system

Abstract

We provide the system. [Solution] Means for receiving various data collected from aircraft equipment, ground imaging equipment, and detection devices worn by workers, The means for preprocessing the aforementioned diverse data, performing noise reduction and missing value imputation, An integrated analysis means for analyzing pre-processed data and evaluating risks in the work environment, A means of generating feedback and issuing warnings based on the analysis results, A system that includes this.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Unexamined Patent Application Publication No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 It is required to ensure the safety of workers and reduce the accident rate in dangerous working environments such as base station construction. In particular, due to the labor shortage associated with the aging population and the need to improve work efficiency, high-precision real-time risk management is required. However, with conventional methods, it is difficult to integrate data from various devices, analyze it quickly, and provide feedback to workers. 【Means for Solving the Problems】 【0005】 This invention provides a system for integrally processing diverse data obtained from aircraft devices, ground imaging devices, and worker-mounted detection devices. Specifically, a server receives data from these devices and performs preprocessing such as noise reduction and missing value imputation. Furthermore, an integrated analysis means analyzes this data, learns from past accident data using machine learning algorithms, and predicts new risks. Feedback generated based on the analysis results is notified to workers and managers in real time, prompting immediate risk avoidance actions. In this way, safety at the work site can be improved in real time. 【0006】 An "aircraft device" is a device that flies for the purpose of surveillance and data collection from above, and usually refers to a drone. 【0007】 A "ground-based imaging device" is a device used to capture video or images from the ground, and includes security cameras and surveillance cameras. 【0008】 "Detection devices worn by workers" are sensor devices used to detect the worker's physical information, movements, and surrounding environmental data. 【0009】 "Diverse data" refers to a collection of data generated from multiple different devices, including video data, environmental data, and vital data. 【0010】 "Noise reduction" refers to the process of removing unnecessary data or errors during data processing. 【0011】 "Missing value imputation" refers to the process of filling in missing data in an appropriate manner when there are gaps in the collected data. 【0012】 "Preprocessing" refers to a series of processes to prepare data before performing data analysis or interpretation, and includes noise reduction and missing value imputation. 【0013】 "Integrated analysis means" refers to methods and mechanisms for analyzing diverse data collected separately. 【0014】 "Feedback generation" refers to the process of creating appropriate actions and information based on data analysis results and communicating them to stakeholders. 【0015】 A "machine learning algorithm" is a mathematical method that uses large amounts of data to learn patterns and perform predictions and classifications. [Brief explanation of the drawing] 【0016】 [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined. 【Mode for Carrying Out the Invention】 【0017】 Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings. 【0018】 First, the terms used in the following description will be explained. 【0019】 In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc. 【0020】 In the following embodiments, the numbered RAM (Random Access Memory) is a memory where information is temporarily stored and is used as a work memory by the processor. 【0021】 In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disk (e.g., hard disk), or magnetic tape, etc. 【0022】 In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark). 【0023】 In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or." 【0024】 [First Embodiment] 【0025】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0026】 As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server. 【0027】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0028】 The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52. 【0029】 The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input. 【0030】 The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor. 【0031】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54. 【0032】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0033】 As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30. 【0034】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0035】 In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0036】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0037】 This invention is a system that integrates and analyzes data collected from an aerial vehicle, a ground-based imaging device, and a detection device worn by a worker, in order to enhance the safety of workers at construction sites. Specific embodiments for carrying out this invention are described below. 【0038】 Server operation 【0039】 The server receives diverse data transmitted from multiple devices. Specifically, it receives aerial footage from drones, surveillance footage from ground cameras, and vital and environmental information from worker sensor devices, and processes this data comprehensively. 【0040】 The server first preprocesses the received data. This preprocessing includes removing data noise and imputing missing values. This improves the accuracy and reliability of the analysis. 【0041】 The pre-processed data is analyzed by an integrated analysis system. This system uses machine learning algorithms to predict risk based on past accident data and similar cases. Based on these predictions, the server generates a risk score for the work situation. This risk score forms the basis for feedback generation. 【0042】 Based on the analysis results, the server generates real-time feedback and determines actions. For example, if a worker's movements are dangerous, it creates and sends an audio alert. Administrators may also receive immediate email notifications. 【0043】 Terminal operation 【0044】 The terminal has the function of receiving feedback from the server and notifying the worker. For high-priority feedback, the worker is immediately alerted through methods such as voice alerts and vibrations. 【0045】 The terminals worn by workers collect new data in real time and send it to the server. This repeated process ensures that the latest work environment information is constantly updated within the system. 【0046】 User interaction 【0047】 Users, namely workers and managers, monitor the site conditions based on real-time feedback. Workers use their own devices to follow safety check instructions and take appropriate safety measures. Managers manage overall safety based on reports received from the system. 【0048】 Specifically, for example, if a worker's heart rate exceeds a certain threshold, the system will immediately instruct the worker to take a break. In other situations, the system can also notify the team working in the area of ​​a hazard detected by a drone and instruct them to take an alternative route. 【0049】 As a result, the present invention significantly improves safety at the work site and reduces the accident rate. 【0050】 The following describes the processing flow. 【0051】 Step 1: 【0052】 The server receives data in real time from aircraft equipment, ground imaging equipment, and detection devices worn by workers. The received data is diverse, including video data, environmental monitoring data, and workers' vital signs. 【0053】 Step 2: 【0054】 The server preprocesses the received data. Specifically, it applies a denoising filter and performs processing to fill in unreliable data points and missing data. This step ensures the data quality necessary for analysis. 【0055】 Step 3: 【0056】 The server analyzes pre-processed data through an integrated analysis system. Using machine learning-based analysis algorithms, it detects risk factors from various data and calculates specific risk scores. This allows for real-time updates of safety indicators for the work environment. 【0057】 Step 4: 【0058】 The server generates feedback based on the analysis results and immediately communicates it to the relevant parties. Depending on the alert content, the feedback is delivered to each terminal as voice guidance, email notifications, or displays on the dashboard. 【0059】 Step 5: 【0060】 The terminal receives feedback sent from the server and notifies the worker on the device. In the case of an emergency alert, it prompts immediate action using visual or audio means. 【0061】 Step 6: 【0062】 Users receive real-time feedback through their devices to understand their current work status. They follow the provided instructions, perform their work safely, and adjust their work plan if necessary. 【0063】 (Example 1) 【0064】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0065】 Maintaining a safe work environment is crucial, especially in hazardous work settings. However, traditional methods have made it difficult to monitor worker safety in real time and provide immediate, appropriate warnings. Furthermore, there is a need to efficiently process and integrate data from diverse sources to improve the accuracy of work risk predictions. 【0066】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0067】 In this invention, the server includes means for receiving diverse information from an aerial mobile body, ground imaging equipment, and detection devices worn by workers; means for integrating and preprocessing the diverse information, performing noise reduction and missing value completion; and integrated analysis means using machine learning to analyze the preprocessed information and evaluate hazards in the work environment. This makes it possible to evaluate the risks at the work site in real time with high accuracy and to quickly provide appropriate feedback to workers and managers. 【0068】 An "aerial mobile device" is a device that collects images and data while moving through the air. 【0069】 "Ground imaging equipment" refers to devices that are fixed or installed on the ground to capture and record images and information of the surrounding environment. 【0070】 A "detection device" is a device worn by workers to monitor their vital signs and the surrounding environment, and to collect data. 【0071】 "Diverse information" refers to a collection of different types of data and information gathered from aerial vehicles, ground-based imaging equipment, and detection devices. 【0072】 "Preprocessing" is the process of removing noise from data and filling in missing values. 【0073】 An "integrated analysis tool" is a method or algorithm for analyzing diverse information and using machine learning to evaluate risks in the work environment. 【0074】 "Machine learning" is a technology that enables computer systems to learn from data and make predictions and decisions. 【0075】 An "evaluation score" is an index calculated using integrated analysis methods that quantifies and shows the risks in the work environment. 【0076】 This invention is a system for ensuring the safety of workers at construction sites. The system collects data from an aerial mobile device, ground-based imaging equipment, and detection devices worn by workers, and evaluates the risks in the work environment by performing integrated analysis. 【0077】 Server operation 【0078】 The server receives video data from aerial vehicles (e.g., drones), video data from ground-based cameras (e.g., surveillance cameras), and vital and environmental information from sensors worn by workers. The received data is preprocessed, for example, by removing noise and imputing missing values. This preprocessing improves the accuracy and reliability of the analysis. 【0079】 The server uses pre-processed data to perform integrated analysis using machine learning algorithms. The analysis predicts risks in the work environment by modeling past accident data and comparing it with current data. Based on this, it generates a risk score for the work situation and creates appropriate feedback. 【0080】 For example, if an increase in heart rate is detected, the server will determine the situation is dangerous and generate feedback prompting the worker to take a break. It can also notify workers if the drone detects unstable terrain and instruct them to bypass the area. 【0081】 Terminal operation 【0082】 The terminal receives feedback from the server and notifies the worker. High-priority alerts include features such as voice alerts and immediate vibrating warnings. Furthermore, the terminal continuously transmits newly acquired data from the worker to the server in real time. This ensures that the system is always up-to-date, allowing the server to reassess risks based on that information. 【0083】 User interaction 【0084】 Users interact with the system as both workers and managers. Workers receive real-time feedback via terminals and are instructed to take safety checks. For example, if a worker's heart rate exceeds a certain threshold, the system automatically notifies them to take a break. 【0085】 An example of a prompt might be: "Describe a system that supports the latest safety measures at construction sites. Please provide specific examples of devices and describe in detail how they collect and process data." This prompt is used as an instruction to explain the system's operation using a generative AI model. 【0086】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0087】 Step 1: 【0088】 The server receives data from aerial vehicles, ground-based imaging equipment, and detection devices worn by workers. Specifically, it receives video from drones, surveillance data from cameras, and vital information from sensors. This data is provided as input. 【0089】 Step 2: 【0090】 The server preprocesses the received data. It applies algorithms to remove noise from the data and impute missing values. Specifically, it corrects blur in drone footage and improves reliability by imputing incomplete data from sensors. The preprocessed data is then output. 【0091】 Step 3: 【0092】 The server performs an integrated analysis of the pre-processed data. Using machine learning algorithms, it assesses risk based on past accident data. Specifically, it uses pattern recognition technology to evaluate the current work situation and generates a risk score. This score becomes the output of the analysis. 【0093】 Step 4: 【0094】 The server generates feedback based on the generated risk score. Specifically, if the risk is high, it creates an audio alert or email notification indicating that immediate action is required. The generated feedback is then output. 【0095】 Step 5: 【0096】 The terminal receives feedback from the server and notifies the worker. Specifically, it alerts the worker by playing an audio alert or vibrating the terminal. This notification is output from the terminal. 【0097】 Step 6: 【0098】 The terminal collects new data from sensors in real time and sends it to the server. This ensures that the latest information on the work environment is always provided. The input of data from sensors and its transmission from the terminal to the server maintains the adaptability and safety of the entire system. 【0099】 (Application Example 1) 【0100】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0101】 Effective safety management at work sites requires real-time risk information and rapid feedback to workers. However, conventional systems lacked timeliness in risk assessment and warning transmission, making it difficult to provide workers with sufficient safety. Therefore, there is a need to develop a system that effectively improves worker safety. 【0102】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0103】 In this invention, the server includes means for receiving diverse data collected from aircraft equipment, ground imaging equipment, and detection devices worn by workers; means for preprocessing the diverse data, performing noise reduction and missing value imputation; integrated analysis means for analyzing the preprocessed data and evaluating risks in the work environment; means for generating feedback and issuing warnings based on the analysis results; and means for providing vibration or audible alerts via terminal devices to notify workers of warnings in real time. This enables effective monitoring of the work environment and immediate implementation of safety measures. 【0104】 "Aircraft equipment" refers to unmanned aerial vehicles or devices used to collect data from the air. 【0105】 A "ground imaging device" is a device used to monitor and photograph the environment and work conditions from the ground. 【0106】 A "detection device" is a device worn by workers to collect information about their physical condition and the environment. 【0107】 "Means for receiving diverse data" refers to methods and mechanisms for receiving various types of data transmitted from multiple devices. 【0108】 "Preprocessing" refers to the process of removing noise and imputing missing values ​​in order to improve the accuracy of data analysis. 【0109】 An "integrated analysis tool" refers to an analysis method or algorithm used to assess risk based on received data. 【0110】 "Means for generating feedback and issuing warnings" refers to methods for creating appropriate feedback from analysis results and notifying workers of warnings. 【0111】 A "terminal device" is a device that can be carried by workers and is used to receive feedback and transmit warnings in real time. 【0112】 "Means for notifying warnings in real time" refers to technologies and methods for immediately transmitting urgent information to workers. 【0113】 A "vibration or voice alert" is a type of alarm that uses vibration or sound to notify workers via a terminal device in order to draw their attention. 【0114】 The system for implementing this invention integrates and manages data collected from aircraft devices, ground imaging devices, and detection devices worn by workers in order to improve safety at construction sites. 【0115】 The server receives data from these devices and first performs preprocessing, including noise reduction and missing value imputation. This process improves the accuracy and reliability of the data. Subsequently, the received data is analyzed using machine learning algorithms. Specifically, it predicts risks in the work environment by learning from past accident data and similar cases. Based on this, the server generates a safety score for workers and provides immediate feedback. 【0116】 The terminal receives feedback transmitted from the server. It immediately alerts workers by providing real-time voice and vibration warnings. The terminal also collects new data from workers and continuously transmits it to the server, ensuring that it always reflects the latest situation. 【0117】 Users can maintain safe working conditions by following the feedback provided through this system. For example, if the work environment is hazardous, an instruction such as "Your heart rate is high, please take a 5-minute break" will be issued. An example of a prompt message is, "What is the greatest safety risk in working at this site?" 