Region management method and system, and device and storage medium

Abstract

The present application belongs to the technical field of data analysis. Disclosed are a region management method and system, and a device and a storage medium. The method comprises: acquiring a plurality of images corresponding to a region, wherein the plurality of images respectively comprise corresponding collection sub-region information and corresponding collection time information; respectively performing image recognition on the plurality of images, so as to obtain image recognition results, and on the basis of the image recognition results, acquiring attribute information of a plurality of objects comprised in the plurality of images; on the basis of the collection sub-region information, the collection time information and the image recognition results which respectively correspond to the plurality of images, acquiring time information of the plurality of objects in different sub-regions in the region; and on the basis of the time information of the plurality of objects in the different sub-regions in the region and the attribute information of the plurality of objects, performing management on the region. In the method, automatic recognition is performed on a plurality of images in a region, such that management is performed on the region with regard to attribute information of objects in the region and time information of the objects in different sub-regions, thereby improving the management efficiency of the region.

Description

Regional management method, system, device and storage medium Technical Field

[0001] The embodiments of the present application relate to the field of data analysis technology, and in particular to a region management method, system, device, and storage medium. Background Art

[0002] With the rapid development of the park, the number of visitors to the park continues to increase. Therefore, how to manage the area where the park is located to improve the management efficiency of the area is an urgent problem to be solved.

[0003] Summary of the Invention

[0004] The embodiments of the present application provide a method, system, device, and storage medium for regional management, which can be used to improve regional management efficiency. The technical solution is as follows:

[0005] In a first aspect, a method for regional management is provided, the method comprising:

[0006] Acquire multiple images corresponding to the area, wherein the multiple images respectively include corresponding acquisition sub-area information and acquisition time information;

[0007] performing image recognition on each of the plurality of images to obtain image recognition results, and acquiring attribute information of a plurality of objects included in the plurality of images according to the image recognition results;

[0008] Based on the acquisition sub-region information, acquisition time information and the image recognition result respectively corresponding to the multiple images, obtaining time information of different sub-regions of the multiple objects within the region;

[0009] The area is managed according to time information of different sub-areas of the multiple objects in the area and attribute information of the multiple objects, and the management includes at least one of campus area object management, campus area facility management or campus area service management.

[0010] On the other hand, a regional management system is also provided, the system comprising a plurality of image acquisition devices, an image recognition module, and a management and analysis module;

[0011] The multiple image acquisition devices are used to acquire multiple images corresponding to the area, wherein the multiple images respectively include corresponding acquisition sub-area information and acquisition time information; and send the multiple images to the image recognition module;

[0012] The image recognition module is configured to perform image recognition on each of the multiple images to obtain image recognition results, and obtain attribute information of multiple objects included in the multiple images based on the image recognition results; obtain time information of the multiple objects in different sub-areas within the area based on the acquisition sub-area information and acquisition time information corresponding to the multiple images, and the image recognition results; and send the time information of the multiple objects in different sub-areas within the area and the attribute information of the multiple objects to the management and analysis module;

[0013] The management analysis module is used to manage the area based on time information of the multiple objects in different sub-areas of the area and attribute information of the multiple objects, and the management includes at least one of area object management, area facility management or area service management.

[0014] On the other hand, a computer device is also provided, comprising a processor and a memory, wherein the memory stores at least one computer program, and the at least one computer program is loaded and executed by the processor so that the computer device implements the area management method described in any of the above aspects.

[0015] On the other hand, a non-volatile computer-readable storage medium is provided, in which at least one computer program is stored. The at least one computer program is loaded and executed by a processor so that the computer implements the area management method described in any of the above aspects.

[0016] In another aspect, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the area management method described in any of the above aspects.

[0017] The technical solutions provided by the embodiments of the present application bring at least the following beneficial effects:

[0018] The technical solution provided in this application automatically identifies multiple images within a region, thereby managing the region based on the attribute information of objects within the region and the time information of objects in different sub-regions, thereby improving the management efficiency of the region. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] 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 description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0020] FIG1 is a schematic diagram of an implementation environment of a regional management method provided in an embodiment of the present application;

[0021] FIG2 is a flow chart of a regional management method provided by an embodiment of the present application;

[0022] FIG3 is a schematic diagram of the installation position of an image acquisition device within a region provided by an embodiment of the present application;

[0023] FIG4 is a schematic diagram of a coincidence ratio calculation process provided in an embodiment of the present application;

[0024] FIG5 is a schematic diagram of an image acquisition process provided by an embodiment of the present application;

[0025] FIG6 is a schematic diagram of an object attribute analysis process provided by an embodiment of the present application;

[0026] FIG7 is a schematic diagram of a process for determining entry and exit direction information provided by an embodiment of the present application;

[0027] FIG8 is a schematic diagram of the structure of a visual interface provided in an embodiment of the present application;

[0028] FIG9 is a crowd warning flow chart provided in an embodiment of the present application;

[0029] FIG10 is a flowchart of a store marketing method analysis provided by an embodiment of the present application;

[0030] FIG11 is a flowchart of an analysis of store operating hours within a region provided by an embodiment of the present application;

[0031] FIG12 is a flowchart of a regional management method provided in an embodiment of the present application;

[0032] FIG13 is a schematic diagram of the structure of a regional management system provided in an embodiment of the present application;

[0033] FIG14 is a schematic structural diagram of a server provided in an embodiment of the present application;

[0034] FIG15 is a schematic structural diagram of a terminal provided in an embodiment of the present application. DETAILED DESCRIPTION

[0035] In order to make the objectives, technical solutions and advantages of this application clearer, the implementation methods of this application will be further described in detail below with reference to the accompanying drawings.

[0036] It should be noted that the terms "first", "second", etc. (if any) in the specification of the present application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application.

[0037] With the rapid development of the park, the number of tourists in the park continues to increase. For the area where the park is located, it is necessary to conduct a detailed analysis of tourists in order to provide personalized services and conduct more effective marketing.

[0038] In related technologies, facial recognition and passenger flow counting technologies are used to automatically identify and count the number of people entering a monitored area during area management. However, analysis of customer attributes and trends is relatively limited. Facial recognition systems use video cameras to capture and analyze facial features, then compare them with information in a database to identify individuals. Facial recognition technology is primarily used in access control systems and security monitoring, but it cannot provide in-depth analysis of crowd attributes, such as age, gender, and emotional state. Passenger flow counting technologies measure passenger flow by monitoring moving objects in video. While passenger flow counting technologies can provide certain patterns and data, they cannot analyze detailed customer behavior, such as their travel paths, duration of stay, and points of interest.

[0039] This embodiment of the present application proposes a regional management method to improve regional management efficiency and further enhance regional service quality. Please refer to Figure 1, which shows a schematic diagram of an implementation environment for the regional management method provided in this embodiment of the present application. This implementation environment may include: an image acquisition device 11 and a server 12, which establish a communication connection between the image acquisition device 11 and the server 12 via a wired or wireless network.

[0040] The image acquisition device 11 is capable of capturing images within the area and transmitting the images to the server 12. The image acquisition device 11 may include multiple cameras installed within the area. For example, the image acquisition device 11 captures multiple images within the area through the cameras. Each of the multiple images includes capture sub-area information corresponding to the captured image location and capture time information. The image acquisition device 11 then transmits the captured multiple images to the server 12.

[0041] In one possible implementation, server 12 may be a big data cloud platform capable of executing the regional management method provided in the embodiments of the present application, and the big data cloud platform may be capable of acquiring multiple images sent by image acquisition device 11. Optionally, server 12 acquires attribute information of multiple objects within the region and time information of multiple objects in different sub-regions within the region based on the multiple images, and then manages the region based on the attribute information of multiple objects within the region and time information of multiple objects in different sub-regions within the region. In the embodiments of the present application, server 12 may be a single server, a server cluster consisting of multiple servers, or a cloud computing service center.

[0042] Exemplarily, the image acquisition device 11 can be connected to the server 12 via a transfer device 13. The embodiment of the present application does not limit the type of the transfer device 13. For example, the transfer device 13 can be a computer. The transfer device 13 is connected to the image acquisition device 11. The multiple images acquired by the image acquisition device 11 can be stored in the transfer device 13. The transfer device 13 can send the multiple images to the server 12 so that the server 12 can manage the area based on the multiple images. Optionally, the image acquisition device 11 is connected to each branch computer room via a network, and each branch computer room is connected to the central computer room via a network. The transfer device 13 is placed in each branch computer room, and the server 12 is placed in the central computer room.

[0043] Those skilled in the art should understand that the above-mentioned image acquisition device 11 and server 12 are only examples. Other existing or future image acquisition devices or servers that are applicable to this application should also be included in the scope of protection of this application and are included here by reference.

[0044] See Figure 2, which is a flow chart of a regional management method provided in an embodiment of the present application. This method can be applied to the implementation environment shown in Figure 1. For example, the method can be executed by server 12 shown in Figure 1; or, alternatively, the method can be executed interactively by transfer device 13 and server 12 shown in Figure 1. As shown in Figure 2, the method includes, but is not limited to, steps 201 through 204.

[0045] Step 201: Acquire multiple images corresponding to a region, where the multiple images respectively include corresponding acquisition sub-region information and acquisition time information.

[0046] In an embodiment of the present application, multiple image acquisition devices are installed in different sub-areas within the area, and any image acquisition device is used to capture images of the corresponding sub-area. Multiple image acquisition devices are distributed in various corners and high points of the area to ensure that each sub-area can be photographed, wherein the vertical distance from the high point to the ground is greater than or equal to the height threshold. Thus, multiple images corresponding to the area can be obtained through images captured by multiple image acquisition devices. Among them, the area can refer to a park, for example, a cultural and tourism park. A cultural and tourism park is a park that serves tourists, with cultural sightseeing and recreational experience as its core attributes, and has multiple functions such as cultural leisure and creativity, tourism, vacation and recuperation, exhibition, science and education, etc.

[0047] For example, refer to the schematic diagram of the installation position of the image acquisition device in an area shown in Figure 3, wherein there are multiple entrances and exits 303 in the sub-area 301, and each entrance and exit 303 is installed with at least one image acquisition device. Taking the image acquisition device as camera 302 as an example, there is at least one camera 302 whose shooting sub-area includes the entrance and exit 303, so as to prevent tourists in the area from entering the sub-area 301 through paths outside the sub-area shot by camera 302, resulting in errors in the subsequent calculation of the number of tourists.

