Intelligent managed methods, systems, devices, media, and program products

Abstract

This disclosure provides a smart pet care method applicable to the field of artificial intelligence technology. The method includes: obtaining the binding relationship between user identity information and pet identity information; performing smart pet care monitoring on the bound pet, wherein the smart pet care monitoring includes obtaining pet monitoring information, obtaining pet emotional status information based on the pet monitoring information, and initiating pet status reminders based on the pet emotional status information; obtaining user behavior collection data, determining the user's in-store status based on the user behavior collection data and a user departure judgment model, wherein the user behavior collection data includes user in-store progress data and user exit movement trajectory data; when the user's in-store status is about to leave or has already left, reminding the user to retrieve the bound pet. This disclosure also provides a smart pet care system, device, storage medium, and program product.

Description

Technical Field

[0001] This disclosure relates to the field of artificial intelligence technology, and more specifically, to an intelligent hosting method, apparatus, device, medium, and program product. Background Technology

[0002] When customers visit bank branches or other institutional business premises, pets are generally prohibited from being brought inside to avoid disturbing other customers. In such cases, customers often temporarily leash their pets on handrails, tree trunks, or bicycle parking areas outside the premises. This approach fails to guarantee the pet's safety, prevent loss, or allow the customer to monitor the pet's condition. Therefore, customers are hesitant to enter the premises to conduct their business under these circumstances. Summary of the Invention

[0003] In view of the above problems, embodiments of this disclosure provide intelligent pet care methods, systems, devices, media, and program products that improve the intelligence and user satisfaction of pet care.

[0004] According to a first aspect of this disclosure, a smart pet custody method is provided, applied to a pet custody service processing server, comprising: obtaining the binding relationship between user identity information and pet identity information; performing smart pet custody monitoring, wherein the smart pet custody monitoring includes obtaining pet monitoring information, obtaining pet emotional status information based on the pet monitoring information, and initiating pet status reminders based on the pet emotional status information; obtaining user behavior collection data, determining the user's in-store status based on the user behavior collection data and a user departure judgment model, wherein the user behavior collection data includes user in-store progress data and user exit movement trajectory data; and reminding the user to retrieve the bound pet when the user's in-store status is about to leave or has already left.

[0005] According to embodiments of this disclosure, determining the user's in-store status based on the user behavior collection data and the user departure judgment model includes: acquiring a user exit mobile video data stream; acquiring a user exit displacement time series image based on the user exit mobile video data stream; calculating user exit movement trajectory data based on the user exit displacement time series image; acquiring user in-store progress data; acquiring user behavior collection data based on the user in-store progress data and the user exit movement trajectory data; inputting the user behavior collection data into the user departure judgment model to obtain the user's in-store status, wherein the user's in-store status includes one of having left the store, about to leave the store, or not having left the store; the user in-store progress data includes user custodian time data, user business completion time data, and user exit identification time data; the user exit movement trajectory data includes user movement direction data.

[0006] According to embodiments of this disclosure, the user departure judgment model is constructed based on a supervised algorithm.

[0007] According to embodiments of this disclosure, the user departure judgment model is constructed based on the Boosting algorithm.

[0008] According to embodiments of this disclosure, the step of calculating the user exit movement trajectory data based on the user exit displacement time series image includes: identifying the user to be tracked based on the baseline facial feature information of the managed user and the facial recognition information in the user exit displacement time series image; obtaining the location data of the user to be tracked based on the frame images in the user exit displacement time series image; and calculating the user exit movement trajectory data based on the location data of the user to be tracked in adjacent frame images in the user exit displacement time series image.

[0009] According to an embodiment of this disclosure, the intelligent management and monitoring of the bound pet includes: acquiring pet monitoring information, which includes pet behavior data and facial expression data; inputting the pet monitoring information into a pet emotion recognition model to acquire the pet's emotional state monitoring information, wherein the pet emotion recognition model is trained based on a deep convolutional neural network.

[0010] According to embodiments of this disclosure, after obtaining the binding relationship between user identity information and pet identity information, the method further includes: locking the pet locking device based on the binding relationship between the user identity information and pet identity information.

[0011] According to embodiments of this disclosure, after reminding the user to retrieve the bound pet, the method further includes: obtaining user identification information; and unlocking the pet locking device when the user identification information matches the user identity information.

[0012] According to embodiments of this disclosure, the method further includes: obtaining pet identity confirmation information when the user identification information matches the user identity information; and unlocking the pet locking device when the pet identity confirmation information is verified.

[0013] According to embodiments of this disclosure, reminding a user to retrieve the bound pet includes: sending a weak reminder message to the user when the user's in-store status is about to leave the store, wherein the weak reminder message includes a text reminder message; and / or sending a strong reminder message to the user when the user's in-store status is already left the store, wherein the strong reminder message includes a voice reminder message.

[0014] The second aspect of this disclosure provides an intelligent hosting method applied to an identity binding device, comprising: acquiring user identity recognition information from an identity recognition device and pet image information from a pet image acquisition device; comparing the user identity recognition information with user identity information, confirming the user identity upon successful matching, and extracting the user identity information; inputting the pet image information into a pet nose print recognition model to obtain pet nose print feature information, using the pet nose print feature information as the pet identity information; and establishing a binding relationship between the user identity information and the pet identity information based on the matching relationship between the user identity information and the pet identity information, wherein the pet nose print recognition model is constructed based on a graph convolutional neural network algorithm.

[0015] A third aspect of this disclosure provides a smart pet hosting method, comprising: an identity binding device acquiring user identity information from an identity recognition device and pet image information from an image acquisition device, and processing the user identity information and pet image information to obtain a binding relationship between user identity information and pet identity information; a pet hosting service processing server acquiring the binding relationship between user identity information and pet identity information from the identity binding device; a pet monitoring server acquiring pet monitoring information from the pet hosting monitoring device; and the pet hosting service processing server performing smart pet hosting monitoring on the bound pet, wherein the smart pet hosting monitoring includes acquiring pet monitoring information from the pet monitoring server, and based on the pet monitoring... The system acquires pet emotional status information and initiates pet status reminders based on this information; the exit image server collects user exit movement trajectory data from the exit monitoring equipment; the pet hosting service processing server acquires user behavior collection data and determines the user's in-store status based on this data and the user departure judgment model. The user behavior collection data includes user in-store progress data and user exit movement trajectory data, with the exit movement trajectory data acquired from the exit image server and the in-store progress data acquired from the service processing server. When the user's in-store status indicates they are about to leave or have already left, the pet hosting service processing server sends a command to remind the user to retrieve their bound pet.

[0016] The fourth aspect of this disclosure provides a smart pet-sitting method, comprising: an identity binding device, a pet-sitting service processing server, a pet monitoring server, an exit image server, and a pet-sitting device, wherein the pet-sitting device includes a pet-sitting monitoring device; the pet monitoring server is used to acquire pet monitoring information from the pet-sitting monitoring device; the exit image server is used to collect user movement trajectory data from the exit monitoring device; the pet-sitting service processing server is used to acquire the binding relationship between user identity information and pet identity information, and to perform smart pet-sitting monitoring on the bound pet, wherein the smart pet-sitting monitoring includes acquiring pet monitoring information, acquiring pet emotional status information based on the pet monitoring information, and initiating pet status reminders based on the pet emotional status information; acquiring user behavior collection data, and determining the user's in-store status based on the user behavior collection data and a user departure judgment model, wherein the user behavior collection data includes User in-store progress data and user movement trajectory data; when the user's in-store status is about to leave or has already left, remind the user to retrieve the bound pet; the identity binding device is used to obtain user identity recognition information from the identity recognition device and pet image information from the pet image acquisition device; compare the user identity recognition information with the user identity information, and confirm the user's identity after a successful match, and extract the user identity information; input the pet image information into the pet nose print recognition model to obtain pet nose print feature information, and use the pet nose print feature information as the pet's identity information; and establish the binding relationship between the user and the pet based on the matching relationship between the user identity information and the pet identity information, wherein the pet nose print recognition model is constructed based on the graph convolutional neural network algorithm, and the hosting business processing server is communicatively connected to the identity binding device, the pet monitoring server, the exit image server, and the hosting monitoring device respectively.

[0017] The fifth aspect of this disclosure provides a hosting service processing server, comprising: a first acquisition module configured to acquire the binding relationship between user identity information and pet identity information; an intelligent monitoring module configured to perform intelligent hosting monitoring on the bound pet, wherein the intelligent hosting monitoring includes acquiring pet monitoring information, acquiring pet emotional status information based on the pet monitoring information, and initiating pet status reminders based on the pet emotional status information; a first calculation module configured to acquire user behavior collection data, determine the user's in-store status based on the user behavior collection data and a user departure judgment model, wherein the user behavior collection data includes user in-store progress data and user exit movement trajectory data; and a reminder module configured to remind the user to retrieve the bound pet when the user's in-store status is about to leave or has already left.

