system

Abstract

We provide the system. [Solution] A means for generating logistics information by integrating land transport information and maritime transport information using an information processing device, A means for automatically generating the optimal logistics route and tasks using an artificial intelligence agent based on the aforementioned logistics information, A means for displaying the generated logistics routes and tasks on a display device and accepting input from the user, A means of determining the final logistics plan that reflects user input data and deploying it to the relevant logistics systems, A system that includes this.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In a conventional logistics system, there is a problem that land transportation information and sea transportation information are separated, making it difficult to generate an optimal logistics route or task. As a result, problems such as a decrease in transportation efficiency, an increase in environmental load, and difficulty in coping with a rapid labor shortage have occurred. Furthermore, it is difficult to take appropriate real-time measures when an abnormality occurs, which may have an adverse effect on the overall logistics efficiency. 【Means for Solving the Problems】 【0005】 This invention solves the above problems by integrating land transport information and sea transport information in an information processing device and automatically generating optimal logistics routes and tasks using an artificial intelligence agent based on the generated logistics information. The generated logistics plan can be viewed by the user via a display device and adjusted based on input. Furthermore, it has a function to monitor the logistics status in real time and immediately notify the user of an alert if an anomaly is detected, providing a system that minimizes the decrease in logistics efficiency and adverse environmental impacts. In addition, even in situations where multiple modes of transport are mixed, sustainable logistics operations are realized by adjusting logistics routes and tasks to evaluate and minimize the environmental impact. 【0006】 An "information processing device" is a computer or computer system that has the function of collecting, processing, analyzing, and outputting data. 【0007】 "Land transport information" refers to information such as schedules, routes, and resources related to transportation using trucks, rail, etc., on roads. 【0008】 "Maritime transport information" refers to information such as schedules, routes, and resources related to maritime transport using ships. 【0009】 "Logistics information" refers to a collection of data used to generate efficient transportation routes and tasks by integrating land and sea transportation information. 【0010】 An "artificial intelligence agent" is a program or system that automatically analyzes data, makes decisions based on the results, and proposes the optimal logistics route and tasks. 【0011】 An "optimal logistics route and task" is a plan that maximizes transportation efficiency and minimizes problems, evaluated based on criteria such as efficiency, cost, and environmental impact. 【0012】 A "display device" refers to a device such as a monitor or screen used to visually output generated data or information. 【0013】 "User input" refers to actions or behaviors by which a user provides new data or feedback through an information processing device. 【0014】 An "alert" refers to a warning message or signal used to notify users of anomalies or problems detected during the logistics process. 【0015】 A "logistics plan" refers to a detailed plan for realizing the generated logistics routes and tasks and executing them in a manageable state. [Brief explanation of the drawing] 【0016】 [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]Shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined. 【Mode for Carrying Out the Invention】 【0017】 Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings. 【0018】 First, the terms used in the following description will be explained. 【0019】 In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like. 【0020】 In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0021】 In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes. 【0022】 In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark). 【0023】 In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or." 【0024】 [First Embodiment] 【0025】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0026】 As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server. 【0027】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0028】 The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52. 【0029】 The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input. 【0030】 The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor. 【0031】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54. 【0032】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0033】 As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30. 【0034】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0035】 In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0036】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0037】 This invention is a logistics optimization system implemented by an information processing device, namely a server, a terminal, and a user operating it, and aims to realize more efficient and sustainable logistics operations. 【0038】 The server first collects land and sea transport information from various data sources on the internet and internal corporate databases. This data includes diverse information related to logistics, such as truck schedules and port usage. The collected data is unified from different formats and centrally managed in a standardized form. 【0039】 Based on this integrated data, the server uses artificial intelligence agents to evaluate multiple transportation options and automatically generate the optimal logistics route, reduce tasks, and efficiently allocate resources. This generation process applies complex algorithms to minimize transportation costs, time, and environmental impact. 【0040】 The generated transportation plan is displayed visually to the user via a terminal. An intuitive and easy-to-understand user interface is used, allowing users to quickly review the plan details. Users can provide feedback and modify conditions via the terminal as needed, and this information is immediately reflected on the server. 【0041】 As a concrete example, consider a case where a user needs to transport a large amount of cargo from Tokyo to Osaka. In this case, the server takes into account the shortage of truck drivers and congestion in sea transport, and proposes a route that combines sea transport from Tokyo to Kobe Port, followed by land transport from Kobe to Osaka. This proposal is designed to reduce environmental impact and minimize overall costs. 【0042】 Even after the plan is executed, real-time monitoring of the logistics status is performed via terminals, and alerts are sent to users if any anticipated problems or delays occur. This information allows users to make quick decisions and ensure that the logistics flow is maintained without interruption. 【0043】 This system can provide sustainable solutions to the driver shortage and environmental problems facing the logistics industry. Furthermore, by effectively utilizing various modes of transport, it can significantly improve overall logistics efficiency. 【0044】 The following describes the processing flow. 【0045】 Step 1: 【0046】 The server collects data related to land and sea transport from the internet and internal corporate databases. Specifically, it obtains truck operational status, port utilization rates, weather information, etc., via APIs, and utilizes scraping techniques as needed. 【0047】 Step 2: 【0048】 The server standardizes the collected data and stores it in an integrated database. Here, different data formats are converted into a unified format, and missing or inconsistent data is automatically identified, supplemented, and corrected. 【0049】 Step 3: 【0050】 The server runs an artificial intelligence agent that uses integrated data to generate optimal logistics routes and tasks. In this process, the best plan is selected from multiple route options based on conditions such as transportation costs, time, and environmental impact. 【0051】 Step 4: 【0052】 The generated plan is visualized on the device. The user interface graphically displays route details, anticipated problems, and cost analysis. Users can make quick decisions based on this information. 【0053】 Step 5: 【0054】 Users review the logistics plan displayed on their device and enter any necessary changes or additional conditions. For example, they can add specific time limits or prioritize certain modes of transport. 【0055】 Step 6: 【0056】 The server takes user input, reconstructs the plan, and finalizes the adjusted route. The final plan is then deployed to the logistics system and moves into the execution phase. 【0057】 Step 7: 【0058】 The terminal monitors the ongoing logistics process in real time and reports progress and anticipated problems to the user. As soon as an anomaly is detected, an alert is sent to the user, prompting a quick response. 【0059】 (Example 1) 【0060】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0061】 Modern logistics systems face challenges of complexity and inefficiency. In particular, finding the optimal transportation route is extremely difficult amidst the diversification of transport methods. Furthermore, increasing environmental impact has become a social issue, demanding sustainable logistics operations. The lack of systems capable of real-time monitoring of logistics status and rapid alert notifications is also a problem. 【0062】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0063】 This invention includes a server that collects transportation information using a data processing device, converts it into a unified format, and centrally manages it; a server that automatically generates the optimal transportation route and resource allocation using a generation AI model; and a server that monitors the logistics status in real time and immediately notifies the user of any problems if they are detected. This enables the construction of optimal logistics routes and flexible real-time responses even under complex transportation conditions. 【0064】 A "data processing device" is a device consisting of hardware and software for collecting, transforming, storing, and managing information. 【0065】 "Transportation information" refers to operational schedules, traffic conditions, port usage, and related data concerning various modes of transport, including land and sea. 【0066】 A "unified format" is a standardized data format used to convert data in different formats into a consistent format, enabling cross-platform data management. 【0067】 A "generative AI model" is an algorithm that uses machine learning and data analysis techniques to extract patterns and insights from data and propose the optimal logistics route and resource allocation. 