system

Abstract

We provide the system. [Solution] By collecting past and present information on civil engineering projects from a data storage system, a means of optimizing planning is provided. A means of analyzing progress and resource status using machine learning algorithms and generating an optimal schedule, A means of monitoring the work status at the work site in real time and proposing resource allocation as needed, A means of predicting risks based on past accident data and issuing necessary warnings for safety management, A means of monitoring progress in real time via smart devices and performing dynamic scheduling based on predictive analytics, A means by which a smart device transmits a warning to workers to encourage them to wear appropriate safety equipment, A system that includes this.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is 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 character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the modern construction industry, the problems faced by on-site supervisors are diverse. With the complexity of work projects increasing, schedule management and optimization of resource allocation have become increasingly important. However, for on-site supervisors who are required to make highly accurate judgments within limited human resources and time, these tasks have become a heavy burden. In addition, due to regulations on working hours and changes in the review system, higher-level monitoring and prediction are required, especially in safety management and quality management. It is a challenge to solve these problems and improve construction quality while enhancing work efficiency.

Means for Solving the Problems

[0005] This invention provides a system that generates an optimal schedule by collecting past and present data on construction projects from a database and using machine learning algorithms to analyze progress and resource status based on that data. It also features a function to dynamically adjust operations by monitoring on-site work status in real time and proposing resource allocation as needed. Furthermore, it includes means to improve construction quality by predicting risks based on past accident data and issuing warnings for safety management. This system enables more flexible and efficient project management by automatically adjusting schedules while also considering weather data and supply chain data.

[0006] A "database" is a systematic collection of information used to efficiently manage, search, and manipulate large amounts of data.

[0007] A "construction project" is a series of activities aimed at systematically designing, constructing, and completing a specific building or infrastructure.

[0008] A "machine learning algorithm" is a type of computer program that automatically learns patterns and rules from data to perform predictions and classifications.

[0009] "Progress" refers to the state of how far along a project or task is in relation to its time-based plan or goals.

[0010] "Resource allocation" refers to the effective distribution of resources such as personnel, equipment, and materials necessary for a project or task.

[0011] "Real-time" means immediately reflecting, processing, and responding to events that are actually happening on the spot.

[0012] "Safety management" refers to efforts to prevent accidents and hazards and ensure safety in work sites and projects.

[0013] "Accident data" refers to information about accidents that have occurred in the past, including detailed records of their causes, impacts, and countermeasures.

[0014] "Weather data" refers to measured and predicted values ​​related to meteorological conditions, including information such as temperature, precipitation, and wind speed.

[0015] A "supply chain" refers to the series of activities and processes involved in delivering raw materials, components, and finished products from suppliers to consumers. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12]It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the language used in the following description will be explained.

[0019] 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 CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), and the like.

[0020] 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.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention is a system for streamlining construction site management and improving construction quality. The system consists of a server, multiple terminals, and users. Specific embodiments of this system are described below.

[0038] First, the server collects historical and current data about construction projects from the database. This includes progress data from previous projects, resource usage history, and accident records. More accurate data is collected as users input data on ongoing projects in real time using terminals.

[0039] Subsequently, the server analyzes this data using machine learning algorithms to evaluate the project's progress and resource availability. Based on this analysis, the server can generate and propose an optimal schedule to the user.

[0040] For example, if the server detects that the progress of foundation work is behind schedule, it will suggest the deployment of additional workers as needed. It can also flexibly adjust the schedule, taking weather data and supply chain information into consideration. For instance, if bad weather is expected, it will notify the user to switch from external work to internal work.

[0041] Furthermore, the terminal also has a safety management function that provides workers with real-time risk warnings. This works by having a server predict risk factors based on analysis of past accident data and issuing appropriate warnings when the risk increases. For example, it checks whether workers are fully equipped with safety gear before working at heights and warns them via the terminal if necessary.

[0042] Thus, the system of the present invention optimizes project management in a data-driven manner, thereby improving construction quality and safety.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The server accesses the database to collect historical and current progress data for the target construction project. This data includes past project schedules, resource allocations, and accident records.

[0046] Step 2:

[0047] Users input real-time information about on-site work status and resource usage via their terminals. The terminals continuously transmit this data to the server, ensuring that the server maintains the most up-to-date information.

[0048] Step 3:

[0049] The server uses historical and current data to initiate analysis using machine learning algorithms. This allows for evaluation of project progress and resource consumption, and a determination of the validity of the current schedule.

[0050] Step 4:

[0051] The server generates a new, optimized schedule based on the analysis results. This schedule includes resource allocation and task prioritization, offering suggestions for more efficient project progress.

[0052] Step 5:

[0053] The server generates a draft schedule, which is then sent to the user's terminal for presentation. The user can review the schedule on their terminal and make adjustments as needed.

[0054] Step 6:

[0055] The user sends the approved schedule and proposal back to the server. The server logs this as the official plan within the system.

[0056] Step 7:

[0057] The server predicts risks from past accident data for safety management purposes and generates warnings tailored to the situation on site. Terminals then notify workers of these warnings, prompting them to pay attention and check their safety equipment.

[0058] This will allow for more efficient overall construction project management and strengthen safety measures.

[0059] (Example 1)

[0060] 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."

[0061] In construction activities, effective management of project progress and resources is crucial. However, traditional methods make it difficult to efficiently collect and analyze real-time information from the site and respond quickly. Furthermore, the proper use of past accident information and the implementation of preventive measures to enhance site safety are not adequately carried out. In addition, the inability to automatically and flexibly adjust schedules in response to weather and supply chain fluctuations leads to project delays and inappropriate resource allocation. These challenges need to be addressed.

[0062] 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.

[0063] In this invention, the server includes means for optimizing plans by collecting past and present information on construction activities from a data management device; means for analyzing progress and resource status using machine learning methods to generate an optimal time plan; means for monitoring the working environment in real time and proposing resource allocation as needed; means for predicting risks based on past accident information and issuing necessary warnings for safety management; and means for inputting feedback at work terminals and using it to improve subsequent analyses and proposals. This makes it possible to grasp the situation on site in real time and respond quickly, enabling resource optimization, improved safety, and flexible adjustment of plans.

[0064] A "data management device" is a system that stores past and present information related to construction activities and provides it in an accessible format.

[0065] "Construction activities" refer to a series of tasks and projects for building, maintaining, and repairing buildings and facilities.

[0066] "Machine learning techniques" refer to algorithmic technologies that analyze data and recognize patterns and trends, thereby enabling efficient decision-making.

[0067] "Progress status" refers to the extent to which a construction project is being achieved in relation to its planned schedule and objectives.

[0068] "Resource status" refers to information indicating the quantity and arrangement of personnel, equipment, materials, etc., used in construction activities.

[0069] A "time plan" is a detailed plan outlining the steps and schedule required to complete a construction project.

[0070] "Work environment" refers to the entire physical and organizational location and conditions under which construction activities are carried out.

[0071] "Real-time" refers to a time concept that enables processing and analysis of information and data almost immediately after they are generated.

[0072] "Risk" refers to factors that indicate the possibility of accidents or problems occurring during construction activities and the resulting impacts.

[0073] "Feedback" refers to information obtained from users and systems, such as reactions and data, that can be used to improve or adjust future systems.

[0074] The system of this invention is designed to optimize construction activities and improve their quality and safety. This system consists of a server, multiple terminals, and users.

[0075] The server first collects past and present information on construction activities from data management devices. The software used includes database management systems (e.g., MySQL®, PostgreSQL), which are used for access and data extraction. The server then analyzes the collected data using machine learning techniques (e.g., Python's scikit-learn library). This allows the server to evaluate project progress and resource status and generate an optimal time plan.

[0076] Users input information in real time using terminals installed on-site. This input includes the current status of the work environment and the completion time of specific tasks. Furthermore, the server processes this information immediately, proposes resource allocation as needed, and supports on-site decision-making.

[0077] The terminal alerts workers when the risk increases based on past accident data. Specifically, it displays a warning on the terminal when a risk is detected based on pre-set criteria. This function aims to enhance worker safety.

[0078] As a concrete example of its operation, the server evaluates the progress of foundation work and, if it finds that there are delays, it includes a function that suggests to the user the necessary allocation of additional resources. Furthermore, if weather analysis predicts bad weather, it also includes a function that recommends switching from external work to internal work.

[0079] An example of a prompt message would be, "Please report on the progress and resource status of the construction project. This should include tasks that are behind schedule and proposed optimizations for resource allocation." This input allows the generating AI model to extract appropriate information, enabling the user to take quick and appropriate action.

[0080] This allows the server to optimize the situation at the construction site through centralized information management and advanced analysis, supporting real-time decision-making and ultimately improving construction quality.

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] The server collects past and present project information from the data management device. This input data includes project progress, resource usage history, and incident records. The server queries this information using a database management system to extract data. As output, the server obtains an integrated dataset ready for analysis.

[0084] Step 2:

[0085] Users input real-time information from the field using a terminal. This input includes details of ongoing work, the amount of resources being used, and worker deployment information. The terminal collects this information and sends it to the server. As output, the server updates the latest field information and uses it for analysis.

[0086] Step 3:

[0087] The server analyzes collected data using machine learning techniques. Input data includes historical and current project information and real-time field data. The server uses this data to perform trend analysis and predictive modeling using the Python scikit-learn library. The output generates suggestions for optimal time planning and resource allocation.

[0088] Step 4:

[0089] The server generates an optimal schedule based on the analysis results and proposes it to the user. The input data is the analysis results obtained in the previous step. The server uses a generating AI model to assemble a schedule in a way that is practical for the user and communicates it to the terminal. As output, the user receives a concrete plan to support on-site decision-making.

[0090] Step 5:

[0091] The terminal alerts workers based on risk predictions from the server. The input data is the result of the server's risk analysis. The terminal uses this data to generate and display warning messages according to specific conditions. As a result, workers prepare safety equipment and take precautions for high-risk tasks.

[0092] Step 6:

[0093] Users input feedback into the server via their devices. This feedback includes reports on the results of implementing the suggestions and the situation on-site. The server receives this feedback and uses it for subsequent data analysis and improvement of the suggestions. As an output, the accuracy and efficiency of the entire system in the next cycle improve.

[0094] (Application Example 1)

[0095] 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."

[0096] Project management at construction sites presents numerous challenges, including delays, inappropriate resource allocation, and safety uncertainties. Traditional management methods have struggled to address these issues quickly and efficiently. In particular, real-time progress monitoring, dynamic scheduling, and enhanced safety measures are essential. Solving these challenges and improving construction quality and safety is crucial.

[0097] 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.

[0098] This invention includes means for optimizing plans by collecting past and present information on civil engineering projects from a data storage system; means for analyzing progress and resource status using machine learning algorithms and generating an optimal schedule; means for monitoring work status at work sites in real time and proposing resource allocation as needed; means for monitoring progress in real time via smart devices and performing dynamic scheduling based on predictive analytics; and means for smart devices to send warnings to workers to encourage the wearing of appropriate safety equipment. This enables efficient management of construction projects.

[0099] A "data storage system" is a system for storing and managing information, and in particular, it enables the efficient collection and access of past and present information necessary for a project.

[0100] A "civil engineering project" is a collection of plans and activities for constructing physical infrastructure, and requires management of progress and resource management.

[0101] A "machine learning algorithm" is a method for computers to learn from data and perform analysis and predictions, and is a means to optimize projects.

[0102] "Progress status" is an indicator that shows whether a project is progressing according to schedule, and it is managed by comparing it to the plan.

[0103] "Resource status" refers to an indicator that shows the utilization of manpower, materials, equipment, etc., allocated to a project, and managing this directly impacts the efficiency of the project.

[0104] A "schedule" is a plan that shows the planned dates and times for each task in a project, and it is the core of schedule management.

[0105] The term "workplace" refers to the physical location where the work is actually performed, and monitoring the work status at this location forms part of project management.

[0106] A "smart device" is an electronic device that has information processing capabilities and utilizes communication functions to support on-site work, and is a tool that enables real-time monitoring and notifications.

[0107] "Safety equipment" refers to protective gear worn by workers to protect themselves from danger and is necessary to maintain a safe working environment.

[0108] The system for implementing this invention consists of a server, a smart device, and a user. The server uses a data storage system to collect information on past and present civil engineering projects and analyzes this data using machine learning algorithms. Specifically, it utilizes cloud computing services such as AWS® and processes the collected data using TENSORFLOW® to evaluate progress and resource availability and generate an optimal schedule.

[0109] Smart devices play a role in collecting real-time information about the work site from users and transmitting it to a server. This allows the server to dynamically adjust progress based on predictive analysis and suggest resource allocation as needed. Smart devices also have a function to display warnings to workers to encourage the wearing of appropriate safety equipment. Specific examples of such equipment include smartphones and tablets.

[0110] Users input and verify project data through their devices. The user interface is built with React Native, enabling intuitive operation. Furthermore, real-time data synchronization is achieved by utilizing AWS cloud services.

[0111] As a concrete example, if weather deteriorates at a construction site, the server can adjust the schedule to reschedule external work to internal work and notify the user via a smart device. In this way, the efficiency of on-site work is maintained while providing flexibility in the plan.

[0112] An example of a prompt to the generated AI model would be: "Please tell me how to optimize the construction schedule, taking into account the progress data and weather information of the construction project. Please also explain strategies for efficient resource allocation and risk management." This allows the AI ​​model to provide effective project management methods and assist in optimization.