【0118】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0119】 Step 1: 【0120】 The server receives data from aircraft equipment, ground imaging equipment, and detection devices worn by workers. The input is real-time data from these devices, and the output is a dataset that centrally manages this data. The received data is first aggregated on the server. 【0121】 Step 2: 【0122】 The server performs preprocessing on the received data. Preprocessing involves removing noise and imputing missing values ​​from the input dataset. The output is a clean and complete dataset, which contributes to improved accuracy in the analysis. 【0123】 Step 3: 【0124】 The server uses pre-processed data to perform analysis for risk assessment. It applies machine learning algorithms to predict future risks from the input data. The output is a risk score for the work environment, based on past accident data and similar cases. 【0125】 Step 4: 【0126】 The server generates feedback based on the analysis results and sends it to the worker's terminal. Based on the risk score obtained from the input data, it generates specific alert information as output. This information is used to identify situations that require immediate attention. 【0127】 Step 5: 【0128】 The terminal notifies workers of feedback received from the server. The input is feedback information, and the output is warning information via voice or vibration notifications. The terminal uses this to alert workers in real time. 【0129】 Step 6: 【0130】 Users, i.e., workers and managers, take safety measures based on feedback provided through the terminal. The input is real-time feedback data from the terminal, and the output is specific safety actions that serve as guidelines for workers. Users use this information to ensure their own safety. 【0131】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0132】 This invention is a system that incorporates an emotion engine to simultaneously ensure the safety and psychological health of workers. This system combines diverse data obtained from aerial devices, ground imaging devices, and worker-mounted detection devices with technology to analyze the user's emotions. 【0133】 Server operation 【0134】 The server centrally manages data transmitted from various devices and receives diverse data. This includes video data, environmental data, worker vital information, and emotional data analyzed by the emotion engine. 【0135】 The received data is first subjected to noise reduction and missing value imputation. This enables reliable data processing and improves the accuracy of the analysis results. During this preprocessing stage, sentiment data is also appropriately filtered. 【0136】 The integrated analysis system performs a detailed risk assessment using pre-processed information. During this process, a machine learning algorithm learns patterns from past data and predicts new risks. Meanwhile, the emotion engine analyzes voice and facial expression data to evaluate the emotional state of the workers. 【0137】 These analysis results form the basis for generating feedback and creating information that leads to improvements in the work environment. The feedback includes not only warnings about physical risks but also instructions to reduce psychological burden. 【0138】 Terminal operation 【0139】 The terminal has the function of receiving feedback provided by the server and notifying workers in real time. Specifically, it prompts immediate risk avoidance actions through warning sounds and visual displays. 【0140】 Furthermore, the terminals continuously collect biometric and environmental information from workers and transmit this data to the server. This allows the system to always make decisions based on the latest information. 【0141】 User interaction 【0142】 Users will proceed with their work while utilizing the feedback provided through their devices. This feedback includes safety warnings and alerts to reduce psychological stress, so users are expected to follow it to ensure safe work practices. 【0143】 For example, if the server detects a "high-stress state" via the emotion engine during work, a message prompting the worker to take a break will be sent from the terminal. Also, if anxiety is detected during work at height, more detailed safety instructions will be automatically generated. 【0144】 Thus, the present invention can provide an optimal working environment by continuously monitoring the physical and psychological safety of workers, thereby preventing accidents and improving efficiency. 【0145】 The following describes the processing flow. 【0146】 Step 1: 【0147】 The server receives data from aircraft equipment, ground cameras, and detection devices worn by workers. In addition, it receives emotional data from the emotion engine. At this stage, the data includes environmental information, vital data, captured video, and audio. 【0148】 Step 2: 【0149】 The server performs preprocessing of the received data. Specifically, it uses noise reduction filters to improve data accuracy and appropriately fills in missing parts. In this process, unnecessary information is also removed in the analysis of sentiment data. 【0150】 Step 3: 【0151】 The server passes pre-processed data to an integrated analysis system for risk analysis. Using machine learning algorithms, it extracts risk factors from each data type and calculates a real-time risk score. The emotion engine determines the emotional state from the worker's voice and facial expression information and assesses whether the worker is under potential stress. 【0152】 Step 4: 【0153】 The server generates feedback based on the analysis results. This feedback comes in two types: warnings about physical risks and psychological alerts based on emotional state. For example, if a high-stress state is detected, a message recommending a break is generated. 【0154】 Step 5: 【0155】 The terminal receives feedback from the server and notifies the worker. For high-priority feedback, the terminal immediately notifies the worker as an audio and visual alert. The work procedure is adjusted according to the content of the feedback. 【0156】 Step 6: 【0157】 Users review the feedback provided through their device and decide whether to continue working. They can also request additional information from the server if necessary. For example, if their emotional state deteriorates under certain working conditions, they can immediately reschedule their work. 【0158】 (Example 2) 【0159】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0160】 Ensuring both worker safety and psychological well-being simultaneously requires comprehensive monitoring utilizing diverse data. However, current safety management systems focus primarily on assessing physical risks and lack feedback that considers changes in emotions and psychological states. Therefore, providing a safe work environment that includes the psychological well-being of workers remains a challenging task. 【0161】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0162】 In this invention, the server includes means for receiving diverse information collected from spatial devices, ground-viewing devices, and measuring devices worn by workers; means for pre-processing, reducing, and supplementing the diverse information; comprehensive analysis means for analyzing the pre-processed information and evaluating hazards in the work environment; means for analyzing the psychological state of workers using emotion analysis means; and means for generating information based on the analysis results and notifying the workers. This makes it possible to provide detailed feedback to enhance the physical and psychological safety of workers. 【0163】 "Spatial devices" refer to devices designed for information gathering in space, and include various types of devices such as flying vehicles. 【0164】 A "ground vision device" is a device used to acquire visual information from the ground, and includes cameras and sensors. 【0165】 A "measuring device" refers to a device worn by a worker that collects biometric information and data related to their movements. 【0166】 "Diverse information" refers collectively to various types of data obtained from spatial devices, ground-based visual devices, and measurement devices. 【0167】 "Preprocessing" refers to the process of reducing noise and filling in missing data on collected information. 【0168】 "Reduced noise processing" refers to the process of removing unnecessary noise contained in information. 【0169】 "Completion processing" refers to the process of filling in missing data based on past information or inferences. 【0170】 "Comprehensive analysis means" refers to a method for conducting a detailed analysis from multiple perspectives using pre-processed information. 【0171】 "Emotional analysis methods" refer to methods for evaluating the psychological state of workers based on their voice and facial expression data. 【0172】 "Information generation means" refers to a means of generating feedback based on analysis results and conveying useful information to workers. 【0173】 This invention is a system that simultaneously improves safety and psychological health in the work environment, and operates primarily using information obtained from spatial devices, ground-based visual devices, and measurement devices. The server receives diverse information and manages it centrally. The received information includes video data, environmental data, and vital information of workers, as well as data necessary for emotion analysis. 【0174】 The server is equipped with software that performs preprocessing on received information, such as noise reduction and data interpolation. This preprocessing is necessary to supply reliable information for subsequent analysis. After preprocessing, the information is sent to a comprehensive analysis system, where detailed analysis is performed using machine learning algorithms. This enables the assessment of physical risks. The server also incorporates an emotion analysis system that evaluates the emotional state of workers based on voice and facial expression data. 【0175】 Based on the analysis results, the server generates feedback. This feedback includes warnings about physical risks as well as advice to reduce psychological burden. For example, if the emotional analysis detects a high-stress state, it will generate a message recommending that the user "take a break." 【0176】 The terminal has the function of receiving feedback from the server in real time and notifying workers visually or audibly. This allows workers to take immediate risk avoidance actions. In addition, the terminal plays a role in providing the latest data by constantly transmitting biometric and environmental information to the server. 【0177】 Users can perform tasks safely by following the feedback provided via their devices. For example, in high-altitude work where anxiety is detected through server analysis, specific instructions such as "check safety equipment" or "adjust work speed" are provided as feedback. 【0178】 A concrete example is a scenario where a worker in a manufacturing plant is advised to take a break. An example of a prompt generated by a generative AI model is: "Explain how emotional data and work environment data are analyzed to generate feedback in order to optimize the safety of the work environment and the mental health of the workers." 【0179】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0180】 Step 1: 【0181】 The server receives diverse information in real time from spatial devices, ground-based visual devices, and measurement devices. This includes video data, environmental data, worker vital information, and audio data. The received data is temporarily stored in a database. Based on this input data, preparations for data preprocessing are made. 【0182】 Step 2: 【0183】 The server preprocesses the received information. Specifically, it applies a noise reduction filter to video data and uses statistical methods to impute missing data in environmental and vital information. This process enhances data reliability and outputs a clear dataset with reduced noise. 【0184】 Step 3: 【0185】 The server passes the pre-processed data to an integrated analysis system, which uses machine learning algorithms to perform a detailed risk assessment. The algorithm, having learned from past data patterns, predicts potential hazards in the work environment. As a result of this analysis, new risk assessment data is output. 【0186】 Step 4: 【0187】 The server uses emotion analysis tools to analyze the psychological state of workers from voice and facial expression data. It utilizes a generative AI model to execute prompts that identify changes in emotional state. This results in the output of emotional data such as the worker's stress level and anxiety level. 【0188】 Step 5: 【0189】 The server generates feedback based on risk assessment data and sentiment data. The generated feedback includes warnings about physical risks and advice to support psychological well-being. This feedback data is generated as the final output. 【0190】 Step 6: 【0191】 The terminal receives feedback from the server in real time and notifies the worker. Notifications include visual displays and audio messages, allowing users to take immediate risk avoidance actions. Thus, the terminal plays a crucial role in providing workers with critical feedback information. 【0192】 Step 7: 【0193】 Users can proceed with their work safely based on feedback received through their devices. For example, if an emergency is detected during work at height, they can receive detailed safety instructions and take appropriate measures accordingly. This use of feedback improves both the safety and efficiency of the work. 【0194】 (Application Example 2) 【0195】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal". 【0196】 This invention aims to simultaneously achieve risk assessment and psychological burden reduction for workers in today's workplace environment, where it is necessary to ensure not only the physical safety of workers but also their psychological health. Conventional systems have been overly focused on the physical aspects of risk, neglecting the psychological aspects. Therefore, the challenge is to grasp workers' stress levels and psychological burden in a timely manner and provide feedback that contributes to their improvement. 【0197】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0198】 In this invention, the server includes means for receiving diverse information collected from flight equipment, ground recording devices, and detection devices worn by workers; means for preprocessing the diverse information, removing noise and imputing missing values; integrated analysis means for analyzing the preprocessed information and evaluating hazards in the work environment; means for generating feedback and issuing warnings based on the analysis results; and means for monitoring the psychological and physiological state of workers and generating auxiliary instructions to reduce psychological burden. This makes it possible to simultaneously evaluate physical and psychological risks and comprehensively ensure the safety and health of workers. 【0199】 A "flying device" is a device that has the ability to monitor and collect information about the environment and the condition of workers from the air. 【0200】 A "ground recording device" is a device that captures and records images and environmental information from the ground and provides the data for analysis. 【0201】 A "detection device" is a device that includes sensor devices worn by workers to collect vital information and environmental data. 【0202】 "Diverse information" refers to data related to the work environment and workers, including video, audio, vital signs, and emotional state analysis information. 【0203】 "Preprocessing" is the process of transforming received data into information suitable for analysis by removing noise and imputing missing values. 【0204】 An "integrated analysis means" is a method or apparatus for analyzing pre-processed data and comprehensively evaluating the risks in the work environment. 【0205】 "Feedback" refers to warnings and instructions provided to workers based on analysis results, with the aim of avoiding physical and psychological risks. 【0206】 "Psychological burden reduction instructions" are notifications or suggestions to reduce psychological stress based on an analysis of the worker's emotional state. 【0207】 The embodiments for carrying out the present invention are shown below. 【0208】 The system of this invention aims to ensure safety and psychological health in the work environment. First, the server receives various information from the flight device, ground recording device, and sensing device worn by the worker. This includes environmental data, video data, audio data, worker vital information, and emotional state analysis information. 【0209】 Upon receiving this data, the server first performs preprocessing to remove noise and impute missing values. Data processing utilizes Python data analysis libraries (e.g., Pandas) and machine learning frameworks (e.g., TENSORFLOW®), enabling highly accurate analysis. 【0210】 Next, using an integrated analysis method, machine learning algorithms analyze the pre-processed data to evaluate the potential hazards in the work environment. In this process, it is possible to predict new hazards using a model that has learned from past accident data. 【0211】 The server generates feedback based on the analysis results. This feedback includes not only warnings about physical risks, but also instructions to reduce psychological burden based on the worker's emotional state. This provides a safe and comfortable working environment. 【0212】 For example, if the emotion engine detects that a worker's stress levels are rising while working at height, the server will generate an instruction such as "Please temporarily stop working and take some time to relax," and notify the worker's terminal in real time. 【0213】 An example of a prompt message is: "Analyze the worker's heart rate and facial expression data, and generate feedback to promote relaxation when psychological stress levels rise." 【0214】 By linking the server and terminals, the physical and psychological safety of workers can be ensured, accident prevention can be achieved, and work efficiency can be improved. 【0215】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0216】 Step 1: 【0217】 The server receives diverse information from aircraft, ground recording devices, and sensing equipment worn by workers. Input information includes environmental data, video data, audio data, vital signs, and emotional state analysis information. The server integrates this data and organizes it into the format required for subsequent processing. 【0218】 Step 2: 【0219】 The server performs noise reduction and missing value imputation on the received data. The input is the data integrated in step 1, and the output is the purified data. Specifically, the data cleaning process is carried out using a Python library (e.g., Pandas). Noisy data is smoothed, and missing data is imputed using statistical methods. 【0220】 Step 3: 【0221】 The server uses pre-processed data to evaluate hazards through an integrated analysis method. The input is the output data from step 2, and the output is risk assessment information for the work environment. Machine learning algorithms are used to learn from past accident information and predict new hazards. Specifically, a model is applied using TensorFlow to perform safety assessments in real time. 