Artificial intelligence-based secure intelligent privacy protection area management server, method thereof, and privacy protection detection device

Abstract

The application relates to an artificial intelligence-based safe intelligent privacy protection area management server, a method thereof and a privacy protection detection device, the privacy protection area management server comprises a detection information collection unit, an information analysis unit and a processor for controlling the operation of a detection area service processing unit, the processor receives the detection area state information containing detection area identification information transmitted from the privacy protection detection device, uses a pre-learned artificial intelligence model, masters the existence of a dangerous condition by behavior analysis on the detection area state information, analyzes whether a dangerous condition exists by judging whether the behavior state of a user in the detection area is consistent with a preset dangerous state, and outputs dangerous alarm information containing relevant detection area identification information when the analysis result exists a dangerous condition.

Description

Technical Field

[0001] This invention relates to an artificial intelligence-based method for managing privacy-preserving areas. More specifically, this invention relates to an artificial intelligence-based secure intelligent privacy-preserving area management server, its method, and a privacy protection detection device. Background Technology

[0002] Public restrooms are facilities used by a large number of people without a specific target audience, and must always be kept in a good clean and safe condition. However, due to reasons such as user privacy, access to and management of restrooms is actually somewhat difficult. Therefore, various situations may occur, such as the lack of supplies like toilet paper that should be available in public restrooms, the occurrence of upskirt photography, or the inability to recognize elderly users or others who suddenly faint. The public restrooms mentioned are not limited to one example and may also include other areas where personal privacy needs to be protected, such as bathhouses, changing rooms, hotel rooms, hospital wards, and homes, where cameras or other recording devices cannot be installed.

[0003] Therefore, several solutions have been proposed for more effective management of privacy-protected areas such as public restrooms. Summary of the Invention

[0004] Technical issues The purpose of the embodiments disclosed in this invention is to provide an AI-based secure and intelligent privacy protection area management server, its method, and a privacy protection detection device that can maintain the security and hygiene of the privacy protection area in a good state.

[0005] The purpose of this invention is not limited to the purposes mentioned above, and other purposes not mentioned can be clearly understood by those skilled in the art based on the following description.

[0006] Technical solution A privacy protection area management server according to an embodiment of the present invention for achieving the aforementioned objective may include: a detection information collection unit for collecting detection area status information; an information analysis unit for understanding dangerous situations by analyzing the detection area status information; and a processor for controlling the operation of a detection area service processing unit, wherein the detection area service processing unit provides various services including danger alarm information. The processor receives the detection area status information containing detection area identification information transmitted from a privacy protection detection device, and uses a pre-learned artificial intelligence model to determine whether a dangerous situation exists by performing behavioral analysis on the detection area status information. It analyzes whether a dangerous situation exists by judging whether the behavioral state of a user within the detection area is consistent with a preset dangerous situation. When the analysis result indicates that a dangerous situation exists, it outputs danger alarm information containing the relevant detection area identification information.

[0007] When the hazard alarm information is output, the processor may output the hazard alarm information which includes at least one of the following: map information displaying the location information of the detection area that matches the detection area identification information, the time of occurrence of the hazard, and the type of hazard.

[0008] The processor can pre-set hazard levels, determine the hazard level of the hazard, and distinguish the objects transmitting the hazard alarm information according to the determined hazard level. The hazard alarm information may also include the hazard level.

[0009] When the hazard level is preset, the processor can assign weighted values ​​to preset items in the hazard alarm information to set the hazard level.

[0010] If the dangerous situation occurs, the processor can match the image of the interval in the image of the detection area status information that matches the preset dangerous situation with the danger alarm information and store it separately.

[0011] The processor can calculate privacy protection area status statistics based on the detection area status information and the analysis information of the detection area status information, and calculate the toilet status statistics. The toilet status statistics can include at least one of the following: privacy protection area location, whether a dangerous situation has occurred, time of occurrence of dangerous situation, type of dangerous situation, level of dangerous situation, number of times dangerous situation has occurred, and post-danger processing results.

[0012] Furthermore, a privacy protection detection device according to another embodiment of the present invention includes: a detection sensor for identifying the status of a detection area in a restroom; and a processor, wherein the processor transmits detection area status information, which includes detection area identification information in the detection information obtained by the detection sensor, to a privacy protection area management server according to preset transmission conditions. The detection sensor may include at least one of a thermal detection sensor, an infrared sensor, an ultrasonic sensor, and a microwave sensor.

