system

Abstract

Provide a system. [Solution] A means of collecting basic information about the target structure; a means for calculating the age of the structure of interest; A means for calculating an average condition score for the target structure from past inspection results; means for calculating the expected lifespan of the target structure based on the average condition score; a means for evaluating the safety of the target structure by comparing the most recent inspection score with a safety threshold; means for determining the need for repairs based on the results of the safety assessment and generating repair proposals; A system including:

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Here is the original draft.

[0005] The infrastructure built in large quantities during the period of rapid economic growth is aging, increasing the risk of collapse due to natural disasters such as earthquakes and typhoons. Therefore, to protect human lives and the infrastructure of daily life, it is essential to predict lifespan, assess safety, and propose optimal repairs. However, conventional methods involve collecting, analyzing, evaluating, and proposing data separately, which is inefficient and makes it difficult to make comprehensive decisions. Furthermore, the need for repairs cannot be intuitively grasped, which can lead to delays in the timing of repairs. A system that can solve these problems and enable effective management and maintenance of aging infrastructure is needed. [Means for solving the problem]

[0006] The present invention provides a system including: means for collecting basic information about a target structure and calculating the structure's age; means for calculating an average condition score from past inspection results and calculating the structure's expected lifespan based on the average condition score; means for evaluating the safety of the structure by comparing the latest inspection score with a safety threshold; and means for determining the need for repairs and generating repair proposals based on the results of the safety evaluation. This system can receive the latest inspection scores of the target structure, compare them with the safety threshold to evaluate safety, and determine whether the structure is safe based on the evaluation results. The system also calculates the expected lifespan as a function of the reference lifespan and the average condition score. In this way, comprehensive data collection, analysis, evaluation, and proposals are performed in an integrated manner, thereby achieving effective management and maintenance of aging infrastructure.

[0007] "Structures" refers to built infrastructure such as roads, bridges, sewers, and utilities.

[0008] "Basic information" refers to basic data about the structure, such as the structure's name, construction date, previous inspection dates, and inspection score.

[0009] "Means for calculating age" refers to a method or device for calculating the number of years that have elapsed between the construction date of a structure and the present date.

[0010] "Inspection Results" refers to scores or data resulting from tests or observations conducted to assess the condition of a structure.

[0011] "Average condition score" refers to the average score indicating the condition of a structure calculated from past inspection results.

[0012] "Expected life" refers to the predicted number of years a structure is expected to remain usable, based on factors such as the average condition score.

[0013] "Safety threshold" refers to the standard score that a structure must meet to be considered safe.

[0014] "Means for assessing safety" refers to a method or device for comparing the most recent inspection score with a safety threshold and assessing whether the structure is safe.

[0015] "Means for determining the need for repairs" means a method or device for determining whether a structure requires repairs based on the results of a safety assessment.

[0016] A "repair proposal" refers to a proposal that presents the content and timing of repairs that should be made to a structure based on the results of a safety assessment.

[0017] "Data Collection Request" means a request sent to collect basic information and inspection results about a structure.

[0018] "Calculating the age" refers to the act of calculating the number of years that have elapsed between the date of construction of a structure and the present date.

[0019] "Getting an inspection score" refers to the act of retrieving the latest inspection score from the saved inspection results.

[0020] "Generating a repair proposal" refers to the act of determining the need for repairs and then creating a proposal that specifies the specific repair content and method. [Brief explanation of the drawings]

[0021] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5]FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0022] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0023] First, the terms used in the following description will be explained.

[0024] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0025] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0026] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0027] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0028] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0029] [First embodiment]

[0030] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0031] 1, a 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.

[0032] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0033] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0034] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0035] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0036] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0037] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0039] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0040] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0041] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0042] This invention is a system that comprehensively manages and evaluates the condition of aging structures and proposes appropriate repairs. This system functions mainly through a server, terminals, and users.

[0043] First, the server collects basic information about the structure. The user uses a terminal to input the structure's name, construction date, past inspection dates, and the inspection result scores, etc. This data is sent to the server and stored in the server's database.

[0044] The server then uses this data to calculate the age of the structure. The method used to calculate the age is to calculate the difference between the current date and the construction date of the structure and convert it to years. For example, a bridge built in 1965 would be approximately 58 years old in 2023.

[0045] The server then calculates an average condition score based on past inspection scores. If the past three inspection results were 80, 75, and 60 points, respectively, the average condition score would be (80 + 75 + 60) / 3 = 71.67 points. Based on this average condition score, the server calculates the expected lifespan of the structure. If the base lifespan is 50 years and the maximum score is 100, a score of 71.67 points means that the expected lifespan is 71.67%, or 35.83 years.

[0046] The server receives the latest inspection score and compares it with a safety threshold (for example, 70 points). Based on this comparison, the server evaluates whether the structure is currently safe. For example, if the latest inspection score is 60 points, it is below the safety threshold of 70 points and is therefore judged to be "unsafe."

[0047] Based on the results of this safety assessment, the server determines whether repairs are necessary. If the assessment result is "unsafe," the server determines that immediate repairs are necessary and generates specific repair proposals. For example, a proposal such as "City Bridge needs immediate repair" may be generated. Users can receive these proposals via their devices and create appropriate repair plans.

[0048] This system enables comprehensive management of aging structures, reducing risks from natural disasters and enabling the development of efficient repair plans. As a concrete example, consider a bridge built in 1965 that received scores of 80, 75, and 60 in three previous inspections (2000, 2010, and 2020). The system operates based on this data, calculating the bridge's current age, calculating its expected lifespan, and conducting a safety assessment, before generating a recommendation for immediate repairs.

[0049] The processing flow will be explained below.

[0050] Step 1:

[0051] The server sends a request to collect data on the structure. The user uses a terminal to input the structure's name, construction date, past inspection dates, and inspection result scores. The terminal sends this information to the server. The server stores the received data in a database.

[0052] Step 2:

[0053] The server calculates the current age of a structure based on the stored data. For example, the server compares the construction date of the structure with the current date and converts the difference into years. For example, a bridge built on June 1, 1965, will be approximately 58 years old on June 1, 2023.

[0054] Step 3:

[0055] The server calculates the average condition score based on past inspection scores. Specifically, it adds up the past inspection scores (for example, 80, 75, and 60 points) and divides the total by the number of inspections. This calculates the average condition score. In this case, it is (80 + 75 + 60) / 3 = 71.67 points.

[0056] Step 4:

[0057] The server calculates the expected lifespan based on the average health score. The reference lifespan is assumed to be 50 years, and the average health score is calculated based on a percentage of 100. For example, if the average health score is 71.67 points, that is 71.67% of the reference lifespan, and the expected lifespan is 35.83 years.

[0058] Step 5:

[0059] The server receives the latest inspection score. Let's say the latest inspection score is 60 points. The server compares this with the safety threshold and evaluates whether it is safe or not.

[0060] Step 6:

[0061] The server compares the safety threshold and evaluates the safety of the structure. For example, if the safety threshold is set at 70 points, a score of 60 will be evaluated as "unsafe."

[0062] Step 7:

[0063] The server determines the need for repairs based on the results of the safety assessment. If the assessment result is "unsafe," the server determines that immediate repairs are required.

[0064] Step 8:

[0065] The server generates a repair proposal. Specifically, it summarizes the reasons why repairs are necessary and the recommended repair methods. For example, a proposal such as "City Bridge needs immediate repair" is generated.

[0066] Step 9:

[0067] The user receives the repair proposal through the terminal. The terminal receives the repair proposal from the server and displays it to the user. The user can then create a repair plan based on the displayed repair proposal.

[0068] These are the specific processing steps of this system, which will enable efficient and comprehensive management and maintenance of aging structures.

[0069] Example 1

[0070] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0071] The lack of a system for comprehensively managing safety assessments and repair needs for aging structures is an issue. In particular, there is a need for efficient calculations of the expected lifespan of structures based on inspection results, safety assessments, and repair proposals.

[0072] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0073] In this invention, the server includes means for collecting basic information about the target structure, means for calculating the age of the target structure, means for calculating an average condition score for the target structure from past inspection results, means for calculating an expected lifespan of the target structure based on the average condition score, means for evaluating the safety of the target structure by comparing the latest inspection score with a safety threshold, means for determining the need for repairs based on the results of the safety evaluation and generating repair proposals, and means for transmitting the generated repair proposals to a user's terminal so that the user can confirm the proposals. This enables comprehensive management of aging structures and the development of efficient repair plans.

[0074] "Structures" are artificially constructed objects such as buildings, bridges, and roads.

[0075] "Basic information" refers to basic data about the structure, such as the structure's name, construction date, location, materials, and design specifications.

[0076] "Age" refers to the period in years between the date of construction of the structure and the present date.

[0077] "Inspection results" are numerical data regarding the condition and performance of a structure collected through inspection or investigation.

[0078] The "average condition score" is a numerical value that evaluates the overall condition of a structure, calculated from past inspection results.

[0079] "Expected life" is the period, in years, that a structure is expected to continue performing as designed.

[0080] A "safety threshold" is the minimum score that a structure must meet to be considered safe.

[0081] A "repair proposal" is a proposal regarding specific repair content and plans that is generated when it is determined that repairs to a structure are necessary based on a safety assessment.

[0082] A "user" is an entity that operates the system, inputs information about the structure, and checks repair proposals.

[0083] "Terminal" refers to a device used by a user to operate the system, including a personal computer or smartphone.

[0084] The "server" is a computer that performs the central processing of this system and has the functions of collecting, storing, calculating, and evaluating data.

[0085] This invention is a system that comprehensively manages and evaluates the condition of aging structures and proposes appropriate repairs. This system functions mainly through a server, terminals, and users. The detailed configuration and specific operation of the system are explained below.

[0086] System configuration

[0087] 1. Hardware Configuration

[0088] Server: A central data processing and storage computer, such as an AWS (registered trademark) EC2 instance or an on-premise server.

[0089] Terminal: A device that allows users to input information about a structure and check repair proposals. This includes personal computers (PCs) and smartphones.

[0090] Network: Infrastructure for sending and receiving data between terminals and servers, using the Internet or dedicated lines.

[0091] 2. Software Configuration

[0092] Database: A system for storing information about the structure and inspection results. For example, a relational database such as MySQL (registered trademark) or PostgreSQL is used.

[0093] AI model: A model for generating repair suggestions. A generative AI model (e.g., GPT-3 (registered trademark)) is used.

[0094] Programming language and framework: The programs that run on the server are written in Python, JavaScript (registered trademark) (Node.js), or similar, and use a web framework (e.g., Django or Express).

[0095] System Operation

[0096] Data collection

[0097] The user inputs basic information about the structure via the terminal, specifically the structure name, construction date, past inspection dates, and inspection score into the form, and then presses the submit button.

[0098] Data transmission and storage

[0099] The terminal sends the data entered by the user to the server using a secure HTTPS request.

[0100] The server stores the received data in a database, which stores a record for each structure.

[0101] Age Calculator

[0102] The server calculates the age of a structure by subtracting the current date from its construction date stored in the database. For example, a structure built in 1965 will be 58 years old in 2023.

[0103] Calculating the average condition score

[0104] The server calculates the average condition score based on the past inspection scores by dividing the total score by the number of inspections.

[0105] Expected life calculation

[0106] The server calculates the expected lifespan by comparing the calculated average condition score with the reference lifespan. For example, if the reference lifespan is 50 years, a score of 71.67 points means the expected lifespan is 35.83 years.

[0107] Safety evaluation

[0108] The server compares the latest inspection score with the safety threshold and evaluates whether it is safe. For example, if the latest score is 60 and the threshold is 70, it is evaluated as unsafe.

[0109] Generate repair proposals

[0110] The server generates repair proposals based on safety assessments. It uses a generative AI model to generate specific repair proposals. For example, it generates a proposal that reads, "City Bridge needs immediate repair."

[0111] Example prompts for generating repair proposals:

[0112] Generate repair recommendations based on basic structure information: age, average condition score, expected lifespan, and safety rating.

[0113] data:

[0114] Structure name: Structure A

[0115] Construction date: 1965

[0116] Past inspection dates and scores:

[0117] 2000: 80 points

[0118] 2010: 75 points

[0119] 2020: 60 points

[0120] Latest inspection score: 60 points

[0121] Safety threshold: 70 points

[0122] Standard life: 50 years

[0123] Proposal Notification

[0124] The server sends the generated repair proposal to the user's device, where the user can review the proposal and create an appropriate repair plan.

[0125] In this way, the system can comprehensively manage information on aging structures and make efficient repair proposals.

[0126] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0127] Step 1:

[0128] The user uses the terminal to input basic information about the structure, including the structure name, construction date, past inspection dates, and inspection score. The input information is then sent to the server by pressing the send button.

[0129] Input: Structure name, construction date, past inspection dates, and inspection score

[0130] Output: The input data is sent to the server

[0131] Step 2:

[0132] The device sends the data entered by the user to the server using a secure HTTPS request.

[0133] Input: Basic information of the structure entered by the user

[0134] Output: Data sent to the server in the HTTPS request

[0135] Step 3:

[0136] The server stores the received data in a database, for example using SQLAlchemy to store the data in a relational database (e.g. MySQL or PostgreSQL).

[0137] Input: Basic information in the structure sent from the terminal

[0138] Output: Basic information about the structure stored in the database

[0139] Step 4:

[0140] The server calculates the age of the structure by retrieving the construction date from the database and calculating the difference between that and the current date, using the Python datetime library for this calculation.

[0141] Input: Construction date retrieved from the database

[0142] Output: Age of the structure (in years)

[0143] Specific operation: The server gets the current date and calculates the age by calculating the difference from the construction date.

[0144] Step 5:

[0145] The server calculates an average condition score based on the past inspection scores by dividing the total score by the number of inspections.

[0146] Input: Past inspection scores retrieved from the database

[0147] Output: Mean condition score

[0148] Specific operation: The server calculates the past inspection scores and divides the total by the number of inspections.

[0149] Step 6:

[0150] The server calculates the expected lifespan based on the average condition score, and calculates the lifespan as a percentage of the score based on a standard lifespan of 50 years.

[0151] Input: average condition score, reference lifespan

[0152] Output: Expected Life (in years)

[0153] Specific behavior: Expected lifespan = Reference lifespan (Average condition score / 100)

[0154] Step 7:

[0155] The server compares the latest inspection score with the safety threshold to evaluate safety. If it is below the threshold, it is judged to be "unsafe."

[0156] Input: Latest inspection score, safety threshold

[0157] Output: Safety assessment results

[0158] Specific operation: The server compares the latest inspection score with the threshold and performs an evaluation.

[0159] Step 8:

[0160] The server generates repair proposals based on the safety assessment results, using a generative AI model to create specific proposals.

[0161] Input: Safety assessment results, basic structure information

[0162] Output: Repair proposal

[0163] Specific actions: Generate prompts for the AI ​​model and generate repair suggestions

[0164] Example prompt sentence:

[0165] Generate repair recommendations based on basic structure information: age, average condition score, expected lifespan, and safety rating.

[0166] data:

[0167] Structure name: Structure A

[0168] Construction date: 1965

[0169] Past inspection dates and scores:

[0170] 2000: 80 points

[0171] 2010: 75 points

[0172] 2020: 60 points

[0173] Latest inspection score: 60 points

[0174] Safety threshold: 70 points

[0175] Standard life: 50 years

[0176] Step 9:

[0177] The server transmits the generated repair proposal to the user's terminal.

[0178] Input: Generated repair proposal

[0179] Output: Repair suggestions sent to the user's device

[0180] Specific behavior: Sends an HTTPS response containing repair suggestions to the user's device.

[0181] Step 10:

[0182] The user checks the proposals via the terminal and creates a repair plan.

[0183] Input: Repair proposal sent from the server

[0184] Output: Repair suggestions confirmed by the user

[0185] Specific behavior: The user checks the repair proposal on the device and contacts the repair company if necessary.

[0186] (Application example 1)

[0187] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0188] Managing aging structures involves a wide range of tasks, including maintenance inspections, repairs, and maintenance plan development. Conventional management systems make it difficult to comprehensively evaluate the condition of structures, making efficient management particularly difficult in locations with many structures, such as logistics centers. Furthermore, repair proposals cannot be received immediately based on inspection results, which can increase risk. There is a need to solve these issues, efficiently grasp the condition of structures in real time, and make appropriate repair proposals.

[0189] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0190] In this invention, the server includes means for collecting basic information about the structure, means for calculating the age of the structure, means for calculating an average condition score of the structure from past inspection results, means for calculating an expected lifespan of the structure based on the average condition score, means for evaluating the safety of the structure by comparing the latest inspection score with a safety threshold, means for determining the need for repairs based on the results of the safety evaluation and generating repair proposals, and means for notifying the user of the repair proposals in real time via a wearable device. This makes it possible to grasp the condition of structures in a logistics center in real time and quickly receive appropriate repair proposals.

[0191] "Structure" is a general term for physical structures that are used for a long period of time, such as bridges and buildings.

[0192] "Basic information" refers to the initial data required for evaluating and managing a structure, such as the structure's name, construction date, past inspection dates and the inspection result scores.

[0193] "Age" means the number of years that have passed since the structure was constructed.

[0194] "Inspection Results" means the scores or ratings recorded as a result of an inspection conducted to assess the condition of a structure.

[0195] The "average condition score" is an average value that indicates the overall condition of a structure, calculated based on the scores of past inspection results.

[0196] "Expected life" is the period during which a structure can be safely used, calculated based on the reference life and taking into account the average condition score.

[0197] A "safety threshold" is a standard inspection score set to determine the safety of a structure.

[0198] "Evaluation" refers to the process of making a comprehensive judgment on the current safety of a structure based on the latest inspection scores.

[0199] A "repair proposal" is a specific recommendation or plan for repair or reinforcement based on the condition of a structure and the results of its assessment.

[0200] A "wearable device" is a device such as smart glasses or a head-mounted display that provides real-time information when worn by a user.

[0201] "Real-time notification" refers to the process or functionality that enables users to receive information immediately.

[0202] The "server" is a central computer system that stores and processes data about the structure and exchanges data with users' terminals and wearable devices.

[0203] This invention is a system that comprehensively manages the status of structures in a logistics center and makes appropriate repair proposals. It functions mainly through a server, terminals, and users. The details of this system are described below.

[0204] First, the server collects basic information about the structure. The user uses a terminal to input the structure's name, construction date, past inspection dates, and the inspection result scores, etc. This data is sent to the server and stored in the server's database.

[0205] The server then uses this data to calculate the age of the structure. The method used to calculate the age is to calculate the difference between the current date and the construction date of the structure and convert it to years. For example, a warehouse built in 2000 will be approximately 23 years old in 2023.

[0206] The server then calculates an average condition score based on past inspection scores. If the past three inspection results were 85, 80, and 75 points, respectively, the average condition score would be (85 + 80 + 75) / 3 = 80 points. Based on this average condition score, the server calculates the expected lifespan of the structure. If the standard lifespan is 50 years and the maximum score is 100, a score of 80 means that the expected lifespan is 80%, which is calculated as 40 years.

[0207] The server receives the latest inspection score and compares it with a safety threshold (for example, 70 points). Based on this comparison, the server evaluates whether the structure is currently safe. For example, if the latest inspection score is 65 points, it is below the safety threshold of 70 points and is therefore judged to be "unsafe."

[0208] Based on the results of this safety assessment, the server determines whether repairs are necessary. If the assessment result is "unsafe," the server determines that immediate repairs are necessary and generates specific repair proposals. For example, a proposal such as "Warehouse A requires immediate repairs" may be generated. The user receives these proposals in real time through the smart glasses and can create an appropriate repair plan.

[0209] This system enables comprehensive management of aging structures within logistics centers and allows for the development of efficient repair plans. As a specific example, consider a warehouse that was built in 2000 and recorded scores of 85, 80, and 75 points in three previous inspections (2015, 2018, and 2021). The server operates based on this data, calculates the warehouse's current age, calculates its expected lifespan, performs a safety assessment, and then generates effective repair proposals.

[0210] The hardware used is smart glasses worn by the user (e.g., RealWear HMT-1), and the software uses the Django framework and SQLite database on the server side, allowing users to grasp the status of structures in the logistics center in real time and take prompt action.

[0211] Example prompt sentence:

[0212] "Enter the results of the last three inspections and construction dates of structures (warehouses, mobile vehicles, conveyor systems, etc.) within a distribution center, and generate the structure's age, average condition score, safety rating, and repair recommendations. For example, if a warehouse was built in 2000 and the results of the last three inspections were 85, 80, and 75, show the specific output."

[0213] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0214] Step 1:

[0215] The user uses a terminal to input basic information about the structure (such as the name, construction date, past inspection dates and the inspection result scores), and the input data is sent to the server.

[0216] Input: Name of structure, construction date, past inspection dates, and score for each inspection date

[0217] Output: Basic information about the structure sent to the server

[0218] Specific operation: The user uses a device (smartphone or tablet) to enter basic information about the structure into the input form and presses the submit button.

[0219] Step 2:

[0220] The server stores the received basic information about the structure in a database.

[0221] Input: Basic information about the structure sent by the user

[0222] Output: Basic information about the structure stored in a database

[0223] Specific operation: The server analyzes the received data and executes an INSERT query to save it in the database (SQLite).

[0224] Step 3:

[0225] The server calculates the age of the structure using the difference between the current date and the structure's construction date.

[0226] Input: Construction date of the structure, current date

[0227] Output: Age of structure

[0228] What it does: The server gets the current date and calculates the age of the structure by calculating the difference in years from the construction date.

[0229] Step 4:

[0230] The server calculates the average condition score of the structure from past inspection results.