[0018] According to embodiments of this disclosure, the pet custody service further includes a locking module. This locking module is configured to lock the pet locking device based on the binding relationship between the user's identity information and the pet's identity information after obtaining that binding relationship.

[0019] According to embodiments of this disclosure, the managed service processing server further includes a second acquisition module and an unlocking module. The second acquisition module is configured to acquire user identification information. The unlocking module is configured to unlock the pet locking device when the user identification information matches the user identity information.

[0020] According to embodiments of this disclosure, the hosting service processing server further includes an extraction module and a release module. The extraction module is configured to obtain the hosting pet's identity confirmation information when the user identification information matches the user's identity information. The release module is configured to unlock the pet locking device when the hosting pet's identity confirmation information is verified.

[0021] According to embodiments of this disclosure, the first calculation module may further include a first acquisition unit, a second acquisition unit, a first processing unit, a third acquisition unit, a fourth acquisition unit, and a second processing unit. The first acquisition unit is configured to acquire a user exit mobile video data stream. The second acquisition unit is configured to acquire a user exit displacement time-series image based on the user exit mobile video data stream. The first processing unit is configured to calculate user exit movement trajectory data based on the user exit displacement time-series image. The third acquisition unit is configured to acquire user in-store progress data. The fourth acquisition unit is configured to acquire user behavior collection data based on the user in-store progress data and the user exit movement trajectory data. The second processing unit is configured to input the user behavior collection data into the user departure judgment model to acquire the user in-store status. The user in-store status includes one of having already left, about to leave, or not yet left; the user in-store progress data includes user custodian time data, user business completion time data, and user exit identification time data; the user exit movement trajectory data includes user movement direction data.

[0022] According to embodiments of this disclosure, the first processing unit further includes an identification subunit, a tracking subunit, and a calculation subunit. The identification subunit is configured to identify the user to be tracked based on baseline facial feature information of a registered user and facial recognition information in the user exit displacement time-series image. The tracking subunit is configured to obtain the user's location data based on frame images in the user exit displacement time-series image. The calculation subunit is configured to calculate the user exit movement trajectory data based on the user's location data in adjacent frame images in the user exit displacement time-series image.

[0023] According to embodiments of this disclosure, the intelligent monitoring module may further include a fifth acquisition unit and a third processing unit. The fifth acquisition unit is configured to acquire pet monitoring information, which includes pet behavior data and facial expression data. The third processing unit inputs the pet monitoring information into a pet emotion recognition model to acquire the pet's emotional state monitoring information, wherein the pet emotion recognition model is trained based on a deep convolutional neural network.

[0024] According to embodiments of this disclosure, the reminder module may further include a sending unit. The sending unit is configured to send a weak reminder message to the user when the user's in-store status is about to leave the store, wherein the weak reminder message includes text reminder information; and / or to send a strong reminder message to the user when the user's in-store status is that the user has already left the store, wherein the strong reminder message includes voice reminder information.

[0025] The sixth aspect of this disclosure provides an identity binding device, comprising: a second acquisition module configured to acquire user identity recognition information from an identity recognition device and pet image information from an image acquisition device; a second calculation module configured to compare the user identity recognition information with user identity information, confirm the user identity upon successful matching, and extract the user identity information; a third calculation module configured to input the pet image information into a pet nose print recognition model to acquire pet nose print feature information, and use the pet nose print feature information as the pet identity information, wherein the pet nose print recognition model is constructed based on a graph convolutional neural network algorithm; and a fourth calculation module configured to establish a binding relationship between the user identity information and the pet identity information based on the matching relationship between the user identity information and the pet identity information.

[0026] A seventh aspect of this disclosure provides an electronic device comprising: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors perform the above-described smart managed method.

[0027] An eighth aspect of this disclosure also provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, cause the processor to perform the above-described smart managed method.

[0028] The ninth aspect of this disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described intelligent managed method.

[0029] The methods provided in this disclosure, based on artificial intelligence technologies such as biometrics, emotion analysis, and behavioral trajectory analysis, enable intelligent pet care. This ensures pet safety, prevents pet loss, allows users to monitor their pet's condition, and provides timely reminders to end pet care, preventing pet forgetting. The methods provided in this disclosure offer a safe and reliable temporary care environment for pets. Attached Figure Description

[0030] The foregoing contents, as well as other objects, features, and advantages of this disclosure, will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0031] Figure 1 The diagram illustrates an application scenario of the smart hosting method according to an embodiment of the present disclosure.

[0032] Figure 2 A flowchart illustrating a smart hosting method according to an embodiment of the present disclosure is shown schematically.

[0033] Figure 3 A flowchart illustrating a method for intelligently managing and monitoring a bound pet according to an embodiment of this disclosure is shown schematically.

[0034] Figure 4 The flowchart illustrates a method for determining a user's in-store status based on the user behavior collection data and the user departure judgment model, according to some specific embodiments of the present disclosure.

[0035] Figure 5 The flowchart illustrates a method for calculating user exit movement trajectory data based on the user exit displacement time series image according to some specific embodiments of the present disclosure.

[0036] Figure 6 The illustration shows a schematic diagram of a method for calculating user exit movement trajectory data according to some specific embodiments of the present disclosure.

[0037] Figure 7 A flowchart illustrating a method for locking a pet according to an embodiment of the present disclosure is shown schematically.

[0038] Figure 8 A flowchart illustrating a method for unlocking a pet according to an embodiment of the present disclosure is shown schematically.

[0039] Figure 9 A flowchart illustrating a method for unlocking a pet according to other embodiments of this disclosure is shown schematically.

[0040] Figure 10 A schematic diagram of a pet locking device according to some specific embodiments of the present disclosure is shown.

[0041] Figure 11 A flowchart illustrating a method for reminding a user to retrieve a bound pet according to an embodiment of this disclosure is shown schematically.

[0042] Figure 12 A flowchart illustrating a smart hosting method according to other embodiments of this disclosure is shown schematically.

[0043] Figure 13 A flowchart illustrating a smart hosting method according to other embodiments of this disclosure is shown schematically.

[0044] Figure 14A A schematic block diagram of an intelligent managed system according to an embodiment of the present disclosure is shown.

[0045] Figure 14B A schematic block diagram of a pet care device according to an embodiment of the present disclosure is shown.

[0046] Figure 15 The illustration shows schematic diagrams of an identity recognition device and an image acquisition device according to some specific embodiments of the present disclosure.

[0047] Figure 16 A schematic block diagram of a managed service processing server according to an embodiment of the present disclosure is shown.

[0048] Figure 17 A schematic block diagram of a managed service processing server according to other embodiments of the present disclosure is shown.

[0049] Figure 18 A schematic block diagram of a managed service processing server according to other embodiments of the present disclosure is shown.

[0050] Figure 19 A schematic block diagram of a managed service processing server according to other embodiments of the present disclosure is shown.

[0051] Figure 20 A schematic block diagram of a first computing module according to an embodiment of the present disclosure is shown.

[0052] Figure 21 A schematic block diagram of the structure of a first processing unit according to an embodiment of the present disclosure is shown.

[0053] Figure 22 A schematic block diagram of an intelligent monitoring module according to an embodiment of the present disclosure is shown.

[0054] Figure 23 A schematic block diagram of a reminder module according to an embodiment of the present disclosure is shown.

[0055] Figure 24A schematic block diagram of an identity binding device according to an embodiment of the present disclosure is shown.

[0056] Figure 25 A block diagram of an electronic device suitable for a real-time intelligent hosting method according to an embodiment of the present disclosure is shown schematically. Detailed Implementation

[0057] The embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0058] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0059] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0060] When using expressions such as "at least one of A, B, and C", they should generally be interpreted in accordance with the meaning that is commonly understood by a person skilled in the art (e.g., "a system having at least one of A, B, and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B, and C, etc.).

[0061] To serve all customers, fixed business locations such as bank branches and branch offices need to consider various special groups and service scenarios in their functional area planning, business design, and equipment placement. For example, for the elderly, people with disabilities, and foreigners, it is often necessary to provide corresponding solutions to address their difficulties and pain points in handling business, in order to provide as considerate service as possible to all types of customers. However, for customers with pets, to avoid disturbing other customers, it is generally not allowed to bring pets into the business premises. In this case, customers with pets often temporarily tether their pets to fixed locations outside the business premises, such as door handles, tree trunks, or bicycle parking areas. This cannot guarantee the safety of the pets or prevent them from getting lost, nor can it allow customers to know the status of their pets. Therefore, in this scenario, customers are hesitant to enter the business premises to conduct business.