【0068】 "Transportation route" is a term that refers to the optimal path or route selected for sending goods or cargo, and may include a combination of different modes of transport. 【0069】 "Resource allocation" is the process of appropriately allocating and managing human, material, and financial resources available for efficient logistics operations. 【0070】 "Real-time monitoring" is a process of continuously observing the current situation without time delay and updating information and taking action as needed. 【0071】 "Notifying a warning" is an action taken to immediately inform users when an anomaly or malfunction is detected, in order to encourage a quick response. 【0072】 This invention relates to an information processing device for the purpose of optimizing logistics. It primarily consists of a server, terminals, and users who operate them. The server functions as a data processing device, collecting data from multiple transportation-related information sources. These information sources include operational schedules, traffic conditions, and port usage related to land and sea transport. This data collection process is performed in real time via the internet. 【0073】 The server processes the collected data, converting different formats into a unified format for centralized management. This ensures data integrity and enables efficient analysis in the next stage. The server then uses a generative AI model to automatically generate optimal transportation routes and resource allocations based on the collected data. This AI model includes complex algorithms that assess challenges within the logistics network and provide scenarios that minimize cost, time, and environmental impact. 【0074】 The generated transportation plan is presented visually to the user via a terminal. The terminal features an intuitive user interface, allowing users to easily understand the plan and provide feedback as needed. User feedback is immediately reflected on the server, and the plan is re-evaluated. 【0075】 For example, in a scenario where a user needs to transport a large volume of cargo from Tokyo to Osaka, the server will propose a combination of sea transport from Tokyo to Kobe Port and land transport from Kobe to Osaka. This is done to reduce overall costs while taking into account congestion and driver shortages, and while mitigating environmental impact. 【0076】 Even after the plan's execution begins, real-time monitoring of the logistics status is performed using terminals. If any anticipated problems or delays are detected, the terminals immediately notify the user and encourage them to ensure the smooth continuation of the logistics flow. 【0077】 An example of a prompt to the generating AI model is, "Optimize the transportation route from Tokyo to Osaka. Propose the best route and cost reduction measures, taking into account the shortage of truck drivers and congestion." In this way, this invention achieves optimization that meets the complex needs of logistics, enabling more sustainable and efficient logistics operations. 【0078】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0079】 Step 1: 【0080】 The server collects transportation-related information from multiple data sources on the internet. The server receives input data such as land transport schedules, traffic information, and port usage data for sea transport. Since the collected data is provided in various formats, the server converts them into a unified format. This process involves data cleaning to remove noise and inconsistencies. 【0081】 Step 2: 【0082】 The server stores the data, converted to a unified format, in a database for centralized management. This database is indexed to enable efficient data retrieval. After storage in the database, the server queries the stored data to extract the necessary information. This process ensures data integrity while enabling rapid access. 【0083】 Step 3: 【0084】 The server utilizes a generative AI model to analyze optimal transportation routes and resource allocation from centrally managed data. The inputs provided are factors related to economic efficiency, time, and environmental impact. Based on these factors, the server applies complex algorithms to perform optimization. The output proposes the most efficient route selection and resource allocation. 【0085】 Step 4: 【0086】 The server generates a transportation plan, which is then provided to the user via a terminal. The terminal displays the plan data in a visually easy-to-understand format. Through the user interface, the user can review the plan details and provide feedback. Upon receiving feedback, the terminal sends the input to the server. This process allows the user to easily react to the plan. 【0087】 Step 5: 【0088】 Upon receiving user feedback, the server re-evaluates the plan and updates it as needed. The updated plan is quickly reflected on the device, allowing users to re-verify it. The server also considers the new parameters and runs the AI ​​model again to further optimize it. 【0089】 Step 6: 【0090】 Once the plan is executed, the terminal monitors the delivery status in real time. The real-time data acquired as input is used to detect delays and anomalies. If an anomaly is detected, the terminal immediately alerts the user and provides a notification that includes a suggested solution. 【0091】 This series of processes improves the efficiency and sustainability of logistics, enabling users to achieve flexible logistics operations. 【0092】 (Application Example 1) 【0093】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0094】 Modern logistics demands optimized transportation routes and efficient task management, with real-time situation monitoring and rapid response being particularly challenging. Furthermore, it's necessary to effectively integrate various transportation methods while minimizing environmental impact, costs, and transit times. Traditional systems have struggled to meet all these requirements simultaneously. 【0095】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0096】 This invention includes a server that generates logistics information by integrating land transport information and sea transport information, a server that automatically generates optimal logistics routes and tasks using an artificial intelligence agent, and a server that monitors the logistics status in real time using a mobile terminal, receives change information, and immediately reflects it in the plan. This enables more efficient and sustainable logistics operations. 【0097】 An "information processing device" is a device that has the function of integrating logistics information and generating the optimal transportation route. 【0098】 An "artificial intelligence agent" is a program that evaluates transportation options and automatically generates the optimal logistics route by applying complex algorithms. 【0099】 "Logistics information" is information generated by integrating data related to land and sea transport, and it forms the basis of logistics planning. 【0100】 A "display device" is a device that visually shows generated logistics routes and tasks, and provides an interface for users to check and input information. 【0101】 A "mobile terminal" is a portable electronic device used by managers at logistics centers and other locations to monitor the situation in real time and immediately input any changes. 【0102】 "Real-time monitoring" is a process that instantly observes the progress of logistics and immediately detects any signs of delays or other anomalies. 【0103】 "Environmental impact" refers to the effects that logistics activities have on the natural environment, and minimizing this impact is required. 【0104】 To implement this invention, a server, acting as an information processing device, plays a central role. The server collects data related to land and sea transport in order to aggregate logistics information. This involves obtaining data from sources such as internal company databases and APIs for providing official transport information. This collected data is then processed and standardized using Python libraries such as Pandas and Requests. 【0105】 Next, the server generates the optimal logistics route based on integrated logistics information through an artificial intelligence agent. It utilizes generative AI models such as Scikit-learn and TENSORFLOW® to apply complex algorithms that take into account minimizing transportation costs, time, and environmental impact. 【0106】 Meanwhile, the terminal provides a user-friendly interface developed with React Native. Users can check the logistics status in real time via the mobile terminal and easily provide necessary feedback and make changes to plans through the terminal. Real-time monitoring of logistics status is achieved using WebSocket technology, and the system is equipped with a function that immediately notifies the user of alerts if delays or anomalies occur. 【0107】 For example, if a logistics center manager wants to optimize cargo transport from Tokyo to Osaka, they can use the app to check the current status and see the most efficient route suggested by AI. If delays are predicted during transport, an alert will be sent to the manager's terminal, and an alternative route will be presented. 【0108】 An example of a prompt message might be: "Please propose the optimal logistics route from Tokyo to Osaka, minimizing environmental impact, cost, and time." 【0109】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0110】 Step 1: 【0111】 The server collects land and sea transport information from APIs and internal corporate databases. Its inputs are various forms of logistics data, and its output is integrated logistics information. The server uses Pandas to standardize and manage the data. 【0112】 Step 2: 【0113】 The server passes integrated logistics information as input to an artificial intelligence agent. Based on this input data, the agent uses TensorFlow, a generative AI model, to apply logic that minimizes transportation costs, time, and environmental impact, and outputs the optimal logistics route. The AI ​​agent utilizes complex algorithms to generate an efficient plan from multiple options. 【0114】 Step 3: 【0115】 The terminal visually presents the user with optimized transportation route information transmitted from the server. The input is optimized logistics route information from the server, and the output is visual information displayed on the user's screen. The interface, developed with React Native, allows users to easily access and review the information. 【0116】 Step 4: 【0117】 The user inputs any necessary changes or feedback based on the information presented. This input is sent to the server via the terminal and reflected in the logistics plan as new conditions. Here, the user's feedback acts as output for revising the plan. 【0118】 Step 5: 【0119】 The terminal uses WebSocket to perform real-time monitoring during the logistics process. Its input is progress data during logistics, and its output is an alert generated when an anomaly is detected. This immediately notifies the user. 