[0231] Input: Score for each inspection date

[0232] Output: Mean condition score

[0233] Specific operation: The server retrieves past inspection scores from the database and calculates their average.

[0234] Step 5:

[0235] The server calculates the expected lifespan of the structure based on the average condition score. The reference lifespan is set to 50 years, and the expected lifespan is calculated in proportion to the average condition score.

[0236] Input: Reference lifespan, average condition score

[0237] Output: Expected life of the structure

[0238] Specific operation: Calculate the expected lifespan as a percentage of the average condition score against the reference lifespan (50 years).

[0239] Step 6:

[0240] The server compares the latest inspection score with a safety threshold to assess the safety of the structure.

[0241] Input: Latest inspection score, safety threshold

[0242] Output: Safety assessment results

[0243] Specific operation: The server retrieves the latest inspection score, compares it with the safety threshold, and executes logic to evaluate whether it is safe or unsafe.

[0244] Step 7:

[0245] The server determines the need for repairs based on the results of the safety assessment and generates repair proposals.

[0246] Input: Safety assessment results

[0247] Output: Repair suggestion message

[0248] Specific operation: If the safety assessment is "unsafe", a message is generated suggesting that immediate repairs are required.

[0249] Step 8:

[0250] The server notifies the user of repair suggestions in real time via a wearable device (smart glasses).

[0251] Input: Repair proposal message

[0252] Output: Repair suggestions displayed on the user's wearable device

[0253] Specific operation: A repair suggestion message is sent via the wearable device's API and displayed on the smart glasses worn by the user.

[0254] This system allows users to understand the condition of structures within the logistics center in real time and receive quick and appropriate repair suggestions.

[0255] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0256] This invention is a system that comprehensively manages and evaluates the condition of aging structures and makes appropriate repair proposals, and also combines an emotion engine that recognizes the user's emotions and optimizes repair proposals. This system functions mainly through a server, terminals, and users, and is implemented in the following steps.

[0257] First, the server collects basic information about the structure. The user uses a terminal to input the structure's name, construction date, past inspection dates, and the inspection result scores. The terminal then sends this information to the server, which then stores the received data in a database.

[0258] The server then calculates the current age of the structure based on the stored data. The method used to calculate the age is to calculate the difference between the current date and the construction date of the structure and convert it into years. For example, a bridge built on June 1, 1965, will be approximately 58 years old on June 1, 2023.

[0259] Furthermore, the server calculates an average condition score based on past inspection scores. The average condition score is calculated by adding up past inspection scores (for example, 80, 75, and 60 points) and dividing the total by the number of inspections. In this case, the result is (80 + 75 + 60) / 3 = 71.67 points. Based on this average condition score, the server calculates the expected lifespan of the structure. If the standard lifespan is 50 years and the maximum score is 100, a score of 71.67 points means that the expected lifespan is 71.67%, or 35.83 years.

[0260] The server receives the latest inspection score and compares it with a safety threshold (for example, 70 points) to evaluate whether it is safe. For example, if the latest inspection score is 60 points, it is judged to be "unsafe" because it is below the safety threshold of 70 points. Based on the results of this safety evaluation, the server determines whether repairs are necessary. If the evaluation result is "unsafe," the server determines that immediate repairs are necessary and generates a specific repair proposal. For example, a proposal such as "City Bridge needs immediate repair" may be generated.

[0261] Next, the system is equipped with an emotion engine to recognize the user's emotions. When the user interacts with the system through the device, the emotion engine collects emotional data from the user's voice, facial expressions, input, etc. The emotion engine analyzes this data and identifies the user's emotional state. For example, it can identify when the user is feeling stressed or relieved.

[0262] The server optimizes repair suggestions based on the emotional data provided by the emotion engine. The content of the suggestions and the notification method can be adjusted based on the user's emotional state when receiving the repair suggestions. For example, if the user is feeling stressed, the server can select a notification method that makes the suggestions clear and concise, and emphasizes that they are easy to implement.

[0263] Finally, the user receives the optimized repair proposals through their device. The device receives the repair proposals from the server and displays them to the user. The user can then create a repair plan based on the displayed repair proposals. For example, even if the user is feeling stressed, the easy-to-understand proposals will make it easier for them to make appropriate decisions and take appropriate action.

[0264] This system enables efficient and comprehensive management and maintenance of aging structures, and provides suggestions that take the user's emotions into consideration, making it easier to encourage actual action. As a specific example, consider a bridge built in 1965 that received scores of 80, 75, and 60 in three previous inspections (2000, 2010, and 2020). Based on this data, the system calculates the bridge's current age, calculates its expected lifespan, and performs a safety assessment, before generating repair suggestions optimized according to the user's emotional state.

[0265] The processing flow will be explained below.

[0266] Step 1:

[0267] The server sends a request to collect data on the structure. The user uses a terminal to input the structure's name, construction date, past inspection dates, and the inspection result score. The terminal sends this information to the server. The server stores the received data in a database.

[0268] Step 2:

[0269] The server calculates the current age of a structure based on the stored data. For example, the server compares the construction date of the structure with the current date and converts the difference into years. For example, a bridge built on June 1, 1965, will be approximately 58 years old on June 1, 2023.

[0270] Step 3:

[0271] The server calculates the average condition score based on past inspection scores. Specifically, it adds up the past inspection scores (for example, 80, 75, and 60 points) and divides the total by the number of inspections. This calculates the average condition score. In this case, it is (80 + 75 + 60) / 3 = 71.67 points.

[0272] Step 4:

[0273] The server calculates the expected lifespan based on the average health score. The reference lifespan is assumed to be 50 years, and the average health score is calculated based on a percentage of 100. For example, if the average health score is 71.67 points, that is 71.67% of the reference lifespan, and the expected lifespan is 35.83 years.

[0274] Step 5:

[0275] The server receives the latest inspection score. For example, let's say the latest inspection score is 60 points. The server compares this with the safety threshold and evaluates whether it is safe or not.

[0276] Step 6:

[0277] The server compares the safety threshold and evaluates the safety of the structure. For example, if the safety threshold is set at 70 points, a score of 60 will be evaluated as "unsafe."

[0278] Step 7:

[0279] The server determines the need for repairs based on the results of the safety assessment. If the assessment result is "unsafe," the server determines that immediate repairs are required.

[0280] Step 8:

[0281] The server generates a repair proposal. Specifically, it summarizes the reasons why repairs are necessary and the recommended repair methods. For example, a proposal such as "City Bridge needs immediate repair" is generated.

[0282] Step 9:

[0283] The server uses an emotion engine to collect user emotion data. When a user receives repair suggestions through their device, the emotion engine recognizes the user's emotional state from their voice, facial expression, and input. For example, it can identify whether the user is feeling stressed or relieved.

[0284] Step 10:

[0285] The server optimizes repair suggestions based on the emotion data provided by the emotion engine. For example, if the user is feeling stressed, the server selects a notification method that makes the suggestions simple and clear, and emphasizes that they are easy to implement.

[0286] Step 11:

[0287] The user receives the optimized repair proposal through the terminal. The terminal receives the repair proposal from the server and displays it to the user. The user can then create a repair plan based on the displayed repair proposal.

[0288] These are the specific processing steps of this system. This flow enables efficient and comprehensive management and maintenance of aging structures, while also providing optimal proposals that take user feelings into consideration.

[0289] Example 2

[0290] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0291] Conventional structure management systems have difficulty comprehensively assessing the condition of aging structures and proposing appropriate repairs, and are particularly unable to provide repair proposals that take user feelings into consideration. Furthermore, they lack the accuracy required for calculating expected lifespans and safety assessments based on inspection scores.

[0292] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0293] In this invention, the server includes means for collecting basic information about the structure, means for calculating the age of the structure, means for calculating an average condition score of the structure from past inspection results, means for calculating an expected lifespan of the structure based on the average condition score, means for evaluating the safety of the structure by comparing the latest inspection score with a safety threshold, means for determining the need for repairs based on the results of the safety evaluation and generating repair proposals, means for collecting and analyzing emotional data, and means for optimizing the content of the repair proposals according to the emotional state of the user. This not only enables efficient and comprehensive management and maintenance of the structure, but also makes it possible to provide repair proposals that take the user's emotions into consideration, making it easier to encourage actual action.

[0294] "Structures" refers to constructed infrastructure and buildings such as bridges, buildings, and tunnels.

[0295] "Basic information" includes data such as the name of the target structure, construction date, and past inspection dates and results.

[0296] "Age" refers to the period in years between the date of construction of the structure and the present date.

[0297] "Inspection results" refers to the scores and notes resulting from inspections conducted to evaluate the condition of a structure.

[0298] The "average condition score" is a score that indicates the average condition of a structure, calculated based on the scores of past inspection results.

[0299] "Expected life" refers to the remaining useful life of the structure in question, predicted from its current condition.

[0300] The "safety threshold" is the standard value used to evaluate the safety of a structure in the inspection score.

[0301] A "repair proposal" refers to a proposal that specifically indicates the content and methods of repairs necessary to maintain or improve the safety of a structure.

[0302] "Emotion data" refers to data relating to emotions collected from the user's voice, facial expressions, input content, and the like.

[0303] "Emotional state" refers to the specific emotional state displayed by the user, and includes stress, relief, anxiety, and the like.

[0304] "Optimization" means adjusting the content of repair suggestions and notification methods according to the user's emotional state to make them more effective.

[0305] This invention is a system that comprehensively manages and evaluates the condition of aging structures and makes appropriate repair proposals, and also combines it with an emotion engine that recognizes the user's emotions and optimizes repair proposals. The system consists of a server, terminals, and users.

[0306] Program Generation and Execution

[0307] Hardware and software used

[0308] The server uses the following major hardware and software:

[0309] Hardware: High-performance server computer

[0310] Software: MySQL (database management system), Python (programming language)

[0311] The device uses the following main hardware and software:

[0312] Hardware: Laptop, smartphone

[0313] Software: Web browser

[0314] The emotion engine uses the following main hardware and software:

[0315] Hardware: High-performance analysis server

[0316] Software: EmotionAPI (emotion analysis library)

[0317] System action

[0318] Data collection

[0319] The server receives basic information about the structure from the device and stores it in a database. For example, a user might enter information such as the bridge name "City Bridge," the construction date "1965-06-01," and past inspection dates and results (2000-01-01: 80 points, 2010-01-01: 75 points, and 2020-01-01: 60 points). This information is sent to the server and stored in a MySQL database.

[0320] Age Calculator

[0321] The server retrieves the construction date from the database and calculates the difference from the current date using Python's datetime library. For example, if the current date is June 1, 2023, a structure built on June 1, 1965 would be calculated to be 58 years old.

[0322] Mean condition score

[0323] The server calculates the average condition score from past inspection results. For example, if the past inspection scores are 80, 75, and 60, the average condition score is calculated by adding these scores together and dividing by 3, resulting in 71.67 points.

[0324] Expected lifespan

[0325] The server calculates the expected lifespan by taking into account the average condition score against the reference lifespan (50 years). For example, if the average condition score is 71.67 points, the expected lifespan is 35.83 years, which is 71.67% of the reference lifespan.

[0326] Safety assessment and repair proposals

[0327] The server obtains the latest inspection score and compares it with a safety threshold (for example, 70 points). For example, if the latest inspection score is 60 points, it is judged to be "unsafe" and generates a repair suggestion such as "City Bridge needs immediate repair."

[0328] Emotion data collection and analysis

[0329] The terminal collects voice, facial expressions, inputs, etc. when the user interacts with the system.

[0330] The emotion engine analyzes this data in real time to identify the user's emotional state. For example, if the user has an anxious expression, it will classify the user as "stressed."

[0331] Optimizing repair proposals

[0332] The server adjusts the content of repair suggestions and notification methods based on the emotional data. For example, if the user is feeling stressed, the server will make the suggestions simple and easy to implement.

[0333] Specific examples

[0334] Examples of specific prompts include:

[0335] "For a bridge built in 1965, the server calculates its current age and condition score based on past inspection scores of 80, 75, and 60, calculates its expected lifespan, evaluates its safety, and generates repair recommendations. In addition, the emotion engine optimizes recommendations to be concise and easy to implement if the user is feeling stressed."

[0336] As a result, the present invention enables efficient and comprehensive management and maintenance of structures, and further provides repair proposals that take into consideration the user's feelings, thereby encouraging actual action.

[0337] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0338] Step 1:

[0339] Data Entry and Submission

[0340] The terminal receives the user's basic information about the structure (name, construction date, past inspection dates, and inspection result scores) through an input form. For example, the user enters "City Bridge," the construction date as "1965-06-01," and the past inspections as "2000-01-01: 80 points," "2010-01-01: 75 points," and "2020-01-01: 60 points." The terminal then sends the entered data to the server. The terminal receives the basic information about the structure from the user as input and sends it to the server as output.

[0341] Step 2:

[0342] Data storage and processing

[0343] The server receives basic information about the structure from the terminal and stores it in a MySQL database. For example, it stores it in the format "City Bridge, 1965-06-01, 2000-01-01:80, 2010-01-01:75, 2020-01-01:60". It takes the received data as input and stores it in the database as output.

[0344] Step 3:

[0345] Age Calculator

[0346] The server retrieves the construction date from the database and calculates the difference with the current date using Python's datetime library. For example, if a structure was built on June 1, 1965, and the current date is June 1, 2023, the structure's age is calculated to be 58 years. It takes the construction date as input, compares it with the current date to calculate the age, and outputs that age.

[0347] Step 4:

[0348] Calculating the average condition score

[0349] The server retrieves past inspection dates and inspection scores from the database, sums the inspection scores, and divides by the number of inspections to calculate the average condition score. For example, if the scores are 80, 75, and 60, the result is (80 + 75 + 60) / 3 = 71.67. It takes in the inspection results as input, performs calculations, and outputs the average condition score.

[0350] Step 5:

[0351] Expected life calculation

[0352] The server calculates the expected lifespan based on the reference lifespan (50 years) and the average condition score. If the average condition score is 71.67 points, the expected lifespan is 35.83 years, which is 71.67% of the reference lifespan. The server receives the average condition score as input, calculates the expected lifespan using the reference lifespan and score, and outputs the value.

[0353] Step 6:

[0354] Safety assessment and repair proposal generation

[0355] The server obtains the latest inspection score and compares it with a safety threshold (for example, 70 points). For example, if the latest inspection score is 60 points, it is judged to be "unsafe" and generates a repair proposal such as "City Bridge needs immediate repair." The server obtains the latest inspection score as input, compares it with the safety threshold for evaluation, and outputs a repair proposal.

[0356] Step 7:

[0357] Emotion data collection and analysis

[0358] The device collects voice, facial expressions, and input content when the user interacts with the system. The emotional data is sent to the server. The emotion engine analyzes the received data and identifies the user's emotional state. It receives voice and facial expression data as input, analyzes it, and outputs the emotional state.

[0359] Step 8:

[0360] Optimizing repair proposals

[0361] The server adjusts the content of repair suggestions and notification methods based on the emotion data provided by the emotion engine. For example, if the user is feeling stressed, the server will simplify the suggestions and make them easier to implement. It receives emotion data as input, adjusts based on it, and outputs optimized suggestions.

[0362] Step 9:

[0363] Receive and view repair proposals

[0364] The terminal receives the optimized repair proposal from the server and displays it to the user, for example, "City Bridge needs immediate repair. Follow these simple steps: [Step 1], [Step 2], [Step 3]". It takes the optimized repair proposal as input and displays it to the user.

[0365] (Application example 2)

[0366] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0367] Currently, detecting the deterioration of equipment and machinery and carrying out repairs at the appropriate time is an important issue in many factories. Furthermore, if repair proposals are made uniformly without considering the emotional state of workers, appropriate decision-making can be difficult. For workers who are feeling stressed or anxious, complex repair proposals and immediate responses can be a significant burden. In these circumstances, there is a need for efficient repair proposals that take into consideration the emotions of workers.

[0368] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting basic information about the target structure, means for calculating the age of the target structure, means for calculating an average condition score of the target structure from past inspection results, means for calculating an expected lifespan of the target structure based on the average condition score, means for evaluating the safety of the target structure by comparing the latest inspection score with a safety threshold, means for determining the need for repairs based on the results of the safety evaluation and generating repair proposals, means for collecting emotion data and recognizing the user's emotional state, and means for optimizing repair proposals based on the recognized emotional state. This makes it possible to efficiently manage the aging state of equipment and machinery and provide optimal repair proposals according to the emotional state of workers.

[0369] "Basic information" is basic data about the structure, such as the structure's name, construction date, past inspection dates, and inspection result scores.

[0370] "Calculating age" means calculating the difference between the current date and the construction date of the structure and converting it to years.

[0371] The "average condition score" is the average of the total past inspection scores divided by the number of inspections.

[0372] "Expected Life" is the estimated life of a structure calculated from the average condition score based on a baseline life.

[0373] A "safety threshold" is a reference point used to evaluate the safety of a structure, usually expressed as a specific number.

[0374] "Evaluating safety" means comparing the most recent inspection score with a safety threshold to determine the safety of the structure.

[0375] "Generating repair proposals" means proposing appropriate repair methods based on the results of safety assessments of structures.

[0376] "Emotion data" refers to data relating to emotions collected from the user's voice, facial expressions, input content, and the like.

[0377] The "emotional state" is the user's current emotional state obtained as a result of analyzing the emotion data.

[0378] "Optimizing" means adjusting the content of suggestions and notification methods based on the user's emotional state.

[0379] This invention is a system that efficiently manages the deterioration state of equipment and machinery in a factory and provides optimal repair proposals according to the emotional state of workers. This system functions mainly through a server, terminals, and users, and is implemented in the following steps.

[0380] First, the server collects basic information about the equipment and machines in the factory, including the equipment name, installation date, past inspection dates and inspection result scores, etc. This data is automatically collected from sensor networks and IoT devices.

[0381] Next, the server calculates the current age of the equipment or machinery based on the collected data. The age is calculated by calculating the difference between the current date and the installation date and converting it into years. For example, if a machine was installed on June 1, 2010, it will be approximately 13 years old as of June 1, 2023.

[0382] Furthermore, the server calculates an average condition score based on past inspection scores. The average condition score is calculated by adding up past inspection scores (for example, 80, 75, and 70) and dividing the total by the number of inspections. In this case, the result is (80 + 75 + 70) / 3 = 75 points. Based on this average condition score, the server calculates the expected lifespan of the equipment or machinery. If the standard lifespan is, for example, 50 years and the maximum score is 100, a score of 75 indicates a 75% expected lifespan, which is calculated as 37.5 years.

[0383] Next, the server receives the latest inspection score and compares it with a safety threshold (e.g., 70 points) to evaluate whether it is safe. For example, if the latest inspection score is 65 points, it is below the safety threshold of 70 points and is therefore judged to be "unsafe." Based on the results of this safety evaluation, the server determines the need for repairs and generates specific repair proposals.

[0384] Furthermore, this system is equipped with an emotion engine. When a user interacts with the system through a terminal, the emotion engine collects the user's emotional data (voice, facial expressions, input content, etc.). The emotion engine analyzes this data and identifies the user's emotional state. For example, it can identify whether the user is feeling stressed or relieved.

[0385] The server optimizes repair suggestions based on the emotional data provided by the emotion engine. The server can adjust the content of the suggestions and the notification method based on the user's emotional state when receiving the repair suggestions. For example, if the user is feeling stressed, the server can select a notification method that emphasizes that the suggestions are simple and easy to implement. Conversely, if the user is relaxed, the server can provide suggestions with detailed explanations.

[0386] Finally, the user receives the optimized repair proposals through their device. The device receives the repair proposals from the server and displays them to the user. The user can then create a repair plan based on the displayed repair proposals. For example, even if the user is feeling stressed, easy-to-understand proposals can help them make appropriate decisions and take appropriate action.

[0387] As a specific example, consider a machine installed in 2010 that has received scores of 80, 75, and 65 over three previous inspections (2015, 2020, and 2022).The system operates based on this data, calculating the machine's current age, its expected lifespan, and safety assessment, and then generates optimized repair proposals based on the results.

[0388] Example prompt sentence:

[0389] "It reads the latest inspection data from within the factory and outputs repair suggestions in a concise format if the user is feeling stressed, or in a detailed format if the user is feeling reassured."

[0390] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0391] Step 1:

[0392] The server collects basic information about the equipment and machines in the factory. Specifically, it obtains the equipment name, installation date, past inspection dates, and inspection result scores from the sensor network and IoT devices, and stores this information in a database. The input is the basic information obtained from the sensor network and IoT devices, and the output is the information stored in the database.

[0393] Step 2:

[0394] The server calculates the age of the equipment or machinery based on the collected basic information. Specifically, it calculates the difference between the current date and the installation date of the equipment and converts it into years. The input is the installation date stored in the database, and the output is the calculated age.

[0395] Step 3:

[0396] The server calculates the average condition score from past inspection scores. Specifically, it calculates the average by dividing the sum of past inspection scores by the number of inspections. The input is the past inspection scores stored in the database, and the output is the calculated average condition score.

[0397] Step 4:

[0398] The server calculates the expected lifespan of equipment and machinery based on the average condition score. Specifically, it calculates the expected lifespan as a function using the reference lifespan and the average condition score. The inputs are the average condition score and the reference lifespan, and the output is the calculated expected lifespan.

[0399] Step 5:

[0400] The server compares the latest inspection score with the safety threshold to evaluate safety. Specifically, it compares the latest inspection score with the threshold and determines whether it is safe or not. The input is the latest inspection score and the safety threshold, and the output is the evaluation result (safe / unsafe).

[0401] Step 6:

[0402] The server determines the need for repairs based on the results of the safety assessment and generates repair proposals. Specifically, if the system is assessed as "unsafe," it generates a proposal for immediate repairs. The input is the safety assessment result, and the output is a repair proposal.