[0048] Optionally, two cameras 302 are installed at one entrance 303 in Figure 3, including a first camera 3021 and a second camera 3022. The first camera 3021 captures a subarea on the side of the entrance 303 closer to the subarea 301, while the second camera 3022 captures a subarea on the side of the entrance 303 farther from the subarea 301. The images captured by cameras 3021 and 3022 can verify each other, thereby improving the accuracy of the image content. This embodiment of the present application does not limit the location of the camera 302 installed at each entrance 303; for example, the camera 302 can be installed at the top or side of the entrance.

[0049] In addition, a camera can also be installed inside sub-area 301 (not shown in FIG3 ) to obtain the distribution of people in sub-area 301. This embodiment of the application does not limit the model of camera 302. It can be determined based on the specific situation of sub-area 301 and the installation position of camera 302. For example, if the distance between the sub-area captured by camera 302 and camera 302 is 10 meters, the focal length of camera 302 is selected to be 4 mm.

[0050] When capturing each image, the image capture device can record the timestamp of capturing each image, and each image capture device includes a corresponding device number and installation location. Therefore, each image includes corresponding capture sub-area information and capture time information.

[0051] Among them, the acquisition sub-area information can be obtained through the device number and installation location of the image acquisition device. The acquisition sub-area information can refer to the information of the specific sub-area of ​​the area covered by the image. If the area includes an entertainment area, a sales area, and a storage area, the acquisition sub-area information will identify which specific sub-area the image corresponds to. For example, if the image acquisition device is installed in a storage area, then the image acquired by the image acquisition device can be marked as a storage area. The acquisition time information can be obtained by acquiring the timestamp of each image. The acquisition sub-area information can refer to the specific time when the image was acquired. The time information can be a specific date and time, or a time interval, such as morning, noon, and evening.

[0052] Exemplarily, the process of acquiring multiple images corresponding to an area may include acquiring multiple acquired images obtained by acquiring different sub-areas of the area using multiple image acquisition devices, the multiple acquired images respectively including corresponding acquisition sub-area information and acquisition time information, performing object detection on the multiple acquired images, acquiring detection frames respectively included in the multiple acquired images, one detection frame corresponding to one object, and determining that the acquired images whose image quality within the detection frames in the multiple acquired images meets the quality requirements are the multiple images.

[0053] Object detection is performed on multiple captured images. An object detection algorithm is used to generate one or more detection frames in each image, each corresponding to a detected object. Exemplarily, object detection can be performed using a terminal box. This embodiment of the present application does not limit the type of terminal box used to perform object detection. For example, the terminal box can be a computer with object detection capabilities.

[0054] Different terminal boxes implement object detection using different object detection models. Optional object detection models include, but are not limited to, YOLOv5-m (You Only Look Once version 5-medium), YOLOv5-s (You Only Look Once version 5-small), YOLOv5-l (You Only Look Once version 5-large), and YOLOv5-x (You Only Look Once version 5-extra large). The terminal box uses the object detection model to adjust the original network output and increase the number of obj (object bounding box) to accommodate different detection targets. During training, different detection target data passes through the same network layer structure, but uses different detection category losses (loss functions). This allows the model to share underlying features while also learning specifically for each target, ensuring accuracy and real-time performance.

[0055] After obtaining the detection frame, it is necessary to further determine which captured images have image quality that meets the quality requirements. Image quality can include a variety of indicators, such as image clarity, brightness, contrast, etc.; it can also be based on the quality of the detection frame, such as whether the target in the detection frame is clear and complete. Quality requirements can be flexibly adjusted according to the application scenario. The quality requirement can be a quality standard or quality threshold set based on image quality. The captured images that meet the quality standard or threshold will be selected as the final multiple images. For example, if the quality requirement is that the image clarity is greater than the clarity threshold, then the images with clarity greater than the clarity threshold among the multiple captured images will be used as the multiple images for area management.

[0056] In one possible implementation, there may be multiple detection frames for an object. For example, for tourists in an area, a tourist may have a corresponding human body detection frame, head detection frame, and face detection frame, etc. The application scenarios corresponding to different types of detection frames may be different, and this embodiment of the present application does not limit this. Optionally, the human body detection frame, head detection frame, and face detection frame of the same tourist can be matched to facilitate the classification and storage of the detection frames of the same tourist. This embodiment of the present application does not limit the method of matching detection frames. For example, matching can be performed by calculating whether the overlap ratio of two detection frames is greater than an overlap threshold. The overlap threshold can be set based on experience, or flexibly adjusted according to the application scenario.

[0057] Referring to the schematic diagram of the overlap ratio calculation process shown in Figure 4, the position of the head detection frame 401 is represented by [x1, y1, x2, y2], where (x1, y1) represents the coordinates of the upper left corner, and (x2, y2) represents the coordinates of the lower right corner. The position of the face detection frame 402 is represented by [x3, y3, x4, y4], where (x3, y3) represents the coordinates of the upper left corner, and (x4, y4) represents the coordinates of the lower right corner.

[0058] The intersection area of ​​head detection frame 401 and face detection frame 402 is denoted by S1, and the union area of ​​head detection frame 401 and face detection frame 402 is denoted by S2, where S1 = |x2-x3|*|y2-y3|, S2 = |x2-x1|*|y2-y1|+|x4-x3|*|y4-y3|-*S1, and the overlap ratio of head detection frame 401 and face detection frame 402 = S1 / S2. If the overlap ratio is greater than the overlap threshold, it is determined that head detection frame 401 and face detection frame 402 correspond to the same tourist. If the overlap ratio is not greater than the overlap threshold, it is determined that head detection frame 401 and face detection frame 402 do not correspond to the same tourist.

[0059] Optionally, a tracking ID can be assigned to the same tourist in the acquisition sub-area of ​​any image acquisition device to facilitate the storage of the tourist's information. For example, when any tourist enters the acquisition sub-area of ​​any image acquisition device for the first time, a corresponding tracking ID is assigned to any tourist, and after the tourist leaves the acquisition sub-area of ​​any image acquisition device, the tracking ID of the tourist is deleted. The embodiment of the present application does not limit the format of the tracking ID, for example, camera number-timestamp-6-digit number (such as 000010) as the number increases, and the head detection frame can be bound to the tracking ID. In addition, if there are other detection frames, such as face detection frame and body detection frame, they can also be bound to the tracking ID.

[0060] In one possible implementation, the server acquiring the multiple images corresponding to the region may include receiving the multiple images from a relay device. In this case, the relay device performs object detection on the multiple captured images and determines that the multiple captured images, within the detection frame, meet quality requirements. The relay device then sends the multiple images to the server.

[0061] Taking the example of a server receiving multiple images corresponding to an acquisition area sent by a transfer device, that is, the transfer device and the server interactively executing the area management method provided by the embodiment of the present application as an example. Referring to the image acquisition process diagram shown in Figure 5, the transfer device reads the video data collected by the camera, performs multi-branch target detection on the multiple acquired images in the video data, and obtains the human body detection frame, face detection frame, and head detection frame of each object in each acquired image. The human body detection frame, face detection frame, and head detection frame of the same visitor are bound and assigned corresponding tracking IDs to facilitate unified storage of the attributes of the same visitor.

[0062] The image quality within the human body detection frame is determined. If the image quality within the human body detection frame meets the quality requirements, an image meeting the quality requirements is sent to the server. For example, the entire captured image meeting the quality requirements or a screenshot of the human body detection frame is sent to the server. A further determination is made as to whether a face detection frame exists. If no face detection frame exists, the video data is reread. If a face detection frame exists, the image quality within the face detection is determined. If the image quality within the face detection frame meets the quality requirements, an image meeting the quality requirements is sent to the server. For example, the entire captured image meeting the quality requirements or a screenshot of the face detection frame is sent to the server.

[0063] In step 202 , image recognition is performed on each of the multiple images to obtain image recognition results, and attribute information of multiple objects included in the multiple images is obtained based on the image recognition results.

[0064] Based on the multiple images acquired in step 201, image recognition technology can be used to perform image recognition on each of the multiple images to identify the object in each image, obtain image recognition results, and then further obtain attribute information of the object based on the image recognition results. The object can be a tourist, an animal, or a vehicle, and the attribute information of the object can be different characteristics of the object.

[0065] The embodiments of this application do not limit the image recognition technology used. It is sufficient that an image recognition result can be obtained. The image recognition result may include the position coordinates of a detection frame of an object within the image and image features within the detection frame. For example, the image recognition technology may be a technology that uses an efficient deep neural network model for recognition. The efficient deep neural network model may be a resnet18 (Residual Network 18, an 18-layer residual network) model.

[0066] In one possible embodiment, the image recognition process may include determining the position of an object in an image, which may be accomplished by a detection frame, i.e., determining a rectangular sub-region that surrounds the target object; analyzing and extracting visual features of the object, such as color, texture, shape, etc., to facilitate subsequent attribute recognition; using a classification algorithm to determine the attributes of the object based on the extracted visual features, for example, determining the color attributes of the object through a color histogram, or determining the shape attributes of the object using a shape descriptor; and obtaining attribute information of multiple objects included in multiple images.

[0067] It is important to note that the accuracy of image recognition and attribute extraction is affected by many factors, including image quality, object complexity and diversity, algorithm selection and parameter settings, etc. Therefore, in practical applications, algorithm tuning and verification may be required to ensure accurate and reliable recognition results.

[0068] For example, a screenshot image of a person detection frame and a screenshot image of a face detection frame are used as the basis for image recognition. The screenshot image of the person detection frame is referred to as a person image, and the screenshot image of the face detection frame is referred to as a face image. The person image is input into the neural network model for recognition to obtain person attribute information, and the face image is input into the neural network model for recognition to obtain face attribute information.

[0069] Facial attribute information may include at least one of gender, facial expression, face shape, age, whether a person wears glasses, and whether a person has a beard. Body attribute information may include at least one of hairstyle and clothing. Based on the facial and body attribute information, at least one of the visitor's occupation and personality information may be obtained.

[0070] For example, mapping relationships between facial attribute information, body attribute information, occupation information, and personality information are preset and stored in a server. The server can determine the visitor's occupation information and personality information based on at least one of the preset mapping relationships between facial attribute information and occupation information, facial attribute information and personality information, body attribute information and occupation information, and body attribute information and personality information.

[0071] Optionally, an input value set including facial attribute information and body attribute information, and an output value set including occupation information and personality information are constructed. The elements in the input value set may include gender (male, female), face shape (round face, square face, triangular face, melon-shaped face, heart-shaped face), glasses (wearing glasses, not wearing glasses), beard (with beard, without beard), hairstyle (straight, wavy, curly, braid, bun, short hair), clothing (formal, business, casual, sports, other). The elements in the output value set may include personality (rational, doubtful, introverted, extroverted, mixed), occupation (business talent, high-paid talent, salaried talent). For each element in the input value set, there is at least one element in the output value set that satisfies the mapping relationship.

