE 1 weight Q lookup table for interference factors
| Personal density value (person/m)2) | Q value |
| ＜0.1 | －0.2 |
| 0.1～0.2 | －0.1 |
| 0.2～0.35 | 0 |
| 0.35～0.50 | 0.2 |
| 0.50～0.70 | 0.4 |
| ＞0.70 | 0.7 |
In the travel mode, as a better implementation choice, preferably, the weight value of the route priority is composed of a travel mode weight value of the customer, a time weight predicted to arrive at the corresponding merchant, and an interference factor weight;
wherein, the trip mode includes: walking, riding, subway, bus or driving; in addition, the first and second substrates are,
when the travel mode is walking, the weight value of the route priority is obtained by the following formula:
Y＝Y1+c1－d1，
wherein Y is the weight value of the route priority, Y1A preset initial weight value for walking trip, c1Weight of elapsed time for walking journey, d1Is the interference factor weight;
when the travel mode is riding, the weight value of the route priority is obtained by the following formula:
Y＝Y2+c2－d2，
wherein Y is the weight value of the route priority, Y2For the preset initial weight value of riding trip, c2Weight of riding time, d2Is the interference factor weight;
when the travel mode is subway, the weight value of the route priority is obtained by the following formula:
Y＝Y3+c3－d3，
wherein Y is the weight value of the route priority, Y3Presetting initial weight value, c, for subway trip3Weight of time spent on subway trips, d3Is the interference factor weight;
when the travel mode is the bus, the weighted value of the route priority is obtained by the following formula:
Y＝Y4+c4－d4，
wherein Y is the weight value of the route priority, Y4For presetting an initial weight value, c, for bus trips4Time-of-use weight for bus travel, d4Is the interference factor weight;
when the travel mode is driving (including riding taxi and other private car), the weight value of the route priority is obtained by the following formula:
Y＝Y5+c5－d5，
wherein Y is the weight value of the route priority, Y5A preset initial weight value, c, for driving5For driving journey, time-of-use weight, d5Is the interference factor weight.
In addition, as an example, the preset initial weight values Y of different travel modes1、Y2、Y3、Y4、Y5The initial weight values can be all taken as 1.0; time of use weight c1、c2、c3、c4、c5Corresponding to preset values under different time consumption conditions, and forming a useful time weight lookup table; as an example of the time-use weight, the time when the current position of the customer reaches the in-store of the merchant or the route switching transit point is taken as a main factor, and the time-use weight c1、c2、c3、c4、c5The specific values may be determined by matching according to a look-up table,
TABLE 2 time weight lookup table
For some cases that a travel mode needs to be converted into a transfer mode, when a travel mode conversion exists in a planned route from a client to a merchant, a weighted value of a route priority is the sum of weighted values of route priorities of different travel modes multiplied by a ratio of a travel of the travel mode to a total travel of a position of the client to a position of the corresponding merchant, for example, if a travel required by the client from the current position to a position of the merchant is 10Km, wherein a subway needs to be taken for 5 kilometers, a person walks for 2 kilometers, and a person rides for 3 kilometers, a weighted value Y of the route priority is 0.5YSubway+0.2YWalking device+0.3YRiding bicycleWherein Y isSubway、YWalking device、YRiding bicycleAnd the weight values respectively represent the priority levels of the routes calculated by subway, walking and riding routes.
Because different travel modes have different interference factors, as a better implementation choice, the interference factor weight d is preferable1、d2、d3、d4Corresponding to preset values under different travel modes and travel conditions, and forming an interference factor weight query table.
As an interference factor weight d1、d2、d3、d4As an example of (1), the interference factor weight d1、d2、d3、d4The specific values may be determined by matching according to the following look-up table:
table 3 interference factor weight look-up table
| Travel mode | Interference factor weight value |
| Walking (d)1) | 0.2 |
| Riding (d)2) | 0.4 |
| Subway (d)3) | 0.3 |
| Bus (d)4) | 0.5 |
The economic cost of walking is lowest, and the accident and delay risk are lowest relative to other travel modes, so the interference factor weight is set to be the lowest; the subway has the characteristics of punctuality and comfortable riding, so that the interference factor is low; the bus has the problems of traffic jam, delayed boarding and alighting at a stop, traffic accidents and the like, so the weighted value of the interference factors of the bus is set to be higher than that of walking, riding and subway departure.
