Client screening and advertisement recommendation method and system based on data analysis

Abstract

The invention relates to a customer screening and advertisement recommendation method and system based on data analysis, and the method comprises the steps: responding to an obtained real-time image, carrying out the analysis of the real-time image, and obtaining a character object in the real-time image; character objects included in the real-time image are grouped to obtain character object groups, and the character objects in each character object group have an association relationship; obtaining a moving track of the character object group and generating analysis data according to the moving track; generating an advertisement recommendation scheme associated with the character object group using the analysis data; and obtaining a newly added moving track of the character object group and implementing an advertisement recommendation scheme on a display terminal associated with the newly added moving track. According to the customer screening and advertisement recommendation method and system based on data analysis, screening and recommendation can be achieved through targeted analysis of customer groups, the mode does not need to depend on historical data, and privacy and targeted content recommendation can be better balanced.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method and system for customer screening and advertisement recommendation based on data analysis. Background Art

[0002] Customer screening and advertising recommendations are two core links in precision marketing and recommendation systems. The former is used to identify high-value or potential target customers, while the latter pushes advertisements or products that meet their needs to these customers through algorithm models. For example, in content browsing, targeted advertising recommendations will be made based on the customer's viewing content and consumption habits. These advertisements are naturally related to customer preferences and have a better conversion rate.

[0003] In shopping mall consumption scenarios, current advertising delivery methods mostly rely on bidding. One advantage of this method is that it guarantees shopping mall profits, but it has inherent defects in conversion and customer experience because the bidding method ignores key factors such as quality and customer matching.

[0004] A better way is to make targeted recommendations based on the customers, but due to practical factors such as customer privacy and customer source complexity, it is difficult to achieve objective and true data recording. Summary of the invention

[0005] The present application provides a customer screening and advertising recommendation method and system based on data analysis, which can achieve screening and recommendation through targeted analysis of customer groups. This method does not need to rely on historical data and can better balance privacy and targeted content recommendations.

[0006] The above-mentioned purpose of the present application is achieved through the following technical solutions: In a first aspect, the present application provides a method for customer screening and advertisement recommendation based on data analysis, comprising: In response to the acquired real-time image, the real-time image is parsed to obtain a person object included in the real-time image; The person objects included in the real-time image are grouped to obtain person object groups, wherein the person objects in each person object group have an associated relationship; Obtain movement trajectories of the person object groups and generate analysis data according to the movement trajectories, wherein each person object group corresponds to one piece of analysis data; Using the analysis data to generate advertising recommendations associated with the group of personas; A newly added movement track of the character object group is obtained and an advertisement recommendation scheme is implemented on a display terminal associated with the newly added movement track.

[0007] In a possible implementation manner of the first aspect, grouping the human objects included in the real-time image includes: Using the human object included in the real-time image, an analysis reference network is established, wherein each grid in the analysis reference network has the same number of edges; Obtain the size variation of each grid in the analysis reference network in time series; When the size variation of the grid exceeds the allowable range, the grid is eliminated until the number of grids in the analysis reference network is less than or equal to the set number; The sparse regions of the analysis reference network are calculated and the analysis reference network is pre-partitioned.

[0008] In a possible implementation manner of the first aspect, calculating the sparse area of ​​the analysis reference network and pre-dividing the analysis reference network includes: Calculate and analyze the segment length of each line segment in the reference network, the average length of the line segment, the grid area of ​​each grid and the average area of ​​the grid; Annotate a first range in the analysis reference network according to the length of the line segment and the mean length of the line segment; Annotate the second range in the analysis reference network based on the grid area and the mean grid area; Determine an overlapping area between the first range and the second range, and record it as an overlapping range; The analysis reference network is pre-partitioned using the coincident range, first range remainder, and second range remainder.

