system

Abstract

We provide the system. [Solution] As a data collection method, a device that collects real-time data from sensors installed at the construction site, A storage means for storing data from the above-mentioned device in a central database, An artificial intelligence processing method that automatically generates construction plans for construction projects using stored data, An adjustment mechanism that detects changes in the external environment and adjusts the construction plan accordingly, A notification means for informing the user of the adjusted construction plan, A learning method that uses the construction results to retrain the system and utilize them in generating the next plan, 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 and includes 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] Optimization of a construction plan requires consideration of various dynamic elements and influencing factors, and it is difficult to ensure sufficient efficiency and accuracy in conventional manual plan making. In particular, at a construction site, sudden changes in weather and changes in the physical condition of workers directly affect the construction, so adjustment of the construction plan that immediately reflects these variable elements is required. Accordingly, it is necessary to achieve efficient construction management while minimizing waste of construction costs and time.

Means for Solving the Problems

[0005] This invention aims to efficiently and flexibly manage construction projects by collecting real-time data from construction sites using data collection means and storing it in a central database. By utilizing generation AI to automatically generate construction plans based on the stored data, the efficiency of plan creation is enhanced. Furthermore, it incorporates adjustment means to detect changes in the external environment and the status of workers in real time and immediately adjust the construction plan, enabling rapid response to fluctuating factors. In addition, by notifying the user of the adjusted construction plan, on-site work can be appropriately instructed, and a learning means is used to retrain the system on construction results, which are then reflected in future plan generation. This entire system achieves improved efficiency and accuracy in construction.

[0006] "Data collection means" refers to devices that use sensors and other equipment installed at a construction site to measure the real-time conditions at the site and collect necessary data.

[0007] A "central database" is a record system for centrally storing and managing information collected by various data collection methods.

[0008] "Artificial intelligence processing means" refers to an algorithm or program that has the ability to analyze collected data and automatically generate a construction plan based on that data.

[0009] "Changes in the external environment" refers to fluctuations in external factors that affect the construction site, such as changes in weather or the health of workers.

[0010] "Adjustment means" refers to technology that has the function of dynamically modifying an already generated construction plan by judging the influence of the external environment and other factors.

[0011] "Notification means" refers to communication technology used to inform users of the latest revised construction plan and any changes thereto.

[0012] A "learning tool" is a system that analyzes the results of construction work and provides continuous feedback to help create future construction plans based on that data. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] 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).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention provides a system for more efficient planning and management of construction projects. The system has the ability to accurately grasp the real-time situation at the construction site and automatically generate and adjust an appropriate construction plan based on that information.

[0035] The system collects real-time data from the field using a data acquisition method consisting of multiple sensors and devices. Terminals acquire weather information, worker health status, equipment operating status, etc., from the sensors and transmit this data to a central database. The server analyzes the acquired data and stores it in the central database.

[0036] Next, the server automatically generates a construction plan using artificial intelligence processing based on the stored data. This artificial intelligence continuously learns from past construction plans and implementation results, improving the accuracy of the plan by considering successful and unsuccessful examples. The server also monitors changes in the external environment in real time and identifies the factors that have an impact.

[0037] If the server determines that the plan needs to be adjusted due to external factors or other reasons, it will use adjustment mechanisms to update the current construction plan. For example, if there is a sudden change in weather during work, it will switch from outdoor work to indoor work to ensure work efficiency.

[0038] The updated construction plan is quickly communicated to users through notification channels. Users can use their devices to review the plan and understand the changes, enabling them to give precise instructions to on-site workers.

[0039] Furthermore, upon completion of construction, the server records the construction results back into the database, and the learning mechanism utilizes this data to create future plans. This feedback loop allows the AI's accuracy to improve over time, resulting in the provision of more optimal construction plans.

[0040] As a concrete example, this system is used from the planning stage in large-scale building construction projects. In the initial stages of construction, work proceeds according to the plan provided by the AI, but if a typhoon is expected to approach, the server immediately readjusts the plan based on that information and instructs workers to suspend any dangerous work. In this way, the system responds flexibly to fluctuating conditions and contributes to the smooth execution of the entire project.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The terminal collects data in real time from various sensors placed at the construction site. This data includes weather information, workers' vital signs, and equipment operating status.

[0044] Step 2:

[0045] The terminal periodically sends the collected real-time data to a central server. Data transmission is carried out quickly and reliably via the communication network.

[0046] Step 3:

[0047] The server stores the received real-time data in a central database. Simultaneously, it retrieves BIM / CIM data from the database and creates the dataset necessary for generating construction plans.

[0048] Step 4:

[0049] The server utilizes generation AI to automatically generate an initial construction plan using stored data as input. The AI ​​optimizes the plan by comparing it with past successful and unsuccessful construction plans.

[0050] Step 5:

[0051] The server analyzes real-time data to determine if there are changes in the external environment or if emergency response is required. If changes are detected, the construction plan will need to be readjusted.

[0052] Step 6:

[0053] The server will use adjustment mechanisms to modify the construction plan if any influencing factors are identified. For example, it might predict bad weather and postpone outdoor work, switching the plan to indoor work.

[0054] Step 7:

[0055] The server notifies the user of the revised construction plan. The user receives this notification and can check the latest construction plan on their device.

[0056] Step 8:

[0057] The user communicates updates and instructions to on-site workers based on the latest construction plan. Workers who receive notifications are required to proceed with their work according to the new instructions.

[0058] Step 9:

[0059] After construction is completed, the server records the construction results data in a database. This data is used to improve the accuracy of future construction plans.

[0060] Step 10:

[0061] The server analyzes the saved construction results using learning methods and uses this information to improve the performance of the AI ​​model. This increases the accuracy of planning in similar scenarios.

[0062] (Example 1)

[0063] 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."

[0064] In recent years, there has been a growing demand for increased efficiency in construction site management, but traditional methods make it difficult to flexibly adjust plans in real time. Furthermore, there is a lack of means to automatically optimize construction plans while considering changes in the external environment and the health status of workers. As a result, decreased productivity and unexpected delays occur during project progress, posing a significant challenge.

[0065] 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.

[0066] In this invention, the server includes means for collecting real-time information using multiple detectors installed at the construction site, artificial intelligence processing means for automatically creating a construction plan using the stored information, and means for monitoring changes in the external environment in real time and dynamically modifying the construction work plan. This enables flexible and rapid adjustment of the construction plan in response to external conditions and the situation at the work site, allowing for safe and efficient work progress.

[0067] A "detector" is a device installed at a construction site to acquire real-time environmental information and work progress.

[0068] "Information" refers to data collected through detectors, including, for example, atmospheric conditions, the physical condition of workers, and the operating status of tools.

[0069] The "Central Records Unit" refers to a central database used to store and manage collected information.

[0070] "Artificial intelligence processing means" refers to technology that automatically creates construction plans using algorithms and models that have been schematicized from accumulated information.

[0071] "External environment" refers to environmental factors that affect work efficiency and safety, such as weather conditions and geographical conditions that impact the construction site.

[0072] A "notification system" refers to a mechanism for quickly communicating coordinated construction plans and important information to on-site workers and managers.

[0073] This invention is a system for streamlining the planning and management of construction projects, and is primarily implemented by servers, terminals, and users. The system collects real-time information from construction sites and automatically generates construction plans based on this data.

[0074] The terminal uses multiple detectors installed at the construction site to collect environmental information such as temperature, humidity, and wind speed, as well as the health status of workers and the operating status of heavy machinery, in real time. This information is transmitted securely to a central recording unit, or central database, via a stable communication module.

[0075] The server stores received information in a central recording unit and uses a generation AI model to automatically and efficiently create construction plans from that information. These construction plans take into account past construction results and success / failure cases, and the AI ​​continuously learns to achieve this. The server also monitors changes in the external environment in real time and dynamically modifies the construction plan as needed. This ensures maximum efficiency throughout the project.

[0076] The updated construction plan is communicated to users quickly and reliably. Users can review the new construction plan notified via their terminal and give appropriate instructions to on-site workers, thereby ensuring smooth progress of the work according to the plan.

[0077] As a concrete example, if a sudden change in weather is expected at a construction site, the server immediately readjusts the plan based on that information and notifies the user that external work will be suspended for safety reasons. This system flexibly responds to dynamic conditions and supports the efficient and safe execution of the entire project.

[0078] An example of a prompt message might be, "Please readjust the construction plan for the construction site, taking weather changes into consideration." Such prompts allow the system to generate an optimal construction plan and enable a quick response.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] The terminal collects real-time environmental information using multiple detectors installed at the construction site. It receives inputs such as weather data (temperature, humidity, wind speed, etc.), worker health status, and heavy equipment operating status. This data is formatted appropriately and output to the central recording unit as transmission data. This step also includes error handling and retransmission functions to ensure the stability and security of data communication.

[0082] Step 2:

[0083] The server receives data transmitted from terminals and stores it in the central recording unit. It receives formatted environmental information as input and stores it in the database. This process includes data purification using algorithms that perform data integrity checks and anomaly detection. The output is data stored in an analyzable format.

[0084] Step 3:

[0085] The server uses a generation AI model based on data stored in the central recording unit to automatically generate construction plans. It references past construction data and real-time environmental information as input, and calculates the most efficient construction sequence based on this. Data calculations performed in this step include optimizing the project schedule and resource allocation. The generated construction plan is obtained as output.

[0086] Step 4:

[0087] The server monitors changes in the external environment in real time and adjusts the construction plan as needed. It receives newly acquired environmental information as input and determines whether it will affect the current construction plan. Based on this information, it dynamically rearranges the plan and generates specific instructions to minimize risk. The adjusted construction plan is output.

[0088] Step 5:

[0089] The server notifies the user of the revised construction plan. Using the latest construction plan information as input, it outputs the plan through a system that visualizes and provides important notifications in a concise and intuitive manner for the user. Notification methods include email and message transmission via a dedicated app.

[0090] Step 6:

[0091] The user reviews the received construction plan using a terminal and issues appropriate instructions to the on-site workers. The input involves formulating specific work instructions based on the notified information and creating an effective action plan to ensure safety and efficiency on site. The output is the instructions for the on-site workers. At this step, appropriate feedback is also sent back to the server, which is used for future planning.

[0092] (Application Example 1)

[0093] 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."

[0094] In modern manufacturing, the increasing complexity of the workplace and the growing demand for efficiency make it essential to accurately understand the work situation in real time and manage production schedules efficiently. However, traditional methods struggle to flexibly adjust plans to respond to environmental changes and unforeseen circumstances, leading to decreased production efficiency.

[0095] 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.

[0096] In this invention, the server includes means for collecting real-time information from detection devices installed at the work site, means for storing the information in a central data management device, means for utilizing the operating rate and energy consumption information of the production line, and means for automatically adjusting the production schedule according to the situation to maximize production efficiency. This makes it possible to generate and adjust flexible and efficient work plans in response to environmental changes.

[0097] A "data collection means" is a device that collects real-time information from detection devices installed at the work site.

[0098] "Storage means" refers to means for storing collected information in a central data management device.

[0099] "Artificial intelligence processing means" refers to means that use artificial intelligence to automatically generate a work plan using stored information.

[0100] "Adjustment means" are means for detecting changes in the external environment and adjusting the work plan as appropriate.

[0101] "Notification means" refers to means of notifying users of the adjusted work plan.

[0102] A "learning method" is a means of relearning the results of a task and using them to generate the next plan.

[0103] "Means for utilizing manufacturing line operating rates and energy consumption information" refers to means for collecting and managing information on the operating status of manufacturing lines and the energy consumed, and using this information to formulate efficient work plans.

[0104] "Methods for automatically adjusting production schedules and maximizing production efficiency" refers to methods for automatically adjusting schedules according to the situation to improve overall production efficiency.

[0105] In this embodiment of the invention, various detection devices installed at the work site collect information in real time and transmit it to a server. The server uses appropriate hardware, such as a Raspberry Pi, to store the acquired information in a central data management device. This enables real-time monitoring of the work site.

[0106] The server uses the Python programming language and TENSORFLOW® to perform artificial intelligence processing based on stored information and generate efficient work plans. These plans are designed taking into account the operating rate and energy consumption of the manufacturing line, with the aim of maximizing work efficiency.

[0107] As a means of adjustment, the server has a built-in function that monitors changes in the external environment in real time and automatically adjusts the work plan as needed. This makes it possible to respond quickly to unexpected situations and environmental fluctuations.

[0108] The adjusted plan is quickly communicated to users via notification systems. Users can review the plan on their smartphones or devices and provide precise instructions to field workers.

[0109] For example, this system can be used to update plans so that, when demand for parts surges at a factory, the server automatically extends operating hours and increases inventory of the necessary parts. It can also issue instructions to use alternative equipment in the event of an unexpected equipment failure.

[0110] A concrete example is the prompt, "Tell me about an app that monitors the production line status in a factory in real time and automatically generates an optimal production plan." This allows the AI ​​model to generate a response or plan appropriate to the situation, enabling more efficient on-site management.