[0403] Step 7:

[0404] The device collects the user's emotional data (voice, facial expressions, input content, etc.). Specifically, it collects the user's emotional data using input devices such as a camera or microphone. The input is the emotional data obtained from the camera or microphone, and the output is the collected emotional data.

[0405] Step 8:

[0406] The server analyzes the emotional data and recognizes the user's emotional state. Specifically, it uses an emotion engine to analyze the data and identify whether the user is stressed or relaxed. The input is the collected emotional data, and the output is the recognized emotional state.

[0407] Step 9:

[0408] The server optimizes repair suggestions based on the recognized emotional state. Specifically, it provides brief suggestions if the user is stressed, and detailed suggestions if the user is relaxed. The input is the recognized emotional state and the generated repair suggestions, and the output is the optimized repair suggestions.

[0409] Step 10:

[0410] The terminal receives the optimized repair proposal from the server and displays it to the user. Specifically, the terminal displays the optimized repair proposal on the display and notifies the user. The input is the optimized repair proposal sent from the server, and the output is the optimized repair proposal displayed to the user.

[0411] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0412] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0413] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0414] [Second embodiment]

[0415] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0416] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0417] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0418] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0419] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0420] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0421] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0422] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0423] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0424] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0425] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0426] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0427] This invention is a system that comprehensively manages and evaluates the condition of aging structures and proposes appropriate repairs. This system functions mainly through a server, terminals, and users.

[0428] First, the server collects basic information about the structure. The user uses a terminal to input the structure's name, construction date, past inspection dates, and the inspection result scores, etc. This data is sent to the server and stored in the server's database.

[0429] The server then uses this data to calculate the age of the structure. The method used to calculate the age is to calculate the difference between the current date and the construction date of the structure and convert it to years. For example, a bridge built in 1965 would be approximately 58 years old in 2023.

[0430] The server then calculates an average condition score based on past inspection scores. If the past three inspection results were 80, 75, and 60 points, respectively, the average condition score would be (80 + 75 + 60) / 3 = 71.67 points. Based on this average condition score, the server calculates the expected lifespan of the structure. If the base lifespan is 50 years and the maximum score is 100, a score of 71.67 points means that the expected lifespan is 71.67%, or 35.83 years.

[0431] The server receives the latest inspection score and compares it with a safety threshold (for example, 70 points). Based on this comparison, the server evaluates whether the structure is currently safe. For example, if the latest inspection score is 60 points, it is below the safety threshold of 70 points and is therefore judged to be "unsafe."

[0432] Based on the results of this safety assessment, the server determines whether repairs are necessary. If the assessment result is "unsafe," the server determines that immediate repairs are necessary and generates specific repair proposals. For example, a proposal such as "City Bridge needs immediate repair" may be generated. Users can receive these proposals via their devices and create appropriate repair plans.

[0433] This system enables comprehensive management of aging structures, reducing risks from natural disasters and enabling the development of efficient repair plans. As a concrete example, consider a bridge built in 1965 that received scores of 80, 75, and 60 in three previous inspections (2000, 2010, and 2020). The system operates based on this data, calculating the bridge's current age, calculating its expected lifespan, and conducting a safety assessment, before generating a recommendation for immediate repairs.

[0434] The processing flow will be explained below.

[0435] Step 1:

[0436] The server sends a request to collect data on the structure. The user uses a terminal to input the structure's name, construction date, past inspection dates, and inspection result scores. The terminal sends this information to the server. The server stores the received data in a database.

[0437] Step 2:

[0438] The server calculates the current age of a structure based on the stored data. For example, the server compares the construction date of the structure with the current date and converts the difference into years. For example, a bridge built on June 1, 1965, will be approximately 58 years old on June 1, 2023.

[0439] Step 3:

[0440] The server calculates the average condition score based on past inspection scores. Specifically, it adds up the past inspection scores (for example, 80, 75, and 60 points) and divides the total by the number of inspections. This calculates the average condition score. In this case, it is (80 + 75 + 60) / 3 = 71.67 points.

[0441] Step 4:

[0442] The server calculates the expected lifespan based on the average health score. The reference lifespan is assumed to be 50 years, and the average health score is calculated based on a percentage of 100. For example, if the average health score is 71.67 points, that is 71.67% of the reference lifespan, and the expected lifespan is 35.83 years.

[0443] Step 5:

[0444] The server receives the latest inspection score. Let's say the latest inspection score is 60 points. The server compares this with the safety threshold and evaluates whether it is safe or not.

[0445] Step 6:

[0446] The server compares the safety threshold and evaluates the safety of the structure. For example, if the safety threshold is set at 70 points, a score of 60 will be evaluated as "unsafe."

[0447] Step 7:

[0448] The server determines the need for repairs based on the results of the safety assessment. If the assessment result is "unsafe," the server determines that immediate repairs are required.

[0449] Step 8:

[0450] The server generates a repair proposal. Specifically, it summarizes the reasons why repairs are necessary and the recommended repair methods. For example, a proposal such as "City Bridge needs immediate repair" is generated.

[0451] Step 9:

[0452] The user receives the repair proposal through the terminal. The terminal receives the repair proposal from the server and displays it to the user. The user can then create a repair plan based on the displayed repair proposal.

[0453] These are the specific processing steps of this system, which will enable efficient and comprehensive management and maintenance of aging structures.

[0454] Example 1

[0455] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0456] The lack of a system for comprehensively managing safety assessments and repair needs for aging structures is an issue. In particular, there is a need for efficient calculations of the expected lifespan of structures based on inspection results, safety assessments, and repair proposals.

[0457] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0458] In this invention, the server includes means for collecting basic information about the target structure, means for calculating the age of the target structure, means for calculating an average condition score for the target structure from past inspection results, means for calculating an expected lifespan of the target structure based on the average condition score, means for evaluating the safety of the target structure by comparing the latest inspection score with a safety threshold, means for determining the need for repairs based on the results of the safety evaluation and generating repair proposals, and means for transmitting the generated repair proposals to a user's terminal so that the user can confirm the proposals. This enables comprehensive management of aging structures and the development of efficient repair plans.

[0459] "Structures" are artificially constructed objects such as buildings, bridges, and roads.

[0460] "Basic information" refers to basic data about the structure, such as the structure's name, construction date, location, materials, and design specifications.

[0461] "Age" refers to the period in years between the date of construction of the structure and the present date.

[0462] "Inspection results" are numerical data regarding the condition and performance of a structure collected through inspection or investigation.

[0463] The "average condition score" is a numerical value that evaluates the overall condition of a structure, calculated from past inspection results.

[0464] "Expected life" is the period, in years, that a structure is expected to continue performing as designed.

[0465] A "safety threshold" is the minimum score that a structure must meet to be considered safe.

[0466] A "repair proposal" is a proposal regarding specific repair content and plans that is generated when it is determined that repairs to a structure are necessary based on a safety assessment.

[0467] A "user" is an entity that operates the system, inputs information about the structure, and checks repair proposals.

[0468] "Terminal" refers to a device used by a user to operate the system, including a personal computer or smartphone.

[0469] The "server" is a computer that performs the central processing of this system and has the functions of collecting, storing, calculating, and evaluating data.

[0470] This invention is a system that comprehensively manages and evaluates the condition of aging structures and proposes appropriate repairs. This system functions mainly through a server, terminals, and users. The detailed configuration and specific operation of the system are explained below.

[0471] System configuration

[0472] 1. Hardware Configuration

[0473] Server: A central data processing and storage computer, such as an EC2 instance on AWS or an on-premise server.

[0474] Terminal: A device that allows users to input information about a structure and check repair proposals. This includes personal computers (PCs) and smartphones.

[0475] Network: Infrastructure for sending and receiving data between terminals and servers, using the Internet or dedicated lines.

[0476] 2. Software Configuration

[0477] Database: A system for storing information about the structure and inspection results, for example a relational database such as MySQL or PostgreSQL.

[0478] AI model: A model for generating repair suggestions. A generative AI model (e.g., GPT-3) is used.

[0479] Programming language and framework: The programs that run on the server are written in Python, JavaScript (Node.js), or similar, and use a web framework (e.g., Django or Express).

[0480] System Operation

[0481] Data collection

[0482] The user inputs basic information about the structure via the terminal, specifically the structure name, construction date, past inspection dates, and inspection score into the form, and then presses the submit button.

[0483] Data transmission and storage

[0484] The terminal sends the data entered by the user to the server using a secure HTTPS request.

[0485] The server stores the received data in a database, which stores a record for each structure.

[0486] Age Calculator

[0487] The server calculates the age of a structure by subtracting the current date from its construction date stored in the database. For example, a structure built in 1965 will be 58 years old in 2023.

[0488] Calculating the average condition score

[0489] The server calculates the average condition score based on the past inspection scores by dividing the total score by the number of inspections.

[0490] Expected life calculation

[0491] The server calculates the expected lifespan by comparing the calculated average condition score with the reference lifespan. For example, if the reference lifespan is 50 years, a score of 71.67 points means the expected lifespan is 35.83 years.

[0492] Safety evaluation

[0493] The server compares the latest inspection score with the safety threshold and evaluates whether it is safe. For example, if the latest score is 60 and the threshold is 70, it is evaluated as unsafe.

[0494] Generate repair proposals

[0495] The server generates repair proposals based on safety assessments. It uses a generative AI model to generate specific repair proposals. For example, it generates a proposal that reads, "City Bridge needs immediate repair."

[0496] Example prompts for generating repair proposals:

[0497] Generate repair recommendations based on basic structure information: age, average condition score, expected lifespan, and safety rating.

[0498] data:

[0499] Structure name: Structure A

[0500] Construction date: 1965

[0501] Past inspection dates and scores:

[0502] 2000: 80 points

[0503] 2010: 75 points

[0504] 2020: 60 points

[0505] Latest inspection score: 60 points

[0506] Safety threshold: 70 points

[0507] Standard life: 50 years

[0508] Proposal Notification

[0509] The server sends the generated repair proposal to the user's device, where the user can review the proposal and create an appropriate repair plan.

[0510] In this way, the system can comprehensively manage information on aging structures and make efficient repair proposals.

[0511] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0512] Step 1:

[0513] The user uses the terminal to input basic information about the structure, including the structure name, construction date, past inspection dates, and inspection score. The input information is then sent to the server by pressing the send button.

[0514] Input: Structure name, construction date, past inspection dates, and inspection score

[0515] Output: The input data is sent to the server

[0516] Step 2:

[0517] The device sends the data entered by the user to the server using a secure HTTPS request.

[0518] Input: Basic information of the structure entered by the user

[0519] Output: Data sent to the server in the HTTPS request

[0520] Step 3:

[0521] The server stores the received data in a database, for example using SQLAlchemy to store the data in a relational database (e.g. MySQL or PostgreSQL).

[0522] Input: Basic information in the structure sent from the terminal

[0523] Output: Basic information about the structure stored in the database

[0524] Step 4:

[0525] The server calculates the age of the structure by retrieving the construction date from the database and calculating the difference between that and the current date, using the Python datetime library for this calculation.

[0526] Input: Construction date retrieved from the database

[0527] Output: Age of the structure (in years)

[0528] Specific operation: The server gets the current date and calculates the age by calculating the difference from the construction date.

[0529] Step 5:

[0530] The server calculates an average condition score based on the past inspection scores by dividing the total score by the number of inspections.

[0531] Input: Past inspection scores retrieved from the database

[0532] Output: Mean condition score

[0533] Specific operation: The server calculates the past inspection scores and divides the total by the number of inspections.

[0534] Step 6:

[0535] The server calculates the expected lifespan based on the average condition score, and calculates the lifespan as a percentage of the score based on a standard lifespan of 50 years.

[0536] Input: average condition score, reference lifespan

[0537] Output: Expected Life (in years)

[0538] Specific behavior: Expected lifespan = Reference lifespan (Average condition score / 100)

[0539] Step 7:

[0540] The server compares the latest inspection score with the safety threshold to evaluate safety. If it is below the threshold, it is judged to be "unsafe."

[0541] Input: Latest inspection score, safety threshold

[0542] Output: Safety assessment results

[0543] Specific operation: The server compares the latest inspection score with the threshold and performs an evaluation.

[0544] Step 8:

[0545] The server generates repair proposals based on the safety assessment results, using a generative AI model to create specific proposals.

[0546] Input: Safety assessment results, basic structure information

[0547] Output: Repair proposal

[0548] Specific actions: Generate prompts for the AI ​​model and generate repair suggestions

[0549] Example prompt sentence:

[0550] Generate repair recommendations based on basic structure information: age, average condition score, expected lifespan, and safety rating.

[0551] data:

[0552] Structure name: Structure A

[0553] Construction date: 1965

[0554] Past inspection dates and scores:

[0555] 2000: 80 points

[0556] 2010: 75 points

[0557] 2020: 60 points

[0558] Latest inspection score: 60 points

[0559] Safety threshold: 70 points

[0560] Standard life: 50 years

[0561] Step 9:

[0562] The server transmits the generated repair proposal to the user's terminal.

[0563] Input: Generated repair proposal

[0564] Output: Repair suggestions sent to the user's device

[0565] Specific behavior: Sends an HTTPS response containing repair suggestions to the user's device.

[0566] Step 10:

[0567] The user checks the proposals via the terminal and creates a repair plan.

[0568] Input: Repair proposal sent from the server

[0569] Output: Repair suggestions confirmed by the user

[0570] Specific behavior: The user checks the repair proposal on the device and contacts the repair company if necessary.

[0571] (Application example 1)

[0572] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0573] Managing aging structures involves a wide range of tasks, including maintenance inspections, repairs, and maintenance plan development. Conventional management systems make it difficult to comprehensively evaluate the condition of structures, making efficient management particularly difficult in locations with many structures, such as logistics centers. Furthermore, repair proposals cannot be received immediately based on inspection results, which can increase risk. There is a need to solve these issues, efficiently grasp the condition of structures in real time, and make appropriate repair proposals.

[0574] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0575] In this invention, the server includes means for collecting basic information about the structure, means for calculating the age of the structure, means for calculating an average condition score of the structure from past inspection results, means for calculating an expected lifespan of the structure based on the average condition score, means for evaluating the safety of the structure by comparing the latest inspection score with a safety threshold, means for determining the need for repairs based on the results of the safety evaluation and generating repair proposals, and means for notifying the user of the repair proposals in real time via a wearable device. This makes it possible to grasp the condition of structures in a logistics center in real time and quickly receive appropriate repair proposals.

[0576] "Structure" is a general term for physical structures that are used for a long period of time, such as bridges and buildings.

[0577] "Basic information" refers to the initial data required for evaluating and managing a structure, such as the structure's name, construction date, past inspection dates and the inspection result scores.

[0578] "Age" means the number of years that have passed since the structure was constructed.

[0579] "Inspection Results" means the scores or ratings recorded as a result of an inspection conducted to assess the condition of a structure.

[0580] The "average condition score" is an average value that indicates the overall condition of a structure, calculated based on the scores of past inspection results.

[0581] "Expected life" is the period during which a structure can be safely used, calculated based on the reference life and taking into account the average condition score.

[0582] A "safety threshold" is a standard inspection score set to determine the safety of a structure.

[0583] "Evaluation" refers to the process of making a comprehensive judgment on the current safety of a structure based on the latest inspection scores.

[0584] A "repair proposal" is a specific recommendation or plan for repair or reinforcement based on the condition of a structure and the results of its assessment.

[0585] A "wearable device" is a device such as smart glasses or a head-mounted display that provides real-time information when worn by a user.

[0586] "Real-time notification" refers to the process or functionality that enables users to receive information immediately.

[0587] The "server" is a central computer system that stores and processes data about the structure and exchanges data with users' terminals and wearable devices.

[0588] This invention is a system that comprehensively manages the status of structures in a logistics center and makes appropriate repair proposals. It functions mainly through a server, terminals, and users. The details of this system are described below.

[0589] First, the server collects basic information about the structure. The user uses a terminal to input the structure's name, construction date, past inspection dates, and the inspection result scores, etc. This data is sent to the server and stored in the server's database.

[0590] The server then uses this data to calculate the age of the structure. The method used to calculate the age is to calculate the difference between the current date and the construction date of the structure and convert it to years. For example, a warehouse built in 2000 will be approximately 23 years old in 2023.

[0591] The server then calculates an average condition score based on past inspection scores. If the past three inspection results were 85, 80, and 75 points, respectively, the average condition score would be (85 + 80 + 75) / 3 = 80 points. Based on this average condition score, the server calculates the expected lifespan of the structure. If the standard lifespan is 50 years and the maximum score is 100, a score of 80 means that the expected lifespan is 80%, which is calculated as 40 years.

[0592] The server receives the latest inspection score and compares it with a safety threshold (for example, 70 points). Based on this comparison, the server evaluates whether the structure is currently safe. For example, if the latest inspection score is 65 points, it is below the safety threshold of 70 points and is therefore judged to be "unsafe."

[0593] Based on the results of this safety assessment, the server determines whether repairs are necessary. If the assessment result is "unsafe," the server determines that immediate repairs are necessary and generates specific repair proposals. For example, a proposal such as "Warehouse A requires immediate repairs" may be generated. The user receives these proposals in real time through the smart glasses and can create an appropriate repair plan.

[0594] This system enables comprehensive management of aging structures within logistics centers and allows for the development of efficient repair plans. As a specific example, consider a warehouse that was built in 2000 and recorded scores of 85, 80, and 75 points in three previous inspections (2015, 2018, and 2021). The server operates based on this data, calculates the warehouse's current age, calculates its expected lifespan, performs a safety assessment, and then generates effective repair proposals.

[0595] The hardware used is smart glasses worn by the user (e.g., RealWear HMT-1), and the software uses the Django framework and SQLite database on the server side, allowing users to grasp the status of structures in the logistics center in real time and take prompt action.

[0596] Example prompt sentence:

[0597] "Enter the results of the last three inspections and construction dates of structures (warehouses, mobile vehicles, conveyor systems, etc.) within a distribution center, and generate the structure's age, average condition score, safety rating, and repair recommendations. For example, if a warehouse was built in 2000 and the results of the last three inspections were 85, 80, and 75, show the specific output."

[0598] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0599] Step 1:

[0600] The user uses a terminal to input basic information about the structure (such as the name, construction date, past inspection dates and the inspection result scores), and the input data is sent to the server.

[0601] Input: Name of structure, construction date, past inspection dates, and score for each inspection date

[0602] Output: Basic information about the structure sent to the server

[0603] Specific operation: The user uses a device (smartphone or tablet) to enter basic information about the structure into the input form and presses the submit button.

[0604] Step 2:

[0605] The server stores the received basic information about the structure in a database.

[0606] Input: Basic information about the structure sent by the user

[0607] Output: Basic information about the structure stored in a database

[0608] Specific operation: The server analyzes the received data and executes an INSERT query to save it in the database (SQLite).

[0609] Step 3:

[0610] The server calculates the age of the structure using the difference between the current date and the structure's construction date.

[0611] Input: Construction date of the structure, current date

[0612] Output: Age of structure

[0613] What it does: The server gets the current date and calculates the age of the structure by calculating the difference in years from the construction date.

[0614] Step 4:

[0615] The server calculates the average condition score of the structure from past inspection results.

[0616] Input: Score for each inspection date

[0617] Output: Mean condition score

[0618] Specific operation: The server retrieves past inspection scores from the database and calculates their average.

[0619] Step 5:

[0620] The server calculates the expected lifespan of the structure based on the average condition score. The reference lifespan is set to 50 years, and the expected lifespan is calculated in proportion to the average condition score.

[0621] Input: Reference lifespan, average condition score

[0622] Output: Expected life of the structure

[0623] Specific operation: Calculate the expected lifespan as a percentage of the average condition score against the reference lifespan (50 years).

[0624] Step 6:

[0625] The server compares the latest inspection score with a safety threshold to assess the safety of the structure.

[0626] Input: Latest inspection score, safety threshold

[0627] Output: Safety assessment results

[0628] Specific operation: The server retrieves the latest inspection score, compares it with the safety threshold, and executes logic to evaluate whether it is safe or unsafe.

[0629] Step 7:

[0630] The server determines the need for repairs based on the results of the safety assessment and generates repair proposals.

[0631] Input: Safety assessment results

[0632] Output: Repair suggestion message

[0633] Specific operation: If the safety assessment is "unsafe", a message is generated suggesting that immediate repairs are required.

[0634] Step 8:

[0635] The server notifies the user of repair suggestions in real time via a wearable device (smart glasses).

[0636] Input: Repair proposal message

[0637] Output: Repair suggestions displayed on the user's wearable device

[0638] Specific operation: A repair suggestion message is sent via the wearable device's API and displayed on the smart glasses worn by the user.

[0639] This system allows users to understand the condition of structures within the logistics center in real time and receive quick and appropriate repair suggestions.

[0640] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0641] This invention is a system that comprehensively manages and evaluates the condition of aging structures and makes appropriate repair proposals, and also combines an emotion engine that recognizes the user's emotions and optimizes repair proposals. This system functions mainly through a server, terminals, and users, and is implemented in the following steps.

[0642] First, the server collects basic information about the structure. The user uses a terminal to input the structure's name, construction date, past inspection dates, and the inspection result scores. The terminal then sends this information to the server, which then stores the received data in a database.

[0643] The server then calculates the current age of the structure based on the stored data. The method used to calculate the age is to calculate the difference between the current date and the construction date of the structure and convert it into years. For example, a bridge built on June 1, 1965, will be approximately 58 years old on June 1, 2023.

