Intelligent process management method

Abstract

The intelligent process management method may include: an operation of asynchronously collecting visual data acquired from a wearable camera worn by a user at a target site, GPS-based location data corresponding to the time of acquisition of said visual data, and said user's site inspection voice data, and integrating them into multimodal construction data; an operation of superimposing in real-time the visual data included in the multimodal construction data with design drawing data previously registered corresponding to said target site to calculate a numerical error for a set major construction item; an operation of sending an error notification message to a user terminal previously registered corresponding to said target site when a target construction item among the major construction items is identified in which the numerical error exceeds a threshold error; and an operation of setting the process for said target site to a suspended state until feedback data regarding said target construction item is received from the user terminal, and sending a warning notification message to said user terminal whenever additional data is acquired from said wearable camera.

Description

Technology Field

[0001] The present invention relates to an intelligent process management method. Background Technology

[0002] Construction sites are complex environments where multiple processes proceed simultaneously, and construction quality management has traditionally relied primarily on visual inspections by site managers or supervisors and document-based records. This approach has limitations, such as results varying depending on skill levels and the difficulty of responding in real-time. In particular, if structural errors are not detected in the early stages, they have a cascading effect on subsequent processes, increasing rework costs and safety risks.

[0003] Recently, attempts have been made to compare design drawings with construction results using BIM (Building Information Modeling) and digital twin technologies. However, most of these operations are centered on post-construction analysis, and structures directly linked to the behavior of on-site workers or real-time data are limited. In other words, an integrated system that immediately detects errors between design and construction and links them to process control has not yet been fully implemented.

[0004] With the advancement of wearable sensors, location tracking devices, and biosignal measurement technologies, technologies for monitoring the location and status of workers are being introduced. However, existing technologies are largely limited to warning levels based on single-sensor information, and direct integration with actual process control or equipment operation is restricted. Furthermore, technologies that calculate risk levels by comprehensively assessing a worker's psychological state or voice information are still in their early stages.

[0005] While voice recognition technology is utilized in industrial settings for equipment control, access authentication, and work reporting, the use of technologies that determine emergency situations by comprehensively analyzing not only the content but also acoustic characteristics is limited. Particularly in noisy environments such as construction sites, simple keyword-based judgments carry the risk of causing false alarms.

[0006] Therefore, there is a need for intelligent process management technology capable of comparing design drawings with actual construction data in real time, comprehensively analyzing worker location, biosignals, and voice data to determine risk levels, and immediately linking the results to on-site equipment control. In particular, a structure is required where risk assessment results go beyond simple notifications and are integrated with physical control devices. The problem to be solved

[0007] According to the present invention, errors in major construction items can be calculated immediately by comparing visual data acquired at the site with design drawings in real time. Accordingly, construction errors can be detected early to prevent them from spreading to subsequent processes, and rework costs and schedule delays can be minimized.

[0008] According to the present invention, the possibility of misjudgment that may occur in a single information-based system can be reduced by integrating visual data, location information, voice data, and biosignals to make a judgment. By comprehensively analyzing multiple data sources, dangerous situations can be recognized more precisely, and the generation of unnecessary warnings can be suppressed.

[0009] According to the present invention, accidents can be prevented in advance by automatically stopping the process or restricting access when an error or dangerous situation is detected. This has great technical significance in that it implements an active safety management system that controls actual equipment operation, going beyond a simple warning level.

[0010] According to the present invention, the presence of an emergency situation can be quantitatively determined by comprehensively analyzing acoustic characteristics such as fundamental frequency, speech rate, amplitude, and silence ratio, as well as the text content of the voice. Accordingly, emotional escalation or urgent situations can be precisely recognized, and the reliability of the response to actual dangerous situations is improved.

[0011] According to the present invention, the site management system can be unified by integrating quality control and safety management functions. Accordingly, this provides the effects of reducing the burden on management personnel, shortening response times to hazardous situations, and improving overall site operational efficiency. Furthermore, through the accumulation of digital data, it can be utilized in the long term to establish policies for improving construction quality and preventing accidents.

[0012] The technical problems of the present invention are not limited to those mentioned above, and other unmentioned technical problems will be clearly understood by those skilled in the art from the description below. means of solving the problem

[0013] An intelligent process management method based on real-time construction quality monitoring and design consistency analysis, performed by an electronic device including a processor according to an embodiment of the present invention, may include the following operations: the processor asynchronously collecting visual data acquired from a wearable camera worn by a user in a target site, GPS-based location data corresponding to the time of acquisition of the visual data, and voice data of the user's site inspection, and integrating them into multimodal construction data; the processor superimposing the visual data included in the multimodal construction data with design drawing data previously registered in real time corresponding to the target site to calculate a numerical error for a set major construction item; the processor, when a target construction item among the major construction items is identified in which the numerical error exceeds a threshold error, sending an error notification message to a user terminal previously registered in correspondence to the target site; and the processor setting the process for the target site to a suspended state until feedback data regarding the target construction item is received from the user terminal, and sending a warning notification message to the user terminal whenever additional data is acquired from the wearable camera.

[0014] According to one embodiment, the intelligent process management method may include: an operation in which, when the target construction item is identified, the processor determines whether the user's current location is within a precision control zone containing the target construction item based on the location data; an operation in which, when the user is confirmed to be within the precision control zone, the processor receives the user's biosignal through a wearable device registered in response to the user and determines whether a tension score calculated based on the biosignal exceeds a set tension threshold; an operation in which, when the tension score exceeds the tension threshold, the processor transmits a notification inducing departure from the precision control zone to the wearable device and transmits a location sharing request to the wearable device at a predetermined period; and an operation in which, in response to the processor identifying that the user has departed from the precision control zone based on the real-time location received from the wearable device at the predetermined period, the processor switches an automatic closing safety gate placed at the boundary of the precision control zone to a locked state.

[0015] According to one embodiment, the intelligent process management method may include the operation of comparing subsequent voice data with reference voice data set corresponding to the user terminal when the processor switches the automatic closing safety gate to a locked state and subsequent voice data is received through a smart voice recognition device installed on the automatic closing safety gate, and the operation of switching the automatic closing safety gate to an open state for repairing the target construction item when the processor confirms that, as a result of the comparison, the speaker of the subsequent voice data is the same as the manager corresponding to the user terminal and that the subsequent voice data contains a modification approval keyword set therein.

[0016] According to one embodiment, the operation of switching an automatic closing safety gate placed at the boundary of the precision control zone to a locked state may include: the operation of the processor converting the field inspection voice data into text data and checking the number of inclusions of words matching a set risk keyword dictionary in the text data; the operation of the processor calculating a voice urgency index based on acoustic characteristics extracted through the field inspection voice data; and the operation of the processor switching the automatic closing safety gate to a locked state only when the number of inclusions of words matching a set risk keyword dictionary in the text data exceeds a threshold number and the voice urgency index exceeds a threshold urgency index.

[0017] According to one embodiment, the operation of calculating the voice emergency index further includes the operation of the processor extracting acoustic characteristics including a fundamental frequency value, a speech rate per unit time, the mean squared amplitude of the voice signal, and a silence ratio from the field inspection voice data, and the operation of the processor calculating the voice emergency index based on the acoustic characteristics, and the operation of switching the automatic closing safety gate to a locked state may further include the operation of outputting a red light based on a flashing cycle of 2 seconds or less through a smart lighting device installed at the target site when the voice emergency index exceeds a risk index greater than the threshold emergency index.

[0018] The above intelligent process management method is,

[0019] It further includes the operation of calculating the above voice urgency index through [Mathematical Formula 1] below, and

[0020] [Mathematical Formula 1]

[0021]

[0022] I_urgency is the voice urgency index, △F_0 is the amount of increase in the fundamental frequency, △V_cap is the limited amount of increase in the speech rate per unit time, A_rms is the real-time amplitude average value of the field inspection voice data, A_base is the reference amplitude value set corresponding to the user, w_snr is the signal-to-noise ratio weight according to the noise environment of the target site, R_silence is the ratio of the silent intervals in the field inspection voice data, a, b, k, c, and d are the set weighting coefficients, and p is the set correction constant.

[0023] The above intelligent process management method is,

[0024] The above processor calculates the user's walking balance index through an accelerometer and a gyroscope included in the wearable device; and

[0025] The processor includes an operation to control the protective airbag of the smart safety vest worn by the user to switch to a pre-inflation state when the user's location on the design drawing data is identified as a set high-altitude work zone and the walking balance index is less than a preset instability threshold.

[0026] The above intelligent process management method is,

[0027] The above processor performs the operation of analyzing the visual data to recognize the identification number of the power tool held by the user; and

[0028] The above processor includes an operation to physically restrict the operation of the said tool by cutting off power to the smart power plug connected to the said tool if, based on the above identification number, the said power tool is identified as a power tool not permitted in the current process stage confirmed through the above design drawing data.

[0029] The above intelligent process management method is,

[0030] The above processor performs the operation of collecting the concentration of volatile organic compounds (VOCs) from an electronic nose sensor deployed at the target site; and

[0031] The above processor includes an operation of outputting an arrow-shaped light signal in the direction of the shortest evacuation path set through the smart lighting device when the concentration of the volatile organic compound reaches a dangerous level and the negative urgency index exceeds the critical urgency index.

[0032] The above intelligent process management method is,

[0033] The above processor performs the operation of collecting brainwave signals of the user from an EEG (electroencephalogram) sensor embedded in a smart helmet worn by the user and calculating a cognitive focus score; and

[0034] The above processor includes the operation of concentrating a High-CRI spotlight placed adjacent to the wearable camera on the target construction item and recollecting the visual data in order to forcibly improve the user's visual perception when the cognitive concentration is below a preset confidence threshold at the time when the numerical error is calculated. Effects of the invention

[0035] The effects of the intelligent process management method according to the embodiments of the present invention are described as follows.

[0036] According to the present invention, errors in major construction items can be calculated immediately by comparing visual data acquired at the site with design drawings in real time. Accordingly, construction errors can be detected early to prevent them from spreading to subsequent processes, and rework costs and schedule delays can be minimized.

[0037] According to the present invention, the possibility of misjudgment that may occur in a single information-based system can be reduced by integrating visual data, location information, voice data, and biosignals to make a judgment. By comprehensively analyzing multiple data sources, dangerous situations can be recognized more precisely, and the generation of unnecessary warnings can be suppressed.

[0038] According to the present invention, accidents can be prevented in advance by automatically stopping the process or restricting access when an error or dangerous situation is detected. This has great technical significance in that it implements an active safety management system that controls actual equipment operation, going beyond a simple warning level.

[0039] According to the present invention, the presence of an emergency situation can be quantitatively determined by comprehensively analyzing acoustic characteristics such as fundamental frequency, speech rate, amplitude, and silence ratio, as well as the text content of the voice. Accordingly, emotional escalation or urgent situations can be precisely recognized, and the reliability of the response to actual dangerous situations is improved.

[0040] According to the present invention, the site management system can be unified by integrating quality control and safety management functions. Accordingly, this provides the effects of reducing the burden on management personnel, shortening response times to hazardous situations, and improving overall site operational efficiency. Furthermore, through the accumulation of digital data, it can be utilized in the long term to establish policies for improving construction quality and preventing accidents.

[0041] In addition, various effects that can be identified directly or indirectly through this document may be provided. Brief explanation of the drawing

[0042] FIG. 1 is a block diagram showing the components of an electronic device according to one embodiment of the present invention. FIG. 2 is a block diagram showing the components of an intelligent process management system according to one embodiment of the present invention. FIG. 3 is a flowchart of the operation of an intelligent process management method according to one embodiment of the present invention. FIG. 4 is a flowchart of the operation of an intelligent process management method according to one embodiment of the present invention. In relation to the description of the drawings, the same or similar reference numerals may be used for identical or similar components. Specific details for implementing the invention

[0043] Hereinafter, some embodiments of the present invention will be described in detail with reference to exemplary drawings. It should be noted that in assigning reference numerals to the components of each drawing, the same components are given the same reference numeral whenever possible, even if they are shown in different drawings. Furthermore, in describing the embodiments of the present invention, if it is determined that a detailed description of related known components or functions would hinder understanding of the embodiments of the present invention, such detailed description is omitted.

[0044] In describing the components of the embodiments of the present invention, terms such as first, second, A, B, (a), (b), etc., may be used. These terms are intended merely to distinguish the components from other components, and the essence, order, or sequence of the components is not limited by the terms. Furthermore, unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art to which the present invention pertains. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an ideal or overly formal sense unless explicitly defined in this application.

[0045] Hereinafter, embodiments of the present invention will be described in detail with reference to FIGS. 1 to 4.

[0047] FIG. 1 is a block diagram showing the components of an electronic device according to one embodiment of the present invention.

[0048] According to one embodiment, the electronic device (100) may include a memory (110), a processor (120), a communication interface (130), and / or a display device (140). The configuration of the electronic device (100) shown in FIG. 1 is exemplary and the embodiments of the present invention are not limited thereto. For example, the electronic device (100) may further include components not shown in FIG. 1 (e.g., a web crawler, a user interface, an input device, a notification unit, a sensor unit, or at least one of any combination thereof).

[0049] According to one embodiment, the memory (110) may store instructions or data. For example, the memory (110) may store one or more instructions that cause the electronic device (100) to perform various operations when executed by the processor (120).