[0062] To address the aforementioned problems in the existing technology, embodiments of this disclosure provide an intelligent pet custody method applied to a pet custody service processing server, comprising: obtaining the binding relationship between user identity information and pet identity information; performing intelligent pet custody monitoring on the bound pet, wherein the intelligent pet custody monitoring includes obtaining pet monitoring information, obtaining pet emotional status information based on the pet monitoring information, and initiating pet status reminders based on the pet emotional status information; obtaining user behavior collection data, determining the user's in-store status based on the user behavior collection data and a user departure judgment model, wherein the user behavior collection data includes user in-store progress data and user exit movement trajectory data; and reminding the user to retrieve the bound pet when the user's in-store status is about to leave or has already left the store.

[0063] It should be noted that the intelligent pet care method, system, device, medium, and program products provided in this disclosure can be used in the application of artificial intelligence technology to pet care, and can also be used in various fields other than artificial intelligence technology, such as the financial field. The application fields of the intelligent pet care method, device, device, medium, and program products provided in this disclosure are not limited.

[0064] In the technical solution disclosed herein, the collection, storage, use, processing, transmission, provision, disclosure, and application of user personal information comply with the provisions of relevant laws and regulations, necessary confidentiality measures have been taken, and there is no violation of public order and good morals.

[0065] In the technical solution disclosed herein, the user's authorization or consent is obtained before acquiring or collecting the user's personal information.

[0066] The above-described operations for achieving at least one objective of this disclosure will be described below in conjunction with the accompanying drawings and their descriptions.

[0067] Figure 1The diagram illustrates an application scenario of the smart hosting method according to an embodiment of the present disclosure.

[0068] like Figure 1 As shown, application scenario 100 according to this embodiment may include terminal devices 101 and 102, a managed monitoring device 103, a network 104, a managed service processing server 105, an identity binding device 106, a pet monitoring server 107, a pet locking device 108, an exit monitoring device 109, an exit image server 110, an exit monitoring device 111, and a service processing server 112. The network 104 serves as a medium for providing communication links between the terminal devices 101 and 102, the managed monitoring device 103, the managed service processing server 105, the identity binding device 106, the pet monitoring server 107, the pet locking device 108, the exit monitoring device 109, the exit image server 110, the exit monitoring device 111, and the service processing server 112. The network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables, etc.

[0069] Users can use terminal devices 101 and 102 to interact with the managed service processing server 105 via network 104 to receive or send messages, etc. For example, terminal devices 101 and 102 can be used to send managed request information to the managed service processing server 105. Various communication client applications can also be installed on terminal devices 101 and 102, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social media platform software, etc. (for example only).

[0070] Terminal devices 101 and 102 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0071] The managed service processing server 105 can be a server that provides various services, such as a backend management server that provides feedback on requests sent by users using terminal devices 101 and 102 (this is just an example). The managed service processing server 105 can analyze and process received user requests and other data, and feed back the processing results (such as web pages, information, or data obtained or generated based on user requests) to the terminal devices. Furthermore, the managed service processing server 105 can also interact with devices such as the identity binding device 106, pet monitoring server 107, pet locking device 108, exit monitoring device 109, exit image server 110, exit monitoring device 111, and service processing server 112 to achieve comprehensive pet boarding service management.

[0072] The identity binding device 106 can be a terminal device or a server. The identity binding device 106 can identify the identities of users and pets and establish a binding relationship so that pet care can be carried out based on the binding relationship.

[0073] The pet monitoring server 107 can be a server that provides various services. The pet monitoring server 107 can obtain pet monitoring information and feed the pet monitoring information back to the hosting service processing server 105 for analysis and processing of the pet's emotional state, thereby promptly reminding users to pay attention to the pet's condition.

[0074] The pet locking device 108 can be a terminal device, used to restrict a pet's activity range to a controllable and easily manageable area. In embodiments of this disclosure, the pet locking device 108 can be connected to a hosting service processing server 105 via a network 104 to achieve smart locking and unlocking.

[0075] The managed monitoring device 103 can be a terminal device. It can be used to collect pet monitoring information, such as images or videos of the managed pet's behavior and expressions, and provide this information to the pet monitoring server 107. Furthermore, the pet monitoring server 107 can transmit the collected pet monitoring information to the managed service processing server 105.

[0076] The exit image server 110 can be a server that provides various services. The exit image server 110 can be used to obtain user behavior data at the exit of the business premises, such as user exit movement trajectory data, so as to provide it to the hosting business processing server 105 to determine the user's departure status and promptly remind the user to pick up the hosted pet.

[0077] Exit monitoring equipment 109 and exit monitoring equipment 111 can be terminal devices. Exit monitoring equipment 109 and exit monitoring equipment 111 can be used to collect the movement trajectory of users at the exit of the business premises and transmit it to the exit image server 110 in real time.

[0078] The business processing server 112 can be a server that provides various services, and it can also be a back-end management server for handling business at a business location. The business processing server 112 can provide the hosted business processing server 105 with the registered original user identity information for user identification.

[0079] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0080] The following will be based on Figure 1The described scene, through Figures 2 to 13 The smart hosting method of the disclosed embodiments will be described in detail.

[0081] Figure 2 A flowchart illustrating a smart hosting method according to an embodiment of the present disclosure is shown schematically.

[0082] like Figure 2 As shown, the smart hosting method of this embodiment includes operations S210 to S240. The smart hosting method can be executed by a hosting service processing server or by any electronic device including a hosting service processing server.

[0083] In operation S210, the binding relationship between user identity information and pet identity information is obtained.

[0084] According to embodiments of this disclosure, before entrusting a pet to a care facility, the binding relationship between user identity information and pet identity information can be obtained first. This binding relationship can be directly entered by the user or obtained using a dedicated identity binding device.

[0085] In operation S220, intelligent hosting and monitoring are performed on the bound pet. This intelligent hosting and monitoring includes acquiring pet monitoring information, acquiring pet emotional status information based on the monitoring information, and initiating pet status alerts based on the emotional status information.

[0086] In the embodiments of this disclosure, to facilitate users' real-time understanding of their pet's emotional state and to promptly alert users when the pet is in a bad mood, pet monitoring information can be acquired and further processed to obtain information about the pet's emotional state. This pet monitoring information can be obtained from monitoring devices, such as cameras. In some typical examples, the pet's behavior over a period of time can be extracted in real-time from the camera, including, for example, the pet's actions and expressions—i.e., time-series behavioral data—and transmitted for further analysis of the pet's emotional state.

[0087] Figure 3 A flowchart illustrating a method for intelligently managing and monitoring a bound pet according to an embodiment of this disclosure is shown schematically.

[0088] like Figure 3 As shown, the method for intelligently managing and monitoring a bound pet in this embodiment includes operations S310 to S320.

[0089] In operation S310, pet monitoring video status information is obtained, which includes pet behavior data and facial expression data.

[0090] In operation S320, the pet behavior data and facial expression data are input into the pet emotion recognition model to obtain the pet's emotional status monitoring information. The pet emotion recognition model is trained based on a deep convolutional neural network.

[0091] According to embodiments of this disclosure, pet monitoring video information can be obtained from pet-sitting monitoring devices, such as cameras. It is understood that pets can be placed in specialized pet-sitting facilities, which may include pet enclosures. Monitoring devices such as cameras can be placed within these facilities, including the pet enclosures, and positioned to capture pet behavior. This allows for real-time capture of pet behavior and facial expression changes. For example, it can capture temporal behavioral and facial expression data of a pet over a period of time. It should be understood that a pet's facial expressions and / or behavioral characteristics will differ in different states, and the pet's state can be determined by analyzing the facial expressions or behavioral characteristics of the pet in each frame or consecutive frames of the video stream. For example, when a dog is anxious, its facial expressions typically include whale-eye features, excessive yawning, licking its tongue, licking its lips, sticking out its tongue to lick its nose, circling, and barking. When a dog is frightened, its facial expressions typically include showing the whites of its eyes. When a dog is bored, it will exhibit quiet staring behavior. When a pet dog is hungry, it may exhibit behaviors such as circling around and howling. The pet emotion recognition model of this disclosure can analyze the corresponding emotional state of a pet based on its behavior and facial expression features in an input image. Specifically, pet monitoring video data streams can be sent to the open-source OpenCV software library. One frame can be extracted from every few frames of the video as an image analysis object. The pet emotion recognition model then analyzes whether the pet's feature information in the image matches the features of a pre-defined pet. To improve data processing efficiency, the pet's emotion can be preset to "good" or a commonly used graph convolutional algorithm can be used to build the pet emotion recognition model. For example, a deep convolutional neural network from an open-source DNN library can be used to train the model, with the training objective being to determine whether the pet's emotion is "good" (1-good, 0-bad). When the judgment result ∈ (0, 0.5), the pet's emotion is considered bad; when the judgment result ∈ (0.5, 1), the pet's emotion is considered good.