[0009] In a possible implementation manner of the first aspect, obtaining the movement trajectory of the person object group and generating analysis data according to the movement trajectory includes: Creating a moving trajectory based on the movement of the group of human objects, the moving trajectory includes sequentially connected line segments and points located on the line segments, the line segments have length and direction, the points have area, and the area of ​​the points is negatively correlated with the moving speed; determining the type of content associated with the point; Generates analytical data based on the content type associated with the point, including content type, content type association time, and content type ratio.

[0010] In a possible implementation manner of the first aspect, the method further includes determining a weight of each character object in the character object group and adjusting the advertisement recommendation scheme according to the weight of the character object.

[0011] In a possible implementation manner of the first aspect, determining the weight of each character object in the character object group includes: Calculate and analyze the degree of change of each grid in the reference network, including the size change and the speed of change; The character objects in the analysis reference network are sorted according to the degree of change, and the weights of the character objects in the sequential sequence decrease in sequence.

[0012] In a possible implementation manner of the first aspect, the method further includes correcting the sorting of the character objects by using the movement trajectory, and correcting the sorting of the character objects by using the movement trajectory includes: Get the area and attributes of all points on the moving trajectory; Group all points according to their attributes and calculate the total area of ​​each point in each group to obtain the group value; Use grouping values ​​to modify the order of person objects in a sequential sequence.

[0013] In a second aspect, the present application provides a customer screening and advertisement recommendation device based on data analysis, comprising: An image analysis unit, configured to analyze the real-time image in response to the acquired real-time image, and obtain a person object included in the real-time image; A grouping processing unit, used for grouping the human objects included in the real-time image to obtain human object groups, wherein the human objects in each human object group have an associated relationship; A data generating unit, used for acquiring the movement trajectory of the person object group and generating analysis data according to the movement trajectory, wherein each person object group corresponds to one piece of analysis data; A scheme generating unit, configured to generate an advertisement recommendation scheme associated with the character object group using the analysis data; The scheme implementation unit is used to obtain a newly added movement track of the character object group and implement the advertisement recommendation scheme on a display terminal associated with the newly added movement track.

[0014] In a third aspect, the present application provides a customer screening and advertising recommendation system based on data analysis, the system comprising: One or more memories, used to store instructions; and one or more processors, used to call and run the instructions from the memories to execute the method as described in the first aspect and any possible implementation of the first aspect.

[0015] In a fourth aspect, the present application provides a computer-readable storage medium, the computer-readable storage medium comprising: Program, when the program is executed by a processor, the method described in the first aspect and any possible implementation of the first aspect is executed.

[0016] In a fifth aspect, the present application provides a computer program product, comprising program instructions. When the program instructions are executed by a computing device, the method described in the first aspect and any possible implementation of the first aspect is executed.

[0017] In a sixth aspect, the present application provides a chip system, which includes a processor for implementing the functions involved in the above aspects, for example, generating, receiving, sending, or processing the data and / or information involved in the above methods.

[0018] The chip system may be composed of chips, or may include chips and other discrete devices.

[0019] In a possible design, the chip system also includes a memory, which is used to store necessary program instructions and data. The processor and the memory can be decoupled and respectively set on different devices, connected by wired or wireless means, or the processor and the memory can also be coupled on the same device. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a step flow chart of a customer screening and advertising recommendation method based on data analysis provided by this application.

[0021] Figure 2 It is a schematic diagram of grouping character objects provided by the present application.

[0022] Figure 3 This is a schematic diagram provided by the present application for implementing an advertisement recommendation scheme on a newly added movement trajectory.

[0023] Figure 4 It is a schematic diagram of a process of obtaining an analysis reference network provided by the present application.

[0024] Figure 5 It is a schematic diagram of a process of decomposing an analysis reference network provided by the present application.

[0025] Figure 6 It is a schematic diagram of a movement trajectory provided by this application. DETAILED DESCRIPTION

[0026] The technical solution in this application is further described in detail below in conjunction with the accompanying drawings.

