system

Abstract

We provide the system. [Solution] A terminal means for receiving user input information and formatting the data, A means of sending data to a server and storing the data, A means including an AI algorithm that analyzes past data and optimizes schedules and resources, A means of notifying the user of the analysis results, A system that includes this.

Description

【Technical Field】 【0001】 The technology of this 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 in response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In the construction industry, site supervisors need to efficiently manage multiple construction sites, and problems such as labor shortages and schedule management constantly occur. Furthermore, due to the reduction of working hour regulations in 2024 and the review exemption system in 2025, the burden of site management has increased, and concerns about the decline in construction quality and safety have risen. Although it is required to address this, the current methods often cannot fully cope with it. Therefore, the development of a more efficient site management system is an issue. 【Means for Solving the Problems】 【0005】 This invention solves the above problems by providing a system that includes terminal means for receiving user input information and formatting the data, means for transmitting and storing the data on a server, means including an AI algorithm that analyzes past data and optimizes schedules and resources, and means for notifying the user of the analysis results. This system enables efficient management of construction sites, leading to improved construction quality and enhanced safety management. 【0006】 "User input information" refers to data entered into the system by users such as site supervisors, including the progress of construction sites, material usage, and the number of workers. 【0007】 "Terminal means" refers to a device or system that receives user input information and converts it into the required format. 【0008】 A "server" is a central management system that receives information sent from terminals, stores it in a database, and provides data to AI algorithms for analysis. 【0009】 An "AI algorithm" is an artificial intelligence technology that analyzes past project data and the current situation to calculate the optimal schedule and resource allocation. 【0010】 "Resource optimization" is the process of reviewing the allocation of personnel, materials, and time on-site to ensure the most efficient use of them. 【0011】 "Analysis results" refer to suggestions and predictive information regarding optimal schedules and resource allocation, generated by AI algorithms. 【0012】 A "notification means" is a system or device for communicating analysis results to the user and proposing improvement measures. [Brief explanation of the drawing] 【0013】 [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] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention] 【0014】 Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings. 【0015】 First, the terms used in the following description will be explained. 【0016】 In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like. 【0017】 In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0018】 In the following embodiments, the labeled 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 disks (e.g., hard disks), or magnetic tapes, and the like. 【0019】 In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like. 【0020】 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." 【0021】 [First Embodiment] 【0022】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0023】 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. 【0024】 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). 【0025】 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. 【0026】 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. 【0027】 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. 【0028】 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. 【0029】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0030】 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. 【0031】 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. 【0032】 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. 【0033】 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". 【0034】 This invention is implemented as a system to streamline the work of site supervisors at construction sites. First, the user inputs site conditions using a terminal and registers data such as the progress of the construction, materials used, and the number of workers. The terminal receives this information and performs a format conversion for secure transmission to the server. 【0035】 The server stores the data received from the terminal and uses an AI algorithm to compare it with past project data and analyze the current progress. Based on this analysis, the server derives the optimal schedule and resource allocation. 【0036】 The analysis results are sent from the server to the terminal and displayed to the user. This allows the user to obtain the optimized schedule suggested by the AI ​​and efficiently manage the site. For example, if foundation work is delayed at a site, the AI ​​will take weather data and material supply status into consideration and suggest a schedule change. The user can then use this as a reference to adjust the construction schedule. 【0037】 AI can also be useful in safety management. The server performs risk analysis and, if a hazard is predicted, sends a warning to the user via the terminal. This warning helps to strengthen safety measures for workers on site. 【0038】 Thus, this invention enhances on-site management efficiency and improves construction quality and ensures safety through cooperation among the user, terminal, and server. 【0039】 The following describes the processing flow. 【0040】 Step 1: 【0041】 The user inputs information about the site situation into the terminal. This input includes the day's work progress, materials used, number of workers, and any special notes. The terminal receives the entered data. 【0042】 Step 2: 【0043】 The terminal formats the data received from the user and sends it to the server. Before sending, it performs necessary data checks and data conversions to ensure that the data is transmitted accurately and completely. 【0044】 Step 3: 【0045】 The server receives data sent from the terminal and stores it in a database. The server then executes an AI algorithm based on the stored data and begins analysis based on past data and the current situation. 【0046】 Step 4: 【0047】 The server generates optimized schedule proposals and resource allocation suggestions based on the analysis. These suggestions create a concrete action plan to maximize on-site construction efficiency. 【0048】 Step 5: 【0049】 The server sends the generated proposal to the terminal. The terminal displays the received proposal to the user. Based on this displayed information, the user manages and coordinates the site. 【0050】 Step 6: 【0051】 Users review the suggestions displayed on their terminals and reallocate on-site schedules and resources based on those suggestions. If necessary, they can enter feedback into their terminals and send it to the server. This feedback will be used for future improvements. 【0052】 Step 7: 【0053】 The server periodically analyzes the work environment and assesses potential risks. If a risk is detected, the server sends an alert to the terminal and provides safety management suggestions to the user. 【0054】 Step 8: 【0055】 Users review safety suggestions from their devices and implement specific safety measures on-site. This improves worker safety and helps prevent accidents. 【0056】 (Example 1) 【0057】 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." 【0058】 In construction sites, operational management often involves manual information updates and progress tracking, resulting in inefficiencies and time-consuming processes. Furthermore, predicting hazards in the work environment and strengthening safety management are crucial challenges, but there is a lack of adequate support for these. These problems can potentially hinder project progress and safety. 【0059】 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. 【0060】 In this invention, the server includes means for receiving user input information and converting the data into a standard format, means for transmitting the data to the server and storing it in an information recording medium, means including an intelligent algorithm that analyzes past data and performs progress management and resource optimization, means for visually reporting the analysis results to the user from a terminal, and means for predicting hazards in the work environment and generating notifications for safety measures. This enables more efficient business management and enhanced on-site safety management. 【0061】 "User input information" refers to data such as the progress of the construction site, the materials used, and the number of workers. 【0062】 "Terminal means" refers to a device or system that receives input information from a user and converts the data into a standard format. 【0063】 "Means of sending data to a server and storing it on an information recording medium" refers to the process of sending data processed on a terminal to the server and securely storing it in a database or similar. 【0064】 An "intelligent algorithm that analyzes past data to manage progress and optimize resources" refers to a technology that uses collected data to compare with previous projects and proposes an evaluation of progress and effective resource allocation. 【0065】 "Means of visually reporting analysis results to the user from the terminal" refers to methods for presenting data analyzed on the server to the user in an easily understandable visual way through the terminal. 【0066】 "Means for predicting hazards in the work environment and generating notifications of safety measures" refers to a process that strengthens safety management by evaluating potential risks based on on-site conditions and notifying that information. 【0067】 This invention provides a system that allows users to streamline the management of construction sites. 【0068】 The user first uses their own device to input site status data. This includes the progress of the construction, the construction materials being used, and the number of workers. This data is entered into the device through a dedicated application. After input, the device converts the information into a standard format (e.g., JSON or XML) and securely transmits it to the server. The device implements encryption technology (such as SSL / TLS protocol) to ensure the secure transfer of data. 【0069】 The server stores the received data in a database. Databases such as MySQL® and PostgreSQL are commonly used. Once the data is stored, the server uses AI algorithms to compare it with past project data and analyze current progress and resource usage. This intelligent algorithm uses statistical models and machine learning techniques to adjust plans and optimize resources. Specific AI models include time series analysis models and risk assessment models. 【0070】 Once the analysis is complete, the server sends the results back to the terminal. The terminal displays these analysis results in a format that is easy for the user to understand visually. This allows the user to manage the site more efficiently. 【0071】 Furthermore, the server predicts hazards in the work environment and generates notifications for necessary safety measures. For example, it assesses project risks by considering factors such as weather conditions and delays in material supply. In situations where hazards are particularly anticipated, it can send warning messages to terminals to prompt workers on-site to take appropriate action. 【0072】 For example, if foundation work is delayed at a construction site and the reason is weather, the AI ​​will analyze the situation and suggest schedule adjustments. An example of a prompt would be, "Please provide the optimal schedule for the next stage, taking into account the delay in foundation work due to bad weather." By considering this suggestion, the user can make quick and accurate decisions. 【0073】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0074】 Step 1: 【0075】 Users input construction site status data via a terminal. This input includes information such as the progress of the work, the construction materials being used, and the number of workers. An application installed on the terminal receives this information and converts it into a standard format (e.g., JSON or XML). This conversion ensures data consistency and compatibility. The converted data is then ready to be sent to the server. 【0076】 Step 2: 【0077】 The terminal sends the converted data to the server using encryption technology (such as the SSL / TLS protocol). The input to this process is encrypted data, and the output is a secure transfer to the server. Sending the data to the server reduces the risk of information leakage and unauthorized access. 【0078】 Step 3: 【0079】 The server stores the received data in a database. The input here is the data sent from the terminal. The server stores this data using a database management system (e.g., MySQL or PostgreSQL). The output is securely stored data, which is used for later analysis. 【0080】 Step 4: 【0081】 The server executes AI algorithms using stored data. The input consists of historical and current data stored in a database. The server applies statistical models and machine learning algorithms to optimize progress and resources compared to past project data. The analysis output is a proposal for an optimized schedule and resource allocation. 【0082】 Step 5: 【0083】 The server sends the generated analysis results to the terminal. The input is the AI-generated analysis results, and the output is information displayed visually to the user. The terminal displays the received results on a dashboard, allowing the user to check and manage the situation on-site based on that information. 【0084】 Step 6: 【0085】 The server further predicts hazards in the work environment and generates notifications for safety measures as needed. The input is data related to the site conditions. The server uses a risk assessment model to evaluate potential hazards and sends warning messages to terminals as output. The terminals notify users of received warnings, supporting site safety management. 【0086】 (Application Example 1) 【0087】 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." 【0088】 In modern construction sites, efficiency and safety management are critical challenges. While it's essential to efficiently track work progress and optimize resource allocation, site conditions are constantly changing, requiring rapid responses. Furthermore, safety management demands proactive risk assessment and appropriate countermeasures. These challenges need to be addressed. 【0089】 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. 【0090】 In this invention, the server includes an information processing means that receives work progress and material information entered by the user, formats and converts the information, an information storage means that transmits the information to a central processing unit and continuously stores the data, an artificial intelligence processing means that optimizes work progress and resources based on past work data, a display means that notifies the user of the analysis results by artificial intelligence in real time, and a hazard detection means that generates and notifies the user of safety management warnings. This enables effective work progress management and safety management at construction sites. 【0091】 A "user" is the entity that uses the system to input workplace information. 【0092】 "Workplace" refers to the on-site environment where a construction project is underway. 【0093】 "Progress status" refers to information that indicates the degree of progress or status of a task. 【0094】 "Materials information" refers to detailed data about the materials and items used in a project. 【0095】 "Information processing means" refers to devices or software that receive input from a user and convert the data into an appropriate format. 【0096】 A "central processing unit" is the central device in a system that receives data remotely and manages and analyzes it centrally. 【0097】 A "data storage means" is a device or system that has the function of storing received data for a long period of time and making it available for retrieval as needed. 【0098】 "Artificial intelligence processing means" refers to devices or software that include algorithms that analyze patterns using past data and make suggestions for improving efficiency. 【0099】 "Analysis results" refer to the results of data analysis derived by artificial intelligence processing tools. 【0100】 "Display means" refers to devices or software used to visualize analysis results and communicate them to the user. 【0101】 A "hazard detection system" is a system that has the function of predicting workplace risks and promoting proactive safety management. 【0102】 To implement this invention, the user first uses a device such as a smartphone or tablet at the worksite to input the progress status and material information of the work area. The device converts this input information into digital data format and transmits it to a server via the internet. In this process, the device uses JavaScript (registered trademark) to convert the data into JSON format and transmits the data securely using the HTTPS protocol. 【0103】 The server stores received data in a database and analyzes it by comparing it with past project data. The server utilizes Python libraries such as pandas and scikit-learn to optimize progress and resource allocation. This analysis also incorporates external weather APIs and material supply databases to take important environmental information into account in real time. 