Artificial intelligence-based server for safe and smart privacy protection zone management, method thereof, and privacy protection detection device

Abstract

The present disclosure relates to an artificial intelligence-based server for safe and smart privacy protection zone management, a method thereof, and a privacy protection detection device. The privacy protection zone management server of the present disclosure comprises: a detection information collection unit; an information analysis unit; and a processor for controlling the operation of a detection zone service processing unit, wherein the processor: receives detection zone state information including detection zone identification information transmitted from the privacy protection detection device; identifies whether a dangerous situation exists by performing behavioral analysis of the detection zone state information using a pre-trained artificial intelligence model, and by determining whether a behavioral state of a user in a detection zone matches a preconfigured dangerous state, analyzes whether the dangerous situation exists; and when the dangerous situation exists as a result of the analysis, outputs danger notification information including relevant detection zone identification information.

Description

AI-based secure smart privacy protection zone management server and method thereof, and privacy protection detection device

[0001] The present disclosure relates to an AI-based privacy protection zone management method. More specifically, the present disclosure relates to an AI-based secure smart privacy protection zone management server, its method, and a privacy protection detection device.

[0002] Public restrooms are facilities used by an unspecified number of people, and must be maintained in a clean and safe state at all times. However, access and management of restrooms are somewhat difficult due to reasons such as user privacy. Consequently, various situations arise, such as the lack of toilet paper and other supplies required in public restrooms, the occurrence of hidden camera crimes, and the failure to recognize the sudden fainting of users, including the elderly. The aforementioned public restroom is not limited to this example; it can also include other areas where personal privacy must be protected, where cameras and other filming devices cannot be installed (e.g., bathrooms, locker rooms, hotel rooms, hospital rooms, and homes).

[0003] Accordingly, various measures are being proposed to more efficiently manage private areas, including public restrooms.

[0004] The purpose of the embodiment disclosed in the present disclosure is to provide an artificial intelligence-based secure smart privacy protection zone management server and method thereof and a privacy protection detection device to maintain the environment, including the safety and sanitary conditions, of a privacy protection zone in a good condition.

[0005] The problems to be solved by the present disclosure are not limited to the problems mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the description below.

[0006] In order to achieve the above-described technical task, a privacy protection zone management server according to one aspect of the present disclosure comprises: a detection information collection unit for collecting detection zone status information; an information analysis unit for analyzing the detection zone status information to identify a risk situation; and a processor for controlling the operation of a detection zone service processing unit for providing various services including risk notification information; wherein the processor receives detection zone status information including detection zone identification information transmitted from a privacy protection detection device, and determines whether a risk situation exists through behavioral analysis of the detection zone status information using a pre-learned artificial intelligence model, and analyzes whether a behavioral state of a user within the detection zone matches a preset risk state and thus a risk situation exists, and if the analysis results show that a risk situation exists, can output risk notification information including the relevant detection zone identification information.

[0007] The processor, when outputting the risk notification information, may output the risk notification information including at least one of map information indicating the detection zone location information matching the detection zone identification information, the time at which the risk situation occurred, and the type of the risk situation.

[0008] The above processor sets a risk situation grade in advance, determines a risk situation grade for the risk situation, and differentiates a target to which the risk notification information is transmitted based on the determined risk situation grade, and the risk notification information may further include the risk situation grade.

[0009] The above processor can set the risk situation-specific grade by assigning weights to preset items among the items in the risk notification information when presetting the risk situation-specific grade.

[0010] The above processor, when the above-mentioned dangerous situation occurs, can separately store an image of a section of the image of the detection zone status information that matches the preset dangerous state by matching it with the dangerous notification information.

[0011] The processor may calculate restroom situation statistics based on the detection zone status information and analysis information on the detection zone status information, and may calculate the restroom situation statistics by including at least one of the following: location of a privacy protection zone, occurrence of a dangerous situation, time of occurrence of a dangerous situation, type of dangerous situation, level of each dangerous situation, number of times a dangerous situation occurred, and processing results after occurrence of a dangerous situation.

[0012] In addition, a privacy protection detection device according to another aspect of the present disclosure includes a detection sensor for recognizing a situation of a detection area in a restroom; and a processor, wherein the processor transmits detection area status information including detection area identification information in detection information acquired through the detection sensor to a privacy protection area management server according to preset transmission conditions, and the detection sensor may include at least one of a heat detection sensor, an infrared sensor, an ultrasonic sensor, and a microwave sensor.