[0050] For example, the memory (110) may be implemented as a single chipset with the processor (120). The processor (120) may include at least one of a communication processor or a modem.

[0051] For example, the memory (110) can store various information related to the electronic device (100). For example, the memory (110) can store information regarding the operation history of the processor (120). For example, the memory (110) can store input data acquired by the electronic device (100), output data output by the electronic device (100), data acquired from an external server, a wearable device, a plurality of cameras and / or user terminals, etc.

[0052] For example, the memory (110) may include multiple storage devices of different types. For example, the memory (110) may include volatile and / or non-volatile storage media. For example, the memory (110) may include at least one of RAM (random-access memory), ROM (read only memory), eMMC (Embedded Multi-Media Card), or any combination thereof.

[0053] The steps of the method or algorithm described in connection with the embodiments disclosed in this specification may be directly implemented in hardware, software modules, or a combination of both, executed by the processor (120). The software modules may reside in a storage medium (i.e., memory (110)) such as RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, a hard disk, a removable disk, or a CD-ROM.

[0054] For example, the memory (110) is coupled to a processor (120), and the processor (120) can read information from a storage medium and write information to a storage medium. Alternatively, the memory (110) may be integrated with the processor (120). The memory (110) and the processor (120) may reside within an application-specific integrated circuit (ASIC). The ASIC may reside within a user terminal. Alternatively, the memory (110) and the processor (120) may reside as separate components within the user terminal.

[0055] According to one embodiment, the processor (120) may be operatively connected to the memory (110), the communication interface (130), and / or the display device (140). For example, the processor (120) may control the operation of the memory (110), the communication interface (130), and / or the display device (140).

[0056] According to one embodiment, the communication interface (130) may support the establishment of a direct (e.g., wired) communication channel or a wireless communication channel between an electronic device (100) and an external device (e.g., voice acquisition device (212) of FIG. 2, automatic closing safety gate (214), smart lighting device (216), wearable camera (222), wearable device (224), user terminal (250), etc.) and the performance of communication through the established communication channel. The communication interface (130) may include one or more communication processors that operate independently of the processor (120) (e.g., application processor) and support direct (e.g., wired) communication or wireless communication. According to one embodiment, the communication interface (130) may include a wireless communication module (e.g., cellular communication module, short-range wireless communication module, or GNSS (global navigation satellite system) communication module) or a wired communication module (e.g., LAN (local area network) communication module, or power line communication module). The corresponding communication module among these communication modules can communicate with an external electronic device through a first network (e.g., a short-range communication network such as Bluetooth, WiFi (wireless fidelity) direct, or IrDA (infrared data association)) or a second network (e.g., a legacy cellular network, a 5G network, a next-generation communication network, the Internet, or a long-range communication network such as a computer network (e.g., LAN or WAN). These various types of communication modules may be integrated into a single component (e.g., a single chip) or implemented as multiple separate components (e.g., multiple chips). The wireless communication module can identify or authenticate the electronic device (100) within a communication network, such as the first network or the second network, using subscriber information (e.g., International Mobile Subscriber Identifier (IMSI)) stored in a subscriber identification module.

[0057] According to one embodiment, the display device (140) may include at least one output device that provides various information and a user interface to the user.

[0058] For example, the display device (140) may include a display device, an audio output device, a virtual reality output device, etc.

[0059] For example, the display device (140) can provide the administrator with various types of user interfaces described in the present disclosure visually and / or audibly.

[0060] The components of the electronic device (100) illustrated in FIG. 1 are exemplary, and the embodiments of the present disclosure are not limited thereto.

[0061] At least some of the embodiments of the present disclosure may be implemented as artificial intelligence (AI) through a processor (120) and memory (110) of an electronic device (100). The processor (120) may be composed of one or more processors, and the one or more processors may be general-purpose processors such as a CPU, AP, DSP (digital signal processor), etc., graphics-dedicated processors such as a GPU, VPU (vision processing unit), or artificial intelligence-dedicated processors such as an NPU. The one or more processors may be controlled to process input data according to predefined operation rules or artificial intelligence models stored in memory (110). Alternatively, if the one or more processors are artificial intelligence-dedicated processors, the artificial intelligence-dedicated processors may be designed with a hardware structure specialized for processing a specific artificial intelligence model.

[0062] The predefined operation rules or artificial intelligence models are characterized by being created through learning. Here, being created through learning means that a predefined operation rules or artificial intelligence models configured to perform a desired characteristic (or purpose) are created by a basic artificial intelligence model being trained using a number of learning data by a learning algorithm. Such learning may be performed within the electronic device (100) itself where the artificial intelligence according to the present disclosure is performed, or it may be performed through 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 the examples described above.

[0063] An artificial intelligence model may be composed of multiple neural network layers. Each of the multiple neural network layers has multiple weight values ​​and can perform neural network operations through operations between the results of previous layers and the multiple weights. The multiple weights possessed by the multiple neural network layers can be optimized based on the learning results of the artificial intelligence model. For example, the multiple weights can be updated so that the loss value or cost value obtained from the artificial intelligence model during the learning process is reduced or minimized. Artificial neural networks may include, but are not limited to, deep neural networks (DNN), convolutional neural networks (CNN), recurrent neural networks (RNN), restricted Boltzmann machines (RBM), deep belief networks (DBN), bidirectional recurrent deep neural networks (BRDNN), or deep Q-networks.

[0064] According to one embodiment, the electronic device (100) may utilize an artificial intelligence-based image analysis model to compare visual data obtained from a wearable camera with pre-registered design drawing data.

[0065] Specifically, visual data can be input into a feature extraction model including a convolutional neural network (CNN) to extract boundary lines, corners, surface shapes, and reference coordinate information of a structure. Meanwhile, design drawing data can be converted into 3D model data and aligned into the same coordinate system.

[0066] The electronic device (100) may apply an attention-based deep learning module to mutually align the feature vector of visual data with the model features of the design drawing. Accordingly, numerical errors including positional deviation, angle error, and area difference for major construction items can be calculated. Additionally, the numerical errors can be input into a pre-trained error judgment model to automatically determine whether they are within the normal range.

[0067] According to one embodiment, the electronic device (100) may apply a time series learning model to calculate a tension score based on biosignals collected from a user's wearable device.

[0068] Specifically, biosignals may include heart rate, heart rate variability, skin conductivity, and acceleration data, and these data may be input into a Long Short-Term Memory (LSTM) model or a Recurrent Neural Network (RNN).

[0069] The artificial intelligence model outputs the user's tension state in the form of a probability value based on pre-learned parameters, and the probability value can be defined as a tension score in the range of 0 to 1.

[0070] The electronic device (100) can generate a notification to induce departure from the precision control zone by determining that there is a possibility of the user's judgment ability deteriorating when the tension score exceeds a set tension threshold.

[0071] According to one embodiment, the electronic device (100) can apply a text analysis model and an acoustic analysis model in parallel to calculate a voice urgency index from field inspection voice data.

[0072] First, the electronic device (100) can convert voice data into text data through a voice recognition model and quantify the contextual risk by applying a natural language processing-based risk analysis model.

[0073] At the same time, the electronic device (100) can extract a fundamental frequency (e.g., F0), a speech rate per unit time, mean squared amplitude, and a silent interval ratio from voice data, and calculate an emergency state probability through an acoustic emotion classification model using these as input values. The electronic device (100) can determine a dangerous situation only when the voice emergency index exceeds a critical emergency index.

[0074] According to one embodiment, the electronic device (100) may apply a multimodal fusion model to determine the construction error value, tension score, and voice urgency index.

[0075] A multimodal fusion model can calculate a comprehensive risk index by converting each input value into a vector form and inputting it into a fully connected network or a transformer-based fusion layer.

[0076] Specifically, the comprehensive risk index can be designed so that process shutdown or safety gate locking operations are performed only when multiple judgment criteria are simultaneously satisfied. Accordingly, the occurrence of false alarms caused by a single sensor malfunction can be suppressed.

[0077] According to one embodiment, the electronic device (100) may apply a reinforcement learning-based policy model to determine whether to stop the process and whether to lock the automatic closed safety gate.

[0078] For example, the policy model can be trained to receive construction error values, tension scores, voice urgency indices, and user location information as state inputs, and to select one of the control actions, such as sending a warning, stopping the process, locking the gate, or flashing lights.

[0079] For example, a policy model can be trained to provide rewards for successful accident prevention and impose penalties for unnecessary alarms. Accordingly, it can automatically derive optimal safety control strategies that adapt to site characteristics.

[0081] FIG. 2 is a block diagram showing the components of an intelligent process management system according to one embodiment of the present invention.

[0082] According to one embodiment, the intelligent process management system may include an electronic device (100), a voice acquisition device (212), an automatic closing safety gate (214), a smart lighting device (216), a wearable camera (222), a wearable device (224), and a user terminal (250).

[0083] According to one embodiment, the electronic device (100) is a core control device of the intelligent process management system of the present invention and may be configured to integrate and analyze a plurality of heterogeneous data collected at a target site and generate a control signal based thereon.

[0084] The electronic device (100) may include at least one processor, memory, communication module, and data bus, and may be implemented in the form of an industrial computing device, an edge server, or a cloud-based server. In particular, it may be implemented in a structure including a GPU, an NPU, or an AI accelerator for high-computation processing such as real-time video analysis and voice processing.

[0085] The memory may store design drawing data, a BIM model, a risk keyword dictionary, a threshold error threshold, a tension threshold, a voice urgency judgment threshold, and learned artificial intelligence model parameters. The electronic device (100) performs construction error calculation, risk judgment, and control action determination by referring to the stored data. At this time, each judgment logic may be configured to be software modularized so that it can be updated independently.

[0086] The electronic device (100) may include a multimodal data synchronization module. The synchronization module aligns time data, location data, and voice data based on time stamps to enable analysis based on the same point in time. In particular, a GPS error correction algorithm or an indoor location correction algorithm may be included to improve location accuracy.

[0087] The electronic device (100) transmits control signals to an automatic closing safety gate (214), a smart lighting device (216), and a user terminal (250) based on the risk assessment result. The control signals can be transmitted in an encrypted form, and security can be ensured through an authentication protocol. This structure has the effect of preventing malfunctions and external intrusions in an industrial environment.

[0088] The electronic device (100) can store risk judgment history and process data logs in a database and perform statistical analysis or AI retraining based on them. Accordingly, an intelligent management system that adapts to site characteristics can be established.

[0089] According to one embodiment, the voice acquisition device (212) is a device for collecting voice data generated during a field inspection process and can be implemented as a single microphone or a plurality of microphone array structures. When using multiple microphones, beamforming technology can be applied to selectively amplify voice generated from a specific direction.

[0090] The voice acquisition device (212) may include an active noise cancellation function in consideration of the noise environment of the construction site. In addition, it may include a wind noise removal filter, a mechanical noise filtering algorithm, and a frequency band limiting filter to improve the quality of the voice signal.

[0091] The voice acquisition device (212) may include an Analog-to-Digital Converter (ADC) for digitizing voice signals, and the sampling frequency and bit resolution may be set considering the accuracy of the urgency analysis. For example, sampling of 16 kHz or higher may be applied.

[0092] The voice acquisition device (212) may include a real-time voice feature extraction module. This module can calculate the fundamental frequency (F0), energy level, spectrum center, speech rate, and silent interval ratio. These features can be used to calculate the voice urgency index.

[0093] The voice acquisition device (212) may be integrated into an automatic closed safety gate (214) or directly connected to an electronic device (100). Additionally, it may be configured to transmit voice data remotely via wireless communication.

[0094] According to one embodiment, an automatic closing safety gate (214) is a safety blocking device installed at the physical boundary of a precision control zone and is configured to automatically perform a locking or opening operation based on a risk judgment result. The automatic closing safety gate (214) may include an electric drive motor, a control module, a locking actuator, and a status detection sensor.

[0095] The control module of the automatic closing safety gate (214) can control the drive motor in response to a control signal received from the electronic device (100). Here, the "control signal" can be set to be activated only when multiple danger conditions are met, and can be configured so as not to switch to a locked state upon the fulfillment of only a single condition.

[0096] The automatic closing safety gate (214) may include an access detection sensor to detect whether a user is approaching, and the access history may be stored as a data log. Additionally, it may include a physical manual release device in preparation for emergency situations.

[0097] The automatic closing safety gate (214) may include a smart voice recognition device and performs a procedure for verifying administrator voice authentication and modification approval keywords. Voice authentication can be performed by comparing feature vectors with reference voice data. Through such a structure, the automatic closing safety gate (214) can function as an intelligent risk response device rather than a simple access control device.

[0098] According to one embodiment, a smart lighting device (216) is a device that provides a visual warning when a dangerous situation occurs, and includes an LED-based lighting module and a control circuit. The lighting device can control color, brightness, and flashing cycle.

[0099] The electronic device (100) can transmit a control signal to the smart lighting device (216) according to the risk index, and in a high-risk state, a red light with a flashing cycle of 2 seconds or less can be output. In a medium-risk state, a yellow flashing light can be implemented, and in a low-risk state, a blue light can be illuminated, and so on, as a step-by-step warning system.

[0100] The smart lighting device (216) may include an ambient light sensor and an automatic brightness adjustment function according to day and night. This allows for minimizing energy consumption while maintaining warning visibility.