[0092] In the embodiments of this disclosure, when a pet's poor emotional state is detected, the pet's owner can be alerted using a preset method. For example, the alert can be sent to the pet owner via intelligent voice calls, text messages, WeChat, or other means.

[0093] In a specific example, when a pet is in poor condition, a reminder can be sent to the pet owner based on the preset statements in Table 1:

[0094] Table 1

[0095]

[0096] In operation S230, user behavior data is acquired, and the user's in-store status is determined based on the user behavior data and the user departure judgment model. The user behavior data includes user in-store progress data and user exit movement trajectory data.

[0097] When operating S240, if a user's store status is "about to leave" or "has already left", remind the user to retrieve their linked pet.

[0098] According to embodiments of this disclosure, to promptly remind users to bring their pets home, the user's in-store behavior trajectory can be automatically tracked. User in-store behavior data is collected, and a user departure judgment model is used to determine the user's in-store status. Specifically, user in-store progress data is used to assess the user's progress in completing transactions at the store, and this data can be partially or entirely obtained from the transaction processing server. User exit movement trajectory data is used to characterize the user's movement trajectory at the exit. It is understood that based on the user's movement trajectory at the exit, such as whether the exit camera captures movement, the direction of movement, and speed, it can be determined whether the user has a tendency to leave the store. For example, if the user does not appear in the exit camera, it indicates that the user has no intention of leaving the store; if the user appears in the exit camera and has a trajectory of gradually approaching the exit and moving outwards from the exit, it indicates that the user is about to leave the store. Furthermore, by collecting sample data of the above behaviors and establishing a user departure judgment model, new user in-store behavior data can be obtained and then input into the user departure judgment model to predict whether the user has a tendency to leave the store.

[0099] It should be noted that in the embodiments of this disclosure, user consent or authorization can be obtained before acquiring user information such as user identity information and behavioral trajectory. For example, a request to acquire user information can be sent to the user before operation S210. If the user consents or authorizes the acquisition of user information, operation S210 is executed.

[0100] In some embodiments, the user departure determination model is built based on a supervised algorithm.

[0101] In some embodiments, the user departure judgment model is built based on the Boosting algorithm. Specifically, algorithms such as GBDT, LIGHTGBM, and XGBOOST can be used to build the model. In some embodiments, to save computing resources and improve processing speed, decision tree algorithms can also be used to train the model.

[0102] Figure 4 The flowchart illustrates a method for determining a user's in-store status based on the user behavior collection data and the user departure judgment model, according to some specific embodiments of the present disclosure.

[0103] like Figure 4 As shown, the method for intelligently managing and monitoring a bound pet in this specific embodiment includes operations S410 to S460.

[0104] When operating S410, acquire the user's outgoing mobile video data stream.

[0105] In operation S420, a time series image of the user exit displacement is acquired based on the user exit mobile video data stream.

[0106] In operation S430, user exit movement trajectory data is calculated based on the user exit displacement time series image.

[0107] Using S440, obtain user in-store progress data.

[0108] In operation S450, user behavior data is acquired based on the user's in-store progress data and the user's exit movement trajectory data.

[0109] In operation S460, the user behavior collection data is input into the user departure judgment model to obtain the user's in-store status.

[0110] According to embodiments of this disclosure, user exit mobile video data streams can be acquired based on exit monitoring equipment, such as exit cameras. These user exit mobile video data streams can be video data containing the user's movement trajectory near the exit (captured by the exit monitoring equipment). It should be understood that the user exit mobile video data stream may contain video of the movement behavior of multiple customers. After identifying a user who has subscribed to a custodial service, a user exit displacement time-series image can be obtained based on that user's mobile video data. For example, consecutive frame images can be extracted sequentially along the time axis of the video data stream to obtain the user exit displacement time-series image. It is understood that the user exit displacement time-series image should be an image set, and user exit movement trajectory data, such as user movement direction and speed, can be calculated from the images at consecutive time points. The process of acquiring user in-store progress data can be combined with... Figure 3The consistency described herein will not be repeated here. Furthermore, user behavior data can be obtained based on the acquired user in-store progress data and the user exit movement trajectory data. The user behavior data can be the sum of the user in-store progress data and the user exit movement trajectory data. The user in-store progress data and the user exit movement trajectory data can each contain multiple types of related data. For example, the user in-store progress data can also include user waiting time, user transaction processing time, user transaction completion time, etc. Preferably, the user in-store progress data includes user servicing duration data, user transaction completion time data, and user exit identification duration data. The user servicing duration data represents the duration from the start of servicing to the current time. The user transaction completion time data represents the duration from the completion of the user transaction to the current time. The user exit identification duration data represents the duration from the time the user was identified by the exit monitoring equipment at the branch exit to the current time. The user exit movement trajectory data can also include user movement direction data, movement speed data, and time data of appearing at the exit, etc. Preferably, the user exit movement trajectory data at least includes user movement direction data, which can be used to represent the user's latest movement direction at the exit. It should be noted that if there are multiple exits, or if one exit has multiple monitoring devices, each monitoring device can collect data and obtain multiple exit movement trajectory data for the same user. Furthermore, when the same exit monitoring device captures multiple movement trajectory data for the same customer, the new data can overwrite the old data to save computing resources.

[0111] Therefore, a data dictionary can be established based on the aforementioned user behavior data to build models and predict user departure behavior based on new data.

[0112] In one specific embodiment, a data dictionary as shown in Table 2 can be established.

[0113] Table 2

[0114]

[0115] In embodiments of this disclosure, the user's in-store status includes one of having left the store, about to leave the store, or not having left the store. Accordingly, when training the user leaving the store determination model, whether the user has left the store (2 - already left the store, 1 - about to leave the store, 0 - not left the store) is used as the model training target or prediction result.

[0116] In a specific example, Table 3 provides an example of user data used for model training.

[0117] Table 3

[0118]

[0119] The sample labels can be set as follows: when the probability of leaving the store is ∈ (0.5, 1.5), it is considered that the store is about to leave; (0, 0.5) and (1.5, 2) are respectively considered as having a low probability of leaving the store and having already left the store.

[0120] It should be noted that after the model training is completed and the model is put into actual use, the system can preset fixed frequencies or specific trigger conditions to trigger model predictions to determine the probability of managed users leaving the store. Preset trigger conditions can be set based on expert experience and business needs, for example, the latest time when a user appears at the branch exit N is not equal to -1 as a trigger condition.

[0121] Figure 5 The flowchart illustrates a method for calculating user exit movement trajectory data based on the user exit displacement time series image according to some specific embodiments of the present disclosure.

[0122] like Figure 5 As shown, the method for calculating user exit movement trajectory data based on the user exit displacement time series image in this specific embodiment includes operations S510 to S530.

[0123] In operation S510, the user to be tracked is identified based on the baseline facial feature information of the managed user and the facial recognition information in the user exit displacement time series image.

[0124] In operation S520, the location data of the user to be tracked is obtained based on the frame images in the user exit displacement time series image.

[0125] According to embodiments of this disclosure, the system pre-stores baseline facial feature information to compare with real-time acquired facial feature information to identify the user. This baseline facial feature information can be pre-acquired based on artificial intelligence technology. Typically, 68 key points of the face (used to mark facial organs such as eyes, nose, and ears) can be obtained by calling the ERT algorithm in the Dlib library. Then, the 68 key points are converted into a 128-dimensional facial descriptor using a deep residual network-based facial recognition algorithm in Dlib, and this data is used as the baseline facial feature information. It should be understood that the user's exit displacement time-series image may contain one or more facial recognition information. Specifically, the video data stream collected by the exit monitoring device can be sent to the open-source OpenCV software library. One frame is extracted from every few frames of the video as the object of facial recognition, and the face detector in the open-source Dlib library is called to detect faces and obtain real-time facial feature information. The user to be tracked can be identified by calculating the Euclidean distance between the real-time facial feature data and the baseline facial feature data. When the distance is less than a certain threshold, the identification is considered successful. The coordinates (x, y, x) of the center point of the user's face are then recorded. iy i (This refers to the user's location.)

[0126] In operation S530, the user exit movement trajectory data is calculated based on the user position data in adjacent frame images in the user exit displacement time series image.

[0127] In embodiments of this disclosure, a model can be built using the user's location data from frame images acquired in adjacent frames or at similar time points to determine the user's movement trajectory at the exit. Typical user exit movement trajectory data includes at least user movement direction data.

[0128] Figure 6 The illustration shows a schematic diagram of a method for calculating user exit movement trajectory data according to some specific embodiments of the present disclosure.