[0013] Furthermore, the present invention also includes a privacy protection area management method executed by a privacy protection area management server, which may include the following steps: receiving detection area status information containing detection area identification information transmitted from a privacy protection detection device; using a pre-learned artificial intelligence model to determine whether a dangerous situation exists by performing behavioral analysis on the detection area status information; analyzing whether a dangerous situation exists by judging whether the behavioral state of a user within the detection area is consistent with a preset dangerous situation; and when the analysis result indicates that a dangerous situation exists, outputting danger alarm information containing the relevant detection area identification information.

[0014] In the privacy protection area management method, when the danger alarm information is output, the output includes at least one of the following: map information displaying the location information of the detection area that matches the detection area identification information, the time of occurrence of the danger situation, and the type of danger situation.

[0015] In addition, the present invention may also provide a computer program stored in a computer-readable recording medium for implementing the methods of the present invention.

[0016] In addition, the present invention may also provide a computer-readable recording medium for recording computer programs for implementing the methods of the present invention.

[0017] The effects of the invention According to the technical solution of the present invention as described above, since the detection area can be monitored 24 hours a day without going directly to the privacy protection area, it provides the effect of dealing with dangerous situations that may occur in the privacy protection area.

[0018] Furthermore, according to the technical solution of the present invention as described above, various dangerous situations occurring in privacy protection areas can be automatically detected based on an artificial intelligence model, and measures such as notifications based on this can be automatically executed, thereby maintaining privacy protection areas such as restrooms in a safe state.

[0019] Furthermore, according to the technical solution of the present invention as described above, the status of the privacy protection area can be monitored by utilizing the data format that protects the privacy of users using the privacy protection area.

[0020] Furthermore, according to the technical solution of the present invention as described above, big data on the actual usage of toilets can be ensured, which can be used to implement toilet management more systematically.

[0021] The effects of this invention are not limited to those mentioned above, and other effects not mentioned can be clearly understood by those skilled in the art based on the following description. Attached Figure Description

[0022] Figure 1 This is a block diagram of the privacy protection area management system of the present invention.

[0023] Figure 2 This is a block diagram of the privacy protection area management server of the present invention.

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

[0025] Figures 4 to 7 This is an example diagram illustrating the privacy protection area management method of the present invention.

[0026] Figure 8This is a flowchart illustrating the privacy protection area management method of the present invention. Detailed Implementation

[0027] Throughout this invention, the same reference numerals denote the same structural elements. This invention does not describe all elements of the various embodiments; common content in the technical field to which this invention pertains, or content repeated between the various embodiments, will be omitted. In this specification, the terms "part, module, component, block" as used may be implemented in software or hardware. According to embodiments, multiple "parts, modules, components, blocks" may be implemented by a single structural element, and a single "part, module, component, block" may also include multiple structural elements.

[0028] Throughout the instruction manual, when it is stated that a part is "connected" to other parts, this includes not only direct connections but also indirect connections, including connections via wireless communication networks.

[0029] Furthermore, when it is stated that a part "includes" a structural element, unless otherwise stated otherwise, it means that other structural elements may also be included, rather than excluding other structural elements.

[0030] In the full text of the instruction manual, when it is said that a component is "on" other components, this includes not only the case where the component is in contact with other components, but also the case where there are other components between the two components.

[0031] The terms “first”, “second”, etc., are used to distinguish one structural element from other structural elements, but do not limit the structural elements to the terms.

[0032] Unless explicitly stated in the text, the singular expression includes the plural expression.

[0033] The reference numerals for each step are for illustrative purposes only and do not indicate the order of the steps. Unless a specific order is explicitly stated in the text, the order of the steps may differ from the order stated.

[0034] The working principle and several embodiments of the present invention will be described below with reference to the accompanying drawings.

[0035] In this specification, "the apparatus of the present invention" includes a variety of devices that can provide results to a user by performing computational processing. For example, the apparatus of the present invention may include a computer, a server device, and a portable terminal, or may be one of these forms.

[0036] Examples of the computer may include laptops, desktops, laptops, tablet PCs, touchscreen tablet PCs, etc., equipped with a web browser.