【0120】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0121】 This invention combines an emotion engine with a logistics optimization system to provide an interface experience that takes user emotions into account, thereby achieving more efficient logistics management. This system is implemented by information processing devices, namely servers, terminals, and the users who use them. 【0122】 The server first collects land and sea transport information from the internet and internal corporate databases, and integrates this information to generate logistics data. Based on this logistics data, an artificial intelligence agent automatically generates the optimal logistics route and tasks. The generated plan is then optimized based on pre-configured conditions within the server. 【0123】 Here, the present invention further incorporates an emotion engine into the terminal to analyze the user's facial expressions, voice, input patterns, etc., when viewing plans, and recognizes the user's emotions. This emotion information is reflected in the display content and style of the user interface. For example, if the user is feeling anxious, detailed information can be presented in a clearer manner, or if the user is satisfied with the choices, an animation can be displayed to encourage smooth execution. 【0124】 As a concrete example, consider a scenario where a user is planning transportation from Tokyo to Fukuoka using the system. In this case, the server generates the optimal transportation route from existing data, and the terminal presents the details to the user. If the emotion engine detects stress from the user's facial expressions and voice while viewing the information, the terminal provides detailed support information and guided navigation. Furthermore, if the user expresses satisfaction with the proposed plan, the execution confirmation process is simplified to reduce response time. 【0125】 Users review the presented plan and provide emotion-based feedback through input on a terminal. Based on this feedback, the server adjusts the final logistics plan and moves to the execution phase. As a result, users can manage their logistics more efficiently and comfortably through an emotion-sensitive system operation. 【0126】 The following describes the processing flow. 【0127】 Step 1: 【0128】 The server collects data on land and sea transport from multiple sources. It obtains real-time data via APIs and generates logistics information by standardizing and centralizing different data formats. 【0129】 Step 2: 【0130】 The server inputs the generated logistics information into an artificial intelligence agent, which automatically generates the optimal logistics route and tasks. The algorithm creates the plan while taking cost, time, and environmental impact into consideration. 【0131】 Step 3: 【0132】 The terminal presents the generated logistics plan to the user. At this time, the emotion engine is activated and analyzes the user's voice, facial expressions, and input patterns to recognize the user's emotions. 【0133】 Step 4: 【0134】 Based on the analysis results of the emotion engine, the device adjusts the user interface. For example, if the user shows anxiety, it displays additional information or guides to reassure them. Also, if the user is satisfied with the plan, the confirmation process is simplified. 【0135】 Step 5: 【0136】 The user operates the device to review the generated plan and enter feedback. This feedback, including emotional aspects, is reflected on the device and sent to the server. 【0137】 Step 6: 【0138】 The server takes feedback into account and makes final adjustments to the plan. The final version of the plan is then deployed throughout the entire logistics system. 【0139】 Step 7: 【0140】 The terminal monitors the logistics process in real time during the execution phase and alerts the user if problems or delays occur. It also uses the functionality of an emotion engine to prompt the user to take appropriate action. 【0141】 (Example 2) 【0142】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0143】 In logistics systems, existing route and task optimization methods often fail to consider the psychological state of users, which can lead to stress and frustration that hinders management efficiency. Therefore, there is a need to achieve efficient and comfortable logistics management that takes users' emotional states into account. Furthermore, systems that lack environmental considerations can, conversely, cause social problems. 【0144】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0145】 In this invention, the server includes means for generating logistics information by integrating land transport information and sea transport information using an information processing device, means for automatically generating optimal logistics routes and operations using an artificial intelligence agent, and means for recognizing the user's emotions using an emotion analysis engine and dynamically adjusting the interface. This makes it possible to provide an interface that takes the user's emotions into consideration and to realize more efficient and comfortable logistics management. 【0146】 An "information processing device" refers to a device that has the ability to collect, integrate, and analyze data related to transportation. 【0147】 "Information on land transport" refers to data related to transport conducted using roads. 【0148】 "Maritime transport information" refers to data related to transportation conducted via the ocean using ships. 【0149】 "Logistics information" refers to information obtained as a result of integrating data from multiple transportation-related sources. 【0150】 An "artificial intelligence agent" refers to artificial intelligence technology that analyzes data and automatically generates optimal transportation plans and tasks. 【0151】 A "logistics route" refers to the path that goods are scheduled to take when they are transported. 【0152】 "Work" refers to a series of activities and processes related to logistics. 【0153】 A "display device" refers to a device used to visually present information to users. 【0154】 "User" refers to a person who operates the system to check and issue instructions regarding logistics information. 【0155】 An "emotion analysis engine" refers to a program that recognizes the user's emotional state and adjusts the information displayed accordingly. 【0156】 An "interface" refers to the operating environment that allows users to interact with a system. 【0157】 "Logistics planning" refers to the final plan for the transportation of goods. 【0158】 "Related logistics systems" refers to a set of systems involved in the execution of logistics plans. 【0159】 "Environmental impact" refers to the impact that logistics activities have on the natural environment. 【0160】 This invention combines a logistics optimization system with an emotion analysis engine, providing users with an emotionally sensitive user experience while achieving efficient logistics management. The system is primarily implemented by servers, terminals, and the users who utilize them. 【0161】 The server first collects information on land and sea transport from the internet and internal corporate databases. This is done using commonly used database management system software. This collected data is processed, integrated, and stored on the server as logistics information. Based on this logistics information, the server utilizes generative AI models to automatically generate optimal transport routes and related logistics tasks. Machine learning algorithms based on Python or R are sometimes used in this process. 【0162】 The terminal is equipped with an emotion analysis engine that uses a camera and microphone to analyze the user's facial expressions, voice, and input patterns as they view logistics plans through the system. Software libraries such as OpenCV and TensorFlow are utilized for this analysis. The resulting user emotions are reflected in the interface in real time. For example, if the user is feeling anxious, the terminal will provide additional information and support guides to aid understanding. 【0163】 Users review the plan presented by the logistics system and input feedback into a terminal to reflect their own feelings. This user input is immediately transmitted to the server, contributing to the final adjustment of the logistics plan. The final plan becomes more efficient and user-friendly, and then proceeds to the execution phase. 【0164】 As a concrete example, a user might input a prompt message into the system saying, "Tell me the optimal transportation plan from Tokyo to Fukuoka." In response to this prompt, the server quickly collects data and uses an AI model to formulate the optimal plan. The terminal then analyzes the user's response and provides an optimal interface. Through this process, the user can manage their logistics efficiently and with minimal stress. 【0165】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0166】 Step 1: 【0167】 The server collects information on land and sea transport from the internet and internal databases. As input, it uses internet and database queries to gather transport-related data (e.g., weather information, traffic conditions, ship location information, etc.). This data is organized and integrated by a database management system and output as a single logistics information dataset. Specifically, a Python script retrieves information from an API, and this data is stored in an SQL database. 【0168】 Step 2: 【0169】 The server receives integrated logistics information as input and uses a generative AI model to generate optimal logistics routes and tasks. In this process, the AI ​​algorithm analyzes the transportation information and creates an optimized route plan and schedule. As output, several recommended transportation routes and their detailed information are generated. Specifically, the model uses the machine learning framework TensorFlow to make predictions based on the training data. 【0170】 Step 3: 【0171】 The terminal receives the logistics plan from the server and displays it on the user interface. It collects data such as facial expressions and voice, as well as data entered by the user during prompting. The emotion analysis engine analyzes the user's emotional state and adapts the displayed content based on the results. The output is a customized interface designed to be easily understood by the user. Specifically, this involves facial recognition using OpenCV and identification of emotion patterns using a local database. 【0172】 Step 4: 【0173】 Users review the displayed logistics plan and input their opinions and feedback on the system. The submitted sentiment feedback is sent to the server and used to fine-tune the logistics plan. The final, adjusted logistics plan is then generated as output. Specifically, the user clicks on options in the GUI and inputs their feedback in text format. 