In addition, since the driving cost is relatively high and there are many important factors affecting the cost, the driving interference factor weight d5Taking individual account of the interference factor weight d5The reference factors comprise violation risk, parking risk and accident risk, and the formula is as follows:
d5＝d51+d52+d53，
wherein d is51For violation risk weight, d52As parking risk weight, d53Is the accident risk weight;
in addition, the first and second substrates are,
d51＝0.003×n，
n is the violation statistics number reported based on merchant return visit and/or customer active evaluation in a preset time period;
d52＝0.002×m，
m is the number of parking poor assessment statistics reported based on merchant return visits and/or customer active evaluations within a preset time period;
d53＝0.001×q，
and q is the statistical frequency of the journey accidents reported based on return visits of merchants and/or active evaluation of customers in a preset time period.
As a simulation example, the following application scenario is constructed to illustrate the push mechanism of the present solution:
(1) real-time position location and merchant matching
The custom database prestores 1 custom model with an operation category A, and the weight values of the custom models are respectively as follows:
the weight value of the habit model with the operation category A is 0.7;
and obtaining the real-time position of the client as O, simultaneously obtaining the map data of the area corresponding to the real-time position of the client, further obtaining the merchant information within the preset range of the real-time position of the client, and obtaining 1 merchant information with the operation category of A.
(2) Route planning
Through route planning, the following 6 ways are obtained to reach the place of the merchant, and the travel and the time are respectively as follows:
TABLE 4 route planning sheet
(3) Based on the route planning table, the following weight values of the route priority are correspondingly obtained, wherein in the weight of the driving interference factor, the weight d of the violation risk is51Is 01, parking risk weight d520.2, accident risk weight d530.1, the distribution of the travel-mode weight values is approximately as follows:
TABLE 5 route priority weight distribution Table
(4) According to the camera image in the shop of the merchant, the density value of the personnel is obtained by digital processing and is led into a personnel density estimation system for estimation, and the Q value is-0.1; thus, the following weight values of the advertisement push priorities in 6 travel modes are obtained:
TABLE 6 advertisement Pushing priority
Based on table 6, the weight value of the advertisement push priority under the condition that the customer has a travel mode can be obtained, when the customer does not have a travel mode, the advertisement of the merchant is pushed based on the item with the highest weight value of the advertisement push priority in the travel mode to the merchant, and then the advertisement of the merchant is listed and recommended to the travel mode of the merchant according to the attached travel route information in a sub-list form and in a route priority from large to small mode.
Through the simulation example, the advertisement push priority can be further expanded to the situations of multiple business categories and multiple merchants in a calculation mode, and therefore the advertisement push lists corresponding to different merchants are formed.
As a further extension, the merchant may also actively select the advertisement delivery range, for example, the time period for the customer to reach the merchant or the travel mode (e.g., no parking space) may be used as an element.
Referring to fig. 4, based on the above method scheme, the present invention further provides an advertisement delivery system based on the real-time location and habit of the client, which includes:
the storage unit 1 is used for storing a customer habit database, map data, merchant information and merchant advertisements;
the position acquisition unit 2 is used for acquiring the position of a client;
the matching unit 3 is used for acquiring map data of an area corresponding to the real-time position of the customer and merchant information in a preset range of the real-time position of the customer, and matching the merchant information in the preset range of the real-time position of the customer with the customer habit database;
the route planning unit 4 is used for planning a route from the real-time position of the customer to the merchant;
the calculation unit 5 is used for acquiring a customer habit database stored in the storage unit 1 and a route from the customer real-time position planned by the route planning unit 4 to a merchant, and outputting advertisement push priority;
and the pushing unit 6 is used for pushing the merchant advertisements to the customers according to the advertisement pushing priorities and pushing the travel route information reaching the corresponding merchants at the same time.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be substantially or partially implemented in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, a network device, or the like) or a processor (processor) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only a part of the embodiments of the present invention, and not intended to limit the scope of the present invention, and all equivalent devices or equivalent processes performed by the present invention through the contents of the specification and the drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. An advertisement pushing method based on real-time position and habit of a client is characterized by comprising the following steps:
acquiring a customer action track in a preset time period, recording the stay time of the customer action track in different merchants, and carrying out statistics and constructing a customer habit database according to the business category of the merchant and the stay time of the customer;
acquiring a real-time position of a client;
obtaining map data of an area corresponding to the real-time position of a client;
acquiring merchant information within a preset range of the real-time position of a customer;
and calling a customer habit database, matching the customer habit database with merchant information in a customer real-time position preset range, pushing merchant advertisements to customers according to advertisement pushing priorities, and pushing travel route information reaching corresponding merchants.
