Passenger flow statistical analysis method and system based on abiotic identity recognition information
Through a method based on non-biological identity identification information, using multiple sensors to generate unique ID and mobile trajectory information, the problems of compliance and privacy protection in existing passenger flow statistics are solved, and high-precision passenger flow analysis is achieved.
Patent Information
- Application Number
- CN202510416980.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-03
- Publication Date
- 2025-08-05
AI Technical Summary
The existing customer flow statistics methods rely on bioidentity identification technology and face compliance tests, making it difficult to achieve accurate customer flow analysis while protecting user privacy.
Using a method based on non-biological identity identification information, the customer's basic information set, appearance location and time information are obtained through multiple sensors, unique ID information is generated, mobile trajectory and passenger flow heat map are generated, and hash algorithm encryption is used to ensure data compliance and privacy protection.
It realizes fast and accurate passenger flow statistics, reduces statistical omissions and data deviations, meets compliance requirements, and protects user privacy. The accuracy of passenger flow statistics is greater than 95%, and the matching degree of traffic prediction and real trajectory is 85%.
Smart Images
Figure CN120430815A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of intelligent data analysis, and in particular to a passenger flow statistical analysis method and system based on non-biometric identity recognition information. Background Art
[0002] Currently, traditional customer counting methods typically rely on biometric identification technologies such as facial and voiceprint recognition, or identification technologies such as Bluetooth and WiFi probes. With growing consumer awareness of privacy protection and increasing government oversight, customer counting and traffic flow analysis solutions in retail settings like shopping malls and stores are facing increasingly stringent compliance challenges. Summary of the Invention
[0003] Based on this, the embodiment of the present application provides a passenger flow statistics analysis method and system based on non-biometric identity recognition information to solve the problem of high compliance requirements for data collection in existing technical solutions.
[0004] In a first aspect, an embodiment of the present application provides a passenger flow statistics analysis method based on non-biometric identity recognition information, the method comprising:
[0005] Based on a plurality of preset sensors, a customer basic information set, appearance location information, and appearance time information corresponding to the appearance location information corresponding to a plurality of target customers are obtained;
[0006] For each target customer: generating unique ID information based on the customer basic information set;
[0007] Based on the unique ID information, generating movement trajectory information according to the appearance position information and the appearance time information;
[0008] Generate customer flow heat map information based on the movement trajectory information corresponding to the multiple target customers.
[0009] Compared with the existing technology, the beneficial effect is: the passenger flow statistical analysis method based on non-biometric identity recognition information provided by the embodiment of the present application, the terminal device can quickly obtain the customer basic information set, appearance location information and appearance time information corresponding to the appearance location information corresponding to multiple target customers based on a preset multiple sensors, and then perform the processing for each target customer: based on the customer basic information set, accurately generate unique ID information, and then based on the unique ID information, effectively generate movement trajectory information according to the appearance location information and appearance time information, and finally accurately generate passenger flow heat map information according to the movement trajectory information corresponding to multiple target customers, thereby reducing statistical omissions and effectively reducing data deviations, and completing passenger flow statistical operations through non-biometric identity information, protecting user privacy to the greatest extent, and meeting the compliance requirements of data collection well, and to a certain extent solving the problem of high compliance requirements for current data collection.
[0010] In a second aspect, an embodiment of the present application provides a passenger flow statistics analysis system based on non-biometric identity recognition information, the system comprising:
[0011] A customer basic information set acquisition module is used to acquire customer basic information sets, appearance location information, and appearance time information corresponding to the appearance location information corresponding to multiple target customers based on a plurality of preset sensors;
[0012] A unique ID information generating module is configured to generate unique ID information for each target customer based on the customer basic information set;
[0013] Movement track information generation module: used for generating movement track information based on the unique ID information, the appearance position information and the appearance time information;
[0014] The passenger flow heat map information generation module is used to generate passenger flow heat map information according to the movement trajectory information corresponding to the plurality of target customers.
[0015] In a third aspect, an embodiment of the present application provides a terminal device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method of the first aspect described above when executing the computer program.
[0016] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method of the first aspect described above are implemented.
[0017] It can be understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS
[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art.
[0019] Figure 1 This is a flow chart of a passenger flow statistics analysis method provided in one embodiment of the present application;
[0020] Figure 2 2 is a flow chart of step S200 in the passenger flow statistics analysis method provided in one embodiment of the present application;
[0021] Figure 3 This is a module block diagram of a passenger flow statistics analysis system provided by an embodiment of the present application;
[0022] Figure 4 This is a schematic diagram of a terminal device provided in one embodiment of the present application. DETAILED DESCRIPTION
[0023] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.
[0024] In the description of this application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.
[0025] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.
[0026] In order to illustrate the technical solution described in this application, specific embodiments are provided below.