【0104】 The analysis results are returned to the terminal in real time and displayed to the user. The user can use this information to optimize schedules and reallocate resources. The AI ​​also predicts risks in the workplace and notifies the user of warnings as needed. 【0105】 As a concrete example, at a construction site, if bad weather is predicted, the AI ​​will suggest revising the schedule. This allows the user to safely transfer workers to other tasks. 【0106】 Examples of prompt statements are as follows: 【0107】 "Based on construction site progress data and weather information, conduct a risk analysis of the current schedule and propose a safe and efficient work plan." 【0108】 In this way, the invention can be effectively implemented. 【0109】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0110】 Step 1: 【0111】 Users input work progress and material information using a terminal. This information is entered into the terminal as text and numerical data and converted into digital data. 【0112】 Step 2: 【0113】 The terminal formats the input information. JavaScript converts the data to JSON format and sends this data to the server via the HTTPS protocol. Input is text and numerical data, and output is JSON format data. 【0114】 Step 3: 【0115】 The server stores the received JSON data in a database for analysis. The database uses either MySQL or PostgreSQL and stores the JSON data in a structured format. The input is JSON data, and the output is a record in the database. 【0116】 Step 4: 【0117】 The server uses Python and the pandas library to analyze progress and resource allocation based on historical business data. The analysis includes time-series and comparative analyses to generate the optimal schedule and resource allocation. Input is business data retrieved from a database, and output is the optimized analysis results. 【0118】 Step 5: 【0119】 The AI ​​uses the obtained analysis results to generate an optimal schedule proposal for the user. Using prompts, the generated AI model is utilized to create a concrete work plan. The input is the analyzed data, and the output is the proposed schedule. 【0120】 Step 6: 【0121】 The server sends the analysis results and proposed schedule to the terminal. The data is processed in real time and is either pushed as a notification or displayed on the user's terminal. The input is the proposed schedule, and the output is the notification message on the terminal. 【0122】 Step 7: 【0123】 The user reviews the displayed suggestions and reallocates work plans and resources as needed. This improves on-site work efficiency and safety. The user's input finalizes the work plan. 【0124】 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. 【0125】 This invention implements a system that combines an emotional engine to improve management efficiency and ensure safety at construction sites. The system consists of a user, a terminal, and a server. The user uses the terminal to input data such as the progress of the site, materials used, and the number of workers. The terminal receives this data and performs a format conversion to send it to the server. 【0126】 The server stores information received from the terminal in a database and uses an AI algorithm to analyze past data and the current situation. The server further analyzes the user's voice and facial expression data with an emotion engine to evaluate the user's psychological state. The emotion engine determines the site supervisor's stress level and motivation, and based on this, suggestions for work improvement are made. 【0127】 For example, if a work delay occurs at a site, and the emotion engine determines that the user is experiencing high stress levels, the server will not only adjust the schedule but also suggest measures to reduce stress, such as readjusting manpower or changing task priorities. Conversely, if the system determines that the user is highly motivated, it can suggest setting more challenging goals or providing resources for skill development. 【0128】 This system enables efficient management of construction sites, improves construction quality, and provides a more human-friendly environment that takes into account the psychological state of site supervisors. 【0129】 The following describes the processing flow. 【0130】 Step 1: 【0131】 Users input information such as the progress of work on-site, material usage, and the number of workers using a terminal. The terminal also simultaneously acquires data on the user's voice and facial expressions. 【0132】 Step 2: 【0133】 The terminal converts the collected data into an appropriate format and prepares it for transmission to the server. This process includes the transmission of voice and facial expression data. 【0134】 Step 3: 【0135】 The server receives data sent from the terminal and stores it in the database. This data includes on-site situation information and user voice and facial expression data. 【0136】 Step 4: 【0137】 The server uses AI algorithms to analyze past data and current on-site conditions to optimize schedules and resources. 【0138】 Step 5: 【0139】 The server inputs the acquired voice and facial expression data into an emotion engine to analyze the user's emotional state, determining stress levels and motivation. 【0140】 Step 6: 【0141】 Based on the analysis results from the emotion engine, the server generates suggestions for business improvements that take into account the user's psychological state. These suggestions include specific actions to reduce stress and improve motivation. 【0142】 Step 7: 【0143】 The server sends analysis results and improvement suggestions to the terminal. The terminal displays this information to the user, providing information that can be used for on-site management. 【0144】 Step 8: 【0145】 Users review the information displayed on their devices and implement the suggested work improvement measures tailored to the specific situation on-site. This increases work efficiency while also reducing the psychological burden on the users themselves. 【0146】 (Example 2) 【0147】 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 will be referred to as the "terminal." 【0148】 Achieving both improved work efficiency and safety at construction sites simultaneously is extremely difficult. Furthermore, conventional management methods have been ineffective in appropriately adjusting tasks while considering the psychological state of site workers and supervisors. Therefore, there is a need for management methods that are efficient and reflect human psychological states. 【0149】 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. 【0150】 In this invention, the server includes emotion analysis means for analyzing the user's voice and facial expressions and evaluating their psychological state, artificial intelligence means for analyzing past information and optimizing action plans and resources, and means for notifying the user of the generated suggestions. This makes it possible to adjust work while considering the psychological stress of site supervisors and workers, while improving work efficiency and safety. 【0151】 "User input information" refers to data directly entered into the device operated by the user, and includes information related to work progress, materials used, and the number of workers. 【0152】 "Device" refers to the hardware and software used to receive data, convert it to an appropriate format, and transfer it to a server. 【0153】 A "server" refers to a central computer system that receives, stores, analyzes, and notifies data on a network. 【0154】 "Artificial intelligence tools" refer to a collection of algorithms that analyze accumulated information to optimize work plans and allocate resources. 【0155】 "Emotional analysis tools" refer to systems that analyze a user's voice and facial expressions to determine their psychological state. 【0156】 "Generated proposals" refer to specific action plans regarding business improvement and resource allocation created using artificial intelligence and sentiment analysis tools. 【0157】 "Notification" refers to the act of informing a user of generated suggestions or analysis results, and is usually done electronically. 【0158】 This invention is a system for efficiently managing construction sites and providing work proposals that take into account the user's psychological state. Specific embodiments are described below. 【0159】 Hardware and software configuration 【0160】 Users input on-site information using devices such as smartphones and tablets. A data entry application is installed on the device. The entered information is formatted into JSON format on the device. 【0161】 The terminal sends information to the server via the HTTPS protocol. Upon receiving the data, the server stores it in a database (MySQL or PostgreSQL). The stored data is then analyzed using artificial intelligence algorithms such as TENSORFLOW® or PyTorch. 【0162】 Furthermore, the server receives the user's voice and facial expression data and performs emotion analysis using facial expression recognition libraries such as DeepFace and voice emotion recognition software. This allows the server to evaluate the user's psychological state. 【0163】 Based on artificial intelligence algorithms and sentiment analysis results, the server generates suggestions for business improvement and skill enhancement. These suggestions are notified to the user via their device. The notifications are made in real time and displayed as push notifications on the user's device. 【0164】 Examples of specific cases and prompt statements 【0165】 For example, if a user enters data such as "50% of the rebar work is complete," the server analyzes this data and assesses the possibility of work delays. If the sentiment analysis determines that the user is highly stressed, a notification will be sent suggesting things like "allow extra time in next week's shift." 【0166】 Examples of prompt messages include, "Generate suggestions on how to adjust tasks when the site supervisor's stress level is high," and "Suggest what kind of goal setting would be effective for a highly motivated site supervisor." In this way, the invention aims to streamline site management while simultaneously providing suggestions that take into account human psychological states, thereby creating a more human-friendly management environment. 【0167】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0168】 Step 1: 【0169】 Users input on-site data such as site conditions, materials used, and the number of workers into a terminal. The input data is then formatted into JSON on the terminal. This prepares the data in a format that the server can process. 【0170】 Step 2: 【0171】 The terminal sends formatted JSON data to the server via the HTTPS protocol. Data encryption is performed during transmission to ensure communication security. Data integrity checks are also conducted during this process to verify the data's defect-free nature. 【0172】 Step 3: 【0173】 The server parses the JSON data received from the terminal. After verifying the validity of the data, the information is stored in a database. Database management systems such as MySQL or PostgreSQL are used for storage, and indexes are set up to allow for quick retrieval of the data. 【0174】 Step 4: 【0175】 The server uses an artificial intelligence model to analyze stored data. This model, utilizing TensorFlow and PyTorch, analyzes past work patterns and delays to generate predictions for future progress. The results of this analysis are used to improve operational efficiency. 【0176】 Step 5: 【0177】 The server analyzes the user's voice and video data received from the terminal. It uses facial recognition libraries such as DeepFace and voice emotion recognition software to evaluate the user's psychological state. The evaluation results are used as a basis for making business proposals. 【0178】 Step 6: 【0179】 The server generates business improvement suggestions based on artificial intelligence analysis results and sentiment analysis results. For example, if high stress levels are detected, suggestions for optimizing task priorities will be created. These suggestions are generated automatically using an AI model. 【0180】 Step 7: 【0181】 The server sends the generated suggestions to the terminal, notifying the user. The notification is configured to arrive at the user's terminal in real time as a push message. Based on this, the user can perform efficient field management. 【0182】 (Application Example 2) 【0183】 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". 【0184】 While there is a need for more efficient progress management and safety management at construction sites, traditional methods make it difficult to manage workers while considering their psychological state, hindering efficient scheduling and resource optimization. In particular, there is a lack of risk prediction and countermeasures for the work environment, necessitating methods to enhance safety while reducing the burden on site managers. 【0185】 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. 【0186】 In this invention, the server includes a device that receives user input information and converts it into a data format, a device that transmits the information to a central computer and stores that information, a device that includes an intelligent algorithm that performs past information analysis including emotion analysis and work efficiency improvement, and a device that notifies the user of suggestions using the analysis results. This makes it possible to improve the efficiency of progress management and safety management at construction sites while taking into account the psychological state of the users. 【0187】 "User input information" refers to information recorded and transmitted by workers using terminals, such as the progress status at construction sites, materials used, and the number of workers. 【0188】 A "data format conversion device" is a device that converts user input information into a format suitable for the server, enabling efficient communication and storage of information. 【0189】 A "central computer" is a computer system that aggregates and processes information and generates optimal management proposals. 【0190】 An "intelligent algorithm" is a computational method that analyzes past information and sentiment to support efficiency improvements and safety enhancements in the field. 【0191】 "Emotional analysis" is a technology that uses user voice and facial expression data to understand their psychological state. 【0192】 A "device for notifying users of suggestions" refers to a notification system that informs users of management suggestions generated based on analyzed information. 【0193】 To implement this invention, it is necessary to construct a system that combines multiple elements. Users input information about the construction site using a device such as a smartphone or tablet. The device has a mechanism for inputting information such as the progress of the site, materials used, and the number of workers. This information is formatted by an application on the device and sent to a server. 【0194】 The server is built using Node.js and stores information in a MongoDB database. The received data is analyzed by an AI algorithm. This algorithm also performs emotion analysis using the Azure® Emotion API. Specifically, user voice data is converted to text using the Google® Cloud Speech-to-Text API, and this text, along with facial expression data obtained from the camera, is analyzed using the Emotion API. This allows the system to determine the user's psychological state and, combined with past data, generate optimal business improvement proposals. 【0195】 The analysis results are notified to the terminal and presented to the user as concrete suggestions. For example, if progress is behind schedule in a certain process, the system can suggest adjusting the work schedule or reallocating personnel based on the user's stress level obtained from the emotion analysis data. This is key to improving overall efficiency and safety within the company. 【0196】 A concrete example of a prompt message is, "Progress delays have been identified on-site, causing high levels of stress for managers. Please suggest ways to streamline the schedule." In this way, the system aims to provide appropriate solutions while reflecting the user's psychological state. 【0197】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0198】 Step 1: 【0199】 Users input information such as the progress of construction sites, materials used, and the number of workers using a terminal. This input information is received by an application on the terminal. The data processing performed here involves format conversion, including manual input by the user. 【0200】 Step 2: 【0201】 The terminal converts the received information into a data format suitable for the central computer. After this format conversion, the terminal sends the data to the server. The input is the user's raw data, and the output is data in a format that the server can process. 【0202】 Step 3: 【0203】 The server receives data sent from the terminal and stores it in the database. MongoDB handles this storage, acting as a permanent storage for the information. The input for this step is formatted data, and the output is the data stored in the database. 【0204】 Step 4: 【0205】 The server performs the process of converting speech data to text using the Google Cloud Speech-to-Text API. Simultaneously, it performs user emotion analysis using the Azure Emotion API. The input is speech and facial expression data, and the output is the transcribed speech data and the results of the emotion state analysis. This analysis performs the data calculations for emotion analysis. 