CN114626900A - Intelligent management system based on feature recognition and big data analysis - Google Patents
Intelligent management system based on feature recognition and big data analysis Download PDFInfo
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Abstract
The invention discloses an intelligent management system based on feature recognition and big data analysis, which comprises a user information collection module, an information processing module, a user action track processing module, a user feedback module, a user flow prediction module, a user APP (application), an identity authentication module, a database and a database audit module. According to the invention, by identifying the action tracks of part of visitors, whether goods or articles in each area are reasonably placed and whether connection between areas is reasonable can be judged according to the action tracks of a plurality of users, so that better service can be provided, and a more reasonable arrangement method of the goods or articles can be determined. By obtaining the user feedback information, removing repeated parts in the feedback information and counting the number of the same feedback information, the feedback of the visitor can be efficiently known. The work content of the staff can be scheduled in advance by predicting the flow of visitors every day.
Description
Technical Field
The invention relates to the field of management systems, in particular to an intelligent management system based on feature recognition and big data analysis.
Background
With the development of social economy and the improvement of living standard of people. People put higher demands on management systems in public places. Management work is programmed and standardized for large public places such as shopping malls, supermarkets, museums and the like or enterprises with standard management through the management system.
Patent No. 202011279208.3 discloses an internet advertisement intelligent recommendation management system based on behavior feature recognition, which comprises a user information collection module, a user information preprocessing module, an advertisement classification module, a user interest analysis module, a database, an analysis server and an intelligent recommendation terminal, and can accurately recommend advertisements according to user preferences.
However, the above patents have the following problems in use: in large-scale occasions such as markets, supermarkets, museums and the like, in order to provide higher-quality services for visitors, the management system needs to determine whether goods or articles in each internal area are reasonably placed or not and whether connection among the areas is reasonable or not. Meanwhile, the existing management system has little interaction with visitors, and the visitors cannot efficiently know the feedback of the visitors to the management aspects of related markets, supermarkets, museums and the like.
An effective solution to the problems in the related art has not been proposed yet.
Disclosure of Invention
Aiming at the problems in the related art, the invention provides an intelligent management system based on feature recognition and big data analysis, so as to overcome the technical problems in the prior related art.
Therefore, the invention adopts the following specific technical scheme:
an intelligent management system based on feature recognition and big data analysis comprises a user information collection module, an information processing module, a user action track processing module, a user feedback module, a user flow prediction module, a user APP, an identity authentication module, a database and a database audit module;
the user information collection module is used for collecting the total number of visits of users in each day and the number of visits of users in each time period;
the information processing module is used for receiving the data of the total number of the user visits of each day and the number of the user visits of each time period, which are collected by the user information collecting module, sequencing the data according to time, and simultaneously connecting the data with a public weather system, adding a weather value into the data of the number of the user visits of each time period to obtain data after information processing;
the user action track processing module is used for randomly selecting part of visitors, identifying the visitors and recording the action tracks of the visitors after the visitors are successfully identified;
the user feedback module is connected with the user APP and used for collecting feedback information of the user, carrying out primary processing and statistics on the feedback information and feeding back a statistical result to a manager;
the user flow prediction module is used for predicting the flow of visitors every day;
the user APP is used for providing a social function for the users and establishing a connection between the users and the management system;
the identity authentication module is used for identifying the identity of a worker and storing the identification record of the worker;
the database is used for storing the data obtained by the information processing module after information processing, the identity identification data of the staff and the identification record data of the staff;
the database auditing module is used for recording database activities in real time, performing auditing compliance management on database operations, alarming risk behaviors suffered by the database and blocking attack behaviors.
Furthermore, the user action track processing module comprises a plurality of image acquisition devices and a central processing device, and the image acquisition devices are distributed in a plurality of artificially divided areas.
Further, the image acquisition device is used for acquiring the posture image data and the acquisition time of the visitor in the area where the image acquisition device is located, and transmitting the posture image data and the acquisition time of the visitor to the central processing device;
the central processing equipment constructs postures according to posture image data of the visitor acquired by each image acquisition equipment to obtain a plurality of posture models, wherein the posture model constructed by the posture image data transmitted by the first image acquisition equipment is used as a standard according to the acquisition time sequence and is compared with the posture models constructed subsequently according to the acquisition time sequence, if the similarity reaches a preset threshold value, the posture models are judged to be the posture models of the same visitor, the acquisition area of the visitor is determined, and the action track of the visitor is determined according to the acquisition area and the acquisition time of the visitor.