【0222】 Step 4: 【0223】 The server generates feedback based on the analysis results and sends it to the terminal. The input is risk assessment information, and the output is warning messages and instructions to reduce psychological burden. The generated feedback is immediately notified to the worker's terminal as audio or text information. Examples of generated feedback include, "Please pause your work and take some time to relax." 【0224】 Step 5: 【0225】 The terminal receives feedback from the server and notifies the worker in real time. Input is feedback information sent from the server, and output is a warning sound, display notification, and vibration alert to the worker. The terminal uses this information to provide specific instructions for the worker to safely continue their work. 【0226】 The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data. 【0227】 Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0228】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14. 【0229】 [Second Embodiment] 【0230】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0231】 As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server. 【0232】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0233】 The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52. 【0234】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0235】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0236】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0237】 Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0238】 The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30. 【0239】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0240】 In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0241】 Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0242】 This invention is a system that integrates and analyzes data collected from an aerial vehicle, a ground-based imaging device, and a detection device worn by a worker, in order to enhance the safety of workers at construction sites. Specific embodiments for carrying out this invention are described below. 【0243】 Server operation 【0244】 The server receives diverse data transmitted from multiple devices. Specifically, it receives aerial footage from drones, surveillance footage from ground cameras, and vital and environmental information from worker sensor devices, and processes this data comprehensively. 【0245】 The server first preprocesses the received data. This preprocessing includes removing data noise and imputing missing values. This improves the accuracy and reliability of the analysis. 【0246】 The pre-processed data is analyzed by an integrated analysis system. This system uses machine learning algorithms to predict risk based on past accident data and similar cases. Based on these predictions, the server generates a risk score for the work situation. This risk score forms the basis for feedback generation. 【0247】 Based on the analysis results, the server generates real-time feedback and determines actions. For example, if a worker's movements are dangerous, it creates and sends an audio alert. Administrators may also receive immediate email notifications. 【0248】 Terminal operation 【0249】 The terminal has the function of receiving feedback from the server and notifying the worker. For high-priority feedback, the worker is immediately alerted through methods such as voice alerts and vibrations. 【0250】 The terminals worn by workers collect new data in real time and send it to the server. This repeated process ensures that the latest work environment information is constantly updated within the system. 【0251】 User interaction 【0252】 Users, namely workers and managers, monitor the site conditions based on real-time feedback. Workers use their own devices to follow safety check instructions and take appropriate safety measures. Managers manage overall safety based on reports received from the system. 【0253】 Specifically, for example, if a worker's heart rate exceeds a certain threshold, the system will immediately instruct the worker to take a break. In other situations, the system can also notify the team working in the area of ​​a hazard detected by a drone and instruct them to take an alternative route. 【0254】 As a result, the present invention significantly improves safety at the work site and reduces the accident rate. 【0255】 The following describes the processing flow. 【0256】 Step 1: 【0257】 The server receives data in real time from aircraft equipment, ground imaging equipment, and detection devices worn by workers. The received data is diverse, including video data, environmental monitoring data, and workers' vital signs. 【0258】 Step 2: 【0259】 The server preprocesses the received data. Specifically, it applies a denoising filter and performs processing to fill in unreliable data points and missing data. This step ensures the data quality necessary for analysis. 【0260】 Step 3: 【0261】 The server analyzes pre-processed data through an integrated analysis system. Using machine learning-based analysis algorithms, it detects risk factors from various data and calculates specific risk scores. This allows for real-time updates of safety indicators for the work environment. 【0262】 Step 4: 【0263】 The server generates feedback based on the analysis results and immediately communicates it to the relevant parties. Depending on the alert content, the feedback is delivered to each terminal as voice guidance, email notifications, or displays on the dashboard. 【0264】 Step 5: 【0265】 The terminal receives feedback sent from the server and notifies the worker on the device. In the case of an emergency alert, it prompts immediate action using visual or audio means. 【0266】 Step 6: 【0267】 Users receive real-time feedback through their devices to understand their current work status. They follow the provided instructions, perform their work safely, and adjust their work plan if necessary. 【0268】 (Example 1) 【0269】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0270】 Maintaining a safe work environment is crucial, especially in hazardous work settings. However, traditional methods have made it difficult to monitor worker safety in real time and provide immediate, appropriate warnings. Furthermore, there is a need to efficiently process and integrate data from diverse sources to improve the accuracy of work risk predictions. 【0271】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0272】 In this invention, the server includes means for receiving diverse information from an aerial mobile body, ground imaging equipment, and detection devices worn by workers; means for integrating and preprocessing the diverse information, performing noise reduction and missing value completion; and integrated analysis means using machine learning to analyze the preprocessed information and evaluate hazards in the work environment. This makes it possible to evaluate the risks at the work site in real time with high accuracy and to quickly provide appropriate feedback to workers and managers. 【0273】 An "aerial mobile device" is a device that collects images and data while moving through the air. 【0274】 "Ground imaging equipment" refers to devices that are fixed or installed on the ground to capture and record images and information of the surrounding environment. 【0275】 A "detection device" is a device worn by workers to monitor their vital signs and the surrounding environment, and to collect data. 【0276】 "Diverse information" refers to a collection of different types of data and information gathered from aerial vehicles, ground-based imaging equipment, and detection devices. 【0277】 "Preprocessing" is the process of removing noise from data and filling in missing values. 【0278】 An "integrated analysis tool" is a method or algorithm for analyzing diverse information and using machine learning to evaluate risks in the work environment. 【0279】 "Machine learning" is a technology that enables computer systems to learn from data and make predictions and decisions. 【0280】 An "evaluation score" is an index calculated using integrated analysis methods that quantifies and shows the risks in the work environment. 【0281】 This invention is a system for ensuring the safety of workers at a construction site. The system collects data from an aerial vehicle, ground imaging equipment, and detection devices worn by workers, and evaluates the risks in the working environment through integrated analysis. 【0282】 Server Operations 【0283】 The server receives video data from an aerial vehicle (e.g., a drone), video data from ground imaging equipment (e.g., surveillance cameras), vital information and environmental information from sensors worn by workers. The received data is preprocessed, for example, noise is removed and missing values are supplemented. This preprocessing improves the accuracy and reliability of the analysis. 【0284】 The server uses the preprocessed data to perform integrated analysis applying machine learning algorithms. In the analysis, past accident data is used as a model and compared with current data to predict the risks of the working environment. Based on this, a risk score for the working situation is generated and appropriate feedback is created. 【0285】 For example, when an increase in heart rate is detected, the server determines that the situation is dangerous and generates feedback to prompt the worker to take a break. Also, when the drone detects unstable terrain, it is possible to notify the worker of that information and send an instruction to bypass that area. 【0286】 Terminal Operations 【0287】 The terminal receives the feedback sent from the server and notifies the worker. High-priority warnings include functions such as immediate attention through voice alerts and vibrations. Furthermore, the terminal continuously sends newly acquired data from the worker to the server in real time. As a result, the latest information within the system is always updated, and based on this information, the server can re-evaluate the risks. 【0288】 User Interaction 【0289】 Users interact with the system as both workers and managers. Workers receive real-time feedback via terminals and are instructed to take safety checks. For example, if a worker's heart rate exceeds a certain threshold, the system automatically notifies them to take a break. 【0290】 An example of a prompt might be: "Describe a system that supports the latest safety measures at construction sites. Please provide specific examples of devices and describe in detail how they collect and process data." This prompt is used as an instruction to explain the system's operation using a generative AI model. 【0291】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0292】 Step 1: 【0293】 The server receives data from aerial vehicles, ground-based imaging equipment, and detection devices worn by workers. Specifically, it receives video from drones, surveillance data from cameras, and vital information from sensors. This data is provided as input. 【0294】 Step 2: 【0295】 The server preprocesses the received data. It applies algorithms to remove noise from the data and impute missing values. Specifically, it corrects blur in drone footage and improves reliability by imputing incomplete data from sensors. The preprocessed data is then output. 【0296】 Step 3: 【0297】 The server performs an integrated analysis of the pre-processed data. Using machine learning algorithms, it assesses risk based on past accident data. Specifically, it uses pattern recognition technology to evaluate the current work situation and generates a risk score. This score becomes the output of the analysis. 【0298】 Step 4: 【0299】 The server generates feedback based on the generated risk score. Specifically, if the risk is high, it creates an audio alert or email notification indicating that immediate action is required. The generated feedback is then output. 【0300】 Step 5: 【0301】 The terminal receives feedback from the server and notifies the worker. Specifically, it alerts the worker by playing an audio alert or vibrating the terminal. This notification is output from the terminal. 【0302】 Step 6: 【0303】 The terminal collects new data from sensors in real time and sends it to the server. This ensures that the latest information on the work environment is always provided. The input of data from sensors and its transmission from the terminal to the server maintains the adaptability and safety of the entire system. 【0304】 (Application Example 1) 【0305】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0306】 Effective safety management at work sites requires real-time risk information and rapid feedback to workers. However, conventional systems lacked timeliness in risk assessment and warning transmission, making it difficult to provide workers with sufficient safety. Therefore, there is a need to develop a system that effectively improves worker safety. 【0307】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0308】 In this invention, the server includes means for receiving various data collected from an aircraft device, a ground imaging device, and a detection device worn by an operator; means for preprocessing the various data to perform noise removal and missing value supplementation; integrated analysis means for analyzing the preprocessed data to evaluate risks in the working environment; means for generating feedback based on the analysis results and issuing warnings; and means for providing alerts by vibration or sound via a terminal device for notifying the operator of the warnings in real time. Thereby, effective monitoring of the working environment and implementation of immediate safety measures become possible. 【0309】 The "aircraft device" is a drone or device for collecting data from the air. 【0310】 The "ground imaging device" is a device for monitoring the environment and working conditions from the ground and performing imaging. 【0311】 The "detection device" is a device worn by an operator for collecting physical condition and environmental information. 【0312】 The "means for receiving various data" is a method or mechanism for receiving various types of data transmitted from multiple devices. 【0313】 "Preprocessing" is a process for performing noise removal and missing value supplementation in order to improve the analysis accuracy of data. 【0314】 The "integrated analysis means" is an analysis method or algorithm for evaluating risks based on the received data. 【0315】 The "means for generating feedback and issuing warnings" is a method for creating appropriate feedback from the analysis results and notifying the operator of the warnings. 【0316】 A "terminal device" is a device that can be carried by workers and is used to receive feedback and transmit warnings in real time. 【0317】 "Means for notifying warnings in real time" refers to technologies and methods for immediately transmitting urgent information to workers. 【0318】 A "vibration or voice alert" is a type of alarm that uses vibration or sound to notify workers via a terminal device in order to draw their attention. 【0319】 The system for implementing this invention integrates and manages data collected from aircraft devices, ground imaging devices, and detection devices worn by workers in order to improve safety at construction sites. 【0320】 The server receives data from these devices and first performs preprocessing, including noise reduction and missing value imputation. This process improves the accuracy and reliability of the data. Subsequently, the received data is analyzed using machine learning algorithms. Specifically, it predicts risks in the work environment by learning from past accident data and similar cases. Based on this, the server generates a safety score for workers and provides immediate feedback. 【0321】 The terminal receives feedback transmitted from the server. It immediately alerts workers by providing real-time voice and vibration warnings. The terminal also collects new data from workers and continuously transmits it to the server, ensuring that it always reflects the latest situation. 【0322】 Users can maintain safe working conditions by following the feedback provided through this system. For example, if the work environment is hazardous, an instruction such as "Your heart rate is high, please take a 5-minute break" will be issued. An example of a prompt message is, "What is the greatest safety risk in working at this site?" 【0323】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0324】 Step 1: 【0325】 The server receives data from aircraft equipment, ground imaging equipment, and detection devices worn by workers. The input is real-time data from these devices, and the output is a dataset that centrally manages this data. The received data is first aggregated on the server. 【0326】 Step 2: 【0327】 The server performs preprocessing on the received data. Preprocessing involves removing noise and imputing missing values ​​from the input dataset. The output is a clean and complete dataset, which contributes to improved accuracy in the analysis. 【0328】 Step 3: 【0329】 The server uses pre-processed data to perform analysis for risk assessment. It applies machine learning algorithms to predict future risks from the input data. The output is a risk score for the work environment, based on past accident data and similar cases. 【0330】 Step 4: 【0331】 The server generates feedback based on the analysis results and sends it to the worker's terminal. Based on the risk score obtained from the input data, it generates specific alert information as output. This information is used to identify situations that require immediate attention. 【0332】 Step 5: 【0333】 The terminal notifies workers of feedback received from the server. The input is feedback information, and the output is warning information via voice or vibration notifications. The terminal uses this to alert workers in real time. 【0334】 Step 6: 【0335】 Users, i.e., workers and managers, take safety measures based on feedback provided through the terminal. The input is real-time feedback data from the terminal, and the output is specific safety actions that serve as guidelines for workers. Users use this information to ensure their own safety. 【0336】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0337】 This invention is a system that incorporates an emotion engine to simultaneously ensure the safety and psychological health of workers. This system combines diverse data obtained from aerial devices, ground imaging devices, and worker-mounted detection devices with technology to analyze the user's emotions. 【0338】 Server operation 【0339】 The server centrally manages data transmitted from various devices and receives diverse data. This includes video data, environmental data, worker vital information, and emotional data analyzed by the emotion engine. 【0340】 The received data is first subjected to noise reduction and missing value imputation. This enables reliable data processing and improves the accuracy of the analysis results. During this preprocessing stage, sentiment data is also appropriately filtered. 【0341】 The integrated analysis system performs a detailed risk assessment using pre-processed information. During this process, a machine learning algorithm learns patterns from past data and predicts new risks. Meanwhile, the emotion engine analyzes voice and facial expression data to evaluate the emotional state of the workers. 