[0101] The smart lighting device (216) can be used as field safety analysis data by recording the number of times danger occurs and the operating time. In this way, the smart lighting device (216) can function as an information transmission means that intuitively conveys the risk level, going beyond a simple warning means.

[0102] According to one embodiment, the wearable camera (222) is implemented in a form that can be attached to a worker's helmet, vest, or body and can transmit real-time video data to an electronic device (100). The wearable camera (222) may include a wide-angle lens, an autofocus function, and an image stabilization function.

[0103] The wearable camera (222) can acquire three-dimensional distance information by including a depth sensor or a stereo camera structure. This can improve the accuracy of coordinate alignment with the design drawing.

[0104] The wearable camera (222) can generate GPS location information or indoor location tracking information at the time of shooting and link it with video data. This location information contributes to providing accurate reference coordinates when analyzing construction errors.

[0105] The wearable camera (222) assigns a time stamp on a frame-by-frame basis, and the data can be compressed and encrypted before transmission. Additionally, it may include a function for local storage and retransmission in case of network instability. Accordingly, the wearable camera (222) functions not merely as a simple shooting device, but as a key data source for design consistency analysis.

[0106] According to one embodiment, the wearable device (224) is a device for collecting biosignals and location information of a worker and can be implemented as a wristband, a smart helmet, or a clothing-attached device.

[0107] The wearable device (224) may include a heart rate sensor, an accelerometer, a gyroscope, and a skin conductivity sensor, and the measured data may be periodically transmitted to the electronic device (100).

[0108] The wearable device (224) can perform GPS or UWB-based indoor location measurement, including a location tracking function. This allows determining whether the location is within a precision control zone.

[0109] The wearable device (224) can intuitively convey a notification of disengagement by including a vibration motor, a speaker, or a display. The wearable device (224) can operate stably by including a battery status detection function and a data encryption communication function.

[0110] According to one embodiment, the user terminal (250) may be implemented as a portable terminal used by an administrator or supervisor and may include a smartphone, tablet, or industrial portable terminal.

[0111] The user terminal (250) receives error notification messages and danger warning messages from the electronic device (100) and visually displays them on the screen. In addition, it can input and transmit feedback data regarding the target construction item.

[0112] The user terminal (250) can store standard voice data for administrator authentication and can be used in the authentication procedure with the automatic closed safety gate (214). In addition, the user terminal (250) can display process status, risk index trends, biosignal statistics, and past log data in the form of a graph.

[0113] That is, the user terminal (250) functions not as a simple notification receiving device, but as an interface device that supports on-site decision-making.

[0115] FIG. 3 is a flowchart of the operation of an intelligent process management method according to one embodiment of the present invention.

[0116] According to one embodiment, the components of an intelligent process management system may perform the operations disclosed in FIG. 3. For example, among the components included in the intelligent process management system, at least some of the components included in the electronic device (100) (e.g., memory (110), processor (120), communication interface (130), and display device (140) of FIG. 1) may be configured to perform the operations of FIG. 3.

[0117] In the following embodiments, the operations S310 to S340 may be performed sequentially, but are not necessarily performed sequentially. For example, the order of each operation may be changed, and at least two operations may be performed in parallel. Additionally, content corresponding to or overlapping with the above description in relation to FIG. 3 may be briefly explained or omitted.

[0118] According to one embodiment, the processor (120) can asynchronously collect visual data acquired from a wearable camera worn by a user in the target site, GPS-based location data corresponding to the time of acquisition of the visual data, and voice data of the user's site inspection, and integrate them into multimodal construction data (S310).

[0119] The electronic device (100) can collect visual data obtained from a wearable camera (222) worn by a user in the target site, GPS-based location data corresponding to the time of acquisition of the visual data, and voice data of the user's site inspection as asynchronous streams having different cycles and delays.

[0120] For example, the wearable camera (222) can generate video frames of 30 frames or 15 frames per second, location data can be updated at intervals of 1 to 5 seconds, and voice data can be collected on an event basis only when the user speaks during inspection.

[0121] Because of this asynchronous nature, each data may not match exactly at the same time, so the electronic device (100) may be configured to assign a timestamp to each data and form a correspondence within a time alignment window (e.g., ±0.5 seconds to ±2 seconds).

[0122] At this time, the time stamp can be generated based on the internal clock of the wearable camera (222) or wearable device (224) and the user terminal (250), and the error can be corrected through standard time synchronization (e.g., NTP-based synchronization or field gateway synchronization).

[0123] The electronic device (100) can generate a record structure of “time event unit” for multimodal integration.

[0124] For example, a specific frame (or video), the location coordinates closest to the time of capturing that frame (e.g., latitude / longitude or field coordinate system transformation values), and the audio segments included in that time interval can be grouped together and defined as “multimodal construction data.”

[0125] At this time, the voice data may be stored as raw voice, but for the possibility of subsequent analysis, the electronic device (100) may separate voice segments (VAD: Voice Activity Detection) and store metadata such as the start / end time, average volume, and major frequency band for each voice segment together.

[0126] In this case, regarding location data, in preparation for cases where the GPS signal quality (e.g., HDOP, number of received satellites, etc.) is low, the electronic device (100) may be configured to calculate a filtering (e.g., Kalman filter) or correction value using the speed of movement from the previous location, the amount of movement based on an inertial sensor (e.g., amount of movement data obtained through the accelerometer / gyroscope sensor of the wearable device (224)), etc.

[0127] From the perspective of a field scenario, it is assumed that a user moves while inspecting a rebar section, films the section with a wearable camera (222), and simultaneously utters verbal inspection comments such as “the spacing between rebars appears narrower than designed.” At this time, the wearable camera (222) continuously generates a video stream, GPS-based location data tracks the movement path, and a microphone included in the voice acquisition device (212) or the wearable device (224) records the voice segments as events. The electronic device (100) combines these three data into a single “inspection session,” divides a specific time interval (e.g., 10:15:32–10:15:40) into a single analysis unit, and then creates a single integrated record containing the representative frame (e.g., keyframe) or set of frames of the segment, the nearest location coordinates, and the voice segments of the segment. This integrated record can be used as input for design drawing overlay and error calculation in subsequent steps.

[0128] The electronic device (100) may include buffering and retransmission strategies in consideration of communication instability situations. For example, if the wearable camera (222) enters a network dead zone, the video may be stored locally and transmitted upon connection restoration, and the electronic device (100) may reorder data with reversed arrival order based on time stamps. Additionally, for personal information and field security, visual data and voice data are encrypted during transmission, and the electronic device (100) may apply an authentication procedure (e.g., token-based authentication, terminal registration-based authentication, etc.) so that only user terminals (250) with access rights can access the relevant data. Such a configuration provides a technical foundation that goes beyond simple “collection” and allows asynchronous multimodal data to be reconstructed into consistent analysis inputs even in actual field environments.

[0129] According to one embodiment, the processor (120) can calculate a numerical error for a set major construction item by overlaying the visual data included in the multimodal construction data and the design drawing data that is pre-registered in real time in correspondence with the target site (S320).

[0130] The electronic device (100) can overlay pre-registered design drawing data (e.g., BIM 3D model, 2D CAD drawing, or reference coordinate data converted from a drawing) in real time corresponding to the target site using visual data among the integrated multimodal construction data.

[0131] The purpose of "overlay" here is to visually align and display design baselines, reference points, design dimensions, or tolerance ranges on field footage, or to calculate numerical errors by performing quantitative comparisons in the aligned state.

[0132] The electronic device (100) can first align (or register) the camera coordinate system (e.g., the shooting coordinate system of the wearable camera (222)) and the design coordinate system for superposition, and to do this, it can perform coordinate transformation by recognizing at least one of the field reference points (e.g., reference marker, structure corner, column centerline, QR / AR marker, or specific reference dimension on the drawing).

[0133] For example, the electronic device (100) can detect corners, reference lines, bolt locations, and embedded locations of a structure in an image frame (e.g., edge / corner-based features) and perform corresponding matching with corresponding elements of a design drawing (e.g., reference line coordinates, bolt hole locations in the design, etc.). After that, the electronic device (100) can update transformation parameters (e.g., rotation / translation / scale) for each frame by taking into account changes in the camera's viewpoint (e.g., situations where a worker turns their head or moves). In particular, when the construction item is a three-dimensional element (e.g., column verticality, wall plan, etc.), the wearable camera (222) can calculate a three-dimensional point cloud in an embodiment that includes a depth sensor or a stereo camera, and calculate an error including depth direction error by aligning it with the three-dimensional shape of the design model. Even in an embodiment without a depth sensor, it can be configured to estimate a relative three-dimensional structure using parallax of multiple frames or IMU-based estimation.

[0134] Numerical error can be calculated for each “defined major construction item.” Major construction items can be defined, for example, as (i) rebar spacing, (ii) distance between anchor bolts, (iii) column centerline position, (iv) wall thickness, (v) diameter and position of buried pipes, (vi) pouring level (height), and (vii) dimensions of openings (e.g., windows, doors), and the comparison method may vary for each item. For example, rebar spacing can be calculated by measuring the pixel spacing between rebar line segments in the image and converting it into actual units (e.g., mm) using camera correction parameters and distance estimates, while anchor bolt position can be defined as an error value by calculating the Euclidean distance (e.g., planar distance or 3D distance) between the detected bolt center coordinates and the design target coordinates. Additionally, angle-based items such as verticality / horizontality can be calculated as the angular difference between the actual vector and the design reference vector.

[0135] The electronic device (100) can adjust the analysis priority based on frame sampling (e.g., 1 to 5 frames per second) or event-based analysis (e.g., prioritizing analysis of frames around the time of user speech) rather than analyzing all frames with high precision in terms of real-time.

[0136] Additionally, “real-time overlay” may include immediately displaying the overlay results on a user terminal (250) or a field monitor, and the displayed overlay information may be provided in the form of a design baseline, an allowable tolerance range (e.g., ±5mm), a current measurement value, an error value (e.g., +8mm), etc. Through such an overlay display, the manager can intuitively determine whether there is a problem immediately at the field.

[0137] The critical error is set differently for each major construction item, and can be set such as 10 mm for rebar spacing, 5 mm for anchor bolt position, and 1 degree for verticality, or / or can be automatically set by referring to a tolerance table defined in the design drawing data. Additionally, the electronic device (100) can be configured to calculate an indicator of measurement reliability (e.g., image shaking, insufficient illumination, occurrence of occlusion, etc.) and to process it in a “re-shooting guide” or “measurement suspended” state if the reliability is low.

[0138] According to one embodiment, when a target construction item among the main construction items is identified in which the numerical error exceeds a threshold error, the processor (120) can send an error notification message to a user terminal that is pre-registered in correspondence with the target site (S330).

[0139] The electronic device (100) can compare the calculated numerical error with the threshold error for each major construction item, and if it exceeds the threshold error, identify the item as a “target construction item.”

[0140] Here, the identification of the target construction item can be structured to include, beyond a mere judgment that the “error is large,” (i) which process section, (ii) which location coordinates, (iii) which item on the drawing (e.g., drawing number / grid location / member ID), (iv) what type of error (e.g., distance / angle / area), and (v) what the current error value and allowable range are. To this end, the electronic device (100) can identify the target construction item by referring to a member identifier (e.g., BIM object ID) included in the design drawing data or a reference coordinate on the drawing (e.g., grid line, level, etc.).

[0141] For example, in a field scenario, when a user photographs a column location, if the video analysis result shows that the column centerline has shifted 12mm relative to the design centerline and the threshold error for that item is set to 5mm, the electronic device (100) can designate the column member as a target construction item. At this time, the electronic device (100) can compose a single notification package containing a keyframe image at the time the error occurred, a superimposed result (e.g., an image showing both the design line and the actual line), GPS coordinates or field coordinates of the location, user utterance (e.g., “The column seems slightly off-center”), and an error calculation log (measurement value / threshold / reliability).

[0142] Error notification messages can be transmitted to a pre-registered user terminal (250), and the transmission method can be implemented as a push notification, SMS, messenger integration, or notification within a dedicated app. The content of the notification message can be specifically configured for feasibility and field usability. For example, it may include (i) “name of the target construction item (part ID / process name)”, (ii) “type and value of error (e.g., position error +12mm)”, (iii) “allowable tolerance and threshold value (±5mm)”, (iv) “location of occurrence (e.g., site area / coordinates)”, (v) “reference data (e.g., keyframe / overlapping image link)”, (vi) “recommended action (e.g., re-measurement, suspension of construction, request for administrator verification)”, etc. Such configuration ensures that it provides actionable information rather than a simple warning.

[0143] The electronic device (100) can classify the importance of errors to set notification priorities. For example, it can be classified into “Caution / Warning / Urgent” grades based on the rate of exceedance relative to a threshold or its association with structural safety (e.g., whether it is a major structural member), and in the case of the Urgent grade, it can perform a notification requiring immediate verification (e.g., including a confirmation button) or a repeated notification. Conversely, if the measurement reliability is low, it can be separated into a “Re-measurement Required” notification to reduce unnecessary false alarms.

[0144] To ensure security and traceability, the electronic device (100) can generate a notification ID when transmitting a notification and store the reception acknowledgment, viewing time, and feedback input time on the user terminal (250) as logs. Additionally, since the notification message may include field data, the electronic device (100) can transmit only to registered terminals and prevent information leakage by applying transmission section encryption and terminal authentication. Such a notification design can satisfy the management and audit requirements required in actual industrial sites.