Furthermore, when the posture model is constructed, a plurality of images of the visitor are acquired through the image acquisition equipment, feature extraction is carried out on the plurality of images of the visitor to obtain a plurality of intermediate feature maps, a two-dimensional coordinate system of a two-dimensional space where the plurality of images of the visitor are located is established, and the intermediate feature maps are processed through the neural network to obtain position information, two-dimensional key points and three-dimensional model parameters of the visitor;
determining a central position parameter and a plurality of relative position parameters of the visitor according to the position information of the visitor, and generating a parameter map of a two-dimensional coordinate system according to the two-dimensional key points of the visitor and the corresponding central position parameters;
and determining the posture model of the visitor in the parameter graph according to the two-dimensional key points and the corresponding central position parameters.
Furthermore, the user action track processing module deletes the posture model of the visitor every day and keeps the picture of the action track of the visitor.
Further, the user feedback module performs preliminary processing and statistics on the feedback information to obtain text data of any two pieces of feedback information, performs vectorization processing on the text data to obtain two text vectors, determines similarity of the two pieces of feedback information according to the text vectors, determines that the two pieces of feedback information are the same information if the similarity is greater than a preset text similarity threshold, randomly deletes one piece of feedback information, and adds a count to the feedback data in the undeleted feedback information, and so on.
Furthermore, when the similarity of the two feedback information is determined according to the text vector, a formula is utilized
The similarity of the two feedback information is calculated, wherein,
represents a 2-norm, sigma represents a 2-norm normalization factor,
and
a text vector representing each of any two pieces of feedback information.
Further, the user traffic prediction module sets the historical traffic data as user traffic prediction data when predicting the traffic of the visitor in each day according to the historical traffic data of the previous period in the same period, and when the previous period in the same period is consistent with the weather of the day, and if the previous period in the same period is not consistent with the weather of the day, the user traffic prediction data of the day refers to the average value of the historical traffic data of the same weather of the previous period.
Further, when the identity of the worker is identified and the identification record of the worker is stored, the working time and the working duration of the worker are recorded.
Further, when the identity of the worker is identified and the identification record of the worker is stored, the identification mode includes, but is not limited to, NFC identification, fingerprint identification, and iris identification.
The invention has the beneficial effects that: according to the invention, by identifying the action tracks of part of visitors, whether goods or articles in each area are reasonably placed and whether connection between areas is reasonable can be judged according to the action tracks of a plurality of users, so that better service can be provided, and a more reasonable arrangement method of the goods or articles can be determined. By obtaining the user feedback information, removing repeated parts in the feedback information and counting the number of the same feedback information, the feedback of the visitor can be efficiently known. The work content of the staff can be scheduled in advance by predicting the flow of visitors every day.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a block diagram of an intelligent management system based on feature recognition and big data analysis according to an embodiment of the present invention.
In the figure:
1. a user information collection module; 2. an information processing module; 3. a user action track processing module; 4. a user feedback module; 5. a user flow prediction module; 6. a user APP; 7. an identity authentication module; 8. a database; 9. and a database audit module.
Detailed Description
For further explanation of the various embodiments, the drawings which form a part of the disclosure and which are incorporated in and constitute a part of this specification, illustrate embodiments and, together with the description, serve to explain the principles of operation of the embodiments, and to enable others of ordinary skill in the art to understand the various embodiments and advantages of the invention, and, by reference to these figures, reference is made to the accompanying drawings, which are not to scale and wherein like reference numerals generally refer to like elements.
According to an embodiment of the invention, an intelligent management system based on feature recognition and big data analysis is provided.
Referring to the drawings and the detailed description, as shown in fig. 1, according to an intelligent management system based on feature recognition and big data analysis of an embodiment of the present invention, the intelligent management system based on feature recognition and big data analysis includes a user information collection module 1, an information processing module 2, a user action track processing module 3, a user feedback module 4, a user traffic prediction module 5, a user APP (abbreviation of Application program) 6, an identity authentication module 7, a database 8, and a database audit module 9;
the user information collecting module 1 is configured to collect a total number of visits of users per day and a number of visits of users per time period;
the information processing module 2 is configured to receive the data of the total number of visits of the user per day and the number of visits of the user in each time period, which are collected by the user information collection module 1, sort the data according to time, connect to a public weather system, add a weather value to the data of the number of visits of the user in each time period, and obtain data after information processing;
the user action track processing module 3 is used for randomly selecting part of the visitors, identifying the visitors and recording the action tracks of the visitors after the visitors are successfully identified;
the user action track processing module 3 comprises a plurality of image acquisition devices and a central processing device, wherein the image acquisition devices are distributed in a plurality of artificially divided areas.