[0129] In conjunction with the preceding text, such as Figure 6 As shown, after identifying the user to be tracked, the coordinates of the center point of the user's face (x, y, y) can be used. i y i This is used as the user's location. Therefore, the user's location data (x) can be obtained from adjacent frames or frames acquired at similar times. i y i ), (x i-1 y i-1 ( ) to determine the user's direction of movement.

[0130] Specifically, the direction of movement α i =-(y i -y i-1 )·|x i -x i-1 +cosθ| -1 .

[0131] Where θ represents the angle between the front of the camera and the direction of customer flow at the exit, θ∈[0, 90].

[0132] Therefore, based on the user's movement direction data α i The sign and value of the value determine the user's direction of movement relative to the exit.

[0133] According to embodiments of this disclosure, a pet locking device can be activated during smart pet care to achieve pet custody and monitoring. Specifically, after the pet is bound to the user, it can be confined to the pet locking device. When the user retrieves the pet, the pet locking device can be unlocked.

[0134] Figure 7 A flowchart illustrating a method for locking a pet according to an embodiment of the present disclosure is shown schematically.

[0135] like Figure 7 As shown, the method for locking a pet in this embodiment includes operation S710.

[0136] During operation S710, the pet locking device is locked based on the binding relationship between the user identity information and the pet identity information.

[0137] In the embodiments of this disclosure, pet identity information can be based on user input or on pet feature recognition technology.

[0138] Typical pet feature recognition technologies include pet nose print recognition. Specifically, pet nose print images can be fed into the open-source OpenCV software library, and a pet identification model can be built by calling commonly used graph convolutional algorithms. For example, a deep convolutional neural network from an open-source DNN library can be used to train the model, with the training objective being to identify a specific pet in a pet database (i.e., a 1:N identification problem). After the pet identification model is trained, nose print images of managed pets can be collected and stored in a managed pet database for subsequent pet identification during the unlocking process.

[0139] Figure 8 A flowchart illustrating a method for unlocking a pet according to an embodiment of the present disclosure is shown schematically.

[0140] like Figure 8 As shown, the method for unlocking the pet in this embodiment includes operations S810-S830 or S810-S820, S840.

[0141] When operating the S810, user identification information is obtained.

[0142] In operation S820, it is determined whether the user identification information matches the user identity information. When the user identification information matches the user identity information, operation S830 is executed.

[0143] Operate S830 to unlock the pet locking device.

[0144] According to embodiments of this disclosure, to enhance the security of the smart pet-sitting method, the user's identity can be verified first when the user retrieves the pet. The user's identity information is the identity information entered during pet-sitting. The user identification information is the authentication information required when retrieving the pet. The user identification information can be obtained from an identity verification device. Typical identity verification devices may include facial recognition devices or identity collection media, including but not limited to ID card and / or bank card recognition devices. Furthermore, user identity can also be verified based on identity information verification. For example, the system can initiate SMS verification of the user's mobile phone number to ensure that the system-reserved user's mobile phone number is the user's currently used mobile phone number. The verified mobile phone number can be used as a reminder mobile phone number for subsequent pet-sitting reminder functions. If the system-reserved mobile phone number is inconsistent with the user's current mobile phone number, or if the system has not stored the user's mobile phone number, the user can modify the system-reserved mobile phone number or enter a new mobile phone number as the reminder mobile phone number. The system can send an SMS verification code to the reminder mobile phone number. After the user enters the correct SMS verification code in the system, the SMS verification is successful. Once the user's identity is verified, the pet locking device can be unlocked. For example, a smart lock can be intelligently opened.

[0145] According to an embodiment of this disclosure, when the user identification information does not match the user identity information, operation S840 can be performed.

[0146] During operation S840, an error message is generated. It should be understood that after generating the error message, unlocking the pet should be refused. In some preferred embodiments, a notification message, such as a warning message, can also be sent to the pet's linked user to further enhance hosting security.

[0147] Figure 9 A flowchart illustrating a method for unlocking a pet according to other embodiments of this disclosure is shown schematically.

[0148] like Figure 9 As shown, the method for unlocking the pet in this embodiment may include operations S810 to S840, as well as operations S910 to S920.

[0149] When the user identification information matches the user identity information, operation S910 can be executed.

[0150] Using S910, obtain the identity confirmation information of the pet being cared for.

[0151] Further, execute operation S920.

[0152] In operation S920, when the identity verification information of the managed pet is successfully verified, the pet locking device is unlocked.

[0153] In other embodiments of this disclosure, the identity information of the pet being cared for is confirmed through dual identification of the pet's identity and the customer's identity, and the smart lock is then deactivated. This further enhances the security of the caring method described in the embodiments of this disclosure.

[0154] In other embodiments of this disclosure, to save storage resources, the binding relationship in the system can be released and the image of the pet that has been unmanaged can be deleted after the pet locking device is unlocked.

[0155] Figure 10 A schematic diagram of a pet locking device according to some specific embodiments of the present disclosure is shown.

[0156] like Figure 10 As shown, the pet locking device can be a smart lock. By applying smart locks to entry / exit devices, pet leash fasteners, and other devices, different forms of smart pet care devices can be formed. Typically, a smart lock can be installed in the pet room. In the pet care system to which the pet care method of this disclosure is applied, one or more pet rooms may be included. Each pet room can be separated by a partition to accommodate pets of different caretakers. Pet care monitoring equipment, such as a camera, can be installed in the pet room. According to embodiments of this disclosure, after obtaining the binding relationship between user identity information and pet identity, the smart lock can automatically open, allowing the user to fix the pet leash to the smart lock and lock it. When the pet locking device is released, the smart lock can also automatically open, allowing the user to remove the pet leash.

[0157] According to embodiments of this disclosure, in order to improve user experience, a tiered reminder system can be used to remind users to retrieve their bound pets.

[0158] Figure 11 A flowchart illustrating a method for reminding a user to retrieve a bound pet according to an embodiment of this disclosure is shown schematically.

[0159] like Figure 11 As shown, the method for unlocking the pet in this embodiment includes operating S1110 to S1120 or operating S1110 and S1130.

[0160] In operation S1110, determine the user's in-store status type.

[0161] Specifically, when a user's in-store status is about to leave the store, operation S1120 is executed.

[0162] In operation S1120, a weak reminder message is sent to the user, wherein the weak reminder message includes a text reminder message.

[0163] When the user's in-store status changes to "left store", execute operation S1130.

[0164] In operation S1130, a strong reminder message is sent to the user, wherein the strong reminder message includes a voice reminder message.

[0165] According to embodiments of this disclosure, a tiered reminder mechanism is established, with differentiated reminder modes based on the probability of a user leaving the store. For customers about to leave, weak text-based reminders such as SMS or WeChat can be used, while for customers who have already left, strong voice-based reminders such as automated voice calls or human calls can be used to minimize disruption to the customer.

[0166] Other embodiments of this disclosure also provide a smart hosting method for identity-bound devices.

[0167] Figure 12 A flowchart illustrating a smart hosting method according to other embodiments of this disclosure is shown schematically.

[0168] like Figure 12 As shown, the smart hosting method of this embodiment includes operations S1210 to S1240.

[0169] In operation S1210, user identification information from the identification device and pet image information from the pet image acquisition device are obtained.

[0170] In operation S1220, the user identification information is compared with the user identity information. If the match is successful, the user identity is confirmed and the user identity information is extracted.

[0171] In operation S1230, the pet image information is input into the pet nose print recognition model to obtain pet nose print feature information, and the pet nose print feature information is used as the pet identity information.

[0172] In operation S1240, a binding relationship between user identity information and pet identity information is established based on the matching relationship between the user identity information and pet identity information.

[0173] According to other embodiments of this disclosure, the identity binding device can be a server or a server cluster. By receiving and processing user identity information from the identity recognition device and pet image information from the pet image acquisition device, user identity information and pet identity information can be extracted and a binding relationship between them can be formed. The pet nose print recognition model can be constructed based on a graph convolutional neural network algorithm. Specific model construction and application methods can be as follows... Figure 7 As shown, it will not be elaborated further here.

[0174] Therefore, some other embodiments of this disclosure also provide an intelligent hosting method.

[0175] Figure 13A flowchart illustrating a smart hosting method according to other embodiments of this disclosure is shown schematically.

[0176] like Figure 13 As shown, the smart hosting method of this embodiment includes operations S1310 to S1370.

[0177] In operation S1310, the identity binding device acquires user identity recognition information from the identity recognition device and pet image information from the image acquisition device, and processes the user identity recognition information and pet image information to obtain the binding relationship between user identity information and pet identity information.

[0178] In operation S1320, the managed service processing server obtains the binding relationship between the user's identity information and the pet's identity information from the identity binding device.

[0179] When operating S1330, the pet monitoring server collects pet monitoring information from the hosted monitoring devices.