[0145] According to one embodiment, the processor (120) may set the process process for the target site to a suspended state until it receives feedback data regarding the target construction item from the user terminal, and may send a warning notification message to the user terminal whenever additional data is acquired from the wearable camera (S340).

[0146] The electronic device (100) can set the process process for the target site to a “suspended state” after the target construction item is identified and an error notification message is transmitted, until feedback data regarding the item is received from the user terminal (250).

[0147] Here, the "state of suspension of the process" is not necessarily limited to the meaning of forcing a physical suspension of construction, and according to one embodiment, it may be implemented in a manner such as (i) restricting the progress of work instructions by displaying the digital process status as "Hold," (ii) restricting access to specific areas, (iii) requiring additional verification procedures from on-site workers, or (iv) restricting operations within the scope necessary for safety by linking with equipment (e.g., pouring equipment, crane operations). Importantly, this can be understood as clearly managing the "progress restriction prior to approval / verification" state at the system level to prevent the problem from escalating as subsequent processes proceed while an error has been identified.

[0148] Feedback data is input from a user terminal (250) and can be selected and input in the form of, for example, (i) “Verification complete / no problem,” (ii) “Request for re-measurement,” (iii) “Instruction for modification work,” (iv) “Approval for design change required,” (v) “Exception allowed (e.g., tolerance reset).” Additionally, the feedback may include additional information such as text comments, attached photos, instructions to be conveyed to field workers, or the estimated time for completion of modifications.

[0149] The electronic device (100) may be configured to release the process state or perform additional verification procedures (request for re-shoot, additional measurement) depending on the type of feedback. For example, when “request for re-measurement” feedback is received, the electronic device (100) may generate a guide to the wearable camera (222) to induce re-shooting at a specific angle / distance, and release the interruption state only if the re-measurement result is within a threshold.

[0150] The electronic device (100) may send a warning notification message to the user terminal (250) whenever additional data is acquired from the wearable camera (222) during the interruption state. This is to ensure that the user is still aware of the danger / error state and to prevent the operation from proceeding without approval. For example, if the system is in a process interruption state but the user continues to receive shooting data in that area (i.e., there is a possibility that the operation is still in progress on-site), the electronic device (100) may send a warning notification such as “Waiting for approval - Operation Continuation Detected.” To prevent the warning notification from becoming excessive spam, it may be limited by (i) sending it only when a certain amount of additional data (e.g., 10 frames or a 5-second interval) has accumulated, or (ii) setting a minimum transmission interval (e.g., 30 seconds to 5 minutes).

[0151] Meanwhile, the process suspension state may be lifted only when various types of conditions are satisfied. For example, the suspension state may be stipulated to be lifted only when (i) "modification completion approval" is entered on the user terminal (or administrator terminal), (ii) it is confirmed that the error in re-captured data within a certain period is below a threshold, and (iii) reliability indicators are above a standard (e.g., low image shake index, sufficient illumination, no occlusion). Conversely, if the feedback indicates "design change approval required," the process status may be switched to "waiting for design change" and configured to require a separate approval process. Such state transitions align with actual process management systems rather than simple logic, thereby enhancing industrial effectiveness.

[0152] The electronic device (100) can log all events occurring during a process interruption state (e.g., error occurrence, notification transmission, warning transmission, user viewing, feedback input, re-measurement results). This log can be used as supporting evidence in subsequent disputes or quality audits and enhances the traceability of on-site safety management. Additionally, since the notifications / feedback / logs may contain personal information and on-site secrets, they can be operated including access control and data retention period policies.

[0153] Meanwhile, if the electronic device (100) determines that the numerical error for the target construction item exceeds a threshold error, the operation of setting the process process to a suspended state may include a process of generating a specific control signal beyond a simple status indication.

[0154] Specifically, the electronic device (100) can be linked with a field process management system to transmit a control command that changes the work step status value from “Active” to “Hold”. The status value is stored in a field work management server or a field gateway device, and can subsequently restrict the work instruction system from automatically proceeding to the next process step.

[0155] Additionally, in an embodiment linked with a specific facility, the electronic device (100) may be configured to transmit a control signal to a pouring equipment, a crane control system, a power tool power control device, etc., to temporarily stop automatic operation in the corresponding area.

[0156] In the process suspension state, the automatic closing safety gate (214) for the area containing the construction item may be set to a locked state or configured to continuously display a warning state through the smart lighting device (216) until administrator approval feedback is entered through the user terminal (250).

[0157] Additionally, the electronic device (100) may operate a notification ID-based verification protocol to determine whether feedback data has been received, and may be configured to send a re-notification or an escalation notification to a senior manager if feedback is not received within a set time (e.g., 5 minutes).

[0158] Unlike simple warning systems, this process interruption structure has technical significance in that it implements an active process management system that includes actual field equipment control and work flow control.

[0160] FIG. 4 is a flowchart of the operation of an intelligent process management method according to one embodiment of the present invention.

[0161] According to one embodiment, the components of an intelligent process management system can perform the operations disclosed in FIG. 4. For example, among the components included in the intelligent process management system, at least some of the components included in the electronic device (100) (e.g., memory (110), processor (120), communication interface (130), and display device (140) of FIG. 1) may be configured to perform the operations of FIG. 4.

[0162] In the following embodiments, the operations S410 to S440 may be performed sequentially, but are not necessarily performed sequentially. For example, the order of each operation may be changed, and at least two operations may be performed in parallel. Additionally, content corresponding to or overlapping with the above description in relation to FIG. 4 may be briefly explained or omitted.

[0163] According to one embodiment, when a target construction item is identified, the processor (120) can determine whether the user's current location is within a precision control zone containing the target construction item based on location data (S410).

[0164] After the target construction item is identified, the electronic device (100) can create or call a “precision control zone” containing the target construction item and use location data to check whether the user’s current location is within the precision control zone.

[0165] Here, a "precision control zone" is a spatially defined area surrounding a target construction item where quality errors may escalate or safety risks may increase. It can be defined as a planar polygon (e.g., polygon), a circle (e.g., radius-based), or a three-dimensional space (e.g., a volume including levels / floor heights). For example, a precision control zone can be set within a radius of 3m to 10m centered on reference coordinates mapped to the target construction item (e.g., member ID, grid coordinates, level information) on a BIM model or design drawing, or it can be set as an asymmetric polygonal area depending on process characteristics (e.g., concrete pouring, work at height, equipment entry routes). Additionally, precision control zones are managed by "Zone IDs" within the site, and attributes such as access permission conditions, emergency notification policies, and gate linkage status can be stored for each zone.

[0166] The user location can be obtained as GPS-based location data, and UWB, BLE beacon, Wi-Fi RTT, or inertial navigation (IMU)-based correction can be combined to account for indoor or GPS dead zones. The electronic device (100) calculates location reliability by referring to accuracy indicators included in the location data (e.g., HDOP, number of satellites, RSSI quality indicator, or UWB positioning error), and if the location reliability is low, it can perform exception processing such as “requesting location” or “conservative judgment (e.g., considered within zone).”

[0167] For example, in a situation where the GPS error is estimated to be ±8m and the zone boundary is within 5m, the electronic device (100) may not conclude that the user is outside the zone and may request additional positioning, or may make a determination after stabilizing using the median / weighted average of multiple location samples for a certain period of time (e.g., 5 seconds to 20 seconds). Additionally, if the location data is updated periodically (e.g., 1 second to 5 seconds), the electronic device (100) may calculate a movement vector for the most recent N location samples (e.g., 3 to 10) and apply filtering (e.g., Kalman filter or moving average) to remove abnormal spike data.

[0168] For example, in a field scenario, the electronic device (100) identifies “A zone column centerline position error 12mm” as a target construction item, and sets a radius of 6m and a corresponding floor level range centered on the design coordinates of the column as a precision control zone. If the user continues to perform inspections around the column, the location data is within the zone boundary, so it is determined to be “within the zone.” Conversely, if the user moves a certain distance away for safety, the electronic device (100) determines whether the real-time location has moved out of the zone boundary. This zone determination result can be used as a condition for a subsequent bio-signal-based tension determination, and prevents the unnecessary repetition of notifications inducing the user to leave the zone.

[0169] In terms of implementation method, the electronic device (100) may store parameters for defining a precision control zone. For example, it may include “radius r,” “polygon vertex coordinates,” “application level (or floor),” “zone validity time (e.g., 2 hours after error occurs),” “zone priority,” etc. Additionally, the user terminal (250) may provide a visualization of the zone (e.g., display on a map / site floor plan) along with a display of the current user’s location to support the manager in intuitively checking the situation inside and outside the zone. In this way, the determination of a precision control zone can be implemented not by simple location comparison, but by the execution of a spatial policy for linking the target construction item with location-based risk management.

[0170] According to one embodiment, when the processor (120) confirms that the user is within the precision control zone, it can receive the user's biosignal through a wearable device registered in response to the user and determine whether the tension score calculated based on the biosignal exceeds a set tension threshold (S420).

[0171] The electronic device (100) can receive biosignals from the wearable device (224) to calculate a tension score and determine whether the tension score exceeds a set tension threshold only when the user is identified as being within a precision control zone. Here, the wearable device (224) can be implemented as a wristband, a smart helmet, or a clothing-attached device and can measure biosignals including heart rate, heart rate variability (HRV), skin conductivity (GSR), skin temperature, respiration rate, acceleration / gyroscope-based activity level, etc.

[0172] The electronic device (100) can receive these biosignals at regular intervals (e.g., 1 second to 10 seconds) or on an event basis (e.g., immediately upon detection of an urgent change). Data transmission can be performed via BLE, Wi-Fi, cellular, or field gateway-based communication, and can prevent forgery and tampering through transmission encryption and device authentication.

[0173] The tension score may be calculated as a normalized indicator combining multiple signals rather than as a single value of the biosignal. For example, an electronic device (100) may store an individual baseline, which may be defined based on the average heart rate, HRV range, and average GSR value collected over a certain period (e.g., 1 to 7 days or 10 to 30 minutes for initial calibration) during the user's normal working state. Subsequently, the degree to which real-time measurements deviate from the baseline (e.g., Z-score or percentage change) may be calculated, and the tension score may be calculated by weighted combination thereof. For example, if an increase in heart rate (△HR), a decrease in HRV (△HRV), and an increase in GSR (△GSR) occur simultaneously, it may be determined that tension has increased; conversely, an increase in heart rate due to a simple increase in activity (e.g., climbing stairs) may be corrected using an acceleration / gyroscope-based activity level to reduce excessive false alarms.

[0174] As an example, assume a situation where a user moves quickly while urgently reconfirming around a structure where an error has been detected. In this case, the heart rate may rise, but this may be attributed to work activity levels and thus misidentified as a state of tension. Therefore, the electronic device (100) may be configured to distinguish between “physical activity” and “mental tension” by considering activity level indicators (e.g., steps per minute, acceleration RMS). For example, if activity levels are high while HRV decreases rapidly and GSR increases, it may be determined that the level of tension is high; conversely, if only activity levels are high and the changes in HRV / GSR are minimal, it may not be considered as an increase in tension.

[0175] The tension threshold can be set according to field policies or individual characteristics. For example, in an embodiment where the tension score is normalized to a range of 0 to 1, the threshold can be set to 0.7 or 0.8, or a rule-based threshold combination such as “HR increase of 20% or more compared to baseline + HRV decrease of 15% or more + GSR increase of 10% or more” can be used. The electronic device (100) can ensure auditability by storing the basis for setting the threshold (e.g., manager settings, safety regulations, learning based on past accident data, etc.). In this way, the tension determination can increase reliability in actual field environments by including individual baselines, activity level correction, and sensor quality verification, rather than a simple comparison of biological values.

[0176] According to one embodiment, the processor (120) can send a notification to the wearable device to induce departure from the precision control zone when the tension score exceeds the tension threshold, and send a location sharing request to the wearable device at predetermined intervals (S430).

[0177] The electronic device (100) can transmit a notification to the wearable device (224) to induce it to move away from the precision control zone when it is determined that the tension score exceeds the tension threshold, and can be configured to simultaneously transmit a location sharing request at a predetermined interval.

[0178] Here, the "exit induction alert" is intended to prevent users from remaining in error zones under high-risk and high-stress conditions, and to ensure safety and minimize judgment errors.

[0179] The notification method can be provided through a vibration motor, speaker, LED display, or simple text / icon display of the wearable device (224), and vibration or visual display can be provided in parallel considering the noise environment at the site.

[0180] For example, the exit induction notification may be provided in the form of a message such as “Caution: High-risk area, move to a safe distance” or an arrow / direction guide (e.g., map linkage of the user terminal (250)).

[0181] The electronic device (100) can not only send a single warning to induce departure, but also apply a step-by-step warning policy that takes into account the user's response. For example, the first warning can be provided with a weak vibration, and if no movement is detected within a certain period of time (e.g., 10 to 30 seconds), the vibration intensity can be increased or a voice guidance can be added to provide a step-by-step warning. Additionally, when the user checks the warning to induce departure (e.g., on a wearable device button or in the user terminal (250) app), the electronic device (100) stores the check event as a log so that a field manager can track whether the user is aware.