[0072] The embodiments of this application do not limit the mapping relationship between the elements in the output value set and the elements in the input value set. For example, the mapping relationship between beard and personality can be that having a beard is mapped to rational type, introversion, and extroversion, while no beard is mapped to rational type, introversion, extroversion, and mixed type. The mapping relationship between glasses and personality can be that wearing glasses is mapped to rational type, doubtful type, introversion, and extroversion, while not wearing glasses is mapped to rational type, introversion, extroversion, and mixed type.

[0073] The mapping relationship between hairstyle and personality can be: straight hair maps to rational, skeptical, and introverted; wavy hair maps to rational, introverted, and extroverted; curly hair maps to rational, skeptical, introverted, extroverted, and mixed; braids map to extroverted and mixed; and buns and short hair map to rational, skeptical, introverted, extroverted, and mixed. The mapping relationship between hairstyle and occupation can be: straight hair maps to high-paid and salaried professionals; wavy hair maps to business professionals and high-paid professionals; curly hair maps to high-paid and salaried professionals; braids map to high-paid and salaried professionals; and short hair maps to business professionals, high-paid professionals, and salaried professionals.

[0074] The mapping relationship between clothing and personality can be: formal attire is mapped to rational, skeptical, and introverted; business attire is mapped to rational, introverted, extroverted, and mixed; casual attire is mapped to skeptical, extroverted, and mixed; sportswear is mapped to rational, extroverted, and mixed; and other is mapped to rational, skeptical, introverted, extroverted, and mixed. The mapping relationship between clothing and occupation can be: formal attire is mapped to high-paid and salaried professionals; business attire is mapped to high-paid and salaried professionals; casual attire is mapped to high-paid and salaried professionals; sportswear is mapped to salaried professionals; and other is mapped to salaried professionals.

[0075] The different personalities and different occupations described in the embodiments of the present application are for illustration only. The demands for different services in a region by different personalities and different occupations can be adjusted in real time or periodically according to the operating conditions of the region.

[0076] For an object, attribute recognition can be performed based on the human body detection frame. If a face detection frame exists, attribute recognition can be performed based on both the face detection frame and the human body detection frame, thereby increasing the accuracy of attribute recognition. Referring to the schematic diagram of the attribute analysis process of an object shown in Figure 6, information stored in a terminal box is received, including a screenshot image of the human body detection frame position. Human body features are extracted and compared based on the screenshot image of the human body detection frame position to determine whether the object corresponding to the human body detection frame has been assigned a tracking ID. If not, a tracking ID is assigned to the object. If so, the attribute information of the object can be obtained through other terminal boxes and combined with the attribute information obtained in this process to improve the attribute information of the object.

[0077] Optionally, the attribute information also includes whether new hand-held items are added, so as to determine whether the object has made a purchase in the sub-area, thereby adjusting the number of stores in the sub-area. Attribute recognition is performed on the screenshot image of the human body detection frame position. The attribute recognition method can be through an efficient deep neural network model. Hand-held item recognition is performed on the screenshot image of the human body detection frame position. If no new hand-held items are added, the tracking ID is marked as unconsumed. If there are new hand-held items, the tracking ID is marked as consumed, and the recognition result is stored.

[0078] In addition, face detection is performed on the information stored in the terminal box to determine whether there is a face detection frame. If not, re-detection is performed. If so, face features are extracted and compared to obtain a face registration ID. The method for obtaining a face registration ID can refer to the process of obtaining a tracking ID. The face registration ID can be the same as the tracking ID. For the recognized face, attribute recognition is performed to determine the attribute information of the object, and the recognition results are stored for subsequent management of the area based on the attribute information.

[0079] Step 203 : Based on the acquisition sub-region information, acquisition time information and image recognition results corresponding to the multiple images, time information of the multiple objects in different sub-regions within the region is obtained.

[0080] The specific sub-region captured by each image can be determined based on the corresponding acquisition sub-region information of multiple images. The acquisition time information can be used to determine the capture time of each image. Time information records the specific moment of image acquisition, which can establish a temporal relationship between multiple images. Combined with the acquisition sub-region information, it can be used to determine which sub-region of an area an object was located at a specific point in time. By combining acquisition sub-region information, acquisition time information, and image recognition results, the movement paths of different objects within the area can be determined, providing more possibilities for area management and analysis.

[0081] Exemplarily, the image recognition results include the position coordinates of the detection frames of each object in each image; based on the acquisition sub-region information, acquisition time information, and image recognition results corresponding to the multiple images, the process of obtaining temporal information of different sub-regions of the multiple objects within the region includes: obtaining, for any of the multiple objects, the position coordinates of the detection frame of the object in multiple reference images, wherein the multiple reference images are images with the same acquisition sub-region information but different acquisition time information, and the detection frames are head detection frames; determining the position sub-regions of the object in the multiple reference images based on the position coordinates of the detection frame of the object; obtaining entry and exit direction information of the sub-region corresponding to the acquisition sub-region information of the multiple reference images based on the position sub-regions of the object in the multiple reference images and the time sequence indicated by the acquisition time information of the multiple reference images; and obtaining temporal information of the multiple objects in different sub-regions of the region based on the entry and exit direction information of the sub-regions corresponding to different acquisition sub-region information of different objects. Exemplarily, the temporal information may include entry time, residence time, and exit time.

[0082] For each reference image, determine the position coordinates of the detection frame of each object, and then determine the position sub-area of ​​the detection frame of each object on multiple reference images. For a specific object, if the object appears in a certain position sub-area in multiple reference images that are consecutive in time, but the position changes, the entry and exit direction of the object in a certain acquisition sub-area within the region can be determined by the trend of its position change. For example, if the position of the detection frame of an object moves from left to right in two consecutive images, it can be determined that the object has entered a certain acquisition sub-area within the region. Combined with the above-mentioned entry and exit direction information, as well as the acquisition time information of each reference image, it can be known at what point in time the object entered or left a certain acquisition sub-area.

[0083] Exemplarily, multiple reference images are respectively divided into entry sub-regions and exit sub-regions. The process of determining the position sub-regions of any object on the multiple reference images according to the position coordinates of the detection frame of any object in the multiple reference images includes: for any reference image in the multiple reference images, if based on the position coordinates of the detection frame of any object on any reference image, it is determined that the overlap ratio between the detection frame of any object and the entry sub-region is greater than the overlap threshold, then it is determined that any object is located in the entry sub-region of any reference image; if based on the position coordinates of the detection frame of any object on any reference image, it is determined that the overlap ratio between the detection frame of any object and the exit sub-region is greater than the overlap threshold, then it is determined that any object is located in the exit sub-region of any reference image.

[0084] The method for calculating the overlap ratio between the detection frame and the entry sub-region or exit sub-region can be found in the calculation process shown in Figure 4 and will not be repeated here. The overlap threshold can be adjusted during actual use. In this embodiment of the application, the overlap threshold is not limited. For example, the overlap threshold is 0.6.

[0085] Exemplarily, multiple reference images are respectively divided into entry sub-regions and exit sub-regions. The entry sub-region includes an entry inner frame, the boundary of which is the boundary of the entry sub-region, reduced by at least one pixel toward the interior of the entry sub-region. The exit sub-region includes an exit inner frame, the boundary of which is the boundary of the exit sub-region, reduced by at least one pixel toward the interior of the exit sub-region. This embodiment of the present application does not limit the size of the entry inner frame and the exit inner frame. For example, the entry inner frame is the boundary of the exit sub-region reduced by 10 pixels toward the interior of the exit sub-region, and the exit inner frame is the boundary of the exit sub-region reduced by 10 pixels toward the interior of the exit sub-region, thereby reducing the probability of erroneous judgment that any object passes through the boundary of the entry sub-region or the exit sub-region and is considered to be within the corresponding sub-region.

[0086] The process of determining, based on the position coordinates of the detection frame of any object in the multiple reference images, the position sub-regions of the object in the multiple reference images includes: for any reference image in the multiple reference images, if, based on the position coordinates of the detection frame of any object in the reference image, an overlap ratio between the detection frame of any object and an entry frame is determined to be greater than a second overlap threshold, then determining that the object is located in an entry sub-region of any reference image. If, based on the position coordinates of the detection frame of any object in the reference image, an overlap ratio between the detection frame of any object and an exit frame is determined to be greater than a second overlap threshold, then determining that the object is located in an exit sub-region of any reference image.

[0087] The method for calculating the overlap ratio between the detection frame and the in-frame or out-frame can be found in the calculation process shown in FIG4 and will not be described in detail here. The second overlap threshold can be adjusted during actual use. The embodiment of the present application does not limit the second overlap threshold. The second overlap threshold can be the same as or different from the first overlap threshold.

[0088] For example, referring to the flowchart for determining entry and exit direction information shown in FIG7 , for a frame of image, the head detection frame is used as the basis for determining entry and exit of any sub-region. If the frame does not have a head detection frame, another frame is re-input and the entry and exit sub-regions are traversed to determine the positions of the entry and exit sub-regions in any frame. The head detection frames are traversed to determine all head detection frames in the frame. For each head detection frame, it is determined whether the head detection frame appears for the first time. If it does, the information corresponding to the head detection frame is initialized and stored, including the number of frames in which the head detection frame is in the sub-region frame and the number of frames in which the person is continuously absent from the sub-region frame. The head detection frame is added to the head detection frame list and the entry and exit status of the sub-region is initialized to facilitate analysis of the head detection frame's position. If it does not appear for the first time, it is determined that the head detection frame is within the entry or exit sub-region frame.

[0089] If the head detection frame is not in the entry sub-region frame or the exit sub-region frame, continue to traverse the next head detection frame. If the head detection frame is in the entry sub-region frame or the exit sub-region frame, the number of frames in which the head detection frame is continuously not in the sub-region frame is reset to 0, the number of frames in which the head is continuously in the sub-region frame is increased by 1, and stored. The time the head detection frame is in the sub-region frame is judged. If the number of consecutive sub-region frames for the head detection frame is less than the intrusion threshold of the sub-region frame, the intrusion threshold can be any positive integer, such as 5, then it is considered that the head detection frame is not in the sub-region frame, and traverse the next head detection frame. If the number of consecutive sub-region frames for the head detection frame is less than the intrusion threshold of the sub-region frame, then it is considered that the head detection frame is in the sub-region frame.

[0090] The sub-area frame number is added to the visitor's entry and exit status. The entry sub-area frame is 1, and the exit sub-area frame is 2. The traversal of the head detection frame and the entry and exit area frame is completed, and the visitor list is traversed. For a visitor, if the visitor's entry and exit status is not [1, 2] or [2, 1], the next visitor corresponding to the head detection frame is traversed. If the visitor's entry and exit status is [1, 2] or [2, 1], the visitor's entry and exit direction information can be stored. If the visitor leaves the sub-area, the corresponding information can be deleted from the visitor list and head detection frame list of the sub-area.