The image acquisition equipment is used for acquiring the posture image data and the acquisition time of the visitor in the area where the image acquisition equipment is located and transmitting the posture image data and the acquisition time of the visitor to the central processing equipment;
the central processing equipment constructs postures according to posture image data of the visitor acquired by each image acquisition equipment to obtain a plurality of posture models, wherein the posture model constructed by the posture image data transmitted by the first image acquisition equipment is used as a standard according to the acquisition time sequence and is compared with the posture models constructed subsequently according to the acquisition time sequence, if the similarity reaches a preset threshold value, the posture models are judged to be the posture models of the same visitor, the acquisition area of the visitor is determined, and the action track of the visitor is determined according to the acquisition area and the acquisition time of the visitor.
When the attitude model is constructed, a plurality of images of a visitor are collected through an image collecting device, feature extraction is carried out on the plurality of images of the visitor to obtain a plurality of intermediate feature maps, a two-dimensional coordinate system of a two-dimensional space where the plurality of visitors are located is established, the intermediate feature maps are processed through a neural network to obtain position information, two-dimensional key points and three-dimensional model parameters of the visitor;
determining a central position parameter and a plurality of relative position parameters of the visitor according to the position information of the visitor, and generating a parameter map of a two-dimensional coordinate system according to the two-dimensional key points of the visitor and the corresponding central position parameters;
and determining the posture model of the visitor in the parameter graph according to the two-dimensional key points and the corresponding central position parameters.
The user action track processing module 3 deletes the posture model of the visitor every day and keeps the picture of the action track of the visitor.
The user feedback module 4 is connected with the user APP6 and is used for collecting feedback information of a user, performing preliminary processing and statistics on the feedback information, and feeding back a statistical result to a manager;
the user feedback module 4 performs preliminary processing and statistics on the feedback information to obtain text data of any two pieces of feedback information, performs vectorization processing on the text data to obtain two text vectors, determines the similarity of the two pieces of feedback information according to the text vectors, determines that the two pieces of feedback information are the same information if the similarity is greater than a preset text similarity threshold, randomly deletes one piece of feedback information, and adds a count to the feedback data in the undeleted feedback information, and so on.
When the similarity of the two feedback information is determined according to the text vector, a formula is utilized
And calculating the similarity of the two feedback information, wherein,
represents a 2-norm, sigma represents a 2-norm normalization factor,
and
a text vector representing each of any two pieces of feedback information.
The user flow prediction module 5 is used for predicting the flow of the visitors every day;
the user flow prediction module 5 sets the historical flow data as the user flow prediction data when predicting the flow of the visitor in each day according to the current period and the current period, and when the current period and the current day are consistent, and if the current period and the current day are not consistent, the current day user flow prediction data refers to the average value of the historical flow data when the current period and the current day are the same.
The period is set by people, and the period can be according to week, month, year, goods period and the like.
The user APP6 is used for providing a social function for the users and enabling the users to establish a connection with the management system;
the identity authentication module 7 is used for identifying the identity of the staff and storing the identification record of the staff;
when the identity of the worker is identified and the identification record of the worker is stored, the working time and the working duration of the worker are recorded.
When the identity of the worker is identified and the identification record of the worker is stored, the identification mode includes but is not limited to NFC identification, fingerprint identification and iris identification.
The database 8 is used for storing the data obtained by the information processing module 2 after the information processing, the identification data of the staff and the identification record data of the staff;
the database auditing module 9 is used for recording database activities in real time, performing compliance management of auditing database operations, alarming risk behaviors suffered by the database and blocking attack behaviors. The database auditing module 9 generates a compliance report and accident root tracing, strengthens the network behavior records of the internal and external databases and improves the data asset safety.