[0644] Furthermore, the server calculates an average condition score based on past inspection scores. The average condition score is calculated by adding up past inspection scores (for example, 80, 75, and 60 points) and dividing the total by the number of inspections. In this case, the result is (80 + 75 + 60) / 3 = 71.67 points. Based on this average condition score, the server calculates the expected lifespan of the structure. If the standard lifespan is 50 years and the maximum score is 100, a score of 71.67 points means that the expected lifespan is 71.67%, or 35.83 years.

[0645] The server receives the latest inspection score and compares it with a safety threshold (for example, 70 points) to evaluate whether it is safe. For example, if the latest inspection score is 60 points, it is judged to be "unsafe" because it is below the safety threshold of 70 points. Based on the results of this safety evaluation, the server determines whether repairs are necessary. If the evaluation result is "unsafe," the server determines that immediate repairs are necessary and generates a specific repair proposal. For example, a proposal such as "City Bridge needs immediate repair" may be generated.

[0646] Next, the system is equipped with an emotion engine to recognize the user's emotions. When the user interacts with the system through the device, the emotion engine collects emotional data from the user's voice, facial expressions, input, etc. The emotion engine analyzes this data and identifies the user's emotional state. For example, it can identify when the user is feeling stressed or relieved.

[0647] The server optimizes repair suggestions based on the emotional data provided by the emotion engine. The content of the suggestions and the notification method can be adjusted based on the user's emotional state when receiving the repair suggestions. For example, if the user is feeling stressed, the server can select a notification method that makes the suggestions clear and concise, and emphasizes that they are easy to implement.

[0648] Finally, the user receives the optimized repair proposals through their device. The device receives the repair proposals from the server and displays them to the user. The user can then create a repair plan based on the displayed repair proposals. For example, even if the user is feeling stressed, the easy-to-understand proposals will make it easier for them to make appropriate decisions and take appropriate action.

[0649] This system enables efficient and comprehensive management and maintenance of aging structures, and provides suggestions that take the user's emotions into consideration, making it easier to encourage actual action. As a specific example, consider a bridge built in 1965 that received scores of 80, 75, and 60 in three previous inspections (2000, 2010, and 2020). Based on this data, the system calculates the bridge's current age, calculates its expected lifespan, and performs a safety assessment, before generating repair suggestions optimized according to the user's emotional state.

[0650] The processing flow will be explained below.

[0651] Step 1:

[0652] The server sends a request to collect data on the structure. The user uses a terminal to input the structure's name, construction date, past inspection dates, and the inspection result score. The terminal sends this information to the server. The server stores the received data in a database.

[0653] Step 2:

[0654] The server calculates the current age of a structure based on the stored data. For example, the server compares the construction date of the structure with the current date and converts the difference into years. For example, a bridge built on June 1, 1965, will be approximately 58 years old on June 1, 2023.

[0655] Step 3:

[0656] The server calculates the average condition score based on past inspection scores. Specifically, it adds up the past inspection scores (for example, 80, 75, and 60 points) and divides the total by the number of inspections. This calculates the average condition score. In this case, it is (80 + 75 + 60) / 3 = 71.67 points.

[0657] Step 4:

[0658] The server calculates the expected lifespan based on the average health score. The reference lifespan is assumed to be 50 years, and the average health score is calculated based on a percentage of 100. For example, if the average health score is 71.67 points, that is 71.67% of the reference lifespan, and the expected lifespan is 35.83 years.

[0659] Step 5:

[0660] The server receives the latest inspection score. For example, let's say the latest inspection score is 60 points. The server compares this with the safety threshold and evaluates whether it is safe or not.

[0661] Step 6:

[0662] The server compares the safety threshold and evaluates the safety of the structure. For example, if the safety threshold is set at 70 points, a score of 60 will be evaluated as "unsafe."

[0663] Step 7:

[0664] The server determines the need for repairs based on the results of the safety assessment. If the assessment result is "unsafe," the server determines that immediate repairs are required.

[0665] Step 8:

[0666] The server generates a repair proposal. Specifically, it summarizes the reasons why repairs are necessary and the recommended repair methods. For example, a proposal such as "City Bridge needs immediate repair" is generated.

[0667] Step 9:

[0668] The server uses an emotion engine to collect user emotion data. When a user receives repair suggestions through their device, the emotion engine recognizes the user's emotional state from their voice, facial expression, and input. For example, it can identify whether the user is feeling stressed or relieved.

[0669] Step 10:

[0670] The server optimizes repair suggestions based on the emotion data provided by the emotion engine. For example, if the user is feeling stressed, the server selects a notification method that makes the suggestions simple and clear, and emphasizes that they are easy to implement.

[0671] Step 11:

[0672] The user receives the optimized repair proposal through the terminal. The terminal receives the repair proposal from the server and displays it to the user. The user can then create a repair plan based on the displayed repair proposal.

[0673] These are the specific processing steps of this system. This flow enables efficient and comprehensive management and maintenance of aging structures, while also providing optimal proposals that take user feelings into consideration.

[0674] Example 2

[0675] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0676] Conventional structure management systems have difficulty comprehensively assessing the condition of aging structures and proposing appropriate repairs, and are particularly unable to provide repair proposals that take user feelings into consideration. Furthermore, they lack the accuracy required for calculating expected lifespans and safety assessments based on inspection scores.

[0677] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0678] In this invention, the server includes means for collecting basic information about the structure, means for calculating the age of the structure, means for calculating an average condition score of the structure from past inspection results, means for calculating an expected lifespan of the structure based on the average condition score, means for evaluating the safety of the structure by comparing the latest inspection score with a safety threshold, means for determining the need for repairs based on the results of the safety evaluation and generating repair proposals, means for collecting and analyzing emotional data, and means for optimizing the content of the repair proposals according to the emotional state of the user. This not only enables efficient and comprehensive management and maintenance of the structure, but also makes it possible to provide repair proposals that take the user's emotions into consideration, making it easier to encourage actual action.

[0679] "Structures" refers to constructed infrastructure and buildings such as bridges, buildings, and tunnels.

[0680] "Basic information" includes data such as the name of the target structure, construction date, and past inspection dates and results.

[0681] "Age" refers to the period in years between the date of construction of the structure and the present date.

[0682] "Inspection results" refers to the scores and notes resulting from inspections conducted to evaluate the condition of a structure.

[0683] The "average condition score" is a score that indicates the average condition of a structure, calculated based on the scores of past inspection results.

[0684] "Expected life" refers to the remaining useful life of the structure in question, predicted from its current condition.

[0685] The "safety threshold" is the standard value used to evaluate the safety of a structure in the inspection score.

[0686] A "repair proposal" refers to a proposal that specifically indicates the content and methods of repairs necessary to maintain or improve the safety of a structure.

[0687] "Emotion data" refers to data relating to emotions collected from the user's voice, facial expressions, input content, and the like.

[0688] "Emotional state" refers to the specific emotional state displayed by the user, and includes stress, relief, anxiety, and the like.

[0689] "Optimization" means adjusting the content of repair suggestions and notification methods according to the user's emotional state to make them more effective.

[0690] This invention is a system that comprehensively manages and evaluates the condition of aging structures and makes appropriate repair proposals, and also combines it with an emotion engine that recognizes the user's emotions and optimizes repair proposals. The system consists of a server, terminals, and users.

[0691] Program Generation and Execution

[0692] Hardware and software used

[0693] The server uses the following major hardware and software:

[0694] Hardware: High-performance server computer

[0695] Software: MySQL (database management system), Python (programming language)

[0696] The device uses the following main hardware and software:

[0697] Hardware: Laptop, smartphone

[0698] Software: Web browser

[0699] The emotion engine uses the following main hardware and software:

[0700] Hardware: High-performance analysis server

[0701] Software: EmotionAPI (emotion analysis library)

[0702] System action

[0703] Data collection

[0704] The server receives basic information about the structure from the device and stores it in a database. For example, a user might enter information such as the bridge name "City Bridge," the construction date "1965-06-01," and past inspection dates and results (2000-01-01: 80 points, 2010-01-01: 75 points, and 2020-01-01: 60 points). This information is sent to the server and stored in a MySQL database.

[0705] Age Calculator

[0706] The server retrieves the construction date from the database and calculates the difference from the current date using Python's datetime library. For example, if the current date is June 1, 2023, a structure built on June 1, 1965 would be calculated to be 58 years old.

[0707] Mean condition score

[0708] The server calculates the average condition score from past inspection results. For example, if the past inspection scores are 80, 75, and 60, the average condition score is calculated by adding these scores together and dividing by 3, resulting in 71.67 points.

[0709] Expected lifespan

[0710] The server calculates the expected lifespan by taking into account the average condition score against the reference lifespan (50 years). For example, if the average condition score is 71.67 points, the expected lifespan is 35.83 years, which is 71.67% of the reference lifespan.

[0711] Safety assessment and repair proposals

[0712] The server obtains the latest inspection score and compares it with a safety threshold (for example, 70 points). For example, if the latest inspection score is 60 points, it is judged to be "unsafe" and generates a repair suggestion such as "City Bridge needs immediate repair."

[0713] Emotion data collection and analysis

[0714] The terminal collects voice, facial expressions, inputs, etc. when the user interacts with the system.

[0715] The emotion engine analyzes this data in real time to identify the user's emotional state. For example, if the user has an anxious expression, it will classify the user as "stressed."

[0716] Optimizing repair proposals

[0717] The server adjusts the content of repair suggestions and notification methods based on the emotional data. For example, if the user is feeling stressed, the server will make the suggestions simple and easy to implement.

[0718] Specific examples

[0719] Examples of specific prompts include:

[0720] "For a bridge built in 1965, the server calculates its current age and condition score based on past inspection scores of 80, 75, and 60, calculates its expected lifespan, evaluates its safety, and generates repair recommendations. In addition, the emotion engine optimizes recommendations to be concise and easy to implement if the user is feeling stressed."

[0721] As a result, the present invention enables efficient and comprehensive management and maintenance of structures, and further provides repair proposals that take into consideration the user's feelings, thereby encouraging actual action.

[0722] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0723] Step 1:

[0724] Data Entry and Submission

[0725] The terminal receives the user's basic information about the structure (name, construction date, past inspection dates, and inspection result scores) through an input form. For example, the user enters "City Bridge," the construction date as "1965-06-01," and the past inspections as "2000-01-01: 80 points," "2010-01-01: 75 points," and "2020-01-01: 60 points." The terminal then sends the entered data to the server. The terminal receives the basic information about the structure from the user as input and sends it to the server as output.

[0726] Step 2:

[0727] Data storage and processing

[0728] The server receives basic information about the structure from the terminal and stores it in a MySQL database. For example, it stores it in the format "City Bridge, 1965-06-01, 2000-01-01:80, 2010-01-01:75, 2020-01-01:60". It takes the received data as input and stores it in the database as output.

[0729] Step 3:

[0730] Age Calculator

[0731] The server retrieves the construction date from the database and calculates the difference with the current date using Python's datetime library. For example, if a structure was built on June 1, 1965, and the current date is June 1, 2023, the structure's age is calculated to be 58 years. It takes the construction date as input, compares it with the current date to calculate the age, and outputs that age.

[0732] Step 4:

[0733] Calculating the average condition score

[0734] The server retrieves past inspection dates and inspection scores from the database, sums the inspection scores, and divides by the number of inspections to calculate the average condition score. For example, if the scores are 80, 75, and 60, the result is (80 + 75 + 60) / 3 = 71.67. It takes in the inspection results as input, performs calculations, and outputs the average condition score.

[0735] Step 5:

[0736] Expected life calculation

[0737] The server calculates the expected lifespan based on the reference lifespan (50 years) and the average condition score. If the average condition score is 71.67 points, the expected lifespan is 35.83 years, which is 71.67% of the reference lifespan. The server receives the average condition score as input, calculates the expected lifespan using the reference lifespan and score, and outputs the value.

[0738] Step 6:

[0739] Safety assessment and repair proposal generation

[0740] The server obtains the latest inspection score and compares it with a safety threshold (for example, 70 points). For example, if the latest inspection score is 60 points, it is judged to be "unsafe" and generates a repair proposal such as "City Bridge needs immediate repair." The server obtains the latest inspection score as input, compares it with the safety threshold for evaluation, and outputs a repair proposal.

[0741] Step 7:

[0742] Emotion data collection and analysis

[0743] The device collects voice, facial expressions, and input content when the user interacts with the system. The emotional data is sent to the server. The emotion engine analyzes the received data and identifies the user's emotional state. It receives voice and facial expression data as input, analyzes it, and outputs the emotional state.

[0744] Step 8:

[0745] Optimizing repair proposals

[0746] The server adjusts the content of repair suggestions and notification methods based on the emotion data provided by the emotion engine. For example, if the user is feeling stressed, the server will simplify the suggestions and make them easier to implement. It receives emotion data as input, adjusts based on it, and outputs optimized suggestions.

[0747] Step 9:

[0748] Receive and view repair proposals

[0749] The terminal receives the optimized repair proposal from the server and displays it to the user, for example, "City Bridge needs immediate repair. Follow these simple steps: [Step 1], [Step 2], [Step 3]". It takes the optimized repair proposal as input and displays it to the user.

[0750] (Application example 2)

[0751] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0752] Currently, detecting the deterioration of equipment and machinery and carrying out repairs at the appropriate time is an important issue in many factories. Furthermore, if repair proposals are made uniformly without considering the emotional state of workers, appropriate decision-making can be difficult. For workers who are feeling stressed or anxious, complex repair proposals and immediate responses can be a significant burden. In these circumstances, there is a need for efficient repair proposals that take into consideration the emotions of workers.

[0753] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting basic information about the target structure, means for calculating the age of the target structure, means for calculating an average condition score of the target structure from past inspection results, means for calculating an expected lifespan of the target structure based on the average condition score, means for evaluating the safety of the target structure by comparing the latest inspection score with a safety threshold, means for determining the need for repairs based on the results of the safety evaluation and generating repair proposals, means for collecting emotion data and recognizing the user's emotional state, and means for optimizing repair proposals based on the recognized emotional state. This makes it possible to efficiently manage the aging state of equipment and machinery and provide optimal repair proposals according to the emotional state of workers.

[0754] "Basic information" is basic data about the structure, such as the structure's name, construction date, past inspection dates, and inspection result scores.

[0755] "Calculating age" means calculating the difference between the current date and the construction date of the structure and converting it to years.

[0756] The "average condition score" is the average of the total past inspection scores divided by the number of inspections.

[0757] "Expected Life" is the estimated life of a structure calculated from the average condition score based on a baseline life.

[0758] A "safety threshold" is a reference point used to evaluate the safety of a structure, usually expressed as a specific number.

[0759] "Evaluating safety" means comparing the most recent inspection score with a safety threshold to determine the safety of the structure.

[0760] "Generating repair proposals" means proposing appropriate repair methods based on the results of safety assessments of structures.

[0761] "Emotion data" refers to data relating to emotions collected from the user's voice, facial expressions, input content, and the like.

[0762] The "emotional state" is the user's current emotional state obtained as a result of analyzing the emotion data.

[0763] "Optimizing" means adjusting the content of suggestions and notification methods based on the user's emotional state.

[0764] This invention is a system that efficiently manages the deterioration state of equipment and machinery in a factory and provides optimal repair proposals according to the emotional state of workers. This system functions mainly through a server, terminals, and users, and is implemented in the following steps.

[0765] First, the server collects basic information about the equipment and machines in the factory, including the equipment name, installation date, past inspection dates and inspection result scores, etc. This data is automatically collected from sensor networks and IoT devices.

[0766] Next, the server calculates the current age of the equipment or machinery based on the collected data. The age is calculated by calculating the difference between the current date and the installation date and converting it into years. For example, if a machine was installed on June 1, 2010, it will be approximately 13 years old as of June 1, 2023.

[0767] Furthermore, the server calculates an average condition score based on past inspection scores. The average condition score is calculated by adding up past inspection scores (for example, 80, 75, and 70) and dividing the total by the number of inspections. In this case, the result is (80 + 75 + 70) / 3 = 75 points. Based on this average condition score, the server calculates the expected lifespan of the equipment or machinery. If the standard lifespan is, for example, 50 years and the maximum score is 100, a score of 75 indicates a 75% expected lifespan, which is calculated as 37.5 years.

[0768] Next, the server receives the latest inspection score and compares it with a safety threshold (e.g., 70 points) to evaluate whether it is safe. For example, if the latest inspection score is 65 points, it is below the safety threshold of 70 points and is therefore judged to be "unsafe." Based on the results of this safety evaluation, the server determines the need for repairs and generates specific repair proposals.

[0769] Furthermore, this system is equipped with an emotion engine. When a user interacts with the system through a terminal, the emotion engine collects the user's emotional data (voice, facial expressions, input content, etc.). The emotion engine analyzes this data and identifies the user's emotional state. For example, it can identify whether the user is feeling stressed or relieved.

[0770] The server optimizes repair suggestions based on the emotional data provided by the emotion engine. The server can adjust the content of the suggestions and the notification method based on the user's emotional state when receiving the repair suggestions. For example, if the user is feeling stressed, the server can select a notification method that emphasizes that the suggestions are simple and easy to implement. Conversely, if the user is relaxed, the server can provide suggestions with detailed explanations.

[0771] Finally, the user receives the optimized repair proposals through their device. The device receives the repair proposals from the server and displays them to the user. The user can then create a repair plan based on the displayed repair proposals. For example, even if the user is feeling stressed, easy-to-understand proposals can help them make appropriate decisions and take appropriate action.

[0772] As a specific example, consider a machine installed in 2010 that has received scores of 80, 75, and 65 over three previous inspections (2015, 2020, and 2022).The system operates based on this data, calculating the machine's current age, its expected lifespan, and safety assessment, and then generates optimized repair proposals based on the results.

[0773] Example prompt sentence:

[0774] "It reads the latest inspection data from within the factory and outputs repair suggestions in a concise format if the user is feeling stressed, or in a detailed format if the user is feeling reassured."

[0775] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0776] Step 1:

[0777] The server collects basic information about the equipment and machines in the factory. Specifically, it obtains the equipment name, installation date, past inspection dates, and inspection result scores from the sensor network and IoT devices, and stores this information in a database. The input is the basic information obtained from the sensor network and IoT devices, and the output is the information stored in the database.

[0778] Step 2:

[0779] The server calculates the age of the equipment or machinery based on the collected basic information. Specifically, it calculates the difference between the current date and the installation date of the equipment and converts it into years. The input is the installation date stored in the database, and the output is the calculated age.

[0780] Step 3:

[0781] The server calculates the average condition score from past inspection scores. Specifically, it calculates the average by dividing the sum of past inspection scores by the number of inspections. The input is the past inspection scores stored in the database, and the output is the calculated average condition score.

[0782] Step 4:

[0783] The server calculates the expected lifespan of equipment and machinery based on the average condition score. Specifically, it calculates the expected lifespan as a function using the reference lifespan and the average condition score. The inputs are the average condition score and the reference lifespan, and the output is the calculated expected lifespan.

[0784] Step 5:

[0785] The server compares the latest inspection score with the safety threshold to evaluate safety. Specifically, it compares the latest inspection score with the threshold and determines whether it is safe or not. The input is the latest inspection score and the safety threshold, and the output is the evaluation result (safe / unsafe).

[0786] Step 6:

[0787] The server determines the need for repairs based on the results of the safety assessment and generates repair proposals. Specifically, if the system is assessed as "unsafe," it generates a proposal for immediate repairs. The input is the safety assessment result, and the output is a repair proposal.

[0788] Step 7:

[0789] The device collects the user's emotional data (voice, facial expressions, input content, etc.). Specifically, it collects the user's emotional data using input devices such as a camera or microphone. The input is the emotional data obtained from the camera or microphone, and the output is the collected emotional data.

[0790] Step 8:

[0791] The server analyzes the emotional data and recognizes the user's emotional state. Specifically, it uses an emotion engine to analyze the data and identify whether the user is stressed or relaxed. The input is the collected emotional data, and the output is the recognized emotional state.

[0792] Step 9:

[0793] The server optimizes repair suggestions based on the recognized emotional state. Specifically, it provides brief suggestions if the user is stressed, and detailed suggestions if the user is relaxed. The input is the recognized emotional state and the generated repair suggestions, and the output is the optimized repair suggestions.

[0794] Step 10:

[0795] The terminal receives the optimized repair proposal from the server and displays it to the user. Specifically, the terminal displays the optimized repair proposal on the display and notifies the user. The input is the optimized repair proposal sent from the server, and the output is the optimized repair proposal displayed to the user.

[0796] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0797] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0798] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0799] [Third embodiment]

[0800] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0801] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0802] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0803] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0804] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0805] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0806] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0807] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0808] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0809] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0810] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0811] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0812] This invention is a system that comprehensively manages and evaluates the condition of aging structures and proposes appropriate repairs. This system functions mainly through a server, terminals, and users.

[0813] First, the server collects basic information about the structure. The user uses a terminal to input the structure's name, construction date, past inspection dates, and the inspection result scores, etc. This data is sent to the server and stored in the server's database.

[0814] The server then uses this data to calculate the age of the structure. The method used to calculate the age is to calculate the difference between the current date and the construction date of the structure and convert it to years. For example, a bridge built in 1965 would be approximately 58 years old in 2023.

[0815] The server then calculates an average condition score based on past inspection scores. If the past three inspection results were 80, 75, and 60 points, respectively, the average condition score would be (80 + 75 + 60) / 3 = 71.67 points. Based on this average condition score, the server calculates the expected lifespan of the structure. If the base lifespan is 50 years and the maximum score is 100, a score of 71.67 points means that the expected lifespan is 71.67%, or 35.83 years.