【0342】 These analysis results form the basis for generating feedback and creating information that leads to improvements in the work environment. The feedback includes not only warnings about physical risks but also instructions to reduce psychological burden. 【0343】 Terminal operation 【0344】 The terminal has the function of receiving feedback provided by the server and notifying workers in real time. Specifically, it prompts immediate risk avoidance actions through warning sounds and visual displays. 【0345】 Furthermore, the terminals continuously collect biometric and environmental information from workers and transmit this data to the server. This allows the system to always make decisions based on the latest information. 【0346】 User interaction 【0347】 Users will proceed with their work while utilizing the feedback provided through their devices. This feedback includes safety warnings and alerts to reduce psychological stress, so users are expected to follow it to ensure safe work practices. 【0348】 For example, if the server detects a "high-stress state" via the emotion engine during work, a message prompting the worker to take a break will be sent from the terminal. Also, if anxiety is detected during work at height, more detailed safety instructions will be automatically generated. 【0349】 Thus, the present invention can provide an optimal working environment by continuously monitoring the physical and psychological safety of workers, thereby preventing accidents and improving efficiency. 【0350】 The following describes the processing flow. 【0351】 Step 1: 【0352】 The server receives data from aircraft equipment, ground cameras, and detection devices worn by workers. In addition, it receives emotional data from the emotion engine. At this stage, the data includes environmental information, vital data, captured video, and audio. 【0353】 Step 2: 【0354】 The server performs preprocessing of the received data. Specifically, it uses noise reduction filters to improve data accuracy and appropriately fills in missing parts. In this process, unnecessary information is also removed in the analysis of sentiment data. 【0355】 Step 3: 【0356】 The server passes pre-processed data to an integrated analysis system for risk analysis. Using machine learning algorithms, it extracts risk factors from each data type and calculates a real-time risk score. The emotion engine determines the emotional state from the worker's voice and facial expression information and assesses whether the worker is under potential stress. 【0357】 Step 4: 【0358】 The server generates feedback based on the analysis results. This feedback comes in two types: warnings about physical risks and psychological alerts based on emotional state. For example, if a high-stress state is detected, a message recommending a break is generated. 【0359】 Step 5: 【0360】 The terminal receives feedback from the server and notifies the worker. For high-priority feedback, the terminal immediately notifies the worker as an audio and visual alert. The work procedure is adjusted according to the content of the feedback. 【0361】 Step 6: 【0362】 Users review the feedback provided through their device and decide whether to continue working. They can also request additional information from the server if necessary. For example, if their emotional state deteriorates under certain working conditions, they can immediately reschedule their work. 【0363】 (Example 2) 【0364】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0365】 Ensuring both worker safety and psychological well-being simultaneously requires comprehensive monitoring utilizing diverse data. However, current safety management systems focus primarily on assessing physical risks and lack feedback that considers changes in emotions and psychological states. Therefore, providing a safe work environment that includes the psychological well-being of workers remains a challenging task. 【0366】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0367】 In this invention, the server includes means for receiving diverse information collected from spatial devices, ground-viewing devices, and measuring devices worn by workers; means for pre-processing, reducing, and supplementing the diverse information; comprehensive analysis means for analyzing the pre-processed information and evaluating hazards in the work environment; means for analyzing the psychological state of workers using emotion analysis means; and means for generating information based on the analysis results and notifying the workers. This makes it possible to provide detailed feedback to enhance the physical and psychological safety of workers. 【0368】 "Spatial devices" refer to devices designed for information gathering in space, and include various types of devices such as flying vehicles. 【0369】 A "ground vision device" is a device used to acquire visual information from the ground, and includes cameras and sensors. 【0370】 A "measuring device" refers to a device worn by a worker that collects biometric information and data related to their movements. 【0371】 "Diverse information" refers collectively to various types of data obtained from spatial devices, ground-based visual devices, and measurement devices. 【0372】 "Preprocessing" refers to the process of reducing noise and filling in missing data on collected information. 【0373】 "Reduced noise processing" refers to the process of removing unnecessary noise contained in information. 【0374】 "Completion processing" refers to the process of filling in missing data based on past information or inferences. 【0375】 "Comprehensive analysis means" refers to a method for conducting a detailed analysis from multiple perspectives using pre-processed information. 【0376】 "Emotional analysis methods" refer to methods for evaluating the psychological state of workers based on their voice and facial expression data. 【0377】 "Information generation means" refers to a means of generating feedback based on analysis results and conveying useful information to workers. 【0378】 This invention is a system that simultaneously improves safety and psychological health in the work environment, and operates primarily using information obtained from spatial devices, ground-based visual devices, and measurement devices. The server receives diverse information and manages it centrally. The received information includes video data, environmental data, and vital information of workers, as well as data necessary for emotion analysis. 【0379】 The server is equipped with software that performs preprocessing on received information, such as noise reduction and data interpolation. This preprocessing is necessary to supply reliable information for subsequent analysis. After preprocessing, the information is sent to a comprehensive analysis system, where detailed analysis is performed using machine learning algorithms. This enables the assessment of physical risks. The server also incorporates an emotion analysis system that evaluates the emotional state of workers based on voice and facial expression data. 【0380】 Based on the analysis results, the server generates feedback. This feedback includes warnings about physical risks as well as advice to reduce psychological burden. For example, if the emotional analysis detects a high-stress state, it will generate a message recommending that the user "take a break." 【0381】 The terminal has the function of receiving feedback from the server in real time and notifying workers visually or audibly. This allows workers to take immediate risk avoidance actions. In addition, the terminal plays a role in providing the latest data by constantly transmitting biometric and environmental information to the server. 【0382】 Users can perform tasks safely by following the feedback provided via their devices. For example, in high-altitude work where anxiety is detected through server analysis, specific instructions such as "check safety equipment" or "adjust work speed" are provided as feedback. 【0383】 A concrete example is a scenario where a worker in a manufacturing plant is advised to take a break. An example of a prompt generated by a generative AI model is: "Explain how emotional data and work environment data are analyzed to generate feedback in order to optimize the safety of the work environment and the mental health of the workers." 【0384】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0385】 Step 1: 【0386】 The server receives diverse information in real time from spatial devices, ground-based visual devices, and measurement devices. This includes video data, environmental data, worker vital information, and audio data. The received data is temporarily stored in a database. Based on this input data, preparations for data preprocessing are made. 【0387】 Step 2: 【0388】 The server preprocesses the received information. Specifically, it applies a noise reduction filter to video data and uses statistical methods to impute missing data in environmental and vital information. This process enhances data reliability and outputs a clear dataset with reduced noise. 【0389】 Step 3: 【0390】 The server passes the pre-processed data to an integrated analysis system, which uses machine learning algorithms to perform a detailed risk assessment. The algorithm, having learned from past data patterns, predicts potential hazards in the work environment. As a result of this analysis, new risk assessment data is output. 【0391】 Step 4: 【0392】 The server uses emotion analysis tools to analyze the psychological state of workers from voice and facial expression data. It utilizes a generative AI model to execute prompts that identify changes in emotional state. This results in the output of emotional data such as the worker's stress level and anxiety level. 【0393】 Step 5: 【0394】 The server generates feedback based on risk assessment data and sentiment data. The generated feedback includes warnings about physical risks and advice to support psychological well-being. This feedback data is generated as the final output. 【0395】 Step 6: 【0396】 The terminal receives feedback from the server in real time and notifies the worker. Notifications include visual displays and audio messages, allowing users to take immediate risk avoidance actions. Thus, the terminal plays a crucial role in providing workers with critical feedback information. 【0397】 Step 7: 【0398】 Users can proceed with their work safely based on feedback received through their devices. For example, if an emergency is detected during work at height, they can receive detailed safety instructions and take appropriate measures accordingly. This use of feedback improves both the safety and efficiency of the work. 【0399】 (Application Example 2) 【0400】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0401】 This invention aims to simultaneously achieve risk assessment and psychological burden reduction for workers in today's workplace environment, where it is necessary to ensure not only the physical safety of workers but also their psychological health. Conventional systems have been overly focused on the physical aspects of risk, neglecting the psychological aspects. Therefore, the challenge is to grasp workers' stress levels and psychological burden in a timely manner and provide feedback that contributes to their improvement. 【0402】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0403】 In this invention, the server includes means for receiving diverse information collected from flight equipment, ground recording devices, and detection devices worn by workers; means for preprocessing the diverse information, removing noise and imputing missing values; integrated analysis means for analyzing the preprocessed information and evaluating hazards in the work environment; means for generating feedback and issuing warnings based on the analysis results; and means for monitoring the psychological and physiological state of workers and generating auxiliary instructions to reduce psychological burden. This makes it possible to simultaneously evaluate physical and psychological risks and comprehensively ensure the safety and health of workers. 【0404】 A "flying device" is a device that has the ability to monitor and collect information about the environment and the condition of workers from the air. 【0405】 A "ground recording device" is a device that captures and records images and environmental information from the ground and provides the data for analysis. 【0406】 A "detection device" is a device that includes sensor devices worn by workers to collect vital information and environmental data. 【0407】 "Diverse information" refers to data related to the work environment and workers, including video, audio, vital signs, and emotional state analysis information. 【0408】 "Preprocessing" is the process of transforming received data into information suitable for analysis by removing noise and imputing missing values. 【0409】 An "integrated analysis means" is a method or apparatus for analyzing pre-processed data and comprehensively evaluating the risks in the work environment. 【0410】 "Feedback" refers to warnings and instructions provided to workers based on analysis results, with the aim of avoiding physical and psychological risks. 【0411】 "Psychological burden reduction instructions" are notifications or suggestions to reduce psychological stress based on an analysis of the worker's emotional state. 【0412】 The embodiments for carrying out the present invention are shown below. 【0413】 The system of this invention aims to ensure safety and psychological health in the work environment. First, the server receives various information from the flight device, ground recording device, and sensing device worn by the worker. This includes environmental data, video data, audio data, worker vital information, and emotional state analysis information. 【0414】 Upon receiving this data, the server first performs preprocessing to remove noise and impute missing values. Data processing utilizes Python data analysis libraries (e.g., Pandas) and machine learning frameworks (e.g., TensorFlow), enabling highly accurate analysis. 【0415】 Next, using an integrated analysis method, machine learning algorithms analyze the pre-processed data to evaluate the potential hazards in the work environment. In this process, it is possible to predict new hazards using a model that has learned from past accident data. 【0416】 The server generates feedback based on the analysis results. This feedback includes not only warnings about physical risks, but also instructions to reduce psychological burden based on the worker's emotional state. This provides a safe and comfortable working environment. 【0417】 For example, if the emotion engine detects that a worker's stress levels are rising while working at height, the server will generate an instruction such as "Please temporarily stop working and take some time to relax," and notify the worker's terminal in real time. 【0418】 An example of a prompt message is: "Analyze the worker's heart rate and facial expression data, and generate feedback to promote relaxation when psychological stress levels rise." 【0419】 By linking the server and terminals, the physical and psychological safety of workers can be ensured, accident prevention can be achieved, and work efficiency can be improved. 【0420】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0421】 Step 1: 【0422】 The server receives diverse information from aircraft, ground recording devices, and sensing equipment worn by workers. Input information includes environmental data, video data, audio data, vital signs, and emotional state analysis information. The server integrates this data and organizes it into the format required for subsequent processing. 【0423】 Step 2: 【0424】 The server performs noise reduction and missing value imputation on the received data. The input is the data integrated in step 1, and the output is the purified data. Specifically, the data cleaning process is carried out using a Python library (e.g., Pandas). Noisy data is smoothed, and missing data is imputed using statistical methods. 【0425】 Step 3: 【0426】 The server uses pre-processed data to evaluate hazards through an integrated analysis method. The input is the output data from step 2, and the output is risk assessment information for the work environment. Machine learning algorithms are used to learn from past accident information and predict new hazards. Specifically, a model is applied using TensorFlow to perform safety assessments in real time. 【0427】 Step 4: 【0428】 The server generates feedback based on the analysis results and sends it to the terminal. The input is risk assessment information, and the output is warning messages and instructions to reduce psychological burden. The generated feedback is immediately notified to the worker's terminal as audio or text information. Examples of generated feedback include, "Please pause your work and take some time to relax." 【0429】 Step 5: 【0430】 The terminal receives feedback from the server and notifies the worker in real time. Input is feedback information sent from the server, and output is a warning sound, display notification, and vibration alert to the worker. The terminal uses this information to provide specific instructions for the worker to safely continue their work. 【0431】 The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0432】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0433】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214. 【0434】 [Third Embodiment] 【0435】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0436】 As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server. 【0437】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0438】 The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52. 【0439】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0440】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0441】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0442】 Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0443】 The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30. 【0444】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0445】 In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0446】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal". 【0447】 This invention is a system that integrates and analyzes data collected from an aerial vehicle, a ground-based imaging device, and a detection device worn by a worker, in order to enhance the safety of workers at construction sites. Specific embodiments for carrying out this invention are described below. 【0448】 Server operation 【0449】 The server receives diverse data transmitted from multiple devices. Specifically, it receives aerial footage from drones, surveillance footage from ground cameras, and vital and environmental information from worker sensor devices, and processes this data comprehensively. 【0450】 The server first preprocesses the received data. This preprocessing includes removing data noise and imputing missing values. This improves the accuracy and reliability of the analysis. 【0451】 The pre-processed data is analyzed by an integrated analysis system. This system uses machine learning algorithms to predict risk based on past accident data and similar cases. Based on these predictions, the server generates a risk score for the work situation. This risk score forms the basis for feedback generation. 