[0091] Step 204 : managing the region based on the time information of the multiple objects in different sub-regions within the region and the attribute information of the multiple objects, wherein the management includes at least one of regional object management, regional facility management, or regional service management.

[0092] The system determines the temporal information of multiple objects within different sub-regions of a region, including when they enter or leave a sub-region and how long they stay within it. It also determines the attributes of multiple objects. By integrating temporal and attribute information, it is possible to accurately understand the dynamic changes of each object within the region.

[0093] Leveraging time and attribute information, anomalies within a zone can be monitored in real time. For example, if an object enters a restricted sub-zone at an unusual time, or if an object of unknown attributes appears in a sub-zone, the system can immediately issue an alarm, prompting security personnel to take prompt action and ensure zone safety. By analyzing the time information of multiple individuals in different sub-zones, the distribution and changing trends of human traffic can be understood. Based on this traffic flow, zone managers can adjust personnel flows to avoid crowding and congestion, thereby improving zone comfort and operational efficiency. Combining object attribute information with time information enables effective management of resources within a zone.

[0094] Based on a wealth of temporal and attribute information, data mining and analysis can be performed to uncover patterns and trends in object movement, providing decision support for regional management. For example, by analyzing time series data on pedestrian and vehicle flows, it is possible to predict future traffic changes, helping regions proactively adjust resources and implement countermeasures. For example, analysis of visitor attributes can also include facial analysis and other visitor information, such as determining whether a visitor has made a purchase based on whether they have any new carry-on items.

[0095] In one possible implementation, based on the time information of multiple objects in different sub-areas within a region, the movement paths of the multiple objects can be obtained; based on the time information of the multiple objects in different sub-areas within a region, and the attribute information of the multiple objects, the attribute information including at least one of gender, age, occupation, personality and clothing type, the distribution of objects in different sub-areas at different times can be obtained.

[0096] By obtaining the action paths of multiple objects, the movement patterns of different objects in the area can be determined. Based on the historical action paths of different objects, the future movement trends of different objects can be predicted, so that resource allocation and management preparations can be made in advance. The distribution of objects in different sub-areas at different times can be obtained, so that the area can be managed more finely. For example, if the first sub-area is mainly composed of people in the first profession in the first time period, the area can provide targeted services or facilities that are more needed by people in the first profession. According to the attribute distribution of the objects, resources can be deployed more accurately, such as configuring corresponding security personnel or facilities according to the distribution of clothing types of people.

[0097] In an embodiment of the present application, after obtaining the movement paths of multiple objects and the distribution of objects in different sub-areas at different times, the area is managed. The embodiment of the present application does not limit the objects to be managed. For example, management includes at least one of area object management, area facility management, or area service management. Scenarios for area management include but are not limited to the following.

[0098] Scenario 1: Visually display at least one of the movement paths of multiple objects and the distribution of objects in different sub-areas at different times.

[0099] The visualization tool provides intuitive decision-making support for regional managers, allowing them to quickly identify problems and respond. Through the visual interface, it is easier to discover patterns and trends hidden in the data, providing data support for further strategy formulation. The embodiment of the present application does not limit the display method of the visualization interface, including but not limited to bar charts, pie charts or line charts. For example, see the structural diagram of the visualization interface shown in Figure 8, which includes a regional passenger flow data interface 801, a regional customer group analysis interface 802, an important holiday visitor number interface 803 and a full-region multi-customer group portrait interface 804.

[0100] For the regional passenger flow data interface 801, the passenger flow in the area can be displayed according to the daily, weekly, monthly or annual stages. Taking daily statistics as an example, the cumulative number of people entering the sub-area today and the cumulative number of people leaving the sub-area today can be displayed, and the corresponding number of people entering the sub-area can be displayed in the form of statistical charts according to different time points. For the regional customer group analysis interface 802, the customer group divination in the area can be displayed according to the daily, weekly, monthly or annual stages. Taking daily statistics as an example, the male and female ratio and age ratio can be displayed. For the number of visitors to important holidays interface 803, the number of people entering the sub-area on different holidays this year and last year can be displayed. For the multi-person customer group portrait interface 804 for the entire area, the distribution of customer groups in the area can be displayed according to the daily, weekly, monthly or annual stages. Taking daily statistics as an example, the distribution of customer groups in the area today can be displayed.

[0101] Scenario 2: managing at least one of the business hours, repair time of service facilities and the number of service personnel in different sub-areas according to the distribution of objects in different sub-areas at different times, wherein the object distribution includes the number of objects.

[0102] After obtaining the object distribution of different sub-areas at different times, the area can dynamically adjust its business hours based on the foot traffic in each sub-area. Adjusting business hours based on foot traffic ensures that the area can fully meet user needs during peak traffic hours, avoiding overcrowding and the resulting degradation of user experience. During non-peak hours, business hours can be appropriately shortened to save operating costs.

[0103] Develop a repair schedule for service facilities based on the number of users in each sub-area at different times and the usage of service facilities. Repairs during low-traffic hours can avoid disrupting user access while ensuring normal operation during high-demand periods. A well-thought-out repair plan can extend the service life of service facilities and improve the overall operational efficiency of the area.

[0104] Dynamically adjust the number of service personnel based on the number of objects in different sub-areas at different times. Deploying additional service personnel during peak traffic hours ensures a quick response to user needs and improves service quality. During quieter traffic hours, the number of service personnel can be appropriately reduced to avoid wasting human resources.

[0105] Scenario three, based on the object distribution in different sub-areas at different times, manage at least one of the facility type of service facilities, the personnel type of service personnel and the store type in different sub-areas, wherein the object distribution includes at least one of gender group characteristics, age group characteristics, occupational group characteristics and personality group characteristics.

[0106] When analyzing target user distribution, we analyze specific demographic characteristics such as gender, age, occupation, and personality. By analyzing these demographic characteristics, we can gain a deeper understanding of the needs and preferences of different user groups. Understanding the characteristics of target user groups helps us accurately position our markets and develop appropriate management strategies.

[0107] Adjust the type and layout of service facilities based on the gender, age, occupation, and personality characteristics of different sub-regions at different times. Providing appropriate service facilities based on the characteristics of the target user groups can improve user satisfaction and experience. Adjusting the type and layout of facilities based on the needs of different groups can help improve facility utilization and avoid resource waste.

[0108] Optionally, the target group types for each sub-region are determined based on at least one of the following: gender, age, occupation, and personality characteristics. Service personnel are assigned to the target group types for each sub-region. Assigning service personnel based on the characteristics of the user groups can provide more personalized and attentive service. Personalized service can increase user engagement with the region, improving user loyalty and return rates.

[0109] Adjust store types and layouts based on the target demographics of different sub-regions. Setting appropriate store types based on user demographics can better meet their shopping and consumption needs. A well-thought-out store layout and type configuration can attract more target users, thereby increasing regional revenue.

[0110] Scenario four: When the ratio between the area of ​​any sub-area in different sub-areas and the number of objects in any sub-area exceeds the ratio threshold, and the duration of exceeding the ratio threshold exceeds the time threshold, a first warning message is issued, and the first warning message is used to remind objects in any sub-area to leave any sub-area; when the movement path of any object among multiple objects overlaps with the warning sub-area of ​​the area, a second warning message is issued, and the second warning message is used to remind any object to stay away from the warning sub-area.

[0111] The system continuously monitors the ratio between the area and the number of objects in different sub-areas. If this ratio exceeds a preset threshold and persists for a set duration, a first warning message is issued. When the ratio of the number of objects to the area of ​​a sub-area exceeds the threshold, it indicates that the sub-area may be overcrowded. The issuance of the first warning message serves to alert objects within the sub-area to leave, preventing safety hazards caused by congestion. Through timely warnings, the number of objects in a sub-area is maintained at an appropriate level, providing a comfortable environment and optimizing the user experience.

[0112] The system analyzes the movement paths of multiple objects and issues a secondary warning if it detects an overlap between an object's path and a warning sub-zone within the area. This overlap indicates that the object is approaching a potentially dangerous sub-zone. The secondary warning alerts the object to move away immediately, avoiding danger. This early warning mechanism ensures that objects within the area do not easily enter dangerous sub-zones, thereby enhancing overall safety.

[0113] Referring to the crowd warning flow chart shown in Figure 9, for a subarea E, gates A, B, C, and D are pre-calibrated as the entrances and exits of E. The area of ​​subarea E is then determined based on the map of the area. The number of people entering and exiting entrances A, B, C, and D is calculated as Ain, Aout, Bin, Bout, Cin, Cout, Din, and Dout, respectively. The number of people staying in E is calculated as Count = (Aout - Ain) + (Bout - Bin) + (Cout - Cin) + (Dout - Din). A check is performed to determine whether Count exceeds a threshold number of people. If Count does not exceed the threshold number of people, the number of people entering and exiting the four entrances and exits is recalculated, and the number of people staying in E is calculated. If Count exceeds the threshold number of people, the time interval between Count exceeding the threshold number of people exceeds a time threshold. If not, the number of people entering and exiting the four entrances and exits is recalculated, and the number of people staying in E is calculated. If the time threshold number of people staying in E is exceeded, a crowd gathering warning is issued.

[0114] Scenario five: different sub-areas within the area include shops, and the attribute information includes the type of carried items, which includes the packaging corresponding to the shops. Based on the time information of multiple objects in different sub-areas within the area and the types of carried items of multiple objects, the number of first objects carrying packaging into the shop, the number of second objects carrying packaging out of the shop, and the total number of objects entering the shop within the reference time period are obtained. The purchase rate of the shop within the reference time period is determined based on the difference number and the total number of objects. The difference number is the difference between the number of second objects and the number of first objects, and the shop is managed according to the purchase rate.

[0115] The purchase rate of the store in the reference time period is determined based on the difference quantity and the total number of objects, and the purchase rate of the store in the reference time period is determined as the ratio of the difference quantity to the total number of objects.

[0116] Exemplarily, the attribute information also includes at least one of gender, age, and occupation. Managing stores based on purchase rates may include, when the purchase rate falls below a probability threshold, determining a target preference for the store based on at least one of the gender, age, and occupation of the person entering the store, and adjusting the types of merchandise within the store based on the target preference. The probability threshold may be set based on experience or flexibly adjusted based on the application scenario.

[0117] Optionally, based on the time information of multiple subjects in different sub-areas within the area and the types of items they carry, the number of subjects entering and leaving the store with store packaging during a reference time period is monitored, and the total number of subjects entering the store is recorded. This allows the user to determine how many people enter and leave the store with store packaging during a specific time period, thereby determining the store's purchase rate.