[0027] This application discloses a method for customer screening and advertising recommendation based on data analysis. Figure 1 In some examples, the customer screening and advertisement recommendation method based on data analysis disclosed in this application includes the following steps: S101, in response to the acquired real-time image, analyzing the real-time image to obtain a person object included in the real-time image; S102, grouping the person objects included in the real-time image to obtain person object groups, wherein the person objects in each person object group have an associated relationship; S103, obtaining movement trajectories of the person object groups and generating analysis data according to the movement trajectories, wherein each person object group corresponds to one piece of analysis data; S104, using the analysis data to generate an advertisement recommendation scheme associated with the character object group; S105, obtaining a newly added movement trajectory of the character object group and implementing an advertisement recommendation scheme on a display terminal associated with the newly added movement trajectory.

[0028] In step S101, the real-time image is parsed according to the received real-time image to obtain the person object included in the real-time image. The real-time image here refers to the surveillance image in the shopping mall, and the surveillance image covers most areas or all areas in the shopping mall.

[0029] The human objects included in the real-time image refer to the people entering the shopping mall, and the purpose of the analysis is to determine the people entering the shopping mall and their number.

[0030] Then, in step S102, the human objects included in the real-time image are grouped. Figure 2 As shown, a character object group is obtained, and the character objects in each character object group have an association relationship. The association relationship here means that when the character objects enter the area corresponding to the real-time image, they have the potential to travel together. The corresponding relationships of traveling together may be friends, family members, colleagues, etc.

[0031] In step S103 , the movement trajectory of the person object group is obtained and analysis data is generated according to the movement trajectory. When there are multiple person object groups, the number of analysis data is also multiple, and each person object group needs to correspond to one analysis data.

[0032] Then, in step S104, the analysis data is used to generate an advertising recommendation scheme associated with the character object group. Finally, in step S105, a new movement trajectory of the character object group is obtained and the advertising recommendation scheme is implemented on the display terminal associated with the new movement trajectory. Figure 3 shown.

[0033] The technical solution provided by the present application analyzes the human objects that enter the corresponding area of ​​the real-time image by grouping and dividing them, and then implements the advertising recommendation scheme based on the grouping. In the process of implementing the advertising recommendation scheme, reference is also made to the newly added movement trajectory, which refers to the new movement trajectory generated by the human object group during the movement process.

[0034] That is, during the movement of the character object group, advertisement recommendations will be made based on the movement trajectory of the character object group. This recommendation method will change with the change of the movement trajectory of the character object group. It does not simply rely on labeling the character objects or character object groups and then making a single type of recommendation, but makes recommendations based on the movement of the character object group.

[0035] Or it can be described as that the advertising recommendation method provided by the present application is based on the current movement of the character object group for recommendation. Because the current hobbies of the character object group may be unpredictable or in a state of change, the recommendation method through labeling will solidify the advertising recommendation.

[0036] In some examples, the specific method of grouping the human objects included in the real-time image is: S201, using the human object included in the real-time image to establish an analysis reference network, wherein each grid in the analysis reference network has the same number of edges; S202, obtaining the size change of each grid in the analysis reference network in time series; S203, when the size variation of the grid exceeds the allowable range, the grid is eliminated until the number of grids in the analysis reference network is less than or equal to the set number; S204, calculating the sparse area of ​​the analysis reference network and pre-dividing the analysis reference network.

[0037] In step S201 to step S203, the human object included in the real-time image is first used to establish an analysis reference network, such as Figure 4 As shown, the mesh obtained here is a basic analysis reference network, and then the analysis reference network is processed.

[0038] The specific processing process is to obtain the size change of each grid in the analysis reference network in time series, and then eliminate the grid when the size change of the grid exceeds the allowable range, until the number of grids in the analysis reference network is less than or equal to the set number.

[0039] Eliminating the grid means that the analysis reference network is decomposed into multiple parts. The corresponding scenario here is when just entering the shopping mall. The human objects are relatively concentrated and the grouping processing described in the previous content cannot be directly performed. However, in order to ensure the continuity of the movement trajectory and the accumulation of previous data, it is necessary to start the analysis from the moment the human objects just enter the shopping mall.