[0037] The server device is a server that can process information by communicating with external devices, and may include application servers, computing servers, database servers, file servers, game servers, mail servers, proxy servers, and web servers, etc.

[0038] The portable terminal is a wireless communication device that ensures portability and mobility. For example, it may include all types of handheld wireless communication devices such as 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) terminals, and smartphones, as well as wearable devices such as watches, rings, bracelets, anklets, necklaces, glasses, contact lenses, or head-mounted devices (HMDs).

[0039] The artificial intelligence-related functions of this invention are operated via a processor and memory. The processor may consist of one or more processors. In this case, the one or more processors may be general-purpose processors such as CPUs, APs, and DSPs (Digital Signal Processors), graphics-specific processors such as GPUs and VPUs (Vision Processing Units), or AI-specific processors such as NPUs. The one or more processors are controlled to process input data according to a predefined operating rule AI model stored in memory. Alternatively, if the one or more processors are AI-specific processors, the AI-specific processors may be designed with a hardware structure dedicated to processing a specific AI model.

[0040] The characteristic of predefined operating rules or artificial intelligence models lies in their creation through learning. Here, "created through learning" means that the underlying artificial intelligence model learns from multiple learning data using a learning algorithm to create predefined operating rules or artificial intelligence models set up in a manner that performs the desired characteristics (or purpose). This learning can occur on the machine itself executing the artificial intelligence of this invention, or via a separate server and / or system. Examples of learning algorithms include supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but are not limited to these examples.

[0041] Artificial intelligence models can consist of multiple neural network layers. Each layer has multiple weight values, and neural network operations are performed by combining the results of previous layers with these weight values. The weight values ​​of these layers can be optimized based on the learning results of the artificial intelligence model. For example, the weight values ​​can be updated in a way that reduces or minimizes the loss or cost values ​​obtained from the artificial intelligence model during the learning process. Artificial neural networks can include deep neural networks (DNNs), such as CNNs (Convolutional Neural Networks), DNNs (Deep Neural Networks), RNNs (Recurrent Neural Networks), RBMs (Restricted Boltzmann Machines), DBNs (Deep Belief Networks), BRDNNs (Bidirectional Recurrent Deep Neural Networks), or deep Q-networks, but are not limited to the examples described.

[0042] According to an exemplary embodiment of the present invention, the processor may embody artificial intelligence. Artificial intelligence means a machine learning method based on artificial neural networks that mimics human biological neurons to enable machine learning. Artificial intelligence methods may include, depending on the learning approach: supervised learning, where both input and output data are provided as training data, and the answer to the question (input data) (output data) has been determined; unsupervised learning, where only input data is provided without output data, and the answer to the question (input data) (output data) has not been determined; and reinforcement learning, where, in the current state, a reward is provided from the external environment for each action taken, and the learning proceeds in the direction of maximizing this reward. Furthermore, artificial intelligence methods can also be distinguished according to the architecture of the learning model structure. The architecture of widely used deep learning technologies can be categorized into Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers, Generative Adversarial Networks (GANs), etc.

[0043] This device and system may include an artificial intelligence model. The artificial intelligence model can be a single model or a combination of multiple models. The artificial intelligence model may consist of a neural network (or artificial neural network), and may include statistical learning algorithms that mimic biological neural networks from machine learning and cognitive science. A neural network can mean an entire model in which artificial neurons (nodes) forming a network of synaptic connections learn to change the strength of synaptic connections to acquire problem-solving capabilities. The neurons of a neural network may include combinations of weights or biases. A neural network may include one or more layers consisting of one or more neurons or nodes. Inventively, the device may include an input layer, a hidden layer, and an output layer. The neural network constituting the device can learn to change the weights of its neurons, thereby inferring the predicted output from any input.

[0044] The processor can generate neural networks, train or learn neural networks, perform operations based on received input data, generate information signals based on the execution results, or retrain the neural network. Neural network models can include, but are not limited to, various types of models such as GoogleNet, AlexNet, VGG Network, CNN (Convolution Neural 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 (Restrected Boltzman Machine), Fully Convolutional Network, LSTM (Long Short-Term Memory) Network, and Classification Network. The processor can include more than one processor for performing operations based on the neural network model. For example, the neural network can include a deep neural network.