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The server collects historical and current information about construction projects from a data storage system. The input is project information in a database, and the output is an analyzable dataset. This step utilizes cloud computing services for efficient data access and extraction.

[0116] Step 2:

[0117] The server analyzes the collected data using machine learning algorithms. The input is the dataset obtained in step 1, and the output is optimized scheduling information based on the analysis results. Specifically, TensorFlow is used to evaluate progress and resource status and generate the optimal schedule.

[0118] Step 3:

[0119] The terminal acquires real-time information about the work location from the user and sends it to the server. The input is work site data obtained via the terminal, and the output is updated project data sent to the server. Users intuitively input data using smartphones or tablets.

[0120] Step 4:

[0121] The server analyzes the real-time data received in step 3 and proposes dynamic resource allocation based on predictive analytics. The input is updated project data, and the output is an optimized resource allocation proposal. In this process, prompts generated using a generative AI model are analyzed to derive appropriate resource adjustment plans.

[0122] Step 5:

[0123] The terminal displays alerts based on predictive analytics and safety equipment warnings for workers. Input is a warning message from the server, and output is a visual notification to the user. This feature allows users to take appropriate actions to enhance workplace safety.

[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 is an advanced system for streamlining construction project management and improving construction quality, and in particular incorporates an emotion engine that recognizes user emotions and provides feedback to project operations. The system consists of a server, multiple terminals, users, and the emotion engine.

[0126] First, the server collects historical and current construction project data from the database. This data includes progress information, resource allocation, and accident records. Users use terminals to input the latest on-site conditions in real time, allowing the server to always have up-to-date information.

[0127] The server uses machine learning algorithms to analyze this data, evaluate project progress and resource status, and generate an optimal schedule. The generated schedule is presented to the user via the terminal, and after user approval, it is officially registered as the project plan.

[0128] The emotion engine analyzes the user's emotions from data such as voice and facial expressions obtained from the device. This analysis evaluates the user's stress level and concentration, and provides the results to the server. The server takes this emotion data into consideration to adjust resource allocation and plans, providing the user with more optimized suggestions.

[0129] For example, if the emotion engine detects that a user's stress level is rising during a project, the server will suggest revising the project schedule and reallocating resources to reduce the burden on the user.

[0130] Furthermore, the terminals receive notifications from the server and provide workers with real-time warnings and advice. For example, in high-risk situations, safety management is strengthened by issuing warnings to workers based on analysis of past accident data.

[0131] Through this process, the system will manage construction projects more effectively, incorporating human emotional elements, thereby improving operational efficiency and construction quality.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The server connects to the project management database and collects historical project records and current progress data. This data includes the schedule for each project, completed and incomplete tasks, and resource usage.

[0135] Step 2:

[0136] Users use terminals on-site to input the latest work status and resource consumption information in real time. The terminals immediately send this data to the server, which maintains a real-time report of the on-site situation.

[0137] Step 3:

[0138] The server utilizes machine learning algorithms to analyze collected project data. This identifies the current project progress, resource usage trends, and potential scheduling issues.

[0139] Step 4:

[0140] The server generates an optimized project schedule based on the data analysis results. This schedule includes the priority and resource allocation for each task.

[0141] Step 5:

[0142] The terminal receives a proposed schedule from the server and presents it to the user. The user reviews the proposed schedule and completes the approval process by making revisions or providing additional feedback as needed.

[0143] Step 6:

[0144] The server records the user-approved schedule in the official management system and sets it as the project execution plan.

[0145] Step 7:

[0146] The device's built-in emotion engine monitors the user's emotional state in real time. This engine analyzes voice and facial expression data to evaluate the user's stress level and concentration level.

[0147] Step 8:

[0148] The emotion engine sends its analysis results to the server, which then uses this data to readjust the project and resource allocation. For example, if the server determines that a user is under excessive stress, it will suggest schedule changes to reduce the load.

[0149] Step 9:

[0150] The server predicts risks based on past accident data to ensure work safety. Terminals notify workers of this risk information, prompting them to pay attention and strictly adhere to safety procedures on-site.

[0151] Through these sequential steps, the system will comprehensively improve construction project management, aiming to enhance efficiency and safety.

[0152] (Example 2)

[0153] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0154] Managing construction projects involves complex progress tracking and resource allocation, and the emotional state of workers can also impact project efficiency. Traditional systems struggle to manage all these elements in an integrated manner, posing challenges to overall project efficiency and quality improvement.

[0155] 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.

[0156] In this invention, the server includes means for optimizing the plan by acquiring past and present information about the construction project from a data storage device, means for analyzing the progress and resource status using machine learning techniques to generate an optimal plan, and means for analyzing voice and facial expression information to evaluate the user's emotions. This enables flexible resource allocation according to the progress of the project and efficient management that takes emotions into consideration.

[0157] A "data storage device" refers to a storage medium or system for accumulating data and retrieving it as needed.

[0158] A "construction project" refers to a series of tasks and plans that include the design, construction, supervision, and maintenance of buildings and infrastructure.

[0159] "Information" refers to a set of data related to a specific purpose, including data used for project progress, resource allocation, and risk analysis.

[0160] "Machine learning technology" refers to algorithms and methods that enable computers to automatically learn from data and adaptively perform inference and prediction.

[0161] "Progress status" refers to information indicating how far along a project is in relation to its plan.

[0162] "Resource status" refers to the allocation and utilization of human and material resources available for the project.

[0163] "Voice and facial information" refers to data obtained from the user's speech and facial movements, and is used to determine their emotions and state of mind.

[0164] "Evaluating emotions" refers to analyzing the user's voice and facial expressions to determine their psychological state at that time.

[0165] This invention is a system for enhancing the management of construction projects. The system includes a server, terminals, users, and an emotion engine.

[0166] The server retrieves historical and current information about construction projects stored in data storage devices via an SQL database. This includes project progress, resource allocation, and accident records. Machine learning algorithms developed using Python and R are used for analysis, examining progress and resource status. Based on the analysis results, project management software (e.g., project management tools) is used to generate an optimal schedule.

[0167] The terminals are used to input the latest on-site information in real time. Users input on-site information using mobile devices such as smartphones and tablets, and send this information to the server. Additionally, voice and facial expression information acquired via voice commands and video communication is passed to an emotion engine to analyze the user's emotions.

[0168] The emotion engine uses open-source facial recognition software (e.g., facial recognition libraries) to analyze voice and facial expression data and assess the user's stress level and concentration. The analysis results are provided to the server, which uses this information to optimize resources and adjust the schedule.

[0169] For example, if the emotion engine detects that a user is experiencing high stress levels during a project, the server can reduce the user's burden by suggesting a reallocation of resources.

[0170] This process allows the system to streamline construction projects, improve safety, and enhance construction quality.

[0171] An example of a prompt message would be, "How can I adjust progress and resources in construction project management while taking user sentiment into consideration?"

[0172] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0173] Step 1:

[0174] The server accesses data storage to retrieve past and present information on the construction project. It uses data from databases, such as project progress, resource allocation, and accident records, as input. After retrieving the data, the server formats it into an initial dataset for analysis, preparing it for transmission to machine learning algorithms. This dataset is then used in subsequent analysis steps.

[0175] Step 2:

[0176] The server launches a machine learning algorithm built in Python and analyzes the dataset obtained in Step 1. The input data includes project progress and resource usage. The analysis outputs predicted project progress and indicators of resource efficiency. This output serves as foundational information for use by project management software.

[0177] Step 3:

[0178] The server generates an optimal schedule using project management software based on the analysis results. The inputs used are progress prediction and resource optimization metrics derived from machine learning. In this step, the software outputs its recommended optimal schedule, preparing it for the next step to be reviewed and approved by the user.

[0179] Step 4:

[0180] The terminal presents the user with an optimal schedule provided by the server. The input is the schedule data generated by the server. The user reviews this schedule and provides feedback for any necessary modifications. If the user approves, the schedule is officially registered as the project plan and used in the next step.

[0181] Step 5:

[0182] The terminal is used to collect voice and facial expression data in the field. The user provides voice instructions and video data as input, which the terminal processes and passes to the emotion engine. The collected data becomes input data for evaluating the user's emotional state.

[0183] Step 6:

[0184] The server analyzes collected voice and facial expression data using an emotion engine. The input data consists of user voice and facial expression information received from the terminal. This analysis outputs evaluations of stress levels and concentration levels. The evaluation results are used for project resource allocation and schedule optimization.

[0185] Step 7:

[0186] The server re-evaluates resource allocation and scheduling based on sentiment data and generates suggestions if necessary. It uses evaluations from the sentiment engine and current project data as input. The output is resource reallocation and schedule revision proposals presented to the user. This improves project efficiency and user experience.

[0187] Step 8:

[0188] The terminal provides real-time notifications from the field based on instructions from the server. This includes alerts and safety suggestions based on past accident information. The input uses suggestions from the server, and the output is specific advice and risk warnings for field workers. These notifications help improve safety at the site.

[0189] (Application Example 2)

[0190] 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".

[0191] Construction projects require efficient management and safety assurance, but achieving this necessitates accurately understanding site conditions and providing optimal instructions that take into account the emotional state of workers. Conventional systems often lack the ability to analyze workers' emotions and have insufficient risk management, limiting improvements in efficiency and safety. To solve this problem, a system is needed that collects and analyzes data in real time and provides appropriate feedback to workers.

[0192] 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.

[0193] In this invention, the server includes means for collecting past and present data on construction projects from a database, means for analyzing progress and resource status using machine learning techniques to generate an optimal schedule, means for monitoring work status in real time and proposing resource allocation as needed, means for analyzing the emotional state of users and adjusting resource allocation and plans based on the results, and means for providing real-time feedback on safety and efficiency based on emotional data. This enables efficient and safe project management that takes into account the emotions of workers in construction project management.

[0194] A "database" is an information management system that stores information about past and present construction projects and allows it to be retrieved and used as needed.

[0195] "Machine learning methods" are data analysis techniques that use algorithms to analyze the progress and resource status of construction projects and generate optimal schedules.

[0196] "Progress status" refers to the state of a construction project, indicating how far along the current work process is compared to the plan.

[0197] "Resource status" refers to information indicating the allocation of resources and workers required for a construction project, as well as their usage.

[0198] "Real-time monitoring" is a monitoring technology that enables immediate observation and evaluation of on-site work conditions, and allows for rapid response as needed.

[0199] "Resource allocation" is a management method for optimally distributing the necessary materials and personnel in a construction project.

[0200] "Risk management" is a process of preventing accidents and incidents by predicting risks based on past accident information and issuing necessary warnings.

[0201] "Emotional state" refers to the user's psychological and emotional state during work, including their stress levels and level of concentration.

[0202] "Feedback" is the act of providing users with information and advice based on emotional data to improve safety and efficiency.

[0203] The system for implementing this invention consists of a server, multiple terminals, users, a database, and an emotion engine. The server first collects historical and current data on construction projects from the database, including progress information, resource status, and accident records. This information is aggregated on the server and analyzed using machine learning techniques. As a result of the analysis, the progress and resource status are evaluated, and an optimal schedule is generated. These schedules are presented to users via terminals, and if approved, they are officially recorded as project plans.

[0204] On the terminal side, real-time work status and emotional data are input from the user. The user's emotional state is analyzed by an emotion engine based on voice and facial expressions acquired from the terminal. The results are provided to the server as emotional states such as the user's stress level and concentration level. The server takes this emotional data into consideration to optimize resource allocation and planning, and provides feedback as needed.

[0205] Furthermore, the server generates feedback based on sentiment analysis of data collected in real time. This feedback is provided to the user via the terminal, contributing to improved project efficiency and security.

[0206] This system uses React Native for the frontend and Python and TensorFlow for the backend. It also utilizes Firebase for its database, enabling cloud-based data management. The sentiment engine's analysis results are integrated into the project management screen, supporting users in safe and efficient work operations.

[0207] For example, when a worker is facing a difficult process, the server detects the worker's stress level based on data recorded on their terminal. The server then suggests reallocating resources or revising the process, and provides specific advice as needed, such as "We recommend taking a break to relax." This feedback and advice is generated based on a prompt message that instructs, "Analyze emotional data from workers at the construction site and provide specific advice to alleviate stress."

[0208] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0209] Step 1:

[0210] The server retrieves historical and current data about construction projects from a database. It accepts progress information, resource status, and accident records stored in the database as input. It organizes this data and converts it into a parseable format. The output is an initial input data list used for analysis.

[0211] Step 2:

[0212] The server uses machine learning techniques to analyze the data obtained in Step 1. It receives progress and resource status as input and uses machine learning algorithms to perform calculations to optimize the future schedule. The output is the proposed optimal schedule.

[0213] Step 3:

[0214] The terminal collects real-time data on the work situation at the site. It receives input data from workers and real-time data from sensors as input. This data is then organized and formatted for transmission to the server. The output is the latest work information from the site.

[0215] Step 4:

[0216] The device collects user emotion data from voice and facial expressions and sends it to the emotion engine. It receives user facial expressions and voice information via camera and microphone as input, and converts this into an analyzable format. The output is the emotion data sent to the emotion engine.

[0217] Step 5:

[0218] The server receives the user's emotional state, analyzed by the emotion engine, and adjusts resource allocation and planning accordingly. It receives user emotional state data as input and uses this to determine if resource allocation is optimal. The output is the adjusted allocation proposal.

