CN114140871A - Data processing-based shopping behavior analysis method for shopping mall - Google Patents
Data processing-based shopping behavior analysis method for shopping mall Download PDFInfo
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Abstract
The invention provides a data processing-based shopping behavior analysis method for a shopping mall, and relates to the technical field of data processing. In the invention, a plurality of historical shopping behavior monitoring videos corresponding to a plurality of shopping behavior monitoring devices are obtained; processing the plurality of historical shopping behavior monitoring videos to obtain a historical shopping behavior video of each shopping user in the plurality of historical shopping behavior monitoring videos, wherein each frame of historical shopping behavior video frame included in the historical shopping behavior video has one shopping user; and analyzing the historical shopping behaviors of the shopping user based on the historical shopping behavior video corresponding to the shopping user aiming at the shopping user corresponding to each obtained historical shopping behavior video to obtain a shopping behavior analysis result corresponding to the shopping user. Based on the method, the problem that the effect of analyzing the shopping behaviors of the shopping mall in the prior art is poor can be solved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a market shopping behavior analysis method based on data processing.
Background
With the continuous development of computer technology, the application range of data processing technology is increased, for example, the data processing technology can be used for analyzing the behavior of a user, so that monitoring, early warning, guidance and the like are realized. For example, in a shopping mall environment, because there are many shopping users, there is a great demand for analyzing shopping behaviors in the shopping mall. However, the inventor has found that, in the prior art, when shopping is performed in a shopping mall, a large number of shopping users are included in one monitoring video frame, and thus, if the monitoring video frame including a large number of shopping users is directly subjected to behavior recognition analysis processing, behavior analysis results of different shopping users may be interfered, and thus, the effect of analyzing shopping behaviors in the shopping mall is not good.
Disclosure of Invention
In view of the above, the present invention provides a method for analyzing shopping behaviors in a shopping mall based on data processing, so as to solve the problem of poor effect of analyzing the shopping behaviors in the shopping mall in the prior art.
In order to achieve the above purpose, the embodiment of the invention adopts the following technical scheme:
a market shopping behavior analysis method based on data processing is applied to a shopping behavior monitoring server, the shopping behavior monitoring server is in communication connection with a plurality of shopping behavior monitoring devices, and the market shopping behavior analysis method based on data processing comprises the following steps:
acquiring historical shopping behavior monitoring videos acquired by a plurality of shopping behavior monitoring devices respectively to obtain a plurality of historical shopping behavior monitoring videos corresponding to the plurality of shopping behavior monitoring devices, wherein the plurality of shopping behavior monitoring devices are arranged in different shopping areas of a target mall respectively and are used for acquiring images of the different shopping areas respectively to obtain corresponding historical shopping behavior monitoring videos;
processing the plurality of historical shopping behavior monitoring videos to obtain a historical shopping behavior video of each shopping user in the plurality of historical shopping behavior monitoring videos, wherein each frame of historical shopping behavior video frame included in the historical shopping behavior video has one shopping user;
and analyzing the historical shopping behaviors of the shopping users based on the historical shopping behavior videos corresponding to the shopping users aiming at the shopping users corresponding to the historical shopping behavior videos to obtain the shopping behavior analysis results corresponding to the shopping users.
In some preferred embodiments, in the method for analyzing shopping behavior in a shopping mall based on data processing, the step of obtaining historical shopping behavior monitoring videos respectively collected by a plurality of shopping behavior monitoring devices to obtain a plurality of historical shopping behavior monitoring videos corresponding to the plurality of shopping behavior monitoring devices includes:
acquiring current time information and determining whether the current time information reaches target time information;
if it is determined that the current time information reaches the target time information, generating historical video acquisition request information, and sending the historical video acquisition request information to each shopping behavior monitoring device of a plurality of shopping behavior monitoring devices in communication connection, wherein each shopping behavior monitoring device is used for sending historical shopping behavior monitoring videos acquired before the target time information to the shopping behavior monitoring server based on the historical video acquisition request information;
and respectively acquiring the historical shopping behavior monitoring videos sent by each shopping behavior monitoring server based on the historical video acquisition request information to obtain a plurality of historical shopping behavior monitoring videos corresponding to the plurality of shopping behavior monitoring devices.
In some preferred embodiments, in the method for analyzing shopping behavior in a shopping mall based on data processing, the step of obtaining historical shopping behavior monitoring videos respectively collected by a plurality of shopping behavior monitoring devices to obtain a plurality of historical shopping behavior monitoring videos corresponding to the plurality of shopping behavior monitoring devices includes:
acquiring current time information and determining whether the current time information reaches target time information;
if the current time information is determined to reach the target time information, generating historical video acquisition request information, and sending the historical video acquisition request information to a historical video storage data server in communication connection, wherein the historical video storage data server is used for sending historical shopping behavior monitoring videos acquired by the plurality of shopping behavior monitoring devices respectively before the target time information to the shopping behavior monitoring server based on the historical video acquisition request information;
and acquiring the historical shopping behavior monitoring videos sent by the historical video storage data server based on the historical video acquisition request information to obtain a plurality of historical shopping behavior monitoring videos corresponding to the plurality of shopping behavior monitoring devices.