【0174】 Step 5: 【0175】 The server deploys the final logistics plan to the relevant logistics systems and arranges for its execution. It receives final feedback and sentiment data from users as input and sends data to each relevant system in an executable format based on this. As output, newly updated logistics network data is distributed to the relevant facilities, and operations begin. Specifically, automated system calls are generated and delivered to logistics partners via corresponding APIs. 【0176】 (Application Example 2) 【0177】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal". 【0178】 In logistics management, there is a need for systems that can efficiently carry out operations while taking into account the mental state of users. Conventional logistics systems have struggled to provide interfaces that consider the emotional state of users, failing to alleviate the stress and anxiety they experience. Furthermore, there is a desire for further optimization of efficient logistics route generation through new technologies. 【0179】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0180】 In this invention, the server includes means for integrating land transport information and sea transport information in order to generate logistics information in an information processing device, means for automatically generating the optimal logistics route and operations using a machine learning system, and means for adjusting the display content of the interface using an emotion analysis engine to analyze the emotional state of the user. This enables flexible logistics management in accordance with the emotional state of the user. 【0181】 An "information processing device" is a device that has the function of integrating information on land transport and sea transport in order to generate logistics information. 【0182】 A "machine learning system" is a technology that automatically generates optimal logistics routes and operations based on logistics information. It is a system that learns using algorithms based on data and can derive optimal results. 【0183】 An "emotion analysis engine" is a system that analyzes the emotional state of a user. It is a technology that determines the user's psychological state based on facial expressions, voice, and input patterns, and adjusts the content displayed on the interface accordingly. 【0184】 A "logistics route" refers to the optimized travel path for products and services from the supplier to the consumer, designed to ensure efficient movement. 【0185】 "Work" refers to various tasks and procedures that must be performed in logistics management, and includes specific tasks such as picking, shipping, and delivery at a logistics center. 【0186】 This invention realizes a system that optimizes logistics management through collaboration between a server, terminals, and users. The server generates logistics information using an information processing device. Specifically, it integrates land transport information and sea transport information to grasp the overall picture of logistics. This enables real-time data processing. 【0187】 The server further uses a machine learning system to automatically generate the optimal logistics routes and operations. It utilizes algorithms to calculate the optimal plan, taking into account economic efficiency and speed. The machine learning system used here learns from historical data and makes highly accurate predictions even for new datasets. 【0188】 The device is equipped with an emotion analysis engine that analyzes the user's emotional state. It analyzes facial expressions, voice, and input patterns in real time and dynamically adjusts the content and style of the interface display. For example, if the user is feeling anxious, it will provide clear information and visual support. 【0189】 Users can review logistics plans and provide necessary feedback through the terminal. The terminal's interface is customized according to the user's emotional state, allowing for smooth and stress-free operation. 【0190】 As a concrete example, smart glasses are used in a logistics center. The glasses analyze the worker's facial expressions and voice, and visually present work instructions. This improves work efficiency. In addition, if a worker is confused, the glasses provide additional support information to assist them in their work. 【0191】 In this way, the user experience can be improved throughout the entire logistics management process by utilizing sentiment analysis. An example of a prompt is: "Explain how to automatically display support information that takes into account the emotional state of workers during picking operations at a logistics center." 【0192】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0193】 Step 1: 【0194】 The server collects land and sea transport information from the company's internal database and the internet. This allows the server to prepare the basic data necessary for generating logistics information. The input is a dataset of land and sea transport data, and the output is logistics information that integrates this data. Data processing includes formatting standardization and reduction of duplicate data. 【0195】 Step 2: 【0196】 The server automatically generates optimal logistics routes and work plans using a machine learning system based on the generated logistics information. Here, the input is integrated logistics information, and the output is an optimized logistics route and work plan. Data calculations include prediction and optimization using algorithms based on historical data. The generative AI model is used to calculate the optimal route under various conditions. 【0197】 Step 3: 【0198】 The terminal visually displays the generated logistics routes and work plans and accepts feedback from users. Input is the logistics route and work plan from the server, and output is user feedback data. Feedback is entered directly by the user through the terminal's interface. 【0199】 Step 4: 【0200】 The device analyzes the user's facial expressions and voice in real time using an emotion analysis engine and adjusts the interface display accordingly. Input is the user's facial expressions and voice data, and output is an interface display corresponding to the user's emotional state. Data processing includes extracting emotional features and determining the emotional state. 【0201】 Step 5: 【0202】 Users review the logistics plan provided on the terminal and provide feedback as needed. Input is displayed via the terminal's interface, while output is the user's evaluation and opinion. Specifically, users provide feedback using touchscreens or voice input. 【0203】 Step 6: 【0204】 The server determines the final logistics plan, incorporating user feedback, and deploys it to the relevant logistics systems. The input is user feedback data, and the output is the final logistics plan that takes feedback into account. Data processing includes feedback analysis and adaptive adjustment of the plan. 【0205】 The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data. 【0206】 Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0207】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14. 【0208】 [Second Embodiment] 【0209】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0210】 As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server. 【0211】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0212】 The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52. 【0213】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0214】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0215】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0216】 Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0217】 The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30. 【0218】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0219】 In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0220】 Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0221】 This invention is a logistics optimization system implemented by an information processing device, namely a server, a terminal, and a user operating it, and aims to realize more efficient and sustainable logistics operations. 【0222】 The server first collects land and sea transport information from various data sources on the internet and internal corporate databases. This data includes diverse information related to logistics, such as truck schedules and port usage. The collected data is unified from different formats and centrally managed in a standardized form. 【0223】 Based on this integrated data, the server uses artificial intelligence agents to evaluate multiple transportation options and automatically generate the optimal logistics route, reduce tasks, and efficiently allocate resources. This generation process applies complex algorithms to minimize transportation costs, time, and environmental impact. 【0224】 The generated transportation plan is displayed visually to the user via a terminal. An intuitive and easy-to-understand user interface is used, allowing users to quickly review the plan details. Users can provide feedback and modify conditions via the terminal as needed, and this information is immediately reflected on the server. 【0225】 As a concrete example, consider a case where a user needs to transport a large amount of cargo from Tokyo to Osaka. In this case, the server takes into account the shortage of truck drivers and congestion in sea transport, and proposes a route that combines sea transport from Tokyo to Kobe Port, followed by land transport from Kobe to Osaka. This proposal is designed to reduce environmental impact and minimize overall costs. 【0226】 Even after the plan is implemented, real-time monitoring of the logistics status is performed via terminals, and alerts are sent to users if any anticipated problems or delays occur. This information allows users to make quick decisions and ensure that the logistics flow is maintained without interruption. 【0227】 This system can provide sustainable solutions to the driver shortage and environmental problems facing the logistics industry. Furthermore, by effectively utilizing various modes of transport, it can significantly improve overall logistics efficiency. 【0228】 The following describes the processing flow. 【0229】 Step 1: 【0230】 The server collects data related to land and sea transport from the internet and internal corporate databases. Specifically, it obtains truck operational status, port utilization rates, weather information, etc., via APIs, and utilizes scraping techniques as needed. 【0231】 Step 2: 【0232】 The server standardizes the collected data and stores it in an integrated database. Here, different data formats are converted into a unified format, and missing or inconsistent data is automatically identified, supplemented, and corrected. 【0233】 Step 3: 【0234】 The server runs an artificial intelligence agent that uses integrated data to generate optimal logistics routes and tasks. In this process, the best plan is selected from multiple route options based on conditions such as transportation costs, time, and environmental impact. 【0235】 Step 4: 【0236】 The generated plan is visualized on the device. The user interface graphically displays route details, anticipated problems, and cost analysis. Users can make quick decisions based on this information. 