2. The method of claim 1, wherein the advertisement push method based on the real-time position and habit of the client,
the advertisement push priority is determined according to the route priority of the client from the real-time position to the position of the merchant, the route priority is sequenced from short to long according to the time consumed by the travel mode of the client from the real-time position to the position of the merchant, and the advertisement push priority is pushed in a list form according to a preset time interval;
the client action track and/or the client real-time position are/is acquired in one of the following modes:
calling a GPS module built in the mobile terminal for positioning through an APP of the client mobile terminal;
calling a BDS module built in the mobile terminal to perform positioning through an APP of the client mobile terminal;
calling a GLONASS module built in the mobile terminal to perform positioning through the APP of the client mobile terminal;
calling a GALILEO module built in the mobile terminal for positioning through the APP of the client mobile terminal;
and positioning through the communication base station of the network service provider corresponding to the client mobile terminal.
3. The advertisement pushing method based on the real-time position and habit of the client as claimed in claim 1, wherein the specific method for constructing the client habit database is as follows:
acquiring a client action track in a preset time period, then extracting a stop point position with the stop time length larger than a preset value in the client action track, and setting the stop point position as a characteristic point position;
obtaining map data of an area corresponding to the action track of the client and associating the feature point position with the map data;
judging whether the area of the map data corresponding to the feature point location is a merchant, if so, calling merchant information, obtaining a merchant operation category, associating the merchant operation category with the stay time corresponding to the feature point location, and meanwhile, constructing a habit model of the operation category or performing habit model adjustment and updating on the constructed habit model;
and (4) carrying out habit weight sequencing on the habit models according to the habit models corresponding to different operation categories to obtain habit priorities under different operation categories.
4. The method of claim 3, wherein the habit model has a formula of:
X＝X0+a，
wherein, X0Is the current weight value of the habit priority, a is the weight adjustment value, X is the adjusted habit priority weight value, and X0The initial value of (a) is 0.5, and a is 0.05.
5. The advertisement push method based on the real-time position and habit of the client according to claim 4, further comprising updating the weight of the habit models corresponding to different business categories in the client habit database according to a preset time period, which is specifically:
when the weight of the habit model corresponding to the operation category is adjusted in a preset period, skipping updating;
when the weight of the habit model corresponding to the operation category is not adjusted in the preset period, the habit model is adjusted according to the following formula:
X`＝X`0－b，
wherein, X ″, is0The current weight value of the habit priority, b is a weight adjustment value, X' is the weight value of the adjusted habit priority, and b is 0.05.
6. The advertisement push method based on the real-time client location and habit according to any one of claims 3 to 5, characterized in that, before pushing the merchant advertisement to the client, it is further determined in advance whether the client has set the current travel mode,
when the current travel mode is not set by the customer, the advertisements of the first N merchants with the advertisement push priorities are pushed in a list form, at least 1 travel route information reaching the corresponding merchant is pushed at the same time, and the travel routes are sequenced from short to long according to the expected use time;
when a current trip mode is set by a client, pushing N front merchant advertisements with the advertisement pushing priorities in a list form according to the current trip mode of the client, and simultaneously pushing at least 1 piece of trip route information containing information of a trip route to a corresponding merchant through the current trip mode of the client;
wherein N is a positive integer greater than 1;
the advertisement push priority is composed of a habit priority and a route priority, and a calculation formula of the advertisement push priority is as follows:
Z＝Y+X－Q，
wherein Z is the weighted value of the advertisement push priority, Y is the weighted value of the route priority, X is the weighted value of the habit priority, Q is the weighted value of the interference factor;
in addition, the weighted value Q of the interference factor is obtained by a camera inside a merchant according to the current in-store personnel density situation, then the personnel density estimated value is obtained after the image digitization processing, and the Q value under different personnel densities is obtained according to a preset query table.