In some preferred embodiments, in the method for analyzing shopping behavior in a shopping mall based on data processing, the step of processing the plurality of historical shopping behavior monitoring videos to obtain a historical shopping behavior video of each shopping user in the plurality of historical shopping behavior monitoring videos includes:
for each historical shopping behavior monitoring video in the multiple historical shopping behavior monitoring videos, carrying out object recognition processing on a historical shopping behavior monitoring video frame included in the historical shopping behavior monitoring video to determine whether a shopping user exists in the historical shopping behavior monitoring video frame;
screening out each frame of historical shopping behavior monitoring video frame without a shopping user in the historical shopping behavior monitoring videos aiming at each historical shopping behavior monitoring video in the plurality of historical shopping behavior monitoring videos, and obtaining updated historical shopping behavior monitoring videos corresponding to the historical shopping behavior monitoring videos based on each frame of historical shopping behavior monitoring video frame with the shopping user in the historical shopping behavior monitoring videos, wherein each frame of historical shopping behavior monitoring video frame has each historical shopping behavior monitoring video of the shopping user, and the historical shopping behavior monitoring videos are used as the corresponding updated historical shopping behavior monitoring videos;
and processing each updated historical shopping behavior monitoring video to obtain the historical shopping behavior video of each shopping user in the plurality of historical shopping behavior monitoring videos.
In some preferred embodiments, in the method for analyzing shopping behavior in a shopping mall based on data processing, the step of processing each updated historical shopping behavior monitoring video to obtain a historical shopping behavior video of each shopping user in the plurality of historical shopping behavior monitoring videos includes:
counting the number of shopping users in each historical shopping behavior monitoring video frame to obtain the number of users corresponding to the historical shopping behavior monitoring video frame;
for each frame of historical shopping behavior monitoring video frame included in each updated historical shopping behavior monitoring video, if the number of users corresponding to the historical shopping behavior monitoring video frame is 1, determining the historical shopping behavior monitoring video frame as a frame of historical shopping behavior video frame;
for each frame of historical shopping behavior monitoring video frame included in each updated historical shopping behavior monitoring video, if the number of users corresponding to the historical shopping behavior monitoring video frame is greater than 1, performing video frame segmentation processing based on monitoring users of the historical shopping behavior monitoring video frame to obtain a corresponding user number frame of historical shopping behavior video frame, wherein each frame of historical shopping behavior video frame includes all image information of one monitoring user, and multiple frames of historical shopping behavior videos corresponding to the same frame of historical shopping behavior monitoring video are spliced to form the historical shopping behavior monitoring video based on a segmentation relation of video segmentation processing;
classifying the obtained multiple frames of historical shopping behavior video frames based on whether shopping users in each frame of historical shopping behavior video frame are the same or not to obtain at least one historical video frame set corresponding to the multiple frames of historical shopping behavior video frames, wherein each historical video frame set comprises at least one historical shopping behavior video frame;
and sequencing each frame of historical shopping behavior video frames included in the historical video frame set according to corresponding video frame acquisition time aiming at each historical video frame set to obtain a historical shopping behavior video of a monitoring user corresponding to the historical video frame set.
In some preferred embodiments, in the method for analyzing shopping behavior of a mall based on data processing, the step of analyzing, for a shopping user corresponding to each obtained historical shopping behavior video, historical shopping behavior of the shopping user based on the historical shopping behavior video corresponding to the shopping user to obtain a shopping behavior analysis result corresponding to the shopping user includes:
aiming at a shopping user corresponding to each obtained historical shopping behavior video, carrying out duplicate removal screening processing on the historical shopping behavior video corresponding to the shopping user to obtain a historical shopping behavior screening video corresponding to the shopping user, wherein each historical shopping behavior screening video comprises at least one frame of historical shopping behavior video frame;
and for each shopping user, screening historical shopping behavior video frames included in the video based on the historical shopping behavior corresponding to the shopping user, and analyzing the historical shopping behavior of the shopping user to obtain a shopping behavior analysis result corresponding to the shopping user.
In some preferred embodiments, in the method for analyzing shopping behavior of a mall based on data processing, the step of performing, for a shopping user corresponding to each obtained historical shopping behavior video, duplicate removal screening processing on the historical shopping behavior video corresponding to the shopping user to obtain a historical shopping behavior screening video corresponding to the shopping user includes:
determining whether the historical shopping behavior video has historical shopping behavior video segments or not according to each obtained historical shopping behavior video, wherein each historical shopping behavior video segment comprises at least two continuous same historical shopping behavior video frames;
aiming at each obtained historical shopping behavior video, if the historical shopping behavior video has at least one historical shopping behavior video segment, performing duplicate removal screening processing on each historical shopping behavior video segment to obtain a historical shopping behavior screening video corresponding to a shopping user corresponding to the historical shopping behavior video;
and for each obtained historical shopping behavior video, if the historical shopping behavior video does not have at least one historical shopping behavior video clip, taking the historical shopping behavior video as a historical shopping behavior screening video corresponding to a shopping user corresponding to the historical shopping behavior video.
In some preferred embodiments, in the method for analyzing shopping behavior of a shopping mall based on data processing, if the obtained historical shopping behavior video has at least one historical shopping behavior video segment, the step of performing deduplication screening processing on each historical shopping behavior video segment to obtain a historical shopping behavior screening video corresponding to a shopping user corresponding to the historical shopping behavior video includes:
and for each obtained historical shopping behavior video, if the historical shopping behavior video has at least one historical shopping behavior video segment, retaining one frame of historical shopping behavior video frame included in each historical shopping behavior video segment and other historical shopping behavior video frames except the historical shopping behavior video segment, and obtaining a historical shopping behavior screening video corresponding to the shopping user corresponding to the historical shopping behavior video.