[0219] Step 6:

[0220] The server notifies the user via the terminal of an optimized schedule and resource suggestions. It receives the schedule obtained in step 2 and the resource suggestions obtained in step 5 as input, and converts them into a user-friendly format. The output is feedback information for the user.

[0221] Step 7:

[0222] The user reviews and modifies the work content and schedule based on feedback received through the terminal. Feedback information from the server is received as input, and the work process is adjusted as needed. The output is a reviewed work procedure document.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] [Second Embodiment]

[0227] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0228] 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.

[0229] 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).

[0230] 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.

[0231] 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.

[0232] 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).

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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".

[0239] This invention is a system for streamlining construction site management and improving construction quality. The system consists of a server, multiple terminals, and users. Specific embodiments of this system are described below.

[0240] First, the server collects historical and current data about construction projects from the database. This includes progress data from previous projects, resource usage history, and accident records. More accurate data is collected as users input data on ongoing projects in real time using terminals.

[0241] Subsequently, the server analyzes this data using machine learning algorithms to evaluate the project's progress and resource availability. Based on this analysis, the server can generate and propose an optimal schedule to the user.

[0242] For example, if the server detects that the progress of foundation work is behind schedule, it will suggest the deployment of additional workers as needed. It can also flexibly adjust the schedule by considering weather data and supply chain information. For instance, if bad weather is expected, it will notify the user to switch from external work to internal work.

[0243] Furthermore, the terminal also has a safety management function that provides workers with real-time risk warnings. This works by having a server predict risk factors based on analysis of past accident data and issuing appropriate warnings when the risk increases. For example, it checks whether workers are fully equipped with safety gear before working at heights and warns them via the terminal if necessary.

[0244] Thus, the system of the present invention optimizes project management in a data-driven manner, thereby improving construction quality and safety.

[0245] The following describes the processing flow.

[0246] Step 1:

[0247] The server accesses the database to collect historical and current progress data for the target construction project. This data includes past project schedules, resource allocations, and accident records.

[0248] Step 2:

[0249] Users input real-time information about on-site work status and resource usage via their terminals. The terminals continuously transmit this data to the server, ensuring that the server maintains the most up-to-date information.

[0250] Step 3:

[0251] The server will use historical and current data to initiate analysis using machine learning algorithms. This will allow us to evaluate the project's progress and resource consumption, and determine the validity of the current schedule.

[0252] Step 4:

[0253] The server generates a new, optimized schedule based on the analysis results. This schedule includes resource allocation and task prioritization, offering suggestions for more efficient project progress.

[0254] Step 5:

[0255] The server generates a draft schedule, which is then sent to the user's terminal for presentation. The user can review the schedule on their terminal and make adjustments as needed.

[0256] Step 6:

[0257] The user sends the approved schedule and proposal back to the server. The server logs this as the official plan within the system.

[0258] Step 7:

[0259] The server predicts risks from past accident data for safety management purposes and generates warnings tailored to the situation on site. Terminals then notify workers of these warnings, prompting them to be cautious and check their safety equipment.

[0260] This will allow for more efficient overall construction project management and strengthen safety measures.

[0261] (Example 1)

[0262] 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."

[0263] In construction activities, effective management of project progress and resources is crucial. However, traditional methods make it difficult to efficiently collect and analyze real-time information from the site and respond quickly. Furthermore, the proper use of past accident information and the implementation of preventive measures to enhance site safety are not adequately carried out. In addition, the inability to automatically and flexibly adjust schedules in response to weather and supply chain fluctuations leads to project delays and inappropriate resource allocation. These challenges need to be addressed.

[0264] 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.

[0265] In this invention, the server includes means for optimizing plans by collecting past and present information on construction activities from a data management device; means for analyzing progress and resource status using machine learning methods to generate an optimal time plan; means for monitoring the working environment in real time and proposing resource allocation as needed; means for predicting risks based on past accident information and issuing necessary warnings for safety management; and means for inputting feedback at work terminals and using it to improve subsequent analyses and proposals. This makes it possible to grasp the situation on site in real time and respond quickly, enabling resource optimization, improved safety, and flexible adjustment of plans.

[0266] A "data management device" is a system that stores past and present information related to construction activities and provides it in an accessible format.

[0267] "Construction activities" refer to a series of tasks and projects for building, maintaining, and repairing buildings and facilities.

[0268] "Machine learning techniques" refer to algorithmic technologies that analyze data and recognize patterns and trends, thereby enabling efficient decision-making.

[0269] "Progress status" refers to the extent to which a construction project is being achieved in relation to its planned schedule and objectives.

[0270] "Resource status" refers to information indicating the quantity and arrangement of personnel, equipment, materials, etc., used in construction activities.

[0271] A "time plan" is a detailed plan outlining the steps and schedule required to complete a construction project.

[0272] "Work environment" refers to the entire physical and organizational location and conditions under which construction activities are carried out.

[0273] "Real-time" refers to a time concept that enables processing and analysis of information and data almost immediately after they are generated.

[0274] "Risk" refers to factors that indicate the possibility of accidents or problems occurring during construction activities and the resulting impacts.

[0275] "Feedback" refers to information obtained from users and systems, such as reactions and data, that can be used to improve or adjust future systems.

[0276] The system of this invention is designed to optimize construction activities and improve their quality and safety. This system consists of a server, multiple terminals, and users.

[0277] The server first collects past and present information on construction activities from data management devices. The software used includes database management systems (e.g., MySQL, PostgreSQL), which are used for access and data extraction. The server then analyzes the collected data using machine learning techniques (e.g., Python's scikit-learn library). This allows the server to evaluate project progress and resource status and generate an optimal time plan.

[0278] Users input information in real time using terminals installed on-site. This input includes the current status of the work environment and the completion time of specific tasks. Furthermore, the server processes this information immediately, proposes resource allocation as needed, and supports on-site decision-making.

[0279] The terminal alerts workers when the risk increases based on past accident data. Specifically, it displays a warning on the terminal when a risk is detected based on pre-set criteria. This function aims to enhance worker safety.

[0280] As a concrete example of its operation, the server evaluates the progress of foundation work and, if it finds that there are delays, it includes a function that suggests to the user the necessary allocation of additional resources. Furthermore, if weather analysis predicts bad weather, it also includes a function that recommends switching from external work to internal work.

[0281] An example of a prompt message would be, "Please report on the progress and resource status of the construction project. This should include tasks that are behind schedule and proposed optimizations for resource allocation." This input allows the generating AI model to extract appropriate information, enabling the user to take quick and appropriate action.

[0282] As a result, the server optimizes the situation at the construction site through unified management and advanced analysis of information, and supports real-time decision-making, thereby realizing the function of improving construction quality.

[0283] The flow of the specific process in Example 1 will be described using FIG. 11.

[0284] Step 1:

[0285] The server collects past and current information of the project from the data management device. This input data includes the progress of the project, resource usage history, accident records, etc. The server queries this information using a database management system and extracts the data. As output, the server obtains an integrated dataset ready for analysis.

[0286] Step 2:

[0287] The user uses the terminal to input real-time information on-site. The input includes details of the ongoing work, the amount of resources in use, and the placement information of the workers. The terminal collects this information and sends it to the server. As output, the server updates the latest information on-site and uses it for analysis.

[0288] Step 3:

[0289] The server analyzes the data collected using machine learning techniques. The input data includes past and current project information and real-time on-site data. The server uses these to execute trend analysis and prediction models using the scikit-learn library in Python. As output, it generates proposals for optimal time planning and resource allocation.

[0290] Step 4:

[0291] The server generates an optimal schedule based on the analysis results and proposes it to the user. The input data is the analysis results obtained in the previous step. The server uses a generating AI model to assemble a schedule in a way that is practical for the user and communicates it to the terminal. As output, the user receives a concrete plan to support on-site decision-making.

[0292] Step 5:

[0293] The terminal alerts workers based on risk predictions from the server. The input data is the result of the server's risk analysis. The terminal uses this data to generate and display warning messages according to specific conditions. As a result, workers prepare safety equipment and take precautions for high-risk tasks.

[0294] Step 6:

[0295] Users input feedback into the server via their devices. This feedback includes reports on the results of implementing the suggestions and the situation on-site. The server receives this feedback and uses it for subsequent data analysis and improvement of the suggestions. As an output, the accuracy and efficiency of the entire system in the next cycle improve.

[0296] (Application Example 1)

[0297] 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."

[0298] Project management at construction sites presents numerous challenges, including delays, inappropriate resource allocation, and safety uncertainties. Traditional management methods have struggled to address these issues quickly and efficiently. In particular, real-time progress monitoring, dynamic scheduling, and enhanced safety measures are essential. Solving these challenges and improving construction quality and safety is crucial.

[0299] 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.

[0300] This invention includes means for optimizing plans by collecting past and present information on civil engineering projects from a data storage system; means for analyzing progress and resource status using machine learning algorithms and generating an optimal schedule; means for monitoring work status at work sites in real time and proposing resource allocation as needed; means for monitoring progress in real time via smart devices and performing dynamic scheduling based on predictive analytics; and means for smart devices to send warnings to workers to encourage the wearing of appropriate safety equipment. This enables efficient management of construction projects.

[0301] A "data storage system" is a system for storing and managing information, and in particular, it enables the efficient collection and access of past and present information necessary for a project.

[0302] A "civil engineering project" is a collection of plans and activities for constructing physical infrastructure, and requires management of progress and resource management.

[0303] A "machine learning algorithm" is a method for computers to learn from data and perform analysis and predictions, and is a means to optimize projects.

[0304] "Progress status" is an indicator that shows whether a project is progressing according to schedule, and it is managed by comparing it to the plan.

[0305] "Resource status" refers to an indicator that shows the utilization of manpower, materials, equipment, etc., allocated to a project, and managing this directly impacts the efficiency of the project.

[0306] "Schedule" refers to a plan indicating the scheduled date and time of each task in a project, and it is the core of schedule management.

[0307] "Work location" refers to the physical location where work is actually carried out, and monitoring the work situation here forms part of project management.

[0308] "Smart device" is an electronic device that has information processing capabilities and utilizes communication functions to support on-site work, and it is a tool that enables real-time monitoring and notification.

[0309] "Safety equipment" refers to protective gear worn by workers to protect themselves from danger, and it is necessary to maintain a safe working environment.

[0310] The system for implementing the present invention is composed of a server, smart devices, and users. The server uses a data storage ring system to collect information on past and current civil engineering projects and analyzes these data using machine learning algorithms. Specifically, by leveraging cloud computing services such as AWS and processing the collected data using TensorFlow, the progress status and resource status are evaluated, and an optimal schedule is generated.

[0311] Smart devices play the role of collecting information on the work location from users in real time and transmitting it to the server. Thereby, the server dynamically adjusts the progress based on predictive analysis and proposes resource allocation as needed. In addition, smart devices have the function of displaying warnings to prompt workers to wear appropriate safety equipment. Specific devices that can be considered include smartphones and tablets.

[0312] Users input and confirm project data through a terminal. The user interface is built with React Native, enabling intuitive operations. Also, by leveraging AWS cloud services, real-time data synchronization is achieved.

[0313] As a concrete example, if weather deteriorates at a construction site, the server can adjust the schedule to reschedule external work to internal work and notify the user via a smart device. In this way, the efficiency of on-site work is maintained while providing flexibility in the plan.

[0314] An example of a prompt to the generated AI model would be: "Please tell me how to optimize the construction schedule, taking into account the progress data and weather information of the construction project. Please also explain strategies for efficient resource allocation and risk management." This allows the AI ​​model to provide effective project management methods and assist in optimization.

[0315] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0316] Step 1:

[0317] The server collects historical and current information about construction projects from a data storage system. The input is project information in a database, and the output is an analyzable dataset. This step utilizes cloud computing services for efficient data access and extraction.

[0318] Step 2:

[0319] The server analyzes the collected data using machine learning algorithms. The input is the dataset obtained in step 1, and the output is optimized scheduling information based on the analysis results. Specifically, TensorFlow is used to evaluate progress and resource status and generate the optimal schedule.

[0320] Step 3:

[0321] The terminal retrieves real-time information about the work location from the user and sends it to the server. Input is work-site data obtained via the terminal, and output is updated project data sent to the server. Users intuitively input data using smartphones or tablets.

[0322] Step 4:

[0323] The server analyzes the real-time data received in step 3 and proposes dynamic resource allocation based on predictive analytics. The input is updated project data, and the output is an optimized resource allocation proposal. In this process, prompts generated by a generative AI model are analyzed to derive appropriate resource adjustment proposals.

[0324] Step 5:

[0325] The terminal displays alerts based on predictive analytics and safety equipment warnings for workers. Input is a warning message from the server, and output is a visual notification to the user. This feature allows users to take appropriate actions to enhance workplace safety.

[0326] 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.

[0327] This invention is an advanced system for streamlining construction project management and improving construction quality, and in particular incorporates an emotion engine that recognizes user emotions and provides feedback to project operations. The system consists of a server, multiple terminals, users, and the emotion engine.

[0328] First, the server collects historical and current construction project data from the database. This data includes progress information, resource allocation, and accident records. Users use terminals to input the latest on-site conditions in real time, allowing the server to always have up-to-date information.

[0329] The server uses machine learning algorithms to analyze this data, evaluate project progress and resource status, and generate an optimal schedule. The generated schedule is presented to the user via the terminal, and after user approval, it is officially registered as the project plan.

