CN115564472A - Equipment interactive information service system and method based on big data - Google Patents

Equipment interactive information service system and method based on big data Download PDF

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CN115564472A
CN115564472A CN202211159514.2A CN202211159514A CN115564472A CN 115564472 A CN115564472 A CN 115564472A CN 202211159514 A CN202211159514 A CN 202211159514A CN 115564472 A CN115564472 A CN 115564472A
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卓大希
张宝宝
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Zhuhai Zhongzhi Intelligent Technology Co ltd
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Abstract

The invention relates to the technical field of information service, in particular to a big data-based equipment interactive information service system and a big data-based equipment interactive information service method, which comprise the following steps: the system comprises a camera, a demand analysis module, an intelligent robot and a database; identifying and positioning position information of a customer through the camera; analyzing the purposiveness of the customer entering the market according to the customer position information through the demand analysis module, and if the purposiveness exists, sending analysis data to the intelligent robot; performing voice interaction with a customer through the intelligent robot, analyzing the demand of the customer, and selecting a shop and displaying a route; storing the facial features, the position information and all the collected voice data of the customer through the database; through the interaction of the equipment, the requirements of customers are analyzed, and information service is provided for the customers, so that the customer can know about shops in a shopping mall, and the purchase rate of the customers is further improved.

Description

Equipment interactive information service system and method based on big data
Technical Field
The invention relates to the technical field of information service, in particular to a device interactive information service system and a device interactive information service method based on big data.
Background
With the improvement of the living standard of people, large stores are in line with the increase of the living standard of people, and now, the large stores become one of the most important retail operation modes in modern society, and the functions of the large stores break through the limit of commodity retail. The method plays a vital role in improving the commercial environment of a city, changing the consumption and leisure modes, optimizing the investment structure, contributing to economic prosperity and developing the commercial service of the city. As the existence of the vital energy and energy for energy consumption and activity of a new city, the development of the city always influences the development of urban economy and business.
However, as the market area is continuously enlarged, the stores inside the store are also increased, and customers who need to enter the store are purposeful, so that the customers want to purchase needed articles as soon as possible in a limited time, however, when the customers enter the store and want to purchase articles, the information and the positions of the specific stores cannot be timely clarified; even if some signboard exists, a customer still cannot find and check the signboard in time; the burden of the customer is greatly increased, and the purchasing desire of the customer is reduced;
therefore, there is a need for a big data-based device interactive information service system and method to solve the above problems, and analyze customer needs through device interaction, thereby providing information services to customers.
Disclosure of Invention
The present invention is directed to a device interactive information service system and method based on big data, so as to solve the problems in the background art.
In order to solve the technical problems, the invention provides the following technical scheme: a big-data based device interactive information service system, the system comprising: the system comprises a camera, a demand analysis module, an intelligent robot and a database;
identifying and positioning position information of a customer through the camera;
analyzing the purposiveness of a customer entering a market according to the customer position information through the demand analysis module, if the customer wants to buy clothes of a certain brand, if the purposiveness exists, sending analysis data to the intelligent robot;
performing voice interaction with a customer through the intelligent robot, analyzing the requirements of the customer, and selecting a shop and displaying a route;
and storing the facial features, the position information and all the collected voice data of the customer by the database.
Furthermore, the cameras are arranged at each corner of a shopping mall, so that the positions of customers can be observed conveniently, and the data accuracy is improved; the face recognition unit is used for recognizing the customers by utilizing OpenCV (open computer vision correction) so as to determine that the recognized users are the same person and sending the recognized customer information to the position positioning unit; the position positioning unit is used for positioning the position of the customer by using a GPS positioning technology and sending positioning information to the database.
Furthermore, the demand analysis module comprises a position extraction unit, a data analysis unit, a demand judgment unit and an intelligent selection unit; the position extracting unit is used for extracting real-time position information of the customer; the data analysis unit is used for forming the real-time position information into a two-dimensional plane coordinate, performing connection processing on each position point and analyzing an included angle of each position point; the demand judging unit is used for judging the purposiveness of the customer entering the mall according to the included angle, and if the included angle of 3 continuous straight lines is larger than a set threshold value and the occurrence frequency of the phenomenon is larger than the threshold value, the traveling route of the customer is close to one straight line, and the purposiveness of the customer is indicated; if the activity is purposeful, predicting the next area of the activity according to the activity track of the customer; the intelligent selection unit is used for sending the customer information to the intelligent robot in the area and closest to the customer.