[0118] The difference between the number of first and second objects is calculated, and then the difference is compared with the total number of objects entering the store to determine the store's purchase rate during the reference time period. The purchase rate intuitively reflects a store's sales performance during a specific time period. By calculating the purchase rate, you can quickly understand the store's operating status and provide data support for subsequent management strategies.

[0119] When the purchase rate falls below a set probability threshold, the store analyzes the store's entry attributes, such as gender, age, and occupation, to determine the store's corresponding user preferences. Based on these preferences, the store adjusts the product offerings to better meet the needs of the target user group. This adjustment in product offerings can specifically increase the target user group's willingness to purchase, thereby boosting purchase rates and increasing store revenue.

[0120] Referring to the store marketing analysis flow chart shown in FIG10 , the camera monitoring sub-area and camera number of store X are identified. Store X data stored on the server is retrieved based on the camera number. This data includes the number of customers entering the store, the number of first subjects carrying packaged items into the store, and the number of second subjects carrying packaged items out of the store. Based on this data, the purchase rate is calculated as (the number of second subjects carrying packaged items out of the store - the number of first subjects carrying packaged items into the store) / the number of customers entering the store. If the purchase rate exceeds a purchase rate threshold, the merchant is provided with operational data analysis, such as daily customer flow statistics and customer flow attribute analysis, to help the merchant refine its service and product categories. If the purchase rate is not greater than the threshold, the merchant calculates the largest category of customers among business, high-income, and salaried customers, e.g., the largest category is salaried professionals.

[0121] Calculate the user preference type = number of women entering the store / number of men entering the store. If the user preference type is greater than 1, indicating that there are more women entering the store than men, then push to the merchant the product types with a high purchase rate and preferred by female salaried talents. This will also be pushed to the merchant for daily customer flow statistics, customer flow attribute analysis, and other operational data analysis. If the user preference type is not greater than 1, indicating that there are more men than women entering the store or the number of men is equal to that of women, if the user preference type indicates that there are more men than women, then push to the merchant the product types with a high purchase rate and preferred by male salaried talents. If the user preference type indicates that men equal women, then push to the merchant the product types with a high purchase rate and preferred by male and female salaried talents. This will also be pushed to the merchant for daily customer flow statistics, customer flow attribute analysis, and other operational data analysis.

[0122] In one possible implementation, the operating hours of stores in the region can be adjusted accordingly based on the different passenger flows at different times. Referring to the flow chart for analyzing the operating hours of stores in the region shown in FIG11 , the commercial sub-regions and entertainment sub-regions are originally demarcated in the region, and the passenger flow statistics stored in the server are obtained. The passenger flow statistics indicate the passenger flows corresponding to different time periods in the region. The passenger flow statistics include the average passenger flow of weekends and the average passenger flow of weekdays. The weekly passenger flow difference is calculated based on the passenger flow statistics = the average passenger flow of weekends / the average passenger flow of weekdays. The time interval for passenger flow statistics analysis is demarcated every N hours. The embodiment of the present application does not limit the value of N, for example, N is 3. Based on the time interval, the passenger flow and passenger flow attribute data in the different time intervals counted by the server are obtained, and the top three time intervals for passenger flow are calculated, for example, 12:00-15:00, 15:00-17:00, and 17:00-21:00.

[0123] Different sub-areas are analyzed separately. The embodiment of the present application takes the commercial sub-area and the entertainment sub-area as examples to determine whether the weekly passenger flow difference of the commercial sub-area is greater than the commercial sub-area difference threshold. If the weekly passenger flow difference of the commercial sub-area is not greater than the commercial sub-area difference threshold, it indicates that the difference in passenger flow proportion between weekdays and weekends in the commercial sub-area is not large. The data is pushed to the regional manager for daily passenger flow statistics, passenger flow attribute analysis and other operational data analysis, and it can be recommended that the three time periods with peak passenger flow be set as regular business hours.

[0124] If the weekly passenger flow difference in the commercial sub-area is greater than the threshold, determine whether the weekly passenger flow difference in the entertainment sub-area is greater than the entertainment sub-area difference threshold. If the weekly passenger flow difference in the entertainment sub-area is not greater than the entertainment sub-area difference threshold, it indicates that the difference in passenger flow proportion between weekdays and weekends in the commercial sub-area is large, and the difference in passenger flow proportion between weekdays and weekends in the entertainment sub-area is small. It is recommended to set the three time periods with peak passenger flow as regular business hours, and hold cultural and tourism promotion activities in the entertainment sub-area from time to time during this time period, so as to increase the attractiveness of the commercial sub-area to tourists.

[0125] When the weekly passenger flow difference in the entertainment sub-area is greater than the entertainment sub-area difference threshold, the user preference type is calculated as the number of females entering the store / the number of males entering the store. The weekly passenger flow difference between the entertainment sub-area and the commercial sub-area is greater than the entertainment sub-area difference threshold, indicating that the difference in passenger flow proportion between the commercial sub-area and the entertainment sub-area on weekdays and weekends is relatively large, indicating that the overall passenger flow attraction of the region is low. It is necessary to first improve the regional entertainment equipment and increase the overall passenger flow. According to the user preference type, the store type in the region should be rectified. For example, if the user preference type is greater than 1, it is recommended to introduce stores preferred by females.

[0126] The embodiments of this application provide the following processes for different scenarios, respectively described. Referring to the flowchart of the area management method shown in FIG12 , the movement paths of multiple objects within an area, the attribute information of multiple objects, and the distribution of objects in different sub-areas within the area at different times are obtained. The object distribution includes at least one of the number of objects, the number of individual tourists, the number of group tourists, gender group characteristics, age group characteristics, occupation group characteristics, and personality group characteristics. Different information is selected according to different usage scenarios.

[0127] For example, scenario one corresponds to the movement paths of multiple objects within an area, as well as the distribution of objects in different subareas within the area at different times. The object distribution includes at least one of the following: the number of objects, the number of individual travelers, the number of group travelers, gender group characteristics, age group characteristics, occupational group characteristics, and personality group characteristics. Scenario two corresponds to the number of objects in different subareas within the area at different times. Scenario three corresponds to at least one of the following: gender group characteristics, age group characteristics, occupational group characteristics, and personality group characteristics in different subareas within the area at different times. Scenario five corresponds to attribute information of multiple objects, including information about items carried.

[0128] The corresponding method description for scenario 1 includes the following steps 1211 to 1212 (not shown in the figure).

[0129] Step 1211, obtaining the movement paths of multiple objects in the area, and the distribution of objects in different sub-areas of the area at different times, wherein the object distribution includes at least one of the number of objects, the number of individual tourists, the number of group tourists, gender group characteristics, age group characteristics, occupation group characteristics, and personality group characteristics.

[0130] Leveraging positioning technology and sensor networks, the movement paths of multiple objects within a region are tracked and recorded in real time. Obtaining the movement paths of objects helps regional managers understand their dynamic distribution and movement patterns in real time. This movement path information can be used for security monitoring, predicting and promptly identifying potential safety hazards.

[0131] By analyzing object movement paths and combining them with sub-area location data, we can calculate the distribution of objects in different sub-areas over time. Understanding the distribution of objects in different sub-areas over time can help assess the popularity and popularity of sub-areas. Based on the object distribution, we can rationally allocate regional resources, such as service personnel and facilities.

[0132] Further refine the analysis of target customer distribution, including the number of customers, the number of individual customers, the number of group customers, and the characteristics of gender, age, occupation, and personality groups. Analyzing group characteristics such as gender, age, and occupation helps build more accurate user profiles and understand user needs and behaviors. Based on these user profiles and group characteristics, more precise marketing strategies can be developed, such as targeted marketing and service optimization. Statistics on quantity and type can reflect the operational effectiveness of a store or region. The number of individual and group customers can be used to analyze which user groups are the primary customer base for a store or region.

[0133] Step 1212: Display at least one of the movement paths of the multiple objects and the distribution of the objects in different sub-areas at different times on the display device.

[0134] Based on management needs, select the content to be displayed on the display device. This can include the movement paths of multiple objects, the distribution of objects in different sub-areas at different times, or a combination of the two. This ensures that the display content matches management goals, allowing managers to focus on the information they need most.

[0135] The acquired action path and object distribution data is processed and presented on a display device in an intuitive and easy-to-understand manner using appropriate visualization methods, such as lines, icons, and colors. Data processing and visualization are key steps, transforming large amounts of raw data into meaningful, intuitive graphics or animations, allowing managers to quickly obtain key information.

[0136] The content on the display device is continuously updated based on the real-time movement paths of objects and the distribution of objects in sub-areas. Real-time updates ensure that the information on the display device is always up-to-date, allowing managers to grasp the latest situation at any time and make decisions quickly.

[0137] Adding interactive features to the display, such as zooming in, zooming out, dragging, and filtering, makes it easier for managers to view and analyze data. These interactive features greatly enhance the practicality of the display, allowing managers to manipulate the display as needed and delve deeper into the data and situations.

[0138] Specific conditions can be set. When the movement paths of one or more objects or the distribution of objects in a sub-area trigger these conditions, the display device will issue an alert or prompt in a prominent manner. These alerts and prompts allow managers to respond quickly to emergencies or important events, ensuring safe and efficient operations in the area.

[0139] The execution process of step 1211 shown in the embodiment of the present application can refer to the relevant description of steps 201 to 203 in Figure 2, and the execution process of step 1212 can refer to the relevant description of scenario 1 in step 204 in Figure 2, which will not be repeated here.

[0140] The corresponding method description for scenario 2 includes the following steps 1221 to 1223 (not shown in the figure).

[0141] Step 1221: Obtain the number of objects in different sub-areas within the area at different times.

[0142] The number of objects in different sub-areas within a region at different times can reflect the activity and usage rate of each sub-area in different time periods, providing data support for subsequent decision-making.

[0143] Step 1222: Determine at least one of the business hours, the repair time of the service facilities, and the number of service personnel deployed in different sub-areas based on the number of objects in different sub-areas at different times.

[0144] Count the average and peak number of objects in each sub-area during each time period. Based on the temporal distribution of the number of objects, set or adjust the operating hours of each sub-area to maximize user satisfaction. Considering the number of objects and facility usage, select time periods with fewer objects for repairs to minimize user impact. Dynamically adjust the number of service personnel assigned to each sub-area based on the number of objects and service demand to ensure service quality. By analyzing the number of objects in different sub-areas at different times, we can more scientifically set operating hours, rationally schedule the repair schedule of service facilities, and optimize the allocation of service personnel, thereby improving overall operational efficiency and enhancing user satisfaction.