[0330] The emotion engine analyzes the user's emotions from data such as voice and facial expressions obtained from the device. This analysis evaluates the user's stress level and concentration, and provides the results to the server. The server takes this emotion data into consideration to adjust resource allocation and plans, providing the user with more optimized suggestions.

[0331] For example, if the emotion engine detects that a user's stress level is rising during a project, the server will suggest revising the project schedule and reallocating resources to reduce the burden on the user.

[0332] Furthermore, the terminals receive notifications from the server and provide workers with real-time warnings and advice. For example, in high-risk situations, safety management is strengthened by issuing warnings to workers based on analysis of past accident data.

[0333] Through this process, the system will manage construction projects more effectively, including the human emotional element, thereby improving operational efficiency and construction quality.

[0334] The following describes the processing flow.

[0335] Step 1:

[0336] The server connects to the project management database and collects historical project records and current progress data. This data includes the schedule for each project, completed and incomplete tasks, and resource usage.

[0337] Step 2:

[0338] Users use terminals on-site to input the latest work status and resource consumption information in real time. The terminals immediately send this data to the server, which maintains a real-time report of the on-site situation.

[0339] Step 3:

[0340] The server utilizes machine learning algorithms to analyze collected project data. This identifies the current project progress, resource usage trends, and potential scheduling issues.

[0341] Step 4:

[0342] The server generates an optimized project schedule based on the data analysis results. This schedule includes the priority and resource allocation for each task.

[0343] Step 5:

[0344] The terminal receives a proposed schedule from the server and presents it to the user. The user reviews the proposed schedule and completes the approval process by making revisions or providing additional feedback as needed.

[0345] Step 6:

[0346] The server records the user-approved schedule in the official management system and sets it as the project execution plan.

[0347] Step 7:

[0348] The device's built-in emotion engine monitors the user's emotional state in real time. This engine analyzes voice and facial expression data to evaluate the user's stress level and concentration level.

[0349] Step 8:

[0350] The emotion engine sends its analysis results to the server, which then uses this data to readjust the project and resource allocation. For example, if the server determines that a user is under excessive stress, it will suggest schedule changes to reduce the load.

[0351] Step 9:

[0352] The server predicts risks based on past accident data to ensure work safety. Terminals notify workers of this risk information, prompting them to pay attention and strictly adhere to safety procedures on-site.

[0353] Through these sequential steps, the system will comprehensively improve construction project management, aiming to enhance efficiency and safety.

[0354] (Example 2)

[0355] 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".

[0356] Managing construction projects involves complex progress tracking and resource allocation, and the emotional state of workers can also impact project efficiency. Traditional systems struggle to integrate and manage all these elements, posing challenges to overall project efficiency and quality improvement.

[0357] 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.

[0358] In this invention, the server includes means for optimizing the plan by acquiring past and present information about the construction project from a data storage device, means for analyzing the progress and resource status using machine learning techniques to generate an optimal plan, and means for analyzing voice and facial expression information to evaluate the user's emotions. This enables flexible resource allocation according to the progress of the project and efficient management that takes emotions into consideration.

[0359] A "data storage device" refers to a storage medium or system for accumulating data and retrieving it as needed.

[0360] A "construction project" refers to a series of tasks and plans that include the design, construction, supervision, and maintenance of buildings and infrastructure.

[0361] "Information" refers to a set of data related to a specific purpose, including data used for project progress, resource allocation, and risk analysis.

[0362] "Machine learning technology" refers to algorithms and methods that enable computers to automatically learn from data and adaptively perform inference and prediction.

[0363] "Progress status" refers to information indicating how far along a project is in relation to its plan.

[0364] "Resource status" refers to the allocation and utilization of human and material resources available for the project.

[0365] "Voice and facial information" refers to data obtained from the user's speech and facial movements, and is used to determine their emotions and state of mind.

[0366] "Evaluating emotions" refers to analyzing the user's voice and facial expressions to determine their psychological state at that time.

[0367] This invention is a system for enhancing the management of construction projects. The system includes a server, terminals, users, and an emotion engine.

[0368] The server retrieves historical and current information about construction projects stored in data storage devices via an SQL database. This includes project progress, resource allocation, and accident records. Machine learning algorithms developed using Python and R are used for analysis, examining progress and resource status. Based on the analysis results, project management software (e.g., project management tools) is used to generate an optimal schedule.

[0369] The terminals are used to input the latest on-site information in real time. Users input on-site information using mobile devices such as smartphones and tablets, and send this information to the server. Additionally, voice and facial expression information acquired via voice commands and video communication is passed to an emotion engine to analyze the user's emotions.

[0370] The emotion engine uses open-source facial recognition software (e.g., facial recognition libraries) to analyze voice and facial expression data and assess the user's stress level and concentration. The analysis results are provided to the server, which uses this information to optimize resources and adjust the schedule.

[0371] For example, if the emotion engine detects that a user is experiencing high stress levels during a project, the server can reduce the user's burden by suggesting a reallocation of resources.

[0372] This process allows the system to streamline construction projects, improve safety, and enhance construction quality.

[0373] An example of a prompt message would be, "How can I adjust progress and resources in construction project management while taking user sentiment into consideration?"

[0374] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0375] Step 1:

[0376] The server accesses data storage to retrieve past and present information on the construction project. It uses data from databases, such as project progress, resource allocation, and accident records, as input. After retrieving the data, the server formats it into an initial dataset for analysis, preparing it for transmission to machine learning algorithms. This dataset is then used in subsequent analysis steps.

[0377] Step 2:

[0378] The server launches a machine learning algorithm built in Python and analyzes the dataset obtained in Step 1. The input data includes project progress and resource usage. The analysis outputs predicted project progress and indicators of resource efficiency. This output serves as foundational information for use by project management software.

[0379] Step 3:

[0380] The server generates an optimal schedule using project management software based on the analysis results. The inputs used are progress prediction and resource optimization metrics derived from machine learning. In this step, the software outputs its recommended optimal schedule, preparing it for the next step to be reviewed and approved by the user.

[0381] Step 4:

[0382] The terminal presents the user with an optimal schedule provided by the server. The input is the schedule data generated by the server. The user reviews this schedule and provides feedback for any necessary modifications. If the user approves, the schedule is officially registered as the project plan and used in the next step.

[0383] Step 5:

[0384] The terminal is used to collect voice and facial expression data in the field. The user provides voice instructions and video data as input, which the terminal processes and passes to the emotion engine. The collected data becomes input data for evaluating the user's emotional state.

[0385] Step 6:

[0386] The server analyzes collected voice and facial expression data using an emotion engine. The input data consists of user voice and facial expression information received from the terminal. This analysis outputs evaluations of stress levels and concentration levels. The evaluation results are used for project resource allocation and schedule optimization.

[0387] Step 7:

[0388] The server re-evaluates resource allocation and scheduling based on sentiment data and generates suggestions if necessary. It uses evaluations from the sentiment engine and current project data as input. The output is resource reallocation and schedule revision proposals presented to the user. This improves project efficiency and user experience.

[0389] Step 8:

[0390] The terminal provides real-time notifications from the field based on instructions from the server. This includes alerts and safety suggestions based on past accident information. The input uses suggestions from the server, and the output is specific advice and risk warnings for field workers. These notifications help improve safety at the site.

[0391] (Application Example 2)

[0392] 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."

[0393] Construction projects require efficient management and safety assurance, but achieving this necessitates accurately understanding site conditions and providing optimal instructions that take into account the emotional state of workers. Conventional systems often lack the ability to analyze workers' emotions and have insufficient risk management, limiting improvements in efficiency and safety. To solve this problem, a system is needed that collects and analyzes data in real time and provides appropriate feedback to workers.

[0394] 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.

[0395] In this invention, the server includes means for collecting past and present data on construction projects from a database, means for analyzing progress and resource status using machine learning techniques to generate an optimal schedule, means for monitoring work status in real time and proposing resource allocation as needed, means for analyzing the emotional state of users and adjusting resource allocation and plans based on the results, and means for providing real-time feedback on safety and efficiency based on emotional data. This enables efficient and safe project management that takes into account the emotions of workers in construction project management.

[0396] A "database" is an information management system that stores information about past and present construction projects and allows it to be retrieved and used as needed.

[0397] "Machine learning methods" are data analysis techniques that use algorithms to analyze the progress and resource status of construction projects and generate optimal schedules.

[0398] "Progress status" refers to the state of a construction project, indicating how far along the current work process is compared to the plan.

[0399] "Resource status" refers to information indicating the allocation of resources and workers required for a construction project, as well as their usage.

[0400] "Real-time monitoring" is a monitoring technology that enables immediate observation and evaluation of on-site work conditions, and allows for rapid response as needed.

[0401] "Resource allocation" is a management method for optimally distributing the necessary materials and personnel in a construction project.

[0402] "Risk management" is a process of preventing accidents and incidents by predicting risks based on past accident information and issuing necessary warnings.

[0403] "Emotional state" refers to the user's psychological and emotional state during work, including their stress levels and level of concentration.

[0404] "Feedback" is the act of providing users with information and advice based on emotional data to improve safety and efficiency.

[0405] The system for implementing this invention consists of a server, multiple terminals, users, a database, and an emotion engine. The server first collects historical and current data on construction projects from the database, including progress information, resource status, and accident records. This information is aggregated on the server and analyzed using machine learning techniques. As a result of the analysis, the progress and resource status are evaluated, and an optimal schedule is generated. These schedules are presented to users via terminals, and if approved, they are officially recorded as project plans.

[0406] On the terminal side, real-time work status and emotional data are input from the user. The user's emotional state is analyzed by an emotion engine based on voice and facial expressions acquired from the terminal. The results are provided to the server as emotional states such as the user's stress level and concentration level. The server takes this emotional data into consideration to optimize resource allocation and planning, and provides feedback as needed.

[0407] Furthermore, the server generates feedback based on sentiment analysis of data collected in real time. This feedback is provided to the user via the terminal, contributing to improved project efficiency and security.

[0408] This system uses React Native for the frontend and Python and TensorFlow for the backend. It also utilizes Firebase for its database, enabling cloud-based data management. The sentiment engine's analysis results are integrated into the project management screen, supporting users in safe and efficient work operations.

[0409] For example, when a worker is facing a difficult process, the server detects the worker's stress level based on data recorded on their terminal. The server then suggests reallocating resources or revising the process, and provides specific advice as needed, such as "We recommend taking a break to relax." This feedback and advice is generated based on a prompt message that instructs, "Analyze emotional data from workers at the construction site and provide specific advice to alleviate stress."

[0410] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0411] Step 1:

[0412] The server retrieves historical and current data about construction projects from a database. It accepts progress information, resource status, and accident records stored in the database as input. It organizes this data and converts it into a parseable format. The output is an initial input data list used for analysis.

[0413] Step 2:

[0414] The server uses machine learning techniques to analyze the data obtained in Step 1. It receives progress and resource status as input and uses machine learning algorithms to perform calculations to optimize the future schedule. The output is the proposed optimal schedule.

[0415] Step 3:

[0416] The terminal collects real-time data on the work situation at the site. It receives input data from workers and real-time data from sensors as input. This data is then organized and formatted for transmission to the server. The output is the latest work information from the site.

[0417] Step 4:

[0418] The device collects user emotion data from voice and facial expressions and sends it to the emotion engine. It receives user facial expressions and voice information via camera and microphone as input, and converts this into an analyzable format. The output is the emotion data sent to the emotion engine.

[0419] Step 5:

[0420] The server receives the user's emotional state, analyzed by the emotion engine, and adjusts resource allocation and planning accordingly. It receives user emotional state data as input and uses this to determine if resource allocation is optimal. The output is the adjusted allocation proposal.

[0421] Step 6:

[0422] The server notifies the user via the terminal of an optimized schedule and resource suggestions. It receives the schedule obtained in step 2 and the resource suggestions obtained in step 5 as input, and converts them into a user-friendly format. The output is feedback information for the user.

[0423] Step 7:

[0424] The user reviews and modifies the work content and schedule based on feedback received through the terminal. Feedback information from the server is received as input, and the work process is adjusted as needed. The output is a reviewed work procedure document.

[0425] 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.

[0426] 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.

[0427] 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.

[0428] [Third Embodiment]

[0429] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0430] 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.

[0431] 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).

[0432] 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.

[0433] 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.

[0434] 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).

[0435] 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.

[0436] 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.

[0437] 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.

[0438] 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.

[0439] 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.

[0440] 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".

[0441] This invention is a system for streamlining construction site management and improving construction quality. The system consists of a server, multiple terminals, and users. Specific embodiments of this system are described below.

[0442] First, the server collects historical and current data about construction projects from the database. This includes progress data from previous projects, resource usage history, and accident records. More accurate data is collected as users input data on ongoing projects in real time using terminals.

[0443] Subsequently, the server analyzes this data using machine learning algorithms to evaluate the project's progress and resource availability. Based on this analysis, the server can generate and propose an optimal schedule to the user.

[0444] For example, if the server detects that the progress of foundation work is behind schedule, it will suggest the deployment of additional workers as needed. It can also flexibly adjust the schedule by considering weather data and supply chain information. For instance, if bad weather is expected, it will notify the user to switch from external work to internal work.

[0445] Furthermore, the terminal also has a safety management function that provides workers with real-time risk warnings. This works by having a server predict risk factors based on analysis of past accident data and issuing appropriate warnings when the risk increases. For example, it checks whether workers are fully equipped with safety gear before working at heights and warns them via the terminal if necessary.

[0446] Thus, the system of the present invention optimizes project management in a data-driven manner, thereby improving construction quality and safety.