【0237】 Step 5: 【0238】 Users review the logistics plan displayed on their device and enter any necessary changes or additional conditions. For example, they can add specific time limits or prioritize certain modes of transport. 【0239】 Step 6: 【0240】 The server takes user input, reconstructs the plan, and finalizes the adjusted route. The final plan is then deployed to the logistics system and moves into the execution phase. 【0241】 Step 7: 【0242】 The terminal monitors the ongoing logistics process in real time and reports progress and anticipated problems to the user. As soon as an anomaly is detected, an alert is sent to the user, prompting a quick response. 【0243】 (Example 1) 【0244】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0245】 Modern logistics systems face challenges of complexity and inefficiency. In particular, finding the optimal transportation route is extremely difficult amidst the diversification of transport methods. Furthermore, increasing environmental impact has become a social issue, demanding sustainable logistics operations. The lack of systems capable of real-time monitoring of logistics status and rapid alert notifications is also a problem. 【0246】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0247】 This invention includes a server that collects transportation information using a data processing device, converts it into a unified format, and centrally manages it; a server that automatically generates the optimal transportation route and resource allocation using a generation AI model; and a server that monitors the logistics status in real time and immediately notifies the user of any problems if they are detected. This enables the construction of optimal logistics routes and flexible real-time responses even under complex transportation conditions. 【0248】 A "data processing device" is a device consisting of hardware and software for collecting, transforming, storing, and managing information. 【0249】 "Transportation information" refers to operational schedules, traffic conditions, port usage, and related data concerning various modes of transport, including land and sea. 【0250】 A "unified format" is a standardized data format used to convert data in different formats into a consistent format, enabling cross-platform data management. 【0251】 A "generative AI model" is an algorithm that uses machine learning and data analysis techniques to extract patterns and insights from data and propose the optimal logistics route and resource allocation. 【0252】 "Transportation route" is a term that refers to the optimal path or route selected for sending goods or cargo, and may include a combination of different modes of transport. 【0253】 "Resource allocation" is the process of appropriately allocating and managing human, material, and financial resources available for efficient logistics operations. 【0254】 "Real-time monitoring" is a process of continuously observing the current situation without time delay and updating information and taking action as needed. 【0255】 "Notifying a warning" is an action taken to immediately inform users when an anomaly or malfunction is detected, in order to encourage a quick response. 【0256】 This invention relates to an information processing device for the purpose of optimizing logistics. It primarily consists of a server, terminals, and users who operate them. The server functions as a data processing device, collecting data from multiple transportation-related information sources. These information sources include operational schedules, traffic conditions, and port usage related to land and sea transport. This data collection process is performed in real time via the internet. 【0257】 The server processes the collected data, converting different formats into a unified format for centralized management. This ensures data integrity and enables efficient analysis in the next stage. The server then uses a generative AI model to automatically generate optimal transportation routes and resource allocations based on the collected data. This AI model includes complex algorithms that assess challenges within the logistics network and provide scenarios that minimize cost, time, and environmental impact. 【0258】 The generated transportation plan is presented visually to the user via a terminal. The terminal features an intuitive user interface, allowing users to easily understand the plan and provide feedback as needed. User feedback is immediately reflected on the server, and the plan is re-evaluated. 【0259】 For example, in a scenario where a user needs to transport a large volume of cargo from Tokyo to Osaka, the server will propose a combination of sea transport from Tokyo to Kobe Port and land transport from Kobe to Osaka. This is done to reduce overall costs while taking into account congestion and driver shortages, and while mitigating environmental impact. 【0260】 Even after the plan's execution begins, real-time monitoring of the logistics status is performed using terminals. If any anticipated problems or delays are detected, the terminals immediately notify the user and encourage them to ensure the smooth continuation of the logistics flow. 【0261】 An example of a prompt to the generating AI model is, "Optimize the transportation route from Tokyo to Osaka. Propose the best route and cost reduction measures, taking into account the shortage of truck drivers and congestion." In this way, this invention achieves optimization that meets the complex needs of logistics, enabling more sustainable and efficient logistics operations. 【0262】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0263】 Step 1: 【0264】 The server collects transportation-related information from multiple data sources on the internet. The server receives input data such as land transport schedules, traffic information, and port usage data for sea transport. Since the collected data is provided in various formats, the server converts them into a unified format. This process involves data cleaning to remove noise and inconsistencies. 【0265】 Step 2: 【0266】 The server stores the data, converted to a unified format, in a database for centralized management. This database is indexed to enable efficient data retrieval. After storage in the database, the server queries the stored data to extract the necessary information. This process ensures data integrity while enabling rapid access. 【0267】 Step 3: 【0268】 The server utilizes a generative AI model to analyze optimal transportation routes and resource allocation from centrally managed data. The inputs provided are factors related to economic efficiency, time, and environmental impact. Based on these factors, the server applies complex algorithms to perform optimization. The output proposes the most efficient route selection and resource allocation. 【0269】 Step 4: 【0270】 The server generates a transportation plan, which is then provided to the user via a terminal. The terminal displays the plan data in a visually easy-to-understand format. Through the user interface, the user can review the plan details and provide feedback. Upon receiving feedback, the terminal sends the input to the server. This process allows the user to easily react to the plan. 【0271】 Step 5: 【0272】 Upon receiving user feedback, the server re-evaluates the plan and updates it as needed. The updated plan is quickly reflected on the device, allowing users to reconfirm it. The server also considers the new parameters and runs the AI ​​model again to further optimize it. 【0273】 Step 6: 【0274】 Once the plan is executed, the terminal monitors the delivery status in real time. The real-time data acquired as input is used to detect delays and anomalies. If an anomaly is detected, the terminal immediately alerts the user and provides a notification that includes a suggested solution. 【0275】 This series of processes improves the efficiency and sustainability of logistics, enabling users to achieve flexible logistics operations. 【0276】 (Application Example 1) 【0277】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0278】 Modern logistics demands optimized transportation routes and efficient task management, with real-time situation monitoring and rapid response being particularly challenging. Furthermore, it's necessary to effectively integrate various transportation methods while minimizing environmental impact, costs, and transit times. Traditional systems have struggled to meet all these requirements simultaneously. 【0279】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0280】 In this invention, the server includes means for generating logistics information by integrating land transportation information and maritime transportation information, means for automatically generating an optimal logistics route and tasks using an artificial intelligence agent, and means for monitoring the logistics situation in real time using a mobile terminal, receiving change information, and immediately reflecting it in the plan. As a result, it becomes possible to improve the efficiency of logistics and achieve sustainable operation. 【0281】 An "information processing device" is a device that has the function of integrating logistics information and generating an optimal transportation route. 【0282】 An "artificial intelligence agent" is a program that evaluates transportation options and applies complex algorithms to automatically generate an optimal logistics route. 【0283】 "Logistics information" is information generated by integrating data related to land transportation and maritime transportation, and serves as the basis for logistics planning. 【0284】 A "display device" is a device that visually shows the generated logistics route and tasks, and provides an interface for the user to confirm information and input data. 【0285】 A "mobile terminal" is a portable electronic device used by administrators of logistics centers, etc. to monitor the situation in real time and immediately input change information. 【0286】 "Real-time monitoring" is a process of immediately observing the progress of logistics and immediately detecting signs of delays and other abnormalities. 【0287】 "Environmental impact" refers to the impact on the natural environment caused by logistics activities, and it is required to minimize this impact. 【0288】 To implement this invention, a server, acting as an information processing device, plays a central role. The server collects data related to land and sea transport in order to aggregate logistics information. This involves obtaining data from sources such as internal company databases and APIs for providing official transport information. This collected data is then processed and standardized using Python libraries such as Pandas and Requests. 【0289】 Next, the server generates the optimal logistics route based on integrated logistics information through an artificial intelligence agent. It utilizes generative AI models such as Scikit-learn and TensorFlow, applying complex algorithms that take into account minimizing transportation costs, time, and environmental impact. 