[0145] Step 1223: Display at least one of the business hours of different sub-areas, the repair time of service facilities, and the number of service personnel deployed on the display device.

[0146] Data on confirmed business hours, facility maintenance times, and staffing levels is transmitted to a display device. This information is presented on the display in intuitive graphical or text format. Update frequency is adjusted based on changes to ensure the information on the display is always up to date. Displaying this information on the display increases transparency and accessibility, making it easier for users and visitors in the area to understand and access the information they need, thereby improving the user experience. It also helps area managers manage time and resources more efficiently.

[0147] The execution process of step 1221 shown in the embodiment of the present application can refer to the relevant description of steps 201 to 203 in Figure 2, and the execution process of steps 1222 and 1223 can refer to the relevant description of scenario 2 in step 204 in Figure 2, which will not be repeated here.

[0148] The corresponding method description for scenario three includes the following steps 1231 to 1233 (not shown in the figure).

[0149] Step 1231 , obtaining at least one of the gender group characteristics, age group characteristics, occupation group characteristics, and personality group characteristics of different sub-regions within the region at different times.

[0150] Through survey analysis and user registration information, we collect demographic data such as gender, age, occupation, and personality across different sub-regions within the region. This information is used to gain a deeper understanding of the target user groups in each sub-region, providing a basis for decision-making on subsequent service and operational strategies.

[0151] Step 1232, determining at least one of the facility type of the service facilities, the personnel type of the service personnel, and the store type of the different sub-areas based on at least one of the gender group characteristics, age group characteristics, occupational group characteristics, and personality group characteristics of the different sub-areas at different times.

[0152] Understand the primary user groups in each sub-area, such as young people, professionals, and families. Designate appropriate service facilities for each user group. For example, areas where young people gather may feature more technological and entertainment facilities. Assign service personnel with relevant experience and skills based on the needs and behavioral characteristics of each user group. Analyze the consumption habits and preferences of each user group and introduce or adjust store types that meet their needs. Targeted configurations can improve service efficiency and user satisfaction, enhancing the region's attractiveness and competitiveness.

[0153] Step 1233: Display at least one of the facility type of the service facilities, the personnel type of the service personnel, and the store type of the service personnel in different sub-areas on the display device.

[0154] Integrate information about service facilities, service personnel, and store types. Displays clearly display information about facilities, personnel, and stores in each sub-area using icons, labels, or brief descriptions. This allows users or visitors within the area to more intuitively understand the services and stores in each sub-area, enabling them to quickly find the services or locations that meet their needs.

[0155] The execution process of step 1231 shown in the embodiment of the present application can refer to the relevant description of steps 201 to 203 in Figure 2, and the execution process of steps 1232 and 1233 can refer to the relevant description of scenario three in step 204 in Figure 2, which will not be repeated here.

[0156] The corresponding method description for scenario five includes the following steps 1241 to 1244 (not shown in the figure).

[0157] Step 1241, obtaining the purchase rates corresponding to multiple stores in the area.

[0158] Collect transaction data from each store in the area and calculate the purchase rate of each store, that is, the ratio of the number of buyers to the total number of visitors. The purchase rate can measure the sales efficiency of each store and provide basic data for subsequent analysis.

[0159] Step 1242: When the purchase rate of any store among the multiple stores is lower than the probability threshold, determine the object preference corresponding to any store based on at least one of the gender, age and occupation of the object entering any store.

[0160] Set a probability threshold, such as a specific percentage of purchase rate. When a store's purchase rate falls below this threshold, analyze the gender, age, and occupation of users entering the store. Determine the target audience preferences for the store, such as which products are preferred by users of a certain age group.

[0161] Step 1243: Determine the type of merchandise in any store based on the object preference corresponding to any store.

[0162] Based on the target customer preferences determined in step 1242, analyze and determine the product types that are suitable for that customer group. Adjust the store's product configuration to better suit the target customer preferences. Adjusting product types helps improve the store's relevance and sales conversion rate, thereby enhancing customer satisfaction.

[0163] Step 1244: Display at least one of the object preference corresponding to any store and the product type in any store on the display device.

[0164] The store's object preference and product type information is transmitted to the display device and displayed graphically and labeled on the display device. Users or visitors can more easily understand the positioning and product characteristics of each store, which helps them make more accurate consumption decisions.

[0165] The execution process of step 1241 shown in the embodiment of the present application can refer to the relevant descriptions in steps 201 to 203 in Figure 2, and the execution process of steps 1242 to 1244 can refer to the relevant descriptions in scenario five in step 204 in Figure 2, which will not be repeated here.

[0166] In summary, the regional management method provided by this application improves regional management efficiency by automatically identifying multiple images within a region, thereby managing the region based on the attribute information of objects within the region and the time information of objects in different sub-regions. In addition, the improved regional management efficiency makes the allocation and mobilization of resources and personnel within the region more reasonable and efficient, thereby improving the service level of the region.

[0167] Referring to FIG. 13 , a schematic structural diagram of a regional management system is shown. As shown in the figure, the system includes a plurality of image acquisition devices 1301 , an image recognition module 1302 , and a management analysis module 1303 .

[0168] Multiple image acquisition devices 1301 are used to acquire multiple images corresponding to the area, each of which includes corresponding acquisition sub-area information and acquisition time information; and send the multiple images to the image recognition module 1302;

[0169] The image recognition module 1302 is configured to perform image recognition on each of the multiple images to obtain image recognition results, and obtain attribute information of multiple objects included in the multiple images based on the image recognition results; obtain time information of the multiple objects in different sub-regions within the region based on the acquisition sub-region information, acquisition time information, and image recognition results corresponding to the multiple images; and send the time information of the multiple objects in different sub-regions within the region and the attribute information of the multiple objects to the management and analysis module 1303;

[0170] The management analysis module 1303 is used to manage the area based on time information of multiple objects in different sub-areas within the area and attribute information of multiple objects, where the management includes at least one of area object management, area facility management, or area service management.

[0171] In one possible implementation, image recognition module 1302 may include a target detection module and a target recognition module. The target detection module and target recognition module may be deployed on the same device, such as a server, or on different devices, such as a terminal and a server. The target detection module is configured to perform image recognition on multiple images and obtain image recognition results. The target recognition module is configured to obtain time information about multiple objects within different sub-regions of a region based on the acquisition sub-region information, acquisition time information, and image recognition results corresponding to the multiple images.

[0172] In one possible implementation, the attribute information includes at least one of gender, age, occupation, personality, and clothing type; the management analysis module 1303 is used to obtain the movement paths of the multiple objects based on the time information of the multiple objects in different sub-areas within the area; obtain the distribution of objects in different sub-areas at different times based on the time information of the multiple objects in different sub-areas within the area, and at least one of the gender, age, occupation, personality, and clothing type of the multiple objects; and visualize at least one of the movement paths of the multiple objects and the distribution of objects in different sub-areas at different times.

[0173] In one possible implementation, the object distribution includes at least one of gender group characteristics, age group characteristics, occupational group characteristics, and personality group characteristics, as well as the number of objects; the management analysis module 1303 is further used to manage at least one of the business hours of different sub-areas, the repair time of service facilities, and the number of service personnel according to the number of objects in different sub-areas at different times; and to manage at least one of the facility type of service facilities, the personnel type of service personnel, and the store type of service facilities in different sub-areas according to at least one of the gender group characteristics, age group characteristics, occupational group characteristics, and personality group characteristics of different sub-areas.

[0174] In one possible implementation, the management analysis module 1303 is used to determine, based on the number of objects in different sub-areas at different times, at least one time period in which the number of objects in different sub-areas exceeds a first quantity threshold, and determine the business hours of any sub-area based on at least one time period of any sub-area; determine, based on the number of objects in different sub-areas at different times, the degree of damage to the service facilities in the different sub-areas, and determine the repair time of the service facilities in any sub-area based on the degree of damage to the service facilities in any sub-area; determine, based on the number of objects in different sub-areas at different times, the number of service personnel required for the different sub-areas at different times, and the number of service personnel required for any sub-area at different times matches the number of objects in any sub-area at different times.

[0175] In one possible implementation, the management analysis module 1303 is used to determine the object group types of different sub-regions based on at least one of the gender group characteristics, age group characteristics, occupational group characteristics, and personality group characteristics of different sub-regions; and to determine the facility types of the service facilities, personnel types of the service personnel, and store types of the different sub-regions based on the object group types of the different sub-regions, so that the facility types of the service facilities, personnel types of the service personnel, and store types of any sub-region match the object group types of any sub-region.

[0176] In one possible implementation, the management and analysis module 1303 is also used to issue a first warning message when the ratio between the area of ​​any sub-region in different sub-regions and the number of objects in any sub-region exceeds a ratio threshold, and the duration of exceeding the ratio threshold exceeds a time threshold. The first warning message is used to remind objects in any sub-region to leave any sub-region; when the movement path of any object among multiple objects overlaps with the warning sub-region of the area, a second warning message is issued. The second warning message is used to remind any object to stay away from the warning sub-region.

[0177] In one possible implementation, different sub-areas within the area include shops, and the attribute information includes the type of carried objects, and the type of carried objects includes the packaging corresponding to the shops; the management analysis module 1303 is used to obtain the number of first objects carrying packaging into the shop, the number of second objects carrying packaging out of the shop, and the total number of objects entering the shop within a reference time period based on the time information of multiple objects in different sub-areas within the area and the types of carried objects of the multiple objects; determine the purchase rate of the shop within the reference time period based on the difference number and the total number of objects, where the difference number is the difference between the number of second objects and the number of first objects; and manage the shop according to the purchase rate.

[0178] In one possible implementation, the attribute information also includes at least one of gender, age, and occupation; the management analysis module 1303 is used to determine the object preference corresponding to the store based on at least one of the gender, age, and occupation of the object entering the store when the purchase rate is lower than the probability threshold; and adjust the type of goods in the store based on the object preference.

[0179] In one possible implementation, the image recognition result includes the position coordinates of the detection frame of each object on each image; the image recognition module 1302 is used to obtain the position coordinates of the detection frame of any object in multiple reference images for any object among multiple objects, where the multiple reference images are images with the same acquisition sub-area information but different acquisition time information in the multiple images, and the detection frame is a head detection frame; based on the position coordinates of the detection frame of any object in the multiple reference images, determine the position sub-area of ​​any object on the multiple reference images respectively; based on the position sub-area of ​​any object on the multiple reference images and the time sequence indicated by the acquisition time information of the multiple reference images, obtain the entry and exit direction information of the sub-area corresponding to the acquisition sub-area information of any object for the multiple reference images; based on the entry and exit direction information of the sub-area corresponding to the different acquisition sub-area information of different objects, obtain the time information of different sub-areas of multiple objects in the area.