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] The terminal acquires data from various sensors installed at the work site. This data includes environmental conditions, equipment operating status, production line utilization rate, and energy consumption. The acquired data is temporarily stored on the terminal and then prepared for transmission to the server.

[0114] Step 2:

[0115] The server receives data transmitted from terminals and stores it in the central data management system. During this process, data formats are standardized and different information is integrated. This allows for the storage of real-time monitoring data from the work site.

[0116] Step 3:

[0117] The server analyzes the stored data using Python and TensorFlow. The analysis results include manufacturing line efficiency, energy consumption optimization, and equipment operating status. Based on this data, the server generates an optimal work plan. Here, an algorithm that has learned from past data using a generative AI model is applied, and future plans are automatically formulated.

[0118] Step 4:

[0119] The server adjusts the generated work plan based on external circumstances and unforeseen events. For example, it modifies the plan and responds flexibly to sudden increases in parts demand or unexpected machine failures. At this stage, prompt statements are used to process the necessary data and provide information for decision-making.

[0120] Step 5:

[0121] The server notifies the user of the adjusted work plan. The user then reviews the adjustments via smartphone or other device and relays specific instructions to the work site. The notified plan is summarized in a way that is easy for workers to understand.

[0122] Step 6:

[0123] Users resend data to the server based on feedback from the field. The server uses this data as a learning tool and incorporates it into future work plans. This allows the AI ​​model's accuracy to improve over time, enabling it to provide better plans.

[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 provides a construction management system that incorporates an emotion engine, thereby enabling more flexible and efficient construction planning that takes into account the user's emotional state. The following describes specific embodiments for carrying out this invention.

[0126] First, data collection devices installed at the construction site collect real-time data such as weather, workers' health status, and equipment operation status. Terminals transmit this data to a server and store it in a central database. In addition, an emotion engine is used to recognize the user's emotions. The emotion engine analyzes the user's facial expressions and tone of voice through sensors such as the terminal's camera and microphone to identify their emotional state.

[0127] The server uses artificial intelligence processing to integrate real-time data stored in the database with user emotion data and automatically generates an initial construction plan. This plan is optimized to reflect past data and the user's emotional state. It also monitors changes in the external environment and adjusts the construction plan as needed. If the emotion engine identifies that the user's emotions are unstable, the server enhances the user's sense of security by presenting the plan details and explanations in an easy-to-understand manner through notification mechanisms.

[0128] As an example, consider a situation where a user feels uneasy about introducing a new construction technique. In this case, the emotion engine detects the user's anxiety, and the server, accordingly, provides additional notifications of detailed guidelines and risk management measures to help the user understand the situation. As a result, the user can confidently adopt the new technology.

[0129] After construction is completed, the server records this construction result data and user feedback based on emotions in a database, which is then used to generate future construction plans. The introduction of an emotion engine enables user-centered construction management, improving construction efficiency and project success rates. Thus, this invention enables the automatic generation and appropriate adjustment of construction plans based on user emotions, resulting in more advanced construction management.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] The device collects real-time data from sensors installed at construction sites, including weather, workers' health status, and equipment operating status. It also records the user's facial expressions and voice through cameras and microphones, collecting data necessary for analyzing their emotional state.

[0133] Step 2:

[0134] The device transmits collected real-time data and sentiment data to the server. The data is transferred to the server quickly and securely via the communication network.

[0135] Step 3:

[0136] The server stores the received data in a central database. This creates a complete dataset that includes the current situation on site and the user's emotional state.

[0137] Step 4:

[0138] The server uses artificial intelligence processing to analyze data stored in the database and automatically generates an initial construction plan. This plan takes into account past construction data and user sentiment data.

[0139] Step 5:

[0140] The server monitors real-time data and assesses whether the external environment is affecting the plan. Adjustments to the construction plan are made as needed.

[0141] Step 6:

[0142] The emotion engine continuously evaluates the user's emotional state, and if signs of anxiety or stress are detected, the server adjusts the method and content of notifications regarding the construction plan to enhance the user's understanding and sense of security.

[0143] Step 7:

[0144] The server will notify the user of the revised construction plan. The notification will be provided along with a text message and detailed guidelines.

[0145] Step 8:

[0146] The user receives notifications and reviews the construction plan. If necessary, they communicate the plan details to the workers and direct the work on site.

[0147] Step 9:

[0148] Once construction is complete, the server records the construction results data and user sentiment feedback and saves it to a database. This information is used when generating the next construction plan.

[0149] Step 10:

[0150] The server uses the stored data for training and aims to improve the performance of the AI ​​model, thereby increasing the accuracy of the next construction plan and user satisfaction.

[0151] (Example 2)

[0152] 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".

[0153] Current construction management systems can only generate construction plans based on real-time site data, making it difficult to respond flexibly while considering the user's emotional state. Therefore, a challenge arises in that it can cause anxiety among users during the planning and execution phase, and adaptive adjustments are not possible.

[0154] 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.

[0155] In this invention, the server includes emotion recognition means for sensing the user's emotions, artificial intelligence processing means for automatically generating a construction plan using stored data and emotion data, and adjustment means for detecting changes in the external environment and the user's emotional state and adjusting the construction plan as appropriate. This makes it possible to generate and adjust a flexible and efficient construction plan that takes the user's emotions into consideration.

[0156] "Data collection means" refers to devices that have the function of collecting real-time data such as weather conditions, workers' health status, and equipment operation status using sensors installed at construction sites.

[0157] A "storage system" refers to a system that has the function of storing collected data in a central database and making it available for later analysis and processing.

[0158] The "artificial intelligence processing means" is a device that has the function of automatically generating a construction plan based on stored data and user sentiment data.

[0159] The "adjustment mechanism" is a system that detects changes in the external environment and the emotional instability of the user, and dynamically adjusts the construction plan based on these factors.

[0160] A "notification mechanism" is a system that provides users with a coordinated construction plan and supplementary information to aid their understanding.

[0161] A "learning tool" is a function that records construction results and user feedback, and uses this information to continuously improve the system in generating future plans.

[0162] An "emotion recognition device" is a device that analyzes the user's facial expressions and tone of voice to identify their emotional state.

[0163] As a specific embodiment for carrying out this invention, an example of a construction management system combined with an emotion engine is shown.

[0164] The terminals are installed at construction sites and collect real-time data using various sensors. Specifically, they measure weather conditions using temperature and humidity sensors, monitor the health of workers through vital signs sensors, and track the operation of equipment. All of this data is transmitted to a server using a secure protocol.

[0165] The server stores data in a central database and recognizes the user's emotions using an emotion engine. Emotion recognition is performed by capturing the user's facial expressions with the terminal's camera and recording their voice tone with a microphone. By integrating this emotion data and collected data using artificial intelligence processing, a construction plan is automatically generated. This plan is constructed in the most optimal form by referring to previously accumulated data and case studies.

[0166] Furthermore, the server monitors changes in the external environment and the user's emotional state. The construction plan is then dynamically adjusted in response to these changes. For example, when introducing a new construction method, if the server detects user anxiety, it provides the user with detailed guidelines and risk management measures. This notification not only deepens the user's understanding but also fosters a sense of security.

[0167] After construction is completed, the server records the construction results and user feedback in a central database. This information is used to generate the next construction plan, contributing to the overall improvement of the system. Users can then use the generated AI model to interact more interactively with the system through prompts such as, "Please suggest the optimal schedule for the next construction plan." In this way, user-centered construction management is achieved, leading to improved efficiency and a higher project success rate.

[0168] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0169] Step 1:

[0170] The terminal collects real-time data using sensors installed at the construction site. Inputs include temperature, humidity, worker heart rate, and equipment operating time. The collected data is processed through digital signal processing to ensure accuracy. As output, this data is ready to be sent to the server.

[0171] Step 2:

[0172] The device utilizes an emotion engine to recognize the user's emotional state. It captures the user's facial expressions with a camera and records their voice tone with a microphone. This data is used as input to identify the user's emotions using image processing and voice analysis algorithms. The user's emotional state is then quantified and sent to the server as output.

[0173] Step 3:

[0174] The server stores real-time data and sentiment data in a central database. Input consists of various data sent from terminals. The server writes the data to the database, ensuring all provided data is accessible. As output, the stored data becomes available for use in the next processing step.

[0175] Step 4:

[0176] The server generates a construction plan using artificial intelligence processing based on stored data. The input consists of real-time data from the database and user sentiment data. The server analyzes the data using a generation AI model and generates an optimal construction plan. The output is an optimized construction plan.

[0177] Step 5:

[0178] The server adjusts the construction plan, taking into account changes in the external environment and user sentiment. Inputs consist of real-time collected data and sentiment data. The server analyzes the new data and adjusts the plan accordingly. The output is a construction plan that has been adjusted as needed.

[0179] Step 6:

[0180] The server notifies the user of the adjusted construction plan and provides necessary guidelines and risk management measures. The input is the adjusted construction plan. The server sends information to the user using a notification system to help them understand the plan. The output is a state where the user can confidently implement the plan.

[0181] Step 7:

[0182] After construction is completed, the server records the construction results and user feedback in a database. Inputs include user feedback and construction results data. The server analyzes this data and uses it to improve future construction plans. The output provides feedback data useful for generating future plans.

[0183] (Application Example 2)

[0184] 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".

[0185] In recent years, improving efficiency in production sites and optimizing the working environment for workers have become important issues. However, there is a problem in that it is difficult to plan and adjust things while dynamically considering changes in the work environment and the emotional state of workers. As a result, there is a problem of decreased work efficiency and increased stress among workers.

[0186] 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.

[0187] In this invention, the server includes, as a data collection mechanism, means for collecting real-time data from detectors installed in the work environment, artificial intelligence processing means for automatically generating a work project plan using the stored data, and emotion analysis means for recognizing the emotional state of the user. This enables efficient work planning and operation adjustments that take into account changes in the external environment and the emotional state of the worker.

[0188] A "data collection mechanism" is a device that collects information in real time from detectors installed in the work environment.

[0189] A "central information repository" is a database used to aggregate and store collected data.

[0190] "Artificial intelligence processing means" refers to technology that analyzes stored data and automatically generates plans for work projects.

[0191] A "modification mechanism" is a system for detecting changes in the external environment and modifying the plan as needed.

[0192] "Notification means" refers to methods for informing users of the adjusted plan.

[0193] A "learning mechanism" is a system that relearns the results of a plan in order to utilize them in generating the next plan.

[0194] "Emotional analysis methods" are technologies that analyze a worker's facial expressions and voice to recognize their emotional state.

[0195] A "control mechanism" is a system that adjusts the movement of a machine based on the emotional state of the operator.

[0196] This system is implemented by robots installed within the factory. The robots collect workers' facial expressions and voices in real time through sensors such as cameras and microphones. This data is transmitted from the terminal to a server and stored in a central data repository. The server uses an emotion analysis model created with Python and TensorFlow to analyze the collected data and recognize the workers' emotional state.

[0197] Furthermore, the server controls the robot's movements using ROS (Robot Operating System). If the worker shows signs of stress, the server sends instructions to the robot to adjust the work speed or change to a simpler task. In this way, the system forms a feedback loop to optimize the work environment in real time. The collected data is also retrained for use in generating the next plan.

[0198] As a concrete example, for workers whose morning work schedules tend to be overcrowded, the robot could play a cheerful, humorous voice message and instruct them to slightly slow down their work speed. This function helps to boost the workers' morale and maintain efficiency. Furthermore, by using a prompt such as, "Please tell me how to analyze workers' emotions in real time and create an optimal robot action plan to alleviate stress in the factory," it becomes possible to more accurately coordinate emotion analysis and action planning.

[0199] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0200] Step 1:

[0201] The terminal collects facial and audio data of workers in real time through cameras and microphones installed in the work environment. The input is raw video and audio data, which is preprocessed to extract facial features and voice patterns. As a result, the data is sent to the server in a formatted state for analysis.

[0202] Step 2:

[0203] The server receives feature data sent from the terminal and inputs it into an emotion analysis model built with Python and TensorFlow. The model uses generative AI techniques to estimate the emotional state of the workers from the feature data and generates emotion labels such as "happy," "stressed," and "concentrated" as its output.

[0204] Step 3:

[0205] The server receives emotion labels and uses ROS (Robot Operating System) to adjust the robot's motion plan. When controlling the robot, it receives the analyzed emotion labels as input and generates corresponding motion commands. For example, if the worker is stressed, it will issue a command to slow down the robot's work speed.

[0206] Step 4:

[0207] The server sends the adjusted motion plan to the robot, which then begins its operation based on it. The input is the robot's motion command, and the output is the physical action based on that command. This action optimizes the work environment.

[0208] Step 5:

[0209] The server records the progress of the work and the history of sentiment labels in a central data repository, which is then used as training data for the next work plan. The input is the work results and sentiment history, and the output is an update to the learning model to improve the next work plan.