In some preferred embodiments, in the method for analyzing shopping behavior of a mall based on data processing, the step of, for each shopping user, screening a historical shopping behavior video frame included in a video based on the historical shopping behavior corresponding to the shopping user, and performing analysis processing on the historical shopping behavior of the shopping user to obtain a shopping behavior analysis result corresponding to the shopping user includes:
for each shopping user, carrying out target sampling and screening processing on a historical shopping behavior video frame included in a historical shopping behavior screening video corresponding to the shopping user to obtain a historical shopping behavior representative video corresponding to the shopping user;
and analyzing the historical shopping behaviors of the shopping users based on the historical shopping behavior video frames included in the historical shopping behavior representative video corresponding to the shopping users to obtain the shopping behavior analysis results corresponding to the shopping users.
In some preferred embodiments, in the method for analyzing shopping behavior of a shopping mall based on data processing, for each shopping user, based on a historical shopping behavior video frame included in a historical shopping behavior representation video corresponding to the shopping user, the step of analyzing the historical shopping behavior of the shopping user to obtain a shopping behavior analysis result corresponding to the shopping user includes:
for each shopping user, performing action recognition processing on each frame of historical shopping behavior video frame included in the historical shopping behavior representative video corresponding to the shopping user to obtain shopping action type information corresponding to each frame of historical shopping behavior video frame;
counting the number ratio of the historical shopping behavior video frames corresponding to each shopping action type information corresponding to each shopping user, updating the number ratio of the historical shopping behavior video frames corresponding to each shopping action type information corresponding to each shopping user based on the number ratio of the historical shopping behavior video frames corresponding to each shopping action type information corresponding to an associated shopping user having an association relation with the shopping user, obtaining the updated number ratio of the historical shopping behavior video frames corresponding to each shopping action type information corresponding to the shopping user as a shopping behavior analysis result corresponding to the shopping user, wherein the number of frames of the historical shopping behavior monitoring video frames of the associated shopping user and the shopping user in the same frame is larger than a preset number of frames.
According to the market shopping behavior analysis method based on data processing provided by the embodiment of the invention, after the plurality of historical shopping behavior monitoring videos corresponding to the plurality of shopping behavior monitoring devices are obtained, the plurality of historical shopping behavior monitoring videos can be processed to obtain the historical shopping behavior video of each shopping user in the plurality of historical shopping behavior monitoring videos, so that the historical shopping behavior of each shopping user can be analyzed and processed based on the historical shopping behavior video corresponding to the shopping user to obtain the shopping behavior analysis result corresponding to the shopping user, the problem of mutual interference among the shopping users in the same frame of video frame can be avoided, the analysis effect is guaranteed, and the problem of poor effect of analyzing the market shopping behavior in the prior art is solved.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
Fig. 1 is a block diagram of a shopping behavior monitoring server according to an embodiment of the present invention.
Fig. 2 is a schematic flow chart of a method for analyzing shopping behavior of a shopping mall based on data processing according to an embodiment of the present invention.
Fig. 3 is a schematic block diagram of a system for analyzing shopping behavior of a mall based on data processing according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, an embodiment of the present invention provides a shopping behavior monitoring server. Wherein the shopping behavior monitoring server may include a memory and a processor.
Optionally, in a preferred embodiment, the memory and the processor are electrically connected, directly or indirectly, to enable data transfer or interaction. For example, they may be electrically connected to each other via one or more communication buses or signal lines. The memory can have stored therein at least one software function (computer program) which can be present in the form of software or firmware. The processor can be used for executing the executable computer program stored in the memory, so as to realize the data processing-based shopping behavior analysis method provided by the embodiment of the invention.
Alternatively, in a preferred embodiment, the Memory may be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Read-Only Memory (EPROM), electrically Erasable Read-Only Memory (EEPROM), and the like.
Alternatively, in a preferred embodiment, the Processor may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), a System on Chip (SoC), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components.
Alternatively, in a preferred embodiment, the structure shown in fig. 1 is only schematic, and the shopping behavior monitoring server may further include more or fewer components than those shown in fig. 1, or have a different configuration than that shown in fig. 1, for example, may include a communication unit for information interaction with other devices (such as shopping behavior monitoring devices, etc.).
With reference to fig. 2, an embodiment of the present invention further provides a data processing-based shopping behavior analysis method for a shopping mall, which is applicable to the shopping behavior monitoring server. The method steps defined by the flow related to the data processing-based market shopping behavior analysis method can be realized by the shopping behavior monitoring server, and the shopping behavior monitoring server is in communication connection with a plurality of shopping behavior monitoring devices. The specific process shown in FIG. 2 will be described in detail below.
In the embodiment of the present invention, the shopping behavior monitoring server may obtain historical shopping behavior monitoring videos respectively collected by a plurality of shopping behavior monitoring devices, and obtain a plurality of historical shopping behavior monitoring videos corresponding to the plurality of shopping behavior monitoring devices. The shopping behavior monitoring devices are respectively arranged in different shopping areas of a target market and used for respectively carrying out image acquisition on the different shopping areas to obtain corresponding historical shopping behavior monitoring videos.
And 120, processing the plurality of historical shopping behavior monitoring videos to obtain a historical shopping behavior video of each shopping user in the plurality of historical shopping behavior monitoring videos.