[0040] The size change degree of a grid refers to the side length and / or area of ​​the grid. When one of the side lengths of a grid or the area of ​​a grid exceeds the set allowable value, the grid is determined to be lost (deleted), corresponding to the grouping processing of character objects recorded in the aforementioned content.

[0041] In this step, a small analysis reference network may be obtained. The number of people corresponding to the analysis reference network at this time includes one person (point), two people (line segment) and multiple people (grid), such as Figure 5 shown.

[0042] For the case of one person (point) and two people (line segment), the subsequent process of generating an advertising recommendation plan and implementing the advertising recommendation plan on the display terminal associated with the newly added mobile trajectory can be directly carried out, but for the case of multiple people (grids), further processing is required.

[0043] The specific processing process is performed in step S204, which is to calculate the sparse area of ​​the analysis reference network and pre-divide the analysis reference network.

[0044] The specific method of calculating the sparse area of ​​the analysis reference network and pre-dividing the analysis reference network is as follows: S301, calculating and analyzing the segment length of each segment in the reference network, the average length of the segment, the grid area of ​​each grid and the average area of ​​the grid; S302, marking a first range in the analysis reference network according to the line segment length and the average length of the line segment; S303, marking a second range in the analysis reference network according to the grid area and the average area of ​​the grid; S304, determining an overlapping area between the first range and the second range, which is recorded as an overlapping range; S305, pre-dividing the analysis reference network using the overlapped range, the first range remainder, and the second range remainder.

[0045] The content in step S301 to step S305 is to pre-divide the analysis reference network according to the mean length and the mean area, that is, to divide the analysis reference network into three parts: an area where the mean area and the mean length are both consistent, an area where the mean area or the mean length is consistent, and an area where neither the mean area nor the mean length is consistent.

[0046] This method can highlight the key areas and non-key areas of the analysis reference network. In the subsequent inspection process, the connection between these three parts will be emphasized instead of conducting a comprehensive analysis of the entire analysis reference network. The advantage of this local analysis is that it can focus on the key points.

[0047] In some examples, the specific method of obtaining the movement trajectory of the group of human objects and generating analysis data according to the movement trajectory is as follows: Create movement trajectories based on the movement of the character object group, Figure 6 As shown, the moving trajectory includes sequentially connected line segments and points located on the line segments. The line segments have length and direction, and the points have area. The area of ​​the points is negatively correlated with the moving speed. determining the type of content associated with the point; Generates analytical data based on the content type associated with the point, including content type, content type association time, and content type ratio.

[0048] In this method, the focus is on what the character object group pays attention to during the movement process. The movement process is represented by a line segment, and the stay time is represented by a point. The longer the stay time, the more interested the character object group is in the content at that point, or it can be described as being willing to spend time on the content at that point.

[0049] Finally, analysis data is generated based on the content type associated with the point, and the analysis data includes content type, content type association time and content type ratio.

[0050] In some examples, it is also added to determine the weight of each character object in the character object group and adjust the advertisement recommendation scheme according to the weight of the character object. The method of determining the weight of each character object in the character object group is as follows: S401, calculating and analyzing the degree of change of each grid in the reference network, the degree of change including the size change amount and the change speed; S402, sorting the character objects in the analysis reference network according to the degree of change, and the weights of the character objects in the sequence decrease in sequence.

[0051] In step S401 to step S402, the change degree of each grid in the reference network is examined in detail. The change degree of the grid corresponds to the activity of the character object. The higher the activity of the character object, the higher its weight.

[0052] That is to say, in this part, the needs or interests of the active characters will be considered as the focus. For the case of one person (point), this part is not involved; for the case of two people (line segments), the degree of change of the two endpoints at both ends is directly examined. The degree of change at this time includes two parameters: moving speed and moving amount.