[0447] The following describes the processing flow.

[0448] Step 1:

[0449] The server accesses the database to collect historical and current progress data for the target construction project. This data includes past project schedules, resource allocations, and accident records.

[0450] Step 2:

[0451] Users input real-time information about on-site work status and resource usage via their terminals. The terminals continuously transmit this data to the server, ensuring that the server maintains the most up-to-date information.

[0452] Step 3:

[0453] The server will use historical and current data to initiate analysis using machine learning algorithms. This will allow us to evaluate the project's progress and resource consumption, and determine the validity of the current schedule.

[0454] Step 4:

[0455] The server generates a new, optimized schedule based on the analysis results. This schedule includes resource allocation and task prioritization, offering suggestions for more efficient project progress.

[0456] Step 5:

[0457] The server generates a draft schedule, which is then sent to the user's terminal for presentation. The user can review the schedule on their terminal and make adjustments as needed.

[0458] Step 6:

[0459] The user sends the approved schedule and proposal back to the server. The server logs this as the official plan within the system.

[0460] Step 7:

[0461] The server predicts risks from past accident data for safety management purposes and generates warnings tailored to the situation on site. Terminals then notify workers of these warnings, prompting them to be cautious and check their safety equipment.

[0462] This will allow for more efficient overall construction project management and strengthen safety measures.

[0463] (Example 1)

[0464] 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."

[0465] In construction activities, effective management of project progress and resources is crucial. However, traditional methods make it difficult to efficiently collect and analyze real-time information from the site and respond quickly. Furthermore, the proper use of past accident information and the implementation of preventive measures to enhance site safety are not adequately carried out. In addition, the inability to automatically and flexibly adjust schedules in response to weather and supply chain fluctuations leads to project delays and inappropriate resource allocation. These challenges need to be addressed.

[0466] 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.

[0467] In this invention, the server includes means for optimizing plans by collecting past and present information on construction activities from a data management device; means for analyzing progress and resource status using machine learning methods to generate an optimal time plan; means for monitoring the working environment in real time and proposing resource allocation as needed; means for predicting risks based on past accident information and issuing necessary warnings for safety management; and means for inputting feedback at work terminals and using it to improve subsequent analyses and proposals. This makes it possible to grasp the situation on site in real time and respond quickly, enabling resource optimization, improved safety, and flexible adjustment of plans.

[0468] A "data management device" is a system that stores past and present information related to construction activities and provides it in an accessible format.

[0469] "Construction activities" refer to a series of tasks and projects for building, maintaining, and repairing buildings and facilities.

[0470] "Machine learning techniques" refer to algorithmic technologies that analyze data and recognize patterns and trends, thereby enabling efficient decision-making.

[0471] "Progress status" refers to the extent to which a construction project is being achieved in relation to its planned schedule and objectives.

[0472] "Resource status" refers to information indicating the quantity and arrangement of personnel, equipment, materials, etc., used in construction activities.

[0473] A "time plan" is a detailed plan outlining the steps and schedule required to complete a construction project.

[0474] "Work environment" refers to the entire physical and organizational location and conditions under which construction activities are carried out.

[0475] "Real-time" refers to a time concept that enables processing and analysis of information and data almost immediately after they are generated.

[0476] "Risk" refers to factors that indicate the possibility of accidents or problems occurring during construction activities and the resulting impacts.

[0477] "Feedback" refers to information obtained from users and systems, such as reactions and data, that can be used to improve or adjust future systems.

[0478] The system of this invention is designed to optimize construction activities and improve their quality and safety. This system consists of a server, multiple terminals, and users.

[0479] The server first collects past and present information on construction activities from data management devices. The software used includes database management systems (e.g., MySQL, PostgreSQL), which are used for access and data extraction. The server then analyzes the collected data using machine learning techniques (e.g., Python's scikit-learn library). This allows the server to evaluate project progress and resource status and generate an optimal time plan.

[0480] Users input information in real time using terminals installed on-site. This input includes the current status of the work environment and the completion time of specific tasks. Furthermore, the server processes this information immediately, proposes resource allocation as needed, and supports on-site decision-making.

[0481] The terminal alerts workers when the risk increases based on past accident data. Specifically, it displays a warning on the terminal when a risk is detected based on pre-set criteria. This function aims to enhance worker safety.

[0482] As a concrete example of its operation, the server evaluates the progress of foundation work and, if it finds that there are delays, it includes a function that suggests to the user the necessary allocation of additional resources. Furthermore, if weather analysis predicts bad weather, it also includes a function that recommends switching from external work to internal work.

[0483] An example of a prompt message would be, "Please report on the progress and resource status of the construction project. This should include tasks that are behind schedule and proposed optimizations for resource allocation." This input allows the generating AI model to extract appropriate information, enabling the user to take quick and appropriate action.

[0484] This allows the server to optimize the situation at the construction site through centralized information management and advanced analysis, supporting real-time decision-making and ultimately improving construction quality.

[0485] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0486] Step 1:

[0487] The server collects past and present project information from the data management device. This input data includes project progress, resource usage history, and incident records. The server queries this information using a database management system to extract data. As output, the server obtains an integrated dataset ready for analysis.

[0488] Step 2:

[0489] Users input real-time information from the field using a terminal. This input includes details of ongoing work, the amount of resources being used, and worker deployment information. The terminal collects this information and sends it to the server. As output, the server updates the latest field information and uses it for analysis.

[0490] Step 3:

[0491] The server analyzes collected data using machine learning techniques. Input data includes historical and current project information and real-time field data. The server uses this data to perform trend analysis and predictive modeling using the Python scikit-learn library. The output generates suggestions for optimal time planning and resource allocation.

[0492] Step 4:

[0493] The server generates an optimal schedule based on the analysis results and proposes it to the user. The input data is the analysis results obtained in the previous step. The server uses a generating AI model to assemble a schedule in a way that is practical for the user and communicates it to the terminal. As output, the user receives a concrete plan to support on-site decision-making.

[0494] Step 5:

[0495] The terminal alerts workers based on risk predictions from the server. The input data is the result of the server's risk analysis. The terminal uses this data to generate and display warning messages according to specific conditions. As a result, workers prepare safety equipment and take precautions for high-risk tasks.

[0496] Step 6:

[0497] Users input feedback into the server via their devices. This feedback includes reports on the results of implementing the suggestions and the situation on-site. The server receives this feedback and uses it for subsequent data analysis and improvement of the suggestions. As an output, the accuracy and efficiency of the entire system in the next cycle improve.

[0498] (Application Example 1)

[0499] 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."

[0500] Project management at construction sites presents numerous challenges, including delays, inappropriate resource allocation, and safety uncertainties. Traditional management methods have struggled to address these issues quickly and efficiently. In particular, real-time progress monitoring, dynamic scheduling, and enhanced safety measures are essential. Solving these challenges and improving construction quality and safety is crucial.

[0501] 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.

[0502] This invention includes means for optimizing plans by collecting past and present information on civil engineering projects from a data storage system; means for analyzing progress and resource status using machine learning algorithms and generating an optimal schedule; means for monitoring work status at work sites in real time and proposing resource allocation as needed; means for monitoring progress in real time via smart devices and performing dynamic scheduling based on predictive analytics; and means for smart devices to send warnings to workers to encourage the wearing of appropriate safety equipment. This enables efficient management of construction projects.

[0503] A "data storage system" is a system for storing and managing information, and in particular, it enables the efficient collection and access of past and present information necessary for a project.

[0504] A "civil engineering project" is a collection of plans and activities for constructing physical infrastructure, and requires management of progress and resource management.

[0505] A "machine learning algorithm" is a method for computers to learn from data and perform analysis and predictions, and is a means to optimize projects.

[0506] "Progress status" is an indicator that shows whether a project is progressing according to schedule, and it is managed by comparing it to the plan.

[0507] "Resource status" refers to an indicator that shows the utilization of manpower, materials, equipment, etc., allocated to a project, and managing this directly impacts the efficiency of the project.

[0508] A "schedule" is a plan that shows the planned dates and times for each task in a project, and it is the core of schedule management.

[0509] The term "workplace" refers to the physical location where the work is actually performed, and monitoring the work status at this location forms part of project management.

[0510] A "smart device" is an electronic device that has information processing capabilities and utilizes communication functions to support on-site work, and is a tool that enables real-time monitoring and notifications.

[0511] "Safety equipment" refers to protective gear worn by workers to protect themselves from danger and is necessary to maintain a safe working environment.

[0512] The system for implementing this invention consists of a server, a smart device, and a user. The server uses a data storage system to collect information on past and present civil engineering projects and analyzes this data using machine learning algorithms. Specifically, it utilizes cloud computing services such as AWS and processes the collected data using TensorFlow to evaluate progress and resource availability and generate an optimal schedule.

[0513] Smart devices play a role in collecting real-time information about the work site from users and transmitting it to a server. This allows the server to dynamically adjust progress based on predictive analysis and suggest resource allocation as needed. Smart devices also have a function to display warnings to workers to encourage the wearing of appropriate safety equipment. Specific examples of such equipment include smartphones and tablets.

[0514] Users input and verify project data through their devices. The user interface is built with React Native, enabling intuitive operation. Furthermore, real-time data synchronization is achieved by utilizing AWS cloud services.

[0515] As a concrete example, if weather deteriorates at a construction site, the server can adjust the schedule to reschedule external work to internal work and notify the user via a smart device. In this way, the efficiency of on-site work is maintained while providing flexibility in the plan.

[0516] An example of a prompt to the generated AI model would be: "Please tell me how to optimize the construction schedule, taking into account the progress data and weather information of the construction project. Please also explain strategies for efficient resource allocation and risk management." This allows the AI ​​model to provide effective project management methods and assist in optimization.

[0517] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0518] Step 1:

[0519] The server collects historical and current information about construction projects from a data storage system. The input is project information in a database, and the output is an analyzable dataset. This step utilizes cloud computing services for efficient data access and extraction.

[0520] Step 2:

[0521] The server analyzes the collected data using machine learning algorithms. The input is the dataset obtained in step 1, and the output is optimized scheduling information based on the analysis results. Specifically, TensorFlow is used to evaluate progress and resource status and generate the optimal schedule.

[0522] Step 3:

[0523] The terminal retrieves real-time information about the work location from the user and sends it to the server. Input is work-site data obtained via the terminal, and output is updated project data sent to the server. Users intuitively input data using smartphones or tablets.

[0524] Step 4:

[0525] The server analyzes the real-time data received in step 3 and proposes dynamic resource allocation based on predictive analytics. The input is updated project data, and the output is an optimized resource allocation proposal. In this process, prompts generated by a generative AI model are analyzed to derive appropriate resource adjustment proposals.

[0526] Step 5:

[0527] The terminal displays alerts based on predictive analytics and safety equipment warnings for workers. Input is a warning message from the server, and output is a visual notification to the user. This feature allows users to take appropriate actions to enhance workplace safety.

[0528] 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.

[0529] This invention is an advanced system for streamlining construction project management and improving construction quality, and in particular incorporates an emotion engine that recognizes user emotions and provides feedback to project operations. The system consists of a server, multiple terminals, users, and the emotion engine.

[0530] First, the server collects historical and current construction project data from the database. This data includes progress information, resource allocation, and accident records. Users use terminals to input the latest on-site conditions in real time, allowing the server to always have up-to-date information.

[0531] The server uses machine learning algorithms to analyze this data, evaluate project progress and resource status, and generate an optimal schedule. The generated schedule is presented to the user via the terminal, and after user approval, it is officially registered as the project plan.

[0532] The emotion engine analyzes the user's emotions from data such as voice and facial expressions obtained from the device. This analysis evaluates the user's stress level and concentration, and provides the results to the server. The server takes this emotion data into consideration to adjust resource allocation and plans, providing the user with more optimized suggestions.

[0533] For example, if the emotion engine detects that a user's stress level is rising during a project, the server will suggest revising the project schedule and reallocating resources to reduce the burden on the user.

[0534] Furthermore, the terminals receive notifications from the server and provide workers with real-time warnings and advice. For example, in high-risk situations, safety management is strengthened by issuing warnings to workers based on analysis of past accident data.

[0535] Through this process, the system will manage construction projects more effectively, including the human emotional element, thereby improving operational efficiency and construction quality.

[0536] The following describes the processing flow.

[0537] Step 1:

[0538] The server connects to the project management database and collects historical project records and current progress data. This data includes the schedule for each project, completed and incomplete tasks, and resource usage.

[0539] Step 2:

[0540] Users use terminals on-site to input the latest work status and resource consumption information in real time. The terminals immediately send this data to the server, which maintains a real-time report of the on-site situation.

[0541] Step 3:

[0542] The server utilizes machine learning algorithms to analyze collected project data. This identifies the current project progress, resource usage trends, and potential scheduling issues.

[0543] Step 4:

[0544] The server generates an optimized project schedule based on the data analysis results. This schedule includes the priority and resource allocation for each task.

[0545] Step 5:

[0546] The terminal receives a proposed schedule from the server and presents it to the user. The user reviews the proposed schedule and completes the approval process by making revisions or providing additional feedback as needed.

[0547] Step 6:

[0548] The server records the user-approved schedule in the official management system and sets it as the project execution plan.

[0549] Step 7:

[0550] The device's built-in emotion engine monitors the user's emotional state in real time. This engine analyzes voice and facial expression data to evaluate the user's stress level and concentration level.