[0180] In operation S1340, the hosting service processing server performs intelligent hosting monitoring on the bound pets. The intelligent hosting monitoring includes obtaining pet monitoring information from the pet monitoring server, obtaining pet emotional status information based on the pet monitoring information, and initiating pet status reminders based on the pet emotional status information.

[0181] When operating S1350, the exit image server collects user exit movement trajectory data from the exit monitoring equipment.

[0182] In operation S1360, the managed service processing server acquires user behavior collection data and determines the user's in-store status based on the user behavior collection data and the user departure judgment model. The user behavior collection data includes user in-store progress data and user exit movement trajectory data. The user exit movement trajectory data is acquired from the exit image server, and the user in-store progress data is acquired from the service processing server.

[0183] In operation S1370, when the user's store status is about to leave or has already left the store, the hosting service processing server sends a command to remind the user to retrieve the bound pet.

[0184] The intelligent pet care method provided in this disclosure utilizes technologies such as biometrics, emotion analysis, and behavioral trajectory analysis to achieve intelligent pet care. On one hand, it allows for independent pet care based on the binding relationship between the pet and the user (pet owner), preventing theft of other people's pets. Furthermore, the system can monitor the pet's emotional state in real time, promptly notifying the user (pet owner) of any pet exhibiting distress, while also allowing the user to check the pet's status at any time. Additionally, the system can promptly remind customers to cancel pet care based on the user's business processing progress and behavioral trajectory, preventing users from forgetting.

[0185] Based on the above-described intelligent hosting method, embodiments of this disclosure also provide an intelligent hosting system. The following will be combined with... Figures 14A-14B The system is described in detail.

[0186] Figure 14A A schematic block diagram of an intelligent managed system according to an embodiment of the present disclosure is shown. Figure 14A As shown, the intelligent pet care system 1400 includes an identity binding device 1401, a pet care business processing server 1402, a pet monitoring server 1403, an export image server 1404, and a pet care device 1405. Figure 14B A schematic block diagram of a pet care device according to an embodiment of the present disclosure is shown. Figure 14B As shown, the pet care device 1405 includes at least a care monitoring device 14051.

[0187] The pet monitoring server 1403 is used to obtain pet monitoring information from the managed monitoring device 14051.

[0188] The export image server 1404 is used to collect user movement trajectory data from the export monitoring equipment.

[0189] The hosting service processing server 1402 is used to obtain the binding relationship between user identity information and pet identity information, and to perform intelligent hosting monitoring on the bound pet. The intelligent hosting monitoring includes obtaining pet monitoring information, obtaining pet emotional status information based on the pet monitoring information, and initiating pet status reminders based on the pet emotional status information; obtaining user behavior collection data, and determining the user's in-store status based on the user behavior collection data and a user departure judgment model. The user behavior collection data includes user in-store progress data and user movement trajectory data. When the user's in-store status is about to leave or has already left, the server reminds the user to retrieve the bound pet.

[0190] The identity binding device 1401 is used to acquire user identity information from an identity recognition device and pet image information from an image acquisition device; compare the user identity information with user identity information, confirm the user's identity upon successful matching, and extract the user identity information; input the pet image information into a pet nose print recognition model to obtain pet nose print feature information, using the pet nose print feature information as the pet's identity information; and establish a binding relationship between the user and the pet based on the matching relationship between the user identity information and the pet identity information, wherein the pet nose print recognition model is constructed based on a graph convolutional neural network algorithm.

[0191] The hosting service processing server 1402 is connected to the identity binding device 1401, the pet monitoring server 1403, the export image server 1404, and the hosting monitoring device 14051.

[0192] Figure 15 The illustration shows schematic diagrams of an identity recognition device and an image acquisition device according to some specific embodiments of the present disclosure.

[0193] like Figure 15 As shown, the identity recognition device 1501 and the image acquisition device 1502 can be integrated to save resources and space. The identity recognition device 1501 is used for collecting and recognizing customer information, including but not limited to one or more of the following: a card reader 15011, an ID card collector 15012, a camera 15013, and a touchscreen 15014. Taking an identity recognition device applied in a bank branch as an example: the card reader can be a financial device that can recognize bank card numbers of magnetic stripe cards or IC cards; the ID card collector is used to recognize the user's personal ID number; the camera is used to capture the user's image and extract baseline facial feature information; the touchscreen is used for user interaction with the bank's internal system, such as displaying the customer's mobile phone number after collecting customer identity information and supporting the customer to enter a mobile phone number SMS verification code. The identity recognition device can connect to the bank's internal back-end server to obtain pre-stored user information for identification and comparison.

[0194] Image acquisition device 1502 can be used for pet information collection and identification, including but not limited to a pluggable camera. Customers can plug and unplug the camera from the device rack to capture nasal print information from the pet's nose, allowing the nasal print features to be extracted using the built-in nasal print image recognition algorithm of the intelligent pet care system. These nasal print features can be used to uniquely identify and recognize the pet.

[0195] Based on the above-described intelligent hosting method, this disclosure also provides a hosting service processing server. The following will combine... Figure 16 The device is described in detail.

[0196] Figure 16 A schematic block diagram of a managed service processing server according to an embodiment of the present disclosure is shown.

[0197] like Figure 16 As shown, the managed service processing server 1600 in this embodiment includes a first acquisition module 1610, an intelligent monitoring module 1620, a first calculation module 1630, and an alert module 1640.

[0198] The first acquisition module 1610 is configured to acquire the binding relationship between user identity information and pet identity information.

[0199] The intelligent monitoring module 1620 is configured to perform intelligent care monitoring of the bound pet. The intelligent care monitoring includes acquiring pet monitoring information, acquiring pet emotional status information based on the pet monitoring information, and initiating pet status reminders based on the pet emotional status information.

[0200] The first calculation module 1630 is configured to acquire user behavior collection data and determine the user's in-store status based on the user behavior collection data and the user departure judgment model. The user behavior collection data includes user in-store progress data and user exit movement trajectory data.

[0201] The reminder module 1640 is configured to remind users to retrieve their bound pets when the user's status in the store is either about to leave or has already left.

[0202] According to other embodiments of this disclosure, the managed service processing server may further include a locking module.

[0203] Figure 17 A schematic block diagram of a managed service processing server according to other embodiments of the present disclosure is shown.

[0204] like Figure 17 As shown, in addition to the first acquisition module 1610, intelligent monitoring module 1620, first calculation module 1630 and reminder module 1640, the managed service processing server 1600 in some other embodiments also includes a locking module 1650.

[0205] The locking module 1650 is configured to lock the pet locking device based on the binding relationship between the user's identity information and the pet's identity information after obtaining the binding relationship between the user's identity information and the pet's identity information.

[0206] According to other embodiments of this disclosure, the managed service processing server may further include an unlocking module.

[0207] Figure 18 A schematic block diagram of a managed service processing server according to other embodiments of the present disclosure is shown.

[0208] like Figure 18 As shown, in addition to the first acquisition module 1610, intelligent monitoring module 1620, first calculation module 1630, reminder module 1640 and locking module 1650, the managed service processing server 1600 in some other embodiments also includes a second acquisition module 1660 and an unlocking module 1670.

[0209] The second acquisition module 1660 is configured to acquire user identification information.

[0210] The unlocking module 1670 is configured to unlock the pet locking device when the user identification information matches the user identity information.

[0211] Figure 19 A schematic block diagram of a managed service processing server according to other embodiments of the present disclosure is shown.

[0212] like Figure 19 As shown, in addition to the first acquisition module 1610, intelligent monitoring module 1620, first calculation module 1630, reminder module 1640, locking module 1650 and second acquisition module 1660, the managed service processing server 1600 in some other embodiments also includes an extraction module 1680 and a release module 1690.

[0213] The extraction module 1680 is configured to obtain the pet's identity confirmation information when the user's identity recognition information matches the user's identity information.

[0214] The unlocking module 1690 is configured to unlock the pet locking device when the identity verification information of the managed pet is successfully verified.

[0215] According to embodiments of this disclosure, the first calculation module may further include a first acquisition unit, a second acquisition unit, a first processing unit, a third acquisition unit, a fourth acquisition unit, and a second processing unit.

[0216] Figure 20 A schematic block diagram of a first computing module according to an embodiment of the present disclosure is shown.

[0217] like Figure 20 As shown, the first calculation module 1630 may further include a first acquisition unit 16301, a second acquisition unit 16302, a first processing unit 16303, a third acquisition unit 16304, a fourth acquisition unit 16305, and a second processing unit 16306.

[0218] The first acquisition unit 16301 is configured to acquire the user's exit mobile video data stream.

[0219] The second acquisition unit 16302 is configured to acquire a user exit displacement time series image based on the user exit mobile video data stream.

[0220] The first processing unit 16303 is configured to calculate user exit movement trajectory data based on the user exit displacement time series image.

[0221] The third acquisition unit 16304 is configured to acquire user in-store progress data.