[0013] In addition, a method for managing a privacy protection zone according to another aspect of the present disclosure may include a step of receiving detection zone status information including detection zone identification information transmitted from a privacy protection detection device, a step of analyzing a behavior of the detection zone status information using a pre-learned artificial intelligence model to determine whether a dangerous situation exists, and analyzing whether a behavioral state of a user within the detection zone matches a preset dangerous state and thus a dangerous situation exists; and a step of outputting dangerous notification information including the relevant detection zone identification information when a dangerous situation exists as a result of the analysis.

[0014] The above privacy protection zone management method can output the risk notification information including at least one of map information indicating the detection zone location information matching the detection zone identification information, the time at which the risk situation occurred, and the type of the risk situation when outputting the risk notification information.

[0015] In addition, a computer program stored in a computer-readable recording medium for executing the present disclosure may be further provided.

[0016] In addition, a computer-readable recording medium recording a computer program for executing a method for implementing the present disclosure may be further provided.

[0017] According to the aforementioned problem solving means of the present disclosure, it is possible to monitor the detection area within the privacy protection area 24 hours a day without having to visit the area in person, thereby providing the effect of being able to prepare for dangerous situations that may occur in the privacy protection area.

[0018] In addition, according to the aforementioned problem solving means of the present disclosure, various dangerous situations occurring in a privacy protection zone can be automatically detected based on an artificial intelligence model, and corresponding actions such as notifications can be automatically performed, thereby maintaining privacy protection zones including restrooms in a safe state.

[0019] In addition, according to the aforementioned problem solving means of the present disclosure, the status of the privacy protection zone can be monitored using a data format that protects the privacy of a user using the privacy protection zone.

[0020] In addition, the aforementioned problem solving means of the present disclosure provides the effect of being able to secure big data on actual toilet usage and, based on this, perform toilet management more systematically.

[0021] The effects of the present disclosure are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description below.

[0022] Figure 1 is a block diagram of the privacy protection zone management system of the present disclosure.

[0023] Figure 2 is a block diagram of the privacy protection zone management server of the present disclosure.

[0024] Figure 3 is a block diagram of a privacy protection detection device of the present disclosure.

[0025] Figures 4 to 7 are exemplary diagrams for explaining the privacy protection zone management method of the present disclosure.

[0026] Figure 8 is a flowchart for explaining the privacy protection zone management method of the present disclosure.

[0027] Throughout this disclosure, the same reference numerals denote the same components. This disclosure does not describe all elements of the embodiments, and any content that is common in the technical field to which this disclosure pertains or that overlaps between embodiments is omitted. The terms "part, module, element, block" used in the specification may be implemented in software or hardware, and depending on the embodiments, multiple "parts, modules, elements, blocks" may be implemented as a single component, or a single "part, module, element, block" may include multiple components.

[0028] Throughout the specification, when a part is said to be "connected" to another part, this includes not only direct connection but also indirect connection, and indirect connection includes connection via a wireless communication network.

[0029] Additionally, when a part is said to "include" a component, this does not mean that it excludes other components, but rather that it may include other components, unless otherwise specifically stated.

[0030] Throughout the specification, when we say that an element is "on" another element, this includes not only cases where the element is in contact with the other element, but also cases where another element exists between the two elements.

[0031] The terms first, second, etc. are used to distinguish one component from another, and the components are not limited by the aforementioned terms.

[0032] Singular expressions include plural expressions unless the context clearly indicates otherwise.

[0033] The identification codes for each step are used for convenience of explanation and do not describe the order of each step. Each step may be performed in a different order than specified unless the context clearly indicates a specific order.

[0034] The operating principle and embodiments of the present disclosure are described below with reference to the attached drawings.

[0035] As used herein, the term "device according to the present disclosure" encompasses a variety of devices capable of performing computational processing and providing results to a user. For example, the device according to the present disclosure may include a computer, a server device, and a portable terminal, or may be any one of them.

[0036] Here, the computer may include, for example, a notebook, desktop, laptop, tablet PC, slate PC, etc. equipped with a web browser.

[0037] The above server device is a server that processes information by communicating with an external device, and may include an application server, a computing server, a database server, a file server, a game server, a mail server, a proxy server, and a web server.

[0038] The above portable terminal may include, for example, a wireless communication device that ensures portability and mobility, and may include all kinds of handheld-based wireless communication devices such as a PCS (Personal Communication System), GSM (Global System for Mobile communications), PDC (Personal Digital Cellular), PHS (Personal Handyphone System), PDA (Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), WiBro (Wireless Broadband Internet) terminal, a smart phone, and a wearable device such as a watch, ring, bracelet, anklet, necklace, glasses, contact lens, or head-mounted-device (HMD).