In summary, the invention can judge whether goods or articles in each area are reasonably placed and whether connection between the areas is reasonable according to the action tracks of a plurality of users by identifying the action tracks of part of visitors, so that better service can be provided and a more reasonable arrangement method of the goods or articles can be determined. By obtaining the user feedback information, removing repeated parts in the feedback information and counting the number of the same feedback information, the feedback of the visitor can be efficiently known. The work content of the staff can be scheduled in advance by predicting the flow of visitors every day.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. An intelligent management system based on feature recognition and big data analysis is characterized by comprising a user information collection module (1), an information processing module (2), a user action track processing module (3), a user feedback module (4), a user flow prediction module (5), a user APP (6), an identity authentication module (7), a database (8) and a database audit module (9);
the user information collection module (1) is used for collecting the total number of visits of users in each day and the number of visits of users in each time period;
the information processing module (2) is used for receiving the data of the total number of the user visits of each day and the number of the user visits of each time period, which are collected by the user information collecting module (1), sequencing the data according to time, connecting with a public weather system, and adding a weather value into the data of the number of the user visits of each time period to obtain data after information processing;
the user action track processing module (3) is used for randomly selecting part of visitors, identifying the visitors and recording the action tracks of the visitors after the visitors are identified successfully;
the user feedback module (4) is connected with the user APP (6) and is used for collecting feedback information of the user, carrying out preliminary processing and statistics on the feedback information and feeding back a statistical result to a manager;
the user flow prediction module (5) is used for predicting the flow of visitors every day;
the user APP (6) is used for providing a social function for the users and enabling the users to establish a connection with the management system;
the identity authentication module (7) is used for identifying the identity of a worker and storing the identification record of the worker;
the database (8) is used for storing the data obtained by the information processing module (2) after information processing, the identity identification data of workers and the identification record data of the workers;
and the database auditing module (9) is used for recording database activities in real time, performing auditing compliance management on database operations, and simultaneously giving an alarm to risk behaviors suffered by the database and blocking attack behaviors.
2. The intelligent management system based on feature recognition and big data analysis as claimed in claim 1, wherein the user action track processing module (3) comprises a plurality of image acquisition devices and a central processing device, and the image acquisition devices are distributed in a plurality of artificially divided areas.
3. The intelligent management system based on feature recognition and big data analysis as claimed in claim 2, wherein the image acquisition device is configured to acquire pose image data and acquisition time of a visitor present in an area where the image acquisition device is located, and transmit the pose image data and acquisition time of the visitor to the central processing device;
the central processing equipment constructs postures according to posture image data of the visitor acquired by each image acquisition equipment to obtain a plurality of posture models, wherein the posture model constructed by the posture image data transmitted by the first image acquisition equipment is used as a standard according to the acquisition time sequence and is compared with the posture models constructed subsequently according to the acquisition time sequence, if the similarity reaches a preset threshold value, the posture models are judged to be the posture models of the same visitor, the acquisition area of the visitor is determined, and the action track of the visitor is determined according to the acquisition area and the acquisition time of the visitor.
4. The intelligent management system based on the feature recognition and the big data analysis as claimed in claim 3, wherein when the gesture model is constructed, the image acquisition device is used for acquiring a plurality of images of the visitor, feature extraction is carried out on the plurality of images of the visitor to obtain a plurality of intermediate feature maps, a two-dimensional coordinate system of a two-dimensional space where the plurality of images of the visitor are located is established, and the intermediate feature maps are processed through a neural network to obtain the position information, two-dimensional key points and three-dimensional model parameters of the visitor;
determining a central position parameter and a plurality of relative position parameters of the visitor according to the position information of the visitor, and generating a parameter map of a two-dimensional coordinate system according to the two-dimensional key points of the visitor and the corresponding central position parameters;
and determining the posture model of the visitor in the parameter graph according to the two-dimensional key points and the corresponding central position parameters.
5. An intelligent management system based on feature recognition and big data analysis according to claim 4, characterized in that the user action track processing module (3) deletes the posture model of the visitor every day and keeps the picture of the action track of the visitor.
6. The intelligent management system based on feature recognition and big data analysis as claimed in claim 1, wherein the user feedback module (4) performs preliminary processing and statistics on the feedback information to obtain text data of any two pieces of feedback information, performs vectorization processing on the text data to obtain two text vectors, determines similarity of the two pieces of feedback information according to the text vectors, determines that the two pieces of feedback information are the same information if the similarity is greater than a preset text similarity threshold, randomly deletes one piece of feedback information, adds a count to the feedback data in the undeleted piece of feedback information, and so on.
7. The intelligent management system based on feature recognition and big data analysis of claim 6, wherein when determining the similarity between two feedback information according to the text vector, a formula is used
The similarity of the two feedback information is calculated, wherein,
represents a 2-norm, sigma represents a 2-norm normalization factor,
and
a text vector representing each of any two pieces of feedback information.