[0210] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0211] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0212] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0213] [Second Embodiment]

[0214] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0215] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0216] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0217] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0218] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0219] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0220] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0221] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0222] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0223] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0224] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0225] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0226] This invention provides a system for more efficient planning and management of construction projects. The system has the ability to accurately grasp the real-time situation at the construction site and automatically generate and adjust an appropriate construction plan based on that information.

[0227] The system collects real-time data from the field using a data acquisition method consisting of multiple sensors and devices. Terminals acquire weather information, worker health status, equipment operating status, etc., from the sensors and transmit this data to a central database. The server analyzes the acquired data and stores it in the central database.

[0228] Next, the server automatically generates a construction plan using artificial intelligence processing based on the stored data. This artificial intelligence continuously learns from past construction plans and implementation results, improving the accuracy of the plan by considering successful and unsuccessful examples. The server also monitors changes in the external environment in real time and identifies the factors that have an impact.

[0229] If the server determines that the plan needs to be adjusted due to external factors or other reasons, it will use adjustment mechanisms to update the current construction plan. For example, if there is a sudden change in weather during work, it will switch from outdoor work to indoor work to ensure work efficiency.

[0230] The updated construction plan is quickly communicated to users through notification channels. Users can use their devices to review the plan and understand the changes, enabling them to give precise instructions to on-site workers.

[0231] Furthermore, upon completion of construction, the server records the construction results back into the database, and the learning mechanism utilizes this data to create future plans. This feedback loop allows the AI's accuracy to improve over time, resulting in the provision of more optimal construction plans.

[0232] As a concrete example, this system is used from the planning stage in large-scale building construction projects. In the initial stages of construction, work proceeds according to the plan provided by the AI, but if a typhoon is expected to approach, the server immediately readjusts the plan based on that information and instructs workers to suspend any dangerous work. In this way, the system responds flexibly to fluctuating conditions and contributes to the smooth execution of the entire project.

[0233] The following describes the processing flow.

[0234] Step 1:

[0235] The terminal collects data in real time from various sensors placed at the construction site. This data includes weather information, workers' vital signs, and equipment operating status.

[0236] Step 2:

[0237] The terminal periodically sends the collected real-time data to a central server. Data transmission is carried out quickly and reliably via the communication network.

[0238] Step 3:

[0239] The server stores the received real-time data in a central database. Simultaneously, it retrieves BIM / CIM data from the database and creates the dataset necessary for generating construction plans.

[0240] Step 4:

[0241] The server utilizes generation AI to automatically generate an initial construction plan using stored data as input. The AI ​​optimizes the plan by comparing it with past successful and unsuccessful construction plans.

[0242] Step 5:

[0243] The server analyzes real-time data to determine if there are changes in the external environment or if emergency response is required. If changes are detected, the construction plan will need to be readjusted.

[0244] Step 6:

[0245] The server will use adjustment mechanisms to modify the construction plan if any influencing factors are identified. For example, it might predict bad weather and postpone outdoor work, switching the plan to indoor work.

[0246] Step 7:

[0247] The server notifies the user of the revised construction plan. The user receives this notification and can check the latest construction plan on their device.

[0248] Step 8:

[0249] The user communicates updates and instructions to on-site workers based on the latest construction plan. Workers who receive notifications are required to proceed with their work according to the new instructions.

[0250] Step 9:

[0251] After construction is completed, the server records the construction results data in a database. This data is used to improve the accuracy of future construction plans.

[0252] Step 10:

[0253] The server analyzes the saved construction results using learning methods and uses this information to improve the performance of the AI ​​model. This increases the accuracy of planning in similar scenarios.

[0254] (Example 1)

[0255] 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."

[0256] In recent years, there has been a growing demand for increased efficiency in construction site management, but traditional methods make it difficult to flexibly adjust plans in real time. Furthermore, there is a lack of means to automatically optimize construction plans while considering changes in the external environment and the health status of workers. As a result, decreased productivity and unexpected delays occur during project progress, posing a significant challenge.

[0257] 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.

[0258] In this invention, the server includes means for collecting real-time information using multiple detectors installed at the construction site, artificial intelligence processing means for automatically creating a construction plan using the stored information, and means for monitoring changes in the external environment in real time and dynamically modifying the construction work plan. This enables flexible and rapid adjustment of the construction plan in response to external conditions and the situation at the work site, allowing for safe and efficient work progress.

[0259] A "detector" is a device installed at a construction site to acquire real-time environmental information and work progress.

[0260] "Information" refers to data collected through detectors, including, for example, atmospheric conditions, the physical condition of workers, and the operating status of tools.

[0261] The "Central Records Unit" refers to a central database used to store and manage collected information.

[0262] "Artificial intelligence processing means" refers to technology that automatically creates construction plans using algorithms and models that have been schematicized from accumulated information.

[0263] "External environment" refers to environmental factors that affect work efficiency and safety, such as weather conditions and geographical conditions that impact the construction site.

[0264] A "notification system" refers to a mechanism for quickly communicating coordinated construction plans and important information to on-site workers and managers.

[0265] This invention is a system for streamlining the planning and management of construction projects, and is primarily implemented by servers, terminals, and users. The system collects real-time information from construction sites and automatically generates construction plans based on this data.

[0266] The terminal uses multiple detectors installed at the construction site to collect environmental information such as temperature, humidity, and wind speed, as well as the health status of workers and the operating status of heavy machinery, in real time. This information is transmitted securely to a central recording unit, or central database, via a stable communication module.

[0267] The server stores received information in a central recording unit and uses a generation AI model to automatically and efficiently create construction plans from that information. These construction plans take into account past construction results and success / failure cases, and the AI ​​continuously learns to achieve this. The server also monitors changes in the external environment in real time and dynamically modifies the construction plan as needed. This ensures maximum efficiency throughout the project.

[0268] The updated construction plan is communicated to users quickly and reliably. Users can review the new construction plan notified via their terminal and give appropriate instructions to on-site workers, thereby ensuring smooth progress of the work according to the plan.

[0269] As a concrete example, if a sudden change in weather is expected at a construction site, the server immediately readjusts the plan based on that information and notifies the user that external work will be suspended for safety reasons. This system flexibly responds to dynamic conditions and supports the efficient and safe execution of the entire project.

[0270] An example of a prompt message might be, "Please readjust the construction plan for the construction site, taking weather changes into consideration." Such prompts allow the system to generate an optimal construction plan and enable a quick response.

[0271] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0272] Step 1:

[0273] The terminal collects real-time environmental information using multiple detectors installed at the construction site. It receives inputs such as weather data (temperature, humidity, wind speed, etc.), worker health status, and heavy equipment operating status. This data is formatted appropriately and output to the central recording unit as transmission data. This step also includes error handling and retransmission functions to ensure the stability and security of data communication.

[0274] Step 2:

[0275] The server receives data transmitted from terminals and stores it in the central recording unit. It receives formatted environmental information as input and stores it in the database. This process includes data purification using algorithms that perform data integrity checks and anomaly detection. The output is data stored in an analyzable format.

[0276] Step 3:

[0277] The server uses a generation AI model based on data stored in the central recording unit to automatically generate construction plans. It references past construction data and real-time environmental information as input, and calculates the most efficient construction sequence based on this. Data calculations performed in this step include optimizing the project schedule and resource allocation. The generated construction plan is obtained as output.

[0278] Step 4:

[0279] The server monitors changes in the external environment in real time and adjusts the construction plan as needed. It receives newly acquired environmental information as input and determines whether it will affect the current construction plan. Based on this information, it dynamically rearranges the plan and generates specific instructions to minimize risk. The adjusted construction plan is output.

[0280] Step 5:

[0281] The server notifies the user of the adjusted construction plan. Using the latest construction plan information as input, it outputs through a system that visualizes and provides important notifications so that the user can understand it simply and intuitively. Notification means include message sending via email or a dedicated app.

[0282] Step 6:

[0283] The user checks the construction plan received using the terminal and gives appropriate instructions to the on-site workers. Using the notified information as input, it formulates specific work instructions and constructs an effective action plan to ensure safety and efficiency at the site. As output, there are instructions for the on-site workers. In this step, it also returns appropriate feedback to the server to be utilized in future plan formulation.

[0284] (Application Example 1)

[0285] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0286] In modern manufacturing, due to the increasing complexity and efficiency requirements of the work site, it is essential to accurately grasp the work situation in real time and efficiently manage the production schedule. However, with conventional methods, it is difficult to flexibly adjust the plan to respond to environmental changes and unexpected situations, and there is a problem of reduced production efficiency.

[0287] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0288] In this invention, the server includes means for collecting real-time information from detection devices installed at the work site, means for storing the information in a central data management device, means for utilizing the operating rate and energy consumption information of the production line, and means for automatically adjusting the production schedule according to the situation to maximize production efficiency. This makes it possible to generate and adjust flexible and efficient work plans in response to environmental changes.

[0289] A "data collection means" is a device that collects real-time information from detection devices installed at the work site.

[0290] "Storage means" refers to means for storing collected information in a central data management device.

[0291] "Artificial intelligence processing means" refers to means that use artificial intelligence to automatically generate a work plan using stored information.

[0292] "Adjustment means" are means for detecting changes in the external environment and adjusting the work plan as appropriate.

[0293] "Notification means" refers to means of notifying users of the adjusted work plan.

[0294] A "learning method" is a means of relearning the results of a task and using them to generate the next plan.

[0295] "Means for utilizing manufacturing line operating rates and energy consumption information" refers to means for collecting and managing information on the operating status of manufacturing lines and the energy consumed, and using this information to formulate efficient work plans.

[0296] "Methods for automatically adjusting production schedules and maximizing production efficiency" refers to methods for automatically adjusting schedules according to the situation to improve overall production efficiency.

[0297] In this embodiment of the invention, various detection devices installed at the work site collect information in real time and transmit it to a server. The server uses appropriate hardware, such as a Raspberry Pi, to store the acquired information in a central data management device. This enables real-time monitoring of the work site.

[0298] The server uses the Python programming language and TensorFlow to perform artificial intelligence processing based on stored information and generate efficient work plans. These plans are designed taking into account the operating rate and energy consumption of the manufacturing line, with the aim of maximizing work efficiency.

[0299] As a means of adjustment, the server has a built-in function that monitors changes in the external environment in real time and automatically adjusts the work plan as needed. This makes it possible to respond quickly to unexpected situations and environmental fluctuations.

[0300] The adjusted plan is quickly communicated to users via notification systems. Users can review the plan on their smartphones or devices and provide precise instructions to field workers.

[0301] For example, this system can be used to update plans so that, when demand for parts surges at a factory, the server automatically extends operating hours and increases inventory of the necessary parts. It can also issue instructions to use alternative equipment in the event of an unexpected equipment failure.

[0302] A concrete example is the prompt, "Tell me about an app that monitors the production line status in a factory in real time and automatically generates an optimal production plan." This allows the AI ​​model to generate a response or plan appropriate to the situation, enabling more efficient on-site management.

[0303] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0304] Step 1:

[0305] The terminal acquires data from various sensors installed at the work site. This data includes environmental conditions, equipment operating status, production line operation rate, energy consumption, etc. The acquired data is temporarily stored in the terminal and prepared to be sent to the server.

[0306] Step 2:

[0307] The server receives the data sent from the terminal and stores it in the central data management device. At this time, the data format is unified and different information is integrated. As a result, real-time monitoring data of the work site is stored.

[0308] Step 3:

[0309] The server analyzes the stored data using Python and TensorFlow. The analysis results include the efficiency of the production line, optimization of energy consumption, and equipment operating status. Based on this data, the server generates an optimal work plan. Here, an algorithm that learns past data using a generated AI model is applied, and future plans are automatically formulated.

[0310] Step 4:

[0311] The server adjusts the generated work plan based on the external environment and unexpected situations. For example, in response to a sudden increase in parts demand or an unexpected machine failure, the plan is changed to respond flexibly. At this stage, prompt sentences are used as the necessary data processing to provide decision-making materials.

[0312] Step 5:

[0313] The server notifies the user of the adjusted work plan. The user checks the adjustment content via a smartphone or terminal and conveys specific instructions to the work site. The notified plan is summarized for easy understanding by the workers.

[0314] Step 6:

[0315] Users resend data to the server based on feedback from the field. The server uses this data as a learning tool and incorporates it into future work plans. This allows the AI ​​model's accuracy to improve over time, enabling it to provide better plans.

[0316] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0317] This invention provides a construction management system that incorporates an emotion engine, thereby enabling more flexible and efficient construction planning that takes into account the user's emotional state. The following describes specific embodiments for carrying out this invention.

[0318] First, data collection devices installed at the construction site collect real-time data such as weather, workers' health status, and equipment operation status. Terminals transmit this data to a server and store it in a central database. In addition, an emotion engine is used to recognize the user's emotions. The emotion engine analyzes the user's facial expressions and tone of voice through sensors such as the terminal's camera and microphone to identify their emotional state.