[0182] The action of sending location sharing requests at predetermined intervals can be understood as an active monitoring procedure to verify whether the user is actually moving and whether they have left the zone. The interval value can be set to 1 second, 2 seconds, 5 seconds, 10 seconds, etc., depending on the field policy, and shorter intervals can be applied as the urgency increases. For example, a variable interval policy may be applied where location is requested at intervals of 1 to 2 seconds in a high-risk state with a tension score of 0.9 or higher, and at intervals of 5 seconds in a warning state in the range of 0.7 to 0.9. Additionally, considering network load and battery consumption, an adaptive policy is also possible that increases the request interval when sufficient movement is detected (e.g., when the speed is greater than 0.5 m / s) and decreases the interval when movement is stagnant.

[0183] As an example, it can be assumed that the user is in a state where judgment is degraded due to increased tension around an error-prone part (i.e., when the tension score exceeds the tension threshold). The electronic device (100) sends an exit inducement notification to the wearable device (224) and requests location sharing at 2-second intervals. If the user actually moves more than 5m, the electronic device (100) can determine that the user is moving toward the zone boundary through the trend of location change and, if necessary, can provide follow-up guidance such as “move to a designated safe waiting point.” Conversely, if the user does not move or wanders inside the zone, additional warnings can be reinforced or an “exit failure” notification can be sent to the administrator terminal (250).

[0184] For example, location sharing requests and responses may include only simple GPS coordinates, but to enhance reliability, they may also include metadata such as location accuracy (e.g., margin of error), positioning method (e.g., GPS / UWB / BLE), and battery status. Additionally, for privacy protection, location data may be stored only for the necessary period (e.g., policy settings may be configured for 24 hours to 30 days), and may be configured so that only designated administrators with access rights can view it. In this way, inducing departure and periodic location sharing can be implemented as specific on-site response procedures that link user status (e.g., tension level) with spatial control (e.g., zone management).

[0185] According to one embodiment, the processor (120) can switch an automatic closing safety gate placed at the boundary of the precision control zone to a locked state in response to identifying that the user has left the precision control zone based on a real-time location received from the wearable device at a predetermined interval (S440).

[0186] The electronic device (100) can identify whether the user has left the precision control zone based on the real-time location received from the wearable device (224) at predetermined intervals, and if the departure is identified, it can switch the automatic closing safety gate (214) placed at the boundary of the precision control zone to a locked state.

[0187] Here, “deviation identification” may not simply mean a single location sample moving outside the boundary, but may be confirmed only when multiple conditions are satisfied, taking into account location error and momentary positioning fluctuations. For example, an deviation may be confirmed only when (i) M consecutive location samples (e.g., 2 to 5) are determined to be outside the zone, (ii) the location is outside a minimum distance (e.g., 1 m or more) from the zone boundary, and (iii) the positioning accuracy index is above a threshold. Additionally, considering situations where a user moves back and forth near the boundary line, a hysteresis interval may be included, and the determination thresholds for “zone exit” and “zone re-entry” may be set differently (e.g., +1 m for exit and -1 m for re-entry).

[0188] When the electronic device (100) confirms that the exit has been confirmed, it can transmit a lock control signal to the automatic closing safety gate (214). Lock control can be performed so that the control module of the automatic closing safety gate (214) drives an electric motor and a lock actuator to form a physical lock state, and whether the lock is completed can be confirmed through a state sensor (e.g., lock switch, door position sensor, etc.) included in the gate. If the lock completion signal is not received within a certain time (e.g., 1 second to 5 seconds) after the lock command, the electronic device (100) may retry or transmit a “gate lock failure” warning to the user terminal (250) to induce on-site action. Additionally, in preparation for a power outage or driving failure, it may include a variation in which the automatic closing safety gate (214) switches to an emergency mode (e.g., manual lock or mechanical lock).

[0189] As an example, it can be assumed that a user receives an exit inducement notification while in a state of heightened tension, moves to a safe waiting area, and exits the precision control area. The electronic device (100) can confirm through a continuous location sample that the user has moved outside the boundary and switch the gate (214) to a locked state to temporarily restrict access to the area. The purpose of the lock is to “prevent re-entry into the danger zone and control access so that no one can access it until repairs are made or administrator approval is obtained.” Thus, the gate lock can function as a means of controlling accessibility to the entire area according to process / safety management policies, rather than simply blocking specific individuals.

[0190] Additionally, the electronic device (100) can enhance visual / managerial response by transmitting a linkage signal to a user terminal (250) and / or a smart lighting device (216) simultaneously with the gate lock. For example, the user terminal (250) may display a “precision control zone lock complete - awaiting approval” status, and the lighting device (216) may be controlled to display a flashing warning at the zone boundary or a danger point. This linkage allows the worker to clearly recognize the current zone status and enables the manager to respond immediately.

[0191] Additionally or generally, the electronic device (100) may store trigger conditions for zone exit determination and gate locking in advance. For example, the electronic device (100) may store conditions such as “determined as exit if three consecutive location samples are outside the zone, the time interval between samples is 2 seconds, and the position accuracy estimation error is 2m or less.” In addition, it may include an administrator authentication-based unlocking procedure to prepare for situations where an administrator needs to allow access to the zone even after gate locking (e.g., repair work, inspection work, etc.).

[0193] According to one embodiment, an intelligent process management method may include, after switching an automatic closed safety gate to a locked state, an operation of comparing subsequent voice data with reference voice data set corresponding to a user terminal when subsequent voice data is received through a smart voice recognition device installed on the automatic closed safety gate, and an operation of switching the automatic closed safety gate to an open state for repairing a target construction item when it is confirmed that the speaker of the subsequent voice data is the same as the manager corresponding to the user terminal and that the subsequent voice data contains a modification approval keyword set in the subsequent voice data as a result of the comparison.

[0194] The electronic device (100) can compare subsequent voice data with reference voice data set in correspondence with the user terminal (250) when subsequent voice data is received through a smart voice recognition device (e.g., voice acquisition device (212) and / or voice recognition module within the gate) installed in the gate (214) after the automatic closing safety gate (214) has been switched to a locked state.

[0195] Here, “subsequent voice data” refers to voice uttered by a site manager or authorized person near the gate after the gate has been locked, and may include utterances related to approval for opening or modifying the gate.

[0196] The smart voice recognition device can collect voice around the gate by including a microphone array or a directional microphone, and can include beamforming and noise removal functions by taking into account field noise (e.g., equipment sounds, wind, surrounding conversation, etc.).

[0197] In addition, to clearly identify the start / end of subsequent voice data, speech activity detection (VAD) can be used to separate utterance segments, and the separated segments can be configured to be used as inputs for authentication and keyword determination.

[0198] Here, "reference voice data" may be stored in association with an administrator (or multiple administrators) registered in the user terminal (250), and depending on the embodiment, may be stored in the form of (i) voice samples previously recorded in the administrator terminal (e.g., 3 to 10 sentences), (ii) voice feature vectors (voiceprint embedding) extracted from the voice samples, or (iii) voice authentication model parameters of the administrator.

[0199] The electronic device (100) can extract a feature vector from the subsequent voice and calculate a similarity (e.g., cosine similarity or distance-based indicator) with the reference voice vector to perform a comparison between the subsequent voice data and the reference voice data. For example, a threshold can be set to determine that there is a “high probability of the same speaker” when the similarity is 0.75 or higher, and this threshold can be adjusted according to the on-site environment (e.g., noise level) and security level. In addition, to increase the reliability of the same speaker determination, a method of accumulating (or weighted averaging) the match in multiple segments (e.g., two utterances) rather than determining based on a single utterance segment may also be applied.

[0200] From the perspective of a field scenario, it is assumed that while the automatic closing safety gate (214) is locked due to an error or risk judgment, an administrator arrives in front of the automatic closing safety gate (214) and speaks, such as, “I am Administrator Hong Gil-dong. I approve the repair.” The smart voice recognition device collects the speech, and the electronic device (100) separates the speech segment to perform an administrator authentication procedure. At this time, the electronic device (100) can determine “who spoke” by comparing voice feature vectors that reflect the speaker’s unique characteristics (e.g., voiceprint characteristics, resonance patterns, etc.), rather than simply comparing the text conversion results of the voice. Additionally, to prevent attacks such as stolen voice playback (e.g., recording playback), the electronic device (100) may add “liveness” verification. For example, an electronic device (100) can make a playback attack difficult by guiding a random challenge phrase (e.g., “Please say the approval code 3-7-2”) through a user terminal (250) or a gate speaker and having an administrator immediately repeat it. Alternatively, a variation is possible in which the biological nature is determined using background noise patterns, temporal variation characteristics of the voice, changes in mouth-microphone distance, etc.

[0201] The electronic device (100) can handle exceptional situations that may occur during the comparison process. For example, if the voice recognition quality is low due to excessive noise or if the reliability of feature vector extraction is evaluated as low, it may output a “re-speech request” notice or require “admin terminal approval (e.g., two-factor authentication).” If the administrator does not have the reference voice data registered, the process may proceed through a temporary registration procedure (e.g., voice registration after two-factor authentication) on the user terminal (250).

[0202] The electronic device (100) can store logs such as voice authentication attempts, comparison scores, thresholds, success / failure results, number of retries, and utterance times. These logs can be used for post-audits and dispute response, and for privacy protection, it is also possible to store only feature vectors or hash values ​​without storing raw voice for the long term. This configuration supports the fact that “receiving subsequent voice data and comparing it with reference voice” can be performed reproducibly even in a field environment.

[0203] The electronic device (100) can switch the automatic closed safety gate (214) to an open state for repairing the target construction item if, as a result of comparing subsequent voice data, it is confirmed that the speaker is the same as the manager corresponding to the user terminal (250) and that the subsequent voice data contains a “modification approval keyword.” Here, the modification approval keyword may be defined as a word or phrase expressing the manager’s intention to explicitly approve repair work or re-entry, and may include, for example, “approval,” “open,” “repair in progress,” “modification permission,” “work resumption,” etc. The keyword is not limited to a single word but may be composed of a combination of multiple words (e.g., “repair approved”) or a specific approval code (e.g., “approval code A-102”), and may be stored in the memory (110) of the user terminal (250) or the electronic device (100) according to a site-specific policy.

[0204] Verification of modified approval keywords can be performed based on text data generated as a result of speech recognition (ASR) or keyword spotting. In an ASR-based embodiment, the subsequent speech is converted into text and then matched with an approval keyword dictionary, and if multiple keywords are included, the approval reliability can be evaluated highly. In a keyword spotting-based embodiment, even if the entire sentence is not accurately recognized, a specific approval phrase can be directly detected as an acoustic pattern, which can be advantageous in a noisy environment. To prevent malfunction, the electronic device (100) may apply multiple conditions to perform opening only when (i) the administrator identity judgment score is above a threshold, (ii) the approval keyword matching reliability is above a threshold, and (iii) the utterance length meets a minimum standard (e.g., 1 second or more) or a specified format (e.g., “I am administrator ○○ + approval phrase”).

[0205] In a field scenario, assume that after locking the automatic closing safety gate (214), the manager completes the field verification and speaks, “Manager Hong Gil-dong, repair approved. Open gate.” The electronic device (100) first verifies through speaker authentication whether the speaker is the same as the registered manager, and then verifies whether the text or keyword detection result of the subsequent voice contains approval keywords such as “repair approved” or “open.” If both conditions are met, the electronic device (100) transmits an opening control signal to the gate (214) and can control the drive motor of the automatic closing safety gate (214) to perform unlock and open operations. At this time, whether the automatic closing safety gate (214) has actually been opened can be verified through a door position sensor or a lock sensor, and if an opening confirmation signal is not received, a retry or a manager terminal warning can be performed.

[0206] The electronic device (100) may limit the opening time for safety after being switched to an open state. For example, it may be configured to keep the automatic closing safety gate (214) open only for a certain period of time (e.g., 30 seconds to 5 minutes) after approval, and to automatically re-lock it when the time is exceeded. Alternatively, the opening range may be minimized based on a pass detection sensor, such as “automatic closing after one repair worker passes through.” Additionally, a variation is possible in which a work status indicator (e.g., a light / flash pattern) is output to a smart lighting device (216) while the repair work is being performed, so that nearby workers can recognize the current status.

[0207] The electronic device (100) may include a processing procedure for approval failures or ambiguous results to prevent mis-authentication and malfunction. For example, if the speaker similarity is uncertain near a threshold, it may require secondary authentication such as “requesting additional phrase utterance” or “entering a confirmation button on the administrator terminal (250).” Additionally, if the approval keyword is not included, it may output a notice such as “authentication was successful, but cannot be opened because there is no approval phrase” to prevent the user from being misunderstood. Since the approval keyword may change depending on the site, the user terminal (250) may be configured to allow an administrator to pre-register and / or modify the approval phrase, and this change history may be logged to ensure auditability.

[0208] Finally, the electronic device (100) can record events related to gate opening in detail. For example, the approval time, administrator ID, authentication score, keyword matching result, opening duration, number of pass detections, etc., can be stored and utilized for post-quality audits and / or safety audits. Additionally, since voice data may constitute personal information, a policy may be adopted in which raw voice is deleted after being stored for a short period, and storage is focused on feature vectors for authentication and event logs.

[0210] According to one embodiment, the operation of switching an automatic closing safety gate placed at the boundary of a precision control zone to a locked state may include converting field inspection voice data into text data and checking the number of words matching a set risk keyword dictionary in the text data; calculating a voice urgency index based on acoustic characteristics extracted from the field inspection voice data; and switching the automatic closing safety gate to a locked state only when the number of words matching a set risk keyword dictionary in the text data exceeds a threshold number and the voice urgency index exceeds a threshold urgency index.