[0816] The server receives the latest inspection score and compares it with a safety threshold (for example, 70 points). Based on this comparison, the server evaluates whether the structure is currently safe. For example, if the latest inspection score is 60 points, it is below the safety threshold of 70 points and is therefore judged to be "unsafe."

[0817] Based on the results of this safety assessment, the server determines whether repairs are necessary. If the assessment result is "unsafe," the server determines that immediate repairs are necessary and generates specific repair proposals. For example, a proposal such as "City Bridge needs immediate repair" may be generated. Users can receive these proposals via their devices and create appropriate repair plans.

[0818] This system enables comprehensive management of aging structures, reducing risks from natural disasters and enabling the development of efficient repair plans. As a concrete example, consider a bridge built in 1965 that received scores of 80, 75, and 60 in three previous inspections (2000, 2010, and 2020). The system operates based on this data, calculating the bridge's current age, calculating its expected lifespan, and conducting a safety assessment, before generating a recommendation for immediate repairs.

[0819] The processing flow will be explained below.

[0820] Step 1:

[0821] The server sends a request to collect data on the structure. The user uses a terminal to input the structure's name, construction date, past inspection dates, and inspection result scores. The terminal sends this information to the server. The server stores the received data in a database.

[0822] Step 2:

[0823] The server calculates the current age of a structure based on the stored data. For example, the server compares the construction date of the structure with the current date and converts the difference into years. For example, a bridge built on June 1, 1965, will be approximately 58 years old on June 1, 2023.

[0824] Step 3:

[0825] The server calculates the average condition score based on past inspection scores. Specifically, it adds up the past inspection scores (for example, 80, 75, and 60 points) and divides the total by the number of inspections. This calculates the average condition score. In this case, it is (80 + 75 + 60) / 3 = 71.67 points.

[0826] Step 4:

[0827] The server calculates the expected lifespan based on the average health score. The reference lifespan is assumed to be 50 years, and the average health score is calculated based on a percentage of 100. For example, if the average health score is 71.67 points, that is 71.67% of the reference lifespan, and the expected lifespan is 35.83 years.

[0828] Step 5:

[0829] The server receives the latest inspection score. Let's say the latest inspection score is 60 points. The server compares this with the safety threshold and evaluates whether it is safe or not.

[0830] Step 6:

[0831] The server compares the safety threshold and evaluates the safety of the structure. For example, if the safety threshold is set at 70 points, a score of 60 will be evaluated as "unsafe."

[0832] Step 7:

[0833] The server determines the need for repairs based on the results of the safety assessment. If the assessment result is "unsafe," the server determines that immediate repairs are required.

[0834] Step 8:

[0835] The server generates a repair proposal. Specifically, it summarizes the reasons why repairs are necessary and the recommended repair methods. For example, a proposal such as "City Bridge needs immediate repair" is generated.

[0836] Step 9:

[0837] The user receives the repair proposal through the terminal. The terminal receives the repair proposal from the server and displays it to the user. The user can then create a repair plan based on the displayed repair proposal.

[0838] These are the specific processing steps of this system, which will enable efficient and comprehensive management and maintenance of aging structures.

[0839] Example 1

[0840] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0841] The lack of a system for comprehensively managing safety assessments and repair needs for aging structures is an issue. In particular, there is a need for efficient calculations of the expected lifespan of structures based on inspection results, safety assessments, and repair proposals.

[0842] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0843] In this invention, the server includes means for collecting basic information about the target structure, means for calculating the age of the target structure, means for calculating an average condition score for the target structure from past inspection results, means for calculating an expected lifespan of the target structure based on the average condition score, means for evaluating the safety of the target structure by comparing the latest inspection score with a safety threshold, means for determining the need for repairs based on the results of the safety evaluation and generating repair proposals, and means for transmitting the generated repair proposals to a user's terminal so that the user can confirm the proposals. This enables comprehensive management of aging structures and the development of efficient repair plans.

[0844] "Structures" are artificially constructed objects such as buildings, bridges, and roads.

[0845] "Basic information" refers to basic data about the structure, such as the structure's name, construction date, location, materials, and design specifications.

[0846] "Age" refers to the period in years between the date of construction of the structure and the present date.

[0847] "Inspection results" are numerical data regarding the condition and performance of a structure collected through inspection or investigation.

[0848] The "average condition score" is a numerical value that evaluates the overall condition of a structure, calculated from past inspection results.

[0849] "Expected life" is the period, in years, that a structure is expected to continue performing as designed.

[0850] A "safety threshold" is the minimum score that a structure must meet to be considered safe.

[0851] A "repair proposal" is a proposal regarding specific repair content and plans that is generated when it is determined that repairs to a structure are necessary based on a safety assessment.

[0852] A "user" is an entity that operates the system, inputs information about the structure, and checks repair proposals.

[0853] "Terminal" refers to a device used by a user to operate the system, including a personal computer or smartphone.

[0854] The "server" is a computer that performs the central processing of this system and has the functions of collecting, storing, calculating, and evaluating data.

[0855] This invention is a system that comprehensively manages and evaluates the condition of aging structures and proposes appropriate repairs. This system functions mainly through a server, terminals, and users. The detailed configuration and specific operation of the system are explained below.

[0856] System configuration

[0857] 1. Hardware Configuration

[0858] Server: A central data processing and storage computer, such as an EC2 instance on AWS or an on-premise server.

[0859] Terminal: A device that allows users to input information about a structure and check repair proposals. This includes personal computers (PCs) and smartphones.

[0860] Network: Infrastructure for sending and receiving data between terminals and servers, using the Internet or dedicated lines.

[0861] 2. Software Configuration

[0862] Database: A system for storing information about the structure and inspection results, for example a relational database such as MySQL or PostgreSQL.

[0863] AI model: A model for generating repair suggestions. A generative AI model (e.g., GPT-3) is used.

[0864] Programming language and framework: The programs that run on the server are written in Python, JavaScript (Node.js), or similar, and use a web framework (e.g., Django or Express).

[0865] System Operation

[0866] Data collection

[0867] The user inputs basic information about the structure via the terminal, specifically the structure name, construction date, past inspection dates, and inspection score into the form, and then presses the submit button.

[0868] Data transmission and storage

[0869] The terminal sends the data entered by the user to the server using a secure HTTPS request.

[0870] The server stores the received data in a database, which stores a record for each structure.

[0871] Age Calculator

[0872] The server calculates the age of a structure by subtracting the current date from its construction date stored in the database. For example, a structure built in 1965 will be 58 years old in 2023.

[0873] Calculating the average condition score

[0874] The server calculates the average condition score based on the past inspection scores by dividing the total score by the number of inspections.

[0875] Expected life calculation

[0876] The server calculates the expected lifespan by comparing the calculated average condition score with the reference lifespan. For example, if the reference lifespan is 50 years, a score of 71.67 points means the expected lifespan is 35.83 years.

[0877] Safety evaluation

[0878] The server compares the latest inspection score with the safety threshold and evaluates whether it is safe. For example, if the latest score is 60 and the threshold is 70, it is evaluated as unsafe.

[0879] Generate repair proposals

[0880] The server generates repair proposals based on safety assessments. It uses a generative AI model to generate specific repair proposals. For example, it generates a proposal that reads, "City Bridge needs immediate repair."

[0881] Example prompts for generating repair proposals:

[0882] Generate repair recommendations based on basic structure information: age, average condition score, expected lifespan, and safety rating.

[0883] data:

[0884] Structure name: Structure A

[0885] Construction date: 1965

[0886] Past inspection dates and scores:

[0887] 2000: 80 points

[0888] 2010: 75 points

[0889] 2020: 60 points

[0890] Latest inspection score: 60 points

[0891] Safety threshold: 70 points

[0892] Standard life: 50 years

[0893] Proposal Notification

[0894] The server sends the generated repair proposal to the user's device, where the user can review the proposal and create an appropriate repair plan.

[0895] In this way, the system can comprehensively manage information on aging structures and make efficient repair proposals.

[0896] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0897] Step 1:

[0898] The user uses the terminal to input basic information about the structure, including the structure name, construction date, past inspection dates, and inspection score. The input information is then sent to the server by pressing the send button.

[0899] Input: Structure name, construction date, past inspection dates, and inspection score

[0900] Output: The input data is sent to the server

[0901] Step 2:

[0902] The device sends the data entered by the user to the server using a secure HTTPS request.

[0903] Input: Basic information of the structure entered by the user

[0904] Output: Data sent to the server in the HTTPS request

[0905] Step 3:

[0906] The server stores the received data in a database, for example using SQLAlchemy to store the data in a relational database (e.g. MySQL or PostgreSQL).

[0907] Input: Basic information in the structure sent from the terminal

[0908] Output: Basic information about the structure stored in the database

[0909] Step 4:

[0910] The server calculates the age of the structure by retrieving the construction date from the database and calculating the difference between that and the current date, using the Python datetime library for this calculation.

[0911] Input: Construction date retrieved from the database

[0912] Output: Age of the structure (in years)

[0913] Specific operation: The server gets the current date and calculates the age by calculating the difference from the construction date.

[0914] Step 5:

[0915] The server calculates an average condition score based on the past inspection scores by dividing the total score by the number of inspections.

[0916] Input: Past inspection scores retrieved from the database

[0917] Output: Mean condition score

[0918] Specific operation: The server calculates the past inspection scores and divides the total by the number of inspections.

[0919] Step 6:

[0920] The server calculates the expected lifespan based on the average condition score, and calculates the lifespan as a percentage of the score based on a standard lifespan of 50 years.

[0921] Input: average condition score, reference lifespan

[0922] Output: Expected Life (in years)

[0923] Specific behavior: Expected lifespan = Reference lifespan (Average condition score / 100)

[0924] Step 7:

[0925] The server compares the latest inspection score with the safety threshold to evaluate safety. If it is below the threshold, it is judged to be "unsafe."

[0926] Input: Latest inspection score, safety threshold

[0927] Output: Safety assessment results

[0928] Specific operation: The server compares the latest inspection score with the threshold and performs an evaluation.

[0929] Step 8:

[0930] The server generates repair proposals based on the safety assessment results, using a generative AI model to create specific proposals.

[0931] Input: Safety assessment results, basic structure information

[0932] Output: Repair proposal

[0933] Specific actions: Generate prompts for the AI ​​model and generate repair suggestions

[0934] Example prompt sentence:

[0935] Generate repair recommendations based on basic structure information: age, average condition score, expected lifespan, and safety rating.

[0936] data:

[0937] Structure name: Structure A

[0938] Construction date: 1965

[0939] Past inspection dates and scores:

[0940] 2000: 80 points

[0941] 2010: 75 points

[0942] 2020: 60 points

[0943] Latest inspection score: 60 points

[0944] Safety threshold: 70 points

[0945] Standard life: 50 years

[0946] Step 9:

[0947] The server transmits the generated repair proposal to the user's terminal.

[0948] Input: Generated repair proposal

[0949] Output: Repair suggestions sent to the user's device

[0950] Specific behavior: Sends an HTTPS response containing repair suggestions to the user's device.

[0951] Step 10:

[0952] The user checks the proposals via the terminal and creates a repair plan.

[0953] Input: Repair proposal sent from the server

[0954] Output: Repair suggestions confirmed by the user

[0955] Specific behavior: The user checks the repair proposal on the device and contacts the repair company if necessary.

[0956] (Application example 1)

[0957] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0958] Managing aging structures involves a wide range of tasks, including maintenance inspections, repairs, and maintenance plan development. Conventional management systems make it difficult to comprehensively evaluate the condition of structures, making efficient management particularly difficult in locations with many structures, such as logistics centers. Furthermore, repair proposals cannot be received immediately based on inspection results, which can increase risk. There is a need to solve these issues, efficiently grasp the condition of structures in real time, and make appropriate repair proposals.

[0959] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0960] In this invention, the server includes means for collecting basic information about the structure, means for calculating the age of the structure, means for calculating an average condition score of the structure from past inspection results, means for calculating an expected lifespan of the structure based on the average condition score, means for evaluating the safety of the structure by comparing the latest inspection score with a safety threshold, means for determining the need for repairs based on the results of the safety evaluation and generating repair proposals, and means for notifying the user of the repair proposals in real time via a wearable device. This makes it possible to grasp the condition of structures in a logistics center in real time and quickly receive appropriate repair proposals.

[0961] "Structure" is a general term for physical structures that are used for a long period of time, such as bridges and buildings.

[0962] "Basic information" refers to the initial data required for evaluating and managing a structure, such as the structure's name, construction date, past inspection dates and the inspection result scores.

[0963] "Age" means the number of years that have passed since the structure was constructed.

[0964] "Inspection Results" means the scores or ratings recorded as a result of an inspection conducted to assess the condition of a structure.

[0965] The "average condition score" is an average value that indicates the overall condition of a structure, calculated based on the scores of past inspection results.

[0966] "Expected life" is the period during which a structure can be safely used, calculated based on the reference life and taking into account the average condition score.

[0967] A "safety threshold" is a standard inspection score set to determine the safety of a structure.

[0968] "Evaluation" refers to the process of making a comprehensive judgment on the current safety of a structure based on the latest inspection scores.

[0969] A "repair proposal" is a specific recommendation or plan for repair or reinforcement based on the condition of a structure and the results of its assessment.

[0970] A "wearable device" is a device such as smart glasses or a head-mounted display that provides real-time information when worn by a user.

[0971] "Real-time notification" refers to the process or functionality that enables users to receive information immediately.

[0972] The "server" is a central computer system that stores and processes data about the structure and exchanges data with users' terminals and wearable devices.

[0973] This invention is a system that comprehensively manages the status of structures in a logistics center and makes appropriate repair proposals. It functions mainly through a server, terminals, and users. The details of this system are described below.

[0974] First, the server collects basic information about the structure. The user uses a terminal to input the structure's name, construction date, past inspection dates, and the inspection result scores, etc. This data is sent to the server and stored in the server's database.

[0975] The server then uses this data to calculate the age of the structure. The method used to calculate the age is to calculate the difference between the current date and the construction date of the structure and convert it to years. For example, a warehouse built in 2000 will be approximately 23 years old in 2023.

[0976] The server then calculates an average condition score based on past inspection scores. If the past three inspection results were 85, 80, and 75 points, respectively, the average condition score would be (85 + 80 + 75) / 3 = 80 points. Based on this average condition score, the server calculates the expected lifespan of the structure. If the standard lifespan is 50 years and the maximum score is 100, a score of 80 means that the expected lifespan is 80%, which is calculated as 40 years.

[0977] The server receives the latest inspection score and compares it with a safety threshold (for example, 70 points). Based on this comparison, the server evaluates whether the structure is currently safe. For example, if the latest inspection score is 65 points, it is below the safety threshold of 70 points and is therefore judged to be "unsafe."

[0978] Based on the results of this safety assessment, the server determines whether repairs are necessary. If the assessment result is "unsafe," the server determines that immediate repairs are necessary and generates specific repair proposals. For example, a proposal such as "Warehouse A requires immediate repairs" may be generated. The user receives these proposals in real time through the smart glasses and can create an appropriate repair plan.

[0979] This system enables comprehensive management of aging structures within logistics centers and allows for the development of efficient repair plans. As a specific example, consider a warehouse that was built in 2000 and recorded scores of 85, 80, and 75 points in three previous inspections (2015, 2018, and 2021). The server operates based on this data, calculates the warehouse's current age, calculates its expected lifespan, performs a safety assessment, and then generates effective repair proposals.

[0980] The hardware used is smart glasses worn by the user (e.g., RealWear HMT-1), and the software uses the Django framework and SQLite database on the server side, allowing users to grasp the status of structures in the logistics center in real time and take prompt action.

[0981] Example prompt sentence:

[0982] "Enter the results of the last three inspections and construction dates of structures (warehouses, mobile vehicles, conveyor systems, etc.) within a distribution center, and generate the structure's age, average condition score, safety rating, and repair recommendations. For example, if a warehouse was built in 2000 and the results of the last three inspections were 85, 80, and 75, show the specific output."

[0983] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0984] Step 1:

[0985] The user uses a terminal to input basic information about the structure (such as the name, construction date, past inspection dates and the inspection result scores), and the input data is sent to the server.

[0986] Input: Name of structure, construction date, past inspection dates, and score for each inspection date

[0987] Output: Basic information about the structure sent to the server

[0988] Specific operation: The user uses a device (smartphone or tablet) to enter basic information about the structure into the input form and presses the submit button.

[0989] Step 2:

[0990] The server stores the received basic information about the structure in a database.

[0991] Input: Basic information about the structure sent by the user

[0992] Output: Basic information about the structure stored in a database

[0993] Specific operation: The server analyzes the received data and executes an INSERT query to save it in the database (SQLite).

[0994] Step 3:

[0995] The server calculates the age of the structure using the difference between the current date and the structure's construction date.

[0996] Input: Construction date of the structure, current date

[0997] Output: Age of structure

[0998] What it does: The server gets the current date and calculates the age of the structure by calculating the difference in years from the construction date.

[0999] Step 4:

[1000] The server calculates the average condition score of the structure from past inspection results.

[1001] Input: Score for each inspection date

[1002] Output: Mean condition score

[1003] Specific operation: The server retrieves past inspection scores from the database and calculates their average.

[1004] Step 5:

[1005] The server calculates the expected lifespan of the structure based on the average condition score. The reference lifespan is set to 50 years, and the expected lifespan is calculated in proportion to the average condition score.

[1006] Input: Reference lifespan, average condition score

[1007] Output: Expected life of the structure

[1008] Specific operation: Calculate the expected lifespan as a percentage of the average condition score against the reference lifespan (50 years).

[1009] Step 6:

[1010] The server compares the latest inspection score with a safety threshold to assess the safety of the structure.

[1011] Input: Latest inspection score, safety threshold

[1012] Output: Safety assessment results

[1013] Specific operation: The server retrieves the latest inspection score, compares it with the safety threshold, and executes logic to evaluate whether it is safe or unsafe.

[1014] Step 7:

[1015] The server determines the need for repairs based on the results of the safety assessment and generates repair proposals.

[1016] Input: Safety assessment results

[1017] Output: Repair suggestion message

[1018] Specific operation: If the safety assessment is "unsafe", a message is generated suggesting that immediate repairs are required.

[1019] Step 8:

[1020] The server notifies the user of repair suggestions in real time via a wearable device (smart glasses).

[1021] Input: Repair proposal message

[1022] Output: Repair suggestions displayed on the user's wearable device

[1023] Specific operation: A repair suggestion message is sent via the wearable device's API and displayed on the smart glasses worn by the user.

[1024] This system allows users to understand the condition of structures within the logistics center in real time and receive quick and appropriate repair suggestions.

[1025] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1026] This invention is a system that comprehensively manages and evaluates the condition of aging structures and makes appropriate repair proposals, and also combines an emotion engine that recognizes the user's emotions and optimizes repair proposals. This system functions mainly through a server, terminals, and users, and is implemented in the following steps.

[1027] First, the server collects basic information about the structure. The user uses a terminal to input the structure's name, construction date, past inspection dates, and the inspection result scores. The terminal then sends this information to the server, which then stores the received data in a database.

[1028] The server then calculates the current age of the structure based on the stored data. The method used to calculate the age is to calculate the difference between the current date and the construction date of the structure and convert it into years. For example, a bridge built on June 1, 1965, will be approximately 58 years old on June 1, 2023.

[1029] Furthermore, the server calculates an average condition score based on past inspection scores. The average condition score is calculated by adding up past inspection scores (for example, 80, 75, and 60 points) and dividing the total by the number of inspections. In this case, the result is (80 + 75 + 60) / 3 = 71.67 points. Based on this average condition score, the server calculates the expected lifespan of the structure. If the standard lifespan is 50 years and the maximum score is 100, a score of 71.67 points means that the expected lifespan is 71.67%, or 35.83 years.

[1030] The server receives the latest inspection score and compares it with a safety threshold (for example, 70 points) to evaluate whether it is safe. For example, if the latest inspection score is 60 points, it is judged to be "unsafe" because it is below the safety threshold of 70 points. Based on the results of this safety evaluation, the server determines whether repairs are necessary. If the evaluation result is "unsafe," the server determines that immediate repairs are necessary and generates a specific repair proposal. For example, a proposal such as "City Bridge needs immediate repair" may be generated.

[1031] Next, the system is equipped with an emotion engine to recognize the user's emotions. When the user interacts with the system through the device, the emotion engine collects emotional data from the user's voice, facial expressions, input, etc. The emotion engine analyzes this data and identifies the user's emotional state. For example, it can identify when the user is feeling stressed or relieved.

[1032] The server optimizes repair suggestions based on the emotional data provided by the emotion engine. The content of the suggestions and the notification method can be adjusted based on the user's emotional state when receiving the repair suggestions. For example, if the user is feeling stressed, the server can select a notification method that makes the suggestions clear and concise, and emphasizes that they are easy to implement.

[1033] Finally, the user receives the optimized repair proposals through their device. The device receives the repair proposals from the server and displays them to the user. The user can then create a repair plan based on the displayed repair proposals. For example, even if the user is feeling stressed, the easy-to-understand proposals will make it easier for them to make appropriate decisions and take appropriate action.

[1034] This system enables efficient and comprehensive management and maintenance of aging structures, and provides suggestions that take the user's emotions into consideration, making it easier to encourage actual action. As a specific example, consider a bridge built in 1965 that received scores of 80, 75, and 60 in three previous inspections (2000, 2010, and 2020). Based on this data, the system calculates the bridge's current age, calculates its expected lifespan, and performs a safety assessment, before generating repair suggestions optimized according to the user's emotional state.

[1035] The processing flow will be explained below.