[0039] The artificial intelligence-related functions according to the present disclosure are operated via a processor and memory. The processor may be comprised of one or more processors. In this case, one or more processors may be a general-purpose processor such as a CPU, an AP, a Digital Signal Processor (DSP), a graphics-only processor such as a GPU or a Vision Processing Unit (VPU), or an artificial intelligence-only processor such as an NPU. One or more processors control the processing of input data according to predefined operating rules or artificial intelligence models stored in memory. Alternatively, if one or more processors are artificial intelligence-only processors, the artificial intelligence-only processor may be designed with a hardware structure specialized for processing a specific artificial intelligence model.

[0040] The predefined operation rules or artificial intelligence models are characterized by being created through learning. Here, being created through learning means that the basic artificial intelligence model is learned by a learning algorithm using a plurality of learning data, thereby creating a predefined operation rules or artificial intelligence model set to perform a desired characteristic (or purpose). This learning may be performed on the device itself on which the artificial intelligence according to the present disclosure is performed, or may be performed through a separate server and / or system. Examples of the learning algorithm include, but are not limited to, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning.

[0041] An artificial intelligence model may be composed of multiple neural network layers. Each of the multiple neural network layers has multiple weight values, and performs neural network operations through operations between the operation results of the previous layer and the multiple weights. The multiple weights of the multiple neural network layers may be optimized based on the learning results of the artificial intelligence model. For example, the multiple weights may be updated so that the loss value or cost value obtained from the artificial intelligence model is reduced or minimized during the learning process. The artificial neural network may include a deep neural network (DNN), and examples thereof include, but are not limited to, a convolutional neural network (CNN), a deep neural network (DNN), a recurrent neural network (RNN), a restricted boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), or deep Q-networks.

[0042] According to an exemplary embodiment of the present disclosure, a processor can implement artificial intelligence. Artificial intelligence refers to a machine learning method based on an artificial neural network that imitates human neurons (biological neurons) to enable machines to learn. Artificial intelligence methodologies can be categorized into supervised learning, in which input data and output data are provided together as training data depending on the learning method, so that the solution (output data) to the problem (input data) is determined; unsupervised learning, in which only input data is provided without output data, so that the solution (output data) to the problem (input data) is not determined; and reinforcement learning, in which a reward (Reward) is provided from an external environment whenever an action (Action) is taken in the current state (State), and learning is performed in a direction to maximize this reward. In addition, artificial intelligence methodologies can be categorized according to the architecture of the learning model. The architectures of widely used deep learning technologies can be categorized into convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and generative adversarial networks (GANs).

[0043] The present device and system may include an artificial intelligence model. The artificial intelligence model may be a single artificial intelligence model or may be implemented as multiple artificial intelligence models. The artificial intelligence model may be composed of a neural network (or artificial neural network) and may include statistical learning algorithms that mimic biological neurons in machine learning and cognitive science. A neural network may refer to a model in general that has problem-solving capabilities by changing the binding strength of synapses through learning, formed by artificial neurons (nodes) that form a network by combining synapses. The neurons of the neural network may include a combination of weights or biases. The neural network may include one or more layers composed of one or more neurons or nodes. For example, the device may include an input layer, a hidden layer, and an output layer. The neural network constituting the device can infer a desired result (output) from an arbitrary input (input) by changing the weights of neurons through learning.

[0044] The processor can create a neural network, train (or learn) a neural network, perform a calculation based on received input data, generate an information signal based on the calculation result, or retrain the neural network. The models of the neural network can include various types of models such as CNN (Convolution Neural Network) such as GoogleNet, AlexNet, VGG Network, R-CNN (Region with Convolution Neural Network), RPN (Region Proposal Network), RNN (Recurrent Neural Network), S-DNN (Stacking-based deep Neural Network), S-SDNN (State-Space Dynamic Neural Network), Deconvolution Network, DBN (Deep Belief Network), RBM (Restrcted Boltzman Machine), Fully Convolutional Network, LSTM (Long Short-Term Memory) Network, Classification Network, etc., but are not limited thereto. The processor can include one or more processors for performing calculations according to the models of the neural network. For example, the neural network can be a deep neural network. It may include a deep neural network.