[0027] See also Figure 1 , Figure 1The figure is a flow chart of a passenger flow statistics and analysis method based on non-biometric identification information provided in an embodiment of the present application. In this embodiment, the execution subject of the passenger flow statistics and analysis method is a terminal device. It is understood that the types of terminal devices include but are not limited to mobile phones, tablet computers, laptop computers, ultra-mobile personal computers (UMPCs), netbooks, personal digital assistants (PDAs), etc., and the embodiments of the present application do not impose any restrictions on the specific types of terminal devices.
[0028] See also Figure 1 The passenger flow statistics analysis method provided in the embodiment of the present application includes but is not limited to the following steps:
[0029] In S100 , based on a plurality of preset sensors, a customer basic information set corresponding to a plurality of target customers, appearance location information, and appearance time information corresponding to the appearance location information are obtained.
[0030] Specifically, the terminal device can first obtain a set of basic customer information corresponding to multiple target customers based on a plurality of preset sensors, and then obtain the appearance location information and the appearance time information corresponding to the appearance location information of each target customer. The plurality of sensors can be non-biometric sensors, such as a gravity sensor (i.e., a gravimeter), an infrared sensor (i.e., an infrared instrument), a temperature sensor (i.e., a body temperature measuring instrument), a barometer, and / or a millimeter-wave radar. The plurality of sensors can be pre-installed at the entrance of the scene. In one possible implementation, the plurality of sensors can be commercial-grade sensors, such as an infrared light curtain and a piezoelectric floor mat.
[0031] Specifically, location information describes the target customer's location within the venue. This location information can be acquired by sensors pre-installed at key locations within the venue (e.g., around the shelf area or checkout counter). Time of appearance information describes the moment the target customer appeared at a certain location within the venue.
[0032] Without loss of generality, the customer basic information set includes weight information, height information, body shape information, body surface temperature distribution information and movement direction information, wherein the weight information is used to describe the target customer's weight data; the height information is used to describe the target customer's height data; the body shape information is used to describe the target customer's body shape data; the surface temperature distribution information is used to describe the target customer's surface temperature distribution data; and the movement direction information is used to describe the target customer's movement direction.
[0033] Exemplarily, the terminal device can determine the target customer's weight information based on a gravity sensor. In one possible implementation, the terminal device can be divided into several segments according to weight; the terminal device can measure the target customer's height based on an infrared sensor to determine the target customer's height information, and measure the target customer's shoulder width and stride based on the infrared sensor to determine the target customer's body shape; the terminal device can detect the temperature difference between the target customer's head and shoulders based on a temperature sensor to determine the body surface temperature distribution information; the terminal device can determine the target customer's moving direction information based on a millimeter wave radar. In one possible implementation, the terminal device can assist in determining the target customer's moving direction based on a preset environmental sensor, such as a barometer.
[0034] In S200 , for each target customer: unique ID information is generated based on the customer basic information set.
[0035] Specifically, after the terminal device obtains the customer basic information set, the terminal device can perform this processing for each target customer: based on the customer basic information set, generate unique ID information, thereby using non-biometric features (such as face) to generate a temporary unique ID.
[0036] In some possible implementations, in order to generate valid unique ID information, please refer to Figure 2 Step S200 includes but is not limited to the following steps:
[0037] In S210 , for each target customer, multi-dimensional feature vector information is generated based on weight information, height information, body shape information, body surface temperature distribution information, and movement direction information.
[0038] Specifically, the terminal device can perform this processing for each target customer: based on weight information, height information, body contour information, body surface temperature distribution information and movement direction information, multi-dimensional feature vector information is generated, thereby realizing the normalization of the collected physical parameters.
[0039] In S220 , the multi-dimensional feature vector information is encrypted based on a hash algorithm to generate unique ID information.
[0040] Specifically, after the terminal device generates the multi-dimensional feature vector information, the terminal device can encrypt the multi-dimensional feature vector information based on a hash algorithm to generate unique ID information. In one possible implementation, the unique ID information is valid until the target customer leaves the store.
[0041] In S300 , based on the unique ID information, movement trajectory information is generated according to the appearance position information and the appearance time information.
[0042] Specifically, after the terminal device generates unique ID information, the terminal device can generate movement trajectory information based on the unique ID information, according to the appearance location information and the appearance time information, wherein the movement trajectory information is used to describe the movement trajectory of the target customer.
[0043] In some possible implementations, in order to effectively generate movement trajectory information, before step S300, the method further includes but is not limited to the following steps:
[0044] In S301, the stay duration information corresponding to the appearance location information is obtained.
[0045] Specifically, the terminal device may obtain the stay duration information corresponding to the appearance location information, wherein the stay duration information is used to describe the stay duration of the target customer at the appearance location information.
[0046] It should be noted that, since there are likely to be multiple target customers in the venue, the terminal device can match the unique ID information and then update the appearance location information and appearance time information.