Furthermore, the intelligent robot comprises an information receiving unit, a route planning unit, a voice interaction unit and a display unit; the information receiving unit is used for receiving the face characteristics of the customer and the predicted position information; the route planning unit is used for planning an optimal path reaching the predicted position point by utilizing an A-x algorithm, and finding the customer according to the facial features after reaching the predicted position point; the voice interaction unit is used for performing voice interaction with customers and analyzing the demands of the customers; the display unit is used for displaying corresponding shop information and a route thereof according to the requirements of customers;
the voice interaction unit comprises an information acquisition subunit and an analysis comparison subunit; the information acquisition subunit is used for acquiring voice information of a customer and sending the voice information to the analysis comparison subunit and the database, so that the database can update data conveniently, and the accuracy of data analysis is improved; the analysis and comparison subunit is used for analyzing and comparing the data of the database by using a cosine similarity algorithm so as to obtain a data result;
the display unit comprises a shop selection subunit and a route display subunit; the shop selection subunit is used for displaying corresponding shop information according to the requirements of customers for the selection of the customers, and if the data display result only has one shop, the selection is not needed; if a plurality of shops exist in the display result, the customer needs to select the shops; the route display subunit is used for displaying a route of the point reached by the customer to the destination according to the shop information selected by the customer.
Further, the database is used for storing the facial features, the position information and all the collected voice data of the customers.
A device interactive information service method based on big data comprises the following steps:
s1: identifying and positioning the position information of the customer by using a camera;
s2: analyzing the purposiveness of the customer entering the mall according to the position information;
s3: the intelligent robot is used for reaching the position of a customer, performing voice interaction with the customer and analyzing the demand of the customer;
s4: displaying corresponding shop information and a route thereof according to the requirements of customers;
s5: and storing the facial features and the position information of the customer and all collected voice data.
Further, in step S1: the method comprises the steps that a plurality of cameras are arranged in a shopping mall and are installed at each corner of the shopping mall, customers entering the shopping mall are identified by using OpenCV, and meanwhile, the cameras identifying the same customer are positioned by using a GPS positioning technology; both the OpenCV and GPS positioning techniques belong to conventional technical means of those skilled in the art, and therefore, are not described in detail herein.
Further, in step S2: to analyze the purposiveness of a patron entering a mall, the patron's real-time location information is first extracted: when the obtained real-time position information exceeds a set threshold value, belonging to the field of E, converting the position information into a two-dimensional plane coordinate set: (X, Y) = { (X) i ,y i )},i＝1,2,…,n,n>2；
And then, performing difference on two continuous coordinate points to form a line segment vector set: (sigma.) x ,σ y )＝{(x 2 -x 1 ,y 2 -y 1 ),…,(x i+1 -x i ,y i+1 -y i ) \8230; }, i =1,2, \8230;, n-1; then, the cosine value of the included angle between every two continuous line segment vectors is obtained:
Figure BDA0003858992260000031
wherein cos theta is [ -1,1 [ ]](ii) a Then the angle:
Figure BDA0003858992260000032
Figure BDA0003858992260000033
theta is 0 deg. or 180 deg. or less]；
Then, the purposiveness of the customer entering the shopping mall is analyzed according to an included angle formula: when theta is i >And alpha, wherein alpha is a set threshold and is an obtuse angle, and the value corresponding to i is recorded, so that a set is formed: a = { a = j },j<n; if a j -a j-1 ＝1,j>1, and corresponding j is consecutive, when it is consecutive, its number of times ζ>Eta, the customer is walking along a path in the area and does not stop at other stores, and the customer goes shoppingWith the purpose property, the customer is possible to find a certain shop or a certain article; on the contrary, if θ i <When alpha is obtained, the path of the customer is changed for many times in the area, the customer enters various different shops, the invisible property of the customer is analyzed, and the customer stops sending information to the intelligent robot;
and finally, if the purposiveness of the customer is analyzed, predicting the next area of the activity of the customer according to the activity track of the customer by using a linear regression model, and sending the position information of the customer to the intelligent robot in charge of the area: is composed of a position coordinate set (X, Y) = { (X) i ,y i )},i＝1,2,…,n,n>2, obtaining the mean value of the abscissa
Figure BDA0003858992260000041
Mean value of ordinate of the same principle
Figure BDA0003858992260000042
Figure BDA0003858992260000043
A linear regression equation set is obtained:
Figure BDA0003858992260000044
thus, a linear equation is obtained:
Figure BDA0003858992260000045
at this time, according to the linear equation
Figure BDA0003858992260000046
The next location coordinates of the customer can be predicted and sent to the intelligent robot responsible for this area.