【0452】 Based on the analysis results, the server generates real-time feedback and determines actions. For example, if a worker's movements are dangerous, it creates and sends an audio alert. Administrators may also receive immediate email notifications. 【0453】 Terminal operation 【0454】 The terminal has the function of receiving feedback from the server and notifying the worker. For high-priority feedback, the worker is immediately alerted through methods such as voice alerts and vibrations. 【0455】 The terminals worn by workers collect new data in real time and send it to the server. This repeated process ensures that the latest work environment information is constantly updated within the system. 【0456】 User interaction 【0457】 Users, namely workers and managers, monitor the site conditions based on real-time feedback. Workers use their own devices to follow safety check instructions and take appropriate safety measures. Managers manage overall safety based on reports received from the system. 【0458】 Specifically, for example, if a worker's heart rate exceeds a certain threshold, the system will immediately instruct the worker to take a break. In other situations, the system can also notify the team working in the area of ​​a hazard detected by a drone and instruct them to take an alternative route. 【0459】 As a result, the present invention significantly improves safety at the work site and reduces the accident rate. 【0460】 The following describes the processing flow. 【0461】 Step 1: 【0462】 The server receives data in real time from aircraft equipment, ground imaging equipment, and detection devices worn by workers. The received data is diverse, including video data, environmental monitoring data, and workers' vital signs. 【0463】 Step 2: 【0464】 The server preprocesses the received data. Specifically, it applies a denoising filter and performs processing to fill in unreliable data points and missing data. This step ensures the data quality necessary for analysis. 【0465】 Step 3: 【0466】 The server analyzes pre-processed data through an integrated analysis system. Using machine learning-based analysis algorithms, it detects risk factors from various data and calculates specific risk scores. This allows for real-time updates of safety indicators for the work environment. 【0467】 Step 4: 【0468】 The server generates feedback based on the analysis results and immediately communicates it to the relevant parties. Depending on the alert content, the feedback is delivered to each terminal as voice guidance, email notifications, or displays on the dashboard. 【0469】 Step 5: 【0470】 The terminal receives feedback sent from the server and notifies the worker on the device. In the case of an emergency alert, it prompts immediate action using visual or audio means. 【0471】 Step 6: 【0472】 Users receive real-time feedback through their devices to understand their current work status. They follow the provided instructions, perform their work safely, and adjust their work plan if necessary. 【0473】 (Example 1) 【0474】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0475】 Maintaining a safe work environment is crucial, especially in hazardous work settings. However, traditional methods have made it difficult to monitor worker safety in real time and provide immediate, appropriate warnings. Furthermore, there is a need to efficiently process and integrate data from diverse sources to improve the accuracy of work risk predictions. 【0476】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0477】 In this invention, the server includes means for receiving diverse information from an aerial mobile body, ground imaging equipment, and detection devices worn by workers; means for integrating and preprocessing the diverse information, performing noise reduction and missing value completion; and integrated analysis means using machine learning to analyze the preprocessed information and evaluate hazards in the work environment. This makes it possible to evaluate the risks at the work site in real time with high accuracy and to quickly provide appropriate feedback to workers and managers. 【0478】 An "aerial mobile device" is a device that collects images and data while moving through the air. 【0479】 "Ground imaging equipment" refers to devices that are fixed or installed on the ground to capture and record images and information of the surrounding environment. 【0480】 A "detection device" is a device worn by workers to monitor their vital signs and the surrounding environment, and to collect data. 【0481】 "Diverse information" refers to a collection of different types of data and information gathered from aerial vehicles, ground-based imaging equipment, and detection devices. 【0482】 "Preprocessing" is the process of removing noise from data and filling in missing values. 【0483】 An "integrated analysis tool" is a method or algorithm for analyzing diverse information and using machine learning to evaluate risks in the work environment. 【0484】 "Machine learning" is a technology that enables computer systems to learn from data and make predictions and decisions. 【0485】 An "evaluation score" is an index calculated using integrated analysis methods that quantifies and shows the risks in the work environment. 【0486】 This invention is a system for ensuring the safety of workers at construction sites. The system collects data from an aerial mobile device, ground-based imaging equipment, and detection devices worn by workers, and evaluates the risks in the work environment by performing integrated analysis. 【0487】 Server operation 【0488】 The server receives video data from aerial vehicles (e.g., drones), video data from ground-based cameras (e.g., surveillance cameras), and vital and environmental information from sensors worn by workers. The received data is preprocessed, for example, by removing noise and imputing missing values. This preprocessing improves the accuracy and reliability of the analysis. 【0489】 The server uses pre-processed data to perform integrated analysis using machine learning algorithms. The analysis predicts risks in the work environment by modeling past accident data and comparing it with current data. Based on this, it generates a risk score for the work situation and creates appropriate feedback. 【0490】 For example, if an increase in heart rate is detected, the server will determine the situation is dangerous and generate feedback prompting the worker to take a break. It can also notify workers if the drone detects unstable terrain and instruct them to bypass the area. 【0491】 Terminal operation 【0492】 The terminal receives feedback from the server and notifies the worker. High-priority alerts include features such as voice alerts and immediate vibrating warnings. Furthermore, the terminal continuously transmits newly acquired data from the worker to the server in real time. This ensures that the system is always up-to-date, allowing the server to reassess risks based on that information. 【0493】 User interaction 【0494】 Users interact with the system as both workers and managers. Workers receive real-time feedback via terminals and are instructed to take safety checks. For example, if a worker's heart rate exceeds a certain threshold, the system automatically notifies them to take a break. 【0495】 An example of a prompt might be: "Describe a system that supports the latest safety measures at construction sites. Please provide specific examples of devices and describe in detail how they collect and process data." This prompt is used as an instruction to explain the system's operation using a generative AI model. 【0496】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0497】 Step 1: 【0498】 The server receives data from aerial vehicles, ground-based imaging equipment, and detection devices worn by workers. Specifically, it receives video from drones, surveillance data from cameras, and vital information from sensors. This data is provided as input. 【0499】 Step 2: 【0500】 The server preprocesses the received data. It applies algorithms to remove noise from the data and impute missing values. Specifically, it corrects blur in drone footage and improves reliability by imputing incomplete data from sensors. The preprocessed data is then output. 【0501】 Step 3: 【0502】 The server performs an integrated analysis of the pre-processed data. Using machine learning algorithms, it assesses risk based on past accident data. Specifically, it uses pattern recognition technology to evaluate the current work situation and generates a risk score. This score becomes the output of the analysis. 【0503】 Step 4: 【0504】 The server generates feedback based on the generated risk score. Specifically, if the risk is high, it creates an audio alert or email notification indicating that immediate action is required. The generated feedback is then output. 【0505】 Step 5: 【0506】 The terminal receives feedback from the server and notifies the worker. Specifically, it alerts the worker by playing an audio alert or vibrating the terminal. This notification is output from the terminal. 【0507】 Step 6: 【0508】 The terminal collects new data from sensors in real time and sends it to the server. This ensures that the latest information on the work environment is always provided. The input of data from sensors and its transmission from the terminal to the server maintains the adaptability and safety of the entire system. 【0509】 (Application Example 1) 【0510】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0511】 Effective safety management at work sites requires real-time risk information and rapid feedback to workers. However, conventional systems lacked timeliness in risk assessment and warning transmission, making it difficult to provide workers with sufficient safety. Therefore, there is a need to develop a system that effectively improves worker safety. 【0512】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0513】 In this invention, the server includes means for receiving diverse data collected from aircraft equipment, ground imaging equipment, and detection devices worn by workers; means for preprocessing the diverse data, performing noise reduction and missing value imputation; integrated analysis means for analyzing the preprocessed data and evaluating risks in the work environment; means for generating feedback and issuing warnings based on the analysis results; and means for providing vibration or audible alerts via terminal devices to notify workers of warnings in real time. This enables effective monitoring of the work environment and immediate implementation of safety measures. 【0514】 "Aircraft equipment" refers to unmanned aerial vehicles or devices used to collect data from the air. 【0515】 A "ground imaging device" is a device used to monitor and photograph the environment and work conditions from the ground. 【0516】 A "detection device" is a device worn by workers to collect information about their physical condition and the environment. 【0517】 "Means for receiving diverse data" refers to methods and mechanisms for receiving various types of data transmitted from multiple devices. 【0518】 "Preprocessing" refers to the process of removing noise and imputing missing values ​​in order to improve the accuracy of data analysis. 【0519】 An "integrated analysis tool" refers to an analysis method or algorithm used to assess risk based on received data. 【0520】 "Means for generating feedback and issuing warnings" refers to methods for creating appropriate feedback from analysis results and notifying workers of warnings. 【0521】 A "terminal device" is a device that can be carried by workers and is used to receive feedback and transmit warnings in real time. 【0522】 "Means for notifying warnings in real time" refers to technologies and methods for immediately transmitting urgent information to workers. 【0523】 A "vibration or voice alert" is a type of alarm that uses vibration or sound to notify workers via a terminal device in order to draw their attention. 【0524】 The system for implementing this invention integrates and manages data collected from aircraft devices, ground imaging devices, and detection devices worn by workers in order to improve safety at construction sites. 【0525】 The server receives data from these devices and first performs preprocessing, including noise reduction and missing value imputation. This process improves the accuracy and reliability of the data. Subsequently, the received data is analyzed using machine learning algorithms. Specifically, it predicts risks in the work environment by learning from past accident data and similar cases. Based on this, the server generates a safety score for workers and provides immediate feedback. 【0526】 The terminal receives feedback transmitted from the server. It immediately alerts workers by providing real-time voice and vibration warnings. The terminal also collects new data from workers and continuously transmits it to the server, ensuring that it always reflects the latest situation. 【0527】 Users can maintain safe working conditions by following the feedback provided through this system. For example, if the work environment is hazardous, an instruction such as "Your heart rate is high, please take a 5-minute break" will be issued. An example of a prompt message is, "What is the greatest safety risk in working at this site?" 【0528】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0529】 Step 1: 【0530】 The server receives data from aircraft equipment, ground imaging equipment, and detection devices worn by workers. The input is real-time data from these devices, and the output is a dataset that centrally manages this data. The received data is first aggregated on the server. 【0531】 Step 2: 【0532】 The server performs preprocessing on the received data. Preprocessing involves removing noise and imputing missing values ​​from the input dataset. The output is a clean and complete dataset, which contributes to improved accuracy in the analysis. 【0533】 Step 3: 【0534】 The server uses pre-processed data to perform analysis for risk assessment. It applies machine learning algorithms to predict future risks from the input data. The output is a risk score for the work environment, based on past accident data and similar cases. 【0535】 Step 4: 【0536】 The server generates feedback based on the analysis results and sends it to the worker's terminal. Based on the risk score obtained from the input data, it generates specific alert information as output. This information is used to identify situations that require immediate attention. 【0537】 Step 5: 【0538】 The terminal notifies workers of feedback received from the server. The input is feedback information, and the output is warning information via voice or vibration notifications. The terminal uses this to alert workers in real time. 【0539】 Step 6: 【0540】 Users, i.e., workers and managers, take safety measures based on feedback provided through the terminal. The input is real-time feedback data from the terminal, and the output is specific safety actions that serve as guidelines for workers. Users use this information to ensure their own safety. 【0541】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0542】 This invention is a system that incorporates an emotion engine to simultaneously ensure the safety and psychological health of workers. This system combines diverse data obtained from aerial devices, ground imaging devices, and worker-mounted detection devices with technology to analyze the user's emotions. 【0543】 Server operation 【0544】 The server centrally manages data transmitted from various devices and receives diverse data. This includes video data, environmental data, worker vital information, and emotional data analyzed by the emotion engine. 【0545】 The received data is first subjected to noise reduction and missing value imputation. This enables reliable data processing and improves the accuracy of the analysis results. During this preprocessing stage, sentiment data is also appropriately filtered. 【0546】 The integrated analysis system performs a detailed risk assessment using pre-processed information. During this process, a machine learning algorithm learns patterns from past data and predicts new risks. Meanwhile, the emotion engine analyzes voice and facial expression data to evaluate the emotional state of the workers. 【0547】 These analysis results form the basis for generating feedback and creating information that leads to improvements in the work environment. The feedback includes not only warnings about physical risks but also instructions to reduce psychological burden. 【0548】 Terminal operation 【0549】 The terminal has the function of receiving feedback provided by the server and notifying workers in real time. Specifically, it prompts immediate risk avoidance actions through warning sounds and visual displays. 【0550】 Furthermore, the terminals continuously collect biometric and environmental information from workers and transmit this data to the server. This allows the system to always make decisions based on the latest information. 【0551】 User interaction 【0552】 Users will proceed with their work while utilizing the feedback provided through their devices. This feedback includes safety warnings and alerts to reduce psychological stress, so users are expected to follow it to ensure safe work practices. 【0553】 For example, if the server detects a "high-stress state" via the emotion engine during work, a message prompting the worker to take a break will be sent from the terminal. Also, if anxiety is detected during work at height, more detailed safety instructions will be automatically generated. 【0554】 Thus, the present invention can provide an optimal working environment by continuously monitoring the physical and psychological safety of workers, thereby preventing accidents and improving efficiency. 【0555】 The following describes the processing flow. 【0556】 Step 1: 【0557】 The server receives data from aircraft equipment, ground cameras, and detection devices worn by workers. In addition, it receives emotional data from the emotion engine. At this stage, the data includes environmental information, vital data, captured video, and audio. 【0558】 Step 2: 【0559】 The server performs preprocessing of the received data. Specifically, it uses noise reduction filters to improve data accuracy and appropriately fills in missing parts. In this process, unnecessary information is also removed in the analysis of sentiment data. 【0560】 Step 3: 【0561】 The server passes pre-processed data to an integrated analysis system for risk analysis. Using machine learning algorithms, it extracts risk factors from each data type and calculates a real-time risk score. The emotion engine determines the emotional state from the worker's voice and facial expression information and assesses whether the worker is under potential stress. 【0562】 Step 4: 【0563】 The server generates feedback based on the analysis results. This feedback comes in two types: warnings about physical risks and psychological alerts based on emotional state. For example, if a high-stress state is detected, a message recommending a break is generated. 【0564】 Step 5: 【0565】 The terminal receives feedback from the server and notifies the worker. For high-priority feedback, the terminal immediately notifies the worker as an audio and visual alert. The work procedure is adjusted according to the content of the feedback. 