[0053] Furthermore, it is also necessary to consider using the movement trajectory to correct the sorting of the character objects. The specific method of using the movement trajectory to correct the sorting of the character objects is as follows: S501, obtaining the areas and attributes of all points on the moving trajectory; S502, grouping all points according to their attributes and calculating the total area of ​​the points in each group to obtain a group value; S503: Use the grouping values ​​to correct the order of the person objects in the sequential sequence.

[0054] The purpose of using the movement trajectory to correct the sorting of character objects is to reduce the weight of the grid variation, because the grid variation is only one aspect, and the actual movement route and the time spent on the movement route must also be considered.

[0055] In the specific process of correction, the obtained grouping value is a value less than 1 (the ratio of the total area of ​​the points in the group to the total area of ​​all the points). This value is multiplied by the order of the character objects (1, 2, 3, and so on), and then arranged in order from large to small to obtain a new sequence.

[0056] The above process is carried out dynamically, for example, reprocessing is performed every five minutes or every ten minutes. The purpose of reprocessing is mainly to take into account the weight changes of the character objects and the dynamic changes of the movement routes. In a complete process, the interests of the active character objects in the character object group and the entire character object group will change.

[0057] The present application also provides a customer screening and advertisement recommendation device based on data analysis, comprising: An image analysis unit, configured to analyze the real-time image in response to the acquired real-time image, and obtain a person object included in the real-time image; A grouping processing unit, used for grouping the human objects included in the real-time image to obtain human object groups, wherein the human objects in each human object group have an associated relationship; A data generating unit, used for acquiring the movement trajectory of the person object group and generating analysis data according to the movement trajectory, wherein each person object group corresponds to one piece of analysis data; A scheme generating unit, configured to generate an advertisement recommendation scheme associated with the character object group using the analysis data; The scheme implementation unit is used to obtain a newly added movement track of the character object group and implement the advertisement recommendation scheme on a display terminal associated with the newly added movement track.

[0058] Furthermore, grouping the human objects included in the real-time image includes: Using the human object included in the real-time image, an analysis reference network is established, wherein each grid in the analysis reference network has the same number of edges; Obtain the size variation of each grid in the analysis reference network in time series; When the size variation of the grid exceeds the allowable range, the grid is eliminated until the number of grids in the analysis reference network is less than or equal to the set number; The sparse regions of the analysis reference network are calculated and the analysis reference network is pre-partitioned.

[0059] Further, calculating the sparse area of ​​the analysis reference network and pre-dividing the analysis reference network includes: Calculate and analyze the segment length of each line segment in the reference network, the average length of the line segment, the grid area of ​​each grid and the average area of ​​the grid; Annotate a first range in the analysis reference network according to the length of the line segment and the mean length of the line segment; Annotate the second range in the analysis reference network based on the grid area and the mean grid area; Determine an overlapping area between the first range and the second range, and record it as an overlapping range; The analysis reference network is pre-partitioned using the coincident range, first range remainder, and second range remainder.

[0060] Further, obtaining the movement trajectory of the person object group and generating analysis data according to the movement trajectory includes: Creating a moving trajectory based on the movement of the group of human objects, the moving trajectory includes sequentially connected line segments and points located on the line segments, the line segments have length and direction, the points have area, and the area of ​​the points is negatively correlated with the moving speed; determining the type of content associated with the point; Generates analytical data based on the content type associated with the point, including content type, content type association time, and content type ratio.

[0061] Furthermore, it also includes determining the weight of each character object in the character object group and adjusting the advertisement recommendation scheme according to the weight of the character object.

[0062] Furthermore, determining the weight of each character object in the character object group includes: Calculate and analyze the degree of change of each grid in the reference network, including the size change and the speed of change; The character objects in the analysis reference network are sorted according to the degree of change, and the weights of the character objects in the sequential sequence decrease in sequence.