【0206】 Step 5: 【0207】 The server uses AI algorithms to analyze historical data and generate business improvement proposals. Inputs are field data and sentiment analysis results, and output is specific business improvement proposals. Data calculations include comparison with historical data and consideration of the user's emotional state. 【0208】 Step 6: 【0209】 The server notifies the terminal of the generated business improvement proposals. The terminal displays the proposals on the screen and notifies the user. The input is the business improvement proposals, and the output is the business improvement proposal information provided to the user. This step includes the action of visually presenting the information in a way that the user can confirm. 【0210】 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. 【0211】 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. 【0212】 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. 【0213】 [Second Embodiment] 【0214】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0215】 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. 【0216】 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). 【0217】 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. 【0218】 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. 【0219】 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). 【0220】 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. 【0221】 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. 【0222】 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. 【0223】 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. 【0224】 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. 【0225】 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". 【0226】 This invention is implemented as a system to streamline the work of site supervisors at construction sites. First, the user inputs site conditions using a terminal and registers data such as the progress of the construction, materials used, and the number of workers. The terminal receives this information and performs a format conversion for secure transmission to the server. 【0227】 The server stores the data received from the terminal and uses an AI algorithm to compare it with past project data and analyze the current progress. Based on this analysis, the server derives the optimal schedule and resource allocation. 【0228】 The analysis results are sent from the server to the terminal and displayed to the user. This allows the user to obtain the optimized schedule suggested by the AI ​​and efficiently manage the site. For example, if foundation work is delayed at a site, the AI ​​will take weather data and material supply status into consideration and suggest a schedule change. The user can then use this as a reference to adjust the construction schedule. 【0229】 AI can also be useful in safety management. The server performs risk analysis and, if a hazard is predicted, sends a warning to the user via the terminal. This warning helps to strengthen safety measures for workers on site. 【0230】 Thus, this invention enhances on-site management efficiency and improves construction quality and ensures safety through cooperation among the user, terminal, and server. 【0231】 The following describes the processing flow. 【0232】 Step 1: 【0233】 The user inputs information about the site situation into the terminal. This input includes the day's work progress, materials used, number of workers, and any special notes. The terminal receives the entered data. 【0234】 Step 2: 【0235】 The terminal formats the data received from the user and sends it to the server. Before sending, it performs necessary data checks and data conversions to ensure that the data is transmitted accurately and completely. 【0236】 Step 3: 【0237】 The server receives data sent from the terminal and stores it in a database. The server then executes an AI algorithm based on the stored data and begins analysis based on past data and the current situation. 【0238】 Step 4: 【0239】 The server generates optimized schedule proposals and resource allocation suggestions based on the analysis. These suggestions create a concrete action plan to maximize on-site construction efficiency. 【0240】 Step 5: 【0241】 The server sends the generated proposal to the terminal. The terminal displays the received proposal to the user. Based on this displayed information, the user manages and coordinates the site. 【0242】 Step 6: 【0243】 Users review the suggestions displayed on their terminals and reallocate on-site schedules and resources based on those suggestions. If necessary, they can enter feedback into their terminals and send it to the server. This feedback will be used for future improvements. 【0244】 Step 7: 【0245】 The server periodically analyzes the work environment and assesses potential risks. If a risk is detected, the server sends an alert to the terminal and provides safety management suggestions to the user. 【0246】 Step 8: 【0247】 Users review safety suggestions from their devices and implement specific safety measures on-site. This improves worker safety and helps prevent accidents. 【0248】 (Example 1) 【0249】 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." 【0250】 In construction sites, operational management often involves manual information updates and progress tracking, resulting in inefficiencies and time-consuming processes. Furthermore, predicting hazards in the work environment and strengthening safety management are crucial challenges, but there is a lack of adequate support for these. These problems can potentially hinder project progress and safety. 【0251】 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. 【0252】 In this invention, the server includes means for receiving user input information and converting the data into a standard format, means for transmitting the data to the server and storing it in an information recording medium, means including an intelligent algorithm that analyzes past data and performs progress management and resource optimization, means for visually reporting the analysis results to the user from a terminal, and means for predicting hazards in the work environment and generating notifications for safety measures. This enables more efficient business management and enhanced on-site safety management. 【0253】 "User input information" refers to data such as the progress of the construction site, the materials used, and the number of workers. 【0254】 "Terminal means" refers to a device or system that receives input information from a user and converts the data into a standard format. 【0255】 "Means of sending data to a server and storing it on an information recording medium" refers to the process of sending data processed on a terminal to the server and securely storing it in a database or similar. 【0256】 An "intelligent algorithm that analyzes past data to manage progress and optimize resources" refers to a technology that uses collected data to compare with previous projects and proposes an evaluation of progress and effective resource allocation. 【0257】 "Means of visually reporting analysis results to the user from the terminal" refers to methods for presenting data analyzed on the server to the user in an easily understandable visual way through the terminal. 【0258】 "Means for predicting hazards in the work environment and generating notifications of safety measures" refers to a process that strengthens safety management by evaluating potential risks based on on-site conditions and notifying that information. 【0259】 This invention provides a system that allows users to streamline the management of construction sites. 【0260】 The user first uses their own device to input site status data. This includes the progress of the construction, the construction materials being used, and the number of workers. This data is entered into the device through a dedicated application. After input, the device converts the information into a standard format (e.g., JSON or XML) and securely transmits it to the server. The device implements encryption technology (such as SSL / TLS protocol) to ensure the secure transfer of data. 【0261】 The server stores the received data in a database. Databases such as MySQL and PostgreSQL are commonly used. Once the data is stored, the server uses AI algorithms to compare it with past project data and analyze current progress and resource usage. These intelligent algorithms use statistical models and machine learning techniques to adjust plans and optimize resources. Specific AI models include time series analysis models and risk assessment models. 【0262】 Once the analysis is complete, the server sends the results back to the terminal. The terminal displays these analysis results in a format that is easy for the user to understand visually. This allows the user to manage the site more efficiently. 【0263】 Furthermore, the server predicts hazards in the work environment and generates notifications for necessary safety measures. For example, it assesses project risks by considering factors such as weather conditions and delays in material supply. In situations where hazards are particularly anticipated, it can send warning messages to terminals to prompt workers on-site to take appropriate action. 【0264】 For example, if foundation work is delayed at a construction site and the reason is weather, the AI ​​will analyze the situation and suggest schedule adjustments. An example of a prompt would be, "Please provide the optimal schedule for the next stage, taking into account the delay in foundation work due to bad weather." By considering this suggestion, the user can make quick and accurate decisions. 【0265】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0266】 Step 1: 【0267】 Users input construction site status data via a terminal. This input includes information such as the progress of the work, the construction materials being used, and the number of workers. An application installed on the terminal receives this information and converts it into a standard format (e.g., JSON or XML). This conversion ensures data consistency and compatibility. The converted data is then ready to be sent to the server. 【0268】 Step 2: 【0269】 The terminal sends the converted data to the server using encryption technology (such as the SSL / TLS protocol). The input to this process is encrypted data, and the output is a secure transfer to the server. Sending the data to the server reduces the risk of information leakage and unauthorized access. 【0270】 Step 3: 【0271】 The server stores the received data in a database. The input here is the data sent from the terminal. The server stores this data using a database management system (e.g., MySQL or PostgreSQL). The output is securely stored data, which is used for later analysis. 【0272】 Step 4: 【0273】 The server executes AI algorithms using stored data. The input consists of historical and current data stored in a database. The server applies statistical models and machine learning algorithms to optimize progress and resources compared to past project data. The analysis output is a proposal for an optimized schedule and resource allocation. 【0274】 Step 5: 【0275】 The server sends the generated analysis results to the terminal. The input is the AI-generated analysis results, and the output is information displayed visually to the user. The terminal displays the received results on a dashboard, allowing the user to check and manage the situation on-site based on that information. 【0276】 Step 6: 【0277】 The server further predicts hazards in the work environment and generates notifications for safety measures as needed. The input is data related to the site conditions. The server uses a risk assessment model to evaluate potential hazards and sends warning messages to terminals as output. The terminals notify users of received warnings, supporting site safety management. 【0278】 (Application Example 1) 【0279】 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." 【0280】 In modern construction sites, improving work efficiency and safety management are important issues. It is required to efficiently grasp the work progress and optimally allocate resources. However, the on-site situation is constantly changing, and prompt responses are necessary. Also, in safety management, it is required to predict risks in advance and take appropriate measures. It is necessary to solve these issues. 【0281】 The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0282】 In this invention, the server includes information processing means for receiving the progress status and material information of the workplace input by the user, formatting and converting the information; information storage means for transmitting the information to the central processing unit and persistently storing the data; artificial intelligence processing means for performing work progress and resource optimization based on past business data; display means for notifying the user of the analysis results by the artificial intelligence in real time; and danger detection means for generating and notifying the user of warnings regarding safety management. Thereby, effective work progress management and safety management at the construction site become possible. 【0283】 The "user" is the entity that uses the system and inputs workplace information. 【0284】 The "workplace" refers to the local environment where the construction project is in progress. 【0285】 The "progress status" is information indicating the progress degree and status of the work. 【0286】 The "material information" is detailed data regarding the materials and articles used in the project. 【0287】 The "information processing means" is a device or software having the function of receiving an input from the user and converting the data into an appropriate format. 【0288】 The "central processing unit" is the central device of a system that receives data remotely and performs centralized management and analysis. 【0289】 A "data storage means" is a device or system that has the function of storing received data for a long period of time and making it available for retrieval as needed. 【0290】 "Artificial intelligence processing means" refers to devices or software that include algorithms that analyze patterns using past data and make suggestions for improving efficiency. 【0291】 "Analysis results" refer to the results of data analysis derived by artificial intelligence processing tools. 【0292】 "Display means" refers to devices or software used to visualize analysis results and communicate them to the user. 【0293】 A "hazard detection system" is a system that has the function of predicting workplace risks and promoting proactive safety management. 【0294】 To implement this invention, the user first uses a device such as a smartphone or tablet at the worksite to input the progress status and material information of the work area. The device converts this input information into digital data format and transmits it to a server via the internet. In this process, the device uses JavaScript to convert the data into JSON format and transmits the data securely using the HTTPS protocol. 【0295】 The server stores received data in a database and analyzes it by comparing it with past project data. The server utilizes Python libraries such as pandas and scikit-learn to optimize progress and resource allocation. This analysis also incorporates external weather APIs and material supply databases to take important environmental information into account in real time. 【0296】 The analysis results are returned to the terminal in real time and displayed to the user. The user can use this information to optimize schedules and reallocate resources. The AI ​​also predicts risks in the workplace and notifies the user of warnings as needed. 【0297】 As a concrete example, at a construction site, if bad weather is predicted, the AI ​​will suggest revising the schedule. This allows the user to safely transfer workers to other tasks. 【0298】 Examples of prompt statements are as follows: 【0299】 "Based on construction site progress data and weather information, conduct a risk analysis of the current schedule and propose a safe and efficient work plan." 【0300】 In this way, the invention can be effectively implemented. 【0301】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0302】 Step 1: 【0303】 Users input work progress and material information using a terminal. This information is entered into the terminal as text and numerical data and converted into digital data. 【0304】 Step 2: 【0305】 The terminal formats the input information. JavaScript converts the data to JSON format and sends this data to the server via the HTTPS protocol. Input is text and numerical data, and output is JSON format data. 【0306】 Step 3: 【0307】 The server saves the received JSON data in the database for analysis. The database uses MySQL or PostgreSQL and stores the JSON data in a structured format. The input is JSON data, and the output is the records in the database. 【0308】 Step 4: 【0309】 Based on past business data, the server uses the Python and pandas libraries to analyze progress and resource allocation. In the analysis, time series and comparative analysis are performed to generate an optimal schedule and resource allocation. The input is the business data obtained from the database, and the output is the optimized analysis result. 【0310】 Step 5: 【0311】 The AI uses the obtained analysis results to generate an optimal schedule plan proposed to the user. It utilizes a generative AI model with prompt texts to create a specific work plan. The input is the analyzed data, and the output is the proposed schedule. 