[0211] Specifically, on-site inspection voice data can be collected through a microphone or voice acquisition device (212) included in a wearable camera (222), and the collected voice data can be divided into analysis intervals of a certain length (e.g., 3 to 15 seconds).

[0212] The electronic device (100) can convert each analysis segment into text data by applying an Automatic Speech Recognition (ASR) module. At this time, considering the noise environment of the construction site, processing such as background noise removal (e.g., filtering low-frequency equipment noise), volume normalization, and voice segment detection (VAD) can be performed during the voice preprocessing stage.

[0213] The result converted into text data can be separated into morpheme units or word units and compared with a danger keyword dictionary. The danger keyword dictionary can be stored in memory (110) in the dictionary and may include safety and structure-related words or compound words such as, for example, “collapse,” “crack,” “danger,” “collapse,” “fall,” “gas leak,” “electric spark,” “fire,” “abnormal vibration,” “support instability,” “rebar breakage,” etc. In addition, to include synonyms and similar expressions, it may include normalized forms of words or a similarity-based extended dictionary (e.g., “cracked,” “looks like it is cracked”).

[0214] The electronic device (100) can calculate the number of words that match the danger keyword dictionary within the text data and can include not only simple frequency calculations but also the following complementary conditions.

[0215] Repeated identical words are counted as one occurrence or a lower weight is applied.

[0216] Increase weight when different risk keywords appear simultaneously

[0217] Negative expressions (e.g., “not dangerous”) are excluded through context analysis.

[0218] Interjections and repeated utterances (e.g., “Ah, danger… danger…”) are reflected only once.

[0219] For example, if a user says, “The pillar is slightly tilted and cracks are visible. It could be dangerous,” three danger-related keywords such as “tilted,” “cracks,” and “danger” can be detected. At this time, the electronic device (100) can calculate the number of N_keywords containing danger keywords as 3.

[0220] The threshold number can be set according to site characteristics, for example, if there are 2 or more, it can be classified as a primary risk candidate, and if there are 3 or more, it can be set to determine a high-risk text state. The threshold number can be configured to be adjustable by a site manager through a user terminal (250).

[0221] Additionally, the electronic device (100) may also consider the text recognition confidence score. If the ASR recognition confidence score is below a certain threshold (e.g., 0.7), the determination of the number of dangerous keywords may be withheld or additional utterances may be requested. This configuration is intended to prevent false alarms caused by misrecognition in a noisy environment.

[0222] As such, this operation can be implemented in a structure that goes beyond simple word search and includes context correction, repetition removal, and confidence-based correction.

[0223] The electronic device (100) may be configured to quantitatively evaluate the urgency of the voice by extracting acoustic characteristics from the field inspection voice data separately from the content analysis of the voice.

[0224] Acoustic characteristics may include a fundamental frequency (F0), a speech rate per unit time, the mean squared amplitude (RMS amplitude) of the voice signal, and a silence ratio. An electronic device (100) may divide voice data into frames (e.g., 20ms to 40ms) and perform frequency analysis and energy calculation for each frame to calculate characteristic values. Subsequently, an average value or statistical value for the entire analysis interval may be calculated to derive a representative acoustic characteristic value for the corresponding voice interval.

[0225] For example, in emergency situations, there may be a tendency for the speech rate to increase, the average amplitude to rise, and the fundamental frequency fluctuation range to expand. Additionally, the silent interval ratio may show a decreasing characteristic. The electronic device (100) can calculate a voice urgency index by normalizing these characteristic changes and then weighting and summing them.

[0226] The electronic device (100) may calculate an urgency index by taking into account individual differences in speech characteristics, setting a baseline based on normal speech data, and calculating a relative deviation from the baseline. This configuration prevents misjudgment caused by a simple increase in volume and allows for a more accurate reflection of emotional heightening in actual emergency situations.

[0227] The electronic device (100) may be configured to switch the automatic closing safety gate (214) to a locked state only when both conditions exceed a set threshold, by comprehensively considering the number of text-based risk keywords and the sound-based voice urgency index.

[0228] That is, the electronic device (100) can be configured to determine a dangerous situation only when the number of dangerous keywords included is greater than or equal to a threshold number and simultaneously the voice urgency index is greater than or equal to the threshold urgency index, and not to perform a locking operation when only a single condition is satisfied. For example, if only one dangerous keyword is detected in a calm report voice and the urgency index is only 0.4, the locking is not performed. On the other hand, if three or more dangerous keywords are detected and the urgency index is calculated to be 0.8 or higher, it can be determined to be in a confirmed dangerous state immediately.

[0229] A gate lock command can be transmitted to the control module of the automatic closing safety gate (214) via the communication interface (130), and the gate can be switched to a physically locked state by driving an electric motor and a lock actuator. Whether the lock is complete can be checked via a status sensor installed in the automatic closing safety gate (214), and if a lock completion signal is not received within a certain period of time, it can be configured to perform a retransmission or administrator notification procedure.

[0230] With such a configuration, the electronic device (100) can implement a multi-condition-based risk judgment system that simultaneously considers the content and acoustic characteristics of the voice, and can realize an active safety management system by directly linking the judgment result to the control of an automatic closed safety gate (214).

[0232] According to one embodiment, the operation of calculating a voice urgency index may further include the operation of extracting acoustic characteristics from on-site inspection voice data, including a fundamental frequency value, a speech rate per unit time, the mean squared amplitude of the voice signal, and a silence ratio, and the operation of calculating a voice urgency index based on the acoustic characteristics. Additionally, the operation of switching an automatic closing safety gate to a locked state may further include the operation of outputting a red light based on a flashing cycle of 2 seconds or less through a smart lighting device installed at the target site when the voice urgency index exceeds a risk index greater than the critical urgency index.

[0233] The electronic device (100) may be configured to extract a plurality of acoustic characteristics as basic data for quantifying the urgency of the voice from field inspection voice data. The acoustic characteristics may include at least a fundamental frequency (F0), a speech rate per unit time, a mean squared amplitude (RMS amplitude) of the voice signal, and a silence ratio.

[0235] Specifically, the electronic device (100) can divide the received voice data into frames of a certain length (e.g., 20ms to 40ms) and perform frequency analysis (e.g., FFT-based analysis) on each frame to estimate the fundamental frequency. The fundamental frequency is a value representing the pitch of the speaker's voice, and in emergency situations, the average value tends to rise or the range of fluctuation expands. The electronic device (100) can calculate a representative value by calculating the average F0, standard deviation, and maximum deviation for the entire analysis interval.

[0236] The speech rate per unit time can be defined as the number of syllables or words per second, and can be calculated using timestamp information from speech recognition results or based on speech energy change patterns. For example, if 50 words are detected during a 10-second interval, it can be calculated as 5 words per second. In emergency situations, the speech rate may tend to increase by more than 20% compared to normal times.

[0237] Mean squared amplitude is an indicator of the energy level of a voice signal and can be calculated by squaring the amplitude of each frame and averaging it. In emergency situations, the mean squared amplitude may show a characteristic of increasing significantly compared to normal times.

[0238] The silent interval ratio can be defined as the proportion of the entire analysis interval in which no speech is present. For example, if 2 seconds of a 10-second interval are silent, the silent interval ratio is calculated as 0.2. In emergency situations, speech tends to continue continuously, so the silent interval ratio may decrease.

[0239] In the preprocessing stage, the electronic device (100) can improve measurement accuracy by performing background noise removal, voice segment detection (VAD), volume normalization, etc. Additionally, if the sensor quality indicator (e.g., signal-to-noise ratio) is below a certain standard, it may be configured to apply a reliability weight to the feature extraction result or withhold analysis.

[0240] The electronic device (100) may be configured to calculate a voice urgency index using extracted acoustic characteristics. The voice urgency index may be an indicator that quantifies the urgency or emotional heightened state of a voice into a continuous value in the range of 0 to 1.

[0241] The electronic device (100) can first normalize each acoustic characteristic. For example, it can set an individual baseline from normal speech data and calculate how much the current measurement deviates from the baseline. The fundamental frequency increase rate, speech rate increase rate, amplitude increase rate, and silence rate decrease rate can each be converted to a range of 0 to 1.

[0242] The electronic device (100) may use a multiple interval average value or a moving average value instead of a single interval value when calculating the urgency index, and may be configured to confirm a high-urgency state only when the threshold value is exceeded two or more times in succession. This prevents misjudgment caused by a temporary increase in volume, etc.

[0243] According to one embodiment, the electronic device (100) may be configured to control a smart lighting device (216) installed at the target site to output a red light having a flashing cycle of 2 seconds or less when the calculated voice urgency index exceeds a risk index greater than a set threshold urgency index.

[0244] The smart lighting device (216) includes an LED-based lighting module and a control circuit, and can control color, brightness, and flashing cycle. The electronic device (100) can transmit a control signal when danger is confirmed to set the red light to flash at a 1-second cycle, such as turning on for 0.5 seconds and turning off for 0.5 seconds. The flashing cycle is set to 2 seconds or less, and can be configured, for example, in the range of 0.5 seconds to 1.5 seconds. This is intended to allow intuitive recognition of an emergency situation.

[0245] The lighting output may be maintained for a set duration (e.g., 30 seconds or until administrator approval), and the flashing may stop if the risk index decreases below a threshold or if an administrator enters a manual release command. Additionally, a multi-stage warning system may be applied to distinguish between medium-risk states (yellow flashing) and high-risk states (red high-speed flashing).

[0246] The electronic device (100) can store the lighting operation time, flashing cycle, risk index value, etc. as a log, and can subsequently be used as safety analysis data. With this configuration, the voice-based risk judgment result can go beyond simple notification and be directly linked with a visual warning device to provide immediate risk awareness throughout the site.

[0248] The above intelligent process management method is,

[0249] It further includes the operation of calculating the above voice urgency index through [Mathematical Formula 1] below, and

[0250] [Mathematical Formula 1]

[0251]

[0252] I_urgency is the voice urgency index, △F_0 is the amount of increase in the fundamental frequency, △V_cap is the limited amount of increase in the speech rate per unit time, A_rms is the real-time amplitude average value of the field inspection voice data, A_base is the reference amplitude value set corresponding to the user, w_snr is the signal-to-noise ratio weight according to the noise environment of the target site, R_silence is the ratio of the silent intervals in the field inspection voice data, a, b, k, c, and d are the set weighting coefficients, and p is the set correction constant.

[0253] The electronic device (100) can calculate a voice urgency index through the above-described mathematical formula 1 in order to quantify the “urgency” of the field inspection voice data collected during the field inspection process.

[0254] The voice urgency index can be defined as a continuous value in the range of 0 to 1, and the closer the value is to 1, the more strongly the utterance is judged to contain characteristics of an emergency situation (e.g., danger notification, report of imminent abnormal signs, possibility of accident). The electronic device (100) can use the calculated voice urgency index as an input value for subsequent control logic, such as gate locking judgment (i.e., comparison with critical urgency index) and risk index judgment (i.e., smart light flashing control).

[0255] According to one embodiment, Equation 1 may be defined as a non-linear combination structure including a log term (e.g., frequency saturation), an exponential term (e.g., reflecting a rapid increase in speech rate), a square term (e.g., emphasizing an energy-based amplitude increase), a square root term (e.g., reflecting a gradual reduction in silence), and sigmoid normalization (e.g., converging to 0 to 1), rather than a method of simply weighting and summing multiple acoustic characteristic values.

[0256] Specifically, the log term provides a gradual saturation effect, sensitive to initial changes in the rise of the fundamental frequency, while ensuring that the contribution does not increase infinitely in excessive high-frequency regions. Additionally, the exponential term is configured to reflect the characteristic that the probability of an emergency utterance increases sharply when the utterance velocity exceeds a certain level, and the square term strongly increases the urgency value in situations such as shouting, screaming, or rapid volume increases, based on the physical characteristic that sound pressure energy is proportional to the square of the amplitude. Furthermore, the square root term increases urgency when utterances continue continuously as silence intervals decrease, but is designed to have a gradual sensitivity so that the urgency is not excessively compromised by the presence of even minute silences. Finally, since the combined result of the aforementioned terms is normalized to a range of 0 to 1 by a sigmoid function, the output indicator can be stably input into the control logic even when characteristic values ​​with different units are combined. Meanwhile, the correction constant can be used to adjust the offset from the reference state to suppress normal utterances from being unnecessarily classified as false alarms near the threshold.

[0257] According to one embodiment, each parameter used in [Mathematical Formula 1] may have the following technical meaning.

[0258] △F_0 can be defined as a fluctuation amount of the fundamental frequency rise. For example, △F_0 is a value indicating how much the fundamental frequency F_0 has risen relative to the user's reference frequency F_base, reflecting the tendency of voice pitch rise that occurs in a state of urgency or excitement. Meanwhile, △F_0 can be defined in the form of max(0, F_0 / F_base-1), thereby preventing the electronic device (100) from distorting the urgency by allowing negative inputs to enter the mathematical formula when the frequency is lower than the reference (e.g., calm state, fatigue state, etc.).