[0063] Furthermore, it also includes using the movement trajectory to correct the sorting of the character objects, and using the movement trajectory to correct the sorting of the character objects includes: Get the area and attributes of all points on the moving trajectory; Group all points according to their attributes and calculate the total area of ​​each point in each group to obtain the group value; Use grouping values ​​to modify the order of person objects in a sequential sequence.

[0064] In one example, the unit in any of the above devices can be one or more integrated circuits configured to implement the above methods, such as: one or more application specific integrated circuits (ASICs), or, one or more digital signal processors (DSPs), or, one or more field programmable gate arrays (FPGAs), or a combination of at least two of these integrated circuit forms.

[0065] For another example, when the units in the device can be implemented in the form of a processing element scheduling program, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call a program. For another example, these units can be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0066] Various objects such as various messages / information / equipment / network elements / systems / devices / actions / operations / processes / concepts that may appear in this application are named. It can be understood that these specific names do not constitute a limitation on the relevant objects. The names assigned may change with factors such as scenarios, contexts or usage habits. The understanding of the technical meaning of the technical terms in this application should be mainly determined from the functions and technical effects embodied / executed in the technical scheme.

[0067] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0068] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0069] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0070] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0071] It should also be understood that in various embodiments of the present application, the first, second, etc. are only used to indicate that multiple objects are different. For example, the first time window and the second time window are only used to indicate different time windows. They should not have any impact on the time window itself, and the first, second, etc. mentioned above should not impose any limitations on the embodiments of the present application.

[0072] It should also be understood that in the various embodiments of the present application, unless otherwise specified or there is a logical conflict, the terms and / or descriptions between different embodiments are consistent and can be referenced to each other, and the technical features in different embodiments can be combined to form new embodiments according to their internal logical relationships.

[0073] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a computer-readable storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned computer-readable storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0074] The present application also provides a customer screening and advertisement recommendation system based on data analysis, the system comprising: One or more memories, used to store instructions; and one or more processors, used to call and run the instructions from the memories to execute the method as described above.

[0075] The present application also provides a computer program product, which includes instructions. When the instructions are executed, the terminal device and the network device perform operations of the terminal device and the network device corresponding to the above method.

[0076] The present application also provides a chip system, which includes a processor for implementing the functions involved in the above content, such as generating, receiving, sending, or processing the data and / or information involved in the above method.

[0077] The chip system may be composed of chips, or may include chips and other discrete devices.

[0078] The processor mentioned in any of the above places can be a CPU, a microprocessor, an ASIC, or one or more integrated circuits for executing programs for controlling the above-mentioned feedback information transmission method.

[0079] In a possible design, the chip system also includes a memory, which is used to store necessary program instructions and data. The processor and the memory can be decoupled and respectively set on different devices, connected by wire or wireless means to support the chip system to implement various functions in the above embodiments. Alternatively, the processor and the memory can also be coupled on the same device.

[0080] Optionally, the computer instructions are stored in a memory.

[0081] Optionally, the memory is a storage unit within the chip, such as a register, a cache, etc. The memory can also be a storage unit within the terminal located outside the chip, such as a ROM or other types of static storage devices that can store static information and instructions, RAM, etc.

[0082] It can be understood that the memory in the present application can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories.

[0083] The non-volatile memory may be a ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), or a flash memory.

[0084] The volatile memory may be a RAM, which is used as an external cache. There are many different types of RAM, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct memory bus RAM.

[0085] The embodiments of this specific implementation method are all preferred embodiments of the present application, and are not intended to limit the protection scope of the present application. Therefore, all equivalent changes made based on the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A customer screening and advertisement recommendation method based on data analysis, characterized in that: include: In response to the acquired real-time image, the real-time image is parsed to obtain a person object included in the real-time image; The person objects included in the real-time image are grouped to obtain person object groups, wherein the person objects in each person object group have an associated relationship; Obtain movement trajectories of the person object groups and generate analysis data according to the movement trajectories, wherein each person object group corresponds to one piece of analysis data; Using the analysis data to generate advertising recommendations associated with the group of personas; A newly added movement track of the character object group is obtained and an advertisement recommendation scheme is implemented on a display terminal associated with the newly added movement track.