Further, in step S3: the intelligent robot plans an optimal path to a predicted position point by using an A-algorithm, identifies the customer according to the received facial features, performs voice interaction, firstly identifies the voice data of the customer by using a Markov model in order to know the demand of the customer, and extracts all the identified vocabulary forming data sets H = { H } r },r＝1,2,…,μ(ii) a Wherein, the a algorithm and the markov model are conventional technical means of those skilled in the art, and therefore, are not described in detail;
then, the cosine similarity algorithm is used for comparing with the data of the database: all words H in the data set H are embedded by a word embedding algorithm r Mapping to an n-dimensional vector space, establishing a coordinate system, and determining one of the words h r Processing to obtain word frequency vector set W = { W = { (W) s S =1,2, \8230:, δ, the set of vectors B = { B is obtained by processing the database s S =1,2, \8230;, δ, where γ is the similarity between vector set W and vector set B, then:
Figure BDA0003858992260000047
Figure BDA0003858992260000048
finally, analyzing the compared content to know the customer demand: if gamma is larger than the set threshold kappa, the vocabulary is similar to the vocabulary corresponding to the database, and the content corresponding to the vocabulary in the database is the result required by the customer; otherwise, if γ is smaller than the set threshold κ, H in the data set H is traversed r And carrying out similarity comparison to obtain a data result.
Further, in step S4: after the intelligent robot obtains the data result, displaying the corresponding shop information, and if the data display result only has one shop, directly displaying a route to the shop; if the display result is a plurality of stores, only all the store information is displayed, and after the customer selects a store, the corresponding route is further displayed.
Further, in step S5: the database is used for storing the face characteristics, the position information and all the collected voice data of the customer, so that the data can be conveniently stored and updated, and the accuracy of data analysis is improved.
Compared with the prior art, the invention has the following beneficial effects:
according to the invention, the customers entering the shopping mall are identified by using OpenCV, and the cameras identifying the same customer are positioned by using GPS positioning technology, so that the activity route of the designated personnel is quickly positioned, and the customer requirements can be conveniently judged according to the position information; the requirements of the customers are analyzed according to the included angles formed by the positions of the customers, so that the behavior of the customers can be pre-judged in time, and the efficiency of information service is greatly improved; the next position of the activity of the customer is predicted by using the linear regression model, and meanwhile, the optimal path reaching the predicted position point is planned by using an A-algorithm, so that the intelligent robot can reach the position point in advance and interact with the customer; the cosine similarity algorithm is compared with the database, so that the result which is desired by the customer can be extracted more conveniently; through the interaction of the equipment, the demands of the customers are analyzed, the information service is provided for the customers, the time for the customers with demands to find the shops is greatly shortened, the knowledge of the customers to the shops in the shopping mall is improved, and the purchase rate of the customers is further improved.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a block diagram of a big data based device interactive information service system of the present invention;
fig. 2 is a flowchart of a big data based device interactive information service method of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
Referring to fig. 1-2, the present invention provides a technical solution: a big data based device interactive information service system, the system comprising: the system comprises a camera, a demand analysis module, an intelligent robot and a database;
identifying and positioning position information of a customer through the camera;
analyzing the purposiveness of a customer entering a market through the demand analysis module according to the position information of the customer, if the customer wants to buy clothes of a certain brand, sending analysis data to the intelligent robot if the purposiveness exists;
performing voice interaction with a customer through the intelligent robot, analyzing the requirements of the customer, and selecting a shop and displaying a route;
and storing the facial features, the position information and all the collected voice data of the customer by the database.
Furthermore, the cameras are arranged at each corner of a shopping mall, so that the positions of customers can be observed conveniently, and the data accuracy is improved; the face recognition unit is used for recognizing the customers by using OpenCV, determining that the recognized users are the same person by recognizing facial features of the customers, and sending the recognized customer information to the position positioning unit and the database, so that the subsequent analysis of data is facilitated; the position positioning unit is used for positioning the camera which identifies the position of the customer by using a GPS positioning technology and sending positioning information to the database.