[0045] Neural networks can include CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), perceptron, multilayer perceptron, FF (FeedForward), RBF (Radial Basis Network), DFF (Deep Feed Forward), LSTM (Long ShortTerm Memory), GRU (Gated Recurrent Unit), AE (Auto Encoder), VAE (Variational) AutoEncoder), DAE (Denoising Auto Encoder), SAE (Sparse Auto Encoder), MC (MarkovChain), 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), GAN (Generative Adversarial Network), LSM (Liquid State Machine), ELM (Extreme Learning Machine), ESN (Echo State Network), DRN (Deep The network can include Residual Network, DNC (Differentiable Neural Computer), NTM (Neural Turning Machine), CN (Capsule Network), KN (Kohonen Network), and AN (Attention Network), but is not limited to these, and can include any neural network that can be understood by a person skilled in the art.

[0046] In exemplary embodiments of the present invention, the processor may utilize CNN (Convolution Neural 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 (Restrected Boltzman Machine), Fully Convolutional Network, LSTM (Long Short-Term Memory) Network, Classification Network, Generative Modeling, eXplainable AI, Continual AI, Representation Learning, AI for Material Design, BERT for Natural Language Processing, SP-BERT, MRC / QA, Text Analysis, Dialog System, GPT-3, GPT-4, Visual Analytics for Visual Processing, Visual Understanding, Video Synthesis, Anomaly Detection, Prediction, and Time-Series for ResNet data intelligence, etc. This invention employs various artificial intelligence structures and algorithms, including Forecasting, Optimization, Recommendation, and Data Creation, but is not limited to these. The embodiments of this invention are described in detail below with reference to the accompanying drawings.

[0047] The definition of the following publicly disclosed privacy-protected areas refers to areas where personal privacy needs to be protected, including public restrooms, bathhouses, changing rooms, hotel rooms, etc., where cameras or other recording devices cannot be installed.

[0048] Figure 1 This is a block diagram of the privacy protection area management system of the present invention. Figure 2 This is a block diagram of the privacy protection zone management server of the present invention. Figure 3This is a block diagram of the privacy protection detection device of the present invention.

[0049] Hereinafter, reference will be made to example diagrams used to illustrate the privacy protection area management method of the present invention. Figures 4 to 7 Please provide an explanation.

[0050] Reference Figure 1 The privacy protection area management system 10 includes a privacy protection area management server 100 and a privacy protection detection device 200. Additionally, the privacy protection area management system 10 may also include a user terminal 300.

[0051] Reference 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. If a dangerous situation occurs, it can send a notification to the user terminal 300 to take appropriate measures. The detection zone refers to the area within the privacy protection zone monitored by the privacy protection detection device 100 (described later), and can be arbitrarily set by the user, such as the entire privacy protection zone or a portion of the privacy protection zone.

[0052] In this case, user terminal 300 may include server administrator terminals, administrator terminals for corresponding privacy protection areas, police terminals, etc., but is not limited to these, and can be added or changed according to the needs of the users. For example, user terminal 300 may also include terminals for ordinary customers utilizing privacy protection areas.

[0053] The privacy protection zone management server 100 can pre-store the user terminal 300 numbers. If the user terminal 300 is a privacy protection zone administrator terminal, it can store the administrator terminal numbers for each privacy protection zone. Furthermore, if the user terminal 300 is a police terminal, it can store the police terminal numbers for each privacy protection zone's responsible police station.

[0054] Reference Figure 2 The privacy protection zone management server 100 includes a processor 110, a memory 150, and a communication unit 160. In this case, the processor 110 may include: a detection information collection unit 120 for collecting detection zone status information; an information analysis unit 130 for understanding the danger situation through the detection zone status information; and a detection zone service processing unit 140 for providing various services such as danger alarm information. The processor 110 can control the operation of the corresponding structures through the structure described above. Figure 1 The structural elements shown are not essential for implementing the privacy protection zone management server 100 of the present invention. The privacy protection zone management server 100 described in this specification may have more or fewer structural elements than those listed above.

[0055] The processor 110 can receive detection area status information, which includes detection area identification information, transmitted from the privacy protection detection device 200.

[0056] Reference Figure 4 The privacy protection detection device 100 detects various situations, such as real-time illegal shooting detection a and real-time fainting detection b, that occur within the detection area of ​​the privacy protection area, and can provide detection area status information including detection area identification information.