[0319] The server uses artificial intelligence processing to integrate real-time data stored in the database with user emotion data and automatically generates an initial construction plan. This plan is optimized to reflect past data and the user's emotional state. It also monitors changes in the external environment and adjusts the construction plan as needed. If the emotion engine identifies that the user's emotions are unstable, the server enhances the user's sense of security by presenting the plan details and explanations in an easy-to-understand manner through notification mechanisms.

[0320] As an example, consider a situation where a user feels uneasy about introducing a new construction technique. In this case, the emotion engine detects the user's anxiety, and the server, accordingly, provides additional notifications of detailed guidelines and risk management measures to help the user understand the situation. As a result, the user can confidently adopt the new technology.

[0321] After construction is completed, the server records this construction result data and user feedback based on emotions in a database, which is then used to generate future construction plans. The introduction of an emotion engine enables user-centered construction management, improving construction efficiency and project success rates. Thus, this invention enables the automatic generation and appropriate adjustment of construction plans based on user emotions, resulting in more advanced construction management.

[0322] The following describes the processing flow.

[0323] Step 1:

[0324] The device collects real-time data from sensors installed at construction sites, including weather, workers' health status, and equipment operating status. It also records the user's facial expressions and voice through cameras and microphones, collecting data necessary for analyzing their emotional state.

[0325] Step 2:

[0326] The device transmits collected real-time data and sentiment data to the server. The data is transferred to the server quickly and securely via the communication network.

[0327] Step 3:

[0328] The server stores the received data in a central database. This creates a complete dataset that includes the current situation on site and the user's emotional state.

[0329] Step 4:

[0330] The server uses artificial intelligence processing to analyze data stored in the database and automatically generates an initial construction plan. This plan takes into account past construction data and user sentiment data.

[0331] Step 5:

[0332] The server monitors real-time data and assesses whether the external environment is affecting the plan. Adjustments to the construction plan are made as needed.

[0333] Step 6:

[0334] The emotion engine continuously evaluates the user's emotional state, and if signs of anxiety or stress are detected, the server adjusts the method and content of notifications regarding the construction plan to enhance the user's understanding and sense of security.

[0335] Step 7:

[0336] The server will notify the user of the revised construction plan. The notification will be provided along with a text message and detailed guidelines.

[0337] Step 8:

[0338] The user receives notifications and reviews the construction plan. If necessary, they communicate the plan details to the workers and direct the work on site.

[0339] Step 9:

[0340] Once construction is complete, the server records the construction results data and user sentiment feedback and saves it to a database. This information is used when generating the next construction plan.

[0341] Step 10:

[0342] The server uses the stored data for training and aims to improve the performance of the AI ​​model, thereby increasing the accuracy of the next construction plan and user satisfaction.

[0343] (Example 2)

[0344] 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".

[0345] Current construction management systems can only generate construction plans based on real-time site data, making it difficult to respond flexibly while considering the user's emotional state. Therefore, a challenge arises in that it can cause anxiety among users during the planning and execution phase, and adaptive adjustments are not possible.

[0346] 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.

[0347] In this invention, the server includes emotion recognition means for sensing the user's emotions, artificial intelligence processing means for automatically generating a construction plan using stored data and emotion data, and adjustment means for detecting changes in the external environment and the user's emotional state and adjusting the construction plan as appropriate. This makes it possible to generate and adjust a flexible and efficient construction plan that takes the user's emotions into consideration.

[0348] "Data collection means" refers to devices that have the function of collecting real-time data such as weather conditions, workers' health status, and equipment operation status using sensors installed at construction sites.

[0349] A "storage system" refers to a system that has the function of storing collected data in a central database and making it available for later analysis and processing.

[0350] The "artificial intelligence processing means" is a device that has the function of automatically generating a construction plan based on stored data and user sentiment data.

[0351] The "adjustment mechanism" is a system that detects changes in the external environment and the emotional instability of the user, and dynamically adjusts the construction plan based on these factors.

[0352] A "notification mechanism" is a system that provides users with a coordinated construction plan and supplementary information to aid their understanding.

[0353] A "learning tool" is a function that records construction results and user feedback, and uses this information to continuously improve the system in generating future plans.

[0354] An "emotion recognition device" is a device that analyzes the user's facial expressions and tone of voice to identify their emotional state.

[0355] As a specific embodiment for carrying out this invention, an example of a construction management system combined with an emotion engine is shown.

[0356] The terminals are installed at construction sites and collect real-time data using various sensors. Specifically, they measure weather conditions using temperature and humidity sensors, monitor the health of workers through vital signs sensors, and track the operation of equipment. All of this data is transmitted to a server using a secure protocol.

[0357] The server stores data in a central database and recognizes the user's emotions using an emotion engine. Emotion recognition is performed by capturing the user's facial expressions with the terminal's camera and recording their voice tone with a microphone. By integrating this emotion data and collected data using artificial intelligence processing, a construction plan is automatically generated. This plan is constructed in the most optimal form by referring to previously accumulated data and case studies.

[0358] Furthermore, the server monitors changes in the external environment and the user's emotional state. The construction plan is then dynamically adjusted in response to these changes. For example, when introducing a new construction method, if the server detects user anxiety, it provides the user with detailed guidelines and risk management measures. This notification not only deepens the user's understanding but also fosters a sense of security.

[0359] After construction is completed, the server records the construction results and user feedback in a central database. This information is used to generate the next construction plan, contributing to the overall improvement of the system. Users can then use the generated AI model to interact more interactively with the system through prompts such as, "Please suggest the optimal schedule for the next construction plan." In this way, user-centered construction management is achieved, leading to improved efficiency and a higher project success rate.

[0360] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0361] Step 1:

[0362] The terminal collects real-time data using sensors installed at the construction site. Inputs include temperature, humidity, worker heart rate, and equipment operating time. The collected data is processed through digital signal processing to ensure accuracy. As output, this data is ready to be sent to the server.

[0363] Step 2:

[0364] The device utilizes an emotion engine to recognize the user's emotional state. It captures the user's facial expressions with a camera and records their voice tone with a microphone. This data is used as input to identify the user's emotions using image processing and voice analysis algorithms. The user's emotional state is then quantified and sent to the server as output.

[0365] Step 3:

[0366] The server stores real-time data and sentiment data in a central database. Input consists of various data sent from terminals. The server writes the data to the database, ensuring all provided data is accessible. As output, the stored data becomes available for use in the next processing step.

[0367] Step 4:

[0368] The server generates a construction plan using artificial intelligence processing based on stored data. The input consists of real-time data from the database and user sentiment data. The server analyzes the data using a generation AI model and generates an optimal construction plan. The output is an optimized construction plan.

[0369] Step 5:

[0370] The server adjusts the construction plan, taking into account changes in the external environment and user sentiment. Inputs consist of real-time collected data and sentiment data. The server analyzes the new data and adjusts the plan accordingly. The output is a construction plan that has been adjusted as needed.

[0371] Step 6:

[0372] The server notifies the user of the adjusted construction plan and provides necessary guidelines and risk management measures. The input is the adjusted construction plan. The server sends information to the user using a notification system to help them understand the plan. The output is a state where the user can confidently implement the plan.

[0373] Step 7:

[0374] After construction is completed, the server records the construction results and user feedback in a database. Inputs include user feedback and construction results data. The server analyzes this data and uses it to improve future construction plans. The output provides feedback data useful for generating future plans.

[0375] (Application Example 2)

[0376] 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."

[0377] In recent years, improving efficiency in production sites and optimizing the working environment for workers have become important issues. However, there is a problem in that it is difficult to plan and adjust things while dynamically considering changes in the work environment and the emotional state of workers. As a result, there is a problem of decreased work efficiency and increased stress among workers.

[0378] 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.

[0379] In this invention, the server includes, as a data collection mechanism, means for collecting real-time data from detectors installed in the work environment, artificial intelligence processing means for automatically generating a work project plan using the stored data, and emotion analysis means for recognizing the emotional state of the user. This enables efficient work planning and operation adjustments that take into account changes in the external environment and the emotional state of the worker.

[0380] A "data collection mechanism" is a device that collects information in real time from detectors installed in the work environment.

[0381] A "central information repository" is a database used to aggregate and store collected data.

[0382] "Artificial intelligence processing means" refers to technology that analyzes stored data and automatically generates plans for work projects.

[0383] A "modification mechanism" is a system for detecting changes in the external environment and modifying the plan as needed.

[0384] "Notification means" refers to methods for informing users of the adjusted plan.

[0385] A "learning mechanism" is a system that relearns the results of a plan in order to utilize them in generating the next plan.

[0386] "Emotional analysis methods" are technologies that analyze a worker's facial expressions and voice to recognize their emotional state.

[0387] A "control mechanism" is a system that adjusts the movement of a machine based on the emotional state of the operator.

[0388] This system is implemented by robots installed within the factory. The robots collect workers' facial expressions and voices in real time through sensors such as cameras and microphones. This data is transmitted from the terminal to a server and stored in a central data repository. The server uses an emotion analysis model created with Python and TensorFlow to analyze the collected data and recognize the workers' emotional state.

[0389] Furthermore, the server controls the robot's movements using ROS (Robot Operating System). If the worker shows signs of stress, the server sends instructions to the robot to adjust the work speed or change to a simpler task. In this way, the system forms a feedback loop to optimize the work environment in real time. The collected data is also retrained for use in generating the next plan.

[0390] As a concrete example, for workers whose morning work schedules tend to be overcrowded, the robot could play a cheerful, humorous voice message and instruct them to slightly slow down their work speed. This function helps to boost the workers' morale and maintain efficiency. Furthermore, by using a prompt such as, "Please tell me how to analyze workers' emotions in real time and create an optimal robot action plan to alleviate stress in the factory," it becomes possible to more accurately coordinate emotion analysis and action planning.

[0391] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0392] Step 1:

[0393] The terminal collects facial and audio data of workers in real time through cameras and microphones installed in the work environment. The input is raw video and audio data, which is preprocessed to extract facial features and voice patterns. As a result, the data is sent to the server in a formatted state for analysis.

[0394] Step 2:

[0395] The server receives feature data sent from the terminal and inputs it into an emotion analysis model built with Python and TensorFlow. The model uses generative AI techniques to estimate the emotional state of the workers from the feature data and generates emotion labels such as "happy," "stressed," and "concentrated" as its output.

[0396] Step 3:

[0397] The server receives emotion labels and uses ROS (Robot Operating System) to adjust the robot's motion plan. When controlling the robot, it receives the analyzed emotion labels as input and generates corresponding motion commands. For example, if the worker is stressed, it will issue a command to slow down the robot's work speed.

[0398] Step 4:

[0399] The server sends the adjusted motion plan to the robot, which then begins its operation based on it. The input is the robot's motion command, and the output is the physical action based on that command. This action optimizes the work environment.

[0400] Step 5:

[0401] The server records the progress of the work and the history of sentiment labels in a central data repository, which is then used as training data for the next work plan. The input is the work results and sentiment history, and the output is an update to the learning model to improve the next work plan.

[0402] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0403] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0404] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0405] [Third Embodiment]

[0406] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0407] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0408] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0409] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0410] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0411] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0412] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0413] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0414] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0415] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0416] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0417] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0418] This invention provides a system for more efficient planning and management of construction projects. The system has the ability to accurately grasp the real-time situation at the construction site and automatically generate and adjust an appropriate construction plan based on that information.

[0419] The system collects real-time data from the field using a data acquisition method consisting of multiple sensors and devices. Terminals acquire weather information, worker health status, equipment operating status, etc., from the sensors and transmit this data to a central database. The server analyzes the acquired data and stores it in the central database.

[0420] Next, the server automatically generates a construction plan using artificial intelligence processing based on the stored data. This artificial intelligence continuously learns from past construction plans and implementation results, improving the accuracy of the plan by considering successful and unsuccessful examples. The server also monitors changes in the external environment in real time and identifies the factors that have an impact.

[0421] If the server determines that the plan needs to be adjusted due to external factors or other reasons, it will use adjustment mechanisms to update the current construction plan. For example, if there is a sudden change in weather during work, it will switch from outdoor work to indoor work to ensure work efficiency.

[0422] The updated construction plan is quickly communicated to users through notification channels. Users can use their devices to review the plan and understand the changes, enabling them to give precise instructions to on-site workers.

[0423] Furthermore, upon completion of construction, the server records the construction results back into the database, and the learning mechanism utilizes this data to create future plans. This feedback loop allows the AI's accuracy to improve over time, resulting in the provision of more optimal construction plans.

[0424] As a concrete example, this system is used from the planning stage in large-scale building construction projects. In the initial stages of construction, work proceeds according to the plan provided by the AI, but if a typhoon is expected to approach, the server immediately readjusts the plan based on that information and instructs workers to suspend any dangerous work. In this way, the system responds flexibly to fluctuating conditions and contributes to the smooth execution of the entire project.

[0425] The following describes the processing flow.