Further, the demand analysis module comprises a position extraction unit, a data analysis unit, a demand judgment unit and an intelligent selection unit; the position extraction unit is used for extracting real-time position information of the customer, and after certain position data are extracted, the data are sent to the data analysis unit; the data analysis unit is used for forming the real-time position information into a two-dimensional plane coordinate, performing connection processing on each position point and analyzing an included angle between line segments; the demand judging unit is used for judging the purposiveness of the customer entering the mall according to the included angle, and if the included angle of 3 continuous straight lines is larger than a set threshold value and the occurrence frequency of the phenomenon is larger than the threshold value, the fact that the traveling route of the customer approaches to one straight line is indicated, other shops are not stopped, and purposiveness of the customer is indicated; if the activity track is purposeful, predicting the next area of the activity of the customer according to the activity track of the customer; the intelligent selection unit is used for sending the customer information to the intelligent robot in the area and closest to the customer.
Furthermore, the intelligent robot comprises an information receiving unit, a route planning unit, a voice interaction unit and a display unit; the information receiving unit is used for receiving the facial features of the customer and the predicted position information; the route planning unit is used for planning an optimal path reaching the predicted position point by utilizing an A-star algorithm, finding the customer according to the facial features of the customer after reaching the predicted position point, and entering the voice interaction unit; the voice interaction unit is used for performing voice interaction with the customer and analyzing the requirement of the customer; the display unit is used for displaying corresponding shop information and a route thereof according to the requirements of customers;
the voice interaction unit comprises an information acquisition subunit and an analysis comparison subunit; the information acquisition subunit is used for acquiring voice information of a customer and sending the voice information to the analysis comparison subunit and the database, so that the database can update data conveniently, and the accuracy of data analysis is improved; the analysis and comparison subunit is used for analyzing and comparing the data of the database by using a cosine similarity algorithm so as to obtain a data result;
the display unit comprises a shop selection subunit and a route display subunit; the shop selection subunit is used for displaying corresponding shop information according to the requirements of customers for the customers to select, and if only one shop is available in the data display result, the customers do not need to select; if the display result has a plurality of shops, the customer needs to select the shop, and the route display subunit is accessed after the customer selects the shop; the route display subunit is used for displaying a route of a point where the customer arrives at the destination according to the store information selected by the customer.
Furthermore, the database is used for storing the face characteristics, the position information and all the collected voice data of the customer, so that the data analysis accuracy is improved.
A device interactive information service method based on big data comprises the following steps:
s1: identifying and positioning the position information of the customer by using a camera;
s2: analyzing the purposiveness of the customer entering the mall according to the position information;
s3: the intelligent robot is used for reaching the position of a customer, performing voice interaction with the customer and analyzing the demand of the customer;
s4: displaying corresponding shop information and a route thereof according to the requirements of customers;
s5: and storing the facial features and the position information of the customer and all collected voice data.
Further, in step S1: the method comprises the steps that a plurality of cameras are arranged in a shopping mall and are installed at each corner of the shopping mall, customers entering the shopping mall are identified by using OpenCV, and meanwhile, the cameras identifying the same customer are positioned by using a GPS positioning technology; both the OpenCV and GPS positioning techniques belong to conventional technical means of those skilled in the art, and therefore, they are not described in detail herein.