[0222] The fourth acquisition unit 16305 is configured to acquire user behavior data based on the user's in-store progress data and the user's exit movement trajectory data.

[0223] The second processing unit 16306 is configured to input the user behavior collection data into the user departure judgment model to obtain the user's in-store status.

[0224] The user's in-store status includes one of having left the store, about to leave the store, or not yet left the store; the user's in-store progress data includes user custodianship duration data, user business completion time data, and user exit identification duration data; the user exit movement trajectory data includes user movement direction data.

[0225] Figure 21 A schematic block diagram of the structure of a first processing unit according to an embodiment of the present disclosure is shown.

[0226] like Figure 21 As shown, the first processing unit 16303 may further include an identification subunit 163031, a tracking subunit 163032, and a calculation subunit 163033.

[0227] The identification subunit 163031 is configured to identify the user to be tracked based on the baseline facial feature information of the user who has been managed and the facial recognition information in the user exit displacement time series image.

[0228] The tracking subunit 163032 is configured to acquire the location data of the user to be tracked based on the frame images in the user exit displacement time series image.

[0229] The calculation subunit 163033 is configured to calculate the user exit movement trajectory data based on the user position data in adjacent frame images in the user exit displacement time series image.

[0230] Figure 22 A schematic block diagram of an intelligent monitoring module according to an embodiment of the present disclosure is shown.

[0231] like Figure 22As shown, the intelligent monitoring module 1620 may also include a fifth acquisition unit 16201 and a third processing unit 16202.

[0232] The fifth acquisition unit 16201 is configured to acquire pet monitoring information, which includes pet behavior data and expression data.

[0233] The third processing unit 16202 inputs the pet monitoring information into the pet emotion recognition model to obtain the pet emotion status monitoring information, wherein the pet emotion recognition model is trained based on a deep convolutional neural network.

[0234] Figure 23 A schematic block diagram of a reminder module according to an embodiment of the present disclosure is shown.

[0235] like Figure 23 As shown, the reminder module 1640 may also include a sending unit 16401.

[0236] The sending unit 16401 is configured to send a weak reminder message to the user when the user's in-store status is about to leave the store, wherein the weak reminder message includes a text reminder message; and / or send a strong reminder message to the user when the user's in-store status is already left the store, wherein the strong reminder message includes a voice reminder message.

[0237] Based on the above-described intelligent hosting method, this disclosure also provides an identity binding device. The following will combine... Figure 24 The device is described in detail.

[0238] Figure 24 A schematic block diagram of an identity binding device according to an embodiment of the present disclosure is shown.

[0239] like Figure 24 As shown, the identity binding device 2400 in this embodiment includes a collection module 2410, a comparison module 2420, a marking module 2430, and a binding module 2440.

[0240] The acquisition module 2410 is configured to acquire user identity information from the identity recognition device and pet image information from the pet image acquisition device.

[0241] The comparison module 2420 is configured to compare the user identification information with the user identity information, and after a successful match, confirm the user's identity and extract the user identity information.

[0242] The tagging module 2430 is configured to input the pet image information into the pet nose print recognition model, obtain the pet nose print feature information, and use the pet nose print feature information as the pet identity information.

[0243] The binding module 2440 is configured to establish a binding relationship between user identity information and pet identity information based on the matching relationship between the user identity information and pet identity information.

[0244] The pet nose print recognition model is constructed based on a graph convolutional neural network algorithm.

[0245] According to embodiments of this disclosure, any and multiple modules among the first acquisition module 1610, intelligent monitoring module 1620, first calculation module 1630, reminder module 1640, locking module 1650, acquisition module 1660, unlocking module 1670, extraction module 1680, release module 1690, first acquisition unit 16301, second acquisition unit 16302, first processing unit 16303, third acquisition unit 16304, fourth acquisition unit 16305, second processing unit 16306, identification subunit 163031, tracking subunit 163032, calculation subunit 163033, fifth acquisition unit 16201, third processing unit 16202, and sending unit 16401 can be combined into one module, or any one of these modules can be split into multiple modules. Similarly, any and multiple modules among the acquisition module 2410, comparison module 2420, marking module 2430, and binding module 2440 can be combined into one module, or any one of these modules can be split into multiple modules. Alternatively, at least some of the functionality of one or more of these modules can be combined with at least some of the functionality of other modules and implemented in one module. According to embodiments of this disclosure, the module includes a first acquisition module 1610, an intelligent monitoring module 1620, a first calculation module 1630, an alert module 1640, a locking module 1650, an acquisition module 1660, an unlocking module 1670, an extraction module 1680, a release module 1690, a first acquisition unit 16301, a second acquisition unit 16302, a first processing unit 16303, a third acquisition unit 16304, a fourth acquisition unit 16305, a second processing unit 16306, an identification subunit 163031, a tracking subunit 163032, a calculation subunit 163033, a fifth acquisition unit 16201, and a third processing unit 16202. At least one of the transmitting unit 16401 can be at least partially implemented as hardware circuitry. Similarly, at least one of the acquisition module 2410, comparison module 2420, tagging module 2430, and binding module 2440 can be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or any other reasonable means of integrating or packaging the circuitry, or implemented in hardware or firmware, or in any one of the three implementation methods of software, hardware, and firmware, or in a suitable combination of any of them.Alternatively, at least one of the following modules can be implemented as a computer program module: first acquisition module 1610, intelligent monitoring module 1620, first calculation module 1630, reminder module 1640, locking module 1650, acquisition module 1660, unlocking module 1670, extraction module 1680, release module 1690, first acquisition unit 16301, second acquisition unit 16302, first processing unit 16303, third acquisition unit 16304, fourth acquisition unit 16305, second processing unit 16306, identification subunit 163031, tracking subunit 163032, calculation subunit 163033, fifth acquisition unit 16201, third processing unit 16202, and sending unit 16401. Similarly, at least one of the following modules can be implemented as a computer program module: acquisition module 2410, comparison module 2420, marking module 2430, and binding module 2440. When the computer program module is run, it can perform the corresponding functions.

[0246] Figure 25 A block diagram of an electronic device suitable for a real-time intelligent hosting method according to an embodiment of the present disclosure is shown schematically.

[0247] like Figure 25 As shown, an electronic device 900 according to an embodiment of the present disclosure includes a processor 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 902 or a program loaded from a storage portion 908 into a random access memory (RAM) 903. The processor 901 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 901 may also include onboard memory for caching purposes. The processor 901 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0248] RAM 903 stores various programs and data required for the operation of electronic device 900. Processor 901, ROM 902, and RAM 903 are interconnected via bus 904. Processor 901 performs various operations of the method flow according to embodiments of the present disclosure by executing programs in ROM 902 and / or RAM 903. It should be noted that the programs may also be stored in one or more memories other than ROM 902 and RAM 903. Processor 901 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in said one or more memories.

[0249] According to embodiments of this disclosure, the electronic device 900 may further include an input / output (I / O) interface 905, which is also connected to a bus 904. The electronic device 900 may also include one or more of the following components connected to the I / O interface 905: an input section 906 including a keyboard, mouse, etc.; an output section 907 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 908 including a hard disk, etc.; and a communication section 909 including a network interface card such as a LAN card, modem, etc. The communication section 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to the I / O interface 905 as needed. A removable medium 911, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 910 as needed so that computer programs read from it can be installed into the storage section 908 as needed.

[0250] This disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs that, when executed, implement the method according to the embodiments of this disclosure.

[0251] According to embodiments of this disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, such as including, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of this disclosure, the computer-readable storage medium may include ROM 902 and / or RAM 903 and / or one or more memories other than ROM 902 and RAM 903 described above.

[0252] Embodiments of this disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code is used to cause the computer system to implement the methods provided in the embodiments of this disclosure.

[0253] When the computer program is executed by the processor 901, it performs the functions defined in the system / apparatus of this disclosure embodiments. According to embodiments of this disclosure, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0254] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and downloaded and installed via the communication section 909, and / or installed from a removable medium 911. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0255] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 909, and / or installed from the removable medium 911. When the computer program is executed by the processor 901, it performs the functions defined in the system of this disclosure embodiment. According to embodiments of this disclosure, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0256] According to embodiments of this disclosure, program code for executing the computer programs provided in embodiments of this disclosure can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can execute entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0257] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0258] Those skilled in the art will understand that the features described in the various embodiments and / or claims of this disclosure can be combined or combined in various ways, even if such combinations or combinations are not explicitly described in this disclosure. In particular, the features described in the various embodiments and / or claims of this disclosure can be combined or combined in various ways without departing from the spirit and teachings of this disclosure. All such combinations and / or combinations fall within the scope of this disclosure.