[0259] △V_cap can be defined as a limited upward fluctuation amount of the firing rate. For example, △V_cap refers to a clipped (limited) upward fluctuation amount of the firing rate to prevent the value of the exponential term from increasing explosively when the change in firing rate becomes excessively large. For example, △V_cap can be defined as min(△V_rate, v_limit). Here, v_limit may correspond to an upper limit value pre-set in response to the target site, and △V_rate may refer to the upward fluctuation amount of the current firing rate relative to the reference firing rate. Accordingly, even if the firing rate is abnormally estimated due to one-time noise or recognition error, it is possible to suppress the occurrence of false alarms caused by the urgency value becoming abnormally saturated.

[0260] A_rms may represent the RMS amplitude of a portion identified as a voice segment in the field inspection voice data (e.g., the square root of the mean square amplitude or an energy-based amplitude index), and A_base may be defined as a user-specific reference amplitude value (i.e., normal utterance size).

[0261] Since the squared term through the two terms above is reflected as a squared term rather than a simple ratio, it has the effect of increasing the urgency more significantly in situations where energy surges, such as shouting or screaming.

[0262] W_snr is a weight calculated based on the signal-to-noise ratio (SNR) according to the noise environment of the target site, and is a term used to adjust the reliability of amplitude, velocity, and frequency estimates in noisy environments. For example, if the SNR is low, w_snr can be set to less than 1 to reduce the contribution of the amplitude term, and if the SNR is high, w_snr can be set close to 1 to reflect a normal contribution. Accordingly, it is possible to suppress misjudgments of increased volume caused by simple noise rise (e.g., equipment sound, impact sound) as an emergency fire.

[0263] R_silence represents the proportion (0 to 1) of periods without speech activity within the analysis interval. The square root term increases as silence decreases (i.e., as continuous speech increases), but has a gentle curve so that points are not deducted sharply even if very small silences are present.

[0264] a, b, k, c, and d are weighting coefficients that adjust the contribution of each term and can be set by site (e.g., noise, work type) or by user (e.g., speech habits).

[0265] k is a coefficient that determines the sensitivity of the firing rate term, controlling how rapidly the urgency increases when the firing rate enters the critical region.

[0266] p is a correction constant that shifts the reference point of the entire input and can be calibrated to maintain the average value of I_urgency in normal utterances within a specific range (e.g., 0.2 to 0.4).

[0267] According to one embodiment, the electronic device (100) may receive field inspection voice data from a microphone that may be included in a voice acquisition device (212) and / or a wearable camera (222), a wearable device (224), etc., and may divide the voice data into a certain analysis interval (e.g., 3 seconds to 15 seconds) to calculate parameters in the following procedure.

[0268] (1) Voice segment detection and preprocessing: The electronic device (100) can separate voice segments and silent segments using VAD (Voice Activity Detection) and perform band limiting filters (e.g., voice band centering), noise reduction, volume normalization, etc. At this time, a signal quality index (e.g., SNR) can be calculated together.

[0269] (2) Calculation of fundamental frequency F_0 and reference frequency F_base: The electronic device (100) can estimate F_0 by performing frame-unit frequency analysis in the voice segment. Additionally, F_base can be set to a user-specific calibration interval (e.g., 1 to 3 minutes after the start of the first check) or a moving average (e.g., the median of the last N intervals). Then, △F_0=max(0, F_0 / F_base-1) is calculated.

[0270] (3) Calculation of speech rate V_rate, △V_rate and △V_cap: The electronic device (100) can calculate the speech rate V_rate using time alignment information of speech recognition results or syllable / word boundary estimation based on speech energy peaks. The reference rate V_base can be set as a reference interval or moving average per user, and after calculating the increase as △V_rate = max(0, V_rate / Vbase-1), it can be limited as △V_cap = min(△V_rate, v_limit). Here, v_limit can be set as, for example, a range of 0.5 to 1.0 (i.e., a 50 to 100% increase relative to the reference) or a value according to a field policy.

[0271] (4) Calculation of amplitude A_rms and reference A_base: The electronic device (100) can calculate A_rms and A_base by calculating the RMS amplitude of the speech segment. A_rms and A_base can be stored as user-specific reference speech amplitudes and can be updated with an initial calibration or a recent moving average. Additionally, to prevent overestimation of amplitude in a noisy environment, the use of only the voice band center energy or the amplitude in the speech segment may be restricted.

[0272] (5) Calculation of SNR weight w_snr: The electronic device (100) can calculate the SNR using the energy ratio of the voice interval and the silent interval or the noise estimate. Then, w_snr can be mapped to a monotonic function that decreases as the SNR decreases, for example, w_snr can be set in the range of 0.5 to 1.2. Accordingly, the amplitude term can be suppressed from raising the urgency excessively in situations of extreme noise.

[0273] (6) Calculation of silence ratio R_silence: The electronic device (100) can calculate R_silence as the ratio of the length of the silence interval to the total length within the analysis interval. The determination of the silence interval can be performed by the VAD result. In this way, the electronic device (100) can repeatedly update parameters from the field inspection voice data to calculate I_urgency for each analysis interval, and if necessary, improve the stability of the final judgment through a moving average or a continuous interval exceedance condition (e.g., exceeding the threshold twice in a row).

[0274] According to one embodiment, Equation 1 has technical significance in that it (i) provides a normalization structure that integrates acoustic characteristics of different units into a range of 0 to 1, (ii) directly reflects the specificity of the construction site by including w_snr that reflects the on-site noise environment, and (iii) includes a stabilization logic that prevents sensor error or estimation error from excessively affecting the urgency index through max processing of △F_0 and min processing (or clipping) of △V_cap.

[0275] In particular, since the firing rate is reflected as an exponential term, emergency situations can be detected quickly, but there is a risk of explosive increase in itself; therefore, the present invention achieves both “sensitivity” and “stability” simultaneously by limiting the input of the exponential term through △V_cap. In addition, ln(1+△F_0) provides the advantage of suppressing index distortion at excessive high frequencies while maintaining the initial sensitivity to the rise in voice frequency.

[0276] Conventional voice-based alarm technologies for industrial sites often rely on single criteria, such as keyword detection or simple volume threshold judgment. This has led to problems such as frequent false alarms in noisy environments and an inability to adequately reflect the "emotional and acoustic characteristics" of emergency utterances. Furthermore, conventional technologies have limited structures in which voice analysis results remain at the level of simple notifications, allowing for direct linkage to the control of physical safety equipment (e.g., gate locking, light flashing).

[0277] On the other hand, Equation 1 of the present invention overcomes the limitations of judgment based on a single characteristic by quantifying urgency through the non-linear combination of complex characteristics of fundamental frequency, speech rate, amplitude, and silence ratio. In particular, since the construction site noise environment is reflected within the formula through the w_snr weight, misjudgment caused by the increase in noise itself can be suppressed. In addition, through devices such as negative blocking and clipping, the problem of speech estimation errors or transient outliers having an excessive influence on the urgency index is reduced.

[0278] Furthermore, the voice urgency index of the present invention is directly linked with physical control devices such as “gate locking” and “smart lighting flashing,” enabling immediate on-site control beyond the recognition of dangerous situations. Accordingly, by recognizing risks early and simultaneously performing access control and visual warnings, it provides a distinctive effect that significantly improves on-site safety and response time.

[0280] The above intelligent process management method is,

[0281] The above processor calculates the user's walking balance index through an accelerometer and a gyroscope included in the wearable device; and

[0282] The processor includes an operation to control the protective airbag of the smart safety vest worn by the user to switch to a pre-inflation state when the user's location on the design drawing data is identified as a set high-altitude work zone and the walking balance index is less than a preset instability threshold.

[0283] The electronic device (100) may be configured to quantify the user’s walking state using an accelerometer and a gyroscope included in a wearable device (224) worn by the user, and to calculate a “walking balance index” based on this. Here, the walking balance index is distinguished from simple movement speed and may be defined as an indicator that reflects lateral swaying, torso rotation amount, regularity of foot placement, and sudden changes in posture within the walking cycle.

[0284] Specifically, the electronic device (100) can collect acceleration / angular velocity time series over a certain time window (e.g., 2 seconds to 10 seconds), perform filtering (e.g., removal of gravity component, low-pass / band-pass filter), and then extract a pattern corresponding to a walking event (e.g., step, step transition). For example, the electronic device (100) can calculate multiple features such as time deviation between steps, left-right acceleration RMS, peak values ​​of roll / pitch angular velocity, or spectral energy distribution, and combine them by weight to normalize them into a walking balance index in the range of 0 to 1 or an arbitrary scale.

[0285] Additionally, the electronic device (100) may be configured to determine whether the user's current location on the design drawing data (or BIM model) corresponds to a “high-altitude work zone.” The high-altitude work zone may be defined by spatial attributes such as floor height / level information, scaffolding installation area, around openings, and fall risk boundary lines (outer railings / slabs), and the electronic device (100) may identify whether the user is within the zone by using location data and the design drawing coordinate system transformation results.

[0286] The electronic device (100) can control the protective airbag of the smart safety vest worn by the user to switch to a “pre-inflation” state when the user’s location is identified as a high-altitude work zone and, at the same time, the walking balance index is below a preset instability threshold. The pre-inflation state is a stage prior to full deployment and can be understood as an action of raising the internal pressure of the airbag to a certain level to shorten the deployment time or to form a state ready for shock absorption. Accordingly, the effect of shortening the protective reaction time in the event of an actual fall or sudden fall can be provided.

[0287] For safety, the electronic device (100) may include exception handling such as (i) activation only when the gait balance index is below a threshold for N consecutive times (e.g., 3 times), (ii) execution only when sensor quality indicators (saturation / disengagement, wear release detection) are normal, and (iii) allowing manual release by an operator or administrator approval procedure so that the pre-inflation trigger is not triggered by a single sensor error. Additionally, the history of pre-inflation state transitions (time, position, balance index, sensor raw summary value) may be stored as a log and used for post-safety audits.

[0288] This embodiment has technical significance in that it goes beyond simply providing a “high-altitude zone warning” and controls the preemptive readiness of protective equipment by combining user status (i.e., balance) and spatial risk (i.e., high-altitude zone). In particular, since response to fall accidents is very limited after the event occurs, a method of reducing reaction time through “pre-inflation” as in this embodiment can provide the effect of improving on-site safety and accident response reliability.

[0289] The electronic device (100) can calculate a gait stability index that quantifies the user's gait stability using 3-axis acceleration data (ax, ay, az) and 3-axis angular velocity data (wx, wy, wz) collected from an acceleration sensor and a gyroscope sensor included in a wearable device (224).

[0290] Specifically, the electronic device (100) can collect sensor data over a set time interval (e.g., 3 to 10 seconds) using a sliding window method and then calculate at least one of (i) the variance of the acceleration in the up and down direction, (ii) the amplitude of the shaking in the left and right direction, (iii) the step interval variability, and (iv) the standard deviation of the rotational angular velocity.

[0291] For example, in a normal walking state, the vertical acceleration waveform has a constant period and the variance of lateral swaying exists within a limited range, whereas in a state of unstable balance, the acceleration variance and angular velocity variability are characterized by increased values.

[0292] The electronic device (100) can calculate a gait balance index in the range of 0 to 1 by normalizing the above parameters and then weighting and summing them, or by inputting them into a pre-learned gait stability classification model. Here, it can be defined such that a value closer to 1 indicates a stable state, and a value closer to 0 indicates an unstable state.

[0293] Additionally, the electronic device (100) may be configured to store the user's initial normal walking data as reference data and to calculate relative stability based on the deviation from the current measurement value. Accordingly, it is possible to make a customized stability judgment that reflects individual walking characteristics.

[0294] If the walking balance index is determined to be below a preset instability threshold (e.g., 0.4), the electronic device (100) may further determine whether the user is located in a high-altitude work zone and then control the protective airbag of the smart safety vest to switch to a pre-inflation state. The pre-inflation state may be defined as a preparatory step to shorten the impact response time by partially injecting air into a low-pressure state rather than full inflation.

[0295] This configuration goes beyond simple sensor detection and implements an active protection mechanism that mitigates the risk of physical falls in advance.

[0297] The above intelligent process management method is,

[0298] The above processor performs the operation of analyzing the visual data to recognize the identification number of the power tool held by the user; and

[0299] The above processor includes an operation to physically restrict the operation of the said tool by cutting off power to the smart power plug connected to the said tool if, based on the above identification number, the said power tool is identified as a power tool not permitted in the current process stage confirmed through the above design drawing data.

[0300] The electronic device (100) may be configured to recognize the identification number of a power tool held by a user by analyzing visual data obtained from a wearable camera (222). The identification number may include, for example, a QR / barcode on the tool body, a laser engraving number, text on a label (e.g., serial number / asset number), or a tool model name.

[0301] Specifically, the electronic device (100) can detect a user's hand area or a tool candidate area in an image frame (e.g., an object detection model), then further estimate an identification number area for the area, and apply an OCR or code recognition algorithm to extract the identification number. Additionally, to account for cases where identification number recognition is uncertain due to illumination / angle / shake, it can be configured to determine the final identification number through voting of multiple results across multiple frames or confidence-weighted combination.

[0302] The electronic device (100) can determine whether the corresponding power tool is an “allowed tool” in the current process stage based on an identification number. For example, a list of tools allowed for each stage (e.g., cutting prohibition stage, power tool restriction in pouring zone, spark tool prohibition in explosion-proof zone, etc.) may be stored in design drawing data or process planning data (e.g., process stage table), and the electronic device (100) can match whether the tool is allowed after identifying the “current process stage” by error analysis results, work location, or manager input.