[0426] Step 1:

[0427] The terminal collects data in real time from various sensors placed at the construction site. This data includes weather information, workers' vital signs, and equipment operating status.

[0428] Step 2:

[0429] The terminal periodically sends the collected real-time data to a central server. Data transmission is carried out quickly and reliably via the communication network.

[0430] Step 3:

[0431] The server stores the received real-time data in a central database. Simultaneously, it retrieves BIM / CIM data from the database and creates the dataset necessary for generating construction plans.

[0432] Step 4:

[0433] The server utilizes generation AI to automatically generate an initial construction plan using stored data as input. The AI ​​optimizes the plan by comparing it with past successful and unsuccessful construction plans.

[0434] Step 5:

[0435] The server analyzes real-time data to determine if there are changes in the external environment or if emergency response is required. If changes are detected, the construction plan will need to be readjusted.

[0436] Step 6:

[0437] The server will use adjustment mechanisms to modify the construction plan if any influencing factors are identified. For example, it might predict bad weather and postpone outdoor work, switching the plan to indoor work.

[0438] Step 7:

[0439] The server notifies the user of the revised construction plan. The user receives this notification and can check the latest construction plan on their device.

[0440] Step 8:

[0441] The user communicates updates and instructions to on-site workers based on the latest construction plan. Workers who receive notifications are required to proceed with their work according to the new instructions.

[0442] Step 9:

[0443] After construction is completed, the server records the construction results data in a database. This data is used to improve the accuracy of future construction plans.

[0444] Step 10:

[0445] The server analyzes the saved construction results using learning methods and uses this information to improve the performance of the AI ​​model. This increases the accuracy of planning in similar scenarios.

[0446] (Example 1)

[0447] 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."

[0448] In recent years, there has been a growing demand for increased efficiency in construction site management, but traditional methods make it difficult to flexibly adjust plans in real time. Furthermore, there is a lack of means to automatically optimize construction plans while considering changes in the external environment and the health status of workers. As a result, decreased productivity and unexpected delays occur during project progress, posing a significant challenge.

[0449] 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.

[0450] In this invention, the server includes means for collecting real-time information using multiple detectors installed at the construction site, artificial intelligence processing means for automatically creating a construction plan using the stored information, and means for monitoring changes in the external environment in real time and dynamically modifying the construction work plan. This enables flexible and rapid adjustment of the construction plan in response to external conditions and the situation at the work site, allowing for safe and efficient work progress.

[0451] A "detector" is a device installed at a construction site to acquire real-time environmental information and work progress.

[0452] "Information" refers to data collected through detectors, including, for example, atmospheric conditions, the physical condition of workers, and the operating status of tools.

[0453] The "Central Records Unit" refers to a central database used to store and manage collected information.

[0454] "Artificial intelligence processing means" refers to technology that automatically creates construction plans using algorithms and models that have been schematicized from accumulated information.

[0455] "External environment" refers to environmental factors that affect work efficiency and safety, such as weather conditions and geographical conditions that impact the construction site.

[0456] A "notification system" refers to a mechanism for quickly communicating coordinated construction plans and important information to on-site workers and managers.

[0457] This invention is a system for streamlining the planning and management of construction projects, and is primarily implemented by servers, terminals, and users. The system collects real-time information from construction sites and automatically generates construction plans based on this data.

[0458] The terminal uses multiple detectors installed at the construction site to collect environmental information such as temperature, humidity, and wind speed, as well as the health status of workers and the operating status of heavy machinery, in real time. This information is transmitted securely to a central recording unit, or central database, via a stable communication module.

[0459] The server stores received information in a central recording unit and uses a generation AI model to automatically and efficiently create construction plans from that information. These construction plans take into account past construction results and success / failure cases, and the AI ​​continuously learns to achieve this. The server also monitors changes in the external environment in real time and dynamically modifies the construction plan as needed. This ensures maximum efficiency throughout the project.

[0460] The updated construction plan is communicated to users quickly and reliably. Users can review the new construction plan notified via their terminal and give appropriate instructions to on-site workers, thereby ensuring smooth progress of the work according to the plan.

[0461] As a concrete example, if a sudden change in weather is expected at a construction site, the server immediately readjusts the plan based on that information and notifies the user that external work will be suspended for safety reasons. This system flexibly responds to dynamic conditions and supports the efficient and safe execution of the entire project.

[0462] An example of a prompt message might be, "Please readjust the construction plan for the construction site, taking weather changes into consideration." Such prompts allow the system to generate an optimal construction plan and enable a quick response.

[0463] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0464] Step 1:

[0465] The terminal collects real-time environmental information using multiple detectors installed at the construction site. It receives inputs such as weather data (temperature, humidity, wind speed, etc.), worker health status, and heavy equipment operating status. This data is formatted appropriately and output to the central recording unit as transmission data. This step also includes error handling and retransmission functions to ensure the stability and security of data communication.

[0466] Step 2:

[0467] The server receives data transmitted from terminals and stores it in the central recording unit. It receives formatted environmental information as input and stores it in the database. This process includes data purification using algorithms that perform data integrity checks and anomaly detection. The output is data stored in an analyzable format.

[0468] Step 3:

[0469] The server uses a generation AI model based on data stored in the central recording unit to automatically generate construction plans. It references past construction data and real-time environmental information as input, and calculates the most efficient construction sequence based on this. Data calculations performed in this step include optimizing the project schedule and resource allocation. The generated construction plan is obtained as output.

[0470] Step 4:

[0471] The server monitors changes in the external environment in real time and adjusts the construction plan as needed. It receives newly acquired environmental information as input and determines whether it will affect the current construction plan. Based on this information, it dynamically rearranges the plan and generates specific instructions to minimize risk. The adjusted construction plan is output.

[0472] Step 5:

[0473] The server notifies the user of the revised construction plan. Using the latest construction plan information as input, it outputs the plan through a system that visualizes and provides important notifications in a concise and intuitive manner for the user. Notification methods include email and message transmission via a dedicated app.

[0474] Step 6:

[0475] The user reviews the received construction plan using a terminal and issues appropriate instructions to the on-site workers. The input involves formulating specific work instructions based on the notified information and creating an effective action plan to ensure safety and efficiency on site. The output is the instructions for the on-site workers. At this step, appropriate feedback is also sent back to the server, which is used for future planning.

[0476] (Application Example 1)

[0477] 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."

[0478] In modern manufacturing, the increasing complexity of the workplace and the growing demand for efficiency make it essential to accurately understand the work situation in real time and manage production schedules efficiently. However, traditional methods struggle to flexibly adjust plans to respond to environmental changes and unforeseen circumstances, leading to decreased production efficiency.

[0479] 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.

[0480] In this invention, the server includes means for collecting real-time information from detection devices installed at the work site, means for storing the information in a central data management device, means for utilizing the operating rate and energy consumption information of the production line, and means for automatically adjusting the production schedule according to the situation to maximize production efficiency. This makes it possible to generate and adjust flexible and efficient work plans in response to environmental changes.

[0481] A "data collection means" is a device that collects real-time information from detection devices installed at the work site.

[0482] "Storage means" refers to means for storing collected information in a central data management device.

[0483] "Artificial intelligence processing means" refers to means that use artificial intelligence to automatically generate a work plan using stored information.

[0484] "Adjustment means" are means for detecting changes in the external environment and adjusting the work plan as appropriate.

[0485] "Notification means" refers to means of notifying users of the adjusted work plan.

[0486] A "learning method" is a means of relearning the results of a task and using them to generate the next plan.

[0487] "Means for utilizing manufacturing line operating rates and energy consumption information" refers to means for collecting and managing information on the operating status of manufacturing lines and the energy consumed, and using this information to formulate efficient work plans.

[0488] "Methods for automatically adjusting production schedules and maximizing production efficiency" refers to methods for automatically adjusting schedules according to the situation to improve overall production efficiency.

[0489] In this embodiment of the invention, various detection devices installed at the work site collect information in real time and transmit it to a server. The server uses appropriate hardware, such as a Raspberry Pi, to store the acquired information in a central data management device. This enables real-time monitoring of the work site.

[0490] The server uses the Python programming language and TensorFlow to perform artificial intelligence processing based on stored information and generate efficient work plans. These plans are designed taking into account the operating rate and energy consumption of the manufacturing line, with the aim of maximizing work efficiency.

[0491] As a means of adjustment, the server has a built-in function that monitors changes in the external environment in real time and automatically adjusts the work plan as needed. This makes it possible to respond quickly to unexpected situations and environmental fluctuations.

[0492] The adjusted plan is quickly communicated to users via notification systems. Users can review the plan on their smartphones or devices and provide precise instructions to field workers.

[0493] For example, this system can be used to update plans so that, when demand for parts surges at a factory, the server automatically extends operating hours and increases inventory of the necessary parts. It can also issue instructions to use alternative equipment in the event of an unexpected equipment failure.

[0494] A concrete example is the prompt, "Tell me about an app that monitors the production line status in a factory in real time and automatically generates an optimal production plan." This allows the AI ​​model to generate a response or plan appropriate to the situation, enabling more efficient on-site management.

[0495] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0496] Step 1:

[0497] The terminal acquires data from various sensors installed at the work site. This data includes environmental conditions, equipment operating status, production line utilization rate, and energy consumption. The acquired data is temporarily stored on the terminal and then prepared for transmission to the server.

[0498] Step 2:

[0499] The server receives data transmitted from terminals and stores it in the central data management system. During this process, data formats are standardized and different information is integrated. This allows for the storage of real-time monitoring data from the work site.

[0500] Step 3:

[0501] The server analyzes the stored data using Python and TensorFlow. The analysis results include manufacturing line efficiency, energy consumption optimization, and equipment operating status. Based on this data, the server generates an optimal work plan. Here, an algorithm that has learned from past data using a generative AI model is applied, and future plans are automatically formulated.

[0502] Step 4:

[0503] The server adjusts the generated work plan based on external circumstances and unforeseen events. For example, it modifies the plan and responds flexibly to sudden increases in parts demand or unexpected machine failures. At this stage, prompt statements are used to process the necessary data and provide information for decision-making.

[0504] Step 5:

[0505] The server notifies the user of the adjusted work plan. The user then reviews the adjustments via smartphone or other device and relays specific instructions to the work site. The notified plan is summarized in a way that is easy for workers to understand.

[0506] Step 6:

[0507] Users resend data to the server based on feedback from the field. The server uses this data as a learning tool and incorporates it into future work plans. This allows the AI ​​model's accuracy to improve over time, enabling it to provide better plans.

[0508] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0509] This invention provides a construction management system that incorporates an emotion engine, thereby enabling more flexible and efficient construction planning that takes into account the user's emotional state. The following describes specific embodiments for carrying out this invention.

[0510] First, data collection devices installed at the construction site collect real-time data such as weather, workers' health status, and equipment operation status. Terminals transmit this data to a server and store it in a central database. In addition, an emotion engine is used to recognize the user's emotions. The emotion engine analyzes the user's facial expressions and tone of voice through sensors such as the terminal's camera and microphone to identify their emotional state.

[0511] The server uses artificial intelligence processing to integrate real-time data stored in the database with user emotion data and automatically generates an initial construction plan. This plan is optimized to reflect past data and the user's emotional state. It also monitors changes in the external environment and adjusts the construction plan as needed. If the emotion engine identifies that the user's emotions are unstable, the server enhances the user's sense of security by presenting the plan details and explanations in an easy-to-understand manner through notification mechanisms.

[0512] As an example, consider a situation where a user feels uneasy about introducing a new construction technique. In this case, the emotion engine detects the user's anxiety, and the server, accordingly, provides additional notifications of detailed guidelines and risk management measures to help the user understand the situation. As a result, the user can confidently adopt the new technology.

[0513] After construction is completed, the server records this construction result data and user feedback based on emotions in a database, which is then used to generate future construction plans. The introduction of an emotion engine enables user-centered construction management, improving construction efficiency and project success rates. Thus, this invention enables the automatic generation and appropriate adjustment of construction plans based on user emotions, resulting in more advanced construction management.

[0514] The following describes the processing flow.

[0515] Step 1:

[0516] The device collects real-time data from sensors installed at construction sites, including weather, workers' health status, and equipment operating status. It also records the user's facial expressions and voice through cameras and microphones, collecting data necessary for analyzing their emotional state.

[0517] Step 2:

[0518] The device transmits collected real-time data and sentiment data to the server. The data is transferred to the server quickly and securely via the communication network.

[0519] Step 3:

[0520] The server stores the received data in a central database. This creates a complete dataset that includes the current situation on site and the user's emotional state.

[0521] Step 4:

[0522] The server uses artificial intelligence processing to analyze data stored in the database and automatically generates an initial construction plan. This plan takes into account past construction data and user sentiment data.

[0523] Step 5:

[0524] The server monitors real-time data and assesses whether the external environment is affecting the plan. Adjustments to the construction plan are made as needed.