【0312】 Step 6: 【0313】 The server sends the analysis results and the proposed schedule to the terminal. The data is processed in real-time and pushed or displayed on the user's terminal. The input is the proposed schedule, and the output is the notification message on the terminal. 【0314】 Step 7: 【0315】 The user checks the displayed proposal and redistributes the work plan and resources as needed. This makes it possible to improve the work efficiency and safety at the site. With the user's input, it is determined as the final work plan. 【0316】 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. 【0317】 This invention implements a system that combines an emotional engine to improve management efficiency and ensure safety at construction sites. The system consists of a user, a terminal, and a server. The user uses the terminal to input data such as the progress of the site, materials used, and the number of workers. The terminal receives this data and performs a format conversion to send it to the server. 【0318】 The server stores information received from the terminal in a database and uses an AI algorithm to analyze past data and the current situation. The server further analyzes the user's voice and facial expression data with an emotion engine to evaluate the user's psychological state. The emotion engine determines the site supervisor's stress level and motivation, and based on this, suggestions for work improvement are made. 【0319】 For example, if a work delay occurs at a site, and the emotion engine determines that the user is experiencing high stress levels, the server will not only adjust the schedule but also suggest measures to reduce stress, such as readjusting manpower or changing task priorities. Conversely, if the system determines that the user is highly motivated, it can suggest setting more challenging goals or providing resources for skill development. 【0320】 This system enables efficient management of construction sites, improves construction quality, and provides a more human-friendly environment that takes into account the psychological state of site supervisors. 【0321】 The following describes the processing flow. 【0322】 Step 1: 【0323】 Users input information such as the progress of work on-site, material usage, and the number of workers using a terminal. The terminal also simultaneously acquires data on the user's voice and facial expressions. 【0324】 Step 2: 【0325】 The terminal converts the collected data into an appropriate format and prepares it for transmission to the server. This process includes the transmission of voice and facial expression data. 【0326】 Step 3: 【0327】 The server receives data sent from the terminal and stores it in the database. This data includes on-site situation information and user voice and facial expression data. 【0328】 Step 4: 【0329】 The server uses AI algorithms to analyze past data and current on-site conditions to optimize schedules and resources. 【0330】 Step 5: 【0331】 The server inputs the acquired voice and facial expression data into an emotion engine to analyze the user's emotional state, determining stress levels and motivation. 【0332】 Step 6: 【0333】 Based on the analysis results from the emotion engine, the server generates suggestions for business improvements that take into account the user's psychological state. These suggestions include specific actions to reduce stress and improve motivation. 【0334】 Step 7: 【0335】 The server sends analysis results and improvement suggestions to the terminal. The terminal displays this information to the user, providing information that can be used for on-site management. 【0336】 Step 8: 【0337】 Users review the information displayed on their devices and implement the suggested work improvement measures tailored to the specific situation on-site. This increases work efficiency while also reducing the psychological burden on the users themselves. 【0338】 (Example 2) 【0339】 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". 【0340】 Achieving both improved work efficiency and safety at construction sites simultaneously is extremely difficult. Furthermore, conventional management methods have been ineffective in appropriately adjusting tasks while considering the psychological state of site workers and supervisors. Therefore, there is a need for management methods that are efficient and reflect human psychological states. 【0341】 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. 【0342】 In this invention, the server includes emotion analysis means for analyzing the user's voice and facial expressions and evaluating their psychological state, artificial intelligence means for analyzing past information and optimizing action plans and resources, and means for notifying the user of the generated suggestions. This makes it possible to adjust work while considering the psychological stress of site supervisors and workers, while improving work efficiency and safety. 【0343】 "User input information" refers to data directly entered into the device operated by the user, and includes information related to work progress, materials used, and the number of workers. 【0344】 "Device" refers to the hardware and software used to receive data, convert it to an appropriate format, and transfer it to a server. 【0345】 A "server" refers to a central computer system that receives, stores, analyzes, and notifies data on a network. 【0346】 "Artificial intelligence tools" refer to a collection of algorithms that analyze accumulated information to optimize work plans and allocate resources. 【0347】 "Emotional analysis tools" refer to systems that analyze a user's voice and facial expressions to determine their psychological state. 【0348】 "Generated proposals" refer to specific action plans regarding business improvement and resource allocation created using artificial intelligence and sentiment analysis tools. 【0349】 "Notification" refers to the act of informing a user of generated suggestions or analysis results, and is usually done electronically. 【0350】 This invention is a system for efficiently managing construction sites and providing work proposals that take into account the user's psychological state. Specific embodiments are described below. 【0351】 Hardware and software configuration 【0352】 Users input on-site information using devices such as smartphones and tablets. A data entry application is installed on the device. The entered information is formatted into JSON format on the device. 【0353】 The terminal sends information to the server via the HTTPS protocol. Upon receiving the data, the server stores it in a database (MySQL or PostgreSQL). The stored data is then analyzed using artificial intelligence algorithms such as TensorFlow or PyTorch. 【0354】 Furthermore, the server receives the user's voice and facial expression data and performs emotion analysis using facial expression recognition libraries such as DeepFace and voice emotion recognition software. This allows the server to evaluate the user's psychological state. 【0355】 Based on artificial intelligence algorithms and sentiment analysis results, the server generates suggestions for business improvement and skill enhancement. These suggestions are notified to the user via their device. The notifications are made in real time and displayed as push notifications on the user's device. 【0356】 Examples of specific cases and prompt statements 【0357】 For example, if a user enters data such as "50% of the rebar work is complete," the server analyzes this data and assesses the possibility of work delays. If the sentiment analysis determines that the user is highly stressed, a notification will be sent suggesting things like "allow extra time in next week's shift." 【0358】 Examples of prompt messages include, "Generate suggestions on how to adjust tasks when the site supervisor's stress level is high," and "Suggest what kind of goal setting would be effective for a highly motivated site supervisor." In this way, the invention aims to streamline site management while simultaneously providing suggestions that take into account human psychological states, thereby creating a more human-friendly management environment. 【0359】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0360】 Step 1: 【0361】 Users input on-site data such as site conditions, materials used, and the number of workers into a terminal. The input data is then formatted into JSON on the terminal. This prepares the data in a format that the server can process. 【0362】 Step 2: 【0363】 The terminal sends formatted JSON data to the server via the HTTPS protocol. Data encryption is performed during transmission to ensure communication security. Data integrity checks are also conducted during this process to verify the data's defect-free nature. 【0364】 Step 3: 【0365】 The server parses the JSON data received from the terminal. After verifying the validity of the data, the information is stored in a database. Database management systems such as MySQL or PostgreSQL are used for storage, and indexes are set up to allow for quick retrieval of the data. 【0366】 Step 4: 【0367】 The server uses an artificial intelligence model to analyze stored data. This model, utilizing TensorFlow and PyTorch, analyzes past work patterns and delays to generate predictions for future progress. The results of this analysis are used to improve operational efficiency. 【0368】 Step 5: 【0369】 The server analyzes the user's voice and video data received from the terminal. It uses facial recognition libraries such as DeepFace and voice emotion recognition software to evaluate the user's psychological state. The evaluation results are used as a basis for making business proposals. 【0370】 Step 6: 【0371】 The server generates business improvement suggestions based on artificial intelligence analysis results and sentiment analysis results. For example, if high stress levels are detected, suggestions for optimizing task priorities will be created. These suggestions are generated automatically using an AI model. 【0372】 Step 7: 【0373】 The server sends the generated suggestions to the terminal, notifying the user. The notification is configured to arrive at the user's terminal in real time as a push message. Based on this, the user can perform efficient field management. 【0374】 (Application Example 2) 【0375】 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." 【0376】 While there is a need for more efficient progress management and safety management at construction sites, traditional methods make it difficult to manage workers while considering their psychological state, hindering efficient scheduling and resource optimization. In particular, there is a lack of risk prediction and countermeasures for the work environment, necessitating methods to enhance safety while reducing the burden on site managers. 【0377】 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. 【0378】 In this invention, the server includes a device that receives user input information and converts it into a data format, a device that transmits the information to a central computer and stores that information, a device that includes an intelligent algorithm that performs past information analysis including emotion analysis and work efficiency improvement, and a device that notifies the user of suggestions using the analysis results. This makes it possible to improve the efficiency of progress management and safety management at construction sites while taking into account the psychological state of the users. 【0379】 "User input information" refers to information recorded and transmitted by workers using terminals, such as the progress status at construction sites, materials used, and the number of workers. 【0380】 A "data format conversion device" is a device that converts user input information into a format suitable for the server, enabling efficient communication and storage of information. 【0381】 A "central computer" is a computer system that aggregates and processes information and generates optimal management proposals. 【0382】 An "intelligent algorithm" is a computational method that analyzes past information and sentiment to support efficiency improvements and safety enhancements in the field. 【0383】 "Emotional analysis" is a technology that uses user voice and facial expression data to understand their psychological state. 【0384】 A "device for notifying users of suggestions" refers to a notification system that informs users of management suggestions generated based on analyzed information. 【0385】 To implement this invention, it is necessary to construct a system that combines multiple elements. Users input information about the construction site using a device such as a smartphone or tablet. The device has a mechanism for inputting information such as the progress of the site, materials used, and the number of workers. This information is formatted by an application on the device and sent to a server. 【0386】 The server is built using Node.js and stores information in a MongoDB database. The received data is analyzed by an AI algorithm. This algorithm also performs emotion analysis using the Azure Emotion API. Specifically, the user's voice data is converted to text using the Google Cloud Speech-to-Text API, and this text, along with facial expression data obtained from the camera, is analyzed using the Emotion API. This allows the system to determine the user's psychological state and, combined with past data, generate optimal business improvement proposals. 【0387】 The analysis results are notified to the terminal and presented to the user as concrete suggestions. For example, if progress is behind schedule in a certain process, the system can suggest adjusting the work schedule or reallocating personnel based on the user's stress level obtained from the emotion analysis data. This is key to improving overall efficiency and safety within the company. 【0388】 A concrete example of a prompt message is, "Progress delays have been identified on-site, causing high levels of stress for managers. Please suggest ways to streamline the schedule." In this way, the system aims to provide appropriate solutions while reflecting the user's psychological state. 【0389】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0390】 Step 1: 【0391】 Users input information such as the progress of construction sites, materials used, and the number of workers using a terminal. This input information is received by an application on the terminal. The data processing performed here involves format conversion, including manual input by the user. 【0392】 Step 2: 【0393】 The terminal converts the received information into a data format suitable for the central computer. After this format conversion, the terminal sends the data to the server. The input is the user's raw data, and the output is data in a format that the server can process. 【0394】 Step 3: 【0395】 The server receives data sent from the terminal and stores it in the database. MongoDB handles this storage, acting as a permanent storage for the information. The input for this step is formatted data, and the output is the data stored in the database. 【0396】 Step 4: 【0397】 The server performs the process of converting speech data to text using the Google Cloud Speech-to-Text API. Simultaneously, it performs user emotion analysis using the Azure Emotion API. The input is speech and facial expression data, and the output is the transcribed speech data and the results of the emotion state analysis. This analysis performs the data calculations for emotion analysis. 【0398】 Step 5: 【0399】 The server uses AI algorithms to analyze historical data and generate business improvement proposals. Inputs are field data and sentiment analysis results, and output is specific business improvement proposals. Data calculations include comparison with historical data and consideration of the user's emotional state. 【0400】 Step 6: 【0401】 The server notifies the terminal of the generated business improvement proposals. The terminal displays the proposals on the screen and notifies the user. The input is the business improvement proposals, and the output is the business improvement proposal information provided to the user. This step includes the action of visually presenting the information in a way that the user can confirm. 【0402】 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. 【0403】 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. 【0404】 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. 【0405】 [Third Embodiment] 【0406】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0407】 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. 【0408】 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). 【0409】 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. 【0410】 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. 【0411】 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). 【0412】 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. 【0413】 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. 【0414】 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. 【0415】 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. 【0416】 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. 【0417】 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". 【0418】 This invention is implemented as a system to streamline the work of site supervisors at construction sites. First, the user inputs site conditions using a terminal and registers data such as the progress of the construction, materials used, and the number of workers. The terminal receives this information and performs a format conversion for secure transmission to the server. 【0419】 The server stores the data received from the terminal and uses an AI algorithm to compare it with past project data and analyze the current progress. Based on this analysis, the server derives the optimal schedule and resource allocation. 