[0180] In one possible implementation, multiple reference images are respectively divided into entry sub-regions and exit sub-regions; the image recognition module 1302 is used to, for any reference image among the multiple reference images, determine that the any object is located in the entry sub-region of any reference image if, based on the position coordinates of the detection frame of any object on any reference image, the overlap ratio between the detection frame of any object and the entry sub-region is greater than an overlap threshold, and determine that the any object is located in the exit sub-region of any reference image if, based on the position coordinates of the detection frame of any object on any reference image, the overlap ratio between the detection frame of any object and the exit sub-region is greater than an overlap threshold,

[0181] In one possible implementation, multiple reference images are respectively divided into entry sub-regions and exit sub-regions, where the entry sub-region includes an entry inner frame, where the boundary of the entry inner frame is the boundary of the entry sub-region that is reduced by at least one pixel toward the interior of the entry sub-region, and the exit sub-region includes an exit inner frame, where the boundary of the exit inner frame is the boundary of the exit sub-region that is reduced by at least one pixel toward the interior of the exit sub-region; the image recognition module 1302 is configured to, for any reference image among the multiple reference images, determine that the object is located within the entry sub-region of any reference image if, based on the position coordinates of the detection frame of any object on any reference image, an overlap ratio between the detection frame of any object and the entry inner frame is greater than a second overlap threshold; and determine that the object is located within the exit sub-region of any reference image if, based on the position coordinates of the detection frame of any object on any reference image, an overlap ratio between the detection frame of any object and the exit inner frame is greater than the second overlap threshold.

[0182] In one possible implementation, multiple image acquisition devices 1301 are used to obtain multiple acquired images obtained by the multiple image acquisition devices 1301 respectively acquiring different sub-areas of the area, and the multiple acquired images respectively include corresponding acquisition sub-area information and acquisition time information; perform object detection on the multiple acquired images, and obtain detection frames respectively included in the multiple acquired images, where one detection frame corresponds to one object; and determine that the acquired images whose image quality within the detection frames in the multiple acquired images meets the quality requirements are multiple images.

[0183] In summary, the regional management system provided by the embodiments of the present application improves regional management efficiency by automatically identifying multiple images within a region, thereby managing the region based on the attribute information of objects within the region and the time information of objects in different sub-regions. Furthermore, the improved regional management efficiency makes the allocation and mobilization of resources and personnel within the region more reasonable and efficient, thereby improving the regional service level.

[0184] It should be noted that when implementing the functions of any of the systems provided in the above embodiments, the functions can be distributed among different entities as needed. That is, the system can be composed of different entities to complete all or part of the functions described above. In addition, the systems and method embodiments provided in the above embodiments are based on the same concept. The specific implementation process is detailed in the method embodiments and will not be repeated here.

[0185] Figure 14 is a schematic diagram of the structure of a server provided in an embodiment of the present application. The server may vary significantly due to different configurations or performance, and may include one or more processors 1401 and one or more memories 1402. The one or more memories 1402 store at least one computer program, which is loaded and executed by the one or more processors 1401 to enable the server to implement the regional management methods provided in the various method embodiments described above. Of course, the server may also have components such as a wired or wireless network interface, a keyboard, and input / output interfaces for input and output. The server may also include other components for implementing device functions, which will not be described in detail here.

[0186] Figure 15 is a schematic diagram of the structure of a terminal provided in an embodiment of the present application. The terminal may be, for example, a smartphone, a tablet computer, a player, a laptop computer, or a desktop computer. The terminal may also be referred to as user equipment, a portable terminal, a laptop terminal, a desktop terminal, or other names.

[0187] Typically, the terminal includes: a processor 1501 and a memory 1502 .

[0188] The processor 1501 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 1501 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), or PLA (Programmable Logic Array). The processor 1501 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 1501 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 1501 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0189] Memory 1502 may include one or more computer-readable storage media, which may be non-transitory. Memory 1502 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices and flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in memory 1502 is used to store at least one instruction, which is executed by processor 1501 to enable the terminal to implement the regional management method provided in the method embodiment of the present application.

[0190] In some embodiments, the terminal may optionally include a peripheral device interface 1503 and at least one peripheral device. The processor 1501, memory 1502, and peripheral device interface 1503 may be connected via a bus or signal lines. Each peripheral device may be connected to the peripheral device interface 1503 via a bus, signal lines, or circuit boards. Specifically, the peripheral device may include at least one of a radio frequency circuit 1504, a display screen 1505, a camera assembly 1506, an audio circuit 1507, and a power supply 1508.

[0191] The peripheral device interface 1503 can be used to connect at least one I / O (Input / Output)-related peripheral device to the processor 1501 and the memory 1502. In some embodiments, the processor 1501, the memory 1502, and the peripheral device interface 1503 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 1501, the memory 1502, and the peripheral device interface 1503 can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0192] RF circuit 1504 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. RF circuit 1504 communicates with communication networks and other communication devices via electromagnetic signals. RF circuit 1504 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals into electrical signals. RF circuit 1504 may optionally include an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, and the like. RF circuit 1504 may communicate with other terminals via at least one wireless communication protocol. Such wireless communication protocols include, but are not limited to, metropolitan area networks, various generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks, and / or WiFi (Wireless Fidelity) networks. In some embodiments, RF circuit 1504 may also include circuitry related to NFC (Near Field Communication), which is not limited in this application.

[0193] Display screen 1505 is used to display a user interface (UI). This UI can include graphics, text, icons, videos, and any combination thereof. When display screen 1505 is a touchscreen display, it can also capture touch signals on or above the surface of display screen 1505. These touch signals can be input as control signals to processor 1501 for processing. Display screen 1505 can also be used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In some embodiments, there can be a single display screen 1505, located on the front panel of the terminal. In other embodiments, there can be at least two display screens 1505, located on different surfaces of the terminal or in a foldable design. In still other embodiments, display screen 1505 can be a flexible display screen, located on a curved or foldable surface of the terminal. Display screen 1505 can also be configured as a non-rectangular, irregular shape, also known as a special-shaped screen. Display screen 1505 can be made of materials such as LCD (Liquid Crystal Display) and OLED (Organic Light-Emitting Diode).

[0194] The camera assembly 1506 is used to capture images or videos. Optionally, the camera assembly 1506 includes a front camera and a rear camera. Typically, the front camera is arranged on the front panel of the terminal, and the rear camera is arranged on the back of the terminal. In some embodiments, there are at least two rear cameras, which are any one of a main camera, a depth of field camera, a wide-angle camera, and a telephoto camera, so as to realize the fusion of the main camera and the depth of field camera to realize the background blur function, the fusion of the main camera and the wide-angle camera to realize panoramic shooting and VR (Virtual Reality) shooting function or other fusion shooting functions. In some embodiments, the camera assembly 1506 may also include a flash. The flash can be a single-color temperature flash or a dual-color temperature flash. A dual-color temperature flash refers to a combination of a warm light flash and a cold light flash, which can be used for light compensation at different color temperatures.

[0195] The audio circuit 1507 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, and convert the sound waves into electrical signals that are input into the processor 1501 for processing, or input into the radio frequency circuit 1504 to achieve voice communication. For the purpose of stereo sound collection or noise reduction, there may be multiple microphones, each disposed at different locations of the terminal. The microphone may also be an array microphone or an omnidirectional collection microphone. The speaker is used to convert electrical signals from the processor 1501 or the radio frequency circuit 1504 into sound waves. The speaker may be a traditional thin film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can not only convert electrical signals into sound waves audible to humans, but also convert electrical signals into sound waves inaudible to humans for purposes such as distance measurement. In some embodiments, the audio circuit 1507 may also include a headphone jack.

[0196] Power supply 1508 is used to power various components in the terminal. Power supply 1508 can be AC ​​power, DC power, a disposable battery, or a rechargeable battery. When power supply 1508 includes a rechargeable battery, the rechargeable battery can support wired charging or wireless charging. The rechargeable battery can also be used to support fast charging technology.

[0197] In some embodiments, the terminal further includes one or more sensors 1509 , including but not limited to: an acceleration sensor 1510 , a gyroscope sensor 1511 , a pressure sensor 1512 , an optical sensor 1513 , and a proximity sensor 1514 .

[0198] The accelerometer 1510 can detect the magnitude of acceleration along the three coordinate axes of the coordinate system established by the terminal. For example, the accelerometer 1510 can be used to detect the components of gravity acceleration along the three coordinate axes. The processor 1501 can control the display screen 1505 to display the user interface in a landscape or portrait view based on the gravity acceleration signal collected by the accelerometer 1510. The accelerometer 1510 can also be used to collect game or user motion data.

[0199] The gyroscope sensor 1511 can detect the terminal's body orientation and rotation angle. It can also work with the accelerometer 1510 to collect the user's 3D movements on the terminal. Based on the data collected by the gyroscope sensor 1511, the processor 1501 can implement the following functions: motion sensing (such as changing the UI based on the user's tilt operation), image stabilization during shooting, game control, and inertial navigation.

[0200] The pressure sensor 1512 can be set in the side frame of the terminal and / or the lower layer of the display screen 1505. When the pressure sensor 1512 is set in the side frame of the terminal, it can detect the user's grip signal of the terminal, and the processor 1501 performs left and right hand recognition or shortcut operations based on the grip signal collected by the pressure sensor 1512. When the pressure sensor 1512 is set in the lower layer of the display screen 1505, the processor 1501 controls the operable controls on the UI interface based on the user's pressure operation on the display screen 1505. Operable controls include at least one of button controls, scroll bar controls, icon controls, and menu controls.

[0201] Optical sensor 1513 is used to detect ambient light intensity. In one embodiment, processor 1501 can control the display brightness of display screen 1505 based on the ambient light intensity detected by optical sensor 1513. Specifically, when the ambient light intensity is high, the display brightness of display screen 1505 is increased; when the ambient light intensity is low, the display brightness of display screen 1505 is decreased. In another embodiment, processor 1501 can also dynamically adjust the shooting parameters of camera assembly 1506 based on the ambient light intensity detected by optical sensor 1513.

[0202] Proximity sensor 1514, also known as a distance sensor, is typically located on the front panel of the terminal. Proximity sensor 1514 is used to detect the distance between the user and the front of the terminal. In one embodiment, when proximity sensor 1514 detects that the distance between the user and the front of the terminal is gradually decreasing, processor 1501 controls display screen 1505 to switch from the screen-on state to the screen-off state. When proximity sensor 1514 detects that the distance between the user and the front of the terminal is gradually increasing, processor 1501 controls display screen 1505 to switch from the screen-off state to the screen-on state.