[0045] Neural networks include CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), perceptron, multilayer perceptron, FF (Feed Forward), RBF (Radial Basis Network), DFF (Deep Feed Forward), LSTM (Long Short Term Memory), GRU (Gated Recurrent Unit), AE (Auto Encoder), VAE (Variational Auto) Encoder), DAE (Denoising Auto Encoder), SAE (Sparse Auto Encoder), MC (Markov Chain), HN (Hopfield Network), BM (Boltzmann Machine), RBM (Restricted Boltzmann Machine), DBN (Depp Belief Network), DCN (Deep Convolutional Network), DN (Deconvolutional Network), DCIGN (Deep Convolutional Inverse Graphics Network), Generative Adversarial Network (GAN), Liquid State Machine (LSM), Extreme Learning Machine (ELM), It will be understood by those skilled in the art that any neural network may be included, including but not limited to ESN (Echo State Network), DRN (Deep Residual Network), DNC (Differentiable Neural Computer), NTM (Neural Turning Machine), CN (Capsule Network), KN (Kohonen Network), and AN (Attention Network).

[0046] According to an exemplary embodiment of the present disclosure, the processor may be configured to perform a process for generating a CNN (Convolution Neural Network) such as GoogleNet, AlexNet, VGG Network, Region with Convolution Neural Network (R-CNN), Region Proposal Network (RPN), Recurrent Neural Network (RNN), Stacking-based deep Neural Network (S-DNN), State-Space Dynamic Neural Network (S-SDNN), Deconvolution Network, Deep Belief Network (DBN), Restrcted Boltzman Machine (RBM), Fully Convolutional Network, Long Short-Term Memory (LSTM) Network, Classification Network, Generative Modeling, eXplainable AI, Continual AI, Representation Learning, AI for Material Design, BERT, SP-BERT, MRC / QA for natural language processing, Text Analysis, Dialog System, GPT-3, GPT-4, Visual Analytics for vision processing, Visual Understanding, Video Synthesis, ResNet for data intelligence, Anomaly Detection, Prediction, Time-Series Forecasting, Various artificial intelligence structures and algorithms, including optimization, recommendation, and data creation, can be utilized, but are not limited thereto. Hereinafter, embodiments of the present disclosure will be described in detail with reference to the attached drawings.

[0047] The privacy protection zone disclosed below is defined as an area where personal privacy must be protected, including public restrooms, bathrooms, changing rooms, hotel guest rooms, etc., where filming devices such as cameras cannot be installed.

[0048] FIG. 1 is a block diagram of a privacy protection zone management system of the present disclosure, FIG. 2 is a block diagram of a privacy protection zone management server of the present disclosure, and FIG. 3 is a block diagram of a privacy protection detection device of the present disclosure.

[0049] Hereinafter, the privacy protection zone management method of the present disclosure will be described with reference to FIGS. 4 to 7, which are exemplary diagrams for explaining the method.

[0050] Referring to FIG. 1, the privacy protection zone management system (10) includes a privacy protection zone management server (100) and a privacy protection detection device (200). Additionally, the privacy protection zone management system (10) may further include a user terminal (300).

[0051] Referring to Figure 1, the privacy protection zone management server (100) analyzes the detection zone status information transmitted in real time from the privacy protection detection device (200) to determine whether a dangerous situation has occurred in the restroom, and if a dangerous situation has occurred, can output a notification to the user terminal (300) to take action. The detection zone refers to an area among the privacy protection zones monitored through the privacy protection detection device (100) described below, and can be arbitrarily set by the operator as the entire privacy protection zone or a portion within the privacy protection zone.

[0052] At this time, the user terminal (300) may include a server administrator terminal, an administrator terminal of the corresponding privacy protection zone, a police terminal, etc., but is not limited thereto, and additions and changes may be possible according to the operator's needs. For example, the user terminal (300) may also include a terminal of a general customer using the privacy protection zone.

[0053] The privacy protection zone management server (100) can store the user terminal (300) number in advance. If the user terminal (300) is an administrator terminal of a privacy protection zone, the administrator terminal number for each privacy protection zone can be stored. Additionally, if the user terminal (300) is a police terminal, the police terminal number of the police station in charge of each privacy protection zone can be stored.

[0054] Referring to FIG. 2, the privacy protection zone management server (100) includes a processor (110), a memory (150), and a communication unit (160). At this time, the processor (110) may control the operation of each of the components, including a detection information collection unit (120) for collecting detection zone status information, an information analysis unit (130) for analyzing the detection zone status information to identify a risk situation, and a detection zone service processing unit (140) for providing various services including risk notification information. The components illustrated in FIG. 1 are not essential for implementing the privacy protection zone management server (100) according to the present disclosure, and thus the privacy protection zone management server (100) described in this specification may have more or fewer components than the components listed above.

[0055] The processor (110) can receive detection zone status information including detection zone identification information transmitted from the privacy protection detection device (200).