【0290】 Meanwhile, the terminal provides a user-friendly interface developed with React Native. Users can check the logistics status in real time via the mobile terminal and easily provide necessary feedback and make changes to plans through the terminal. Real-time monitoring of logistics status is achieved using WebSocket technology, and the system is equipped with a function that immediately notifies the user of alerts if delays or anomalies occur. 【0291】 For example, if a logistics center manager wants to optimize cargo transport from Tokyo to Osaka, they can use the app to check the current status and see the most efficient route suggested by AI. If delays are predicted during transport, an alert will be sent to the manager's terminal, and an alternative route will be presented. 【0292】 An example of a prompt message might be: "Please propose the optimal logistics route from Tokyo to Osaka, minimizing environmental impact, cost, and time." 【0293】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0294】 Step 1: 【0295】 The server collects land and sea transport information from APIs and internal corporate databases. Its inputs are various forms of logistics data, and its output is integrated logistics information. The server uses Pandas to standardize and manage the data. 【0296】 Step 2: 【0297】 The server passes integrated logistics information as input to an artificial intelligence agent. Based on this input data, the agent uses TensorFlow, a generative AI model, to apply logic that minimizes transportation costs, time, and environmental impact, and outputs the optimal logistics route. The AI ​​agent utilizes complex algorithms to generate an efficient plan from multiple options. 【0298】 Step 3: 【0299】 The terminal visually presents the user with optimized transportation route information transmitted from the server. The input is optimized logistics route information from the server, and the output is visual information displayed on the user's screen. The interface, developed with React Native, allows users to easily access and review the information. 【0300】 Step 4: 【0301】 The user inputs any necessary changes or feedback based on the information presented. This input is sent to the server via the terminal and reflected in the logistics plan as new conditions. Here, the user's feedback acts as output for revising the plan. 【0302】 Step 5: 【0303】 At the stage when logistics is executed, the terminal performs real-time monitoring using WebSocket. Its input is the progress data during logistics, and the output is an alert generated when an abnormality is detected. Thus, the user is immediately notified. 【0304】 Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion. 【0305】 The present invention combines an emotion engine with a logistics optimization system to provide an interface experience considering the user's emotion and to achieve more efficient logistics management. This system is implemented by an information processing device, that is, a server, a terminal, and a user who uses them. 【0306】 The server first collects information on land transportation and maritime transportation from the Internet and the enterprise's internal database, integrates them to generate logistics information. Based on this logistics information, an optimal logistics route and tasks are automatically generated using an artificial intelligence agent. The generated plan is optimized based on conditions preset in the server. 【0307】 Here, the present invention further incorporates an emotion engine into the terminal, analyzes the user's expression, voice, input pattern, etc. when the user views the plan, and recognizes the user's emotion. This emotion information is reflected in the display content and style of the user interface. For example, when the user feels anxious, detailed information can be presented more clearly, or when the user is satisfied with the options, an animation promoting smooth execution can be displayed. 【0308】 As a concrete example, consider a scenario where a user is planning transportation from Tokyo to Fukuoka using the system. In this case, the server generates the optimal transportation route from existing data, and the terminal presents the details to the user. If the emotion engine detects stress from the user's facial expressions and voice while viewing the information, the terminal provides detailed support information and guided navigation. Furthermore, if the user expresses satisfaction with the proposed plan, the execution confirmation process is simplified to reduce response time. 【0309】 Users review the presented plan and provide emotion-based feedback through input on a terminal. Based on this feedback, the server adjusts the final logistics plan and moves to the execution phase. As a result, users can manage their logistics more efficiently and comfortably through an emotion-sensitive system operation. 【0310】 The following describes the processing flow. 【0311】 Step 1: 【0312】 The server collects data on land and sea transport from multiple sources. It obtains real-time data via APIs and generates logistics information by standardizing and centralizing different data formats. 【0313】 Step 2: 【0314】 The server inputs the generated logistics information into an artificial intelligence agent, which automatically generates the optimal logistics route and tasks. The algorithm creates the plan while taking cost, time, and environmental impact into consideration. 【0315】 Step 3: 【0316】 The terminal presents the generated logistics plan to the user. At this time, the emotion engine is activated and analyzes the user's voice, facial expressions, and input patterns to recognize the user's emotions. 【0317】 Step 4: 【0318】 Based on the analysis results of the emotion engine, the device adjusts the user interface. For example, if the user shows anxiety, it displays additional information or guides to reassure them. Also, if the user is satisfied with the plan, the confirmation process is simplified. 【0319】 Step 5: 【0320】 The user operates the device to review the generated plan and enter feedback. This feedback, including emotional aspects, is reflected on the device and sent to the server. 【0321】 Step 6: 【0322】 The server takes feedback into account and makes final adjustments to the plan. The final version of the plan is then deployed throughout the entire logistics system. 【0323】 Step 7: 【0324】 The terminal monitors the logistics process in real time during the execution phase and alerts the user if problems or delays occur. It also uses the functionality of an emotion engine to prompt the user to take appropriate action. 【0325】 (Example 2) 【0326】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0327】 In logistics systems, existing route and task optimization methods often fail to consider the psychological state of users, which can lead to stress and frustration that hinders management efficiency. Therefore, there is a need to achieve efficient and comfortable logistics management that takes users' emotional states into account. Furthermore, systems that lack environmental considerations can, conversely, cause social problems. 【0328】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0329】 In this invention, the server includes means for generating logistics information by integrating land transport information and sea transport information using an information processing device, means for automatically generating optimal logistics routes and operations using an artificial intelligence agent, and means for recognizing the user's emotions using an emotion analysis engine and dynamically adjusting the interface. This makes it possible to provide an interface that takes the user's emotions into consideration and to realize more efficient and comfortable logistics management. 【0330】 An "information processing device" refers to a device that has the ability to collect, integrate, and analyze data related to transportation. 【0331】 "Information on land transport" refers to data related to transport conducted using roads. 【0332】 "Maritime transport information" refers to data related to transportation conducted via the ocean using ships. 【0333】 "Logistics information" refers to information obtained as a result of integrating data from multiple transportation-related sources. 【0334】 An "artificial intelligence agent" refers to artificial intelligence technology that analyzes data and automatically generates optimal transportation plans and tasks. 【0335】 A "logistics route" refers to the path that goods are scheduled to take when they are transported. 【0336】 "Work" refers to a series of activities and processes related to logistics. 【0337】 A "display device" refers to a device used to visually present information to users. 【0338】 "User" refers to a person who operates the system to check and issue instructions regarding logistics information. 【0339】 An "emotion analysis engine" refers to a program that recognizes the user's emotional state and adjusts the information displayed accordingly. 【0340】 An "interface" refers to the operating environment that allows users to interact with a system. 【0341】 "Logistics planning" refers to the final plan for the transportation of goods. 【0342】 "Related logistics systems" refers to a set of systems involved in the execution of logistics plans. 【0343】 "Environmental impact" refers to the impact that logistics activities have on the natural environment. 【0344】 This invention combines a logistics optimization system with an emotion analysis engine, providing users with an emotionally sensitive user experience while achieving efficient logistics management. The system is primarily implemented by servers, terminals, and the users who utilize them. 【0345】 The server first collects information on land and sea transport from the internet and internal corporate databases. This is done using commonly used database management system software. This collected data is processed, integrated, and stored on the server as logistics information. Based on this logistics information, the server utilizes generative AI models to automatically generate optimal transport routes and related logistics tasks. Machine learning algorithms based on Python or R are sometimes used in this process. 【0346】 The terminal is equipped with an emotion analysis engine that uses a camera and microphone to analyze the user's facial expressions, voice, and input patterns as they view logistics plans through the system. Software libraries such as OpenCV and TensorFlow are utilized for this analysis. The resulting user emotions are reflected in the interface in real time. For example, if the user is feeling anxious, the terminal will provide additional information and support guides to aid understanding. 【0347】 Users review the plan presented by the logistics system and input feedback into a terminal to reflect their own feelings. This user input is immediately transmitted to the server, contributing to the final adjustment of the logistics plan. The final plan becomes more efficient and user-friendly, and then proceeds to the execution phase. 