【0566】 Step 6: 【0567】 Users review the feedback provided through their device and decide whether to continue working. They can also request additional information from the server if necessary. For example, if their emotional state deteriorates under certain working conditions, they can immediately reschedule their work. 【0568】 (Example 2) 【0569】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0570】 Ensuring both worker safety and psychological well-being simultaneously requires comprehensive monitoring utilizing diverse data. However, current safety management systems focus primarily on assessing physical risks and lack feedback that considers changes in emotions and psychological states. Therefore, providing a safe work environment that includes the psychological well-being of workers remains a challenging task. 【0571】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0572】 In this invention, the server includes means for receiving diverse information collected from spatial devices, ground-viewing devices, and measuring devices worn by workers; means for pre-processing, reducing, and supplementing the diverse information; comprehensive analysis means for analyzing the pre-processed information and evaluating hazards in the work environment; means for analyzing the psychological state of workers using emotion analysis means; and means for generating information based on the analysis results and notifying the workers. This makes it possible to provide detailed feedback to enhance the physical and psychological safety of workers. 【0573】 "Spatial devices" refer to devices designed for information gathering in space, and include various types of devices such as flying vehicles. 【0574】 A "ground vision device" is a device used to acquire visual information from the ground, and includes cameras and sensors. 【0575】 A "measuring device" refers to a device worn by a worker that collects biometric information and data related to their movements. 【0576】 "Diverse information" refers collectively to various types of data obtained from spatial devices, ground-based visual devices, and measurement devices. 【0577】 "Preprocessing" refers to the process of reducing noise and filling in missing data on collected information. 【0578】 "Reduced noise processing" refers to the process of removing unnecessary noise contained in information. 【0579】 "Completion processing" refers to the process of filling in missing data based on past information or inferences. 【0580】 "Comprehensive analysis means" refers to a method for conducting a detailed analysis from multiple perspectives using pre-processed information. 【0581】 "Emotional analysis methods" refer to methods for evaluating the psychological state of workers based on their voice and facial expression data. 【0582】 "Information generation means" refers to a means of generating feedback based on analysis results and conveying useful information to workers. 【0583】 This invention is a system that simultaneously improves safety and psychological health in the work environment, and operates primarily using information obtained from spatial devices, ground-based visual devices, and measurement devices. The server receives diverse information and manages it centrally. The received information includes video data, environmental data, and vital information of workers, as well as data necessary for emotion analysis. 【0584】 The server is equipped with software that performs preprocessing on received information, such as noise reduction and data interpolation. This preprocessing is necessary to supply reliable information for subsequent analysis. After preprocessing, the information is sent to a comprehensive analysis system, where detailed analysis is performed using machine learning algorithms. This enables the assessment of physical risks. The server also incorporates an emotion analysis system that evaluates the emotional state of workers based on voice and facial expression data. 【0585】 Based on the analysis results, the server generates feedback. This feedback includes warnings about physical risks as well as advice to reduce psychological burden. For example, if the emotional analysis detects a high-stress state, it will generate a message recommending that the user "take a break." 【0586】 The terminal has the function of receiving feedback from the server in real time and notifying workers visually or audibly. This allows workers to take immediate risk avoidance actions. In addition, the terminal plays a role in providing the latest data by constantly transmitting biometric and environmental information to the server. 【0587】 Users can perform tasks safely by following the feedback provided via their devices. For example, in high-altitude work where anxiety is detected through server analysis, specific instructions such as "check safety equipment" or "adjust work speed" are provided as feedback. 【0588】 A concrete example is a scenario where a worker in a manufacturing plant is advised to take a break. An example of a prompt generated by a generative AI model is: "Explain how emotional data and work environment data are analyzed to generate feedback in order to optimize the safety of the work environment and the mental health of the workers." 【0589】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0590】 Step 1: 【0591】 The server receives diverse information in real time from spatial devices, ground-based visual devices, and measurement devices. This includes video data, environmental data, worker vital information, and audio data. The received data is temporarily stored in a database. Based on this input data, preparations for data preprocessing are made. 【0592】 Step 2: 【0593】 The server preprocesses the received information. Specifically, it applies a noise reduction filter to video data and uses statistical methods to impute missing data in environmental and vital information. This process enhances data reliability and outputs a clear dataset with reduced noise. 【0594】 Step 3: 【0595】 The server passes the pre-processed data to an integrated analysis system, which uses machine learning algorithms to perform a detailed risk assessment. The algorithm, having learned from past data patterns, predicts potential hazards in the work environment. As a result of this analysis, new risk assessment data is output. 【0596】 Step 4: 【0597】 The server uses emotion analysis tools to analyze the psychological state of workers from voice and facial expression data. It utilizes a generative AI model to execute prompts that identify changes in emotional state. This results in the output of emotional data such as the worker's stress level and anxiety level. 【0598】 Step 5: 【0599】 The server generates feedback based on risk assessment data and sentiment data. The generated feedback includes warnings about physical risks and advice to support psychological well-being. This feedback data is generated as the final output. 【0600】 Step 6: 【0601】 The terminal receives feedback from the server in real time and notifies the worker. Notifications include visual displays and audio messages, allowing users to take immediate risk avoidance actions. Thus, the terminal plays a crucial role in providing workers with critical feedback information. 【0602】 Step 7: 【0603】 Users can proceed with their work safely based on feedback received through their devices. For example, if an emergency is detected during work at height, they can receive detailed safety instructions and take appropriate measures accordingly. This use of feedback improves both the safety and efficiency of the work. 【0604】 (Application Example 2) 【0605】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0606】 This invention aims to simultaneously achieve risk assessment and psychological burden reduction for workers in today's workplace environment, where it is necessary to ensure not only the physical safety of workers but also their psychological health. Conventional systems have been overly focused on the physical aspects of risk, neglecting the psychological aspects. Therefore, the challenge is to grasp workers' stress levels and psychological burden in a timely manner and provide feedback that contributes to their improvement. 【0607】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0608】 In this invention, the server includes means for receiving diverse information collected from flight equipment, ground recording devices, and detection devices worn by workers; means for preprocessing the diverse information, removing noise and imputing missing values; integrated analysis means for analyzing the preprocessed information and evaluating hazards in the work environment; means for generating feedback and issuing warnings based on the analysis results; and means for monitoring the psychological and physiological state of workers and generating auxiliary instructions to reduce psychological burden. This makes it possible to simultaneously evaluate physical and psychological risks and comprehensively ensure the safety and health of workers. 【0609】 A "flying device" is a device that has the ability to monitor and collect information about the environment and the condition of workers from the air. 【0610】 A "ground recording device" is a device that captures and records images and environmental information from the ground and provides the data for analysis. 【0611】 A "detection device" is a device that includes sensor devices worn by workers to collect vital information and environmental data. 【0612】 "Diverse information" refers to data related to the work environment and workers, including video, audio, vital signs, and emotional state analysis information. 【0613】 "Preprocessing" is the process of transforming received data into information suitable for analysis by removing noise and imputing missing values. 【0614】 An "integrated analysis means" is a method or apparatus for analyzing pre-processed data and comprehensively evaluating the risks in the work environment. 【0615】 "Feedback" refers to warnings and instructions provided to workers based on analysis results, with the aim of avoiding physical and psychological risks. 【0616】 "Psychological burden reduction instructions" are notifications or suggestions to reduce psychological stress based on an analysis of the worker's emotional state. 【0617】 The embodiments for carrying out the present invention are shown below. 【0618】 The system of this invention aims to ensure safety and psychological health in the work environment. First, the server receives various information from the flight device, ground recording device, and sensing device worn by the worker. This includes environmental data, video data, audio data, worker vital information, and emotional state analysis information. 【0619】 Upon receiving this data, the server first performs preprocessing to remove noise and impute missing values. Data processing utilizes Python data analysis libraries (e.g., Pandas) and machine learning frameworks (e.g., TensorFlow), enabling highly accurate analysis. 【0620】 Next, using an integrated analysis method, machine learning algorithms analyze the pre-processed data to evaluate the potential hazards in the work environment. In this process, it is possible to predict new hazards using a model that has learned from past accident data. 【0621】 The server generates feedback based on the analysis results. This feedback includes not only warnings about physical risks, but also instructions to reduce psychological burden based on the worker's emotional state. This provides a safe and comfortable working environment. 【0622】 For example, if the emotion engine detects that a worker's stress levels are rising while working at height, the server will generate an instruction such as "Please temporarily stop working and take some time to relax," and notify the worker's terminal in real time. 【0623】 An example of a prompt message is: "Analyze the worker's heart rate and facial expression data, and generate feedback to promote relaxation when psychological stress levels rise." 【0624】 By linking the server and terminals, the physical and psychological safety of workers can be ensured, accident prevention can be achieved, and work efficiency can be improved. 【0625】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0626】 Step 1: 【0627】 The server receives diverse information from aircraft, ground recording devices, and sensing equipment worn by workers. Input information includes environmental data, video data, audio data, vital signs, and emotional state analysis information. The server integrates this data and organizes it into the format required for subsequent processing. 【0628】 Step 2: 【0629】 The server performs noise reduction and missing value imputation on the received data. The input is the data integrated in step 1, and the output is the purified data. Specifically, the data cleaning process is carried out using a Python library (e.g., Pandas). Noisy data is smoothed, and missing data is imputed using statistical methods. 【0630】 Step 3: 【0631】 The server uses pre-processed data to evaluate hazards through an integrated analysis method. The input is the output data from step 2, and the output is risk assessment information for the work environment. Machine learning algorithms are used to learn from past accident information and predict new hazards. Specifically, a model is applied using TensorFlow to perform safety assessments in real time. 【0632】 Step 4: 【0633】 The server generates feedback based on the analysis results and sends it to the terminal. The input is risk assessment information, and the output is warning messages and instructions to reduce psychological burden. The generated feedback is immediately notified to the worker's terminal as audio or text information. Examples of generated feedback include, "Please pause your work and take some time to relax." 【0634】 Step 5: 【0635】 The terminal receives feedback from the server and notifies the worker in real time. Input is feedback information sent from the server, and output is a warning sound, display notification, and vibration alert to the worker. The terminal uses this information to provide specific instructions for the worker to safely continue their work. 【0636】 The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0637】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0638】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314. 【0639】 [Fourth Embodiment] 【0640】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0641】 As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server. 【0642】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0643】 The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52. 【0644】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0645】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0646】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0647】 The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes. 【0648】 Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0649】 The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30. 【0650】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0651】 In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0652】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0653】 This invention is a system that integrates and analyzes data collected from an aerial vehicle, a ground-based imaging device, and a detection device worn by a worker, in order to enhance the safety of workers at construction sites. Specific embodiments for carrying out this invention are described below. 【0654】 Server operation 【0655】 The server receives diverse data transmitted from multiple devices. Specifically, it receives aerial footage from drones, surveillance footage from ground cameras, and vital and environmental information from worker sensor devices, and processes this data comprehensively. 【0656】 The server first preprocesses the received data. This preprocessing includes removing data noise and imputing missing values. This improves the accuracy and reliability of the analysis. 【0657】 The pre-processed data is analyzed by an integrated analysis system. This system uses machine learning algorithms to predict risk based on past accident data and similar cases. Based on these predictions, the server generates a risk score for the work situation. This risk score forms the basis for feedback generation. 【0658】 Based on the analysis results, the server generates real-time feedback and determines actions. For example, if a worker's movements are dangerous, it creates and sends an audio alert. Administrators may also receive immediate email notifications. 【0659】 Terminal operation 【0660】 The terminal has the function of receiving feedback from the server and notifying the worker. For high-priority feedback, the worker is immediately alerted through methods such as voice alerts and vibrations. 【0661】 The terminals worn by workers collect new data in real time and send it to the server. This repeated process ensures that the latest work environment information is constantly updated within the system. 【0662】 User interaction 【0663】 Users, namely workers and managers, monitor the site conditions based on real-time feedback. Workers use their own devices to follow safety check instructions and take appropriate safety measures. Managers manage overall safety based on reports received from the system. 【0664】 Specifically, for example, if a worker's heart rate exceeds a certain threshold, the system will immediately instruct the worker to take a break. In other situations, the system can also notify the team working in the area of ​​a hazard detected by a drone and instruct them to take an alternative route. 【0665】 As a result, the present invention significantly improves safety at the work site and reduces the accident rate. 【0666】 The following describes the processing flow. 【0667】 Step 1: 【0668】 The server receives data in real time from aircraft equipment, ground imaging equipment, and detection devices worn by workers. The received data is diverse, including video data, environmental monitoring data, and workers' vital signs. 【0669】 Step 2: 【0670】 The server preprocesses the received data. Specifically, it applies a denoising filter and performs processing to fill in unreliable data points and missing data. This step ensures the data quality necessary for analysis. 【0671】 Step 3: 【0672】 The server analyzes pre-processed data through an integrated analysis system. Using machine learning-based analysis algorithms, it detects risk factors from various data and calculates specific risk scores. This allows for real-time updates of safety indicators for the work environment. 【0673】 Step 4: 【0674】 The server generates feedback based on the analysis results and immediately communicates it to the relevant parties. Depending on the alert content, the feedback is delivered to each terminal as voice guidance, email notifications, or displays on the dashboard. 【0675】 Step 5: 【0676】 The terminal receives feedback sent from the server and notifies the worker on the device. In the case of an emergency alert, it prompts immediate action using visual or audio means. 【0677】 Step 6: 【0678】 Users receive real-time feedback through their devices to understand their current work status. They follow the provided instructions, perform their work safely, and adjust their work plan if necessary. 