[0525] Step 6:

[0526] The emotion engine continuously evaluates the user's emotional state, and if signs of anxiety or stress are detected, the server adjusts the method and content of notifications regarding the construction plan to enhance the user's understanding and sense of security.

[0527] Step 7:

[0528] The server will notify the user of the revised construction plan. The notification will be provided along with a text message and detailed guidelines.

[0529] Step 8:

[0530] The user receives notifications and reviews the construction plan. If necessary, they communicate the plan details to the workers and direct the work on site.

[0531] Step 9:

[0532] Once construction is complete, the server records the construction results data and user sentiment feedback and saves it to a database. This information is used when generating the next construction plan.

[0533] Step 10:

[0534] The server uses the stored data for training and aims to improve the performance of the AI ​​model, thereby increasing the accuracy of the next construction plan and user satisfaction.

[0535] (Example 2)

[0536] 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."

[0537] Current construction management systems can only generate construction plans based on real-time site data, making it difficult to respond flexibly while considering the user's emotional state. Therefore, a challenge arises in that it can cause anxiety among users during the planning and execution phase, and adaptive adjustments are not possible.

[0538] 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.

[0539] In this invention, the server includes emotion recognition means for sensing the user's emotions, artificial intelligence processing means for automatically generating a construction plan using stored data and emotion data, and adjustment means for detecting changes in the external environment and the user's emotional state and adjusting the construction plan as appropriate. This makes it possible to generate and adjust a flexible and efficient construction plan that takes the user's emotions into consideration.

[0540] "Data collection means" refers to devices that have the function of collecting real-time data such as weather conditions, workers' health status, and equipment operation status using sensors installed at construction sites.

[0541] A "storage system" refers to a system that has the function of storing collected data in a central database and making it available for later analysis and processing.

[0542] The "artificial intelligence processing means" is a device that has the function of automatically generating a construction plan based on stored data and user sentiment data.

[0543] The "adjustment mechanism" is a system that detects changes in the external environment and the emotional instability of the user, and dynamically adjusts the construction plan based on these factors.

[0544] A "notification mechanism" is a system that provides users with a coordinated construction plan and supplementary information to aid their understanding.

[0545] A "learning tool" is a function that records construction results and user feedback, and uses this information to continuously improve the system in generating future plans.

[0546] An "emotion recognition device" is a device that analyzes the user's facial expressions and tone of voice to identify their emotional state.

[0547] As a specific embodiment for carrying out this invention, an example of a construction management system combined with an emotion engine is shown.

[0548] The terminals are installed at construction sites and collect real-time data using various sensors. Specifically, they measure weather conditions using temperature and humidity sensors, monitor the health of workers through vital signs sensors, and track the operation of equipment. All of this data is transmitted to a server using a secure protocol.

[0549] The server stores data in a central database and recognizes the user's emotions using an emotion engine. Emotion recognition is performed by capturing the user's facial expressions with the terminal's camera and recording their voice tone with a microphone. By integrating this emotion data and collected data using artificial intelligence processing, a construction plan is automatically generated. This plan is constructed in the most optimal form by referring to previously accumulated data and case studies.

[0550] Furthermore, the server monitors changes in the external environment and the user's emotional state. The construction plan is then dynamically adjusted in response to these changes. For example, when introducing a new construction method, if the server detects user anxiety, it provides the user with detailed guidelines and risk management measures. This notification not only deepens the user's understanding but also fosters a sense of security.

[0551] After construction is completed, the server records the construction results and user feedback in a central database. This information is used to generate the next construction plan, contributing to the overall improvement of the system. Users can then use the generated AI model to interact more interactively with the system through prompts such as, "Please suggest the optimal schedule for the next construction plan." In this way, user-centered construction management is achieved, leading to improved efficiency and a higher project success rate.

[0552] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0553] Step 1:

[0554] The terminal collects real-time data using sensors installed at the construction site. Inputs include temperature, humidity, worker heart rate, and equipment operating time. The collected data is processed through digital signal processing to ensure accuracy. As output, this data is ready to be sent to the server.

[0555] Step 2:

[0556] The device utilizes an emotion engine to recognize the user's emotional state. It captures the user's facial expressions with a camera and records their voice tone with a microphone. This data is used as input to identify the user's emotions using image processing and voice analysis algorithms. The user's emotional state is then quantified and sent to the server as output.

[0557] Step 3:

[0558] The server stores real-time data and sentiment data in a central database. Input consists of various data sent from terminals. The server writes the data to the database, ensuring all provided data is accessible. As output, the stored data becomes available for use in the next processing step.

[0559] Step 4:

[0560] The server generates a construction plan using artificial intelligence processing based on stored data. The input consists of real-time data from the database and user sentiment data. The server analyzes the data using a generation AI model and generates an optimal construction plan. The output is an optimized construction plan.

[0561] Step 5:

[0562] The server adjusts the construction plan, taking into account changes in the external environment and user sentiment. Inputs consist of real-time collected data and sentiment data. The server analyzes the new data and adjusts the plan accordingly. The output is a construction plan that has been adjusted as needed.

[0563] Step 6:

[0564] The server notifies the user of the adjusted construction plan and provides necessary guidelines and risk management measures. The input is the adjusted construction plan. The server sends information to the user using a notification system to help them understand the plan. The output is a state where the user can confidently implement the plan.

[0565] Step 7:

[0566] After construction is completed, the server records the construction results and user feedback in a database. Inputs include user feedback and construction results data. The server analyzes this data and uses it to improve future construction plans. The output provides feedback data useful for generating future plans.

[0567] (Application Example 2)

[0568] 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."

[0569] In recent years, improving efficiency in production sites and optimizing the working environment for workers have become important issues. However, there is a problem in that it is difficult to plan and adjust things while dynamically considering changes in the work environment and the emotional state of workers. As a result, there is a problem of decreased work efficiency and increased stress among workers.

[0570] 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.

[0571] In this invention, the server includes, as a data collection mechanism, means for collecting real-time data from detectors installed in the work environment, artificial intelligence processing means for automatically generating a work project plan using the stored data, and emotion analysis means for recognizing the emotional state of the user. This enables efficient work planning and operation adjustments that take into account changes in the external environment and the emotional state of the worker.

[0572] A "data collection mechanism" is a device that collects information in real time from detectors installed in the work environment.

[0573] A "central information repository" is a database used to aggregate and store collected data.

[0574] "Artificial intelligence processing means" refers to technology that analyzes stored data and automatically generates plans for work projects.

[0575] A "modification mechanism" is a system for detecting changes in the external environment and modifying the plan as needed.

[0576] "Notification means" refers to methods for informing users of the adjusted plan.

[0577] A "learning mechanism" is a system that relearns the results of a plan in order to utilize them in generating the next plan.

[0578] "Emotional analysis methods" are technologies that analyze a worker's facial expressions and voice to recognize their emotional state.

[0579] A "control mechanism" is a system that adjusts the movement of a machine based on the emotional state of the operator.

[0580] This system is implemented by robots installed within the factory. The robots collect workers' facial expressions and voices in real time through sensors such as cameras and microphones. This data is transmitted from the terminal to a server and stored in a central data repository. The server uses an emotion analysis model created with Python and TensorFlow to analyze the collected data and recognize the workers' emotional state.

[0581] Furthermore, the server controls the robot's movements using ROS (Robot Operating System). If the worker shows signs of stress, the server sends instructions to the robot to adjust the work speed or change to a simpler task. In this way, the system forms a feedback loop to optimize the work environment in real time. The collected data is also retrained for use in generating the next plan.

[0582] As a concrete example, for workers whose morning work schedules tend to be overcrowded, the robot could play a cheerful, humorous voice message and instruct them to slightly slow down their work speed. This function helps to boost the workers' morale and maintain efficiency. Furthermore, by using a prompt such as, "Please tell me how to analyze workers' emotions in real time and create an optimal robot action plan to alleviate stress in the factory," it becomes possible to more accurately coordinate emotion analysis and action planning.

[0583] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0584] Step 1:

[0585] The terminal collects facial and audio data of workers in real time through cameras and microphones installed in the work environment. The input is raw video and audio data, which is preprocessed to extract facial features and voice patterns. As a result, the data is sent to the server in a formatted state for analysis.

[0586] Step 2:

[0587] The server receives feature data sent from the terminal and inputs it into an emotion analysis model built with Python and TensorFlow. The model uses generative AI techniques to estimate the emotional state of the workers from the feature data and generates emotion labels such as "happy," "stressed," and "concentrated" as its output.

[0588] Step 3:

[0589] The server receives emotion labels and uses ROS (Robot Operating System) to adjust the robot's motion plan. When controlling the robot, it receives the analyzed emotion labels as input and generates corresponding motion commands. For example, if the worker is stressed, it will issue a command to slow down the robot's work speed.

[0590] Step 4:

[0591] The server sends the adjusted motion plan to the robot, which then begins its operation based on it. The input is the robot's motion command, and the output is the physical action based on that command. This action optimizes the work environment.

[0592] Step 5:

[0593] The server records the progress of the work and the history of sentiment labels in a central data repository, which is then used as training data for the next work plan. The input is the work results and sentiment history, and the output is an update to the learning model to improve the next work plan.

[0594] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0595] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0596] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0597] [Fourth Embodiment]

[0598] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0599] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0600] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0601] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0602] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0603] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0604] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0605] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0606] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0607] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0608] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0609] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0610] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0611] This invention provides a system for more efficient planning and management of construction projects. The system has the ability to accurately grasp the real-time situation at the construction site and automatically generate and adjust an appropriate construction plan based on that information.

[0612] The system collects real-time data from the field using a data acquisition method consisting of multiple sensors and devices. Terminals acquire weather information, worker health status, equipment operating status, etc., from the sensors and transmit this data to a central database. The server analyzes the acquired data and stores it in the central database.

[0613] Next, the server automatically generates a construction plan using artificial intelligence processing based on the stored data. This artificial intelligence continuously learns from past construction plans and implementation results, improving the accuracy of the plan by considering successful and unsuccessful examples. The server also monitors changes in the external environment in real time and identifies the factors that have an impact.

[0614] If the server determines that the plan needs to be adjusted due to external factors or other reasons, it will use adjustment mechanisms to update the current construction plan. For example, if there is a sudden change in weather during work, it will switch from outdoor work to indoor work to ensure work efficiency.

[0615] The updated construction plan is quickly communicated to users through notification channels. Users can use their devices to review the plan and understand the changes, enabling them to give precise instructions to on-site workers.

[0616] Furthermore, upon completion of construction, the server records the construction results back into the database, and the learning mechanism utilizes this data to create future plans. This feedback loop allows the AI's accuracy to improve over time, resulting in the provision of more optimal construction plans.

[0617] As a concrete example, this system is used from the planning stage in large-scale building construction projects. In the initial stages of construction, work proceeds according to the plan provided by the AI, but if a typhoon is expected to approach, the server immediately readjusts the plan based on that information and instructs workers to suspend any dangerous work. In this way, the system responds flexibly to fluctuating conditions and contributes to the smooth execution of the entire project.

[0618] The following describes the processing flow.

[0619] Step 1:

[0620] The terminal collects data in real time from various sensors placed at the construction site. This data includes weather information, workers' vital signs, and equipment operating status.

[0621] Step 2:

[0622] The terminal periodically sends the collected real-time data to a central server. Data transmission is carried out quickly and reliably via the communication network.

[0623] Step 3:

[0624] The server stores the received real-time data in a central database. Simultaneously, it retrieves BIM / CIM data from the database and creates the dataset necessary for generating construction plans.

[0625] Step 4:

[0626] The server utilizes generation AI to automatically generate an initial construction plan using stored data as input. The AI ​​optimizes the plan by comparing it with past successful and unsuccessful construction plans.

[0627] Step 5:

[0628] The server analyzes real-time data to determine if there are changes in the external environment or if emergency response is required. If changes are detected, the construction plan will need to be readjusted.

[0629] Step 6:

[0630] The server will use adjustment mechanisms to modify the construction plan if any influencing factors are identified. For example, it might predict bad weather and postpone outdoor work, switching the plan to indoor work.

[0631] Step 7:

[0632] The server notifies the user of the revised construction plan. The user receives this notification and can check the latest construction plan on their device.

[0633] Step 8:

[0634] The user communicates updates and instructions to on-site workers based on the latest construction plan. Workers who receive notifications are required to proceed with their work according to the new instructions.

[0635] Step 9:

[0636] After construction is completed, the server records the construction results data in a database. This data is used to improve the accuracy of future construction plans.

[0637] Step 10:

[0638] The server analyzes the saved construction results using learning methods and uses this information to improve the performance of the AI ​​model. This increases the accuracy of planning in similar scenarios.

[0639] (Example 1)

[0640] 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".

[0641] In recent years, there has been a growing demand for increased efficiency in construction site management, but traditional methods make it difficult to flexibly adjust plans in real time. Furthermore, there is a lack of means to automatically optimize construction plans while considering changes in the external environment and the health status of workers. As a result, decreased productivity and unexpected delays occur during project progress, posing a significant challenge.