[0551] Step 8:

[0552] The emotion engine sends its analysis results to the server, which then uses this data to readjust the project and resource allocation. For example, if the server determines that a user is under excessive stress, it will suggest schedule changes to reduce the load.

[0553] Step 9:

[0554] The server predicts risks based on past accident data to ensure work safety. Terminals notify workers of this risk information, prompting them to pay attention and strictly adhere to safety procedures on-site.

[0555] Through these sequential steps, the system will comprehensively improve construction project management, aiming to enhance efficiency and safety.

[0556] (Example 2)

[0557] 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."

[0558] Managing construction projects involves complex progress tracking and resource allocation, and the emotional state of workers can also impact project efficiency. Traditional systems struggle to integrate and manage all these elements, posing challenges to overall project efficiency and quality improvement.

[0559] 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.

[0560] In this invention, the server includes means for optimizing the plan by acquiring past and present information about the construction project from a data storage device, means for analyzing the progress and resource status using machine learning techniques to generate an optimal plan, and means for analyzing voice and facial expression information to evaluate the user's emotions. This enables flexible resource allocation according to the progress of the project and efficient management that takes emotions into consideration.

[0561] A "data storage device" refers to a storage medium or system for accumulating data and retrieving it as needed.

[0562] A "construction project" refers to a series of tasks and plans that include the design, construction, supervision, and maintenance of buildings and infrastructure.

[0563] "Information" refers to a set of data related to a specific purpose, including data used for project progress, resource allocation, and risk analysis.

[0564] "Machine learning technology" refers to algorithms and methods that enable computers to automatically learn from data and adaptively perform inference and prediction.

[0565] "Progress status" refers to information indicating how far along a project is in relation to its plan.

[0566] "Resource status" refers to the allocation and utilization of human and material resources available for the project.

[0567] "Voice and facial information" refers to data obtained from the user's speech and facial movements, and is used to determine their emotions and state of mind.

[0568] "Evaluating emotions" refers to analyzing the user's voice and facial expressions to determine their psychological state at that time.

[0569] This invention is a system for enhancing the management of construction projects. The system includes a server, terminals, users, and an emotion engine.

[0570] The server retrieves historical and current information about construction projects stored in data storage devices via an SQL database. This includes project progress, resource allocation, and accident records. Machine learning algorithms developed using Python and R are used for analysis, examining progress and resource status. Based on the analysis results, project management software (e.g., project management tools) is used to generate an optimal schedule.

[0571] The terminals are used to input the latest on-site information in real time. Users input on-site information using mobile devices such as smartphones and tablets, and send this information to the server. Additionally, voice and facial expression information acquired via voice commands and video communication is passed to an emotion engine to analyze the user's emotions.

[0572] The emotion engine uses open-source facial recognition software (e.g., facial recognition libraries) to analyze voice and facial expression data and assess the user's stress level and concentration. The analysis results are provided to the server, which uses this information to optimize resources and adjust the schedule.

[0573] For example, if the emotion engine detects that a user is experiencing high stress levels during a project, the server can reduce the user's burden by suggesting a reallocation of resources.

[0574] This process allows the system to streamline construction projects, improve safety, and enhance construction quality.

[0575] An example of a prompt message would be, "How can I adjust progress and resources in construction project management while taking user sentiment into consideration?"

[0576] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0577] Step 1:

[0578] The server accesses data storage to retrieve past and present information on the construction project. It uses data from databases, such as project progress, resource allocation, and accident records, as input. After retrieving the data, the server formats it into an initial dataset for analysis, preparing it for transmission to machine learning algorithms. This dataset is then used in subsequent analysis steps.

[0579] Step 2:

[0580] The server launches a machine learning algorithm built in Python and analyzes the dataset obtained in Step 1. The input data includes project progress and resource usage. The analysis outputs predicted project progress and indicators of resource efficiency. This output serves as foundational information for use by project management software.

[0581] Step 3:

[0582] The server generates an optimal schedule using project management software based on the analysis results. The inputs used are progress prediction and resource optimization metrics derived from machine learning. In this step, the software outputs its recommended optimal schedule, preparing it for the next step to be reviewed and approved by the user.

[0583] Step 4:

[0584] The terminal presents the user with an optimal schedule provided by the server. The input is the schedule data generated by the server. The user reviews this schedule and provides feedback for any necessary modifications. If the user approves, the schedule is officially registered as the project plan and used in the next step.

[0585] Step 5:

[0586] The terminal is used to collect voice and facial expression data in the field. The user provides voice instructions and video data as input, which the terminal processes and passes to the emotion engine. The collected data becomes input data for evaluating the user's emotional state.

[0587] Step 6:

[0588] The server analyzes collected voice and facial expression data using an emotion engine. The input data consists of user voice and facial expression information received from the terminal. This analysis outputs evaluations of stress levels and concentration levels. The evaluation results are used for project resource allocation and schedule optimization.

[0589] Step 7:

[0590] The server re-evaluates resource allocation and scheduling based on sentiment data and generates suggestions if necessary. It uses evaluations from the sentiment engine and current project data as input. The output is resource reallocation and schedule revision proposals presented to the user. This improves project efficiency and user experience.

[0591] Step 8:

[0592] The terminal provides real-time notifications from the field based on instructions from the server. This includes alerts and safety suggestions based on past accident information. The input uses suggestions from the server, and the output is specific advice and risk warnings for field workers. These notifications help improve safety at the site.

[0593] (Application Example 2)

[0594] 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."

[0595] Construction projects require efficient management and safety assurance, but achieving this necessitates accurately understanding site conditions and providing optimal instructions that take into account the emotional state of workers. Conventional systems often lack the ability to analyze workers' emotions and have insufficient risk management, limiting improvements in efficiency and safety. To solve this problem, a system is needed that collects and analyzes data in real time and provides appropriate feedback to workers.

[0596] 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.

[0597] In this invention, the server includes means for collecting past and present data on construction projects from a database, means for analyzing progress and resource status using machine learning techniques to generate an optimal schedule, means for monitoring work status in real time and proposing resource allocation as needed, means for analyzing the emotional state of users and adjusting resource allocation and plans based on the results, and means for providing real-time feedback on safety and efficiency based on emotional data. This enables efficient and safe project management that takes into account the emotions of workers in construction project management.

[0598] A "database" is an information management system that stores information about past and present construction projects and allows it to be retrieved and used as needed.

[0599] "Machine learning methods" are data analysis techniques that use algorithms to analyze the progress and resource status of construction projects and generate optimal schedules.

[0600] "Progress status" refers to the state of a construction project, indicating how far along the current work process is compared to the plan.

[0601] "Resource status" refers to information indicating the allocation of resources and workers required for a construction project, as well as their usage.

[0602] "Real-time monitoring" is a monitoring technology that enables immediate observation and evaluation of on-site work conditions, and allows for rapid response as needed.

[0603] "Resource allocation" is a management method for optimally distributing the necessary materials and personnel in a construction project.

[0604] "Risk management" is a process of preventing accidents and incidents by predicting risks based on past accident information and issuing necessary warnings.

[0605] "Emotional state" refers to the user's psychological and emotional state during work, including their stress levels and level of concentration.

[0606] "Feedback" is the act of providing users with information and advice based on emotional data to improve safety and efficiency.

[0607] The system for implementing this invention consists of a server, multiple terminals, users, a database, and an emotion engine. The server first collects historical and current data on construction projects from the database, including progress information, resource status, and accident records. This information is aggregated on the server and analyzed using machine learning techniques. As a result of the analysis, the progress and resource status are evaluated, and an optimal schedule is generated. These schedules are presented to users via terminals, and if approved, they are officially recorded as project plans.

[0608] On the terminal side, real-time work status and emotional data are input from the user. The user's emotional state is analyzed by an emotion engine based on voice and facial expressions acquired from the terminal. The results are provided to the server as emotional states such as the user's stress level and concentration level. The server takes this emotional data into consideration to optimize resource allocation and planning, and provides feedback as needed.

[0609] Furthermore, the server generates feedback based on sentiment analysis of data collected in real time. This feedback is provided to the user via the terminal, contributing to improved project efficiency and security.

[0610] This system uses React Native for the frontend and Python and TensorFlow for the backend. It also utilizes Firebase for its database, enabling cloud-based data management. The sentiment engine's analysis results are integrated into the project management screen, supporting users in safe and efficient work operations.

[0611] For example, when a worker is facing a difficult process, the server detects the worker's stress level based on data recorded on their terminal. The server then suggests reallocating resources or revising the process, and provides specific advice as needed, such as "We recommend taking a break to relax." This feedback and advice is generated based on a prompt message that instructs, "Analyze emotional data from workers at the construction site and provide specific advice to alleviate stress."

[0612] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0613] Step 1:

[0614] The server retrieves historical and current data about construction projects from a database. It accepts progress information, resource status, and accident records stored in the database as input. It organizes this data and converts it into a parseable format. The output is an initial input data list used for analysis.

[0615] Step 2:

[0616] The server uses machine learning techniques to analyze the data obtained in Step 1. It receives progress and resource status as input and uses machine learning algorithms to perform calculations to optimize the future schedule. The output is the proposed optimal schedule.

[0617] Step 3:

[0618] The terminal collects real-time data on the work situation at the site. It receives input data from workers and real-time data from sensors as input. This data is then organized and formatted for transmission to the server. The output is the latest work information from the site.

[0619] Step 4:

[0620] The device collects user emotion data from voice and facial expressions and sends it to the emotion engine. It receives user facial expressions and voice information via camera and microphone as input, and converts this into an analyzable format. The output is the emotion data sent to the emotion engine.

[0621] Step 5:

[0622] The server receives the user's emotional state, analyzed by the emotion engine, and adjusts resource allocation and planning accordingly. It receives user emotional state data as input and uses this to determine if resource allocation is optimal. The output is the adjusted allocation proposal.

[0623] Step 6:

[0624] The server notifies the user via the terminal of an optimized schedule and resource suggestions. It receives the schedule obtained in step 2 and the resource suggestions obtained in step 5 as input, and converts them into a user-friendly format. The output is feedback information for the user.

[0625] Step 7:

[0626] The user reviews and modifies the work content and schedule based on feedback received through the terminal. Feedback information from the server is received as input, and the work process is adjusted as needed. The output is a reviewed work procedure document.

[0627] 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.

[0628] 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.

[0629] 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.

[0630] [Fourth Embodiment]

[0631] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0632] 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.

[0633] 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).

[0634] 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.

[0635] 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.

[0636] 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).

[0637] 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.

[0638] 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.

[0639] 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.

[0640] 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.

[0641] 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.

[0642] 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.

[0643] 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".

[0644] This invention is a system for streamlining construction site management and improving construction quality. The system consists of a server, multiple terminals, and users. Specific embodiments of this system are described below.

[0645] First, the server collects historical and current data about construction projects from the database. This includes progress data from previous projects, resource usage history, and accident records. More accurate data is collected as users input data on ongoing projects in real time using terminals.

[0646] Subsequently, the server analyzes this data using machine learning algorithms to evaluate the project's progress and resource availability. Based on this analysis, the server can generate and propose an optimal schedule to the user.

[0647] For example, if the server detects that the progress of foundation work is behind schedule, it will suggest the deployment of additional workers as needed. It can also flexibly adjust the schedule by considering weather data and supply chain information. For instance, if bad weather is expected, it will notify the user to switch from external work to internal work.

[0648] Furthermore, the terminal also has a safety management function that provides workers with real-time risk warnings. This works by having a server predict risk factors based on analysis of past accident data and issuing appropriate warnings when the risk increases. For example, it checks whether workers are fully equipped with safety gear before working at heights and warns them via the terminal if necessary.

[0649] Thus, the system of the present invention optimizes project management in a data-driven manner, thereby improving construction quality and safety.

[0650] The following describes the processing flow.

[0651] Step 1:

[0652] The server accesses the database to collect historical and current progress data for the target construction project. This data includes past project schedules, resource allocations, and accident records.

[0653] Step 2:

[0654] Users input real-time information about on-site work status and resource usage via their terminals. The terminals continuously transmit this data to the server, ensuring that the server maintains the most up-to-date information.

[0655] Step 3:

[0656] The server will use historical and current data to initiate analysis using machine learning algorithms. This will allow us to evaluate the project's progress and resource consumption, and determine the validity of the current schedule.

[0657] Step 4:

[0658] The server generates a new, optimized schedule based on the analysis results. This schedule includes resource allocation and task prioritization, offering suggestions for more efficient project progress.

[0659] Step 5:

[0660] The server generates a draft schedule, which is then sent to the user's terminal for presentation. The user can review the schedule on their terminal and make adjustments as needed.

[0661] Step 6:

[0662] The user sends the approved schedule and proposal back to the server. The server logs this as the official plan within the system.

[0663] Step 7:

[0664] The server predicts risks from past accident data for safety management purposes and generates warnings tailored to the situation on site. Terminals then notify workers of these warnings, prompting them to be cautious and check their safety equipment.

[0665] This will allow for more efficient overall construction project management and strengthen safety measures.

[0666] (Example 1)

[0667] 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".

[0668] In construction activities, effective management of project progress and resources is crucial. However, traditional methods make it difficult to efficiently collect and analyze real-time information from the site and respond quickly. Furthermore, the proper use of past accident information and the implementation of preventive measures to enhance site safety are not adequately carried out. In addition, the inability to automatically and flexibly adjust schedules in response to weather and supply chain fluctuations leads to project delays and inappropriate resource allocation. These challenges need to be addressed.