【0420】 The analysis results are sent from the server to the terminal and displayed to the user. This allows the user to obtain the optimized schedule suggested by the AI ​​and efficiently manage the site. For example, if foundation work is delayed at a site, the AI ​​will take weather data and material supply status into consideration and suggest a schedule change. The user can then use this as a reference to adjust the construction schedule. 【0421】 AI can also be useful in safety management. The server performs risk analysis and, if a hazard is predicted, sends a warning to the user via the terminal. This warning helps to strengthen safety measures for workers on site. 【0422】 Thus, this invention enhances on-site management efficiency and improves construction quality and ensures safety through cooperation among the user, terminal, and server. 【0423】 The following describes the processing flow. 【0424】 Step 1: 【0425】 The user inputs information about the site situation into the terminal. This input includes the day's work progress, materials used, number of workers, and any special notes. The terminal receives the entered data. 【0426】 Step 2: 【0427】 The terminal formats the data received from the user and sends it to the server. Before sending, it performs necessary data checks and data conversions to ensure that the data is transmitted accurately and completely. 【0428】 Step 3: 【0429】 The server receives data sent from the terminal and stores it in a database. The server then executes an AI algorithm based on the stored data and begins analysis based on past data and the current situation. 【0430】 Step 4: 【0431】 The server generates optimized schedule proposals and resource allocation suggestions based on the analysis. These suggestions create a concrete action plan to maximize on-site construction efficiency. 【0432】 Step 5: 【0433】 The server sends the generated proposal to the terminal. The terminal displays the received proposal to the user. Based on this displayed information, the user manages and coordinates the site. 【0434】 Step 6: 【0435】 Users review the suggestions displayed on their terminals and reallocate on-site schedules and resources based on those suggestions. If necessary, they can enter feedback into their terminals and send it to the server. This feedback will be used for future improvements. 【0436】 Step 7: 【0437】 The server periodically analyzes the work environment and assesses potential risks. If a risk is detected, the server sends an alert to the terminal and provides safety management suggestions to the user. 【0438】 Step 8: 【0439】 Users review safety suggestions from their devices and implement specific safety measures on-site. This improves worker safety and helps prevent accidents. 【0440】 (Example 1) 【0441】 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." 【0442】 In construction sites, operational management often involves manual information updates and progress tracking, resulting in inefficiencies and time-consuming processes. Furthermore, predicting hazards in the work environment and strengthening safety management are crucial challenges, but there is a lack of adequate support for these. These problems can potentially hinder project progress and safety. 【0443】 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. 【0444】 In this invention, the server includes means for receiving user input information and converting the data into a standard format, means for transmitting the data to the server and storing it in an information recording medium, means including an intelligent algorithm that analyzes past data and performs progress management and resource optimization, means for visually reporting the analysis results to the user from a terminal, and means for predicting hazards in the work environment and generating notifications for safety measures. This enables more efficient business management and enhanced on-site safety management. 【0445】 "User input information" refers to data such as the progress of the construction site, the materials used, and the number of workers. 【0446】 "Terminal means" refers to a device or system that receives input information from a user and converts the data into a standard format. 【0447】 "Means of sending data to a server and storing it on an information recording medium" refers to the process of sending data processed on a terminal to the server and securely storing it in a database or similar. 【0448】 An "intelligent algorithm that analyzes past data to manage progress and optimize resources" refers to a technology that uses collected data to compare with previous projects and proposes an evaluation of progress and effective resource allocation. 【0449】 "Means of visually reporting analysis results to the user from the terminal" refers to methods for presenting data analyzed on the server to the user in an easily understandable visual way through the terminal. 【0450】 "Means for predicting hazards in the work environment and generating notifications of safety measures" refers to a process that strengthens safety management by evaluating potential risks based on on-site conditions and notifying that information. 【0451】 This invention provides a system that allows users to streamline the management of construction sites. 【0452】 The user first uses their own device to input site status data. This includes the progress of the construction, the construction materials being used, and the number of workers. This data is entered into the device through a dedicated application. After input, the device converts the information into a standard format (e.g., JSON or XML) and securely transmits it to the server. The device implements encryption technology (such as SSL / TLS protocol) to ensure the secure transfer of data. 【0453】 The server stores the received data in a database. Databases such as MySQL and PostgreSQL are commonly used. Once the data is stored, the server uses AI algorithms to compare it with past project data and analyze current progress and resource usage. These intelligent algorithms use statistical models and machine learning techniques to adjust plans and optimize resources. Specific AI models include time series analysis models and risk assessment models. 【0454】 Once the analysis is complete, the server sends the results back to the terminal. The terminal displays these analysis results in a format that is easy for the user to understand visually. This allows the user to manage the site more efficiently. 【0455】 Furthermore, the server predicts hazards in the work environment and generates notifications for necessary safety measures. For example, it assesses project risks by considering factors such as weather conditions and delays in material supply. In situations where hazards are particularly anticipated, it can send warning messages to terminals to prompt workers on-site to take appropriate action. 【0456】 For example, if foundation work is delayed at a construction site and the reason is weather, the AI ​​will analyze the situation and suggest schedule adjustments. An example of a prompt would be, "Please provide the optimal schedule for the next stage, taking into account the delay in foundation work due to bad weather." By considering this suggestion, the user can make quick and accurate decisions. 【0457】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0458】 Step 1: 【0459】 Users input construction site status data via a terminal. This input includes information such as the progress of the work, the construction materials being used, and the number of workers. An application installed on the terminal receives this information and converts it into a standard format (e.g., JSON or XML). This conversion ensures data consistency and compatibility. The converted data is then ready to be sent to the server. 【0460】 Step 2: 【0461】 The terminal sends the converted data to the server using encryption technology (such as the SSL / TLS protocol). The input to this process is encrypted data, and the output is a secure transfer to the server. Sending the data to the server reduces the risk of information leakage and unauthorized access. 【0462】 Step 3: 【0463】 The server stores the received data in a database. The input here is the data sent from the terminal. The server stores this data using a database management system (e.g., MySQL or PostgreSQL). The output is securely stored data, which is used for later analysis. 【0464】 Step 4: 【0465】 The server executes AI algorithms using stored data. The input consists of historical and current data stored in a database. The server applies statistical models and machine learning algorithms to optimize progress and resources compared to past project data. The analysis output is a proposal for an optimized schedule and resource allocation. 【0466】 Step 5: 【0467】 The server sends the generated analysis results to the terminal. The input is the AI-generated analysis results, and the output is information displayed visually to the user. The terminal displays the received results on a dashboard, allowing the user to check and manage the situation on-site based on that information. 【0468】 Step 6: 【0469】 The server further predicts hazards in the work environment and generates notifications for safety measures as needed. The input is data related to the site conditions. The server uses a risk assessment model to evaluate potential hazards and sends warning messages to terminals as output. The terminals notify users of received warnings, supporting site safety management. 【0470】 (Application Example 1) 【0471】 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." 【0472】 In modern construction sites, efficiency and safety management are critical challenges. While it's essential to efficiently track work progress and optimize resource allocation, site conditions are constantly changing, requiring rapid responses. Furthermore, safety management demands proactive risk assessment and appropriate countermeasures. These challenges need to be addressed. 【0473】 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. 【0474】 In this invention, the server includes an information processing means that receives work progress and material information entered by the user, formats and converts the information, an information storage means that transmits the information to a central processing unit and continuously stores the data, an artificial intelligence processing means that optimizes work progress and resources based on past work data, a display means that notifies the user of the analysis results by artificial intelligence in real time, and a hazard detection means that generates and notifies the user of safety management warnings. This enables effective work progress management and safety management at construction sites. 【0475】 A "user" is the entity that uses the system to input workplace information. 【0476】 "Workplace" refers to the on-site environment where a construction project is underway. 【0477】 "Progress status" refers to information that indicates the degree of progress or status of a task. 【0478】 "Materials information" refers to detailed data about the materials and items used in a project. 【0479】 "Information processing means" refers to devices or software that receive input from a user and convert the data into an appropriate format. 【0480】 A "central processing unit" is the central device in a system that receives data remotely and manages and analyzes it centrally. 【0481】 A "data storage means" is a device or system that has the function of storing received data for a long period of time and making it available for retrieval as needed. 【0482】 "Artificial intelligence processing means" refers to devices or software that include algorithms that analyze patterns using past data and make suggestions for improving efficiency. 【0483】 "Analysis results" refer to the results of data analysis derived by artificial intelligence processing tools. 【0484】 "Display means" refers to devices or software used to visualize analysis results and communicate them to the user. 【0485】 A "hazard detection system" is a system that has the function of predicting workplace risks and promoting proactive safety management. 【0486】 To implement this invention, the user first uses a device such as a smartphone or tablet at the worksite to input the progress status and material information of the work area. The device converts this input information into digital data format and transmits it to a server via the internet. In this process, the device uses JavaScript to convert the data into JSON format and transmits the data securely using the HTTPS protocol. 【0487】 The server stores received data in a database and analyzes it by comparing it with past project data. The server utilizes Python libraries such as pandas and scikit-learn to optimize progress and resource allocation. This analysis also incorporates external weather APIs and material supply databases to take important environmental information into account in real time. 【0488】 The analysis results are returned to the terminal in real time and displayed to the user. The user can use this information to optimize schedules and reallocate resources. The AI ​​also predicts risks in the workplace and notifies the user of warnings as needed. 【0489】 As a concrete example, at a construction site, if bad weather is predicted, the AI ​​will suggest revising the schedule. This allows the user to safely transfer workers to other tasks. 【0490】 Examples of prompt statements are as follows: 【0491】 "Based on construction site progress data and weather information, conduct a risk analysis of the current schedule and propose a safe and efficient work plan." 【0492】 In this way, the invention can be effectively implemented. 【0493】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0494】 Step 1: 【0495】 Users input work progress and material information using a terminal. This information is entered into the terminal as text and numerical data and converted into digital data. 【0496】 Step 2: 【0497】 The terminal formats the input information. JavaScript converts the data to JSON format and sends this data to the server via the HTTPS protocol. Input is text and numerical data, and output is JSON format data. 【0498】 Step 3: 【0499】 The server stores the received JSON data in a database for analysis. The database uses either MySQL or PostgreSQL and stores the JSON data in a structured format. The input is JSON data, and the output is a record in the database. 【0500】 Step 4: 【0501】 The server uses Python and the pandas library to analyze progress and resource allocation based on historical business data. The analysis includes time-series and comparative analyses to generate the optimal schedule and resource allocation. Input is business data retrieved from a database, and output is the optimized analysis results. 【0502】 Step 5: 【0503】 The AI ​​uses the obtained analysis results to generate an optimal schedule proposal for the user. Using prompts, the generated AI model is utilized to create a concrete work plan. The input is the analyzed data, and the output is the proposed schedule. 【0504】 Step 6: 【0505】 The server sends the analysis results and proposed schedule to the terminal. The data is processed in real time and is either pushed as a notification or displayed on the user's terminal. The input is the proposed schedule, and the output is the notification message on the terminal. 【0506】 Step 7: 【0507】 The user reviews the displayed suggestions and reallocates work plans and resources as needed. This improves on-site work efficiency and safety. The user's input finalizes the work plan. 【0508】 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. 【0509】 This invention implements a system that combines an emotional engine to improve management efficiency and ensure safety at construction sites. The system consists of a user, a terminal, and a server. The user uses the terminal to input data such as the progress of the site, materials used, and the number of workers. The terminal receives this data and performs a format conversion to send it to the server. 【0510】 The server stores information received from the terminal in a database and uses an AI algorithm to analyze past data and the current situation. The server further analyzes the user's voice and facial expression data with an emotion engine to evaluate the user's psychological state. The emotion engine determines the site supervisor's stress level and motivation, and based on this, suggestions for work improvement are made. 【0511】 For example, if a work delay occurs at a site, and the emotion engine determines that the user is experiencing high stress levels, the server will not only adjust the schedule but also suggest measures to reduce stress, such as readjusting manpower or changing task priorities. Conversely, if the system determines that the user is highly motivated, it can suggest setting more challenging goals or providing resources for skill development. 【0512】 This system enables efficient management of construction sites, improves construction quality, and provides a more human-friendly environment that takes into account the psychological state of site supervisors. 【0513】 The following describes the processing flow. 【0514】 Step 1: 【0515】 Users input information such as the progress of work on-site, material usage, and the number of workers using a terminal. The terminal also simultaneously acquires data on the user's voice and facial expressions. 【0516】 Step 2: 【0517】 The terminal converts the collected data into an appropriate format and prepares it for transmission to the server. This process includes the transmission of voice and facial expression data. 