[0056] Referring to FIG. 4, the privacy protection detection device (100) can detect various situations, including real-time illegal filming detection (a) and real-time fainting detection (b) occurring within a detection zone within a privacy protection zone, and provide detection zone status information including detection zone identification information.

[0057] The detection zone status information of the present disclosure may include at least one of a still image, a video, audio, and text. In this case, the video may be a real-time streaming video or a recorded video, but is not limited thereto. The still images and videos of the present disclosure may be in a form that makes it impossible to determine the actual appearance of the user in order to protect the privacy of the user being recognized. For example, as illustrated in FIG. 5, the still images and videos of the present disclosure may be in the form of information detected by at least one or more of a heat detection sensor, an infrared sensor, an ultrasonic sensor, and a microwave sensor. In other words, the still images and videos of the present disclosure may be in a form that allows for the distinction of boundaries between different objects, but makes it impossible to recognize the actual appearance. For example, the distinction of boundaries between the different objects may be defined as the distinction of boundaries between objects such as people, floors in a bathroom, toilets, and cell phones based on temperature conditions (temperature differences between different areas of a person's body, temperature differences between living and non-living objects) or the distance from a privacy protection detection device (200) equipped with a detection sensor.

[0058] Meanwhile, the collected detection zone status information may include information in an encrypted or converted state so that the user's personal identification information cannot be detected.

[0059] The processor (110) can determine whether a dangerous situation exists by analyzing the behavior of the detection zone status information using a pre-learned artificial intelligence model.

[0060] At this time, the processor (110) can analyze whether a dangerous situation exists because the user's behavioral state within the detection area matches a preset dangerous state.

[0061] The behavioral state of the user of the present disclosure is defined not only to mean a person, but also to include objects such as cameras, cell phones, and weapons.

[0062] The above pre-learned artificial intelligence model may be an artificial intelligence model trained to take as input a plurality of detection zone status information and a risk status matching the same and output whether a risk status corresponding to a risk situation exists.

[0063] Referring to FIG. 5, the processor (110) can recognize the user's behavioral state, including a person, a camera, a mobile phone, a weapon, etc., from an image within the detection zone status information using a pre-learned artificial intelligence model, and determine whether the recognized user's behavioral state matches a preset risk state.

[0064] If the analysis results indicate that a dangerous situation exists, the processor (110) may output danger alert information including relevant detection zone identification information. In this case, the processor (110) may determine that a dangerous situation has occurred if the recognized user's behavioral state matches a preset danger state by a threshold value or higher.

[0065] When outputting risk notification information, the processor (110) may output risk notification information including at least one of map information displaying detection zone location information matching detection zone identification information, the time at which a risk situation occurred, and the type of risk situation.

[0066] The processor (110) can set risk situation grades in advance, determine risk situation grades for risk situations, and differentiate targets to which risk notification information is transmitted based on the determined risk situation grades. The risk notification information may further include risk situation grades.

[0067] For example, a risk level could range from 1 to 5, with 1 representing the highest risk level and 5 representing the lowest. In other words, as the risk level increases from 5 to 1, the risk level becomes increasingly serious.

[0068] The processor (110) of the present disclosure transmits risk notification information to a user terminal (300), and can differentially designate the target of transmission for each risk situation level as the server administrator terminal alone, the server administrator terminal and the administrator terminal of the corresponding privacy protection zone, or the server administrator terminal, the administrator terminal of the corresponding privacy protection zone, and the police terminal. In this case, the target of transmission of risk notification information may include not only the server administrator terminal, the administrator terminal of the corresponding privacy protection zone, and the police terminal, but also a lower-level terminal of each terminal (e.g., a terminal of another person in charge).

[0069] When presetting a grade for each risk situation, the processor (110) can set a grade for each risk situation by assigning weights to preset items among the items in the risk notification information.

[0070] Specifically, the processor (110) may assign weights to items within the risk notification information that are relatively more dangerous to the type of risk situation, so that these can be reflected when determining the grade for each risk situation. In addition, the processor (110) may assign weights to items within the risk notification information that are longer or shorter than the current time when the risk situation occurred.

[0071] Not limited to the description described above, the processor (110) may assign various weights through a combination of multiple items in the risk notification information (map information indicating the location information of the detection area matching the detection area identification information, the time when the risk situation occurred, the type of risk situation, etc.), so that they can be reflected when determining the level for each risk situation.

[0072] When a dangerous situation occurs, the processor (110) can separately store images of a section of the detection zone status information that matches a preset dangerous state by matching it with the dangerous notification information. In this case, when storing the images, the processor (110) can store the images in the memory (150) together with identification information that can identify the images.