【0348】 As a concrete example, a user might input a prompt message into the system saying, "Tell me the optimal transportation plan from Tokyo to Fukuoka." In response to this prompt, the server quickly collects data and uses an AI model to formulate the optimal plan. The terminal then analyzes the user's response and provides an optimal interface. Through this process, the user can manage their logistics efficiently and with minimal stress. 【0349】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0350】 Step 1: 【0351】 The server collects information on land and sea transport from the internet and internal databases. As input, it uses internet and database queries to gather transport-related data (e.g., weather information, traffic conditions, ship location information, etc.). This data is organized and integrated by a database management system and output as a single logistics information dataset. Specifically, a Python script retrieves information from an API, and this data is stored in an SQL database. 【0352】 Step 2: 【0353】 The server receives integrated logistics information as input and uses a generative AI model to generate optimal logistics routes and tasks. In this process, the AI ​​algorithm analyzes the transportation information and creates an optimized route plan and schedule. As output, several recommended transportation routes and their detailed information are generated. Specifically, the model uses the machine learning framework TensorFlow to make predictions based on the training data. 【0354】 Step 3: 【0355】 The terminal receives the logistics plan from the server and displays it on the user interface. It collects data such as facial expressions and voice, as well as data entered by the user during prompting. The emotion analysis engine analyzes the user's emotional state and adapts the displayed content based on the results. The output is a customized interface designed to be easily understood by the user. Specifically, this involves facial recognition using OpenCV and identification of emotion patterns using a local database. 【0356】 Step 4: 【0357】 Users review the displayed logistics plan and input their opinions and feedback on the system. The submitted sentiment feedback is sent to the server and used to fine-tune the logistics plan. The final, adjusted logistics plan is then generated as output. Specifically, the user clicks on options in the GUI and inputs their feedback in text format. 【0358】 Step 5: 【0359】 The server deploys the final logistics plan to the relevant logistics systems and arranges for its execution. It receives final feedback and sentiment data from users as input and sends data to each relevant system in an executable format based on this. As output, newly updated logistics network data is distributed to the relevant facilities, and operations begin. Specifically, automated system calls are generated and delivered to logistics partners via corresponding APIs. 【0360】 (Application Example 2) 【0361】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0362】 In logistics management, there is a need for systems that can efficiently carry out operations while taking into account the mental state of users. Conventional logistics systems have struggled to provide interfaces that consider the emotional state of users, failing to alleviate the stress and anxiety they experience. Furthermore, there is a desire for further optimization of efficient logistics route generation through new technologies. 【0363】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0364】 In this invention, the server includes means for integrating land transport information and sea transport information in order to generate logistics information in an information processing device, means for automatically generating the optimal logistics route and operations using a machine learning system, and means for adjusting the display content of the interface using an emotion analysis engine to analyze the emotional state of the user. This enables flexible logistics management in accordance with the emotional state of the user. 【0365】 An "information processing device" is a device that has the function of integrating information on land transport and sea transport in order to generate logistics information. 【0366】 A "machine learning system" is a technology that automatically generates optimal logistics routes and operations based on logistics information. It is a system that learns using algorithms based on data and can derive optimal results. 【0367】 An "emotion analysis engine" is a system that analyzes the emotional state of a user. It is a technology that determines the user's psychological state based on facial expressions, voice, and input patterns, and adjusts the content displayed on the interface accordingly. 【0368】 A "logistics route" refers to the optimized travel path for products and services from the supplier to the consumer, designed to ensure efficient movement. 【0369】 "Work" refers to various tasks and procedures that must be performed in logistics management, and includes specific tasks such as picking, shipping, and delivery at a logistics center. 【0370】 This invention realizes a system that optimizes logistics management through collaboration between a server, terminals, and users. The server generates logistics information using an information processing device. Specifically, it integrates land transport information and sea transport information to grasp the overall picture of logistics. This enables real-time data processing. 【0371】 The server further uses a machine learning system to automatically generate the optimal logistics routes and operations. It utilizes algorithms to calculate the optimal plan, taking into account economic efficiency and speed. The machine learning system used here learns from historical data and makes highly accurate predictions even for new datasets. 【0372】 The device is equipped with an emotion analysis engine that analyzes the user's emotional state. It analyzes facial expressions, voice, and input patterns in real time and dynamically adjusts the content and style of the interface display. For example, if the user is feeling anxious, it will provide clear information and visual support. 【0373】 Users can review logistics plans and provide necessary feedback through the terminal. The terminal's interface is customized according to the user's emotional state, allowing for smooth and stress-free operation. 【0374】 As a concrete example, smart glasses are used in a logistics center. The glasses analyze the worker's facial expressions and voice, and visually present work instructions. This improves work efficiency. In addition, if a worker is confused, the glasses provide additional support information to assist them in their work. 【0375】 In this way, the user experience can be improved throughout the entire logistics management process by utilizing sentiment analysis. An example of a prompt is: "Explain how to automatically display support information that takes into account the emotional state of workers during picking operations at a logistics center." 【0376】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0377】 Step 1: 【0378】 The server collects land and sea transport information from the company's internal database and the internet. This allows the server to prepare the basic data necessary for generating logistics information. The input is a dataset of land and sea transport data, and the output is logistics information that integrates this data. Data processing includes formatting standardization and reduction of duplicate data. 【0379】 Step 2: 【0380】 The server automatically generates optimal logistics routes and work plans using a machine learning system based on the generated logistics information. Here, the input is integrated logistics information, and the output is an optimized logistics route and work plan. Data calculations include prediction and optimization using algorithms based on historical data. The generative AI model is used to calculate the optimal route under various conditions. 【0381】 Step 3: 【0382】 The terminal visually displays the generated logistics routes and work plans and accepts feedback from users. Input is the logistics route and work plan from the server, and output is user feedback data. Feedback is entered directly by the user through the terminal's interface. 【0383】 Step 4: 【0384】 The device analyzes the user's facial expressions and voice in real time using an emotion analysis engine and adjusts the interface display accordingly. Input is the user's facial expressions and voice data, and output is an interface display corresponding to the user's emotional state. Data processing includes extracting emotional features and determining the emotional state. 【0385】 Step 5: 【0386】 Users review the logistics plan provided on the terminal and provide feedback as needed. Input is displayed via the terminal's interface, while output is the user's evaluation and opinions. Specifically, users provide feedback using touchscreens or voice input. 【0387】 Step 6: 【0388】 The server determines the final logistics plan, incorporating user feedback, and deploys it to the relevant logistics systems. The input is user feedback data, and the output is the final logistics plan that takes feedback into account. Data processing includes feedback analysis and adaptive adjustment of the plan. 【0389】 The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0390】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0391】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214. 【0392】 [Third Embodiment] 【0393】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0394】 As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server. 【0395】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0396】 The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52. 【0397】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0398】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0399】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0400】 Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0401】 The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30. 【0402】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0403】 In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0404】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal". 【0405】 This invention is a logistics optimization system implemented by an information processing device, namely a server, a terminal, and a user operating it, and aims to realize more efficient and sustainable logistics operations. 【0406】 The server first collects land and sea transport information from various data sources on the internet and internal corporate databases. This data includes diverse information related to logistics, such as truck schedules and port usage. The collected data is unified from different formats and centrally managed in a standardized form. 【0407】 Based on this integrated data, the server uses artificial intelligence agents to evaluate multiple transportation options and automatically generate the optimal logistics route, reduce tasks, and efficiently allocate resources. This generation process applies complex algorithms to minimize transportation costs, time, and environmental impact. 