[0057] The detection area status information of this invention may include at least one of still images, video, audio, and text. In this case, the video may be a real-time streaming video or a recorded video, but is not limited to these. The still images and videos of this invention can be presented in a way that prevents the user's actual appearance from being determined, thereby protecting the privacy of the identified user. For example, such as... Figure 5 As shown, the still images and videos of the present invention can be information forms detected by at least one of a thermal detection sensor, an infrared sensor, an ultrasonic sensor, and a microwave sensor. That is, the still images and videos of the present invention can be forms in which the boundaries between different objects can be distinguished but their actual shapes cannot be identified. For example, the boundaries between the different objects can be defined as the separation distance between objects such as people, bathroom floors, toilet seats, and mobile phones and the privacy protection detection device 200 equipped with temperature state (temperature difference between human body parts, temperature difference between living and non-living things) or detection sensors.

[0058] On the other hand, the collected detection area status information may include encrypted information or information on changes in status that prevents the detection of the user's personal identification information.

[0059] The processor 110 can utilize a pre-learned artificial intelligence model to determine the presence of dangerous situations through behavioral analysis of detected area state information.

[0060] In this case, the processor 110 can analyze whether there is a dangerous situation by determining whether the user's behavior state within the detection area is consistent with a preset dangerous state.

[0061] The user's behavioral state in this invention is not limited to just people, but is defined to also include objects such as cameras, mobile phones, and weapons.

[0062] The pre-learned artificial intelligence model can be an artificial intelligence model that is trained by taking multiple detection area state information and their corresponding dangerous states as input values ​​and outputting whether or not a dangerous state exists corresponding to the dangerous situation.

[0063] Reference Figure 5The processor 110 can use a pre-learned artificial intelligence model to identify the user's behavior status, including people, cameras, mobile phones, and weapons, from the image within the detection area's state information, and can determine whether the identified user's behavior status is consistent with the preset danger status.

[0064] When the analysis results indicate a dangerous situation, the processor 110 can output a danger alarm message containing relevant detection area identification information. In this case, if the consistency between the identified user's behavior and the preset danger state reaches a benchmark value or higher, the processor 110 can determine it as a dangerous situation.

[0065] When outputting hazard alarm information, the processor 110 may output hazard alarm information that includes at least one of the following: map information displaying the location information of the detection area that matches the detection area identification information, the time of occurrence of the hazard, and the type of hazard.

[0066] The processor 110 can pre-set hazard levels, determine the hazard level of a hazard situation, and distinguish the objects transmitting hazard alarm information based on the determined hazard level. The hazard alarm information may also include the hazard level.

[0067] For example, hazard levels can range from 1 to 5. In this case, level 1 could mean the highest hazard level, and level 5 could mean the lowest hazard level. That is, the hazard level gradually increases from 5 to 1.

[0068] The processor 110 of this invention can transmit danger alarm information to user terminals 300. It can specify the transmission recipients differently according to various danger levels: transmission may be sent solely to server administrator terminals, to both server administrator terminals and administrator terminals in corresponding privacy protection zones, or to all three. In this case, the transmission recipients of the danger alarm information include not only server administrator terminals, administrator terminals in corresponding privacy protection zones, and police terminals, but also subordinate terminals (e.g., terminals of other responsible persons).

[0069] When a hazard level is preset, the processor 110 can assign weighted values ​​to preset items in the hazard alarm information to set the hazard level.

[0070] Specifically, the processor 110 can assign a weighted value to items within the hazard alarm information that are relatively more dangerous, and can reflect this when the hazard level can be determined. Furthermore, within items within the hazard alarm information, the processor 110 can assign a weighted value based on whether the time elapsed since the hazard occurred is longer or shorter than the current time.

[0071] The present invention is not limited to the description described herein. The processor 110 can assign multiple weighted values ​​to the combination of multiple items in the hazard alarm information (map information that displays detection area identification information and matching detection area location information, time of occurrence of hazard situation and type of hazard situation, etc.) to reflect the determination of the hazard situation level.

[0072] If a dangerous situation occurs, the processor 110 will match the image of the region whose status information matches the preset dangerous situation with the danger alarm information and store them separately. In this case, when storing the image, the processor 110 can also store the identification information that can identify the corresponding image in the memory 150.