Further, in step S2: to analyze the purposiveness of a patron entering a mall, the patron's real-time location information is first extracted: when the obtained real-time position information exceeds a set threshold value and belongs to a epsilon, converting the position information into a two-dimensional plane coordinate set: (X, Y) = { (X) i ,y i )},i＝1,2,…,n,n>2；
And then, performing difference on two continuous coordinate points to form a line segment vector set: (sigma.) x ,σ y )＝{(x 2 -x 1 ,y 2 -y 1 ),…,(x i+1 -x i ,y i+1 -y i ) \8230, i =1,2, \8230, n-1; then, the cosine value of the included angle between every two continuous line segment vectors is obtained:
Figure BDA0003858992260000071
wherein cos theta is [ -1,1 [ ]](ii) a Then the angle is: θ = { θ i }＝
Figure BDA0003858992260000081
Theta is 0 deg. or 180 deg. or less]；
Then, the purposiveness of the customer entering the shopping mall is analyzed according to an included angle formula: when theta is i >And when alpha is a set threshold and is an obtuse angle, recording the value corresponding to i, and forming a set: a = { a = j },j<n; if a j -a j-1 ＝1,j>1, and corresponding j is consecutive, then when it is consecutive times ζ>Eta, the customer walks along a path all the way in the area and does not stay in other stores, so that the customer visits the shopping mall in a targeted manner, which indicates that the customer may be searching for a certain store or a certain article; on the contrary, if θ i <When the information is alpha, the route of the customer is changed for many times in the area, the customer enters various different shops, the unexpected property of the customer is analyzed, and the information is stopped being sent to the intelligent robot;
and finally, if the purposiveness of the customer is analyzed, predicting the next area of the activity of the customer according to the activity track of the customer by using a linear regression model, and sending the position information of the customer to the intelligent robot in charge of the area: is composed of a position coordinate set (X, Y) = { (X) i ,y i )},i＝1,2,…,n,n>2, obtaining the mean value of the abscissa
Figure BDA0003858992260000082
Mean value of ordinate of the same principle
Figure BDA0003858992260000083
Figure BDA0003858992260000084
Then a linear regression equation set is obtained:
Figure BDA0003858992260000085
thus, a linear equation is obtained:
Figure BDA0003858992260000086
at this time, according to the linear equation
Figure BDA0003858992260000087
The next location coordinate of the customer can be predicted and sent to the intelligent robot responsible for this area.
Further, in step S3: after planning the optimal path to the predicted position point by the intelligent robot by using the A-star algorithm, the intelligent robot receives the optimal pathThe face feature of (2) identifies the customer and performs voice interaction, and in order to understand the customer's needs, first, voice data of the customer is identified using a markov model, and all the identified vocabulary formation data sets H = { H } are extracted r }, r =1,2, \8230;, μ; wherein, the a-algorithm and the markov model are conventional technical means of those skilled in the art, and therefore, the description is not given to them;
then, a cosine similarity algorithm is used for comparing with the data of the database: all words H in the data set H are embedded by a word embedding algorithm r Mapping to an n-dimensional vector space, establishing a coordinate system, and performing mapping on one of the words h r Processing to obtain a word frequency vector set W = { W = { (W) } s S =1,2, \8230;, δ, by processing the database, the set of vectors B = { B } is obtained s S =1,2, \8230;, δ, where γ is the similarity between vector set W and vector set B, then:
Figure BDA0003858992260000088
Figure BDA0003858992260000089
finally, analyzing the compared content to know the customer demand: if gamma is larger than the set threshold value kappa, the vocabulary is similar to the vocabulary corresponding to the database, and the content corresponding to the vocabulary in the database is the result required by the customer; otherwise, if γ is smaller than the set threshold κ, H in the data set H is traversed r And carrying out similarity comparison to obtain a data result.
Further, in step S4: after the intelligent robot obtains the data result, displaying corresponding shop information, and if the data display result is only one shop, directly displaying a route to the shop; if the display result is a plurality of stores, only all the store information is displayed, and after the customer selects a store, the corresponding route is further displayed.
Further, in step S5: the database is used for storing the face characteristics, the position information and all the collected voice data of the customer, so that the data can be conveniently stored and updated, and the accuracy of data analysis is improved.
Furthermore, through the interaction between the camera, the demand analysis module and the intelligent robot, customer demands are analyzed, and information service is provided for customers, so that the customer can know shops in a shopping mall, and the purchase rate of the customers is further improved.
The first embodiment is as follows:
in step S1: the method comprises the steps that a plurality of cameras are arranged in a market and are installed at each corner of the market, customers entering the market are identified through OpenCV, meanwhile, the cameras identifying the same customer are located through GPS locating technology, and at the moment, one customer entering the market is identified and located.