[0669] 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.

[0670] In this invention, the server includes means for optimizing plans by collecting past and present information on construction activities from a data management device; means for analyzing progress and resource status using machine learning methods to generate an optimal time plan; means for monitoring the working environment in real time and proposing resource allocation as needed; means for predicting risks based on past accident information and issuing necessary warnings for safety management; and means for inputting feedback at work terminals and using it to improve subsequent analyses and proposals. This makes it possible to grasp the situation on site in real time and respond quickly, enabling resource optimization, improved safety, and flexible adjustment of plans.

[0671] A "data management device" is a system that stores past and present information related to construction activities and provides it in an accessible format.

[0672] "Construction activities" refer to a series of tasks and projects for building, maintaining, and repairing buildings and facilities.

[0673] "Machine learning techniques" refer to algorithmic technologies that analyze data and recognize patterns and trends, thereby enabling efficient decision-making.

[0674] "Progress status" refers to the extent to which a construction project is being achieved in relation to its planned schedule and objectives.

[0675] "Resource status" refers to information indicating the quantity and arrangement of personnel, equipment, materials, etc., used in construction activities.

[0676] A "time plan" is a detailed plan outlining the steps and schedule required to complete a construction project.

[0677] "Work environment" refers to the entire physical and organizational location and conditions under which construction activities are carried out.

[0678] "Real-time" refers to a time concept that enables processing and analysis of information and data almost immediately after they are generated.

[0679] "Risk" refers to factors that indicate the possibility of accidents or problems occurring during construction activities and the resulting impacts.

[0680] "Feedback" refers to information obtained from users and systems, such as reactions and data, that can be used to improve or adjust future systems.

[0681] The system of this invention is designed to optimize construction activities and improve their quality and safety. This system consists of a server, multiple terminals, and users.

[0682] The server first collects past and present information on construction activities from data management devices. The software used includes database management systems (e.g., MySQL, PostgreSQL), which are used for access and data extraction. The server then analyzes the collected data using machine learning techniques (e.g., Python's scikit-learn library). This allows the server to evaluate project progress and resource status and generate an optimal time plan.

[0683] Users input information in real time using terminals installed on-site. This input includes the current status of the work environment and the completion time of specific tasks. Furthermore, the server processes this information immediately, proposes resource allocation as needed, and supports on-site decision-making.

[0684] The terminal alerts workers when the risk increases based on past accident data. Specifically, it displays a warning on the terminal when a risk is detected based on pre-set criteria. This function aims to enhance worker safety.

[0685] As a concrete example of its operation, the server evaluates the progress of foundation work and, if it finds that there are delays, it includes a function that suggests to the user the necessary allocation of additional resources. Furthermore, if weather analysis predicts bad weather, it also includes a function that recommends switching from external work to internal work.

[0686] An example of a prompt message would be, "Please report on the progress and resource status of the construction project. This should include tasks that are behind schedule and proposed optimizations for resource allocation." This input allows the generating AI model to extract appropriate information, enabling the user to take quick and appropriate action.

[0687] This allows the server to optimize the situation at the construction site through centralized information management and advanced analysis, supporting real-time decision-making and ultimately improving construction quality.

[0688] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0689] Step 1:

[0690] The server collects past and present project information from the data management device. This input data includes project progress, resource usage history, and incident records. The server queries this information using a database management system to extract data. As output, the server obtains an integrated dataset ready for analysis.

[0691] Step 2:

[0692] Users input real-time information from the field using a terminal. This input includes details of ongoing work, the amount of resources being used, and worker deployment information. The terminal collects this information and sends it to the server. As output, the server updates the latest field information and uses it for analysis.

[0693] Step 3:

[0694] The server analyzes collected data using machine learning techniques. Input data includes historical and current project information and real-time field data. The server uses this data to perform trend analysis and predictive modeling using the Python scikit-learn library. The output generates suggestions for optimal time planning and resource allocation.

[0695] Step 4:

[0696] The server generates an optimal schedule based on the analysis results and proposes it to the user. The input data is the analysis results obtained in the previous step. The server uses a generating AI model to assemble a schedule in a way that is practical for the user and communicates it to the terminal. As output, the user receives a concrete plan to support on-site decision-making.

[0697] Step 5:

[0698] The terminal alerts workers based on risk predictions from the server. The input data is the result of the server's risk analysis. The terminal uses this data to generate and display warning messages according to specific conditions. As a result, workers prepare safety equipment and take precautions for high-risk tasks.

[0699] Step 6:

[0700] Users input feedback into the server via their devices. This feedback includes reports on the results of implementing the suggestions and the situation on-site. The server receives this feedback and uses it for subsequent data analysis and improvement of the suggestions. As an output, the accuracy and efficiency of the entire system in the next cycle improve.

[0701] (Application Example 1)

[0702] 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".

[0703] Project management at construction sites presents numerous challenges, including delays, inappropriate resource allocation, and safety uncertainties. Traditional management methods have struggled to address these issues quickly and efficiently. In particular, real-time progress monitoring, dynamic scheduling, and enhanced safety measures are essential. Solving these challenges and improving construction quality and safety is crucial.

[0704] 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.

[0705] This invention includes means for optimizing plans by collecting past and present information on civil engineering projects from a data storage system; means for analyzing progress and resource status using machine learning algorithms and generating an optimal schedule; means for monitoring work status at work sites in real time and proposing resource allocation as needed; means for monitoring progress in real time via smart devices and performing dynamic scheduling based on predictive analytics; and means for smart devices to send warnings to workers to encourage the wearing of appropriate safety equipment. This enables efficient management of construction projects.

[0706] A "data storage system" is a system for storing and managing information, and in particular, it enables the efficient collection and access of past and present information necessary for a project.

[0707] A "civil engineering project" is a collection of plans and activities for constructing physical infrastructure, and requires management of progress and resource management.

[0708] A "machine learning algorithm" is a method for computers to learn from data and perform analysis and predictions, and is a means to optimize projects.

[0709] "Progress status" is an indicator that shows whether a project is progressing according to schedule, and it is managed by comparing it to the plan.

[0710] "Resource status" refers to an indicator that shows the utilization of manpower, materials, equipment, etc., allocated to a project, and managing this directly impacts the efficiency of the project.

[0711] A "schedule" is a plan that shows the planned dates and times for each task in a project, and it is the core of schedule management.

[0712] The term "workplace" refers to the physical location where the work is actually performed, and monitoring the work status at this location forms part of project management.

[0713] A "smart device" is an electronic device that has information processing capabilities and utilizes communication functions to support on-site work, and is a tool that enables real-time monitoring and notifications.

[0714] "Safety equipment" refers to protective gear worn by workers to protect themselves from danger and is necessary to maintain a safe working environment.

[0715] The system for implementing this invention consists of a server, a smart device, and a user. The server uses a data storage system to collect information on past and present civil engineering projects and analyzes this data using machine learning algorithms. Specifically, it utilizes cloud computing services such as AWS and processes the collected data using TensorFlow to evaluate progress and resource availability and generate an optimal schedule.

[0716] Smart devices play a role in collecting real-time information about the work site from users and transmitting it to a server. This allows the server to dynamically adjust progress based on predictive analysis and suggest resource allocation as needed. Smart devices also have a function to display warnings to workers to encourage the wearing of appropriate safety equipment. Specific examples of such equipment include smartphones and tablets.

[0717] Users input and verify project data through their devices. The user interface is built with React Native, enabling intuitive operation. Furthermore, real-time data synchronization is achieved by utilizing AWS cloud services.

[0718] As a concrete example, if weather deteriorates at a construction site, the server can adjust the schedule to reschedule external work to internal work and notify the user via a smart device. In this way, the efficiency of on-site work is maintained while providing flexibility in the plan.

[0719] An example of a prompt to the generated AI model would be: "Please tell me how to optimize the construction schedule, taking into account the progress data and weather information of the construction project. Please also explain strategies for efficient resource allocation and risk management." This allows the AI ​​model to provide effective project management methods and assist in optimization.

[0720] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0721] Step 1:

[0722] The server collects historical and current information about construction projects from a data storage system. The input is project information in a database, and the output is an analyzable dataset. This step utilizes cloud computing services for efficient data access and extraction.

[0723] Step 2:

[0724] The server analyzes the collected data using machine learning algorithms. The input is the dataset obtained in step 1, and the output is optimized scheduling information based on the analysis results. Specifically, TensorFlow is used to evaluate progress and resource status and generate the optimal schedule.

[0725] Step 3:

[0726] The terminal retrieves real-time information about the work location from the user and sends it to the server. Input is work-site data obtained via the terminal, and output is updated project data sent to the server. Users intuitively input data using smartphones or tablets.

[0727] Step 4:

[0728] The server analyzes the real-time data received in step 3 and proposes dynamic resource allocation based on predictive analytics. The input is updated project data, and the output is an optimized resource allocation proposal. In this process, prompts generated by a generative AI model are analyzed to derive appropriate resource adjustment proposals.

[0729] Step 5:

[0730] The terminal displays alerts based on predictive analytics and safety equipment warnings for workers. Input is a warning message from the server, and output is a visual notification to the user. This feature allows users to take appropriate actions to enhance workplace safety.

[0731] 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.

[0732] This invention is an advanced system for streamlining construction project management and improving construction quality, and in particular incorporates an emotion engine that recognizes user emotions and provides feedback to project operations. The system consists of a server, multiple terminals, users, and the emotion engine.

[0733] First, the server collects historical and current construction project data from the database. This data includes progress information, resource allocation, and accident records. Users use terminals to input the latest on-site conditions in real time, allowing the server to always have up-to-date information.

[0734] The server uses machine learning algorithms to analyze this data, evaluate project progress and resource status, and generate an optimal schedule. The generated schedule is presented to the user via the terminal, and after user approval, it is officially registered as the project plan.

[0735] The emotion engine analyzes the user's emotions from data such as voice and facial expressions obtained from the device. This analysis evaluates the user's stress level and concentration, and provides the results to the server. The server takes this emotion data into consideration to adjust resource allocation and plans, providing the user with more optimized suggestions.

[0736] For example, if the emotion engine detects that a user's stress level is rising during a project, the server will suggest revising the project schedule and reallocating resources to reduce the burden on the user.

[0737] Furthermore, the terminals receive notifications from the server and provide workers with real-time warnings and advice. For example, in high-risk situations, safety management is strengthened by issuing warnings to workers based on analysis of past accident data.

[0738] Through this process, the system will manage construction projects more effectively, including the human emotional element, thereby improving operational efficiency and construction quality.

[0739] The following describes the processing flow.

[0740] Step 1:

[0741] The server connects to the project management database and collects historical project records and current progress data. This data includes the schedule for each project, completed and incomplete tasks, and resource usage.

[0742] Step 2:

[0743] Users use terminals on-site to input the latest work status and resource consumption information in real time. The terminals immediately send this data to the server, which maintains a real-time report of the on-site situation.

[0744] Step 3:

[0745] The server utilizes machine learning algorithms to analyze collected project data. This identifies the current project progress, resource usage trends, and potential scheduling issues.

[0746] Step 4:

[0747] The server generates an optimized project schedule based on the data analysis results. This schedule includes the priority and resource allocation for each task.

[0748] Step 5:

[0749] The terminal receives a proposed schedule from the server and presents it to the user. The user reviews the proposed schedule and completes the approval process by making revisions or providing additional feedback as needed.

[0750] Step 6:

[0751] The server records the user-approved schedule in the official management system and sets it as the project execution plan.

[0752] Step 7:

[0753] The device's built-in emotion engine monitors the user's emotional state in real time. This engine analyzes voice and facial expression data to evaluate the user's stress level and concentration level.

[0754] Step 8:

[0755] The emotion engine sends its analysis results to the server, which then uses this data to readjust the project and resource allocation. For example, if the server determines that a user is under excessive stress, it will suggest schedule changes to reduce the load.

[0756] Step 9:

[0757] The server predicts risks based on past accident data to ensure work safety. Terminals notify workers of this risk information, prompting them to pay attention and strictly adhere to safety procedures on-site.

[0758] Through these sequential steps, the system will comprehensively improve construction project management, aiming to enhance efficiency and safety.

[0759] (Example 2)

[0760] 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".

[0761] Managing construction projects involves complex progress tracking and resource allocation, and the emotional state of workers can also impact project efficiency. Traditional systems struggle to integrate and manage all these elements, posing challenges to overall project efficiency and quality improvement.

[0762] 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.

[0763] In this invention, the server includes means for optimizing the plan by acquiring past and present information about the construction project from a data storage device, means for analyzing the progress and resource status using machine learning techniques to generate an optimal plan, and means for analyzing voice and facial expression information to evaluate the user's emotions. This enables flexible resource allocation according to the progress of the project and efficient management that takes emotions into consideration.

[0764] A "data storage device" refers to a storage medium or system for accumulating data and retrieving it as needed.

[0765] A "construction project" refers to a series of tasks and plans that include the design, construction, supervision, and maintenance of buildings and infrastructure.

[0766] "Information" refers to a set of data related to a specific purpose, including data used for project progress, resource allocation, and risk analysis.

[0767] "Machine learning technology" refers to algorithms and methods that enable computers to automatically learn from data and adaptively perform inference and prediction.

[0768] "Progress status" refers to information indicating how far along a project is in relation to its plan.

[0769] "Resource status" refers to the allocation and utilization of human and material resources available for the project.