[0259] The embodiments of this disclosure have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of this disclosure. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. The scope of this disclosure is defined by the appended claims and their equivalents. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of this disclosure, and all such substitutions and modifications should fall within the scope of this disclosure.

Claims

1. A smart hosting method applied to a hosted business processing server, characterized in that, include: Obtain the binding relationship between user identity information and pet identity information; The system provides intelligent care and monitoring for bound pets, including acquiring pet monitoring information, acquiring pet emotional status information based on the pet monitoring information, and initiating pet status reminders based on the pet emotional status information. Acquire user behavior data, and determine the user's in-store status based on the user behavior data and the user exit judgment model. The user behavior data includes user in-store progress data and user exit movement trajectory data. When a user's status changes to "about to leave" or "has already left," remind the user to retrieve their linked pet.

2. The smart hosting method according to claim 1, wherein, The process of determining the user's in-store status based on the user behavior collection data and the user departure judgment model includes: Obtain the user's outgoing mobile video data stream; Based on the user exit mobile video data stream, obtain the user exit displacement time series image; The user's exit movement trajectory data is calculated based on the user's exit displacement time series image. Obtain user's in-store progress data; User behavior data is collected based on the user's in-store progress data and the user's exit movement trajectory data. The user behavior data is input into the user departure judgment model to obtain the user's in-store status. The user's in-store status includes one of having left the store, about to leave the store, or not yet left the store; the user's in-store progress data includes user custodianship duration data, user business completion time data, and user exit identification duration data; the user exit movement trajectory data includes user movement direction data.

3. The smart hosting method according to claim 1 or 2, wherein, The user departure judgment model is built based on a supervised algorithm.

4. The intelligent hosting method according to claim 3, wherein, The user departure judgment model is built based on the Boosting algorithm.

5. The intelligent hosting method according to claim 2, wherein, The user exit movement trajectory data calculated based on the user exit displacement time series image includes: The user to be tracked is identified based on the baseline facial feature information of the users who have already been managed and the facial recognition information in the time series image of the user's exit displacement. The location data of the user to be tracked is obtained based on the frame images in the user exit displacement time series image; The user exit movement trajectory data is calculated based on the user position data in adjacent frames of the user exit displacement time series image.

6. The smart hosting method according to claim 1, wherein, The intelligent management and monitoring of the bound pets includes: Acquire pet monitoring information, which includes pet behavior data and facial expression data; The pet monitoring information is input into the pet emotion recognition model to obtain the pet's emotional state monitoring information, wherein the pet emotion recognition model is trained based on a deep convolutional neural network.

7. The intelligent hosting method according to claim 1, wherein, After obtaining the binding relationship between user identity information and pet identity information, the method further includes: The pet locking device is locked based on the binding relationship between the user's identity information and the pet's identity information.

8. The smart hosting method according to claim 1, wherein, After reminding the user to claim the bound pet, the method further includes: Obtaining user identification information; and When the user identification information matches the user identity information, the pet locking device is unlocked.

9. The intelligent hosting method according to claim 8, wherein, The method further includes: When the user identification information matches the user identity information, the pet's identity confirmation information is obtained; and When the identity verification information of the pet being cared for is successfully verified, the pet locking device is unlocked.

10. The smart hosting method according to claim 1, wherein, The reminder to users to retrieve the bound pets includes: When a user's in-store status is about to leave the store, a weak reminder message is sent to the user, wherein the weak reminder message includes a text reminder message; and / or When a user's in-store status changes to "has left the store," a strong reminder message is sent to the user, which includes a voice reminder message.

11. A smart hosting method applied to an identity-bound device, characterized in that, include: Acquire user identification information from an identification device and pet image information from a pet image acquisition device; The user identification information is compared with the user identity information. If a match is successful, the user identity is confirmed and the user identity information is extracted. The pet image information is input into the pet nose print recognition model to obtain the pet nose print feature information, and the pet nose print feature information is used as the pet identity information. as well as Based on the matching relationship between the user identity information and the pet identity information, a binding relationship between the user identity information and the pet identity information is established, so that the hosting service processing server can obtain the binding relationship between the user identity information and the pet identity information; The system provides intelligent pet care and monitoring, which includes acquiring pet monitoring information, obtaining pet emotional status information based on the pet monitoring information, and initiating pet status reminders based on the pet emotional status information; acquiring user behavior data, determining the user's in-store status based on the user behavior data and a user departure judgment model, wherein the user behavior data includes user in-store progress data and user exit movement trajectory data; and reminding the user to retrieve their pet when the user's in-store status indicates they are about to leave or have already left the store. The pet nose print recognition model is constructed based on a graph convolutional neural network algorithm.

12. A smart hosting method, characterized in that, The method includes: The identity binding device acquires user identity information from the identity recognition device and pet image information from the image acquisition device, and processes the user identity information and pet image information to obtain the binding relationship between user identity information and pet identity information. The hosting service processing server obtains the binding relationship between the user's identity information and the pet's identity information from the identity binding device; The pet monitoring server collects pet monitoring information from the hosted monitoring devices. The hosting service processing server performs intelligent hosting monitoring on the bound pets. The intelligent hosting monitoring includes obtaining pet monitoring information from the pet monitoring server, obtaining pet emotional status information based on the pet monitoring information, and initiating pet status reminders based on the pet emotional status information. The exit image server collects user exit movement trajectory data from the exit monitoring equipment; The hosting service processing server acquires user behavior collection data and determines the user's in-store status based on the user behavior collection data and the user departure judgment model. The user behavior collection data includes user in-store progress data and user exit movement trajectory data. The user exit movement trajectory data is acquired from the exit image server, and the user in-store progress data is acquired from the service processing server. When a user's status in the store is either about to leave or has already left, the hosting service server sends a command to remind the user to retrieve their bound pet.

13. An intelligent hosting system, characterized in that, include: The system includes an identity binding device, a hosting service processing server, a pet monitoring server, an outgoing image server, and pet hosting equipment, wherein the pet hosting equipment includes hosting monitoring equipment. The pet monitoring server is used to obtain pet monitoring information from the hosted monitoring equipment. The exit image server is used to collect user movement trajectory data from the exit monitoring equipment. The pet hosting service processing server is used to obtain the binding relationship between user identity information and pet identity information, and to perform intelligent pet hosting monitoring. The intelligent pet hosting monitoring includes: obtaining pet monitoring information; obtaining pet emotional status information based on the pet monitoring information; and initiating pet status reminders based on the pet emotional status information; obtaining user behavior data; determining the user's in-store status based on the user behavior data and a user departure judgment model; and reminding the user to retrieve the bound pet when the user's in-store status indicates they are about to leave or have already left. The identity binding device is used to acquire user identity information from an identity recognition device and pet image information from a pet image acquisition device; compare the user identity information with the user identity information, and confirm the user's identity upon successful matching, then extract the user identity information; input the pet image information into a pet nose print recognition model to obtain pet nose print feature information, using the pet nose print feature information as the pet's identity information; and establish a binding relationship between the user and the pet based on the matching relationship between the user identity information and the pet identity information, wherein the pet nose print recognition model is constructed based on a graph convolutional neural network algorithm. The hosting service processing server is connected to the identity binding device, the pet monitoring server, the export image server, and the hosting monitoring device.

14. The intelligent hosting system according to claim 13, wherein, The hosting service processing server includes: The first acquisition module is configured to acquire the binding relationship between user identity information and pet identity information; The intelligent monitoring module is configured to perform intelligent care monitoring of the bound pet. The intelligent care monitoring includes obtaining pet monitoring information, obtaining pet emotional status information based on the pet monitoring information, and initiating pet status reminders based on the pet emotional status information. The first calculation module is configured to acquire user behavior data and determine the user's in-store status based on the user behavior data and a user exit judgment model. The user behavior data includes user in-store progress data and user exit movement trajectory data. The reminder module is configured to remind users to retrieve their linked pets when their in-store status is either about to leave or has already left the store.

15. The intelligent hosting system according to claim 13, wherein, The identity binding device includes: The second acquisition module is configured to acquire user identity recognition information from the identity recognition device and pet image information from the image acquisition device. The second calculation module is configured to compare the user identification information with the user identity information, confirm the user identity after a successful match, and extract the user identity information. The third calculation module is configured to input the pet image information into a pet nose print recognition model to obtain pet nose print feature information, and use the pet nose print feature information as the pet's identity information. The pet nose print recognition model is constructed based on a graph convolutional neural network algorithm. The fourth calculation module is configured to establish a binding relationship between the user identity information and the pet identity information based on the matching relationship between the user identity information and the pet identity information.

16. An electronic device comprising: One or more processors; Storage device for storing one or more programs. Wherein, when the one or more programs are executed by the one or more processors, the one or more processors perform the method according to any one of claims 1 to 11.

17. A computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to perform the method according to any one of claims 1 to 11.

18. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1 to 11.

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