In step S2: in order to analyze the purposiveness of the customer entering the mall, the real-time position information of the customer is firstly extracted: when the acquired real-time position information of the customer exceeds a set threshold value 3, converting the position information into a two-dimensional plane coordinate set: (X, Y) = { (X) i ,y i )},i＝1,2,…,n,n>2；
And then, performing difference on two continuous coordinate points to form a line segment vector set: (sigma) x ,σ y )＝{(x 2 -x 1 ,y 2 -y 1 ),…,(x i+1 -x i ,y i+1 -y i ) \8230, i =1,2, \8230, n-1; then, the cosine value of the included angle between every two continuous line segment vectors is obtained:
Figure BDA0003858992260000091
wherein cos theta is [ -1,1 [ ]](ii) a Then the angle:
Figure BDA0003858992260000092
Figure BDA0003858992260000093
theta is 0 deg. or 180 deg. or less]；
Then, the purposiveness of the customer entering the shopping mall is analyzed according to an included angle formula: when theta in the data set theta i >When a threshold value of 120 degrees is set, recording the value corresponding to i, and forming a set: a = { a = j },j<n; will be provided witha j -a j-1 ＝1,j>1, and corresponding j is a continuous data extraction, the number of which is the same>2, the position change of the customers in several areas is approximately in a straight line, and the system judges that purchasers have purposiveness when entering the shopping mall;
and finally, predicting the next region of the activity of the customer according to the activity track of the customer by using a linear regression model, and sending the position information of the customer to an intelligent robot in charge of the region: is composed of a position coordinate set (X, Y) = { (X) i ,y i )},i＝1,2,…,n,n>2, obtaining the mean value of the abscissa
Figure BDA0003858992260000101
Mean value of ordinate of the same principle
Figure BDA0003858992260000102
Then a linear regression equation set is obtained:
Figure BDA0003858992260000103
thus, a linear equation is obtained:
Figure BDA0003858992260000104
at this time, according to the linear equation
Figure BDA0003858992260000105
The next location coordinates of the customer can be predicted and sent to the intelligent robot responsible for this area.
In step S3: after planning an optimal path to a predicted position point by using an A-algorithm, the intelligent robot identifies the customer according to the received facial features so as to perform voice interaction, firstly, the voice data of the customer is identified by using a Markov model in order to know the requirement of the customer, and all identified vocabulary forming data sets H = { "i want to go to a Lining clothing shop to buy a sweater" } H = are extracted r },r＝1,2,…,μ；
Then, a cosine similarity algorithm is used for comparing with the data of the database: all words H in the data set H are embedded by a word embedding algorithm r Mapping to an n-dimensional directionIn the volume space, a coordinate system is established, and one of the words h is subjected to r Processing the word frequency vector set W = { W { "Lining s S =1,2, \8230:, δ, the set of vectors B = { B is obtained by processing the database s S =1,2, \ 8230 }, δ, where γ is set as the similarity between the vector set W and the vector set B, then:
Figure BDA0003858992260000106
Figure BDA0003858992260000107
and finally, analyzing the compared content to know the customer demand: at this time, the threshold value of gamma > is 0.9, which indicates that the vocabulary is similar to the vocabulary corresponding to the database, and indicates that the content "Lining clothing shop" corresponding to the vocabulary in the database is the result required by the customer.
In step S4: after the intelligent robot obtains the data result, the corresponding shop information is displayed, and a specific route is directly displayed beside the shop because only one 'Lining clothing shop' exists in the shop.
In step S5: the database is used for storing the face characteristics, the position information and all the collected voice data of the customer, so that the data can be conveniently stored and updated, and the accuracy of data analysis is improved.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (9)

1. An interactive information service system based on big data, which is characterized in that: the system comprises: the system comprises a camera, a demand analysis module, an intelligent robot and a database;
identifying and positioning position information of a customer through the camera;
analyzing the purposiveness of the customer entering the market according to the customer position information through the demand analysis module, and if the purposiveness exists, sending analysis data to the intelligent robot;
performing voice interaction with a customer through the intelligent robot, analyzing the demand of the customer, and selecting a shop and displaying a route;
and storing the facial features, the position information and all the collected voice data of the customer by the database.
2. The big-data-based device interactive information service system according to claim 1, wherein: the camera comprises a face recognition unit and a position positioning unit; the face recognition unit is used for recognizing the customers by utilizing OpenCV and sending the recognized customer information to the position positioning unit; the position positioning unit is used for positioning the position of the customer by using a GPS positioning technology and sending positioning information to the database.