In the embodiment of the present invention, the shopping behavior monitoring server may process the plurality of historical shopping behavior monitoring videos to obtain a historical shopping behavior video of each shopping user in the plurality of historical shopping behavior monitoring videos. Wherein, the historical shopping behavior video comprises each frame of historical shopping behavior video with a shopping user.
In the embodiment of the present invention, the shopping behavior monitoring server may analyze, based on the historical shopping behavior video corresponding to the shopping user, the historical shopping behavior of the shopping user for the shopping user corresponding to each obtained historical shopping behavior video, so as to obtain a shopping behavior analysis result corresponding to the shopping user.
Based on the steps included in the method, after the plurality of historical shopping behavior monitoring videos corresponding to the plurality of shopping behavior monitoring devices are obtained, the plurality of historical shopping behavior monitoring videos can be processed to obtain the historical shopping behavior video of each shopping user in the plurality of historical shopping behavior monitoring videos, so that the historical shopping behavior of each shopping user can be analyzed based on the historical shopping behavior video corresponding to the shopping user to obtain the shopping behavior analysis result corresponding to the shopping user, the problem of mutual interference among the shopping users in the same frame of video frame can be avoided, the analysis effect is guaranteed, and the problem of poor effect of analyzing the shopping behavior of the shopping mall in the prior art is solved.
Optionally, in a preferred embodiment, the step 110 in the above embodiment may further include the following steps:
firstly, acquiring current time information and determining whether the current time information reaches target time information; if it is determined that the current time information reaches the target time information, generating historical video acquisition request information, and sending the historical video acquisition request information to each shopping behavior monitoring device of a plurality of shopping behavior monitoring devices in communication connection, wherein each shopping behavior monitoring device is used for sending historical shopping behavior monitoring videos acquired before the target time information to the shopping behavior monitoring server based on the historical video acquisition request information;
secondly, historical shopping behavior monitoring videos sent by each shopping behavior monitoring server based on the historical video acquisition request information are acquired respectively, and a plurality of historical shopping behavior monitoring videos corresponding to the plurality of shopping behavior monitoring devices are obtained.
Optionally, in another preferred embodiment, the step 110 in the foregoing embodiment may also further include the following steps:
firstly, acquiring current time information and determining whether the current time information reaches target time information; if it is determined that the current time information reaches the target time information, generating historical video acquisition request information, and sending the historical video acquisition request information to a historical video storage data server in communication connection (for example, the shopping behavior monitoring device cannot store more monitoring videos), wherein the historical video storage data server is used for sending historical shopping behavior monitoring videos acquired by the plurality of shopping behavior monitoring devices respectively before the target time information to the shopping behavior monitoring server based on the historical video acquisition request information;
and secondly, acquiring the historical shopping behavior monitoring videos sent by the historical video storage data server based on the historical video acquisition request information to obtain a plurality of historical shopping behavior monitoring videos corresponding to the plurality of shopping behavior monitoring devices.
Optionally, in a preferred embodiment, the step 120 in the above embodiment may further include the following steps:
firstly, aiming at each historical shopping behavior monitoring video in the multiple historical shopping behavior monitoring videos, carrying out object recognition processing on a historical shopping behavior monitoring video frame included in the historical shopping behavior monitoring video so as to determine whether a shopping user exists in the historical shopping behavior monitoring video frame;
secondly, screening out each frame of historical shopping behavior monitoring video frame without the shopping user in the historical shopping behavior monitoring videos aiming at each historical shopping behavior monitoring video in the plurality of historical shopping behavior monitoring videos, and obtaining updated historical shopping behavior monitoring videos corresponding to the historical shopping behavior monitoring videos based on each frame of historical shopping behavior monitoring video frame with the shopping user in the historical shopping behavior monitoring videos, wherein each frame of historical shopping behavior monitoring video frame has each historical shopping behavior monitoring video of the shopping user, and the historical shopping behavior monitoring videos are used as the corresponding updated historical shopping behavior monitoring videos;
then, each updated historical shopping behavior monitoring video (each frame of video has a shopping user) is processed to obtain a historical shopping behavior video of each shopping user in the plurality of historical shopping behavior monitoring videos.
Optionally, in a preferred embodiment, the step "processing each updated historical shopping behavior monitoring video to obtain a historical shopping behavior video of each shopping user in the multiple historical shopping behavior monitoring videos" in the foregoing embodiment may further include:
firstly, counting the number of shopping users in each frame of historical shopping behavior monitoring video frame included in each updated historical shopping behavior monitoring video to obtain the number of users corresponding to the historical shopping behavior monitoring video frame;
secondly, determining each frame of historical shopping behavior monitoring video frame included in each updated historical shopping behavior monitoring video as a frame of historical shopping behavior video frame if the number of users corresponding to the historical shopping behavior monitoring video frame is 1;
then, for each frame of historical shopping behavior monitoring video frame included in each updated historical shopping behavior monitoring video, if the number of users corresponding to the historical shopping behavior monitoring video frame is greater than 1, performing video frame segmentation processing on the basis of monitoring users of the historical shopping behavior monitoring video frame to obtain a corresponding user number frame of historical shopping behavior video frame, wherein each frame of historical shopping behavior video frame includes all image information of one monitoring user, and multiple frames of historical shopping behavior videos corresponding to the same frame of historical shopping behavior monitoring video are spliced to form the historical shopping behavior monitoring video based on a segmentation relation of the video segmentation processing;
then classifying the obtained multiple frames of historical shopping behavior video frames based on whether shopping users in each frame of historical shopping behavior video frame are the same or not to obtain at least one historical video frame set corresponding to the multiple frames of historical shopping behavior video frames, wherein each historical video frame set comprises at least one historical shopping behavior video frame;
and finally, sequencing each frame of historical shopping behavior video frames included in the historical video frame set according to the corresponding video frame acquisition time aiming at each historical video frame set to obtain the historical shopping behavior video of the monitoring user corresponding to the historical video frame set.