[0770] "Voice and facial information" refers to data obtained from the user's speech and facial movements, and is used to determine their emotions and state of mind.

[0771] "Evaluating emotions" refers to analyzing the user's voice and facial expressions to determine their psychological state at that time.

[0772] This invention is a system for enhancing the management of construction projects. The system includes a server, terminals, users, and an emotion engine.

[0773] The server retrieves historical and current information about construction projects stored in data storage devices via an SQL database. This includes project progress, resource allocation, and accident records. Machine learning algorithms developed using Python and R are used for analysis, examining progress and resource status. Based on the analysis results, project management software (e.g., project management tools) is used to generate an optimal schedule.

[0774] The terminals are used to input the latest on-site information in real time. Users input on-site information using mobile devices such as smartphones and tablets, and send this information to the server. Additionally, voice and facial expression information acquired via voice commands and video communication is passed to an emotion engine to analyze the user's emotions.

[0775] The emotion engine uses open-source facial recognition software (e.g., facial recognition libraries) to analyze voice and facial expression data and assess the user's stress level and concentration. The analysis results are provided to the server, which uses this information to optimize resources and adjust the schedule.

[0776] For example, if the emotion engine detects that a user is experiencing high stress levels during a project, the server can reduce the user's burden by suggesting a reallocation of resources.

[0777] This process allows the system to streamline construction projects, improve safety, and enhance construction quality.

[0778] An example of a prompt message would be, "How can I adjust progress and resources in construction project management while taking user sentiment into consideration?"

[0779] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0780] Step 1:

[0781] The server accesses data storage to retrieve past and present information on the construction project. It uses data from databases, such as project progress, resource allocation, and accident records, as input. After retrieving the data, the server formats it into an initial dataset for analysis, preparing it for transmission to machine learning algorithms. This dataset is then used in subsequent analysis steps.

[0782] Step 2:

[0783] The server launches a machine learning algorithm built in Python and analyzes the dataset obtained in Step 1. The input data includes project progress and resource usage. The analysis outputs predicted project progress and indicators of resource efficiency. This output serves as foundational information for use by project management software.

[0784] Step 3:

[0785] The server generates an optimal schedule using project management software based on the analysis results. The inputs used are progress prediction and resource optimization metrics derived from machine learning. In this step, the software outputs its recommended optimal schedule, preparing it for the next step to be reviewed and approved by the user.

[0786] Step 4:

[0787] The terminal presents the user with an optimal schedule provided by the server. The input is the schedule data generated by the server. The user reviews this schedule and provides feedback for any necessary modifications. If the user approves, the schedule is officially registered as the project plan and used in the next step.

[0788] Step 5:

[0789] The terminal is used to collect voice and facial expression data in the field. The user provides voice instructions and video data as input, which the terminal processes and passes to the emotion engine. The collected data becomes input data for evaluating the user's emotional state.

[0790] Step 6:

[0791] The server analyzes collected voice and facial expression data using an emotion engine. The input data consists of user voice and facial expression information received from the terminal. This analysis outputs evaluations of stress levels and concentration levels. The evaluation results are used for project resource allocation and schedule optimization.

[0792] Step 7:

[0793] The server re-evaluates resource allocation and scheduling based on sentiment data and generates suggestions if necessary. It uses evaluations from the sentiment engine and current project data as input. The output is resource reallocation and schedule revision proposals presented to the user. This improves project efficiency and user experience.

[0794] Step 8:

[0795] The terminal provides real-time notifications from the field based on instructions from the server. This includes alerts and safety suggestions based on past accident information. The input uses suggestions from the server, and the output is specific advice and risk warnings for field workers. These notifications help improve safety at the site.

[0796] (Application Example 2)

[0797] 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".

[0798] Construction projects require efficient management and safety assurance, but achieving this necessitates accurately understanding site conditions and providing optimal instructions that take into account the emotional state of workers. Conventional systems often lack the ability to analyze workers' emotions and have insufficient risk management, limiting improvements in efficiency and safety. To solve this problem, a system is needed that collects and analyzes data in real time and provides appropriate feedback to workers.

[0799] 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.

[0800] In this invention, the server includes means for collecting past and present data on construction projects from a database, means for analyzing progress and resource status using machine learning techniques to generate an optimal schedule, means for monitoring work status in real time and proposing resource allocation as needed, means for analyzing the emotional state of users and adjusting resource allocation and plans based on the results, and means for providing real-time feedback on safety and efficiency based on emotional data. This enables efficient and safe project management that takes into account the emotions of workers in construction project management.

[0801] A "database" is an information management system that stores information about past and present construction projects and allows it to be retrieved and used as needed.

[0802] "Machine learning methods" are data analysis techniques that use algorithms to analyze the progress and resource status of construction projects and generate optimal schedules.

[0803] "Progress status" refers to the state of a construction project, indicating how far along the current work process is compared to the plan.

[0804] "Resource status" refers to information indicating the allocation of resources and workers required for a construction project, as well as their usage.

[0805] "Real-time monitoring" is a monitoring technology that enables immediate observation and evaluation of on-site work conditions, and allows for rapid response as needed.

[0806] "Resource allocation" is a management method for optimally distributing the necessary materials and personnel in a construction project.

[0807] "Risk management" is a process of preventing accidents and incidents by predicting risks based on past accident information and issuing necessary warnings.

[0808] "Emotional state" refers to the user's psychological and emotional state during work, including their stress levels and level of concentration.

[0809] "Feedback" is the act of providing users with information and advice based on emotional data to improve safety and efficiency.

[0810] The system for implementing this invention consists of a server, multiple terminals, users, a database, and an emotion engine. The server first collects historical and current data on construction projects from the database, including progress information, resource status, and accident records. This information is aggregated on the server and analyzed using machine learning techniques. As a result of the analysis, the progress and resource status are evaluated, and an optimal schedule is generated. These schedules are presented to users via terminals, and if approved, they are officially recorded as project plans.

[0811] On the terminal side, real-time work status and emotional data are input from the user. The user's emotional state is analyzed by an emotion engine based on voice and facial expressions acquired from the terminal. The results are provided to the server as emotional states such as the user's stress level and concentration level. The server takes this emotional data into consideration to optimize resource allocation and planning, and provides feedback as needed.

[0812] Furthermore, the server generates feedback based on sentiment analysis of data collected in real time. This feedback is provided to the user via the terminal, contributing to improved project efficiency and security.

[0813] This system uses React Native for the frontend and Python and TensorFlow for the backend. It also utilizes Firebase for its database, enabling cloud-based data management. The sentiment engine's analysis results are integrated into the project management screen, supporting users in safe and efficient work operations.

[0814] For example, when a worker is facing a difficult process, the server detects the worker's stress level based on data recorded on their terminal. The server then suggests reallocating resources or revising the process, and provides specific advice as needed, such as "We recommend taking a break to relax." This feedback and advice is generated based on a prompt message that instructs, "Analyze emotional data from workers at the construction site and provide specific advice to alleviate stress."

[0815] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0816] Step 1:

[0817] The server retrieves historical and current data about construction projects from a database. It accepts progress information, resource status, and accident records stored in the database as input. It organizes this data and converts it into a parseable format. The output is an initial input data list used for analysis.

[0818] Step 2:

[0819] The server uses machine learning techniques to analyze the data obtained in Step 1. It receives progress and resource status as input and uses machine learning algorithms to perform calculations to optimize the future schedule. The output is the proposed optimal schedule.

[0820] Step 3:

[0821] The terminal collects real-time data on the work situation at the site. It receives input data from workers and real-time data from sensors as input. This data is then organized and formatted for transmission to the server. The output is the latest work information from the site.

[0822] Step 4:

[0823] The device collects user emotion data from voice and facial expressions and sends it to the emotion engine. It receives user facial expressions and voice information via camera and microphone as input, and converts this into an analyzable format. The output is the emotion data sent to the emotion engine.

[0824] Step 5:

[0825] The server receives the user's emotional state, analyzed by the emotion engine, and adjusts resource allocation and planning accordingly. It receives user emotional state data as input and uses this to determine if resource allocation is optimal. The output is the adjusted allocation proposal.

[0826] Step 6:

[0827] The server notifies the user via the terminal of an optimized schedule and resource suggestions. It receives the schedule obtained in step 2 and the resource suggestions obtained in step 5 as input, and converts them into a user-friendly format. The output is feedback information for the user.

[0828] Step 7:

[0829] The user reviews and modifies the work content and schedule based on feedback received through the terminal. Feedback information from the server is received as input, and the work process is adjusted as needed. The output is a reviewed work procedure document.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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."

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0851] The following is further disclosed regarding the embodiments described above.

[0852] (Claim 1)

[0853] By collecting historical and current data on construction projects from a database, a means to optimize the plan is provided.

[0854] A means of generating an optimal schedule by analyzing progress and resource status using machine learning algorithms,

[0855] A means of monitoring on-site work status in real time and proposing resource allocation as needed,

[0856] A means of predicting risks based on past accident data and issuing necessary warnings for safety management,

[0857] A system that includes this.

[0858] (Claim 2)

[0859] The system according to claim 1, which dynamically replans resource allocation based on field data collected in real time.

[0860] (Claim 3)

[0861] The system according to claim 1, which automatically adjusts the schedule taking into account weather data and supply chain data.

[0862] "Example 1"

[0863] (Claim 1)

[0864] By collecting past and present information on construction activities from a data management device, a means is provided to optimize the plan.

[0865] A means of analyzing progress and resource status using machine learning techniques and generating an optimal time plan,

[0866] A means of monitoring the status of the work environment in real time and proposing resource allocation as needed,

[0867] A means of predicting risks based on past accident information and issuing necessary warnings for safety management,

[0868] A means of inputting feedback on a work terminal and using it to improve subsequent analysis and proposals,

[0869] A system that includes this.

[0870] (Claim 2)

[0871] The system according to claim 1, which dynamically replans resources based on work information collected in real time.

[0872] (Claim 3)

[0873] The system according to claim 1, which automatically adjusts the time schedule taking into account weather information and supply chain information.

[0874] "Application Example 1"

[0875] (Claim 1)

[0876] By collecting past and present information on civil engineering projects from a data storage system, a means of optimizing planning is provided.

[0877] A means of analyzing progress and resource status using machine learning algorithms and generating an optimal schedule,

[0878] A means of monitoring the work status at the work site in real time and proposing resource allocation as needed,

[0879] A means of predicting risks based on past accident data and issuing necessary warnings for safety management,

[0880] A means of monitoring progress in real time via smart devices and performing dynamic scheduling based on predictive analytics,

[0881] A means by which a smart device transmits a warning to workers to encourage them to wear appropriate safety equipment,

[0882] A system that includes this.

[0883] (Claim 2)

[0884] The system according to claim 1, which dynamically replans resource allocation based on work location data collected in real time.

[0885] (Claim 3)

[0886] The system according to claim 1, which automatically adjusts the schedule taking into account surrounding condition data and supply route information.

[0887] "Example 2 of combining an emotion engine"

[0888] (Claim 1)

[0889] By obtaining past and present information about the construction project from data storage devices, a means of optimizing the plan is provided.

[0890] A means of analyzing progress and resource status using machine learning techniques to generate an optimal plan,

[0891] A means of monitoring work status in real time and proposing resource allocation as needed,

[0892] A means of predicting dangers based on past accident information and issuing necessary warnings for safety management,

[0893] A means for analyzing voice and facial expression information to evaluate the user's emotions,

[0894] A means of optimizing resource allocation and planning based on emotional information and providing suggestions,

[0895] A system that includes this.

[0896] (Claim 2)

[0897] The system according to claim 1, which dynamically replans resource allocation based on work information collected in real time.

[0898] (Claim 3)

[0899] The system according to claim 1, which automatically adjusts the plan taking into account weather information and supply chain information.

[0900] "Application example 2 when combining with an emotional engine"

[0901] (Claim 1)

[0902] By collecting historical and current data on construction projects from a database, a means to optimize the plan is provided.

[0903] A means of analyzing progress and resource status using machine learning techniques and generating an optimal schedule,

[0904] A means of monitoring the work status at construction sites in real time and proposing resource allocation as needed,

[0905] A means of predicting risks based on past accident information and issuing necessary warnings for safety management,

[0906] A means of analyzing the user's emotional state, adjusting resource allocation and plans based on that analysis, and providing optimized suggestions to the user.

[0907] A means of providing real-time feedback on safety and efficiency using acquired emotional data,

[0908] A system that includes this.

[0909] (Claim 2)

[0910] The system according to claim 1, which dynamically replans resource allocation based on field data and sentiment data collected in real time.

[0911] (Claim 3)

[0912] The system according to claim 1, which automatically adjusts the schedule considering weather information and supply chain information, and flexibly responds to project progress based on sentiment data. [Explanation of Symbols]

[0913] 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

1. By collecting past and present information on civil engineering projects from a data storage system, a means of optimizing planning is provided. A means of analyzing progress and resource status using machine learning algorithms and generating an optimal schedule, A means of monitoring the work status at the work site in real time and proposing resource allocation as needed, A means of predicting risks based on past accident data and issuing necessary warnings for safety management, A means of monitoring progress in real time via smart devices and performing dynamic scheduling based on predictive analytics, A means by which a smart device transmits a warning to workers to encourage them to wear appropriate safety equipment, A system that includes this.

2. The system according to claim 1, which dynamically replans resource allocation based on work location data collected in real time.

3. The system according to claim 1, which automatically adjusts the schedule taking into account surrounding condition data and supply route information.

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