【0679】 (Example 1) 【0680】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0681】 Maintaining a safe work environment is crucial, especially in hazardous work settings. However, traditional methods have made it difficult to monitor worker safety in real time and provide immediate, appropriate warnings. Furthermore, there is a need to efficiently process and integrate data from diverse sources to improve the accuracy of work risk predictions. 【0682】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0683】 In this invention, the server includes means for receiving diverse information from an aerial mobile body, ground imaging equipment, and detection devices worn by workers; means for integrating and preprocessing the diverse information, performing noise reduction and missing value completion; and integrated analysis means using machine learning to analyze the preprocessed information and evaluate hazards in the work environment. This makes it possible to evaluate the risks at the work site in real time with high accuracy and to quickly provide appropriate feedback to workers and managers. 【0684】 An "aerial mobile device" is a device that collects images and data while moving through the air. 【0685】 "Ground imaging equipment" refers to devices that are fixed or installed on the ground to capture and record images and information of the surrounding environment. 【0686】 A "detection device" is a device worn by workers to monitor their vital signs and the surrounding environment, and to collect data. 【0687】 "Diverse information" refers to a collection of different types of data and information gathered from aerial vehicles, ground-based imaging equipment, and detection devices. 【0688】 "Preprocessing" is the process of removing noise from data and filling in missing values. 【0689】 An "integrated analysis tool" is a method or algorithm for analyzing diverse information and using machine learning to evaluate risks in the work environment. 【0690】 "Machine learning" is a technology that enables computer systems to learn from data and make predictions and decisions. 【0691】 An "evaluation score" is an index calculated using integrated analysis methods that quantifies and shows the risks in the work environment. 【0692】 This invention is a system for ensuring the safety of workers at construction sites. The system collects data from an aerial mobile device, ground-based imaging equipment, and detection devices worn by workers, and evaluates the risks in the work environment by performing integrated analysis. 【0693】 Server operation 【0694】 The server receives video data from aerial vehicles (e.g., drones), video data from ground-based cameras (e.g., surveillance cameras), and vital and environmental information from sensors worn by workers. The received data is preprocessed, for example, by removing noise and imputing missing values. This preprocessing improves the accuracy and reliability of the analysis. 【0695】 The server uses pre-processed data to perform integrated analysis using machine learning algorithms. The analysis predicts risks in the work environment by modeling past accident data and comparing it with current data. Based on this, it generates a risk score for the work situation and creates appropriate feedback. 【0696】 For example, if an increase in heart rate is detected, the server will determine the situation is dangerous and generate feedback prompting the worker to take a break. It can also notify workers if the drone detects unstable terrain and instruct them to bypass the area. 【0697】 Terminal operation 【0698】 The terminal receives feedback from the server and notifies the worker. High-priority alerts include features such as voice alerts and immediate vibrating warnings. Furthermore, the terminal continuously transmits newly acquired data from the worker to the server in real time. This ensures that the system is always up-to-date, allowing the server to reassess risks based on that information. 【0699】 User interaction 【0700】 Users interact with the system as both workers and managers. Workers receive real-time feedback via terminals and are instructed to take safety checks. For example, if a worker's heart rate exceeds a certain threshold, the system automatically notifies them to take a break. 【0701】 An example of a prompt might be: "Describe a system that supports the latest safety measures at construction sites. Please provide specific examples of devices and describe in detail how they collect and process data." This prompt is used as an instruction to explain the system's operation using a generative AI model. 【0702】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0703】 Step 1: 【0704】 The server receives data from aerial vehicles, ground-based imaging equipment, and detection devices worn by workers. Specifically, it receives video from drones, surveillance data from cameras, and vital information from sensors. This data is provided as input. 【0705】 Step 2: 【0706】 The server preprocesses the received data. It applies algorithms to remove noise from the data and impute missing values. Specifically, it corrects blur in drone footage and improves reliability by imputing incomplete data from sensors. The preprocessed data is then output. 【0707】 Step 3: 【0708】 The server performs an integrated analysis of the pre-processed data. Using machine learning algorithms, it assesses risk based on past accident data. Specifically, it uses pattern recognition technology to evaluate the current work situation and generates a risk score. This score becomes the output of the analysis. 【0709】 Step 4: 【0710】 The server generates feedback based on the generated risk score. Specifically, if the risk is high, it creates an audio alert or email notification indicating that immediate action is required. The generated feedback is then output. 【0711】 Step 5: 【0712】 The terminal receives feedback from the server and notifies the worker. Specifically, it alerts the worker by playing an audio alert or vibrating the terminal. This notification is output from the terminal. 【0713】 Step 6: 【0714】 The terminal collects new data from sensors in real time and sends it to the server. This ensures that the latest information on the work environment is always provided. The input of data from sensors and its transmission from the terminal to the server maintains the adaptability and safety of the entire system. 【0715】 (Application Example 1) 【0716】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0717】 Effective safety management at work sites requires real-time risk information and rapid feedback to workers. However, conventional systems lacked timeliness in risk assessment and warning transmission, making it difficult to provide workers with sufficient safety. Therefore, there is a need to develop a system that effectively improves worker safety. 【0718】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0719】 In this invention, the server includes means for receiving diverse data collected from aircraft equipment, ground imaging equipment, and detection devices worn by workers; means for preprocessing the diverse data, performing noise reduction and missing value imputation; integrated analysis means for analyzing the preprocessed data and evaluating risks in the work environment; means for generating feedback and issuing warnings based on the analysis results; and means for providing vibration or audible alerts via terminal devices to notify workers of warnings in real time. This enables effective monitoring of the work environment and immediate implementation of safety measures. 【0720】 "Aircraft equipment" refers to unmanned aerial vehicles or devices used to collect data from the air. 【0721】 A "ground imaging device" is a device used to monitor and photograph the environment and work conditions from the ground. 【0722】 A "detection device" is a device worn by workers to collect information about their physical condition and the environment. 【0723】 "Means for receiving diverse data" refers to methods and mechanisms for receiving various types of data transmitted from multiple devices. 【0724】 "Preprocessing" refers to the process of removing noise and imputing missing values ​​in order to improve the accuracy of data analysis. 【0725】 An "integrated analysis tool" refers to an analysis method or algorithm used to assess risk based on received data. 【0726】 "Means for generating feedback and issuing warnings" refers to methods for creating appropriate feedback from analysis results and notifying workers of warnings. 【0727】 A "terminal device" is a device that can be carried by workers and is used to receive feedback and transmit warnings in real time. 【0728】 "Means for notifying warnings in real time" refers to technologies and methods for immediately transmitting urgent information to workers. 【0729】 A "vibration or voice alert" is a type of alarm that uses vibration or sound to notify workers via a terminal device in order to draw their attention. 【0730】 The system for implementing this invention integrates and manages data collected from aircraft devices, ground imaging devices, and detection devices worn by workers in order to improve safety at construction sites. 【0731】 The server receives data from these devices and first performs preprocessing, including noise reduction and missing value imputation. This process improves the accuracy and reliability of the data. Subsequently, the received data is analyzed using machine learning algorithms. Specifically, it predicts risks in the work environment by learning from past accident data and similar cases. Based on this, the server generates a safety score for workers and provides immediate feedback. 【0732】 The terminal receives feedback transmitted from the server. It immediately alerts workers by providing real-time voice and vibration warnings. The terminal also collects new data from workers and continuously transmits it to the server, ensuring that it always reflects the latest situation. 【0733】 Users can maintain safe working conditions by following the feedback provided through this system. For example, if the work environment is hazardous, an instruction such as "Your heart rate is high, please take a 5-minute break" will be issued. An example of a prompt message is, "What is the greatest safety risk in working at this site?" 【0734】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0735】 Step 1: 【0736】 The server receives data from aircraft equipment, ground imaging equipment, and detection devices worn by workers. The input is real-time data from these devices, and the output is a dataset that centrally manages this data. The received data is first aggregated on the server. 【0737】 Step 2: 【0738】 The server performs preprocessing on the received data. Preprocessing involves removing noise and imputing missing values ​​from the input dataset. The output is a clean and complete dataset, which contributes to improved accuracy in the analysis. 【0739】 Step 3: 【0740】 The server uses pre-processed data to perform analysis for risk assessment. It applies machine learning algorithms to predict future risks from the input data. The output is a risk score for the work environment, based on past accident data and similar cases. 【0741】 Step 4: 【0742】 The server generates feedback based on the analysis results and sends it to the worker's terminal. Based on the risk score obtained from the input data, it generates specific alert information as output. This information is used to identify situations that require immediate attention. 【0743】 Step 5: 【0744】 The terminal notifies workers of feedback received from the server. The input is feedback information, and the output is warning information via voice or vibration notifications. The terminal uses this to alert workers in real time. 【0745】 Step 6: 【0746】 Users, i.e., workers and managers, take safety measures based on feedback provided through the terminal. The input is real-time feedback data from the terminal, and the output is specific safety actions that serve as guidelines for workers. Users use this information to ensure their own safety. 【0747】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0748】 This invention is a system that incorporates an emotion engine to simultaneously ensure the safety and psychological health of workers. This system combines diverse data obtained from aerial devices, ground imaging devices, and worker-mounted detection devices with technology to analyze the user's emotions. 【0749】 Server operation 【0750】 The server centrally manages data transmitted from various devices and receives diverse data. This includes video data, environmental data, worker vital information, and emotional data analyzed by the emotion engine. 【0751】 The received data is first subjected to noise reduction and missing value imputation. This enables reliable data processing and improves the accuracy of the analysis results. During this preprocessing stage, sentiment data is also appropriately filtered. 【0752】 The integrated analysis system performs a detailed risk assessment using pre-processed information. During this process, a machine learning algorithm learns patterns from past data and predicts new risks. Meanwhile, the emotion engine analyzes voice and facial expression data to evaluate the emotional state of the workers. 【0753】 These analysis results form the basis for generating feedback and creating information that leads to improvements in the work environment. The feedback includes not only warnings about physical risks but also instructions to reduce psychological burden. 【0754】 Terminal operation 【0755】 The terminal has the function of receiving feedback provided by the server and notifying workers in real time. Specifically, it prompts immediate risk avoidance actions through warning sounds and visual displays. 【0756】 Furthermore, the terminals continuously collect biometric and environmental information from workers and transmit this data to the server. This allows the system to always make decisions based on the latest information. 【0757】 User interaction 【0758】 Users will proceed with their work while utilizing the feedback provided through their devices. This feedback includes safety warnings and alerts to reduce psychological stress, so users are expected to follow it to ensure safe work practices. 【0759】 For example, if the server detects a "high-stress state" via the emotion engine during work, a message prompting the worker to take a break will be sent from the terminal. Also, if anxiety is detected during work at height, more detailed safety instructions will be automatically generated. 【0760】 Thus, the present invention can provide an optimal working environment by continuously monitoring the physical and psychological safety of workers, thereby preventing accidents and improving efficiency. 【0761】 The following describes the processing flow. 【0762】 Step 1: 【0763】 The server receives data from aircraft equipment, ground cameras, and detection devices worn by workers. In addition, it receives emotional data from the emotion engine. At this stage, the data includes environmental information, vital data, captured video, and audio. 【0764】 Step 2: 【0765】 The server performs preprocessing of the received data. Specifically, it uses noise reduction filters to improve data accuracy and appropriately fills in missing parts. In this process, unnecessary information is also removed in the analysis of sentiment data. 【0766】 Step 3: 【0767】 The server passes pre-processed data to an integrated analysis system for risk analysis. Using machine learning algorithms, it extracts risk factors from each data type and calculates a real-time risk score. The emotion engine determines the emotional state from the worker's voice and facial expression information and assesses whether the worker is under potential stress. 【0768】 Step 4: 【0769】 The server generates feedback based on the analysis results. This feedback comes in two types: warnings about physical risks and psychological alerts based on emotional state. For example, if a high-stress state is detected, a message recommending a break is generated. 【0770】 Step 5: 【0771】 The terminal receives feedback from the server and notifies the worker. For high-priority feedback, the terminal immediately notifies the worker as an audio and visual alert. The work procedure is adjusted according to the content of the feedback. 【0772】 Step 6: 【0773】 Users review the feedback provided through their device and decide whether to continue working. They can also request additional information from the server if necessary. For example, if their emotional state deteriorates under certain working conditions, they can immediately reschedule their work. 【0774】 (Example 2) 【0775】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0776】 Ensuring both worker safety and psychological well-being simultaneously requires comprehensive monitoring utilizing diverse data. However, current safety management systems focus primarily on assessing physical risks and lack feedback that considers changes in emotions and psychological states. Therefore, providing a safe work environment that includes the psychological well-being of workers remains a challenging task. 【0777】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0778】 In this invention, the server includes means for receiving diverse information collected from spatial devices, ground-viewing devices, and measuring devices worn by workers; means for pre-processing, reducing, and supplementing the diverse information; comprehensive analysis means for analyzing the pre-processed information and evaluating hazards in the work environment; means for analyzing the psychological state of workers using emotion analysis means; and means for generating information based on the analysis results and notifying the workers. This makes it possible to provide detailed feedback to enhance the physical and psychological safety of workers. 【0779】 "Spatial devices" refer to devices designed for information gathering in space, and include various types of devices such as flying vehicles. 【0780】 A "ground vision device" is a device used to acquire visual information from the ground, and includes cameras and sensors. 【0781】 A "measuring device" refers to a device worn by a worker that collects biometric information and data related to their movements. 【0782】 "Diverse information" refers collectively to various types of data obtained from spatial devices, ground-based visual devices, and measurement devices. 【0783】 "Preprocessing" refers to the process of reducing noise and filling in missing data on collected information. 【0784】 "Reduced noise processing" refers to the process of removing unnecessary noise contained in information. 【0785】 "Completion processing" refers to the process of filling in missing data based on past information or inferences. 【0786】 "Comprehensive analysis means" refers to a method for conducting a detailed analysis from multiple perspectives using pre-processed information. 【0787】 "Emotional analysis methods" refer to methods for evaluating the psychological state of workers based on their voice and facial expression data. 【0788】 "Information generation means" refers to a means of generating feedback based on analysis results and conveying useful information to workers. 