Optionally, in a preferred embodiment, the step 130 in the above embodiment may further include the following steps:
firstly, aiming at a shopping user corresponding to each obtained historical shopping behavior video, carrying out duplicate removal screening processing on the historical shopping behavior video corresponding to the shopping user to obtain a historical shopping behavior screening video corresponding to the shopping user, wherein each historical shopping behavior screening video comprises at least one frame of historical shopping behavior video frame;
secondly, for each shopping user, screening historical shopping behavior video frames included in the video based on the historical shopping behavior corresponding to the shopping user, and analyzing and processing the historical shopping behavior of the shopping user to obtain a shopping behavior analysis result corresponding to the shopping user.
Optionally, in a preferred embodiment, the step "performing, for a shopping user corresponding to each obtained historical shopping behavior video, deduplication screening processing on the historical shopping behavior video corresponding to the shopping user to obtain a historical shopping behavior screening video corresponding to the shopping user" in the above embodiment may further include:
firstly, determining whether the historical shopping behavior video has historical shopping behavior video segments or not according to each obtained historical shopping behavior video, wherein each historical shopping behavior video segment comprises at least two continuous same historical shopping behavior video frames;
secondly, aiming at each obtained historical shopping behavior video, if the historical shopping behavior video has at least one historical shopping behavior video segment, performing duplicate removal screening processing on each historical shopping behavior video segment to obtain a historical shopping behavior screening video corresponding to a shopping user corresponding to the historical shopping behavior video;
then, for each obtained historical shopping behavior video, if the historical shopping behavior video does not have at least one historical shopping behavior video segment, the historical shopping behavior video is used as a historical shopping behavior screening video corresponding to a shopping user corresponding to the historical shopping behavior video.
Optionally, in a preferred embodiment, the step "for each obtained historical shopping behavior video, if the historical shopping behavior video has at least one historical shopping behavior video segment, performing deduplication screening processing on each historical shopping behavior video segment to obtain a historical shopping behavior screening video corresponding to a shopping user corresponding to the historical shopping behavior video" in the above embodiment may further include:
and for each obtained historical shopping behavior video, if the historical shopping behavior video has at least one historical shopping behavior video segment, retaining one frame of historical shopping behavior video frame included in each historical shopping behavior video segment and other historical shopping behavior video frames except the historical shopping behavior video segment, and obtaining a historical shopping behavior screening video corresponding to the shopping user corresponding to the historical shopping behavior video.
Optionally, in a preferred embodiment, the step "for each shopping user, screening a historical shopping behavior video frame included in a video based on the historical shopping behavior corresponding to the shopping user, and performing analysis processing on the historical shopping behavior of the shopping user to obtain a shopping behavior analysis result corresponding to the shopping user" in the foregoing embodiment may further include:
firstly, for each shopping user, carrying out target sampling and screening processing on a historical shopping behavior video frame included in a historical shopping behavior screening video corresponding to the shopping user to obtain a historical shopping behavior representative video corresponding to the shopping user;
secondly, analyzing and processing the historical shopping behaviors of the shopping users based on the historical shopping behavior video frames included in the historical shopping behavior representative video corresponding to the shopping users to obtain the shopping behavior analysis results corresponding to the shopping users.