[0642] 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.

[0643] In this invention, the server includes means for collecting real-time information using multiple detectors installed at the construction site, artificial intelligence processing means for automatically creating a construction plan using the stored information, and means for monitoring changes in the external environment in real time and dynamically modifying the construction work plan. This enables flexible and rapid adjustment of the construction plan in response to external conditions and the situation at the work site, allowing for safe and efficient work progress.

[0644] A "detector" is a device installed at a construction site to acquire real-time environmental information and work progress.

[0645] "Information" refers to data collected through detectors, including, for example, atmospheric conditions, the physical condition of workers, and the operating status of tools.

[0646] The "Central Records Unit" refers to a central database used to store and manage collected information.

[0647] "Artificial intelligence processing means" refers to technology that automatically creates construction plans using algorithms and models that have been schematicized from accumulated information.

[0648] "External environment" refers to environmental factors that affect work efficiency and safety, such as weather conditions and geographical conditions that impact the construction site.

[0649] A "notification system" refers to a mechanism for quickly communicating coordinated construction plans and important information to on-site workers and managers.

[0650] This invention is a system for streamlining the planning and management of construction projects, and is primarily implemented by servers, terminals, and users. The system collects real-time information from construction sites and automatically generates construction plans based on this data.

[0651] The terminal uses multiple detectors installed at the construction site to collect environmental information such as temperature, humidity, and wind speed, as well as the health status of workers and the operating status of heavy machinery, in real time. This information is transmitted securely to a central recording unit, or central database, via a stable communication module.

[0652] The server stores received information in a central recording unit and uses a generation AI model to automatically and efficiently create construction plans from that information. These construction plans take into account past construction results and success / failure cases, and the AI ​​continuously learns to achieve this. The server also monitors changes in the external environment in real time and dynamically modifies the construction plan as needed. This ensures maximum efficiency throughout the project.

[0653] The updated construction plan is communicated to users quickly and reliably. Users can review the new construction plan notified via their terminal and give appropriate instructions to on-site workers, thereby ensuring smooth progress of the work according to the plan.

[0654] As a concrete example, if a sudden change in weather is expected at a construction site, the server immediately readjusts the plan based on that information and notifies the user that external work will be suspended for safety reasons. This system flexibly responds to dynamic conditions and supports the efficient and safe execution of the entire project.

[0655] An example of a prompt message might be, "Please readjust the construction plan for the construction site, taking weather changes into consideration." Such prompts allow the system to generate an optimal construction plan and enable a quick response.

[0656] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0657] Step 1:

[0658] The terminal collects real-time environmental information using multiple detectors installed at the construction site. It receives inputs such as weather data (temperature, humidity, wind speed, etc.), worker health status, and heavy equipment operating status. This data is formatted appropriately and output to the central recording unit as transmission data. This step also includes error handling and retransmission functions to ensure the stability and security of data communication.

[0659] Step 2:

[0660] The server receives data transmitted from terminals and stores it in the central recording unit. It receives formatted environmental information as input and stores it in the database. This process includes data purification using algorithms that perform data integrity checks and anomaly detection. The output is data stored in an analyzable format.

[0661] Step 3:

[0662] The server uses a generation AI model based on data stored in the central recording unit to automatically generate construction plans. It references past construction data and real-time environmental information as input, and calculates the most efficient construction sequence based on this. Data calculations performed in this step include optimizing the project schedule and resource allocation. The generated construction plan is obtained as output.

[0663] Step 4:

[0664] The server monitors changes in the external environment in real time and adjusts the construction plan as needed. It receives newly acquired environmental information as input and determines whether it will affect the current construction plan. Based on this information, it dynamically rearranges the plan and generates specific instructions to minimize risk. The adjusted construction plan is output.

[0665] Step 5:

[0666] The server notifies the user of the revised construction plan. Using the latest construction plan information as input, it outputs the plan through a system that visualizes and provides important notifications in a concise and intuitive manner for the user. Notification methods include email and message transmission via a dedicated app.

[0667] Step 6:

[0668] The user reviews the received construction plan using a terminal and issues appropriate instructions to the on-site workers. The input involves formulating specific work instructions based on the notified information and creating an effective action plan to ensure safety and efficiency on site. The output is the instructions for the on-site workers. At this step, appropriate feedback is also sent back to the server, which is used for future planning.

[0669] (Application Example 1)

[0670] 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".

[0671] In modern manufacturing, the increasing complexity of the workplace and the growing demand for efficiency make it essential to accurately understand the work situation in real time and manage production schedules efficiently. However, traditional methods struggle to flexibly adjust plans to respond to environmental changes and unforeseen circumstances, leading to decreased production efficiency.

[0672] 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.

[0673] In this invention, the server includes means for collecting real-time information from detection devices installed at the work site, means for storing the information in a central data management device, means for utilizing the operating rate and energy consumption information of the production line, and means for automatically adjusting the production schedule according to the situation to maximize production efficiency. This makes it possible to generate and adjust flexible and efficient work plans in response to environmental changes.

[0674] A "data collection means" is a device that collects real-time information from detection devices installed at the work site.

[0675] "Storage means" refers to means for storing collected information in a central data management device.

[0676] "Artificial intelligence processing means" refers to means that use artificial intelligence to automatically generate a work plan using stored information.

[0677] "Adjustment means" are means for detecting changes in the external environment and adjusting the work plan as appropriate.

[0678] "Notification means" refers to means of notifying users of the adjusted work plan.

[0679] A "learning method" is a means of relearning the results of a task and using them to generate the next plan.

[0680] "Means for utilizing manufacturing line operating rates and energy consumption information" refers to means for collecting and managing information on the operating status of manufacturing lines and the energy consumed, and using this information to formulate efficient work plans.

[0681] "Methods for automatically adjusting production schedules and maximizing production efficiency" refers to methods for automatically adjusting schedules according to the situation to improve overall production efficiency.

[0682] In this embodiment of the invention, various detection devices installed at the work site collect information in real time and transmit it to a server. The server uses appropriate hardware, such as a Raspberry Pi, to store the acquired information in a central data management device. This enables real-time monitoring of the work site.

[0683] The server uses the Python programming language and TensorFlow to perform artificial intelligence processing based on stored information and generate efficient work plans. These plans are designed taking into account the operating rate and energy consumption of the manufacturing line, with the aim of maximizing work efficiency.

[0684] As a means of adjustment, the server has a built-in function that monitors changes in the external environment in real time and automatically adjusts the work plan as needed. This makes it possible to respond quickly to unexpected situations and environmental fluctuations.

[0685] The adjusted plan is quickly communicated to users via notification systems. Users can review the plan on their smartphones or devices and provide precise instructions to field workers.

[0686] For example, this system can be used to update plans so that, when demand for parts surges at a factory, the server automatically extends operating hours and increases inventory of the necessary parts. It can also issue instructions to use alternative equipment in the event of an unexpected equipment failure.

[0687] A concrete example is the prompt, "Tell me about an app that monitors the production line status in a factory in real time and automatically generates an optimal production plan." This allows the AI ​​model to generate a response or plan appropriate to the situation, enabling more efficient on-site management.

[0688] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0689] Step 1:

[0690] The terminal acquires data from various sensors installed at the work site. This data includes environmental conditions, equipment operating status, production line utilization rate, and energy consumption. The acquired data is temporarily stored on the terminal and then prepared for transmission to the server.

[0691] Step 2:

[0692] The server receives data transmitted from terminals and stores it in the central data management system. During this process, data formats are standardized and different information is integrated. This allows for the storage of real-time monitoring data from the work site.

[0693] Step 3:

[0694] The server analyzes the stored data using Python and TensorFlow. The analysis results include manufacturing line efficiency, energy consumption optimization, and equipment operating status. Based on this data, the server generates an optimal work plan. Here, an algorithm that has learned from past data using a generative AI model is applied, and future plans are automatically formulated.

[0695] Step 4:

[0696] The server adjusts the generated work plan based on external circumstances and unforeseen events. For example, it modifies the plan and responds flexibly to sudden increases in parts demand or unexpected machine failures. At this stage, prompt statements are used to process the necessary data and provide information for decision-making.

[0697] Step 5:

[0698] The server notifies the user of the adjusted work plan. The user then reviews the adjustments via smartphone or other device and relays specific instructions to the work site. The notified plan is summarized in a way that is easy for workers to understand.

[0699] Step 6:

[0700] Users resend data to the server based on feedback from the field. The server uses this data as a learning tool and incorporates it into future work plans. This allows the AI ​​model's accuracy to improve over time, enabling it to provide better plans.

[0701] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0702] This invention provides a construction management system that incorporates an emotion engine, thereby enabling more flexible and efficient construction planning that takes into account the user's emotional state. The following describes specific embodiments for carrying out this invention.

[0703] First, data collection devices installed at the construction site collect real-time data such as weather, workers' health status, and equipment operation status. Terminals transmit this data to a server and store it in a central database. In addition, an emotion engine is used to recognize the user's emotions. The emotion engine analyzes the user's facial expressions and tone of voice through sensors such as the terminal's camera and microphone to identify their emotional state.

[0704] The server uses artificial intelligence processing to integrate real-time data stored in the database with user emotion data and automatically generates an initial construction plan. This plan is optimized to reflect past data and the user's emotional state. It also monitors changes in the external environment and adjusts the construction plan as needed. If the emotion engine identifies that the user's emotions are unstable, the server enhances the user's sense of security by presenting the plan details and explanations in an easy-to-understand manner through notification mechanisms.

[0705] As an example, consider a situation where a user feels uneasy about introducing a new construction technique. In this case, the emotion engine detects the user's anxiety, and the server, accordingly, provides additional notifications of detailed guidelines and risk management measures to help the user understand the situation. As a result, the user can confidently adopt the new technology.

[0706] After construction is completed, the server records this construction result data and user feedback based on emotions in a database, which is then used to generate future construction plans. The introduction of an emotion engine enables user-centered construction management, improving construction efficiency and project success rates. Thus, this invention enables the automatic generation and appropriate adjustment of construction plans based on user emotions, resulting in more advanced construction management.

[0707] The following describes the processing flow.

[0708] Step 1:

[0709] The device collects real-time data from sensors installed at construction sites, including weather, workers' health status, and equipment operating status. It also records the user's facial expressions and voice through cameras and microphones, collecting data necessary for analyzing their emotional state.

[0710] Step 2:

[0711] The device transmits collected real-time data and sentiment data to the server. The data is transferred to the server quickly and securely via the communication network.

[0712] Step 3:

[0713] The server stores the received data in a central database. This creates a complete dataset that includes the current situation on site and the user's emotional state.

[0714] Step 4:

[0715] The server uses artificial intelligence processing to analyze data stored in the database and automatically generates an initial construction plan. This plan takes into account past construction data and user sentiment data.

[0716] Step 5:

[0717] The server monitors real-time data and assesses whether the external environment is affecting the plan. Adjustments to the construction plan are made as needed.

[0718] Step 6:

[0719] The emotion engine continuously evaluates the user's emotional state, and if signs of anxiety or stress are detected, the server adjusts the method and content of notifications regarding the construction plan to enhance the user's understanding and sense of security.

[0720] Step 7:

[0721] The server will notify the user of the revised construction plan. The notification will be provided along with a text message and detailed guidelines.

[0722] Step 8:

[0723] The user receives notifications and reviews the construction plan. If necessary, they communicate the plan details to the workers and direct the work on site.

[0724] Step 9:

[0725] Once construction is complete, the server records the construction results data and user sentiment feedback and saves it to a database. This information is used when generating the next construction plan.

[0726] Step 10:

[0727] The server uses the stored data for training and aims to improve the performance of the AI ​​model, thereby increasing the accuracy of the next construction plan and user satisfaction.

[0728] (Example 2)

[0729] 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".

[0730] Current construction management systems can only generate construction plans based on real-time site data, making it difficult to respond flexibly while considering the user's emotional state. Therefore, a challenge arises in that it can cause anxiety among users during the planning and execution phase, and adaptive adjustments are not possible.

[0731] 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.

[0732] In this invention, the server includes emotion recognition means for sensing the user's emotions, artificial intelligence processing means for automatically generating a construction plan using stored data and emotion data, and adjustment means for detecting changes in the external environment and the user's emotional state and adjusting the construction plan as appropriate. This makes it possible to generate and adjust a flexible and efficient construction plan that takes the user's emotions into consideration.

[0733] "Data collection means" refers to devices that have the function of collecting real-time data such as weather conditions, workers' health status, and equipment operation status using sensors installed at construction sites.

[0734] A "storage system" refers to a system that has the function of storing collected data in a central database and making it available for later analysis and processing.

[0735] The "artificial intelligence processing means" is a device that has the function of automatically generating a construction plan based on stored data and user sentiment data.

[0736] The "adjustment mechanism" is a system that detects changes in the external environment and the emotional instability of the user, and dynamically adjusts the construction plan based on these factors.