[0303] The electronic device (100) can physically restrict the operation of a tool by cutting off power to a smart power plug connected to the tool when the power tool is identified as a tool not permitted in the current process stage. Here, the smart power plug can be implemented as a cutoff device connected inline to a wired power line and can be configured to cut off the power supply via a relay or semiconductor switch in response to a control signal from the electronic device (100).

[0304] To prevent malfunction, the electronic device (100) may apply multiple conditions such as (i) the reliability of tool identification number recognition is above a threshold, (ii) the tool permission is confirmed as “prohibited,” and (iii) a first warning is sent to the user terminal (250) before blocking, and there is no release response within a certain period of time. In addition, to prepare for cases where it is necessary for safety even after blocking, it may include an exception procedure (e.g., “temporary permission for 60 seconds upon administrator approval”) that allows power to be temporarily restored through administrator voice authentication or terminal authentication.

[0305] This embodiment provides a differentiated effect in that it goes beyond simple warning notifications and enforces compliance with on-site process steps through “physical power cutoff.” In particular, it can reduce the risk of prohibited tools being used due to human error or variations in worker proficiency, and by implementing an active management system that connects process, design, and on-site control, it can provide the effect of reducing the likelihood of safety accidents and quality degradation (e.g., overcutting, structural damage, explosion-proof violations, etc.).

[0307] The above intelligent process management method is,

[0308] The above processor performs the operation of collecting the concentration of volatile organic compounds (VOCs) from an electronic nose sensor deployed at the target site; and

[0309] The above processor includes an operation of outputting an arrow-shaped light signal in the direction of the shortest evacuation path set through the smart lighting device when the concentration of the volatile organic compound reaches a dangerous level and the negative urgency index exceeds the critical urgency index.

[0310] The electronic device (100) may be configured to collect the concentration of volatile organic compounds (VOCs) from an electronic nose sensor deployed at the target site. The electronic nose sensor may be implemented based on a plurality of gas sensor arrays (e.g., MOS, electrochemical, etc.) and may detect an increase in the concentration of specific organic solvents, fuel vapors, adhesive / paint components, or fire precursors.

[0311] The electronic device (100) can determine not only a simple concentration exceedance but also “reaching danger” by analyzing temporal changes in VOCs concentration data (e.g., rate of increase, pattern of rapid increase). For example, a danger condition can be set when the absolute concentration is above a danger level or when a rapid increase in a short period is observed, and false alarms can be reduced by applying sensor calibration values, temperature / humidity correction values, or sensor drift correction.

[0312] In this embodiment, the electronic device (100) can more reliably identify situations where actual danger is “perceived and reported” by combining the condition that the VOCs concentration reaches a dangerous level with the condition that the voice urgency index calculated from on-site inspection voice data exceeds a critical urgency index. That is, the reliability of the control operation can be increased by distinguishing between cases where the on-site worker continues work calmly even though there is a rise in concentration on the sensor, and cases where an urgent ignition such as “severe odor / dizziness / flame hazard” occurs simultaneously with the rise in concentration.

[0313] The electronic device (100) can be controlled to output an arrow pattern light signal in the direction of the shortest evacuation path set through the smart lighting device when conditions are met. Here, the “shortest evacuation path” can be calculated based on passageways, entrances, evacuation stairs, and danger zone boundary information stored in a site drawing or BIM model, and the electronic device (100) can determine the direction of the arrow based on the path search results between the danger location (e.g., sensor location or user location) and the exit location.

[0314] The light signal can be implemented as a pattern having “direction” (e.g., a sequence of moving lights, an arrow LED array) rather than a simple flashing, and can provide more intuitive evacuation guidance than voice broadcasting in noisy environments. Additionally, the electronic device (100) can be configured to control multiple lighting devices by zone to provide specific guidance, such as “a blocking indicator (e.g., red X) for the direction opposite the danger zone, and an arrow indicator for the direction of the safe passage.”

[0315] This embodiment has technical significance in that it cross-checks the possibility of false alarms (e.g., use of work solvents, temporary sensor fluctuations) associated with a standalone environmental sensor (VOCs) using a voice urgency index and immediately outputs the result as a 'direction indicator' of the evacuation route. In other words, it goes beyond a simple notification of "danger" and can provide a differentiated effect of reducing evacuation delays by inducing immediate action (selection of evacuation direction) by on-site workers.

[0317] The above intelligent process management method is,

[0318] The above processor performs the operation of collecting brainwave signals of the user from an EEG (electroencephalogram) sensor embedded in a smart helmet worn by the user and calculating a cognitive focus score; and

[0319] The above processor includes the operation of concentrating a High-CRI spotlight placed adjacent to the wearable camera on the target construction item and recollecting the visual data in order to forcibly improve the user's visual perception when the cognitive concentration is below a preset confidence threshold at the time when the numerical error is calculated.

[0320] The electronic device (100) may be configured to collect brainwave signals from an EEG sensor embedded in a smart helmet worn by a user and to calculate a cognitive focus score based thereon. The cognitive focus score is a value that quantifies the state in which a user can accurately interpret and judge visual information, and may decrease due to fatigue, distraction, stress, etc.

[0321] Specifically, the electronic device (100) can perform preprocessing of the EEG signal (e.g., power noise removal, motion artifact removal, bandpass filter, etc.) and then calculate power (e.g., alpha / beta ratio) or feature vectors for each frequency band. Subsequently, the electronic device (100) can calculate cognitive concentration as a score in the range of 0 to 1 using a pre-set rule or learning model, and this score can be corrected as a relative value compared to a user-specific baseline (normal average).

[0322] The electronic device (100) can determine whether the cognitive concentration at a given point in time is below a pre-set confidence threshold at the point in time when a numerical error is calculated through the analysis of visual data (i.e., the moment when the design-construction comparison result is derived as an “error exceeding” or “error candidate”). This can function as a confidence correction procedure to distinguish whether the error calculation result is an actual construction defect or a measurement uncertainty caused by insufficient illumination, obstructed view, or lack of worker attention.

[0323] When cognitive concentration is below a confidence threshold, the electronic device (100) may be configured to concentrate a High-CRI spotlight placed adjacent to a wearable camera on a target construction item to forcibly improve the user's visual perception, and to re-collect visual data. High-CRI lighting may have the effect of improving the detection performance of visual features such as cracks, lines, and surface defects by providing clearer color / boundary contrast of the target even at the same brightness.

[0324] The re-collection operation may be performed not merely as a simple re-shooting guide, but by the electronic device (100) controlling the lighting direction, intensity, and irradiation time (e.g., 1 to 3 seconds of intensive irradiation), and prioritizing the collection and analysis of frames in that section. Additionally, the electronic device (100) may be configured to compare the error value calculated from the re-collected visual data with the existing error value, determine it as a “confirmed error” only if the error persists, and classify it as a “candidate error due to insufficient measurement reliability” if the error is significantly reduced, thereby reducing unnecessary process interruptions.

[0325] This embodiment is differentiated by the fact that, based on the observation that a person's condition can be directly linked to measurement reliability, it automatically triggers the conditions for re-collecting image measurements based on biosignals (EEG) and directly controls high color rendering illumination as a means to achieve this. In other words, by controlling the "reliability of error judgment" not merely based on simple image quality indicators (shake / illumination) but also by including the user's perception state, it is possible to provide the effect of reducing false alarms while ensuring that actual defects are not missed.

[0326] In this embodiment, the electronic device (100) can calculate the user's cognitive focus score using brainwave signals collected from an electroencephalogram (EEG) sensor embedded in a smart helmet.

[0327] The EEG sensor may include a plurality of electrodes positioned adjacent to the frontal lobe or parietal region and may collect brainwave signals at a sampling frequency of 250 Hz to 512 Hz. The electronic device (100) may remove electromyography and power noise by applying a noise removal filter (e.g., a 0.5 Hz to 40 Hz bandpass filter) to the collected raw EEG signal.

[0328] Subsequently, the electronic device (100) can decompose the EEG signal by frequency band to produce delta, theta, alpha, beta, and gamma power spectra. Generally, there is a characteristic that the proportion of beta waves (13–30 Hz) increases in a state of concentration, and the proportion of theta waves increases in a state of drowsiness or reduced attention.

[0329] The electronic device (100) can calculate an intensity index based on the ratio of beta waves to theta waves, or the ratio of beta waves to alpha waves. For example, cognitive intensity can be defined as follows:

[0330] Concentration = (Beta Wave Power) / (Theta Wave Power + Alpha Wave Power)

[0331] The calculated value can be mapped to a range of 0 to 1 through a normalization process.

[0332] Additionally, the electronic device (100) may be configured to use an average value over a certain time interval to reflect time series changes or to apply an LSTM-based time series model to mitigate instantaneous noise effects.

[0333] When the cognitive concentration is below a preset confidence threshold at the time when a numerical error is calculated, the electronic device (100) may be configured to control a high-color rendering (High-CRI) spotlight to focus on the target construction item to improve the user's visual perception, and to re-collect visual data in a state where the lighting environment is improved.

[0334] This structure provides a technical effect that corrects for the possibility that measurement errors may be caused by reduced worker perception rather than simple construction errors.

[0336] The above description is merely an illustrative explanation of the technical concept of the present invention, and those skilled in the art to which the present invention pertains will be able to make various modifications and variations within the scope of the essential characteristics of the present invention.

[0337] Accordingly, the embodiments disclosed in this invention are intended to illustrate, not limit, the technical concept of the invention, and the scope of the technical concept of the invention is not limited by these embodiments. The scope of protection of this invention shall be interpreted by the claims below, and all technical concepts within an equivalent scope shall be interpreted as being included within the scope of rights of this invention.

Claims

Claim 1 An intelligent process management method performed by an electronic device including a processor, wherein the processor asynchronously collects and integrates into multimodal data visual data acquired from a wearable camera worn by a user within a target site, GPS-based location data corresponding to the time of acquisition of said visual data, and field inspection voice data including inspection details regarding said target site spoken by said user; the processor superimposes the visual data included in said multimodal data with design drawing data previously registered corresponding to said target site to calculate a numerical error for a set major construction item; and when a target construction item among said major construction items is identified in which the numerical error exceeds a threshold error, the processor transmits an error notification message to a user terminal previously registered corresponding to said target site. The processor includes the operation of setting the process process for the target site to a suspended state until feedback data regarding the target construction item is received from the user terminal, and sending a warning notification message to the user terminal whenever additional data is acquired from the wearable camera; the multimodal data corresponds to data generated by combining a specific frame of the visual data, the location data corresponding to the time of shooting of the specific frame, and the site inspection voice data of the time interval corresponding to the specific frame into a single bundle; and the intelligent process management method comprises the operation of the processor, when the target construction item is identified, checking whether the user's current location is within a precision control zone including the target construction item based on the location data; and when the processor confirms that the user is within the precision control zone, receiving the user's biosignal through a wearable device registered in relation to the user and determining whether a tension score calculated based on the biosignal exceeds a set tension threshold.The above processor transmits a notification inducing departure from the precision control zone to the wearable device and transmits a location sharing request to the wearable device at a predetermined period when the tension score exceeds the tension threshold; and the processor converts an automatic closing safety gate placed at the boundary of the precision control zone to a locked state in response to identifying that the user has departed from the precision control zone based on the real-time location received from the wearable device at the predetermined period, and the intelligent process management method comprises: the processor comparing subsequent voice data with reference voice data set corresponding to the user terminal when subsequent voice data is received through a smart voice recognition device installed on the automatic closing safety gate after converting the automatic closing safety gate to a locked state; An intelligent process management method comprising: an operation in which, if the processor confirms that the speaker of the subsequent voice data is the same as the manager corresponding to the user terminal as a result of comparison and that the subsequent voice data contains a modification approval keyword set therein, the operation of switching the automatic closing safety gate to an open state for the repair of the target construction item; and an operation of switching the automatic closing safety gate placed at the boundary of the precision control zone to a locked state, wherein the processor converts the field inspection voice data into text data and checks the number of words included in the text data that match a risk keyword dictionary set therein; the processor calculates a voice urgency index based on acoustic characteristics extracted through the field inspection voice data; and the processor switches the automatic closing safety gate to a locked state only when the number of words included in the text data that match a risk keyword dictionary set therein exceeds a threshold number and the voice urgency index exceeds a threshold urgency index. Claim 2 delete Claim 3 delete Claim 4 delete Claim 5 An intelligent process management method according to claim 1, wherein the operation of calculating the voice urgency index further includes: the operation of the processor extracting acoustic characteristics including a fundamental frequency value, a speech rate per unit time, the mean squared amplitude of the voice signal, and a silence ratio from the field inspection voice data; and the operation of the processor further including the operation of calculating the voice urgency index based on the acoustic characteristics; and the operation of switching the automatic closing safety gate to a locked state further includes the operation of outputting a red light based on a flashing cycle of 2 seconds or less through a lighting device installed at the target site when the voice urgency index exceeds a risk index greater than the threshold urgency index, wherein the lighting device is a device that provides a visual warning and includes an LED-based lighting module, a control circuit, and an illuminance sensor, and is configured to control at least one of the color, brightness, and flashing cycle of the lighting module based on a control signal transmitted by the electronic device.

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