[0073] The processor (110) can calculate privacy protection zone status statistics based on detection zone status information and analysis information regarding the detection zone status information. The privacy protection zone status statistics may refer to processed information that can systematically confirm general management and safety status management, such as the presence or availability of equipment related to the privacy protection zone.

[0074] Specifically, the processor (110) can produce privacy protection zone situation statistics including at least one of the following: location of the privacy protection zone, whether a dangerous situation has occurred, time when a dangerous situation has occurred, type of dangerous situation, level of each dangerous situation, number of times a dangerous situation has occurred, and processing results after a dangerous situation has occurred.

[0075] Meanwhile, the processor (110) can receive detection zone status information such as privacy protection zone hygiene status and equipment information (e.g., toilet paper in a restroom) from the privacy protection detection device (200) as well as the user terminal (300).

[0076] Additionally, the processor (110) can receive the presence of a user in the restroom as detection zone status information detected through the detection sensor (210) provided in the privacy protection detection device (200).

[0077] Referring to FIG. 6, the processor (110) may generate a restroom location (a) and use / management information (b) using an artificial intelligence model based on detection zone status information, and provide the information to a terminal of a general user who wishes to use a privacy protection zone, but the provision target is not limited thereto. The privacy protection zone location may be provided in the form of displaying privacy protection zone information (e.g., restroom information) located in an adjacent area based on the location of the user terminal (300) on a map. The use / management information may refer to various information related to the use of the privacy protection zone, including whether the privacy protection zone (e.g., by restroom or by each area within the restroom (by restroom stall)) is available, whether there is a risk situation (safety, risk), sanitary conditions, and equipment conditions.

[0078] At this time, the processor (110) may provide a privacy protection zone management service application to the user terminal (300) so that the user can receive various privacy protection zone management services provided based on the detection zone status information, and may enable the user to install the application. The processor (110) of the present disclosure may also provide a simple complaint function ((c) of FIG. 6) so that the user can register his / her opinion related to the use of a privacy protection zone (e.g., a restroom) through the privacy protection zone management service application. At this time, the user can more easily register his / her complaint by scanning an identification code, such as a QR code, pre-attached to the entire restroom or each area of ​​the restroom (restroom stall, washstand, etc.) using the user terminal (300). To this end, the processor (110) may match and set a guidance window in advance for registering a complaint opinion for each QR code, so that when the QR code is scanned, it can be connected to a guidance window (mobile guidance window).

[0079] The processor (110) can include the user's opinion collected through the simple complaint function as detection zone status information and use this as a basis to create various services related to the use of the privacy protection zone (e.g., toilet use / management information (b), etc.).

[0080] Referring to FIG. 7, the processor (110) analyzes and processes the collected detection zone status information according to preset analysis criteria to generate complaint data (a), management data (b), risk data (c), and privacy protection zone use data (e.g., restroom use data) (d), and provides a service so that users, including administrators and general privacy protection zone users, can check the collected detection zone status information through a user terminal (300).

[0081] The above complaint data may refer to opinions registered by users of the privacy protection zone. Management data may refer to various information related to the management of the privacy protection zone. In this case, the processor (110) may be configured to allow the administrator to not only confirm management data but also perform various inputs and modifications via his or her user terminal (300).

[0082] The above risk data refers to information regarding the occurrence of a risk situation, which may include the time and location of the risk situation. The above privacy zone usage data may include information such as the duration of stay in each privacy zone (e.g., duration of stay in each restroom stall), as well as whether the privacy zone is available for use.

[0083] The above-described complaint data, management data, risk data, and privacy protection zone usage data may be provided through the above-described privacy protection zone management service application.

[0084] The processor (110) can differentiate the scope of data access by setting access rights for the aforementioned civil complaint data, management data, risk data, and privacy protection zone usage data. For example, for a service manager, the processor (110) can be set to allow access to all data, including access to input and edit data. For a general privacy protection zone user, the processor (110) can be set to allow access only to privacy protection zone usage data and to register civil complaint opinions.

[0085] The memory (150) can store a computer program for providing a privacy protection zone management service method, and the stored computer program can be read and operated by the processor (110). The memory (150) can store any form of information generated or determined by the processor (110) and any form of information received by the communication unit (160).

[0086] The memory (150) can store data supporting various functions of the privacy protection zone management server (100), a program for the operation of the processor (110), can store input / output data, and can store a plurality of application programs (or applications) run on the privacy protection zone management server (100), data for the operation of the privacy protection zone management server (100), and commands. At least some of these application programs can be downloaded from an external server via wireless communication.