[1036] Step 1:

[1037] The server sends a request to collect data on the structure. The user uses a terminal to input the structure's name, construction date, past inspection dates, and the inspection result score. The terminal sends this information to the server. The server stores the received data in a database.

[1038] Step 2:

[1039] The server calculates the current age of a structure based on the stored data. For example, the server compares the construction date of the structure with the current date and converts the difference into years. For example, a bridge built on June 1, 1965, will be approximately 58 years old on June 1, 2023.

[1040] Step 3:

[1041] The server calculates the average condition score based on past inspection scores. Specifically, it adds up the past inspection scores (for example, 80, 75, and 60 points) and divides the total by the number of inspections. This calculates the average condition score. In this case, it is (80 + 75 + 60) / 3 = 71.67 points.

[1042] Step 4:

[1043] The server calculates the expected lifespan based on the average health score. The reference lifespan is assumed to be 50 years, and the average health score is calculated based on a percentage of 100. For example, if the average health score is 71.67 points, that is 71.67% of the reference lifespan, and the expected lifespan is 35.83 years.

[1044] Step 5:

[1045] The server receives the latest inspection score. For example, let's say the latest inspection score is 60 points. The server compares this with the safety threshold and evaluates whether it is safe or not.

[1046] Step 6:

[1047] The server compares the safety threshold and evaluates the safety of the structure. For example, if the safety threshold is set at 70 points, a score of 60 will be evaluated as "unsafe."

[1048] Step 7:

[1049] The server determines the need for repairs based on the results of the safety assessment. If the assessment result is "unsafe," the server determines that immediate repairs are required.

[1050] Step 8:

[1051] The server generates a repair proposal. Specifically, it summarizes the reasons why repairs are necessary and the recommended repair methods. For example, a proposal such as "City Bridge needs immediate repair" is generated.

[1052] Step 9:

[1053] The server uses an emotion engine to collect user emotion data. When a user receives repair suggestions through their device, the emotion engine recognizes the user's emotional state from their voice, facial expression, and input. For example, it can identify whether the user is feeling stressed or relieved.

[1054] Step 10:

[1055] The server optimizes repair suggestions based on the emotion data provided by the emotion engine. For example, if the user is feeling stressed, the server selects a notification method that makes the suggestions simple and clear, and emphasizes that they are easy to implement.

[1056] Step 11:

[1057] The user receives the optimized repair proposal through the terminal. The terminal receives the repair proposal from the server and displays it to the user. The user can then create a repair plan based on the displayed repair proposal.

[1058] These are the specific processing steps of this system. This flow enables efficient and comprehensive management and maintenance of aging structures, while also providing optimal proposals that take user feelings into consideration.

[1059] Example 2

[1060] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1061] Conventional structure management systems have difficulty comprehensively assessing the condition of aging structures and proposing appropriate repairs, and are particularly unable to provide repair proposals that take user feelings into consideration. Furthermore, they lack the accuracy required for calculating expected lifespans and safety assessments based on inspection scores.

[1062] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1063] In this invention, the server includes means for collecting basic information about the structure, means for calculating the age of the structure, means for calculating an average condition score of the structure from past inspection results, means for calculating an expected lifespan of the structure based on the average condition score, means for evaluating the safety of the structure by comparing the latest inspection score with a safety threshold, means for determining the need for repairs based on the results of the safety evaluation and generating repair proposals, means for collecting and analyzing emotional data, and means for optimizing the content of the repair proposals according to the emotional state of the user. This not only enables efficient and comprehensive management and maintenance of the structure, but also makes it possible to provide repair proposals that take the user's emotions into consideration, making it easier to encourage actual action.

[1064] "Structures" refers to constructed infrastructure and buildings such as bridges, buildings, and tunnels.

[1065] "Basic information" includes data such as the name of the target structure, construction date, and past inspection dates and results.

[1066] "Age" refers to the period in years between the date of construction of the structure and the present date.

[1067] "Inspection results" refers to the scores and notes resulting from inspections conducted to evaluate the condition of a structure.

[1068] The "average condition score" is a score that indicates the average condition of a structure, calculated based on the scores of past inspection results.

[1069] "Expected life" refers to the remaining useful life of the structure in question, predicted from its current condition.

[1070] The "safety threshold" is the standard value used to evaluate the safety of a structure in the inspection score.

[1071] A "repair proposal" refers to a proposal that specifically indicates the content and methods of repairs necessary to maintain or improve the safety of a structure.

[1072] "Emotion data" refers to data relating to emotions collected from the user's voice, facial expressions, input content, and the like.

[1073] "Emotional state" refers to the specific emotional state displayed by the user, and includes stress, relief, anxiety, and the like.

[1074] "Optimization" means adjusting the content of repair suggestions and notification methods according to the user's emotional state to make them more effective.

[1075] This invention is a system that comprehensively manages and evaluates the condition of aging structures and makes appropriate repair proposals, and also combines it with an emotion engine that recognizes the user's emotions and optimizes repair proposals. The system consists of a server, terminals, and users.

[1076] Program Generation and Execution

[1077] Hardware and software used

[1078] The server uses the following major hardware and software:

[1079] Hardware: High-performance server computer

[1080] Software: MySQL (database management system), Python (programming language)

[1081] The device uses the following main hardware and software:

[1082] Hardware: Laptop, smartphone

[1083] Software: Web browser

[1084] The emotion engine uses the following main hardware and software:

[1085] Hardware: High-performance analysis server

[1086] Software: EmotionAPI (emotion analysis library)

[1087] System action

[1088] Data collection

[1089] The server receives basic information about the structure from the device and stores it in a database. For example, a user might enter information such as the bridge name "City Bridge," the construction date "1965-06-01," and past inspection dates and results (2000-01-01: 80 points, 2010-01-01: 75 points, and 2020-01-01: 60 points). This information is sent to the server and stored in a MySQL database.

[1090] Age Calculator

[1091] The server retrieves the construction date from the database and calculates the difference from the current date using Python's datetime library. For example, if the current date is June 1, 2023, a structure built on June 1, 1965 would be calculated to be 58 years old.

[1092] Mean condition score

[1093] The server calculates the average condition score from past inspection results. For example, if the past inspection scores are 80, 75, and 60, the average condition score is calculated by adding these scores together and dividing by 3, resulting in 71.67 points.

[1094] Expected lifespan

[1095] The server calculates the expected lifespan by taking into account the average condition score against the reference lifespan (50 years). For example, if the average condition score is 71.67 points, the expected lifespan is 35.83 years, which is 71.67% of the reference lifespan.

[1096] Safety assessment and repair proposals

[1097] The server obtains the latest inspection score and compares it with a safety threshold (for example, 70 points). For example, if the latest inspection score is 60 points, it is judged to be "unsafe" and generates a repair suggestion such as "City Bridge needs immediate repair."

[1098] Emotion data collection and analysis

[1099] The terminal collects voice, facial expressions, inputs, etc. when the user interacts with the system.

[1100] The emotion engine analyzes this data in real time to identify the user's emotional state. For example, if the user has an anxious expression, it will classify the user as "stressed."

[1101] Optimizing repair proposals

[1102] The server adjusts the content of repair suggestions and notification methods based on the emotional data. For example, if the user is feeling stressed, the server will make the suggestions simple and easy to implement.

[1103] Specific examples

[1104] Examples of specific prompts include:

[1105] "For a bridge built in 1965, the server calculates its current age and condition score based on past inspection scores of 80, 75, and 60, calculates its expected lifespan, evaluates its safety, and generates repair recommendations. In addition, the emotion engine optimizes recommendations to be concise and easy to implement if the user is feeling stressed."

[1106] As a result, the present invention enables efficient and comprehensive management and maintenance of structures, and further provides repair proposals that take into consideration the user's feelings, thereby encouraging actual action.

[1107] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1108] Step 1:

[1109] Data Entry and Submission

[1110] The terminal receives the user's basic information about the structure (name, construction date, past inspection dates, and inspection result scores) through an input form. For example, the user enters "City Bridge," the construction date as "1965-06-01," and the past inspections as "2000-01-01: 80 points," "2010-01-01: 75 points," and "2020-01-01: 60 points." The terminal then sends the entered data to the server. The terminal receives the basic information about the structure from the user as input and sends it to the server as output.

[1111] Step 2:

[1112] Data storage and processing

[1113] The server receives basic information about the structure from the terminal and stores it in a MySQL database. For example, it stores it in the format "City Bridge, 1965-06-01, 2000-01-01:80, 2010-01-01:75, 2020-01-01:60". It takes the received data as input and stores it in the database as output.

[1114] Step 3:

[1115] Age Calculator

[1116] The server retrieves the construction date from the database and calculates the difference with the current date using Python's datetime library. For example, if a structure was built on June 1, 1965, and the current date is June 1, 2023, the structure's age is calculated to be 58 years. It takes the construction date as input, compares it with the current date to calculate the age, and outputs that age.

[1117] Step 4:

[1118] Calculating the average condition score

[1119] The server retrieves past inspection dates and inspection scores from the database, sums the inspection scores, and divides by the number of inspections to calculate the average condition score. For example, if the scores are 80, 75, and 60, the result is (80 + 75 + 60) / 3 = 71.67. It takes in the inspection results as input, performs calculations, and outputs the average condition score.

[1120] Step 5:

[1121] Expected life calculation

[1122] The server calculates the expected lifespan based on the reference lifespan (50 years) and the average condition score. If the average condition score is 71.67 points, the expected lifespan is 35.83 years, which is 71.67% of the reference lifespan. The server receives the average condition score as input, calculates the expected lifespan using the reference lifespan and score, and outputs the value.

[1123] Step 6:

[1124] Safety assessment and repair proposal generation

[1125] The server obtains the latest inspection score and compares it with a safety threshold (for example, 70 points). For example, if the latest inspection score is 60 points, it is judged to be "unsafe" and generates a repair proposal such as "City Bridge needs immediate repair." The server obtains the latest inspection score as input, compares it with the safety threshold for evaluation, and outputs a repair proposal.

[1126] Step 7:

[1127] Emotion data collection and analysis

[1128] The device collects voice, facial expressions, and input content when the user interacts with the system. The emotional data is sent to the server. The emotion engine analyzes the received data and identifies the user's emotional state. It receives voice and facial expression data as input, analyzes it, and outputs the emotional state.

[1129] Step 8:

[1130] Optimizing repair proposals

[1131] The server adjusts the content of repair suggestions and notification methods based on the emotion data provided by the emotion engine. For example, if the user is feeling stressed, the server will simplify the suggestions and make them easier to implement. It receives emotion data as input, adjusts based on it, and outputs optimized suggestions.

[1132] Step 9:

[1133] Receive and view repair proposals

[1134] The terminal receives the optimized repair proposal from the server and displays it to the user, for example, "City Bridge needs immediate repair. Follow these simple steps: [Step 1], [Step 2], [Step 3]". It takes the optimized repair proposal as input and displays it to the user.

[1135] (Application example 2)

[1136] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1137] Currently, detecting the deterioration of equipment and machinery and carrying out repairs at the appropriate time is an important issue in many factories. Furthermore, if repair proposals are made uniformly without considering the emotional state of workers, appropriate decision-making can be difficult. For workers who are feeling stressed or anxious, complex repair proposals and immediate responses can be a significant burden. In these circumstances, there is a need for efficient repair proposals that take into consideration the emotions of workers.

[1138] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting basic information about the target structure, means for calculating the age of the target structure, means for calculating an average condition score of the target structure from past inspection results, means for calculating an expected lifespan of the target structure based on the average condition score, means for evaluating the safety of the target structure by comparing the latest inspection score with a safety threshold, means for determining the need for repairs based on the results of the safety evaluation and generating repair proposals, means for collecting emotion data and recognizing the user's emotional state, and means for optimizing repair proposals based on the recognized emotional state. This makes it possible to efficiently manage the aging state of equipment and machinery and provide optimal repair proposals according to the emotional state of workers.

[1139] "Basic information" is basic data about the structure, such as the structure's name, construction date, past inspection dates, and inspection result scores.

[1140] "Calculating age" means calculating the difference between the current date and the construction date of the structure and converting it to years.

[1141] The "average condition score" is the average of the total past inspection scores divided by the number of inspections.

[1142] "Expected Life" is the estimated life of a structure calculated from the average condition score based on a baseline life.

[1143] A "safety threshold" is a reference point used to evaluate the safety of a structure, usually expressed as a specific number.

[1144] "Evaluating safety" means comparing the most recent inspection score with a safety threshold to determine the safety of the structure.

[1145] "Generating repair proposals" means proposing appropriate repair methods based on the results of safety assessments of structures.

[1146] "Emotion data" refers to data relating to emotions collected from the user's voice, facial expressions, input content, and the like.

[1147] The "emotional state" is the user's current emotional state obtained as a result of analyzing the emotion data.

[1148] "Optimizing" means adjusting the content of suggestions and notification methods based on the user's emotional state.

[1149] This invention is a system that efficiently manages the deterioration state of equipment and machinery in a factory and provides optimal repair proposals according to the emotional state of workers. This system functions mainly through a server, terminals, and users, and is implemented in the following steps.

[1150] First, the server collects basic information about the equipment and machines in the factory, including the equipment name, installation date, past inspection dates and inspection result scores, etc. This data is automatically collected from sensor networks and IoT devices.

[1151] Next, the server calculates the current age of the equipment or machinery based on the collected data. The age is calculated by calculating the difference between the current date and the installation date and converting it into years. For example, if a machine was installed on June 1, 2010, it will be approximately 13 years old as of June 1, 2023.

[1152] Furthermore, the server calculates an average condition score based on past inspection scores. The average condition score is calculated by adding up past inspection scores (for example, 80, 75, and 70) and dividing the total by the number of inspections. In this case, the result is (80 + 75 + 70) / 3 = 75 points. Based on this average condition score, the server calculates the expected lifespan of the equipment or machinery. If the standard lifespan is, for example, 50 years and the maximum score is 100, a score of 75 indicates a 75% expected lifespan, which is calculated as 37.5 years.

[1153] Next, the server receives the latest inspection score and compares it with a safety threshold (e.g., 70 points) to evaluate whether it is safe. For example, if the latest inspection score is 65 points, it is below the safety threshold of 70 points and is therefore judged to be "unsafe." Based on the results of this safety evaluation, the server determines the need for repairs and generates specific repair proposals.

[1154] Furthermore, this system is equipped with an emotion engine. When a user interacts with the system through a terminal, the emotion engine collects the user's emotional data (voice, facial expressions, input content, etc.). The emotion engine analyzes this data and identifies the user's emotional state. For example, it can identify whether the user is feeling stressed or relieved.

[1155] The server optimizes repair suggestions based on the emotional data provided by the emotion engine. The server can adjust the content of the suggestions and the notification method based on the user's emotional state when receiving the repair suggestions. For example, if the user is feeling stressed, the server can select a notification method that emphasizes that the suggestions are simple and easy to implement. Conversely, if the user is relaxed, the server can provide suggestions with detailed explanations.

[1156] Finally, the user receives the optimized repair proposals through their device. The device receives the repair proposals from the server and displays them to the user. The user can then create a repair plan based on the displayed repair proposals. For example, even if the user is feeling stressed, easy-to-understand proposals can help them make appropriate decisions and take appropriate action.

[1157] As a specific example, consider a machine installed in 2010 that has received scores of 80, 75, and 65 over three previous inspections (2015, 2020, and 2022).The system operates based on this data, calculating the machine's current age, its expected lifespan, and safety assessment, and then generates optimized repair proposals based on the results.

[1158] Example prompt sentence:

[1159] "It reads the latest inspection data from within the factory and outputs repair suggestions in a concise format if the user is feeling stressed, or in a detailed format if the user is feeling reassured."

[1160] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1161] Step 1:

[1162] The server collects basic information about the equipment and machines in the factory. Specifically, it obtains the equipment name, installation date, past inspection dates, and inspection result scores from the sensor network and IoT devices, and stores this information in a database. The input is the basic information obtained from the sensor network and IoT devices, and the output is the information stored in the database.

[1163] Step 2:

[1164] The server calculates the age of the equipment or machinery based on the collected basic information. Specifically, it calculates the difference between the current date and the installation date of the equipment and converts it into years. The input is the installation date stored in the database, and the output is the calculated age.

[1165] Step 3:

[1166] The server calculates the average condition score from past inspection scores. Specifically, it calculates the average by dividing the sum of past inspection scores by the number of inspections. The input is the past inspection scores stored in the database, and the output is the calculated average condition score.

[1167] Step 4:

[1168] The server calculates the expected lifespan of equipment and machinery based on the average condition score. Specifically, it calculates the expected lifespan as a function using the reference lifespan and the average condition score. The inputs are the average condition score and the reference lifespan, and the output is the calculated expected lifespan.

[1169] Step 5:

[1170] The server compares the latest inspection score with the safety threshold to evaluate safety. Specifically, it compares the latest inspection score with the threshold and determines whether it is safe or not. The input is the latest inspection score and the safety threshold, and the output is the evaluation result (safe / unsafe).

[1171] Step 6:

[1172] The server determines the need for repairs based on the results of the safety assessment and generates repair proposals. Specifically, if the system is assessed as "unsafe," it generates a proposal for immediate repairs. The input is the safety assessment result, and the output is a repair proposal.

[1173] Step 7:

[1174] The device collects the user's emotional data (voice, facial expressions, input content, etc.). Specifically, it collects the user's emotional data using input devices such as a camera or microphone. The input is the emotional data obtained from the camera or microphone, and the output is the collected emotional data.

[1175] Step 8:

[1176] The server analyzes the emotional data and recognizes the user's emotional state. Specifically, it uses an emotion engine to analyze the data and identify whether the user is stressed or relaxed. The input is the collected emotional data, and the output is the recognized emotional state.

[1177] Step 9:

[1178] The server optimizes repair suggestions based on the recognized emotional state. Specifically, it provides brief suggestions if the user is stressed, and detailed suggestions if the user is relaxed. The input is the recognized emotional state and the generated repair suggestions, and the output is the optimized repair suggestions.

[1179] Step 10:

[1180] The terminal receives the optimized repair proposal from the server and displays it to the user. Specifically, the terminal displays the optimized repair proposal on the display and notifies the user. The input is the optimized repair proposal sent from the server, and the output is the optimized repair proposal displayed to the user.

[1181] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1182] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1183] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1184] [Fourth embodiment]

[1185] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1186] 7, a 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.

[1187] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1188] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1189] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1190] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1191] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1192] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1193] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1194] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1195] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1196] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1197] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1198] This invention is a system that comprehensively manages and evaluates the condition of aging structures and proposes appropriate repairs. This system functions mainly through a server, terminals, and users.

[1199] First, the server collects basic information about the structure. The user uses a terminal to input the structure's name, construction date, past inspection dates, and the inspection result scores, etc. This data is sent to the server and stored in the server's database.

[1200] The server then uses this data to calculate the age of the structure. The method used to calculate the age is to calculate the difference between the current date and the construction date of the structure and convert it to years. For example, a bridge built in 1965 would be approximately 58 years old in 2023.

[1201] The server then calculates an average condition score based on past inspection scores. If the past three inspection results were 80, 75, and 60 points, respectively, the average condition score would be (80 + 75 + 60) / 3 = 71.67 points. Based on this average condition score, the server calculates the expected lifespan of the structure. If the base lifespan is 50 years and the maximum score is 100, a score of 71.67 points means that the expected lifespan is 71.67%, or 35.83 years.

[1202] The server receives the latest inspection score and compares it with a safety threshold (for example, 70 points). Based on this comparison, the server evaluates whether the structure is currently safe. For example, if the latest inspection score is 60 points, it is below the safety threshold of 70 points and is therefore judged to be "unsafe."

[1203] Based on the results of this safety assessment, the server determines whether repairs are necessary. If the assessment result is "unsafe," the server determines that immediate repairs are necessary and generates specific repair proposals. For example, a proposal such as "City Bridge needs immediate repair" may be generated. Users can receive these proposals via their devices and create appropriate repair plans.

[1204] This system enables comprehensive management of aging structures, reducing risks from natural disasters and enabling the development of efficient repair plans. As a concrete example, consider a bridge built in 1965 that received scores of 80, 75, and 60 in three previous inspections (2000, 2010, and 2020). The system operates based on this data, calculating the bridge's current age, calculating its expected lifespan, and conducting a safety assessment, before generating a recommendation for immediate repairs.

[1205] The processing flow will be explained below.

[1206] Step 1:

[1207] The server sends a request to collect data on the structure. The user uses a terminal to input the structure's name, construction date, past inspection dates, and inspection result scores. The terminal sends this information to the server. The server stores the received data in a database.

[1208] Step 2:

[1209] The server calculates the current age of a structure based on the stored data. For example, the server compares the construction date of the structure with the current date and converts the difference into years. For example, a bridge built on June 1, 1965, will be approximately 58 years old on June 1, 2023.

[1210] Step 3:

[1211] The server calculates the average condition score based on past inspection scores. Specifically, it adds up the past inspection scores (for example, 80, 75, and 60 points) and divides the total by the number of inspections. This calculates the average condition score. In this case, it is (80 + 75 + 60) / 3 = 71.67 points.

[1212] Step 4:

[1213] The server calculates the expected lifespan based on the average health score. The reference lifespan is assumed to be 50 years, and the average health score is calculated based on a percentage of 100. For example, if the average health score is 71.67 points, that is 71.67% of the reference lifespan, and the expected lifespan is 35.83 years.

[1214] Step 5:

[1215] The server receives the latest inspection score. Let's say the latest inspection score is 60 points. The server compares this with the safety threshold and evaluates whether it is safe or not.

[1216] Step 6:

[1217] The server compares the safety threshold and evaluates the safety of the structure. For example, if the safety threshold is set at 70 points, a score of 60 will be evaluated as "unsafe."