[0073] The processor 110 can calculate privacy protection area status statistics based on the detection area status information and the analysis information of the detection area status information. The privacy protection area status statistics can mean information processed in a way that can systematically confirm whether it is related to the privacy protection area, whether it is usable, etc., in the form of general management and security status management.

[0074] Specifically, the processor 110 calculates privacy protection area status statistics in a manner that includes at least one of the following: location of privacy protection area, whether a dangerous situation has occurred, time of occurrence of dangerous situation, type of dangerous situation, level of dangerous situation, number of times dangerous situation has occurred, and post-occurrence processing results of dangerous situation.

[0075] On the other hand, the processor 110 not only receives detection area status information from the privacy protection detection device 200, but also receives detection area status information such as the hygiene status and supplies information (e.g., toilet paper in the bathroom) from the user terminal 300.

[0076] Furthermore, the processor 110 can receive the presence of a user in the restroom as detection area status information, which is detected by the detection sensor 210 in the privacy protection detection device 200.

[0077] Reference Figure 6 The processor 110 can generate a privacy protection zone for ordinary users' terminals based on the detected area status information using an artificial intelligence model, along with toilet location a and usage / management information b. The target audience is not limited to this. The privacy protection zone location can be provided as a map displaying privacy protection zone information (e.g., toilet information) located in adjacent areas with the user terminal 300 as a reference. The usage / management information can include various information related to the use of the privacy protection zone (e.g., whether each toilet or area within a toilet (toilet cubicle) is available, whether there are any dangerous conditions (safe, dangerous), hygiene status, and availability of supplies).

[0078] In this case, the processor 110 can provide and configure a privacy-protected area management service application to the user terminal 300 in a manner that makes the user acceptable by providing various privacy-protected area management services based on the detected area status information. The processor 110 of the present invention can also provide a convenient public opinion function by allowing users to express their opinions related to the use of privacy-protected areas (e.g., restrooms) through the privacy-protected area management service application. Figure 6 (part (c)). In this case, users can use user terminal 300 to scan QR codes or other identification codes pre-attached throughout the restroom or in various areas of the restroom (toilet stalls, sinks, etc.) to more easily express their opinions. To this end, processor 110 can pre-match and set a guide window for expressing opinions for each QR code, and can connect to the guide window (moving window) as the QR code is scanned.

[0079] The processor 110 can include user opinions collected through the convenient public opinion function into the detection area status information, which can be used as a basis to generate various services related to the use of privacy-protected areas (e.g., toilet usage / management information b, etc.).

[0080] Reference Figure 7 The processor 110 can analyze and process the collected detection area status information according to the preset analysis benchmark, so as to provide services in a way that allows managers, ordinary privacy protection area users and other users to confirm the generation of public opinion data a, management data b, danger data c and privacy protection area usage data d (e.g., toilet usage data) through the user terminal 300.

[0081] The public opinion data may represent the opinions expressed by users in the privacy protection area. Management data may represent various information related to the management of the privacy protection area. In this case, the processor 110 can be configured so that administrators can confirm the management data through their own user terminal 300, and can also perform various input and modification operations.

[0082] The danger data refers to information about the occurrence of a dangerous situation, which may include the time and location of the dangerous situation. The privacy protection area usage data may include information such as the time spent in each privacy protection area (e.g., the time spent in each toilet stall) and whether the privacy protection area can be used.

[0083] The aforementioned public opinion data, management data, risk data, and privacy protection area usage data can be provided through the privacy protection area management service application.

[0084] The processor 110 can set data browsing permissions for the aforementioned public opinion data, management data, dangerous data, and privacy-protected area usage data, thereby differentiating the scope of verifiable data. For example, for service administrators, the processor 110 can be set to allow them to view, input, and modify all data. For ordinary privacy-protected area users, the processor 110 can be set to only view privacy-protected area usage data and express public opinions.

[0085] The memory 150 may store a computer program for providing a privacy-protected area management service method, and the stored computer program may be read and driven by the processor 110. The memory 150 may store information of any form generated or determined by the processor 110 and information of any form received by the communication unit 160.

[0086] The memory 150 may store: data supporting various functions of the privacy-protected zone management server 100; programs for the operation of the processor 110; multiple data that can store input / output; multiple application programs or applications driven in the privacy-protected zone management server 100; multiple data for the operation of the privacy-protected zone management server 100; and multiple instructions. At least a portion of such applications may be downloaded wirelessly from an external server.