【0518】 Step 3: 【0519】 The server receives data sent from the terminal and stores it in the database. This data includes on-site situation information and user voice and facial expression data. 【0520】 Step 4: 【0521】 The server uses AI algorithms to analyze past data and current on-site conditions to optimize schedules and resources. 【0522】 Step 5: 【0523】 The server inputs the acquired voice and facial expression data into an emotion engine to analyze the user's emotional state, determining stress levels and motivation. 【0524】 Step 6: 【0525】 Based on the analysis results from the emotion engine, the server generates suggestions for business improvements that take into account the user's psychological state. These suggestions include specific actions to reduce stress and improve motivation. 【0526】 Step 7: 【0527】 The server sends analysis results and improvement suggestions to the terminal. The terminal displays this information to the user, providing information that can be used for on-site management. 【0528】 Step 8: 【0529】 Users review the information displayed on their devices and implement the suggested work improvement measures tailored to the specific situation on-site. This increases work efficiency while also reducing the psychological burden on the users themselves. 【0530】 (Example 2) 【0531】 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." 【0532】 Achieving both improved work efficiency and safety at construction sites simultaneously is extremely difficult. Furthermore, conventional management methods have been ineffective in appropriately adjusting tasks while considering the psychological state of site workers and supervisors. Therefore, there is a need for management methods that are efficient and reflect human psychological states. 【0533】 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. 【0534】 In this invention, the server includes emotion analysis means for analyzing the user's voice and facial expressions and evaluating their psychological state, artificial intelligence means for analyzing past information and optimizing action plans and resources, and means for notifying the user of the generated suggestions. This makes it possible to adjust work while considering the psychological stress of site supervisors and workers, while improving work efficiency and safety. 【0535】 "User input information" refers to data directly entered into the device operated by the user, and includes information related to work progress, materials used, and the number of workers. 【0536】 "Device" refers to the hardware and software used to receive data, convert it to an appropriate format, and transfer it to a server. 【0537】 A "server" refers to a central computer system that receives, stores, analyzes, and notifies data on a network. 【0538】 "Artificial intelligence tools" refer to a collection of algorithms that analyze accumulated information to optimize work plans and allocate resources. 【0539】 "Emotional analysis tools" refer to systems that analyze a user's voice and facial expressions to determine their psychological state. 【0540】 "Generated proposals" refer to specific action plans regarding business improvement and resource allocation created using artificial intelligence and sentiment analysis tools. 【0541】 "Notification" refers to the act of informing a user of generated suggestions or analysis results, and is usually done electronically. 【0542】 This invention is a system for efficiently managing construction sites and providing work proposals that take into account the user's psychological state. Specific embodiments are described below. 【0543】 Hardware and software configuration 【0544】 Users input on-site information using devices such as smartphones and tablets. A data entry application is installed on the device. The entered information is formatted into JSON format on the device. 【0545】 The terminal sends information to the server via the HTTPS protocol. Upon receiving the data, the server stores it in a database (MySQL or PostgreSQL). The stored data is then analyzed using artificial intelligence algorithms such as TensorFlow or PyTorch. 【0546】 Furthermore, the server receives the user's voice and facial expression data and performs emotion analysis using facial expression recognition libraries such as DeepFace and voice emotion recognition software. This allows the server to evaluate the user's psychological state. 【0547】 Based on artificial intelligence algorithms and sentiment analysis results, the server generates suggestions for business improvement and skill enhancement. These suggestions are notified to the user via their device. The notifications are made in real time and displayed as push notifications on the user's device. 【0548】 Examples of specific cases and prompt statements 【0549】 For example, if a user enters data such as "50% of the rebar work is complete," the server analyzes this data and assesses the possibility of work delays. If the sentiment analysis determines that the user is highly stressed, a notification will be sent suggesting things like "allow extra time in next week's shift." 【0550】 Examples of prompt messages include, "Generate suggestions on how to adjust tasks when the site supervisor's stress level is high," and "Suggest what kind of goal setting would be effective for a highly motivated site supervisor." In this way, the invention aims to streamline site management while simultaneously providing suggestions that take into account human psychological states, thereby creating a more human-friendly management environment. 【0551】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0552】 Step 1: 【0553】 Users input on-site data such as site conditions, materials used, and the number of workers into a terminal. The input data is then formatted into JSON on the terminal. This prepares the data in a format that the server can process. 【0554】 Step 2: 【0555】 The terminal sends formatted JSON data to the server via the HTTPS protocol. Data encryption is performed during transmission to ensure communication security. Data integrity checks are also conducted during this process to verify the data's defect-free nature. 【0556】 Step 3: 【0557】 The server parses the JSON data received from the terminal. After verifying the validity of the data, the information is stored in a database. Database management systems such as MySQL or PostgreSQL are used for storage, and indexes are set up to allow for quick retrieval of the data. 【0558】 Step 4: 【0559】 The server uses an artificial intelligence model to analyze stored data. This model, utilizing TensorFlow and PyTorch, analyzes past work patterns and delays to generate predictions for future progress. The results of this analysis are used to improve operational efficiency. 【0560】 Step 5: 【0561】 The server analyzes the user's voice and video data received from the terminal. It uses facial recognition libraries such as DeepFace and voice emotion recognition software to evaluate the user's psychological state. The evaluation results are used as a basis for making business proposals. 【0562】 Step 6: 【0563】 The server generates business improvement suggestions based on artificial intelligence analysis results and sentiment analysis results. For example, if high stress levels are detected, suggestions for optimizing task priorities will be created. These suggestions are generated automatically using an AI model. 【0564】 Step 7: 【0565】 The server sends the generated suggestions to the terminal, notifying the user. The notification is configured to arrive at the user's terminal in real time as a push message. Based on this, the user can perform efficient field management. 【0566】 (Application Example 2) 【0567】 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." 【0568】 While there is a need for more efficient progress management and safety management at construction sites, traditional methods make it difficult to manage workers while considering their psychological state, hindering efficient scheduling and resource optimization. In particular, there is a lack of risk prediction and countermeasures for the work environment, necessitating methods to enhance safety while reducing the burden on site managers. 【0569】 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. 【0570】 In this invention, the server includes a device that receives user input information and converts it into a data format, a device that transmits the information to a central computer and stores that information, a device that includes an intelligent algorithm that performs past information analysis including emotion analysis and work efficiency improvement, and a device that notifies the user of suggestions using the analysis results. This makes it possible to improve the efficiency of progress management and safety management at construction sites while taking into account the psychological state of the users. 【0571】 "User input information" refers to information recorded and transmitted by workers using terminals, such as the progress status at construction sites, materials used, and the number of workers. 【0572】 A "data format conversion device" is a device that converts user input information into a format suitable for the server, enabling efficient communication and storage of information. 【0573】 A "central computer" is a computer system that aggregates and processes information and generates optimal management proposals. 【0574】 An "intelligent algorithm" is a computational method that analyzes past information and sentiment to support efficiency improvements and safety enhancements in the field. 【0575】 "Emotional analysis" is a technology that uses user voice and facial expression data to understand their psychological state. 【0576】 A "device for notifying users of suggestions" refers to a notification system that informs users of management suggestions generated based on analyzed information. 【0577】 To implement this invention, it is necessary to construct a system that combines multiple elements. Users input information about the construction site using a device such as a smartphone or tablet. The device has a mechanism for inputting information such as the progress of the site, materials used, and the number of workers. This information is formatted by an application on the device and sent to a server. 【0578】 The server is built using Node.js and stores information in a MongoDB database. The received data is analyzed by an AI algorithm. This algorithm also performs emotion analysis using the Azure Emotion API. Specifically, the user's voice data is converted to text using the Google Cloud Speech-to-Text API, and this text, along with facial expression data obtained from the camera, is analyzed using the Emotion API. This allows the system to determine the user's psychological state and, combined with past data, generate optimal business improvement proposals. 【0579】 The analysis results are notified to the terminal and presented to the user as concrete suggestions. For example, if progress is behind schedule in a certain process, the system can suggest adjusting the work schedule or reallocating personnel based on the user's stress level obtained from the emotion analysis data. This is key to improving overall efficiency and safety within the company. 【0580】 A concrete example of a prompt message is, "Progress delays have been identified on-site, causing high levels of stress for managers. Please suggest ways to streamline the schedule." In this way, the system aims to provide appropriate solutions while reflecting the user's psychological state. 【0581】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0582】 Step 1: 【0583】 Users input information such as the progress of construction sites, materials used, and the number of workers using a terminal. This input information is received by an application on the terminal. The data processing performed here involves format conversion, including manual input by the user. 【0584】 Step 2: 【0585】 The terminal converts the received information into a data format suitable for the central computer. After this format conversion, the terminal sends the data to the server. The input is the user's raw data, and the output is data in a format that the server can process. 【0586】 Step 3: 【0587】 The server receives data sent from the terminal and stores it in the database. MongoDB handles this storage, acting as a permanent storage for the information. The input for this step is formatted data, and the output is the data stored in the database. 【0588】 Step 4: 【0589】 The server performs the process of converting speech data to text using the Google Cloud Speech-to-Text API. Simultaneously, it performs user emotion analysis using the Azure Emotion API. The input is speech and facial expression data, and the output is the transcribed speech data and the results of the emotion state analysis. This analysis performs the data calculations for emotion analysis. 【0590】 Step 5: 【0591】 The server uses AI algorithms to analyze historical data and generate business improvement proposals. Inputs are field data and sentiment analysis results, and output is specific business improvement proposals. Data calculations include comparison with historical data and consideration of the user's emotional state. 【0592】 Step 6: 【0593】 The server notifies the terminal of the generated business improvement proposals. The terminal displays the proposals on the screen and notifies the user. The input is the business improvement proposals, and the output is the business improvement proposal information provided to the user. This step includes the action of visually presenting the information in a way that the user can confirm. 【0594】 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. 【0595】 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. 【0596】 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. 【0597】 [Fourth Embodiment] 【0598】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0599】 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. 【0600】 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). 【0601】 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. 【0602】 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. 【0603】 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). 【0604】 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. 【0605】 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. 【0606】 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. 【0607】 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. 【0608】 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. 【0609】 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. 【0610】 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". 【0611】 This invention is implemented as a system to streamline the work of site supervisors at construction sites. First, the user inputs site conditions using a terminal and registers data such as the progress of the construction, materials used, and the number of workers. The terminal receives this information and performs a format conversion for secure transmission to the server. 【0612】 The server stores the data received from the terminal and uses an AI algorithm to compare it with past project data and analyze the current progress. Based on this analysis, the server derives the optimal schedule and resource allocation. 【0613】 The analysis results are sent from the server to the terminal and displayed to the user. This allows the user to obtain the optimized schedule suggested by the AI ​​and efficiently manage the site. For example, if foundation work is delayed at a site, the AI ​​will take weather data and material supply status into consideration and suggest a schedule change. The user can then use this as a reference to adjust the construction schedule. 【0614】 AI can also be useful in safety management. The server performs risk analysis and, if a hazard is predicted, sends a warning to the user via the terminal. This warning helps to strengthen safety measures for workers on site. 【0615】 Thus, this invention enhances on-site management efficiency and improves construction quality and ensures safety through cooperation among the user, terminal, and server. 【0616】 The following describes the processing flow. 【0617】 Step 1: 【0618】 The user inputs information about the site situation into the terminal. This input includes the day's work progress, materials used, number of workers, and any special notes. The terminal receives the entered data. 【0619】 Step 2: 【0620】 The terminal formats the data received from the user and sends it to the server. Before sending, it performs necessary data checks and data conversions to ensure that the data is transmitted accurately and completely. 【0621】 Step 3: 【0622】 The server receives data sent from the terminal and stores it in a database. The server then executes an AI algorithm based on the stored data and begins analysis based on past data and the current situation. 【0623】 Step 4: 【0624】 The server generates optimized schedule proposals and resource allocation suggestions based on the analysis. These suggestions create a concrete action plan to maximize on-site construction efficiency. 【0625】 Step 5: 【0626】 The server sends the generated proposal to the terminal. The terminal displays the received proposal to the user. Based on this displayed information, the user manages and coordinates the site. 【0627】 Step 6: 【0628】 Users review the suggestions displayed on their terminals and reallocate on-site schedules and resources based on those suggestions. If necessary, they can enter feedback into their terminals and send it to the server. This feedback will be used for future improvements. 