[0047] Accordingly, the above step S300 includes but is not limited to the following steps:
[0048] In S310 , based on the unique ID information and the stay duration information, the appearance position information is sorted in descending order of the appearance time information to generate movement trajectory information.
[0049] Specifically, the terminal device can sort the appearance location information in order from the earliest to the latest appearance time based on the unique ID information and the length of stay information, and generate movement trajectory information, thereby tracking the target customer's route of action.
[0050] In S400 , customer flow heat map information is generated based on the movement trajectory information corresponding to the plurality of target customers.
[0051] Specifically, after the terminal device generates movement trajectory information, it can generate customer flow heat map information based on the movement trajectory information corresponding to multiple target customers, thereby facilitating in-depth analysis of the movement trajectories of multiple target customers. In one possible implementation, the terminal device can use time series analysis and clustering algorithms (such as DBSCAN) to associate movement trajectories with the same ID to construct customer flow heat map information.
[0052] In some possible implementations, to further improve data security, after step S400, the method further includes but is not limited to the following steps:
[0053] In S401 , in response to a customer's exit instruction, the unique ID information of the target customer is invalidated.
[0054] Specifically, the terminal device may automatically invalidate the unique ID information of the target customer in response to a customer exit instruction, wherein the customer exit instruction is used to instruct the target customer to leave the venue.
[0055] In some possible implementations, in order to further improve data security, after step S400, the method further includes but is not limited to the following steps:
[0056] In S402, the passenger flow heat map information is uploaded to a designated cloud server.
[0057] Specifically, the terminal device can upload the passenger flow heat map information to the designated cloud server. It should be noted that the original data does not need to be uploaded to the cloud server.
[0058] It should be noted that the passenger flow statistics analysis method of the present application has a passenger flow statistics accuracy rate greater than 95%, and the matching degree between the movement line prediction and the actual trajectory reaches 85%, and it fully complies with relevant data privacy requirements.
[0059] The implementation principle of the passenger flow statistical analysis method based on non-biometric identity recognition information in the embodiment of the present application is as follows: the terminal device can quickly obtain the customer basic information set, appearance location information and appearance time information corresponding to the appearance location information corresponding to multiple target customers based on a preset plurality of sensors, and then perform the processing for each target customer: based on the customer basic information set, accurately generate unique ID information, and then based on the unique ID information, effectively generate movement trajectory information according to the appearance location information and appearance time information, and finally accurately generate passenger flow heat map information according to the movement trajectory information corresponding to multiple target customers, thereby realizing cross-validation using multiple sensors and reducing the possibility of misjudgment by a single sensor.
[0060] It should be noted that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.
[0061] The embodiment of the present application also provides a passenger flow statistics analysis system based on non-biological identity recognition information. For ease of explanation, only the parts related to the present application are shown, such as Figure 3 As shown, the system 30 includes:
[0062] The customer basic information set acquisition module 31 is used to acquire the customer basic information sets, appearance location information, and appearance time information corresponding to the appearance location information corresponding to multiple target customers based on a plurality of preset sensors;
[0063] Unique ID information generation module 32: for generating unique ID information for each target customer based on the customer basic information set;
[0064] Movement track information generating module 33: used to generate movement track information based on unique ID information, appearance position information and appearance time information;
[0065] The passenger flow heat map information generating module 34 is used to generate passenger flow heat map information according to the movement trajectory information corresponding to multiple target customers.
[0066] Optionally, the sensors include a gravimeter, an infrared instrument, a body temperature measuring instrument, a barometer, and a millimeter wave radar.
[0067] Optionally, the customer basic information set includes weight information, height information, body shape information, body surface temperature distribution information and movement direction information; the unique ID information generation module 32 includes:
[0068] Multi-dimensional feature vector information generation submodule: for each target customer: generating multi-dimensional feature vector information based on weight information, height information, body shape information, body surface temperature distribution information and movement direction information;
[0069] Unique ID information generation submodule: used to encrypt multi-dimensional feature vector information based on the hash algorithm to generate unique ID information.
[0070] Optionally, the system 30 further includes:
[0071] Stay duration information acquisition module: used to obtain the stay duration information corresponding to the location information;
[0072] Accordingly, the movement trajectory information generating module 33 includes:
[0073] Movement trajectory information generation submodule: used to sort the appearance location information in descending order based on the unique ID information and the stay duration information to generate movement trajectory information.
[0074] Optionally, the system 30 further includes:
[0075] Unique ID information failure module: used to invalidate the unique ID information of the target customer in response to the customer's exit instruction.
[0076] Optionally, the system 30 further includes:
[0077] Passenger flow heat map information upload module: used to upload passenger flow heat map information to the designated cloud server.
[0078] It should be noted that the information interaction, execution process and other contents between the above modules are based on the same concept as the method embodiment of this application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.