7. The advertisement push method based on the real-time position and habit of the customer according to claim 6, wherein the weighted value of the route priority is composed of a travel mode weighted value of the customer, a time weighted value estimated to arrive at the corresponding merchant, and an interference factor weighted value;
wherein, the trip mode includes: walking, riding, subway, bus or driving; in addition, the first and second substrates are,
when the travel mode is walking, the weight value of the route priority is obtained by the following formula:
Y＝Y1+c1－d1，
wherein Y is the weight value of the route priority, Y1A preset initial weight value for walking trip, c1Weight of elapsed time for walking journey, d1Is the interference factor weight;
when the travel mode is riding, the weight value of the route priority is obtained by the following formula:
Y＝Y2+c2－d2，
wherein Y is the weight value of the route priority, Y2For the preset initial weight value of riding trip, c2Weight of riding time, d2Is the interference factor weight;
when the travel mode is subway, the weight value of the route priority is obtained by the following formula:
Y＝Y3+c3－d3，
wherein Y is the weight value of the route priority, Y3Presetting initial weight value, c, for subway trip3Weight of time spent on subway trips, d3Is the interference factor weight;
when the travel mode is the bus, the weighted value of the route priority is obtained by the following formula:
Y＝Y4+c4－d4，
wherein Y is the weight value of the route priority, Y4For presetting an initial weight value, c, for bus trips4Time-of-use weight for bus travel, d4Is the interference factor weight;
when the travel mode is driving, the weight value of the route priority is obtained by the following formula:
Y＝Y5+c5－d5，
wherein Y is the weight value of the route priority, Y5A preset initial weight value, c, for driving5For driving journey, time-of-use weight, d5Is the interference factor weight;
in addition, the elapsed time weight c1、c2、c3、c4、c5Corresponding to preset values under different time consumption conditions, and forming a useful time weight lookup table;
when travel mode conversion exists in a planned route from a client to a merchant currently, the weighted value of the route priority is the sum of the weighted value of the route priority of different travel modes and the ratio of the travel mode to the total travel of the position of the client to the position of the corresponding merchant.
8. The method of claim 7, wherein the interference factor weight d is a weight of real-time location and habit of the client1、d2、d3、d4Corresponding to preset values under different travel modes and travel conditions, and forming an interference factor weight query table;
in addition, the interference factor weight d5Reference to (2)The factors include violation risk, parking risk and accident risk, and the formula is as follows:
d5＝d51+d52+d53，
wherein d is51For violation risk weight, d52As parking risk weight, d53Is the accident risk weight;
in addition, the first and second substrates are,
d51＝0.003×n，
n is the violation statistics number reported based on merchant return visit and/or customer active evaluation in a preset time period;
d52＝0.002×m，
m is the number of parking poor assessment statistics reported based on merchant return visits and/or customer active evaluations within a preset time period;
d53＝0.001×q，
and q is the statistical frequency of the journey accidents reported based on return visits of merchants and/or active evaluation of customers in a preset time period.
9. Advertisement push system based on customer's real-time position and custom, its characterized in that, it includes:
the storage unit is used for storing a customer habit database, map data, merchant information and merchant advertisements;
the position acquisition unit is used for acquiring the position of a client;
the matching unit is used for acquiring map data of an area corresponding to the real-time position of the customer and merchant information in a preset range of the real-time position of the customer, and matching the merchant information in the preset range of the real-time position of the customer with the customer habit database;
the route planning unit is used for planning a route from the client real-time position to the merchant;
the calculation unit is used for acquiring a customer habit database stored in the storage unit and a route from a customer real-time position planned by the route planning unit to a merchant, and outputting advertisement push priority;
and the pushing unit is used for pushing the merchant advertisements to the customers according to the advertisement pushing priorities and pushing the travel route information reaching the corresponding merchants at the same time.
10. A computer-readable storage medium, characterized in that: the storage medium stores at least one instruction, at least one program, a set of codes, or a set of instructions, which is loaded and executed by a processor to implement the advertisement push method according to any one of claims 1 to 8 based on the real-time location and habits of the client.
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