8. The intelligent management system based on feature recognition and big data analysis as claimed in claim 1, wherein the user traffic prediction module (5) sets the historical traffic data as user traffic prediction data when predicting the traffic of the visitor in each day according to the historical traffic data of the previous period in the same period, and when the weather of the previous period in the same period is consistent with the weather of the day, and if the weather of the previous period in the same period is not consistent with the weather of the day, the user traffic prediction data of the day refers to the average value of the historical traffic data of the same weather in the previous period.
9. The intelligent management system based on feature recognition and big data analysis of claim 1, wherein when the identification of the staff is performed and the identification record of the staff is saved, the working time and the working duration of the staff are recorded.
10. An intelligent management system based on feature recognition and big data analysis according to claim 1, wherein when identifying the identity of the staff and saving the identification record of the staff, the identification modes include but are not limited to NFC identification, fingerprint identification and iris identification.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117079351A (en) * | 2023-10-12 | 2023-11-17 | 成都崇信大数据服务有限公司 | Method and system for analyzing personnel behaviors in key areas |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106022878A (en) * | 2016-05-19 | 2016-10-12 | 华南理工大学 | Community comment emotion tendency analysis-based mobile phone game ranking list construction method |
| CN110139075A (en) * | 2019-05-10 | 2019-08-16 | 银河水滴科技（北京）有限公司 | Video data handling procedure, device, computer equipment and storage medium |
| CN111353828A (en) * | 2020-03-30 | 2020-06-30 | 中国工商银行股份有限公司 | Method and device for predicting number of people arriving at store from network |
| CN111653010A (en) * | 2020-06-11 | 2020-09-11 | 中国建设银行股份有限公司 | Intelligent passenger flow control system and method for unmanned place |
| CN111860992A (en) * | 2020-07-13 | 2020-10-30 | 上海云角信息技术有限公司 | Passenger flow volume prediction method, device, equipment and storage medium |
| CN111950321A (en) * | 2019-05-14 | 2020-11-17 | 杭州海康威视数字技术股份有限公司 | Gait recognition method and device, computer equipment and storage medium |
| CN112329430A (en) * | 2021-01-04 | 2021-02-05 | 恒生电子股份有限公司 | Model training method, text similarity determination method and text similarity determination device |
| CN112948142A (en) * | 2021-03-03 | 2021-06-11 | 上海掌门科技有限公司 | Method, apparatus, medium, and program product for determining target feedback information |
| CN113164098A (en) * | 2018-11-26 | 2021-07-23 | 林德拉有限责任公司 | Human gait analysis system and method |
-
2022
- 2022-05-16 CN CN202210525562.2A patent/CN114626900A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106022878A (en) * | 2016-05-19 | 2016-10-12 | 华南理工大学 | Community comment emotion tendency analysis-based mobile phone game ranking list construction method |
| CN113164098A (en) * | 2018-11-26 | 2021-07-23 | 林德拉有限责任公司 | Human gait analysis system and method |
| CN110139075A (en) * | 2019-05-10 | 2019-08-16 | 银河水滴科技（北京）有限公司 | Video data handling procedure, device, computer equipment and storage medium |
| CN111950321A (en) * | 2019-05-14 | 2020-11-17 | 杭州海康威视数字技术股份有限公司 | Gait recognition method and device, computer equipment and storage medium |
| CN111353828A (en) * | 2020-03-30 | 2020-06-30 | 中国工商银行股份有限公司 | Method and device for predicting number of people arriving at store from network |
| CN111653010A (en) * | 2020-06-11 | 2020-09-11 | 中国建设银行股份有限公司 | Intelligent passenger flow control system and method for unmanned place |
| CN111860992A (en) * | 2020-07-13 | 2020-10-30 | 上海云角信息技术有限公司 | Passenger flow volume prediction method, device, equipment and storage medium |
| CN112329430A (en) * | 2021-01-04 | 2021-02-05 | 恒生电子股份有限公司 | Model training method, text similarity determination method and text similarity determination device |
| CN112948142A (en) * | 2021-03-03 | 2021-06-11 | 上海掌门科技有限公司 | Method, apparatus, medium, and program product for determining target feedback information |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117079351A (en) * | 2023-10-12 | 2023-11-17 | 成都崇信大数据服务有限公司 | Method and system for analyzing personnel behaviors in key areas |
| CN117079351B (en) * | 2023-10-12 | 2024-01-30 | 成都崇信大数据服务有限公司 | Method and system for analyzing personnel behaviors in key areas |
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