Optionally, in a preferred embodiment, the target sample screening process in the foregoing embodiment may further include:
firstly, performing pixel value calculation processing on each frame of historical shopping behavior video frame included in the historical shopping behavior screening video, determining a pixel value and a value corresponding to each frame of the historical shopping behavior video frame, calculating a discrete degree value of the pixel value of each pixel point in the historical shopping behavior video frame aiming at each frame of the historical shopping behavior video frame, and determining one frame of the historical shopping behavior video frame with the maximum discrete degree value;
secondly, for each pixel value interval (such as 0-49, 50-99 and the like) in a plurality of continuous pixel value intervals divided in advance, constructing and obtaining a historical shopping behavior video frame sequence corresponding to the pixel value interval based on the corresponding pixel value and each historical shopping behavior video frame of which the value belongs to the pixel value interval, determining the historical shopping behavior video frame sequence corresponding to the historical shopping behavior video frame with the maximum discrete degree value as a first historical shopping behavior video frame sequence, and determining each other historical shopping behavior video frame sequence except the first historical shopping behavior video frame sequence as a second historical shopping behavior video frame sequence;
thirdly, counting the number of frames of historical shopping behavior video frames included in the first historical shopping behavior video frame sequence to obtain a first statistical frame number corresponding to the first historical shopping behavior video frame sequence, and determining each second historical shopping behavior video frame sequence of which the number of frames of the historical shopping behavior video frames is greater than or equal to the first statistical frame number to serve as a candidate second historical shopping behavior video frame sequence;
fourthly, determining a target statistical frame number (any one or the first statistical frame number) which is less than or equal to the first statistical frame number, determining continuous historical shopping behavior video frames of the target statistical frame number in the first historical shopping behavior video frame sequence to form a corresponding first historical video frame subsequence, and determining continuous historical shopping behavior video frames of the target statistical frame number in each candidate second historical shopping behavior video frame sequence to form a corresponding second historical video frame subsequence respectively;
fifthly, aiming at each second historical video frame subsequence, matching each frame of historical shopping behavior video frame in the second historical video frame subsequence with the historical shopping behavior video frame at the corresponding sequence position in the first historical video frame subsequence, and determining a target historical shopping behavior video frame (if the video frame similarity is greater than a preset threshold) matched with the historical shopping behavior video frame at the corresponding sequence position in the first historical video frame subsequence in the second historical video frame subsequence;
sixthly, determining whether a corresponding second historical video frame subsequence is a target second historical video frame subsequence matched with the first historical video frame subsequence (if the number of the target historical shopping behavior video frames included by the target second historical video frame subsequence is larger than or equal to the target frame number), and determining a collection time interval formed by video frame collection time of the historical shopping behavior video frames included by each target second historical video frame subsequence;
seventhly, determining whether a candidate second historical shopping behavior video frame sequence corresponding to the target second historical video frame subsequence is a target second historical shopping behavior video frame sequence or not based on the ratio of the acquisition time interval of the target second historical video frame subsequence to the number of frames of the target historical shopping behavior video frames included in the target second historical video frame subsequence for each target second historical video frame subsequence, and each frame of historical shopping behavior video frame included in the first historical shopping behavior video frame sequence and each frame of historical shopping behavior video frame included in the target second historical shopping behavior video frame sequence are used as historical shopping behavior representative videos corresponding to shopping users, and the ratio corresponding to the target second historical video frame subsequence corresponding to the target second historical shopping behavior video frame sequence is less than or equal to a preset frame ratio threshold value.
Optionally, in a preferred embodiment, the step "for each shopping user, analyzing the historical shopping behavior of the shopping user based on a historical shopping behavior video frame included in the historical shopping behavior representative video corresponding to the shopping user to obtain a shopping behavior analysis result corresponding to the shopping user" in the foregoing embodiment may further include:
firstly, aiming at each shopping user, performing action recognition processing on each frame of historical shopping behavior video frame included in a historical shopping behavior representative video corresponding to the shopping user to obtain shopping action type information corresponding to each frame of historical shopping behavior video frame;
secondly, counting the number ratio of the historical shopping behavior video frames corresponding to each shopping action type information corresponding to each shopping user, and updating the number ratio of the historical shopping behavior video frames corresponding to each shopping action type information corresponding to each shopping user based on the number ratio of the historical shopping action video frames corresponding to each shopping action type information corresponding to the associated shopping user having an association relationship with the shopping user (for example, if the number ratio of the B type action of the shopping user A is C, and the number ratio of the B type action of the associated shopping user D is E, then updating C based on E can be performed, wherein the value of E is larger, the value of the updated C is larger, for example, the sum of E and C is directly calculated, or the product of E and a coefficient smaller than 1 can be calculated first, and then calculating the sum of the product and C) to obtain the updated number ratio of the historical shopping behavior video frames corresponding to each shopping action type information corresponding to the shopping user as the shopping behavior analysis result corresponding to the shopping user, wherein the number of the associated shopping user and the number of the shopping user in the same frame of historical shopping behavior monitoring video frame are more than the preset number of frames.
With reference to fig. 3, an embodiment of the present invention further provides a data processing-based shopping behavior analysis system for a shopping mall, which is applicable to the shopping behavior monitoring server. The data processing-based shopping behavior analysis system for the shopping mall may include:
a monitoring video obtaining module (as in step 110 above), configured to obtain historical shopping behavior monitoring videos respectively collected by multiple shopping behavior monitoring devices, and obtain multiple historical shopping behavior monitoring videos corresponding to the multiple shopping behavior monitoring devices, where the multiple shopping behavior monitoring devices are respectively disposed in different shopping areas of a target mall, and are used to respectively perform image collection on the different shopping areas, so as to obtain corresponding historical shopping behavior monitoring videos;
a monitoring video processing module (as in step 120 above) configured to process the plurality of historical shopping behavior monitoring videos to obtain a historical shopping behavior video of each shopping user in the plurality of historical shopping behavior monitoring videos, where each frame of the historical shopping behavior video includes a shopping user;
and a monitoring video analysis module (step 130 described above) configured to, for a shopping user corresponding to each obtained historical shopping behavior video, analyze the historical shopping behavior of the shopping user based on the historical shopping behavior video corresponding to the shopping user, and obtain a shopping behavior analysis result corresponding to the shopping user.