[0203] Those skilled in the art will understand that the structure shown in FIG15 does not constitute a limitation on the terminal, and may include more or fewer components than shown in the figure, or combine certain components, or adopt a different component arrangement.

[0204] In an exemplary embodiment, a computer device is further provided, comprising a processor and a memory, wherein the memory stores at least one computer program. The at least one computer program is loaded and executed by one or more processors to enable the computer device to implement any of the above-mentioned area management methods.

[0205] In an exemplary embodiment, a non-transitory computer-readable storage medium is also provided, in which at least one computer program is stored. The at least one computer program is loaded and executed by a processor of a computer device to enable the computer to implement any of the above-mentioned area management methods.

[0206] In one possible implementation, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0207] In an exemplary embodiment, a computer program product or computer program is also provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform any of the above-described area management methods.

[0208] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.) and signals involved in this application are all authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions. For example, the relevant data in the area involved in this application are obtained with full authorization.

[0209] It should be understood that the term "plurality" used herein refers to two or more. "And / or" describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, or B exists alone. The character " / " generally indicates an "or" relationship between the associated objects.

[0210] The above description is merely an exemplary embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A regional management method, characterized in that, the method includes: obtaining multiple images corresponding to a region, where the multiple images respectively include corresponding acquisition sub-region information and acquisition time information; performing image recognition on the multiple images respectively to obtain an image recognition result, and obtaining attribute information of multiple objects included in the multiple images according to the image recognition result; based on the acquisition sub-region information, acquisition time information respectively corresponding to the multiple images, and the image recognition result, obtaining time information of the multiple objects in different sub-regions within the region; managing the region according to the time information of the multiple objects in different sub-regions within the region and the attribute information of the multiple objects, where the management includes at least one of regional object management, regional facility management, or regional service management.

2. The method according to claim 1, characterized in that, the attribute information includes at least one of gender, age, occupation, personality, and clothing type; the managing the region according to the time information of the multiple objects in different sub-regions within the region and the attribute information of the multiple objects includes: obtaining the action paths of the multiple objects according to the time information of the multiple objects in different sub-regions within the region; obtaining the object distribution in different sub-regions at different times according to the time information of the multiple objects in different sub-regions within the region and at least one of the gender, age, occupation, personality, and clothing type of the multiple objects; visually displaying at least one of the action paths of the multiple objects and the object distribution in different sub-regions at different times.

3. The method according to claim 2, characterized in that, the object distribution includes at least one of gender group characteristics, age group characteristics, occupation group characteristics, and personality group characteristics and the number of objects; after obtaining the object distribution in different sub-regions at different times, it further includes: managing at least one of the business hours of different sub-regions, the repair time of service facilities, and the configured number of service personnel according to the number of objects in different sub-regions at different times; managing at least one of the facility type of service facilities, the personnel type of service personnel, and the store type of different sub-regions according to at least one of the gender group characteristics, age group characteristics, occupation group characteristics, and personality group characteristics of different sub-regions.

4. The method according to claim 3, characterized in that, the managing at least one of the business hours of different sub-regions, the repair time of service facilities, and the configured number of service personnel according to the number of objects in different sub-regions at different times includes: determining at least one time period in which the number of objects in different sub-regions exceeds a first quantity threshold according to the number of objects in different sub-regions at different times, and determining the business hours of any sub-region based on at least one time period of any sub-region; Determine the damage degree of the service facilities in the different sub - regions according to the number of objects in the different sub - regions at different times, and determine the repair time of the service facilities in any one of the sub - regions based on the damage degree of the service facilities in any one of the sub - regions; Determine the required number of service staff configurations in the different sub - regions at different times according to the number of objects in the different sub - regions at different times, and the number of service staff configurations in any one of the sub - regions at different times matches the number of objects in any one of the sub - regions at different times.

5. The method according to claim 3, wherein, managing at least one of the facility types of the service facilities, the staff types of the service staff, and the store types in the different sub - regions according to at least one of the gender group characteristics, age group characteristics, occupation group characteristics, and personality group characteristics in the different sub - regions includes: Determine the object group types in the different sub - regions according to at least one of the gender group characteristics, age group characteristics, occupation group characteristics, and personality group characteristics in the different sub - regions; Based on the object group types in the different sub - regions, determine the facility types of the service facilities, the staff types of the service staff, and the store types in the different sub - regions. The facility types of the service facilities, the staff types of the service staff, and the store types in any one of the sub - regions all match the object group type in any one of the sub - regions.

6. The method according to claim 2, wherein, after obtaining the object distribution in the different sub - regions at different times, it further includes: When the ratio between the area of any one of the different sub - regions and the number of objects in any one of the sub - regions exceeds a ratio threshold, and the duration exceeding the ratio threshold exceeds a time threshold issue a first warning message, and the first warning message is used to remind the objects in any one of the sub - regions to leave any one of the sub - regions; When the action path of any one of the multiple objects coincides with the warning sub - region of the region, issue a second warning message, and the second warning message is used to remind any one of the objects to stay away from the warning sub - region.

7. The method according to claim 1, wherein, the different sub - regions in the region include stores, the attribute information includes the type of carried items, and the type of carried items includes the packaging corresponding to the store; managing the region according to the time information of the multiple objects in the different sub - regions in the region and the attribute information of the multiple objects includes: According to the time information of the multiple objects in the different sub - regions in the region and the type of carried items of the multiple objects, obtain the number of the first objects carrying the packaging into the store, the number of the second objects carrying the packaging out of the store, and the total number of objects entering the store during a reference time period; Based on the difference quantity and the total number of objects, determine the purchase rate of the store during the reference time period, and the difference quantity is the difference between the number of the second objects and the number of the first objects; Manage the store according to the purchase rate.

8. The method according to claim 7, It is characterized in that the attribute information further includes at least one of gender, age, and occupation; and the management of the store according to the purchase rate includes: when the purchase rate is lower than the probability threshold, determining the object preference corresponding to the store according to at least one of the gender, age, and occupation of the object entering the store; adjusting the types of goods in the store based on the object preference.

9. The method according to any one of claims 1-8, It is characterized in that the image recognition result includes the position coordinates of the detection frames of each object on each image; and the obtaining of the time information of the multiple objects in different sub-regions within the region based on the acquisition sub-region information, acquisition time information respectively corresponding to the multiple images, and the image recognition result includes: for any one of the multiple objects, obtaining the position coordinates of the detection frame of the any one object in multiple reference images, where the multiple reference images are images among the multiple images with the same acquisition sub-region information and different acquisition time information, and the detection frame is a head detection frame; determining the position sub-regions of the any one object on the multiple reference images respectively according to the position coordinates of the detection frame of the any one object in the multiple reference images; obtaining the in-out direction information of the sub-region corresponding to the acquisition sub-region information of the any one object for the multiple reference images according to the position sub-regions of the any one object on the multiple reference images respectively and the time sequence indicated by the acquisition time information of the multiple reference images; obtaining the time information of the multiple objects in different sub-regions within the region based on the in-out direction information of the sub-region corresponding to different acquisition sub-region information for different objects.

10. The method according to claim 9, It is characterized in that the multiple reference images are respectively divided into an in sub-region and an out sub-region; and the determining of the position sub-regions of the any one object on the multiple reference images respectively according to the position coordinates of the detection frame of the any one object in the multiple reference images includes: for any one of the multiple reference images, if it is determined that the coincidence ratio between the detection frame of the any one object and the in sub-region is greater than the first coincidence threshold based on the position coordinates of the detection frame of the any one object on the any one reference image, then it is determined that the any one object is located in the in sub-region of the any one reference image; if it is determined that the coincidence ratio between the detection frame of the any one object and the out sub-region is greater than the first coincidence threshold based on the position coordinates of the detection frame of the any one object on the any one reference image, then it is determined that the any one object is located in the out sub-region of the any one reference image.

11. The method according to claim 9, It is characterized in that The multiple reference images are respectively divided into an incoming sub-region and an outgoing sub-region. The incoming sub-region includes an inner incoming frame, and the boundary of the inner incoming frame is the boundary of the incoming sub-region shrunk inward by at least one pixel. The outgoing sub-region includes an inner outgoing frame, and the boundary of the inner outgoing frame is the boundary of the outgoing sub-region shrunk inward by at least one pixel. Determining the position sub-region of any one object on each of the multiple reference images according to the position coordinates of the detection frame of the any one object in the multiple reference images includes: For any one of the multiple reference images, if it is determined based on the position coordinates of the detection frame of the any one object on the any one reference image that the overlap ratio between the detection frame of the any one object and the inner incoming frame is greater than a second overlap threshold, it is determined that the any one object is located within the incoming sub-region of the any one reference image; If it is determined based on the position coordinates of the detection frame of the any one object on the any one reference image that the overlap ratio between the detection frame of the any one object and the inner outgoing frame is greater than the second overlap threshold, it is determined that the any one object is located within the outgoing sub-region of the any one reference image.

12. The method according to any one of claims 1-8, characterized in that obtaining the multiple images corresponding to the region includes: obtaining multiple acquisition images respectively acquired by multiple image acquisition devices for different sub-regions of the region, the multiple acquisition images respectively including corresponding acquisition sub-region information and acquisition time information; performing object detection on the multiple acquisition images to obtain the detection frames included in the multiple acquisition images, one detection frame corresponding to one object; determining the acquisition images whose image quality within the detection frames in the multiple acquisition images meets the quality requirements as the multiple images.

13. A region management system, characterized in that the system includes multiple image acquisition devices, an image recognition module and a management analysis module; the multiple image acquisition devices are used to obtain multiple images corresponding to a region, the multiple images respectively including corresponding acquisition sub-region information and acquisition time information; sending the multiple images to the image recognition module; the image recognition module is used to perform image recognition on each of the multiple images to obtain an image recognition result, and obtain the attribute information of multiple objects included in the multiple images according to the image recognition result; based on the acquisition sub-region information, acquisition time information respectively corresponding to the multiple images and the image recognition result, obtaining the time information of the multiple objects in different sub-regions within the region; sending the time information of the multiple objects in different sub-regions within the region and the attribute information of the multiple objects to the management analysis module; the management analysis module is used to manage the region according to the time information of the multiple objects in different sub-regions within the region and the attribute information of the multiple objects, and the management includes at least one of region object management, region facility management or region service management.

14. A computer device, characterized in that The computer device includes a processor and a memory. At least one computer program is stored in the memory and is loaded and executed by the processor so that the computer device implements the area management method as described in any one of claims 1 to 12.

15. A non-transitory computer-readable storage medium, characterized in that, at least one computer program is stored in the non-transitory computer-readable storage medium and is loaded and executed by a processor so that a computer implements the area management method as described in any one of claims 1 to 12.

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