【0629】 Step 7: 【0630】 The server periodically analyzes the work environment and assesses potential risks. If a risk is detected, the server sends an alert to the terminal and provides safety management suggestions to the user. 【0631】 Step 8: 【0632】 Users review safety suggestions from their devices and implement specific safety measures on-site. This improves worker safety and helps prevent accidents. 【0633】 (Example 1) 【0634】 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". 【0635】 In construction sites, operational management often involves manual information updates and progress tracking, resulting in inefficiencies and time-consuming processes. Furthermore, predicting hazards in the work environment and strengthening safety management are crucial challenges, but there is a lack of adequate support for these. These problems can potentially hinder project progress and safety. 【0636】 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. 【0637】 In this invention, the server includes means for receiving user input information and converting the data into a standard format, means for transmitting the data to the server and storing it in an information recording medium, means including an intelligent algorithm that analyzes past data and performs progress management and resource optimization, means for visually reporting the analysis results to the user from a terminal, and means for predicting hazards in the work environment and generating notifications for safety measures. This enables more efficient business management and enhanced on-site safety management. 【0638】 "User input information" refers to data such as the progress of the construction site, the materials used, and the number of workers. 【0639】 "Terminal means" refers to a device or system that receives input information from a user and converts the data into a standard format. 【0640】 "Means of sending data to a server and storing it on an information recording medium" refers to the process of sending data processed on a terminal to the server and securely storing it in a database or similar. 【0641】 An "intelligent algorithm that analyzes past data to manage progress and optimize resources" refers to a technology that uses collected data to compare with previous projects and proposes an evaluation of progress and effective resource allocation. 【0642】 "Means of visually reporting analysis results to the user from the terminal" refers to methods for presenting data analyzed on the server to the user in an easily understandable visual way through the terminal. 【0643】 "Means for predicting hazards in the work environment and generating notifications of safety measures" refers to a process that strengthens safety management by evaluating potential risks based on on-site conditions and notifying that information. 【0644】 This invention provides a system that allows users to streamline the management of construction sites. 【0645】 The user first uses their own device to input site status data. This includes the progress of the construction, the construction materials being used, and the number of workers. This data is entered into the device through a dedicated application. After input, the device converts the information into a standard format (e.g., JSON or XML) and securely transmits it to the server. The device implements encryption technology (such as SSL / TLS protocol) to ensure the secure transfer of data. 【0646】 The server stores the received data in a database. Databases such as MySQL and PostgreSQL are commonly used. Once the data is stored, the server uses AI algorithms to compare it with past project data and analyze current progress and resource usage. These intelligent algorithms use statistical models and machine learning techniques to adjust plans and optimize resources. Specific AI models include time series analysis models and risk assessment models. 【0647】 Once the analysis is complete, the server sends the results back to the terminal. The terminal displays these analysis results in a format that is easy for the user to understand visually. This allows the user to manage the site more efficiently. 【0648】 Furthermore, the server predicts hazards in the work environment and generates notifications for necessary safety measures. For example, it assesses project risks by considering factors such as weather conditions and delays in material supply. In situations where hazards are particularly anticipated, it can send warning messages to terminals to prompt workers on-site to take appropriate action. 【0649】 For example, if foundation work is delayed at a construction site and the reason is weather, the AI ​​will analyze the situation and suggest schedule adjustments. An example of a prompt would be, "Please provide the optimal schedule for the next stage, taking into account the delay in foundation work due to bad weather." By considering this suggestion, the user can make quick and accurate decisions. 【0650】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0651】 Step 1: 【0652】 Users input construction site status data via a terminal. This input includes information such as the progress of the work, the construction materials being used, and the number of workers. An application installed on the terminal receives this information and converts it into a standard format (e.g., JSON or XML). This conversion ensures data consistency and compatibility. The converted data is then ready to be sent to the server. 【0653】 Step 2: 【0654】 The terminal sends the converted data to the server using encryption technology (such as the SSL / TLS protocol). The input to this process is encrypted data, and the output is a secure transfer to the server. Sending the data to the server reduces the risk of information leakage and unauthorized access. 【0655】 Step 3: 【0656】 The server stores the received data in a database. The input here is the data sent from the terminal. The server stores this data using a database management system (e.g., MySQL or PostgreSQL). The output is securely stored data, which is used for later analysis. 【0657】 Step 4: 【0658】 The server executes AI algorithms using stored data. The input consists of historical and current data stored in a database. The server applies statistical models and machine learning algorithms to optimize progress and resources compared to past project data. The analysis output is a proposal for an optimized schedule and resource allocation. 【0659】 Step 5: 【0660】 The server sends the generated analysis results to the terminal. The input is the AI-generated analysis results, and the output is information displayed visually to the user. The terminal displays the received results on a dashboard, allowing the user to check and manage the situation on-site based on that information. 【0661】 Step 6: 【0662】 The server further predicts hazards in the work environment and generates notifications for safety measures as needed. The input is data related to the site conditions. The server uses a risk assessment model to evaluate potential hazards and sends warning messages to terminals as output. The terminals notify users of received warnings, supporting site safety management. 【0663】 (Application Example 1) 【0664】 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". 【0665】 In modern construction sites, efficiency and safety management are critical challenges. While it's essential to efficiently track work progress and optimize resource allocation, site conditions are constantly changing, requiring rapid responses. Furthermore, safety management demands proactive risk assessment and appropriate countermeasures. These challenges need to be addressed. 【0666】 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. 【0667】 In this invention, the server includes an information processing means that receives work progress and material information entered by the user, formats and converts the information, an information storage means that transmits the information to a central processing unit and continuously stores the data, an artificial intelligence processing means that optimizes work progress and resources based on past work data, a display means that notifies the user of the analysis results by artificial intelligence in real time, and a hazard detection means that generates and notifies the user of safety management warnings. This enables effective work progress management and safety management at construction sites. 【0668】 A "user" is the entity that uses the system to input workplace information. 【0669】 "Workplace" refers to the on-site environment where a construction project is underway. 【0670】 "Progress status" refers to information that indicates the degree of progress or status of a task. 【0671】 "Materials information" refers to detailed data about the materials and items used in a project. 【0672】 "Information processing means" refers to devices or software that receive input from a user and convert the data into an appropriate format. 【0673】 A "central processing unit" is the central device in a system that receives data remotely and manages and analyzes it centrally. 【0674】 A "data storage means" is a device or system that has the function of storing received data for a long period of time and making it available for retrieval as needed. 【0675】 "Artificial intelligence processing means" refers to devices or software that include algorithms that analyze patterns using past data and make suggestions for improving efficiency. 【0676】 "Analysis results" refer to the results of data analysis derived by artificial intelligence processing tools. 【0677】 "Display means" refers to devices or software used to visualize analysis results and communicate them to the user. 【0678】 A "hazard detection system" is a system that has the function of predicting workplace risks and promoting proactive safety management. 【0679】 To implement this invention, the user first uses a device such as a smartphone or tablet at the worksite to input the progress status and material information of the work area. The device converts this input information into digital data format and transmits it to a server via the internet. In this process, the device uses JavaScript to convert the data into JSON format and transmits the data securely using the HTTPS protocol. 【0680】 The server stores received data in a database and analyzes it by comparing it with past project data. The server utilizes Python libraries such as pandas and scikit-learn to optimize progress and resource allocation. This analysis also incorporates external weather APIs and material supply databases to take important environmental information into account in real time. 【0681】 The analysis results are returned to the terminal in real time and displayed to the user. The user can use this information to optimize schedules and reallocate resources. The AI ​​also predicts risks in the workplace and notifies the user of warnings as needed. 【0682】 As a concrete example, at a construction site, if bad weather is predicted, the AI ​​will suggest revising the schedule. This allows the user to safely transfer workers to other tasks. 【0683】 Examples of prompt statements are as follows: 【0684】 "Based on construction site progress data and weather information, conduct a risk analysis of the current schedule and propose a safe and efficient work plan." 【0685】 In this way, the invention can be effectively implemented. 【0686】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0687】 Step 1: 【0688】 Users input work progress and material information using a terminal. This information is entered into the terminal as text and numerical data and converted into digital data. 【0689】 Step 2: 【0690】 The terminal formats the input information. JavaScript converts the data to JSON format and sends this data to the server via the HTTPS protocol. Input is text and numerical data, and output is JSON format data. 【0691】 Step 3: 【0692】 The server stores the received JSON data in a database for analysis. The database uses either MySQL or PostgreSQL and stores the JSON data in a structured format. The input is JSON data, and the output is a record in the database. 【0693】 Step 4: 【0694】 The server uses Python and the pandas library to analyze progress and resource allocation based on historical business data. The analysis includes time-series and comparative analyses to generate the optimal schedule and resource allocation. Input is business data retrieved from a database, and output is the optimized analysis results. 【0695】 Step 5: 【0696】 The AI ​​uses the obtained analysis results to generate an optimal schedule proposal for the user. Using prompts, the generated AI model is utilized to create a concrete work plan. The input is the analyzed data, and the output is the proposed schedule. 【0697】 Step 6: 【0698】 The server sends the analysis results and proposed schedule to the terminal. The data is processed in real time and is either pushed as a notification or displayed on the user's terminal. The input is the proposed schedule, and the output is the notification message on the terminal. 【0699】 Step 7: 【0700】 The user reviews the displayed suggestions and reallocates work plans and resources as needed. This improves on-site work efficiency and safety. The user's input finalizes the work plan. 【0701】 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. 【0702】 This invention implements a system that combines an emotional engine to improve management efficiency and ensure safety at construction sites. The system consists of a user, a terminal, and a server. The user uses the terminal to input data such as the progress of the site, materials used, and the number of workers. The terminal receives this data and performs a format conversion to send it to the server. 【0703】 The server stores information received from the terminal in a database and uses an AI algorithm to analyze past data and the current situation. The server further analyzes the user's voice and facial expression data with an emotion engine to evaluate the user's psychological state. The emotion engine determines the site supervisor's stress level and motivation, and based on this, suggestions for work improvement are made. 【0704】 For example, if a work delay occurs at a site, and the emotion engine determines that the user is experiencing high stress levels, the server will not only adjust the schedule but also suggest measures to reduce stress, such as readjusting manpower or changing task priorities. Conversely, if the system determines that the user is highly motivated, it can suggest setting more challenging goals or providing resources for skill development. 【0705】 This system enables efficient management of construction sites, improves construction quality, and provides a more human-friendly environment that takes into account the psychological state of site supervisors. 【0706】 The following describes the processing flow. 【0707】 Step 1: 【0708】 Users input information such as the progress of work on-site, material usage, and the number of workers using a terminal. The terminal also simultaneously acquires data on the user's voice and facial expressions. 【0709】 Step 2: 【0710】 The terminal converts the collected data into an appropriate format and prepares it for transmission to the server. This process includes the transmission of voice and facial expression data. 【0711】 Step 3: 【0712】 The server receives data sent from the terminal and stores it in the database. This data includes on-site situation information and user voice and facial expression data. 【0713】 Step 4: 【0714】 The server uses AI algorithms to analyze past data and current on-site conditions to optimize schedules and resources. 【0715】 Step 5: 【0716】 The server inputs the acquired voice and facial expression data into an emotion engine to analyze the user's emotional state, determining stress levels and motivation. 【0717】 Step 6: 【0718】 Based on the analysis results from the emotion engine, the server generates suggestions for business improvements that take into account the user's psychological state. These suggestions include specific actions to reduce stress and improve motivation. 【0719】 Step 7: 【0720】 The server sends analysis results and improvement suggestions to the terminal. The terminal displays this information to the user, providing information that can be used for on-site management. 【0721】 Step 8: 【0722】 Users review the information displayed on their devices and implement the suggested work improvement measures tailored to the specific situation on-site. This increases work efficiency while also reducing the psychological burden on the users themselves. 【0723】 (Example 2) 【0724】 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". 【0725】 Achieving both improved work efficiency and safety at construction sites simultaneously is extremely difficult. Furthermore, conventional management methods have been ineffective in appropriately adjusting tasks while considering the psychological state of site workers and supervisors. Therefore, there is a need for management methods that are efficient and reflect human psychological states. 【0726】 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. 【0727】 In this invention, the server includes emotion analysis means for analyzing the user's voice and facial expressions and evaluating their psychological state, artificial intelligence means for analyzing past information and optimizing action plans and resources, and means for notifying the user of the generated suggestions. This makes it possible to adjust work while considering the psychological stress of site supervisors and workers, while improving work efficiency and safety. 【0728】 "User input information" refers to data directly entered into the device operated by the user, and includes information related to work progress, materials used, and the number of workers. 