[0087] The memory (150) may include at least one type of storage medium among a flash memory type, a hard disk type, an SSD type, an SDD type, a multimedia card micro type, a card type memory (e.g., SD or XD memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, and an optical disk. In addition, the memory may be a database that is separate from the device but is connected by wire or wirelessly.

[0088] The communication unit (160) may include one or more components that enable communication with an external device, and may include, for example, at least one of a broadcast reception module, a wired communication module, a wireless communication module, a short-range communication module, and a location information module.

[0089] Although not shown, the privacy protection zone management server (100) of the present disclosure may further include an output section and an input section.

[0090] The output unit may display a user interface (UI) for providing information related to the privacy protection zone management service. The output unit may output any form of information generated or determined by the processor (110) and any form of information received by the communication unit (160).

[0091] The output unit may include at least one of a liquid crystal display (LCD), a thin film transistor-liquid crystal display (TFT LCD), an organic light-emitting diode (OLED), a flexible display, and a three-dimensional display (3D display). Some of these display modules may be configured as transparent or light-transmitting so that the outside can be viewed through them. This may be referred to as a transparent display module, and a representative example of the transparent display module is TOLED (Transparent OLED).

[0092] The input unit can receive information input by a user. The input unit can include keys and / or buttons on a user interface, or physical keys and / or buttons, for receiving information input by a user. A computer program for controlling a display according to embodiments of the present disclosure can be executed based on user input through the input unit.

[0093] Referring to FIG. 3, the privacy protection detection device (200) may include a detection sensor (210) and a processor (220) for recognizing the situation of a detection area within a privacy protection zone.

[0094] The detection sensor (210) may include at least one of a heat detection sensor, an infrared sensor, an ultrasonic sensor, and a microwave sensor, but is not limited thereto.

[0095] The processor (220) can transmit detection zone status information including detection zone identification information in the detection information acquired through the detection sensor (210) to the privacy protection zone management server (100) according to preset transmission conditions.

[0096] At this time, the preset transmission conditions may include at least one of real-time, a condition in which a preset event occurs, a preset time cycle condition, and a preset time condition.

[0097] The above preset events may include arbitrarily set events, such as a state in which no movement is detected for a preset period of time after a target, including a user, is detected, or a state in which the amount of change in the recognized movement exceeds a threshold. To this end, the processor (220) may analyze the detection information to determine whether it matches a preset event.

[0098] The processor (220) may process the information into an encrypted or converted state so that the user's personal identification information cannot be detected before providing the detection zone status information.

[0099] Figure 8 is a flowchart illustrating the privacy protection zone management service method of the present disclosure. The privacy protection zone management method disclosed below can apply all of the roles of the privacy protection zone management server (100) disclosed through Figures 1 to 7 described above, and for the sake of convenience, redundant detailed descriptions will be omitted.

[0100] The processor (110) of the privacy protection zone management server (100) can receive detection zone status information including detection zone identification information transmitted from the privacy protection detection device (200) through the detection information collection unit (120) (1100).

[0101] The processor (110) uses an information analysis unit (130) to analyze the behavior of the detection zone status information using a pre-learned artificial intelligence model to determine whether a dangerous situation exists, and can analyze whether a dangerous situation exists because the behavioral status of a user within the detection zone matches a preset dangerous state (1200).

[0102] If the analysis results indicate that a dangerous situation exists, the processor (110) can output danger notification information including related detection zone identification information through the detection zone service processing unit (140) (1300).

[0103] Specifically, when the processor (110) outputs risk notification information through the detection zone service processing unit (140), it may output risk notification information including at least one of map information displaying detection zone location information matching detection zone identification information, the time at which a risk situation occurred, and the type of risk situation.

[0104] Meanwhile, the disclosed embodiments may be implemented in the form of a recording medium storing computer-executable instructions. The instructions may be stored in the form of program code, and when executed by a processor, may generate program modules to perform the operations of the disclosed embodiments. The recording medium may be implemented as a computer-readable recording medium.

[0105] Computer-readable storage media include all types of storage media that store instructions that can be deciphered by a computer. Examples include read-only memory (ROM), random access memory (RAM), magnetic tape, magnetic disks, flash memory, and optical data storage devices.

[0106] The disclosed embodiments have been described with reference to the attached drawings as described above. Those skilled in the art will understand that the present disclosure can be implemented in forms other than the disclosed embodiments without altering the technical spirit or essential features of the present disclosure. The disclosed embodiments are illustrative and should not be construed as limiting.