[0079] The present application also provides a terminal device, such as Figure 4 As shown, the terminal device 40 of this embodiment includes: a processor 41, a memory 42, and a computer program 43 stored in the memory 42 and executable on the processor 41. When the processor 41 executes the computer program 43, the steps in the above-mentioned passenger flow statistical analysis method embodiment are implemented, such as Figure 1 Steps S100 to S400 shown; or, when the processor 41 executes the computer program 43, the functions of each module in the above device are realized, for example Figure 3 The functions of modules 31 to 34 are shown.
[0080] The terminal device 40 can be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server, and the terminal device 40 includes but is not limited to a processor 41 and a memory 42. Those skilled in the art will understand that Figure 4 It is merely an example of the terminal device 40 and does not constitute a limitation on the terminal device 40. The terminal device 40 may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal device 40 may also include input and output devices, network access devices, buses, etc.
[0081] Among them, the processor 41 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.; the general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.
[0082] The memory 42 can be an internal storage unit of the terminal device 40, such as a hard disk or memory of the terminal device 40, or the memory 42 can be an external storage device of the terminal device 40, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the terminal device 40; further, the memory 42 can also include both the internal storage unit of the terminal device 40 and the external storage device, and the memory 42 can also store the computer program 43 and other programs and data required by the terminal device 40, and the memory 42 can also be used to temporarily store data that has been output or is to be output.
[0083] One embodiment of the present application further provides a computer-readable storage medium, which stores a computer program, which, when executed by a processor, can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form; the computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium.
[0084] The above are all preferred embodiments of the present application, and are not intended to limit the scope of protection of the present application. Therefore, any equivalent changes made based on the methods, principles, and structures of the present application should be included in the scope of protection of the present application.
Claims
1. A passenger flow statistical analysis method based on non-biometric identity recognition information, characterized in that: The method comprises: Based on a plurality of preset sensors, a customer basic information set, appearance location information, and appearance time information corresponding to the appearance location information corresponding to a plurality of target customers are obtained; For each target customer: generating unique ID information based on the customer basic information set; Based on the unique ID information, generating movement trajectory information according to the appearance position information and the appearance time information; Generate customer flow heat map information based on the movement trajectory information corresponding to the multiple target customers.
2. The method according to claim 1, characterized in that The sensors include a gravimeter, an infrared instrument, a body temperature measuring instrument, a barometer and a millimeter wave radar.
3. The method according to claim 1, characterized in that The customer basic information set includes weight information, height information, body shape information, body surface temperature distribution information and movement direction information; for each target customer: based on the customer basic information set, generating unique ID information, including: For each target customer: generating multidimensional feature vector information based on the weight information, the height information, the body shape information, the body surface temperature distribution information, and the movement direction information; Based on the hash algorithm, the multi-dimensional feature vector information is encrypted to generate unique ID information.
4. The method according to claim 1, wherein Before generating movement trajectory information based on the unique ID information and according to the appearance location information and the appearance time information, the method further includes: Obtaining the stay duration information corresponding to the appearance location information; Accordingly, the generating of movement trajectory information based on the unique ID information and the appearance location information and the appearance time information includes: Based on the unique ID information and the stay duration information, the appearance position information is sorted in the order of the appearance time information from earliest to latest to generate movement trajectory information.
5. The method according to claim 1, wherein After generating customer flow heat map information based on the movement trajectory information corresponding to the plurality of target customers, the method further includes: In response to a customer exit instruction, invalidating the unique ID information of the target customer.
6. The method according to claim 1, characterized in that After generating customer flow heat map information based on the movement trajectory information corresponding to the plurality of target customers, the method further includes: Upload the passenger flow heat map information to the designated cloud server.
7. A passenger flow statistics analysis system based on non-biological identity recognition information, characterized in that: The system comprises: A customer basic information set acquisition module is used to acquire customer basic information sets, appearance location information, and appearance time information corresponding to the appearance location information corresponding to multiple target customers based on a plurality of preset sensors; A unique ID information generating module is configured to generate unique ID information for each target customer based on the customer basic information set; Movement track information generation module: used for generating movement track information based on the unique ID information, the appearance position information and the appearance time information; The passenger flow heat map information generation module is used to generate passenger flow heat map information according to the movement trajectory information corresponding to the plurality of target customers.
8. The system according to claim 6, wherein: The customer basic information set includes weight information, height information, body shape information, body surface temperature distribution information and movement direction information; the movement trajectory information generation module includes: A multi-dimensional feature vector information generating submodule is configured to generate multi-dimensional feature vector information for each target customer based on the weight information, the height information, the body shape information, the body surface temperature distribution information, and the movement direction information; Unique ID information generation submodule: used to encrypt the multi-dimensional feature vector information based on the hash algorithm to generate unique ID information.
9. A terminal device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.
10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.