【0729】 "Device" refers to the hardware and software used to receive data, convert it to an appropriate format, and transfer it to a server. 【0730】 A "server" refers to a central computer system that receives, stores, analyzes, and notifies data on a network. 【0731】 "Artificial intelligence tools" refer to a collection of algorithms that analyze accumulated information to optimize work plans and allocate resources. 【0732】 "Emotional analysis tools" refer to systems that analyze a user's voice and facial expressions to determine their psychological state. 【0733】 "Generated proposals" refer to specific action plans regarding business improvement and resource allocation created using artificial intelligence and sentiment analysis tools. 【0734】 "Notification" refers to the act of informing a user of generated suggestions or analysis results, and is usually done electronically. 【0735】 This invention is a system for efficiently managing construction sites and providing work proposals that take into account the user's psychological state. Specific embodiments are described below. 【0736】 Hardware and software configuration 【0737】 Users input on-site information using devices such as smartphones and tablets. A data entry application is installed on the device. The entered information is formatted into JSON format on the device. 【0738】 The terminal sends information to the server via the HTTPS protocol. Upon receiving the data, the server stores it in a database (MySQL or PostgreSQL). The stored data is then analyzed using artificial intelligence algorithms such as TensorFlow or PyTorch. 【0739】 Furthermore, the server receives the user's voice and facial expression data and performs emotion analysis using facial expression recognition libraries such as DeepFace and voice emotion recognition software. This allows the server to evaluate the user's psychological state. 【0740】 Based on artificial intelligence algorithms and sentiment analysis results, the server generates suggestions for business improvement and skill enhancement. These suggestions are notified to the user via their device. The notifications are made in real time and displayed as push notifications on the user's device. 【0741】 Examples of specific cases and prompt statements 【0742】 For example, if a user enters data such as "50% of the rebar work is complete," the server analyzes this data and assesses the possibility of work delays. If the sentiment analysis determines that the user is highly stressed, a notification will be sent suggesting things like "allow extra time in next week's shift." 【0743】 Examples of prompt messages include, "Generate suggestions on how to adjust tasks when the site supervisor's stress level is high," and "Suggest what kind of goal setting would be effective for a highly motivated site supervisor." In this way, the invention aims to streamline site management while simultaneously providing suggestions that take into account human psychological states, thereby creating a more human-friendly management environment. 【0744】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0745】 Step 1: 【0746】 Users input on-site data such as site conditions, materials used, and the number of workers into a terminal. The input data is then formatted into JSON on the terminal. This prepares the data in a format that the server can process. 【0747】 Step 2: 【0748】 The terminal sends formatted JSON data to the server via the HTTPS protocol. Data encryption is performed during transmission to ensure communication security. Data integrity checks are also conducted during this process to verify the data's defect-free nature. 【0749】 Step 3: 【0750】 The server parses the JSON data received from the terminal. After verifying the validity of the data, the information is stored in a database. Database management systems such as MySQL or PostgreSQL are used for storage, and indexes are set up to allow for quick retrieval of the data. 【0751】 Step 4: 【0752】 The server uses an artificial intelligence model to analyze stored data. This model, utilizing TensorFlow and PyTorch, analyzes past work patterns and delays to generate predictions for future progress. The results of this analysis are used to improve operational efficiency. 【0753】 Step 5: 【0754】 The server analyzes the user's voice and video data received from the terminal. It uses facial recognition libraries such as DeepFace and voice emotion recognition software to evaluate the user's psychological state. The evaluation results are used as a basis for making business proposals. 【0755】 Step 6: 【0756】 The server generates business improvement suggestions based on artificial intelligence analysis results and sentiment analysis results. For example, if high stress levels are detected, a suggestion to optimize task priorities will be created. The suggestions are generated automatically using an AI model. 【0757】 Step 7: 【0758】 The server sends the generated suggestions to the terminal, notifying the user. The notification is configured to arrive at the user's terminal in real time as a push message. Based on this, the user can perform efficient field management. 【0759】 (Application Example 2) 【0760】 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". 【0761】 While there is a need for more efficient progress management and safety management at construction sites, traditional methods make it difficult to manage workers while considering their psychological state, hindering efficient scheduling and resource optimization. In particular, there is a lack of risk prediction and countermeasures for the work environment, necessitating methods to enhance safety while reducing the burden on site managers. 【0762】 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. 【0763】 In this invention, the server includes a device that receives user input information and converts it into a data format, a device that transmits the information to a central computer and stores that information, a device that includes an intelligent algorithm that performs past information analysis including emotion analysis and work efficiency improvement, and a device that notifies the user of suggestions using the analysis results. This makes it possible to improve the efficiency of progress management and safety management at construction sites while taking into account the psychological state of the users. 【0764】 "User input information" refers to information recorded and transmitted by workers using terminals, such as the progress status at construction sites, materials used, and the number of workers. 【0765】 A "data format conversion device" is a device that converts user input information into a format suitable for the server, enabling efficient communication and storage of information. 【0766】 A "central computer" is a computer system that aggregates and processes information and generates optimal management proposals. 【0767】 An "intelligent algorithm" is a computational method that analyzes past information and sentiment to support efficiency improvements and safety enhancements in the field. 【0768】 "Emotional analysis" is a technology that uses user voice and facial expression data to understand their psychological state. 【0769】 A "device for notifying users of suggestions" refers to a notification system that informs users of management suggestions generated based on analyzed information. 【0770】 To implement this invention, it is necessary to construct a system that combines multiple elements. Users input information about the construction site using a device such as a smartphone or tablet. The device has a mechanism for inputting information such as the progress of the site, materials used, and the number of workers. This information is formatted by an application on the device and sent to a server. 【0771】 The server is built using Node.js and stores information in a MongoDB database. The received data is analyzed by an AI algorithm. This algorithm also performs emotion analysis using the Azure Emotion API. Specifically, the user's voice data is converted to text using the Google Cloud Speech-to-Text API, and this text, along with facial expression data obtained from the camera, is analyzed using the Emotion API. This allows the system to determine the user's psychological state and, combined with past data, generate optimal business improvement proposals. 【0772】 The analysis results are notified to the terminal and presented to the user as concrete suggestions. For example, if progress is behind schedule in a certain process, the system can suggest adjusting the work schedule or reallocating personnel based on the user's stress level obtained from the emotion analysis data. This is key to improving overall efficiency and safety within the company. 【0773】 A concrete example of a prompt message is, "Progress delays have been identified on-site, causing high levels of stress for managers. Please suggest ways to streamline the schedule." In this way, the system aims to provide appropriate solutions while reflecting the user's psychological state. 【0774】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0775】 Step 1: 【0776】 Users input information such as the progress of construction sites, materials used, and the number of workers using a terminal. This input information is received by an application on the terminal. The data processing performed here involves format conversion, including manual input by the user. 【0777】 Step 2: 【0778】 The terminal converts the received information into a data format suitable for the central computer. After this format conversion, the terminal sends the data to the server. The input is the user's raw data, and the output is data in a format that the server can process. 【0779】 Step 3: 【0780】 The server receives data sent from the terminal and stores it in the database. MongoDB handles this storage, acting as a permanent storage for the information. The input for this step is formatted data, and the output is the data stored in the database. 【0781】 Step 4: 【0782】 The server performs the process of converting speech data to text using the Google Cloud Speech-to-Text API. Simultaneously, it performs user emotion analysis using the Azure Emotion API. The input is speech and facial expression data, and the output is the transcribed speech data and the results of the emotion state analysis. This analysis performs the data calculations for emotion analysis. 【0783】 Step 5: 【0784】 The server uses AI algorithms to analyze historical data and generate business improvement proposals. Inputs are field data and sentiment analysis results, and output is specific business improvement proposals. Data calculations include comparison with historical data and consideration of the user's emotional state. 【0785】 Step 6: 【0786】 The server notifies the terminal of the generated business improvement proposals. The terminal displays the proposals on the screen and notifies the user. The input is the business improvement proposals, and the output is the business improvement proposal information provided to the user. This step includes the action of visually presenting the information in a way that the user can confirm. 【0787】 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. 【0788】 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. 【0789】 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. 【0790】 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. 【0791】 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. 【0792】 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. 【0793】 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. 【0794】 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. 【0795】 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." 【0796】 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. 【0797】 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. 【0798】 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. 【0799】 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. 【0800】 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. 【0801】 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. 【0802】 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. 【0803】 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. 【0804】 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. 【0805】 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. 【0806】 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. 【0807】 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 as being incorporated by reference. 【0808】 The following is further disclosed regarding the embodiments described above. 【0809】 (Claim 1) 【0810】 A terminal means for receiving user input information and formatting the data, 【0811】 A means of sending data to a server and storing the data, 【0812】 A means including an AI algorithm that analyzes past data and optimizes schedules and resources, 【0813】 A means of notifying the user of the analysis results, 【0814】 A system that includes this. 【0815】 (Claim 2) 【0816】 The system according to claim 1, which displays suggestions for business improvement to the user. 【0817】 (Claim 3) 【0818】 The system according to claim 1, which performs risk prediction based on the work environment and provides safety management proposals. 【0819】 "Example 1" 【0820】 (Claim 1) 【0821】 A terminal means that receives user input information and converts the data into a standard format, 【0822】 A means of sending data to a server and storing it on an information recording medium, 【0823】 A means including an intelligent algorithm that analyzes past data and performs progress management and resource optimization, 【0824】 A means of visually reporting the analysis results to the user from the terminal, 【0825】 A means for predicting hazards in the work environment and generating notifications for safety measures, 【0826】 A system that includes this. 【0827】 (Claim 2) 【0828】 The system according to claim 1, which proposes efficient business operations to the user and provides specific operating instructions. 【0829】 (Claim 3) 【0830】 The system according to claim 1, which performs a risk assessment based on changes in the work environment and provides information on improving maintenance management. 【0831】 "Application Example 1" 【0832】 (Claim 1) 【0833】 An information processing means that receives work progress status and material information entered by the user, formats and converts the information, 【0834】 Information storage means for transmitting information to a central processing unit and continuously storing data, 【0835】 An artificial intelligence processing method that optimizes work progress and resources based on past business data, 【0836】 A display means that notifies the user of the results of analysis by artificial intelligence in real time, 【0837】 A risk detection means that generates and notifies users of safety management warnings, 【0838】 A system that includes this. 【0839】 (Claim 2) 【0840】 The system according to claim 1, which presents the user with an optimized work plan for improving work efficiency. 【0841】 (Claim 3) 【0842】 The system according to claim 1, which predicts hazardous elements related to the work site environment and provides instructions to enhance safety. 【0843】 "Example 2 of combining an emotion engine" 【0844】 (Claim 1) 【0845】 A device that receives user input information and formats the information, 【0846】 A device that transmits and stores information over a network, 【0847】 An artificial intelligence system that analyzes past information and optimizes action plans and resources, 【0848】 An emotion analysis method that analyzes the user's voice and facial expressions to evaluate their psychological state, 【0849】 A means of proposing business improvements and resource allocation based on the analysis results, 【0850】 A means of notifying the user of the generated suggestions, 【0851】 A system that includes this. 【0852】 (Claim 2) 【0853】 The system according to claim 1, which displays suggestions for business improvement based on the progress of the work. 【0854】 (Claim 3) 【0855】 The system according to claim 1, which performs hazard prediction based on working conditions and provides safety management proposals. 【0856】 "Application example 2 when combining with an emotional engine" 【0857】 (Claim 1) 【0858】 A device that receives user input information and converts it into a data format, 【0859】 A device that transmits information to a central computer and stores that information, 【0860】 A device including an intelligent algorithm for analyzing past information, including emotion analysis, and for improving business efficiency, 【0861】 A device that notifies the user of the proposal using the analysis results, 【0862】 A control device that includes this. 【0863】 (Claim 2) 【0864】 The management device according to claim 1, which displays suggestions for business improvement based on the user's emotional state. 【0865】 (Claim 3) 【0866】 A management device according to claim 1 that predicts hazards based on information about the work environment and provides safety measures. [Explanation of symbols] 【0867】 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] A terminal means for receiving user input information and formatting the data, A means of sending data to a server and storing the data, A means including an AI algorithm that analyzes past data and optimizes schedules and resources, A means of notifying the user of the analysis results, A system that includes this. [Claim 2] The system according to claim 1, which displays suggestions for business improvement to the user. [Claim 3] The system according to claim 1, which performs risk prediction based on the work environment and provides proposals for safety management.

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