In summary, according to the method for analyzing shopping behaviors in a shopping mall based on data processing provided by the present invention, after obtaining a plurality of historical shopping behavior monitoring videos corresponding to a plurality of shopping behavior monitoring devices, the plurality of historical shopping behavior monitoring videos may be processed to obtain a historical shopping behavior video of each shopping user in the plurality of historical shopping behavior monitoring videos, so that the historical shopping behavior of each shopping user may be analyzed based on the historical shopping behavior video corresponding to the shopping user to obtain a shopping behavior analysis result corresponding to the shopping user, and thus, a problem of mutual interference among the shopping users in the same frame of video frame may be avoided, an analysis effect may be ensured, and a problem of poor effect of analyzing the shopping behaviors in the shopping mall in the prior art may be improved.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. 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 (10)
1. A market shopping behavior analysis method based on data processing is characterized in that the method is applied to a shopping behavior monitoring server, the shopping behavior monitoring server is in communication connection with a plurality of shopping behavior monitoring devices, and the market shopping behavior analysis method based on data processing comprises the following steps:
acquiring historical shopping behavior monitoring videos acquired by a plurality of shopping behavior monitoring devices respectively to obtain a plurality of historical shopping behavior monitoring videos corresponding to the plurality of shopping behavior monitoring devices, wherein the plurality of shopping behavior monitoring devices are arranged in different shopping areas of a target mall respectively and are used for acquiring images of the different shopping areas respectively to obtain corresponding historical shopping behavior monitoring videos;
processing the plurality of historical shopping behavior monitoring videos to obtain a historical shopping behavior video of each shopping user in the plurality of historical shopping behavior monitoring videos, wherein each frame of historical shopping behavior video frame included in the historical shopping behavior video has one shopping user;
and analyzing the historical shopping behaviors of the shopping users based on the historical shopping behavior videos corresponding to the shopping users aiming at the shopping users corresponding to the historical shopping behavior videos to obtain the shopping behavior analysis results corresponding to the shopping users.
2. A mall shopping behavior analysis method according to claim 1, wherein the step of obtaining historical shopping behavior monitoring videos collected by a plurality of shopping behavior monitoring devices respectively to obtain a plurality of historical shopping behavior monitoring videos corresponding to the plurality of shopping behavior monitoring devices comprises:
acquiring current time information and determining whether the current time information reaches target time information;
if it is determined that the current time information reaches the target time information, generating historical video acquisition request information, and sending the historical video acquisition request information to each shopping behavior monitoring device of a plurality of shopping behavior monitoring devices in communication connection, wherein each shopping behavior monitoring device is used for sending historical shopping behavior monitoring videos acquired before the target time information to the shopping behavior monitoring server based on the historical video acquisition request information;
and respectively acquiring the historical shopping behavior monitoring videos sent by each shopping behavior monitoring server based on the historical video acquisition request information to obtain a plurality of historical shopping behavior monitoring videos corresponding to the plurality of shopping behavior monitoring devices.
3. A mall shopping behavior analysis method according to claim 1, wherein the step of obtaining historical shopping behavior monitoring videos collected by a plurality of shopping behavior monitoring devices respectively to obtain a plurality of historical shopping behavior monitoring videos corresponding to the plurality of shopping behavior monitoring devices comprises:
acquiring current time information and determining whether the current time information reaches target time information;
if the current time information is determined to reach the target time information, generating historical video acquisition request information, and sending the historical video acquisition request information to a historical video storage data server in communication connection, wherein the historical video storage data server is used for sending historical shopping behavior monitoring videos acquired by the plurality of shopping behavior monitoring devices respectively before the target time information to the shopping behavior monitoring server based on the historical video acquisition request information;
and acquiring the historical shopping behavior monitoring videos sent by the historical video storage data server based on the historical video acquisition request information to obtain a plurality of historical shopping behavior monitoring videos corresponding to the plurality of shopping behavior monitoring devices.
4. A method as claimed in claim 1, wherein said step of processing said plurality of historical shopping behavior monitoring videos to obtain historical shopping behavior videos of each shopping user in said plurality of historical shopping behavior monitoring videos comprises:
for each historical shopping behavior monitoring video in the multiple historical shopping behavior monitoring videos, carrying out object recognition processing on a historical shopping behavior monitoring video frame included in the historical shopping behavior monitoring video to determine whether a shopping user exists in the historical shopping behavior monitoring video frame;
screening out each frame of historical shopping behavior monitoring video frame without a shopping user in the historical shopping behavior monitoring videos aiming at each historical shopping behavior monitoring video in the plurality of historical shopping behavior monitoring videos, and obtaining updated historical shopping behavior monitoring videos corresponding to the historical shopping behavior monitoring videos based on each frame of historical shopping behavior monitoring video frame with the shopping user in the historical shopping behavior monitoring videos, wherein each frame of historical shopping behavior monitoring video frame has each historical shopping behavior monitoring video of the shopping user, and the historical shopping behavior monitoring videos are used as the corresponding updated historical shopping behavior monitoring videos;
and processing each updated historical shopping behavior monitoring video to obtain the historical shopping behavior video of each shopping user in the plurality of historical shopping behavior monitoring videos.