3. The big-data-based device interactive information service system according to claim 1, wherein: the demand analysis module comprises a position extraction unit, a data analysis unit, a demand judgment unit and an intelligent selection unit; the position extracting unit is used for extracting real-time position information of the customer; the data analysis unit is used for forming the real-time position information into a two-dimensional plane coordinate, performing connection processing on each position point and analyzing an included angle of each position point; the demand judging unit is used for judging the purposiveness of the customer entering the market according to the included angle, and if the purposiveness is achieved, the next area of the activity of the customer is predicted according to the activity track of the customer; the intelligent selection unit is used for sending the customer information with purposiveness to the intelligent robot.
4. The big-data-based device interactive information service system according to claim 1, wherein: the intelligent robot comprises an information receiving unit, a route planning unit, a voice interaction unit and a display unit; the information receiving unit is used for receiving the face characteristics of the customer and the predicted position information; the route planning unit is used for planning an optimal path reaching the predicted position point by utilizing an A-algorithm; the voice interaction unit is used for performing voice interaction with customers and analyzing the demands of the customers; the display unit is used for displaying corresponding shop information and a route thereof according to the requirements of customers;
the voice interaction unit comprises an information acquisition subunit and an analysis comparison subunit; the information acquisition subunit is used for acquiring voice information of the customer and sending the voice information to the analysis and comparison subunit and the database; the analysis and comparison subunit is used for analyzing and comparing the data of the database by using a cosine similarity algorithm so as to obtain a data result;
the display unit comprises a shop selection subunit and a route display subunit; the shop selection subunit is used for displaying corresponding shop information according to the requirements of customers for the selection of the customers; the route display subunit is used for displaying a route of a point where the customer arrives at the destination according to the store information selected by the customer.
5. A device interactive information service method based on big data is characterized in that: the method comprises the following steps:
s1: identifying and positioning position information of a customer by using a camera;
s2: analyzing the purposiveness of the customer entering the mall according to the position information;
s3: the intelligent robot is used for reaching the position of a customer, performing voice interaction with the customer and analyzing the demand of the customer;
s4: displaying corresponding shop information and a route thereof according to the requirements of customers;
s5: and storing the face characteristics, the position information and all the collected voice data of the customer.
6. The big data based device interactive information service method according to claim 5, wherein: in step S1: the cameras are arranged in the shopping mall and are installed at all corners of the shopping mall, customers entering the shopping mall are identified by using OpenCV, and meanwhile, the cameras identifying the same customer are positioned by using GPS positioning technology.
7. The big data based device interactive information service method according to claim 5, wherein: in step S2: to analyze the purposiveness of a patron entering a mall, the patron's real-time location information is first extracted: when the obtained real-time position information exceeds a set threshold value and belongs to a epsilon, converting the position information into a two-dimensional plane coordinate set: (X, Y) = { (X) i ,y i )},i＝1,2,…,n,n>2；
And then, performing difference on two continuous coordinate points to form a line segment vector set: (sigma.) x ,σ y )＝{(x 2 -x 1 ,y 2 -y 1 ),…,(x i+1 -x i ,y i+1 -y i ) \8230, i =1,2, \8230, n-1; then, the cosine value of the included angle between every two continuous line segment vectors is obtained:
Figure FDA0003858992250000021
then the angle is:
Figure FDA0003858992250000022
then, the purposiveness of the customer entering the shopping mall is analyzed according to an included angle formula: when theta is i >And when alpha is set, wherein alpha represents a set threshold value, and a value corresponding to i is recorded, a set is formed: a = { a = j },j<n; if a j -a j-1 ＝1,j>1, and corresponding j is consecutive, then when it is consecutive times ζ>Eta, the customer is shown to be shopping with the target property;
and finally, if the purposiveness of the customer is analyzed, predicting the next region of the customer activity according to the activity track of the customer by using a linear regression model, and sending the position information of the customer to an intelligent robot in charge of the region: from a set of position coordinates(X,Y)＝{(x i ,y i )},i＝1,2,…,n,n>2, obtaining the mean value of the abscissa
Figure FDA0003858992250000031
Mean value of ordinate of the same principle
Figure FDA0003858992250000032
Figure FDA0003858992250000033
A linear regression equation set is obtained:
Figure FDA0003858992250000034
thus, a linear equation is obtained:
Figure FDA0003858992250000035
at this time, according to the linear equation
Figure FDA0003858992250000036
The next location coordinate of the customer can be predicted and sent to the intelligent robot responsible for this area.