[0087] This memory 150 may include at least one type of storage medium selected from flash memory, hard disk, SSD (Solid State Disk), SDD (Silicon Disk Drive), Multimedia Card Micro, card-type memory (e.g., SD or XD memory), Random Access Memory (RAM), SRAM (Static Random Access Memory), Read-Only Memory (ROM), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory), magnetic storage, magnetic disk, and optical disk. Furthermore, although the memory is separate from this device, it may also be a database connected via wired or wireless means.

[0088] The communication unit 160 may include one or more structural elements capable of communicating with external devices, such as at least one of a broadcast receiving module, a wired communication module, a wireless communication module, a short-range communication module, and a location information module.

[0089] Although not shown in the figure, the privacy protection area management server 100 of the present invention may also include an output section and an input section.

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

[0091] The output section 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, or a 3D display. Some of these display modules may be formed in a transparent or light-transmitting manner, allowing the outside to be seen through them. This can be referred to as a transparent display module, and representative examples of such transparent display modules include transparent organic light-emitting diodes (TOLEDs).

[0092] The input unit can receive information input by the user. The input unit may include multiple buttons and / or physical buttons on a user interface for receiving user input. As user input is made through the input unit, a computer program for controlling the display, according to various embodiments of the present invention, can be executed.

[0093] Reference Figure 3 The privacy protection detection device 200 may include a detection sensor 210 and a processor 220 for identifying the condition of the detection area within the privacy protection area.

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

[0095] The processor 220 can transmit detection area status information, which includes detection area identification information in the detection information obtained by the detection sensor 210, to the privacy protection area management server 100 according to preset transmission conditions.

[0096] In this case, the preset transmission conditions may include at least one of real-time, a preset event occurrence condition, a preset time period condition, and a preset time condition.

[0097] The preset events can include any set events, such as a state where no movement is detected after a user or other object has been detected for a preset time, or a state where the detected amount of movement change reaches or exceeds a benchmark value. Therefore, the processor 220 can analyze the detection information to determine whether it is consistent with the preset events.

[0098] The processor 220 can be configured to provide encrypted or state-transforming information that prevents the user's personal identification information from being detected before providing the detection area state information.

[0099] Figure 8 This is a flowchart illustrating the privacy protection area management method of the present invention. The privacy protection area management method disclosed below is applicable to [various applications / methods]. Figures 1 to 7 For ease of explanation, detailed descriptions of all the functions of the publicly disclosed privacy protection zone management server 100 will be omitted.

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

[0101] The processor 110 can use the information analysis unit 130 and the pre-learned artificial intelligence model to determine whether a dangerous situation exists by analyzing the behavior of the detected area status information, and analyze whether a dangerous situation exists by judging whether the behavior of the user in the detected area is consistent with the preset dangerous situation (step 1200).

[0102] When the analysis results indicate a dangerous situation, the processor 110 can output a danger alarm message containing relevant detection area identification information through the detection area service processing unit 140 (step 1300).

[0103] Specifically, when the detection area service processing unit 140 outputs hazard alarm information, the processor 110 can output hazard alarm information that includes at least one of the following: map information displaying the location information of the detection area that matches the detection area identification information, the time of occurrence of the hazard, and the type of hazard.

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

[0105] Computer-readable recording media include all kinds of recording media that store instructions that can be read by a computer. For example, they may include ROM (Read Only Memory), RAM (Random Access Memory), magnetic tape, magnetic disk, flash memory, optical data storage devices, etc.

[0106] The above description, with reference to the accompanying drawings, illustrates several disclosed embodiments. Those skilled in the art will understand that the invention can be implemented in different ways than the disclosed embodiments without altering the technical concept or essential features of the invention. The disclosed embodiments are exemplary and should not be construed as limiting embodiments.

Claims

1. A privacy-protected area management server, characterized in that, include: The detection information collection unit is used to collect status information of the detection area; The information analysis unit is used to understand the dangerous situation by analyzing the status information of the detection area; and A processor controls the operation of the detection area service processing unit, which provides various services, including hazard alarm information. The processor receives the detection area status information, which includes detection area identification information, transmitted from the privacy protection detection device. By utilizing a pre-learned artificial intelligence model, the presence or absence of a dangerous situation is determined through behavioral analysis of the state information of the detection area. The existence of a dangerous situation is further analyzed by determining whether the behavioral state of users within the detection area matches a preset dangerous state. When the analysis results indicate a dangerous situation, a danger alarm message containing the relevant detection area identification information is output.