5. A method as claimed in claim 4, wherein said step of processing each of said updated historical shopping behavior monitor videos to obtain a historical shopping behavior video of each shopping user in said plurality of historical shopping behavior monitor videos comprises:
counting the number of shopping users in each historical shopping behavior monitoring video frame to obtain the number of users corresponding to the historical shopping behavior monitoring video frame;
for each frame of historical shopping behavior monitoring video frame included in each updated historical shopping behavior monitoring video, if the number of users corresponding to the historical shopping behavior monitoring video frame is 1, determining the historical shopping behavior monitoring video frame as a frame of historical shopping behavior video frame;
for each frame of historical shopping behavior monitoring video frame included in each updated historical shopping behavior monitoring video, if the number of users corresponding to the historical shopping behavior monitoring video frame is greater than 1, performing video frame segmentation processing based on monitoring users of the historical shopping behavior monitoring video frame to obtain a corresponding user number frame of historical shopping behavior video frame, wherein each frame of historical shopping behavior video frame includes all image information of one monitoring user, and multiple frames of historical shopping behavior videos corresponding to the same frame of historical shopping behavior monitoring video are spliced to form the historical shopping behavior monitoring video based on a segmentation relation of video segmentation processing;
classifying the obtained multiple frames of historical shopping behavior video frames based on whether shopping users in each frame of historical shopping behavior video frame are the same or not to obtain at least one historical video frame set corresponding to the multiple frames of historical shopping behavior video frames, wherein each historical video frame set comprises at least one historical shopping behavior video frame;
and sequencing each frame of historical shopping behavior video frames included in the historical video frame set according to corresponding video frame acquisition time aiming at each historical video frame set to obtain a historical shopping behavior video of a monitoring user corresponding to the historical video frame set.
6. A mall shopping behavior analysis method based on data processing as claimed in any one of claims 1 to 5, wherein said step of analyzing the historical shopping behavior of the shopping user based on the historical shopping behavior video corresponding to the shopping user for the shopping user corresponding to each obtained historical shopping behavior video to obtain the shopping behavior analysis result corresponding to the shopping user comprises:
aiming at a shopping user corresponding to each obtained historical shopping behavior video, carrying out duplicate removal screening processing on the historical shopping behavior video corresponding to the shopping user to obtain a historical shopping behavior screening video corresponding to the shopping user, wherein each historical shopping behavior screening video comprises at least one frame of historical shopping behavior video frame;
and for each shopping user, screening historical shopping behavior video frames included in the video based on the historical shopping behavior corresponding to the shopping user, and analyzing the historical shopping behavior of the shopping user to obtain a shopping behavior analysis result corresponding to the shopping user.
7. A mall shopping behavior analysis method according to claim 6, wherein the step of performing a de-duplication screening process on the historical shopping behavior video corresponding to the shopping user for the shopping user corresponding to each obtained historical shopping behavior video to obtain the historical shopping behavior screening video corresponding to the shopping user comprises:
determining whether the historical shopping behavior video has historical shopping behavior video segments or not according to each obtained historical shopping behavior video, wherein each historical shopping behavior video segment comprises at least two continuous same historical shopping behavior video frames;
aiming at each obtained historical shopping behavior video, if the historical shopping behavior video has at least one historical shopping behavior video segment, performing duplicate removal screening processing on each historical shopping behavior video segment to obtain a historical shopping behavior screening video corresponding to a shopping user corresponding to the historical shopping behavior video;
and for each obtained historical shopping behavior video, if the historical shopping behavior video does not have at least one historical shopping behavior video clip, taking the historical shopping behavior video as a historical shopping behavior screening video corresponding to a shopping user corresponding to the historical shopping behavior video.
8. A method as claimed in claim 7, wherein the step of performing a duplicate elimination screening process on each of the historical shopping behavior video segments to obtain a historical shopping behavior screening video corresponding to a shopping user corresponding to the historical shopping behavior video, if the historical shopping behavior video has at least one of the historical shopping behavior video segments, comprises:
and for each obtained historical shopping behavior video, if the historical shopping behavior video has at least one historical shopping behavior video segment, retaining one frame of historical shopping behavior video frame included in each historical shopping behavior video segment and other historical shopping behavior video frames except the historical shopping behavior video segment, and obtaining a historical shopping behavior screening video corresponding to the shopping user corresponding to the historical shopping behavior video.
9. A method as claimed in claim 6, wherein the step of screening the video frames of the historical shopping behaviors included in the video based on the historical shopping behaviors corresponding to the shopping users and analyzing the historical shopping behaviors of the shopping users to obtain the analysis result of the shopping behavior corresponding to the shopping user comprises:
for each shopping user, carrying out target sampling and screening processing on a historical shopping behavior video frame included in a historical shopping behavior screening video corresponding to the shopping user to obtain a historical shopping behavior representative video corresponding to the shopping user;
and analyzing the historical shopping behaviors of the shopping users based on the historical shopping behavior video frames included in the historical shopping behavior representative video corresponding to the shopping users to obtain the shopping behavior analysis results corresponding to the shopping users.
10. A method as claimed in claim 9, wherein said step of analyzing the historical shopping behavior of the shopping user based on the historical shopping behavior video frame included in the historical shopping behavior representation video corresponding to the shopping user to obtain the shopping behavior analysis result corresponding to the shopping user comprises:
for each shopping user, performing action recognition processing on each frame of historical shopping behavior video frame included in the historical shopping behavior representative video corresponding to the shopping user to obtain shopping action type information corresponding to each frame of historical shopping behavior video frame;
counting the number ratio of the historical shopping behavior video frames corresponding to each shopping action type information corresponding to each shopping user, updating the number ratio of the historical shopping behavior video frames corresponding to each shopping action type information corresponding to each shopping user based on the number ratio of the historical shopping behavior video frames corresponding to each shopping action type information corresponding to an associated shopping user having an association relation with the shopping user, obtaining the updated number ratio of the historical shopping behavior video frames corresponding to each shopping action type information corresponding to the shopping user as a shopping behavior analysis result corresponding to the shopping user, wherein the number of frames of the historical shopping behavior monitoring video frames of the associated shopping user and the shopping user in the same frame is larger than a preset number of frames.
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