[1218] Step 7:

[1219] The server determines the need for repairs based on the results of the safety assessment. If the assessment result is "unsafe," the server determines that immediate repairs are required.

[1220] Step 8:

[1221] The server generates a repair proposal. Specifically, it summarizes the reasons why repairs are necessary and the recommended repair methods. For example, a proposal such as "City Bridge needs immediate repair" is generated.

[1222] Step 9:

[1223] The user receives the repair proposal through the terminal. The terminal receives the repair proposal from the server and displays it to the user. The user can then create a repair plan based on the displayed repair proposal.

[1224] These are the specific processing steps of this system, which will enable efficient and comprehensive management and maintenance of aging structures.

[1225] Example 1

[1226] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1227] The lack of a system for comprehensively managing safety assessments and repair needs for aging structures is an issue. In particular, there is a need for efficient calculations of the expected lifespan of structures based on inspection results, safety assessments, and repair proposals.

[1228] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1229] In this invention, the server includes means for collecting basic information about the target structure, means for calculating the age of the target structure, means for calculating an average condition score for the target structure from past inspection results, means for calculating an expected lifespan of the target structure based on the average condition score, means for evaluating the safety of the target structure by comparing the latest inspection score with a safety threshold, means for determining the need for repairs based on the results of the safety evaluation and generating repair proposals, and means for transmitting the generated repair proposals to a user's terminal so that the user can confirm the proposals. This enables comprehensive management of aging structures and the development of efficient repair plans.

[1230] "Structures" are artificially constructed objects such as buildings, bridges, and roads.

[1231] "Basic information" refers to basic data about the structure, such as the structure's name, construction date, location, materials, and design specifications.

[1232] "Age" refers to the period in years between the date of construction of the structure and the present date.

[1233] "Inspection results" are numerical data regarding the condition and performance of a structure collected through inspection or investigation.

[1234] The "average condition score" is a numerical value that evaluates the overall condition of a structure, calculated from past inspection results.

[1235] "Expected life" is the period, in years, that a structure is expected to continue performing as designed.

[1236] A "safety threshold" is the minimum score that a structure must meet to be considered safe.

[1237] A "repair proposal" is a proposal regarding specific repair content and plans that is generated when it is determined that repairs to a structure are necessary based on a safety assessment.

[1238] A "user" is an entity that operates the system, inputs information about the structure, and checks repair proposals.

[1239] "Terminal" refers to a device used by a user to operate the system, including a personal computer or smartphone.

[1240] The "server" is a computer that performs the central processing of this system and has the functions of collecting, storing, calculating, and evaluating data.

[1241] This invention is a system that comprehensively manages and evaluates the condition of aging structures and proposes appropriate repairs. This system functions mainly through a server, terminals, and users. The detailed configuration and specific operation of the system are explained below.

[1242] System configuration

[1243] 1. Hardware Configuration

[1244] Server: A central data processing and storage computer, such as an EC2 instance on AWS or an on-premise server.

[1245] Terminal: A device that allows users to input information about a structure and check repair proposals. This includes personal computers (PCs) and smartphones.

[1246] Network: Infrastructure for sending and receiving data between terminals and servers, using the Internet or dedicated lines.

[1247] 2. Software Configuration

[1248] Database: A system for storing information about the structure and inspection results, for example a relational database such as MySQL or PostgreSQL.

[1249] AI model: A model for generating repair suggestions. A generative AI model (e.g., GPT-3) is used.

[1250] Programming language and framework: The programs that run on the server are written in Python, JavaScript (Node.js), or similar, and use a web framework (e.g., Django or Express).

[1251] System Operation

[1252] Data collection

[1253] The user inputs basic information about the structure via the terminal, specifically the structure name, construction date, past inspection dates, and inspection score into the form, and then presses the submit button.

[1254] Data transmission and storage

[1255] The terminal sends the data entered by the user to the server using a secure HTTPS request.

[1256] The server stores the received data in a database, which stores a record for each structure.

[1257] Age Calculator

[1258] The server calculates the age of a structure by subtracting the current date from its construction date stored in the database. For example, a structure built in 1965 will be 58 years old in 2023.

[1259] Calculating the average condition score

[1260] The server calculates the average condition score based on the past inspection scores by dividing the total score by the number of inspections.

[1261] Expected life calculation

[1262] The server calculates the expected lifespan by comparing the calculated average condition score with the reference lifespan. For example, if the reference lifespan is 50 years, a score of 71.67 points means the expected lifespan is 35.83 years.

[1263] Safety evaluation

[1264] The server compares the latest inspection score with the safety threshold and evaluates whether it is safe. For example, if the latest score is 60 and the threshold is 70, it is evaluated as unsafe.

[1265] Generate repair proposals

[1266] The server generates repair proposals based on safety assessments. It uses a generative AI model to generate specific repair proposals. For example, it generates a proposal that reads, "City Bridge needs immediate repair."

[1267] Example prompts for generating repair proposals:

[1268] Generate repair recommendations based on basic structure information: age, average condition score, expected lifespan, and safety rating.

[1269] data:

[1270] Structure name: Structure A

[1271] Construction date: 1965

[1272] Past inspection dates and scores:

[1273] 2000: 80 points

[1274] 2010: 75 points

[1275] 2020: 60 points

[1276] Latest inspection score: 60 points

[1277] Safety threshold: 70 points

[1278] Standard life: 50 years

[1279] Proposal Notification

[1280] The server sends the generated repair proposal to the user's device, where the user can review the proposal and create an appropriate repair plan.

[1281] In this way, the system can comprehensively manage information on aging structures and make efficient repair proposals.

[1282] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1283] Step 1:

[1284] The user uses the terminal to input basic information about the structure, including the structure name, construction date, past inspection dates, and inspection score. The input information is then sent to the server by pressing the send button.

[1285] Input: Structure name, construction date, past inspection dates, and inspection score

[1286] Output: The input data is sent to the server

[1287] Step 2:

[1288] The device sends the data entered by the user to the server using a secure HTTPS request.

[1289] Input: Basic information of the structure entered by the user

[1290] Output: Data sent to the server in the HTTPS request

[1291] Step 3:

[1292] The server stores the received data in a database, for example using SQLAlchemy to store the data in a relational database (e.g. MySQL or PostgreSQL).

[1293] Input: Basic information in the structure sent from the terminal

[1294] Output: Basic information about the structure stored in the database

[1295] Step 4:

[1296] The server calculates the age of the structure by retrieving the construction date from the database and calculating the difference between that and the current date, using the Python datetime library for this calculation.

[1297] Input: Construction date retrieved from the database

[1298] Output: Age of the structure (in years)

[1299] Specific operation: The server gets the current date and calculates the age by calculating the difference from the construction date.

[1300] Step 5:

[1301] The server calculates an average condition score based on the past inspection scores by dividing the total score by the number of inspections.

[1302] Input: Past inspection scores retrieved from the database

[1303] Output: Mean condition score

[1304] Specific operation: The server calculates the past inspection scores and divides the total by the number of inspections.

[1305] Step 6:

[1306] The server calculates the expected lifespan based on the average condition score, and calculates the lifespan as a percentage of the score based on a standard lifespan of 50 years.

[1307] Input: average condition score, reference lifespan

[1308] Output: Expected Life (in years)

[1309] Specific behavior: Expected lifespan = Reference lifespan (Average condition score / 100)

[1310] Step 7:

[1311] The server compares the latest inspection score with the safety threshold to evaluate safety. If it is below the threshold, it is judged to be "unsafe."

[1312] Input: Latest inspection score, safety threshold

[1313] Output: Safety assessment results

[1314] Specific operation: The server compares the latest inspection score with the threshold and performs an evaluation.

[1315] Step 8:

[1316] The server generates repair proposals based on the safety assessment results, using a generative AI model to create specific proposals.

[1317] Input: Safety assessment results, basic structure information

[1318] Output: Repair proposal

[1319] Specific actions: Generate prompts for the AI ​​model and generate repair suggestions

[1320] Example prompt sentence:

[1321] Generate repair recommendations based on basic structure information: age, average condition score, expected lifespan, and safety rating.

[1322] data:

[1323] Structure name: Structure A

[1324] Construction date: 1965

[1325] Past inspection dates and scores:

[1326] 2000: 80 points

[1327] 2010: 75 points

[1328] 2020: 60 points

[1329] Latest inspection score: 60 points

[1330] Safety threshold: 70 points

[1331] Standard life: 50 years

[1332] Step 9:

[1333] The server transmits the generated repair proposal to the user's terminal.

[1334] Input: Generated repair proposal

[1335] Output: Repair suggestions sent to the user's device

[1336] Specific behavior: Sends an HTTPS response containing repair suggestions to the user's device.

[1337] Step 10:

[1338] The user checks the proposals via the terminal and creates a repair plan.

[1339] Input: Repair proposal sent from the server

[1340] Output: Repair suggestions confirmed by the user

[1341] Specific behavior: The user checks the repair proposal on the device and contacts the repair company if necessary.

[1342] (Application example 1)

[1343] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1344] Managing aging structures involves a wide range of tasks, including maintenance inspections, repairs, and maintenance plan development. Conventional management systems make it difficult to comprehensively evaluate the condition of structures, making efficient management particularly difficult in locations with many structures, such as logistics centers. Furthermore, repair proposals cannot be received immediately based on inspection results, which can increase risk. There is a need to solve these issues, efficiently grasp the condition of structures in real time, and make appropriate repair proposals.

[1345] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1346] In this invention, the server includes means for collecting basic information about the structure, means for calculating the age of the structure, means for calculating an average condition score of the structure from past inspection results, means for calculating an expected lifespan of the structure based on the average condition score, means for evaluating the safety of the structure by comparing the latest inspection score with a safety threshold, means for determining the need for repairs based on the results of the safety evaluation and generating repair proposals, and means for notifying the user of the repair proposals in real time via a wearable device. This makes it possible to grasp the condition of structures in a logistics center in real time and quickly receive appropriate repair proposals.

[1347] "Structure" is a general term for physical structures that are used for a long period of time, such as bridges and buildings.

[1348] "Basic information" refers to the initial data required for evaluating and managing a structure, such as the structure's name, construction date, past inspection dates and the inspection result scores.

[1349] "Age" means the number of years that have passed since the structure was constructed.

[1350] "Inspection Results" means the scores or ratings recorded as a result of an inspection conducted to assess the condition of a structure.

[1351] The "average condition score" is an average value that indicates the overall condition of a structure, calculated based on the scores of past inspection results.

[1352] "Expected life" is the period during which a structure can be safely used, calculated based on the reference life and taking into account the average condition score.

[1353] A "safety threshold" is a standard inspection score set to determine the safety of a structure.

[1354] "Evaluation" refers to the process of making a comprehensive judgment on the current safety of a structure based on the latest inspection scores.

[1355] A "repair proposal" is a specific recommendation or plan for repair or reinforcement based on the condition of a structure and the results of its assessment.

[1356] A "wearable device" is a device such as smart glasses or a head-mounted display that provides real-time information when worn by a user.

[1357] "Real-time notification" refers to the process or functionality that enables users to receive information immediately.

[1358] The "server" is a central computer system that stores and processes data about the structure and exchanges data with users' terminals and wearable devices.

[1359] This invention is a system that comprehensively manages the status of structures in a logistics center and makes appropriate repair proposals. It functions mainly through a server, terminals, and users. The details of this system are described below.

[1360] First, the server collects basic information about the structure. The user uses a terminal to input the structure's name, construction date, past inspection dates, and the inspection result scores, etc. This data is sent to the server and stored in the server's database.

[1361] The server then uses this data to calculate the age of the structure. The method used to calculate the age is to calculate the difference between the current date and the construction date of the structure and convert it to years. For example, a warehouse built in 2000 will be approximately 23 years old in 2023.

[1362] The server then calculates an average condition score based on past inspection scores. If the past three inspection results were 85, 80, and 75 points, respectively, the average condition score would be (85 + 80 + 75) / 3 = 80 points. Based on this average condition score, the server calculates the expected lifespan of the structure. If the standard lifespan is 50 years and the maximum score is 100, a score of 80 means that the expected lifespan is 80%, which is calculated as 40 years.

[1363] The server receives the latest inspection score and compares it with a safety threshold (for example, 70 points). Based on this comparison, the server evaluates whether the structure is currently safe. For example, if the latest inspection score is 65 points, it is below the safety threshold of 70 points and is therefore judged to be "unsafe."

[1364] Based on the results of this safety assessment, the server determines whether repairs are necessary. If the assessment result is "unsafe," the server determines that immediate repairs are necessary and generates specific repair proposals. For example, a proposal such as "Warehouse A requires immediate repairs" may be generated. The user receives these proposals in real time through the smart glasses and can create an appropriate repair plan.

[1365] This system enables comprehensive management of aging structures within logistics centers and allows for the development of efficient repair plans. As a specific example, consider a warehouse that was built in 2000 and recorded scores of 85, 80, and 75 points in three previous inspections (2015, 2018, and 2021). The server operates based on this data, calculates the warehouse's current age, calculates its expected lifespan, performs a safety assessment, and then generates effective repair proposals.

[1366] The hardware used is smart glasses worn by the user (e.g., RealWear HMT-1), and the software uses the Django framework and SQLite database on the server side, allowing users to grasp the status of structures in the logistics center in real time and take prompt action.

[1367] Example prompt sentence:

[1368] "Enter the results of the last three inspections and construction dates of structures (warehouses, mobile vehicles, conveyor systems, etc.) within a distribution center, and generate the structure's age, average condition score, safety rating, and repair recommendations. For example, if a warehouse was built in 2000 and the results of the last three inspections were 85, 80, and 75, show the specific output."

[1369] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1370] Step 1:

[1371] The user uses a terminal to input basic information about the structure (such as the name, construction date, past inspection dates and the inspection result scores), and the input data is sent to the server.

[1372] Input: Name of structure, construction date, past inspection dates, and score for each inspection date

[1373] Output: Basic information about the structure sent to the server

[1374] Specific operation: The user uses a device (smartphone or tablet) to enter basic information about the structure into the input form and presses the submit button.

[1375] Step 2:

[1376] The server stores the received basic information about the structure in a database.

[1377] Input: Basic information about the structure sent by the user

[1378] Output: Basic information about the structure stored in a database

[1379] Specific operation: The server analyzes the received data and executes an INSERT query to save it in the database (SQLite).

[1380] Step 3:

[1381] The server calculates the age of the structure using the difference between the current date and the structure's construction date.

[1382] Input: Construction date of the structure, current date

[1383] Output: Age of structure

[1384] What it does: The server gets the current date and calculates the age of the structure by calculating the difference in years from the construction date.

[1385] Step 4:

[1386] The server calculates the average condition score of the structure from past inspection results.

[1387] Input: Score for each inspection date

[1388] Output: Mean condition score

[1389] Specific operation: The server retrieves past inspection scores from the database and calculates their average.

[1390] Step 5:

[1391] The server calculates the expected lifespan of the structure based on the average condition score. The reference lifespan is set to 50 years, and the expected lifespan is calculated in proportion to the average condition score.

[1392] Input: Reference lifespan, average condition score

[1393] Output: Expected life of the structure

[1394] Specific operation: Calculate the expected lifespan as a percentage of the average condition score against the reference lifespan (50 years).

[1395] Step 6:

[1396] The server compares the latest inspection score with a safety threshold to assess the safety of the structure.

[1397] Input: Latest inspection score, safety threshold

[1398] Output: Safety assessment results

[1399] Specific operation: The server retrieves the latest inspection score, compares it with the safety threshold, and executes logic to evaluate whether it is safe or unsafe.

[1400] Step 7:

[1401] The server determines the need for repairs based on the results of the safety assessment and generates repair proposals.

[1402] Input: Safety assessment results

[1403] Output: Repair suggestion message

[1404] Specific operation: If the safety assessment is "unsafe", a message is generated suggesting that immediate repairs are required.

[1405] Step 8:

[1406] The server notifies the user of repair suggestions in real time via a wearable device (smart glasses).

[1407] Input: Repair proposal message

[1408] Output: Repair suggestions displayed on the user's wearable device

[1409] Specific operation: A repair suggestion message is sent via the wearable device's API and displayed on the smart glasses worn by the user.

[1410] This system allows users to understand the condition of structures within the logistics center in real time and receive quick and appropriate repair suggestions.

[1411] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1412] This invention is a system that comprehensively manages and evaluates the condition of aging structures and makes appropriate repair proposals, and also combines an emotion engine that recognizes the user's emotions and optimizes repair proposals. This system functions mainly through a server, terminals, and users, and is implemented in the following steps.

[1413] First, the server collects basic information about the structure. The user uses a terminal to input the structure's name, construction date, past inspection dates, and the inspection result scores. The terminal then sends this information to the server, which then stores the received data in a database.

[1414] The server then calculates the current age of the structure based on the stored data. The method used to calculate the age is to calculate the difference between the current date and the construction date of the structure and convert it into years. For example, a bridge built on June 1, 1965, will be approximately 58 years old on June 1, 2023.

[1415] Furthermore, the server calculates an average condition score based on past inspection scores. The average condition score is calculated by adding up past inspection scores (for example, 80, 75, and 60 points) and dividing the total by the number of inspections. In this case, the result is (80 + 75 + 60) / 3 = 71.67 points. Based on this average condition score, the server calculates the expected lifespan of the structure. If the standard lifespan is 50 years and the maximum score is 100, a score of 71.67 points means that the expected lifespan is 71.67%, or 35.83 years.

[1416] The server receives the latest inspection score and compares it with a safety threshold (for example, 70 points) to evaluate whether it is safe. For example, if the latest inspection score is 60 points, it is judged to be "unsafe" because it is below the safety threshold of 70 points. Based on the results of this safety evaluation, the server determines whether repairs are necessary. If the evaluation result is "unsafe," the server determines that immediate repairs are necessary and generates a specific repair proposal. For example, a proposal such as "City Bridge needs immediate repair" may be generated.

[1417] Next, the system is equipped with an emotion engine to recognize the user's emotions. When the user interacts with the system through the device, the emotion engine collects emotional data from the user's voice, facial expressions, input, etc. The emotion engine analyzes this data and identifies the user's emotional state. For example, it can identify when the user is feeling stressed or relieved.

[1418] The server optimizes repair suggestions based on the emotional data provided by the emotion engine. The content of the suggestions and the notification method can be adjusted based on the user's emotional state when receiving the repair suggestions. For example, if the user is feeling stressed, the server can select a notification method that makes the suggestions clear and concise, and emphasizes that they are easy to implement.

[1419] Finally, the user receives the optimized repair proposals through their device. The device receives the repair proposals from the server and displays them to the user. The user can then create a repair plan based on the displayed repair proposals. For example, even if the user is feeling stressed, the easy-to-understand proposals will make it easier for them to make appropriate decisions and take appropriate action.

[1420] This system enables efficient and comprehensive management and maintenance of aging structures, and provides suggestions that take the user's emotions into consideration, making it easier to encourage actual action. As a specific example, consider a bridge built in 1965 that received scores of 80, 75, and 60 in three previous inspections (2000, 2010, and 2020). Based on this data, the system calculates the bridge's current age, calculates its expected lifespan, and performs a safety assessment, before generating repair suggestions optimized according to the user's emotional state.

[1421] The processing flow will be explained below.

[1422] Step 1:

[1423] The server sends a request to collect data on the structure. The user uses a terminal to input the structure's name, construction date, past inspection dates, and the inspection result score. The terminal sends this information to the server. The server stores the received data in a database.

[1424] Step 2:

[1425] The server calculates the current age of a structure based on the stored data. For example, the server compares the construction date of the structure with the current date and converts the difference into years. For example, a bridge built on June 1, 1965, will be approximately 58 years old on June 1, 2023.

[1426] Step 3:

[1427] The server calculates the average condition score based on past inspection scores. Specifically, it adds up the past inspection scores (for example, 80, 75, and 60 points) and divides the total by the number of inspections. This calculates the average condition score. In this case, it is (80 + 75 + 60) / 3 = 71.67 points.

[1428] Step 4:

[1429] The server calculates the expected lifespan based on the average health score. The reference lifespan is assumed to be 50 years, and the average health score is calculated based on a percentage of 100. For example, if the average health score is 71.67 points, that is 71.67% of the reference lifespan, and the expected lifespan is 35.83 years.

[1430] Step 5:

[1431] The server receives the latest inspection score. For example, let's say the latest inspection score is 60 points. The server compares this with the safety threshold and evaluates whether it is safe or not.

[1432] Step 6:

[1433] The server compares the safety threshold and evaluates the safety of the structure. For example, if the safety threshold is set at 70 points, a score of 60 will be evaluated as "unsafe."

[1434] Step 7:

[1435] The server determines the need for repairs based on the results of the safety assessment. If the assessment result is "unsafe," the server determines that immediate repairs are required.

[1436] Step 8:

[1437] The server generates a repair proposal. Specifically, it summarizes the reasons why repairs are necessary and the recommended repair methods. For example, a proposal such as "City Bridge needs immediate repair" is generated.

[1438] Step 9:

[1439] The server uses an emotion engine to collect user emotion data. When a user receives repair suggestions through their device, the emotion engine recognizes the user's emotional state from their voice, facial expression, and input. For example, it can identify whether the user is feeling stressed or relieved.

[1440] Step 10:

[1441] The server optimizes repair suggestions based on the emotion data provided by the emotion engine. For example, if the user is feeling stressed, the server selects a notification method that makes the suggestions simple and clear, and emphasizes that they are easy to implement.

[1442] Step 11:

[1443] The user receives the optimized repair proposal through the terminal. The terminal receives the repair proposal from the server and displays it to the user. The user can then create a repair plan based on the displayed repair proposal.