2. The privacy protection zone management server according to claim 1, characterized in that, When the hazard alarm information is output, the processor outputs hazard alarm information that includes at least one of the following: map information displaying the location information of the detection area that matches the detection area identification information, the time of occurrence of the hazard, and the type of hazard.

3. The privacy protection zone management server according to claim 2, characterized in that, The processor pre-sets hazard levels, determines the hazard level of each hazard, and distinguishes the objects transmitting the hazard alarm information based on the determined hazard level. The danger alarm information also includes the danger level.

4. The privacy protection zone management server according to claim 3, characterized in that, When the hazard level is preset, the processor assigns a weighted value to the preset item in the hazard alarm information to set the hazard level.

5. The privacy protection zone management server according to claim 3, characterized in that, If the dangerous situation occurs, the processor will match the image of the region in the image of the detection area status information that matches the preset dangerous situation with the danger alarm information and store them separately.

6. The privacy protection zone management server according to claim 1, characterized in that, The processor calculates privacy protection area status statistics based on the detection area status information and the analysis information of the detection area status information, and calculates toilet status statistics. The toilet status statistics include at least one of the following: privacy protection area location, whether a dangerous situation has occurred, time of occurrence of a dangerous situation, type of dangerous situation, level of dangerous situation, number of times a dangerous situation has occurred, and post-danger processing results.

7. The privacy protection zone management server according to claim 1, characterized in that, The privacy protection detection device acquires detection information using detection sensors to identify the status of detection areas within the restroom, and transmits this detection area status information, which includes the detection area identification information, to the privacy protection area management server according to preset transmission conditions. The detection sensor includes at least one of a thermal detection sensor, an infrared sensor, an ultrasonic sensor, and a microwave sensor.

8. A method for managing privacy protection zones, executed by the processor of a privacy protection zone management server, characterized in that, Includes the following steps: Receive detection area status information, which includes detection area identification information, transmitted from the privacy protection detection device; By using a pre-learned artificial intelligence model, the presence or absence of a dangerous situation can be determined by analyzing the state information of the detection area. The existence of a dangerous situation can be analyzed by judging whether the behavior of users in the detection area is consistent with the preset dangerous situation. as well as When the analysis results indicate a dangerous situation, a danger alarm message containing the relevant detection area identification information is output.

9. The privacy protection area management method according to claim 8, characterized in that, The processor outputs hazard alarm information that includes at least one of the following: map information displaying the location information of the detection area that matches the detection area identification information, the time of occurrence of the hazard, and the type of hazard.

10. The privacy protection area management method according to claim 9, characterized in that, The processor pre-sets hazard levels, determines the hazard level of each hazard, and distinguishes the objects transmitting the hazard alarm information based on the determined hazard level. The danger alarm information also includes the danger level.

11. The privacy protection area management method according to claim 10, characterized in that, When the hazard level is preset, the processor assigns a weighted value to the preset item in the hazard alarm information to set the hazard level.

12. The privacy protection area management method according to claim 10, characterized in that, If the dangerous situation occurs, the processor will match the image of the region in the image of the detection area status information that matches the preset dangerous situation with the danger alarm information and store them separately.

13. The privacy protection area management method according to claim 8, characterized in that, The processor calculates privacy protection area status statistics based on the detection area status information and the analysis information of the detection area status information, and calculates toilet status statistics. The toilet status statistics include at least one of the following: privacy protection area location, whether a dangerous situation has occurred, time of occurrence of a dangerous situation, type of dangerous situation, level of dangerous situation, number of times a dangerous situation has occurred, and post-danger processing results.

14. The privacy protection area management method according to claim 8, characterized in that, The privacy protection detection device acquires detection information using detection sensors to identify the status of detection areas within the restroom, and transmits this detection area status information, which includes the detection area identification information, to the privacy protection area management server according to preset transmission conditions. The detection sensor includes at least one of a thermal detection sensor, an infrared sensor, an ultrasonic sensor, and a microwave sensor.

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