8. The big-data-based device interactive information service method according to claim 7, wherein: in step S3: the intelligent robot plans an optimal path to the predicted position point by using an A-algorithm, identifies the customer according to the received facial features, performs voice interaction, firstly identifies the voice data of the customer by using a Markov model in order to know the demand of the customer, and extracts all the identified vocabulary forming data sets H = { H = r },r＝1,2,…,μ；
Then, the cosine similarity algorithm is used for comparing with the data of the database: all words H in the data set H are embedded by using a word embedding algorithm r Mapping to an n-dimensional vector space, establishing a coordinate system, and performing mapping on one of the words h r Processing to obtain word frequency vector set W = { W = { (W) s },s＝12, \8230δ, obtaining a vector set B = { B ] by processing the database s S =1,2, \ 8230 }, δ, setting γ to the similarity of vector set W and vector set B, then:
Figure FDA0003858992250000037
Figure FDA0003858992250000038
finally, analyzing the compared content to know the customer demand: if gamma is larger than the set threshold value kappa, the vocabulary is similar to the vocabulary corresponding to the database, and the content corresponding to the vocabulary in the database is the result required by the customer; otherwise, if γ is smaller than the set threshold κ, H in the data set H is traversed r And carrying out similarity comparison to obtain a data result.
9. The big data based device interactive information service method of claim 8, wherein: in step S4: after the intelligent robot obtains the data result, displaying corresponding shop information, and if the data display result is only one shop, directly displaying a route to the shop; if the display result is a plurality of stores, only all the store information is displayed, and after the customer selects a store, the corresponding route is further displayed.
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Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2006185293A (en) * 2004-12-28 2006-07-13 Hitachi Software Eng Co Ltd Customer information collection system
JP2007079939A (en) * 2005-09-14 2007-03-29 Sumitomo Mitsui Construction Co Ltd Customer information management system
CN106921945A (en) * 2017-02-22 2017-07-04 上海斐讯数据通信技术有限公司 The positioning shopping guide method and system of a kind of commodity
KR102122608B1 (en) * 2019-11-26 2020-06-15 주식회사 립페이 Method for providing customized store display service with propensity to consume of individual on blockchain based easy payment platform
CN112598486A (en) * 2021-01-07 2021-04-02 开封大学 Marketing accurate screening push system based on big data and intelligent Internet of things
CN113657959A (en) * 2020-06-17 2021-11-16 周丽 Shopping mall shopping consumption recommendation system based on block chain
CN113674037A (en) * 2021-10-21 2021-11-19 西安超嗨网络科技有限公司 Data acquisition and recommendation method based on shopping behaviors
KR20220020047A (en) * 2020-08-11 2022-02-18 주식회사 클럽 Method and System for Predicting Customer Tracking and Shopping Time in Stores

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2006185293A (en) * 2004-12-28 2006-07-13 Hitachi Software Eng Co Ltd Customer information collection system
JP2007079939A (en) * 2005-09-14 2007-03-29 Sumitomo Mitsui Construction Co Ltd Customer information management system
CN106921945A (en) * 2017-02-22 2017-07-04 上海斐讯数据通信技术有限公司 The positioning shopping guide method and system of a kind of commodity
KR102122608B1 (en) * 2019-11-26 2020-06-15 주식회사 립페이 Method for providing customized store display service with propensity to consume of individual on blockchain based easy payment platform
CN113657959A (en) * 2020-06-17 2021-11-16 周丽 Shopping mall shopping consumption recommendation system based on block chain
KR20220020047A (en) * 2020-08-11 2022-02-18 주식회사 클럽 Method and System for Predicting Customer Tracking and Shopping Time in Stores
CN112598486A (en) * 2021-01-07 2021-04-02 开封大学 Marketing accurate screening push system based on big data and intelligent Internet of things
CN113674037A (en) * 2021-10-21 2021-11-19 西安超嗨网络科技有限公司 Data acquisition and recommendation method based on shopping behaviors

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
ULLA LINDBERG等: "《Thermal comfort in the supermarket environment multiple enquiry methods and simultaneous measurements of the thermal environment》", INTERNATIONAL JOURNAL OF REFRIGERATION *
张翔宇等: "《基于GPS轨迹挖掘的兴趣地点个性化推荐方法》", 高技术通讯, vol. 31, no. 1 *

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