[1444] These are the specific processing steps of this system. This flow enables efficient and comprehensive management and maintenance of aging structures, while also providing optimal proposals that take user feelings into consideration.

[1445] Example 2

[1446] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1447] Conventional structure management systems have difficulty comprehensively assessing the condition of aging structures and proposing appropriate repairs, and are particularly unable to provide repair proposals that take user feelings into consideration. Furthermore, they lack the accuracy required for calculating expected lifespans and safety assessments based on inspection scores.

[1448] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1449] In this invention, the server includes means for collecting basic information about the structure, means for calculating the age of the structure, means for calculating an average condition score of the structure from past inspection results, means for calculating an expected lifespan of the structure based on the average condition score, means for evaluating the safety of the structure by comparing the latest inspection score with a safety threshold, means for determining the need for repairs based on the results of the safety evaluation and generating repair proposals, means for collecting and analyzing emotional data, and means for optimizing the content of the repair proposals according to the emotional state of the user. This not only enables efficient and comprehensive management and maintenance of the structure, but also makes it possible to provide repair proposals that take the user's emotions into consideration, making it easier to encourage actual action.

[1450] "Structures" refers to constructed infrastructure and buildings such as bridges, buildings, and tunnels.

[1451] "Basic information" includes data such as the name of the target structure, construction date, and past inspection dates and results.

[1452] "Age" refers to the period in years between the date of construction of the structure and the present date.

[1453] "Inspection results" refers to the scores and notes resulting from inspections conducted to evaluate the condition of a structure.

[1454] The "average condition score" is a score that indicates the average condition of a structure, calculated based on the scores of past inspection results.

[1455] "Expected life" refers to the remaining useful life of the structure in question, predicted from its current condition.

[1456] The "safety threshold" is the standard value used to evaluate the safety of a structure in the inspection score.

[1457] A "repair proposal" refers to a proposal that specifically indicates the content and methods of repairs necessary to maintain or improve the safety of a structure.

[1458] "Emotion data" refers to data relating to emotions collected from the user's voice, facial expressions, input content, and the like.

[1459] "Emotional state" refers to the specific emotional state displayed by the user, and includes stress, relief, anxiety, and the like.

[1460] "Optimization" means adjusting the content of repair suggestions and notification methods according to the user's emotional state to make them more effective.

[1461] This invention is a system that comprehensively manages and evaluates the condition of aging structures and makes appropriate repair proposals, and also combines it with an emotion engine that recognizes the user's emotions and optimizes repair proposals. The system consists of a server, terminals, and users.

[1462] Program Generation and Execution

[1463] Hardware and software used

[1464] The server uses the following major hardware and software:

[1465] Hardware: High-performance server computer

[1466] Software: MySQL (database management system), Python (programming language)

[1467] The device uses the following main hardware and software:

[1468] Hardware: Laptop, smartphone

[1469] Software: Web browser

[1470] The emotion engine uses the following main hardware and software:

[1471] Hardware: High-performance analysis server

[1472] Software: EmotionAPI (emotion analysis library)

[1473] System action

[1474] Data collection

[1475] The server receives basic information about the structure from the device and stores it in a database. For example, a user might enter information such as the bridge name "City Bridge," the construction date "1965-06-01," and past inspection dates and results (2000-01-01: 80 points, 2010-01-01: 75 points, and 2020-01-01: 60 points). This information is sent to the server and stored in a MySQL database.

[1476] Age Calculator

[1477] The server retrieves the construction date from the database and calculates the difference from the current date using Python's datetime library. For example, if the current date is June 1, 2023, a structure built on June 1, 1965 would be calculated to be 58 years old.

[1478] Mean condition score

[1479] The server calculates the average condition score from past inspection results. For example, if the past inspection scores are 80, 75, and 60, the average condition score is calculated by adding these scores together and dividing by 3, resulting in 71.67 points.

[1480] Expected lifespan

[1481] The server calculates the expected lifespan by taking into account the average condition score against the reference lifespan (50 years). For example, if the average condition score is 71.67 points, the expected lifespan is 35.83 years, which is 71.67% of the reference lifespan.

[1482] Safety assessment and repair proposals

[1483] The server obtains the latest inspection score and compares it with a safety threshold (for example, 70 points). For example, if the latest inspection score is 60 points, it is judged to be "unsafe" and generates a repair suggestion such as "City Bridge needs immediate repair."

[1484] Emotion data collection and analysis

[1485] The terminal collects voice, facial expressions, inputs, etc. when the user interacts with the system.

[1486] The emotion engine analyzes this data in real time to identify the user's emotional state. For example, if the user has an anxious expression, it will classify the user as "stressed."

[1487] Optimizing repair proposals

[1488] The server adjusts the content of repair suggestions and notification methods based on the emotional data. For example, if the user is feeling stressed, the server will make the suggestions simple and easy to implement.

[1489] Specific examples

[1490] Examples of specific prompts include:

[1491] "For a bridge built in 1965, the server calculates its current age and condition score based on past inspection scores of 80, 75, and 60, calculates its expected lifespan, evaluates its safety, and generates repair recommendations. In addition, the emotion engine optimizes recommendations to be concise and easy to implement if the user is feeling stressed."

[1492] As a result, the present invention enables efficient and comprehensive management and maintenance of structures, and further provides repair proposals that take into consideration the user's feelings, thereby encouraging actual action.

[1493] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1494] Step 1:

[1495] Data Entry and Submission

[1496] The terminal receives the user's basic information about the structure (name, construction date, past inspection dates, and inspection result scores) through an input form. For example, the user enters "City Bridge," the construction date as "1965-06-01," and the past inspections as "2000-01-01: 80 points," "2010-01-01: 75 points," and "2020-01-01: 60 points." The terminal then sends the entered data to the server. The terminal receives the basic information about the structure from the user as input and sends it to the server as output.

[1497] Step 2:

[1498] Data storage and processing

[1499] The server receives basic information about the structure from the terminal and stores it in a MySQL database. For example, it stores it in the format "City Bridge, 1965-06-01, 2000-01-01:80, 2010-01-01:75, 2020-01-01:60". It takes the received data as input and stores it in the database as output.

[1500] Step 3:

[1501] Age Calculator

[1502] The server retrieves the construction date from the database and calculates the difference with the current date using Python's datetime library. For example, if a structure was built on June 1, 1965, and the current date is June 1, 2023, the structure's age is calculated to be 58 years. It takes the construction date as input, compares it with the current date to calculate the age, and outputs that age.

[1503] Step 4:

[1504] Calculating the average condition score

[1505] The server retrieves past inspection dates and inspection scores from the database, sums the inspection scores, and divides by the number of inspections to calculate the average condition score. For example, if the scores are 80, 75, and 60, the result is (80 + 75 + 60) / 3 = 71.67. It takes in the inspection results as input, performs calculations, and outputs the average condition score.

[1506] Step 5:

[1507] Expected life calculation

[1508] The server calculates the expected lifespan based on the reference lifespan (50 years) and the average condition score. If the average condition score is 71.67 points, the expected lifespan is 35.83 years, which is 71.67% of the reference lifespan. The server receives the average condition score as input, calculates the expected lifespan using the reference lifespan and score, and outputs the value.

[1509] Step 6:

[1510] Safety assessment and repair proposal generation

[1511] The server obtains the latest inspection score and compares it with a safety threshold (for example, 70 points). For example, if the latest inspection score is 60 points, it is judged to be "unsafe" and generates a repair proposal such as "City Bridge needs immediate repair." The server obtains the latest inspection score as input, compares it with the safety threshold for evaluation, and outputs a repair proposal.

[1512] Step 7:

[1513] Emotion data collection and analysis

[1514] The device collects voice, facial expressions, and input content when the user interacts with the system. The emotional data is sent to the server. The emotion engine analyzes the received data and identifies the user's emotional state. It receives voice and facial expression data as input, analyzes it, and outputs the emotional state.

[1515] Step 8:

[1516] Optimizing repair proposals

[1517] The server adjusts the content of repair suggestions and notification methods based on the emotion data provided by the emotion engine. For example, if the user is feeling stressed, the server will simplify the suggestions and make them easier to implement. It receives emotion data as input, adjusts based on it, and outputs optimized suggestions.

[1518] Step 9:

[1519] Receive and view repair proposals

[1520] The terminal receives the optimized repair proposal from the server and displays it to the user, for example, "City Bridge needs immediate repair. Follow these simple steps: [Step 1], [Step 2], [Step 3]". It takes the optimized repair proposal as input and displays it to the user.

[1521] (Application example 2)

[1522] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1523] Currently, detecting the deterioration of equipment and machinery and carrying out repairs at the appropriate time is an important issue in many factories. Furthermore, if repair proposals are made uniformly without considering the emotional state of workers, appropriate decision-making can be difficult. For workers who are feeling stressed or anxious, complex repair proposals and immediate responses can be a significant burden. In these circumstances, there is a need for efficient repair proposals that take into consideration the emotions of workers.

[1524] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting basic information about the target structure, means for calculating the age of the target structure, means for calculating an average condition score of the target structure from past inspection results, means for calculating an expected lifespan of the target structure based on the average condition score, means for evaluating the safety of the target structure by comparing the latest inspection score with a safety threshold, means for determining the need for repairs based on the results of the safety evaluation and generating repair proposals, means for collecting emotion data and recognizing the user's emotional state, and means for optimizing repair proposals based on the recognized emotional state. This makes it possible to efficiently manage the aging state of equipment and machinery and provide optimal repair proposals according to the emotional state of workers.

[1525] "Basic information" is basic data about the structure, such as the structure's name, construction date, past inspection dates, and inspection result scores.

[1526] "Calculating age" means calculating the difference between the current date and the construction date of the structure and converting it to years.

[1527] The "average condition score" is the average of the total past inspection scores divided by the number of inspections.

[1528] "Expected Life" is the estimated life of a structure calculated from the average condition score based on a baseline life.

[1529] A "safety threshold" is a reference point used to evaluate the safety of a structure, usually expressed as a specific number.

[1530] "Evaluating safety" means comparing the most recent inspection score with a safety threshold to determine the safety of the structure.

[1531] "Generating repair proposals" means proposing appropriate repair methods based on the results of safety assessments of structures.

[1532] "Emotion data" refers to data relating to emotions collected from the user's voice, facial expressions, input content, and the like.

[1533] The "emotional state" is the user's current emotional state obtained as a result of analyzing the emotion data.

[1534] "Optimizing" means adjusting the content of suggestions and notification methods based on the user's emotional state.

[1535] This invention is a system that efficiently manages the deterioration state of equipment and machinery in a factory and provides optimal repair proposals according to the emotional state of workers. This system functions mainly through a server, terminals, and users, and is implemented in the following steps.

[1536] First, the server collects basic information about the equipment and machines in the factory, including the equipment name, installation date, past inspection dates and inspection result scores, etc. This data is automatically collected from sensor networks and IoT devices.

[1537] Next, the server calculates the current age of the equipment or machinery based on the collected data. The age is calculated by calculating the difference between the current date and the installation date and converting it into years. For example, if a machine was installed on June 1, 2010, it will be approximately 13 years old as of June 1, 2023.

[1538] Furthermore, the server calculates an average condition score based on past inspection scores. The average condition score is calculated by adding up past inspection scores (for example, 80, 75, and 70) and dividing the total by the number of inspections. In this case, the result is (80 + 75 + 70) / 3 = 75 points. Based on this average condition score, the server calculates the expected lifespan of the equipment or machinery. If the standard lifespan is, for example, 50 years and the maximum score is 100, a score of 75 indicates a 75% expected lifespan, which is calculated as 37.5 years.

[1539] Next, the server receives the latest inspection score and compares it with a safety threshold (e.g., 70 points) to evaluate whether it is safe. For example, if the latest inspection score is 65 points, it is below the safety threshold of 70 points and is therefore judged to be "unsafe." Based on the results of this safety evaluation, the server determines the need for repairs and generates specific repair proposals.

[1540] Furthermore, this system is equipped with an emotion engine. When a user interacts with the system through a terminal, the emotion engine collects the user's emotional data (voice, facial expressions, input content, etc.). The emotion engine analyzes this data and identifies the user's emotional state. For example, it can identify whether the user is feeling stressed or relieved.

[1541] The server optimizes repair suggestions based on the emotional data provided by the emotion engine. The server can adjust the content of the suggestions and the notification method based on the user's emotional state when receiving the repair suggestions. For example, if the user is feeling stressed, the server can select a notification method that emphasizes that the suggestions are simple and easy to implement. Conversely, if the user is relaxed, the server can provide suggestions with detailed explanations.

[1542] Finally, the user receives the optimized repair proposals through their device. The device receives the repair proposals from the server and displays them to the user. The user can then create a repair plan based on the displayed repair proposals. For example, even if the user is feeling stressed, easy-to-understand proposals can help them make appropriate decisions and take appropriate action.

[1543] As a specific example, consider a machine installed in 2010 that has received scores of 80, 75, and 65 over three previous inspections (2015, 2020, and 2022).The system operates based on this data, calculating the machine's current age, its expected lifespan, and safety assessment, and then generates optimized repair proposals based on the results.

[1544] Example prompt sentence:

[1545] "It reads the latest inspection data from within the factory and outputs repair suggestions in a concise format if the user is feeling stressed, or in a detailed format if the user is feeling reassured."

[1546] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1547] Step 1:

[1548] The server collects basic information about the equipment and machines in the factory. Specifically, it obtains the equipment name, installation date, past inspection dates, and inspection result scores from the sensor network and IoT devices, and stores this information in a database. The input is the basic information obtained from the sensor network and IoT devices, and the output is the information stored in the database.

[1549] Step 2:

[1550] The server calculates the age of the equipment or machinery based on the collected basic information. Specifically, it calculates the difference between the current date and the installation date of the equipment and converts it into years. The input is the installation date stored in the database, and the output is the calculated age.

[1551] Step 3:

[1552] The server calculates the average condition score from past inspection scores. Specifically, it calculates the average by dividing the sum of past inspection scores by the number of inspections. The input is the past inspection scores stored in the database, and the output is the calculated average condition score.

[1553] Step 4:

[1554] The server calculates the expected lifespan of equipment and machinery based on the average condition score. Specifically, it calculates the expected lifespan as a function using the reference lifespan and the average condition score. The inputs are the average condition score and the reference lifespan, and the output is the calculated expected lifespan.

[1555] Step 5:

[1556] The server compares the latest inspection score with the safety threshold to evaluate safety. Specifically, it compares the latest inspection score with the threshold and determines whether it is safe or not. The input is the latest inspection score and the safety threshold, and the output is the evaluation result (safe / unsafe).

[1557] Step 6:

[1558] The server determines the need for repairs based on the results of the safety assessment and generates repair proposals. Specifically, if the system is assessed as "unsafe," it generates a proposal for immediate repairs. The input is the safety assessment result, and the output is a repair proposal.

[1559] Step 7:

[1560] The device collects the user's emotional data (voice, facial expressions, input content, etc.). Specifically, it collects the user's emotional data using input devices such as a camera or microphone. The input is the emotional data obtained from the camera or microphone, and the output is the collected emotional data.

[1561] Step 8:

[1562] The server analyzes the emotional data and recognizes the user's emotional state. Specifically, it uses an emotion engine to analyze the data and identify whether the user is stressed or relaxed. The input is the collected emotional data, and the output is the recognized emotional state.

[1563] Step 9:

[1564] The server optimizes repair suggestions based on the recognized emotional state. Specifically, it provides brief suggestions if the user is stressed, and detailed suggestions if the user is relaxed. The input is the recognized emotional state and the generated repair suggestions, and the output is the optimized repair suggestions.

[1565] Step 10:

[1566] The terminal receives the optimized repair proposal from the server and displays it to the user. Specifically, the terminal displays the optimized repair proposal on the display and notifies the user. The input is the optimized repair proposal sent from the server, and the output is the optimized repair proposal displayed to the user.

[1567] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1568] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1569] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1570] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1571] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1572] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1573] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1574] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1575] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1576] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1577] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1578] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1579] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1580] 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.

[1581] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1582] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1583] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1584] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1585] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1586] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1587] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1588] The following is further disclosed regarding the above embodiment.

[1589] (Claim 1)

[1590] A means of collecting basic information about the target structure;

[1591] a means for calculating the age of the structure of interest;

[1592] A means for calculating an average condition score for the target structure from past inspection results;

[1593] means for calculating the expected lifespan of the target structure based on the average condition score;

[1594] a means for evaluating the safety of the target structure by comparing the most recent inspection score with a safety threshold;

[1595] means for determining the need for repairs based on the results of the safety assessment and generating repair proposals;

[1596] A system including:

[1597] (Claim 2)

[1598] The system of claim 1 further comprises means for receiving the latest inspection score of the target structure, comparing it with a safety threshold to evaluate safety, and determining whether the structure is safe based on the results of the evaluation.

[1599] (Claim 3)

[1600] 10. The system of claim 1, wherein the expected lifespan is calculated as a function of the baseline lifespan and the average condition score.

[1601] "Example 1"

[1602] (Claim 1)

[1603] a means of collecting basic information about the target structure;

[1604] a means for calculating the age of the structure of interest;

[1605] means for calculating an average condition score for the target structure from past inspection results;

[1606] means for calculating an expected lifespan of the target structure based on the average condition score;

[1607] a means for assessing the safety of the target structure by comparing the most recent inspection score with a safety threshold;

[1608] means for determining the need for repairs based on the results of the safety assessment and generating repair proposals;

[1609] means for transmitting the generated repair proposal to a user's terminal so that the user can confirm the proposal;

[1610] A system including:

[1611] (Claim 2)

[1612] The system of claim 1 further comprising a means for receiving the latest inspection score of the target structure, comparing it with a safety threshold to evaluate safety, and determining whether the structure is safe based on the results of the evaluation.

[1613] (Claim 3)

[1614] 10. The system of claim 1, wherein the expected lifespan is calculated as a function of the baseline lifespan and the average condition score.

[1615] "Application Example 1"

[1616] (Claim 1)

[1617] A means of collecting basic information about the target structure;

[1618] a means for calculating the age of the structure of interest;

[1619] A means for calculating an average condition score for the target structure from past inspection results;

[1620] means for calculating the expected lifespan of the target structure based on the average condition score;

[1621] a means for evaluating the safety of the target structure by comparing the most recent inspection score with a safety threshold;

[1622] means for determining the need for repairs based on the results of the safety assessment and generating repair proposals;

[1623] means for notifying a user of repair suggestions in real time via a wearable device;

[1624] A system including:

[1625] (Claim 2)

[1626] The system of claim 1 includes a means for receiving the latest inspection score of the target structure, comparing it with a safety threshold to evaluate safety, determining whether it is safe based on the results of the evaluation, and notifying the user of the information via a wearable device.

[1627] (Claim 3)

[1628] 10. The system of claim 1, wherein the expected lifespan is calculated as a function of the baseline lifespan and the average condition score.

[1629] "Example 2: Combining Emotion Engines"

[1630] (Claim 1)

[1631] A means of collecting basic information about the target structure;

[1632] a means for calculating the age of the structure of interest;

[1633] A means for calculating an average condition score for the target structure from past inspection results;

[1634] means for calculating the expected lifespan of the target structure based on the average condition score;

[1635] a means for evaluating the safety of the target structure by comparing the most recent inspection score with a safety threshold;

[1636] means for determining the need for repairs based on the results of the safety assessment and generating repair proposals;

[1637] a means for collecting and analyzing emotion data;

[1638] means for optimizing repair suggestions according to the emotional state of the user;

[1639] A system including:

[1640] (Claim 2)

[1641] The system of claim 1 further comprises means for receiving the latest inspection score of the target structure, comparing it with a safety threshold to evaluate safety, and determining whether the structure is safe based on the results of the evaluation.

[1642] (Claim 3)

[1643] 10. The system of claim 1, wherein the expected lifespan is calculated as a function of the baseline lifespan and the average condition score.

[1644] "Application example 2 when combining emotion engines"

[1645] (Claim 1)

[1646] A means of collecting basic information about the target structure;

[1647] a means for calculating the age of the structure of interest;

[1648] A means for calculating an average condition score for the target structure from past inspection results;

[1649] means for calculating the expected lifespan of the target structure based on the average condition score;

[1650] a means for evaluating the safety of the target structure by comparing the most recent inspection score with a safety threshold;

[1651] means for determining the need for repairs based on the results of the safety assessment and generating repair proposals;

[1652] means for collecting emotional data and recognizing the emotional state of a user;

[1653] a means for optimizing repair suggestions based on the recognized emotional state;

[1654] A system including:

[1655] (Claim 2)

[1656] The system of claim 1 further comprises means for receiving the latest inspection score of the target structure, comparing it with a safety threshold to evaluate safety, and determining whether the structure is safe based on the results of the evaluation.

[1657] (Claim 3)

[1658] 10. The system of claim 1, wherein the expected lifespan is calculated as a function of the baseline lifespan and the average condition score. [Explanation of symbols]

[1659] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting basic information about the target structure; a means for calculating the age of the structure of interest; A means for calculating an average condition score for the target structure from past inspection results; means for calculating the expected lifespan of the target structure based on the average condition score; a means for evaluating the safety of the target structure by comparing the most recent inspection score with a safety threshold; means for determining the need for repairs based on the results of the safety assessment and generating repair proposals; A system including:

2. The system according to claim 1, further comprising means for receiving the latest inspection score of the target structure, comparing the score with a safety threshold to evaluate safety, and determining whether the structure is safe based on the results of the evaluation.

3. 10. The system of claim 1, wherein the expected lifespan is calculated as a function of the baseline lifespan and the average condition score.

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