【0789】 This invention is a system that simultaneously improves safety and psychological health in the work environment, and operates primarily using information obtained from spatial devices, ground-based visual devices, and measurement devices. The server receives diverse information and manages it centrally. The received information includes video data, environmental data, and vital information of workers, as well as data necessary for emotion analysis. 【0790】 The server is equipped with software that performs preprocessing on received information, such as noise reduction and data interpolation. This preprocessing is necessary to supply reliable information for subsequent analysis. After preprocessing, the information is sent to a comprehensive analysis system, where detailed analysis is performed using machine learning algorithms. This enables the assessment of physical risks. The server also incorporates an emotion analysis system that evaluates the emotional state of workers based on voice and facial expression data. 【0791】 Based on the analysis results, the server generates feedback. This feedback includes warnings about physical risks as well as advice to reduce psychological burden. For example, if the emotional analysis detects a high-stress state, it will generate a message recommending that the user "take a break." 【0792】 The terminal has the function of receiving feedback from the server in real time and notifying workers visually or audibly. This allows workers to take immediate risk avoidance actions. In addition, the terminal plays a role in providing the latest data by constantly transmitting biometric and environmental information to the server. 【0793】 Users can perform tasks safely by following the feedback provided via their devices. For example, in high-altitude work where anxiety is detected through server analysis, specific instructions such as "check safety equipment" or "adjust work speed" are provided as feedback. 【0794】 A concrete example is a scenario where a worker in a manufacturing plant is advised to take a break. An example of a prompt generated by a generative AI model is: "Explain how emotional data and work environment data are analyzed to generate feedback in order to optimize the safety of the work environment and the mental health of the workers." 【0795】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0796】 Step 1: 【0797】 The server receives diverse information in real time from spatial devices, ground-based visual devices, and measurement devices. This includes video data, environmental data, worker vital information, and audio data. The received data is temporarily stored in a database. Based on this input data, preparations for data preprocessing are made. 【0798】 Step 2: 【0799】 The server preprocesses the received information. Specifically, it applies a noise reduction filter to video data and uses statistical methods to impute missing data in environmental and vital information. This process enhances data reliability and outputs a clear dataset with reduced noise. 【0800】 Step 3: 【0801】 The server passes the pre-processed data to an integrated analysis system, which uses machine learning algorithms to perform a detailed risk assessment. The algorithm, having learned from past data patterns, predicts potential hazards in the work environment. As a result of this analysis, new risk assessment data is output. 【0802】 Step 4: 【0803】 The server uses emotion analysis tools to analyze the psychological state of workers from voice and facial expression data. It utilizes a generative AI model to execute prompts that identify changes in emotional state. This results in the output of emotional data such as the worker's stress level and anxiety level. 【0804】 Step 5: 【0805】 The server generates feedback based on risk assessment data and sentiment data. The generated feedback includes warnings about physical risks and advice to support psychological well-being. This feedback data is generated as the final output. 【0806】 Step 6: 【0807】 The terminal receives feedback from the server in real time and notifies the worker. Notifications include visual displays and audio messages, allowing users to take immediate risk avoidance actions. Thus, the terminal plays a crucial role in providing workers with critical feedback information. 【0808】 Step 7: 【0809】 Users can proceed with their work safely based on feedback received through their devices. For example, if an emergency is detected during work at height, they can receive detailed safety instructions and take appropriate measures accordingly. This use of feedback improves both the safety and efficiency of the work. 【0810】 (Application Example 2) 【0811】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0812】 This invention aims to simultaneously achieve risk assessment and psychological burden reduction for workers in today's workplace environment, where it is necessary to ensure not only the physical safety of workers but also their psychological health. Conventional systems have been overly focused on the physical aspects of risk, neglecting the psychological aspects. Therefore, the challenge is to grasp workers' stress levels and psychological burden in a timely manner and provide feedback that contributes to their improvement. 【0813】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0814】 In this invention, the server includes means for receiving diverse information collected from flight equipment, ground recording devices, and detection devices worn by workers; means for preprocessing the diverse information, removing noise and imputing missing values; integrated analysis means for analyzing the preprocessed information and evaluating hazards in the work environment; means for generating feedback and issuing warnings based on the analysis results; and means for monitoring the psychological and physiological state of workers and generating auxiliary instructions to reduce psychological burden. This makes it possible to simultaneously evaluate physical and psychological risks and comprehensively ensure the safety and health of workers. 【0815】 A "flying device" is a device that has the ability to monitor and collect information about the environment and the condition of workers from the air. 【0816】 A "ground recording device" is a device that captures and records images and environmental information from the ground and provides the data for analysis. 【0817】 A "detection device" is a device that includes sensor devices worn by workers to collect vital information and environmental data. 【0818】 "Diverse information" refers to data related to the work environment and workers, including video, audio, vital signs, and emotional state analysis information. 【0819】 "Preprocessing" is the process of transforming received data into information suitable for analysis by removing noise and imputing missing values. 【0820】 An "integrated analysis means" is a method or apparatus for analyzing pre-processed data and comprehensively evaluating the risks in the work environment. 【0821】 "Feedback" refers to warnings and instructions provided to workers based on analysis results, with the aim of avoiding physical and psychological risks. 【0822】 "Psychological burden reduction instructions" are notifications or suggestions to reduce psychological stress based on an analysis of the worker's emotional state. 【0823】 The embodiments for carrying out the present invention are shown below. 【0824】 The system of this invention aims to ensure safety and psychological health in the work environment. First, the server receives various information from the flight device, ground recording device, and sensing device worn by the worker. This includes environmental data, video data, audio data, worker vital information, and emotional state analysis information. 【0825】 Upon receiving this data, the server first performs preprocessing to remove noise and impute missing values. Data processing utilizes Python data analysis libraries (e.g., Pandas) and machine learning frameworks (e.g., TensorFlow), enabling highly accurate analysis. 【0826】 Next, using an integrated analysis method, machine learning algorithms analyze the pre-processed data to evaluate the potential hazards in the work environment. In this process, it is possible to predict new hazards using a model that has learned from past accident data. 【0827】 The server generates feedback based on the analysis results. This feedback includes not only warnings about physical risks, but also instructions to reduce psychological burden based on the worker's emotional state. This provides a safe and comfortable working environment. 【0828】 For example, if the emotion engine detects that a worker's stress levels are rising while working at height, the server will generate an instruction such as "Please temporarily stop working and take some time to relax," and notify the worker's terminal in real time. 【0829】 An example of a prompt message is: "Analyze the worker's heart rate and facial expression data, and generate feedback to promote relaxation when psychological stress levels rise." 【0830】 By linking the server and terminals, the physical and psychological safety of workers can be ensured, accident prevention can be achieved, and work efficiency can be improved. 【0831】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0832】 Step 1: 【0833】 The server receives diverse information from aircraft, ground recording devices, and sensing equipment worn by workers. Input information includes environmental data, video data, audio data, vital signs, and emotional state analysis information. The server integrates this data and organizes it into the format required for subsequent processing. 【0834】 Step 2: 【0835】 The server performs noise reduction and missing value imputation on the received data. The input is the data integrated in step 1, and the output is the purified data. Specifically, the data cleaning process is carried out using a Python library (e.g., Pandas). Noisy data is smoothed, and missing data is imputed using statistical methods. 【0836】 Step 3: 【0837】 The server uses pre-processed data to evaluate hazards through an integrated analysis method. The input is the output data from step 2, and the output is risk assessment information for the work environment. Machine learning algorithms are used to learn from past accident information and predict new hazards. Specifically, a model is applied using TensorFlow to perform safety assessments in real time. 【0838】 Step 4: 【0839】 The server generates feedback based on the analysis results and sends it to the terminal. The input is risk assessment information, and the output is warning messages and instructions to reduce psychological burden. The generated feedback is immediately notified to the worker's terminal as audio or text information. Examples of generated feedback include, "Please pause your work and take some time to relax." 【0840】 Step 5: 【0841】 The terminal receives feedback from the server and notifies the worker in real time. Input is feedback information sent from the server, and output is a warning sound, display notification, and vibration alert to the worker. The terminal uses this information to provide specific instructions for the worker to safely continue their work. 【0842】 The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0843】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0844】 In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414. 【0845】 Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion. 【0846】 Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together. 【0847】 These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression. 【0848】 The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become. 【0849】 Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant. 【0850】 The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more." 【0851】 The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values. 【0852】 The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format. 【0853】 In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data. 【0854】 In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56. 【0855】 Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12. 【0856】 Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56. 【0857】 The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory. 【0858】 The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor. 【0859】 Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources. 【0860】 Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose. 【0861】 The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above. 【0862】 All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference. 【0863】 The following is further disclosed regarding the embodiments described above. 【0864】 (Claim 1) 【0865】 Means for receiving various data collected from aircraft equipment, ground imaging equipment, and detection devices worn by workers, 【0866】 The means for preprocessing the aforementioned diverse data, performing noise reduction and missing value imputation, 【0867】 An integrated analysis means for analyzing pre-processed data and evaluating risks in the work environment, 【0868】 A means of generating feedback and issuing warnings based on the analysis results, 【0869】 A system that includes this. 【0870】 (Claim 2) 【0871】 The system according to claim 1, further comprising means for the integrated analysis means to learn past accident data using a machine learning algorithm and predict new risks. 【0872】 (Claim 3) 【0873】 The system according to claim 1, further comprising means for notifying workers in real time of high-priority warnings and prompting them to take action to avoid risks. 【0874】 "Example 1" 【0875】 (Claim 1) 【0876】 Means for receiving diverse information collected from aerial moving objects, ground imaging equipment, and detection devices worn by workers, 【0877】 A means for integrating and preprocessing the aforementioned diverse information, and for performing noise reduction and missing value imputation, 【0878】 An integrated analysis method using machine learning to analyze pre-processed information and evaluate hazards in the work environment, 【0879】 A means of generating an evaluation score based on the analysis results and issuing a warning, 【0880】 A means of monitoring the work status in real time based on the received information and taking appropriate measures, 【0881】 A system that includes this. 【0882】 (Claim 2) 【0883】 The system according to claim 1, further comprising means for learning past accident information using a learning algorithm and predicting new hazards. 【0884】 (Claim 3) 【0885】 The system according to claim 1, further comprising means for notifying the worker in real time based on the evaluation score and prompting them to take action to avoid danger. 【0886】 "Application Example 1" 【0887】 (Claim 1) 【0888】 Means for receiving various data collected from aircraft equipment, ground imaging equipment, and detection devices worn by workers, 【0889】 The means for preprocessing the aforementioned diverse data, performing noise reduction and missing value imputation, 【0890】 An integrated analysis means for analyzing pre-processed data and evaluating risks in the work environment, 【0891】 A means of generating feedback and issuing warnings based on the analysis results, 【0892】 A means of providing vibratory or audible alerts to workers via a terminal device for notifying them of warnings in real time, 【0893】 A system that includes this. 【0894】 (Claim 2) 【0895】 The aforementioned integrated analysis means includes a means for learning past accident data using a machine learning algorithm and predicting new risks, 【0896】 The system according to claim 1, further comprising means for transmitting real-time feedback to a worker's terminal based on the risk prediction. 【0897】 (Claim 3) 【0898】 The feedback generation means includes means for notifying workers in real time of high-priority warnings and prompting them to take action to avoid risks, 【0899】 The system according to claim 1, further comprising means for sending an instruction to the worker to take a break if the feedback detects a safety check instruction or an abnormal heart rate. 【0900】 "Example 2 of combining an emotion engine" 【0901】 (Claim 1) 【0902】 Means for receiving diverse information collected from spatial devices, ground-based visual devices, and measuring devices worn by workers, 【0903】 means for preprocessing the aforementioned diverse information and performing reduction and supplementation processing, 【0904】 A comprehensive analysis means for analyzing pre-processed information and evaluating hazards in the work environment, 【0905】 A means of analyzing the psychological state of workers using emotion analysis tools, 【0906】 A means of generating information based on the analysis results and notifying workers, 【0907】 A system that includes this. 【0908】 (Claim 2) 【0909】 The system according to claim 1, wherein the comprehensive analysis means further includes means for learning past accident information using a machine learning algorithm and predicting new hazards. 【0910】 (Claim 3) 【0911】 The system according to claim 1, further comprising means for notifying workers in real time of high-priority warnings and prompting them to take action to avoid danger. 【0912】 "Application example 2 when combining with an emotional engine" 【0913】 (Claim 1) 【0914】 Means for receiving diverse information collected from flying devices, ground recording devices, and detection devices worn by workers, 【0915】 The means for preprocessing the aforementioned diverse information and performing noise reduction and missing value imputation, 【0916】 An integrated analysis means for analyzing pre-processed information and evaluating hazards in the work environment, 【0917】 A means of generating feedback and issuing warnings based on the analysis results, 【0918】 A means for monitoring the psychological and physiological state of workers and generating supplementary instructions to reduce their psychological burden, 【0919】 A system that includes this. 【0920】 (Claim 2) 【0921】 The system according to claim 1, further comprising means for learning past accident information using a machine learning algorithm and predicting new risks. 【0922】 (Claim 3) 【0923】 The system according to claim 1, further comprising means for notifying an operator in real time of a high-priority warning and prompting them to take action to avoid danger. [Explanation of symbols] 【0924】 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

[Claim 1] Means for receiving various data collected from aircraft equipment, ground imaging equipment, and detection devices worn by workers, The means for preprocessing the aforementioned diverse data, performing noise reduction and missing value imputation, An integrated analysis means for analyzing pre-processed data and evaluating risks in the work environment, A means of generating feedback and issuing warnings based on the analysis results, A system that includes this. [Claim 2] The system according to claim 1, further comprising means for the integrated analysis means to learn past accident data using a machine learning algorithm and predict new risks. [Claim 3] The system according to claim 1, further comprising means for notifying workers in real time of high-priority warnings and prompting them to take action to avoid risks.

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