[0737] A "notification mechanism" is a system that provides users with a coordinated construction plan and supplementary information to aid their understanding.

[0738] A "learning tool" is a function that records construction results and user feedback, and uses this information to continuously improve the system in generating future plans.

[0739] An "emotion recognition device" is a device that analyzes the user's facial expressions and tone of voice to identify their emotional state.

[0740] As a specific embodiment for carrying out this invention, an example of a construction management system combined with an emotion engine is shown.

[0741] The terminals are installed at construction sites and collect real-time data using various sensors. Specifically, they measure weather conditions using temperature and humidity sensors, monitor the health of workers through vital signs sensors, and track the operation of equipment. All of this data is transmitted to a server using a secure protocol.

[0742] The server stores data in a central database and recognizes the user's emotions using an emotion engine. Emotion recognition is performed by capturing the user's facial expressions with the terminal's camera and recording their voice tone with a microphone. By integrating this emotion data and collected data using artificial intelligence processing, a construction plan is automatically generated. This plan is constructed in the most optimal form by referring to previously accumulated data and case studies.

[0743] Furthermore, the server monitors changes in the external environment and the user's emotional state. The construction plan is then dynamically adjusted in response to these changes. For example, when introducing a new construction method, if the server detects user anxiety, it provides the user with detailed guidelines and risk management measures. This notification not only deepens the user's understanding but also fosters a sense of security.

[0744] After construction is completed, the server records the construction results and user feedback in a central database. This information is used to generate the next construction plan, contributing to the overall improvement of the system. Users can then use the generated AI model to interact more interactively with the system through prompts such as, "Please suggest the optimal schedule for the next construction plan." In this way, user-centered construction management is achieved, leading to improved efficiency and a higher project success rate.

[0745] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0746] Step 1:

[0747] The terminal collects real-time data using sensors installed at the construction site. Inputs include temperature, humidity, worker heart rate, and equipment operating time. The collected data is processed through digital signal processing to ensure accuracy. As output, this data is ready to be sent to the server.

[0748] Step 2:

[0749] The device utilizes an emotion engine to recognize the user's emotional state. It captures the user's facial expressions with a camera and records their voice tone with a microphone. This data is used as input to identify the user's emotions using image processing and voice analysis algorithms. The user's emotional state is then quantified and sent to the server as output.

[0750] Step 3:

[0751] The server stores real-time data and sentiment data in a central database. Input consists of various data sent from terminals. The server writes the data to the database, ensuring all provided data is accessible. As output, the stored data becomes available for use in the next processing step.

[0752] Step 4:

[0753] The server generates a construction plan using artificial intelligence processing based on stored data. The input consists of real-time data from the database and user sentiment data. The server analyzes the data using a generation AI model and generates an optimal construction plan. The output is an optimized construction plan.

[0754] Step 5:

[0755] The server adjusts the construction plan, taking into account changes in the external environment and user sentiment. Inputs consist of real-time collected data and sentiment data. The server analyzes the new data and adjusts the plan accordingly. The output is a construction plan that has been adjusted as needed.

[0756] Step 6:

[0757] The server notifies the user of the adjusted construction plan and provides necessary guidelines and risk management measures. The input is the adjusted construction plan. The server sends information to the user using a notification system to help them understand the plan. The output is a state where the user can confidently implement the plan.

[0758] Step 7:

[0759] After construction is completed, the server records the construction results and user feedback in a database. Inputs include user feedback and construction results data. The server analyzes this data and uses it to improve future construction plans. The output provides feedback data useful for generating future plans.

[0760] (Application Example 2)

[0761] 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".

[0762] In recent years, improving efficiency in production sites and optimizing the working environment for workers have become important issues. However, there is a problem in that it is difficult to plan and adjust things while dynamically considering changes in the work environment and the emotional state of workers. As a result, there is a problem of decreased work efficiency and increased stress among workers.

[0763] 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.

[0764] In this invention, the server includes, as a data collection mechanism, means for collecting real-time data from detectors installed in the work environment, artificial intelligence processing means for automatically generating a work project plan using the stored data, and emotion analysis means for recognizing the emotional state of the user. This enables efficient work planning and operation adjustments that take into account changes in the external environment and the emotional state of the worker.

[0765] A "data collection mechanism" is a device that collects information in real time from detectors installed in the work environment.

[0766] A "central information repository" is a database used to aggregate and store collected data.

[0767] "Artificial intelligence processing means" refers to technology that analyzes stored data and automatically generates plans for work projects.

[0768] A "modification mechanism" is a system for detecting changes in the external environment and modifying the plan as needed.

[0769] "Notification means" refers to methods for informing users of the adjusted plan.

[0770] A "learning mechanism" is a system that relearns the results of a plan in order to utilize them in generating the next plan.

[0771] "Emotional analysis methods" are technologies that analyze a worker's facial expressions and voice to recognize their emotional state.

[0772] A "control mechanism" is a system that adjusts the movement of a machine based on the emotional state of the operator.

[0773] This system is implemented by robots installed within the factory. The robots collect workers' facial expressions and voices in real time through sensors such as cameras and microphones. This data is transmitted from the terminal to a server and stored in a central data repository. The server uses an emotion analysis model created with Python and TensorFlow to analyze the collected data and recognize the workers' emotional state.

[0774] Furthermore, the server controls the robot's movements using ROS (Robot Operating System). If the worker shows signs of stress, the server sends instructions to the robot to adjust the work speed or change to a simpler task. In this way, the system forms a feedback loop to optimize the work environment in real time. The collected data is also retrained for use in generating the next plan.

[0775] As a concrete example, for workers whose morning work schedules tend to be overcrowded, the robot could play a cheerful, humorous voice message and instruct them to slightly slow down their work speed. This function helps to boost the workers' morale and maintain efficiency. Furthermore, by using a prompt such as, "Please tell me how to analyze workers' emotions in real time and create an optimal robot action plan to alleviate stress in the factory," it becomes possible to more accurately coordinate emotion analysis and action planning.

[0776] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0777] Step 1:

[0778] The terminal collects facial and audio data of workers in real time through cameras and microphones installed in the work environment. The input is raw video and audio data, which is preprocessed to extract facial features and voice patterns. As a result, the data is sent to the server in a formatted state for analysis.

[0779] Step 2:

[0780] The server receives feature data sent from the terminal and inputs it into an emotion analysis model built with Python and TensorFlow. The model uses generative AI techniques to estimate the emotional state of the workers from the feature data and generates emotion labels such as "happy," "stressed," and "concentrated" as its output.

[0781] Step 3:

[0782] The server receives emotion labels and uses ROS (Robot Operating System) to adjust the robot's motion plan. When controlling the robot, it receives the analyzed emotion labels as input and generates corresponding motion commands. For example, if the worker is stressed, it will issue a command to slow down the robot's work speed.

[0783] Step 4:

[0784] The server sends the adjusted motion plan to the robot, which then begins its operation based on it. The input is the robot's motion command, and the output is the physical action based on that command. This action optimizes the work environment.

[0785] Step 5:

[0786] The server records the progress of the work and the history of sentiment labels in a central data repository, which is then used as training data for the next work plan. The input is the work results and sentiment history, and the output is an update to the learning model to improve the next work plan.

[0787] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0788] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0789] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0790] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0791] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0792] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0793] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0794] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0795] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0796] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0797] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0798] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0799] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0800] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0801] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0802] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0803] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0804] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0805] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0806] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0807] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0808] The following is further disclosed regarding the embodiments described above.

[0809] (Claim 1)

[0810] As a data collection method, a device that collects real-time data from sensors installed at the construction site,

[0811] A storage means for storing data from the above-mentioned device in a central database,

[0812] An artificial intelligence processing method that automatically generates construction plans for construction projects using stored data,

[0813] An adjustment mechanism that detects changes in the external environment and adjusts the construction plan accordingly,

[0814] A notification means for informing the user of the adjusted construction plan,

[0815] A learning method that uses the construction results to retrain the system and utilize them in generating the next plan,

[0816] A system that includes this.

[0817] (Claim 2)

[0818] The system according to claim 1, wherein the real-time data acquired by the data acquisition means includes weather, worker health status, and equipment operating status.

[0819] (Claim 3)

[0820] The system according to claim 1, wherein the adjustment of the construction plan is based on past success and failure patterns.

[0821] "Example 1"

[0822] (Claim 1)

[0823] A means of collecting real-time information using multiple detectors installed at a construction site,

[0824] A means for storing the information obtained by the above collection means in a central recording unit,

[0825] An artificial intelligence processing means that automatically creates a construction project plan using stored information,

[0826] A means of monitoring changes in the external environment in real time and dynamically modifying the construction work plan,

[0827] A means of promptly notifying users of the revised construction plan,

[0828] A means of learning from construction results again and using them to create future plans,

[0829] A system that includes this.

[0830] (Claim 2)

[0831] The system according to claim 1, wherein the real-time information acquired by the data acquisition means includes atmospheric conditions, the physical condition of the workers, and the operating status of the tools.

[0832] (Claim 3)

[0833] The system according to claim 1, wherein the revision of the construction work plan is based on past successes and failures.

[0834] "Application Example 1"

[0835] (Claim 1)

[0836] As a means of data collection, a device that collects real-time information from a detection device installed at the work site,

[0837] A storage means for storing information from the above-mentioned device in a central data management device,

[0838] An artificial intelligence processing means that automatically generates a work plan using stored information,

[0839] An adjustment mechanism that detects changes in the external environment and adjusts the work plan accordingly,

[0840] A notification means for informing users of the adjusted work plan,

[0841] A learning method that uses the results of the work to retrain and utilize them in generating the next plan,

[0842] A means of utilizing real-time data including the operating rate and energy consumption information of the manufacturing line,

[0843] A means to automatically adjust the production schedule according to the situation and maximize production efficiency,

[0844] A system that includes this.

[0845] (Claim 2)

[0846] The system according to claim 1, wherein the real-time information acquired by the data collection means includes environmental conditions, worker health status, equipment operating status, production line operating rate, and energy consumption.

[0847] (Claim 3)

[0848] The system according to claim 1, wherein the adjustment of the work plan is based on past success and failure patterns, and production efficiency is improved by automatically adjusting the production schedule according to the situation.

[0849] "Example 2 of combining an emotion engine"

[0850] (Claim 1)

[0851] A data collection method that collects real-time data from sensors installed at a construction site,

[0852] A storage means for saving the above data to a central database,

[0853] An artificial intelligence processing method that automatically generates a construction plan using stored data and user sentiment data,

[0854] An adjustment mechanism that detects changes in the external environment and the emotional state of the user, and adjusts the construction plan accordingly,

[0855] A notification mechanism to inform users of the adjusted construction plan and provide additional information to aid their understanding,

[0856] A learning method that records construction results and user feedback and uses them to generate future plans,

[0857] A means of sensing the user's emotions,

[0858] A system that includes this.

[0859] (Claim 2)

[0860] The system according to claim 1, wherein the real-time data acquired by the data acquisition means includes weather conditions, the health status of workers, and the operating status of equipment, and the emotion recognition means analyzes the user's facial expressions and tone of voice.

[0861] (Claim 3)

[0862] The system according to claim 1, wherein the adjustment of the construction plan is based on past success and failure patterns and user sentiment data.

[0863] "Application example 2 when combining with an emotional engine"

[0864] (Claim 1)

[0865] The data collection mechanism includes a device that collects real-time data from detectors installed in the work environment,

[0866] A storage means for storing data from the above-mentioned device in a central information storage facility,

[0867] An artificial intelligence processing means that automatically generates a work project plan using stored data,

[0868] A means of adjusting the plan as needed by detecting changes in the external environment,

[0869] A notification method for informing users of the adjusted plan,

[0870] A learning method that uses the results of the plan to be retrained and utilized in the generation of the next plan,

[0871] An emotion analysis method for recognizing the emotional state of the user,

[0872] A control means that adjusts the operation of a work machine based on the emotional state of the worker,

[0873] A system that includes this.

[0874] (Claim 2)

[0875] The system according to claim 1, wherein the real-time data acquired by the data collection mechanism includes weather conditions, the health status of workers, and the operating status of equipment.

[0876] (Claim 3)

[0877] The system according to claim 1, wherein the adjustment of the plan is based on past success and failure patterns and further takes into account the emotional state of the workers. [Explanation of Symbols]

[0878] 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. As a data collection method, a device that collects real-time data from sensors installed at the construction site, A storage means for storing data from the above-mentioned device in a central database, An artificial intelligence processing method that automatically generates construction plans for construction projects using stored data, An adjustment mechanism that detects changes in the external environment and adjusts the construction plan accordingly, A notification means for informing the user of the adjusted construction plan, A learning method that uses the construction results to retrain the system and utilize them in generating the next plan, A system that includes this.

2. The system according to claim 1, wherein the real-time data acquired by the data acquisition means includes weather, worker health status, and equipment operating status.

3. The system according to claim 1, wherein the adjustment of the construction plan is based on past success and failure patterns.

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