2. The method for customer screening and advertisement recommendation based on data analysis according to claim 1, characterized in that: The grouping process of the human objects included in the real-time image includes: Using the human object included in the real-time image, an analysis reference network is established, wherein each grid in the analysis reference network has the same number of edges; Obtain the size variation of each grid in the analysis reference network in time series; When the size variation of the grid exceeds the allowable range, the grid is eliminated until the number of grids in the analysis reference network is less than or equal to the set number; The sparse regions of the analysis reference network are calculated and the analysis reference network is pre-partitioned.

3. The method for customer screening and advertisement recommendation based on data analysis according to claim 2, characterized in that: Calculating the sparse area of ​​the analysis reference network and pre-dividing the analysis reference network includes: Calculate and analyze the segment length of each line segment in the reference network, the average length of the line segment, the grid area of ​​each grid and the average area of ​​the grid; Annotate a first range in the analysis reference network according to the length of the line segment and the mean length of the line segment; Annotate the second range in the analysis reference network based on the grid area and the mean grid area; Determine an overlapping area between the first range and the second range, and record it as an overlapping range; The analysis reference network is pre-partitioned using the coincident range, first range remainder, and second range remainder.

4. The method for customer screening and advertisement recommendation based on data analysis according to any one of claims 1 to 3, characterized in that: Obtaining the movement trajectory of the person object group and generating analysis data according to the movement trajectory includes: Creating a moving trajectory based on the movement of the group of human objects, the moving trajectory includes sequentially connected line segments and points located on the line segments, the line segments have length and direction, the points have area, and the area of ​​the points is negatively correlated with the moving speed; determining the type of content associated with the point; Generates analytical data based on the content type associated with the point, including content type, content type association time, and content type ratio.

5. The method for customer screening and advertisement recommendation based on data analysis according to any one of claims 1 to 3, characterized in that: The method also includes determining the weight of each character object in the character object group and adjusting the advertisement recommendation scheme according to the weight of the character object.

6. The method for customer screening and advertisement recommendation based on data analysis according to claim 5, characterized in that: Determining the weight of each character object in the character object group includes: Calculate and analyze the degree of change of each grid in the reference network, including the size change and the speed of change; The character objects in the analysis reference network are sorted according to the degree of change, and the weights of the character objects in the sequential sequence decrease in sequence.

7. The method for customer screening and advertisement recommendation based on data analysis according to claim 6, characterized in that: It also includes using the moving track to correct the sorting of character objects. Using the moving track to correct the sorting of character objects includes: Get the area and attributes of all points on the moving trajectory; Group all points according to their attributes and calculate the total area of ​​each point in each group to obtain the group value; Use grouping values ​​to modify the order of person objects in a sequential sequence.

8. A customer screening and advertisement recommendation device based on data analysis, characterized in that: include: An image analysis unit, configured to analyze the real-time image in response to the acquired real-time image, and obtain a person object included in the real-time image; A grouping processing unit, used for grouping the human objects included in the real-time image to obtain human object groups, wherein the human objects in each human object group have an associated relationship; A data generating unit, used for acquiring the movement trajectory of the person object group and generating analysis data according to the movement trajectory, wherein each person object group corresponds to one piece of analysis data; A scheme generating unit, configured to generate an advertisement recommendation scheme associated with the character object group using the analysis data; The scheme implementation unit is used to obtain a newly added movement track of the character object group and implement the advertisement recommendation scheme on a display terminal associated with the newly added movement track.

9. A customer screening and advertising recommendation system based on data analysis, characterized in that: The system comprises: one or more memories for storing instructions; and One or more processors, configured to call and execute the instructions from the memory to perform the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer readable storage medium comprises: The program, when the program is executed by a processor, the method according to any one of claims 1 to 7 is executed.

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