Claims

1. Detection information collection unit for collecting detection area status information; An information analysis unit for analyzing the above detection zone status information to identify a risk situation; and A processor that controls the operation of a detection zone service processing unit to provide various services including risk notification information; The above processor, Receive the detection zone status information including detection zone identification information transmitted from the privacy protection detection device, By using a pre-learned artificial intelligence model, the presence of a dangerous situation is determined through behavioral analysis of the above detection zone status information, and whether a dangerous situation exists is analyzed because the behavioral status of the user within the detection zone matches the preset dangerous status, A privacy protection zone management server that outputs risk notification information including the relevant detection zone identification information when a risk situation exists as a result of the analysis.

2. In paragraph 1, The above processor, A privacy protection zone management server that outputs the above risk notification information, including at least one of map information displaying the detection zone location information matching the detection zone identification information, the time at which the risk situation occurred, and the type of the risk situation.

3. In paragraph 2, The above processor, Set the risk situation grade in advance, determine the risk situation grade for the risk situation, and differentiate the target to which the risk notification information is transmitted according to the determined risk situation grade. The above risk notification information further includes a risk situation-specific level, a privacy protection zone management server.

4. In paragraph 3, The above processor, A privacy protection zone management server that sets the risk situation grade by assigning weights to preset items among the items in the risk notification information when presetting the risk situation grade.

5. In paragraph 3, The above processor, A privacy protection zone management server that, when the above-mentioned dangerous situation occurs, separately stores the image of the section matching the preset dangerous state among the images of the detection zone status information by matching it with the dangerous notification information.

6. In paragraph 1, The above processor, A privacy protection zone management server that calculates privacy protection zone situation statistics based on the above detection zone status information and analysis information on the above detection zone status information, and includes at least one of the following: privacy protection zone location, occurrence of a dangerous situation, time of occurrence of a dangerous situation, type of dangerous situation, level of each dangerous situation, number of times a dangerous situation occurred, and processing results after occurrence of a dangerous situation.

7. In paragraph 1, The above privacy protection detection device, Obtain detection information to recognize the situation of the detection area in the bathroom through the detection sensor, The detection zone status information including detection zone identification information in the above-mentioned acquired detection information is transmitted to the privacy protection zone management server according to preset transmission conditions. A privacy protection zone management server, wherein the above detection sensor includes at least one of a heat detection sensor, an infrared sensor, an ultrasonic sensor, and a microwave sensor.

8. In a method performed by a processor of a privacy protection zone management server, A step of receiving detection zone status information including detection zone identification information transmitted from a privacy protection detection device; A step of analyzing whether a dangerous situation exists by analyzing the behavior of the detection zone status information using a pre-learned artificial intelligence model, and analyzing whether a dangerous situation exists because the behavioral status of the user within the detection zone matches a preset dangerous status; and A method for managing a privacy protection zone, comprising: a step of outputting risk notification information including the relevant detection zone identification information when a risk situation exists as a result of the analysis; 9. In paragraph 8, The above processor, A privacy protection zone management method, which outputs map information displaying detection zone location information matching the detection zone identification information, and risk notification information including at least one of the time at which a risk situation occurred and the type of risk situation.

10. In paragraph 9, The above processor, Set the risk level in advance, Determine the risk level for each of the above risk situations, Differentiate the target to which the above risk notification information is transmitted according to the determined risk situation level. A method for managing a privacy protection zone, wherein the above risk notification information further includes a level for each risk situation.

11. In paragraph 10, The above processor, A method for managing a privacy protection zone, wherein when presetting the grade for each risk situation, weights are given to preset items among the items in the risk notification information to set the grade for each risk situation.

12. In paragraph 10, The above processor, A privacy protection zone management method in which, when the above-mentioned dangerous situation occurs, the image of the section matching the preset dangerous state among the images of the detection zone status information is matched with the dangerous notification information and stored separately.

13. In paragraph 8, The above processor, A privacy protection zone management method, wherein privacy protection zone status statistics are calculated based on the above detection zone status information and analysis information on the above detection zone status information, and wherein the toilet status statistics are calculated by including at least one of the following: privacy protection zone location, whether a dangerous situation has occurred, time when a dangerous situation has occurred, type of dangerous situation, level of each dangerous situation, number of times a dangerous situation has occurred, and result of processing after a dangerous situation has occurred.

14. In paragraph 8, The above privacy protection detection device, Obtain detection information to recognize the situation of the detection area in the bathroom through the detection sensor, The detection zone status information including detection zone identification information in the above-mentioned acquired detection information is transmitted to the privacy protection zone management server according to preset transmission conditions. A method for managing a privacy protection zone, wherein the above detection sensor comprises at least one of a heat detection sensor, an infrared sensor, an ultrasonic sensor, and a microwave sensor.

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