【0408】 The generated transportation plan is displayed visually to the user via a terminal. An intuitive and easy-to-understand user interface is used, allowing users to quickly review the plan details. Users can provide feedback and modify conditions via the terminal as needed, and this information is immediately reflected on the server. 【0409】 As a concrete example, consider a case where a user needs to transport a large amount of cargo from Tokyo to Osaka. In this case, the server takes into account the shortage of truck drivers and congestion in sea transport, and proposes a route that combines sea transport from Tokyo to Kobe Port, followed by land transport from Kobe to Osaka. This proposal is designed to reduce environmental impact and minimize overall costs. 【0410】 Even after the plan is implemented, real-time monitoring of the logistics status is performed via terminals, and alerts are sent to users if any anticipated problems or delays occur. This information allows users to make quick decisions and ensure that the logistics flow is maintained without interruption. 【0411】 This system can provide sustainable solutions to the driver shortage and environmental problems facing the logistics industry. Furthermore, by effectively utilizing various modes of transport, it can significantly improve overall logistics efficiency. 【0412】 The following describes the processing flow. 【0413】 Step 1: 【0414】 The server collects data related to land and sea transport from the internet and internal corporate databases. Specifically, it obtains truck operational status, port utilization rates, weather information, etc., via APIs, and utilizes scraping techniques as needed. 【0415】 Step 2: 【0416】 The server standardizes the collected data and stores it in an integrated database. Here, different data formats are converted into a unified format, and missing or inconsistent data is automatically identified, supplemented, and corrected. 【0417】 Step 3: 【0418】 The server runs an artificial intelligence agent that uses integrated data to generate optimal logistics routes and tasks. In this process, the best plan is selected from multiple route options based on conditions such as transportation costs, time, and environmental impact. 【0419】 Step 4: 【0420】 The generated plan is visualized on the device. The user interface graphically displays route details, anticipated problems, and cost analysis. Users can make quick decisions based on this information. 【0421】 Step 5: 【0422】 Users review the logistics plan displayed on their device and enter any necessary changes or additional conditions. For example, they can add specific time limits or prioritize certain modes of transport. 【0423】 Step 6: 【0424】 The server takes user input, reconstructs the plan, and finalizes the adjusted route. The final plan is then deployed to the logistics system and moves into the execution phase. 【0425】 Step 7: 【0426】 The terminal monitors the ongoing logistics process in real time and reports progress and anticipated problems to the user. As soon as an anomaly is detected, an alert is sent to the user, prompting a quick response. 【0427】 (Example 1) 【0428】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0429】 Modern logistics systems face challenges of complexity and inefficiency. In particular, finding the optimal transportation route is extremely difficult amidst the diversification of transport methods. Furthermore, increasing environmental impact has become a social issue, demanding sustainable logistics operations. The lack of systems capable of real-time monitoring of logistics status and rapid alert notifications is also a problem. 【0430】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0431】 This invention includes a server that collects transportation information using a data processing device, converts it into a unified format, and centrally manages it; a server that automatically generates the optimal transportation route and resource allocation using a generation AI model; and a server that monitors the logistics status in real time and immediately notifies the user of any problems if they are detected. This enables the construction of optimal logistics routes and flexible real-time responses even under complex transportation conditions. 【0432】 A "data processing device" is a device consisting of hardware and software for collecting, transforming, storing, and managing information. 【0433】 "Transportation information" refers to operational schedules, traffic conditions, port usage, and related data concerning various modes of transport, including land and sea. 【0434】 A "unified format" is a standardized data format used to convert data in different formats into a consistent format, enabling cross-platform data management. 【0435】 A "generative AI model" is an algorithm that uses machine learning and data analysis techniques to extract patterns and insights from data and propose the optimal logistics route and resource allocation. 【0436】 "Transportation route" is a term that refers to the optimal path or route selected for sending goods or cargo, and may include a combination of different modes of transport. 【0437】 "Resource allocation" is the process of appropriately allocating and managing human, material, and financial resources available for efficient logistics operations. 【0438】 "Real-time monitoring" is a process of continuously observing the current situation without time delay and updating information and taking action as needed. 【0439】 "Notifying a warning" is an action taken to immediately inform users when an anomaly or malfunction is detected, in order to encourage a quick response. 【0440】 This invention relates to an information processing device for the purpose of optimizing logistics. It primarily consists of a server, terminals, and users who operate them. The server functions as a data processing device, collecting data from multiple transportation-related information sources. These information sources include operational schedules, traffic conditions, and port usage related to land and sea transport. This data collection process is performed in real time via the internet. 【0441】 The server processes the collected data, converting different formats into a unified format for centralized management. This ensures data integrity and enables efficient analysis in the next stage. The server then uses a generative AI model to automatically generate optimal transportation routes and resource allocations based on the collected data. This AI model includes complex algorithms that assess challenges within the logistics network and provide scenarios that minimize cost, time, and environmental impact. 【0442】 The generated transportation plan is presented visually to the user via a terminal. The terminal features an intuitive user interface, allowing users to easily understand the plan and provide feedback as needed. User feedback is immediately reflected on the server, and the plan is re-evaluated. 【0443】 For example, in a scenario where a user needs to transport a large volume of cargo from Tokyo to Osaka, the server will propose a combination of sea transport from Tokyo to Kobe Port and land transport from Kobe to Osaka. This is done to reduce overall costs while taking into account congestion and driver shortages, and while mitigating environmental impact. 【0444】 Even after the plan's execution begins, real-time monitoring of the logistics status is performed using terminals. If any anticipated problems or delays are detected, the terminals immediately notify the user and encourage them to ensure the smooth continuation of the logistics flow. 【0445】 An example of a prompt to the generating AI model is, "Optimize the transportation route from Tokyo to Osaka. Propose the best route and cost reduction measures, taking into account the shortage of truck drivers and congestion." In this way, this invention achieves optimization that meets the complex needs of logistics, enabling more sustainable and efficient logistics operations. 【0446】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0447】 Step 1: 【0448】 The server collects transportation-related information from multiple data sources on the internet. The server receives input data such as land transport schedules, traffic information, and port usage data for sea transport. Since the collected data is provided in various formats, the server converts them into a unified format. This process involves data cleaning to remove noise and inconsistencies. 【0449】 Step 2: 【0450】 The server stores the data, converted to a unified format, in a database for centralized management. This database is indexed to enable efficient data retrieval. After storage in the database, the server queries the stored data to extract the necessary information. This process ensures data integrity while enabling rapid access. 【0451】 Step 3: 【0452】 The server utilizes a generative AI model to analyze optimal transportation routes and resource allocation from centrally managed data. The inputs provided are factors related to economic efficiency, time, and environmental impact. Based on these factors, the server applies complex algorithms to perform optimization. The output proposes the most efficient route selection and resource allocation. 【0453】 Step 4: 【0454】 The server generates a transportation plan, which is then provided to the user via a terminal. The terminal displays the plan data in a visually easy-to-understand format. Through the user interface, the user can review the plan details and provide feedback. Upon receiving feedback, the terminal sends the input to the server. This process allows the user to easily react to the plan. 【0455】 Step 5: 【0456】 Upon receiving user feedback, the server re-evaluates the plan and updates it as needed. The updated plan is quickly reflected on the device, allowing users to reconfirm it. The server also considers the new parameters and runs the AI ​​model again to further optimize it. 【0457】 Step 6: 【0458】 Once the plan is executed, the terminal monitors the delivery status in real time. The real-time data acquired as input is used to detect delays and anomalies. If an anomaly is detected, the terminal immediately alerts the user and provides a notification that includes a suggested solution. 【0459】 This series of processes improves the efficiency and sustainability of logistics, enabling users to achieve flexible logistics operations. 【0460】 (Application Example 1) 【0461】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0462】 Modern logistics demands optimized transportation routes and efficient task management, with real-time situation monitoring and rapid response being particularly challenging. Furthermore, it's necessary to effectively integrate various transportation methods while minimizing environmental impact, costs, and transit times. Traditional systems have struggled to meet all these requirements simultaneously. 【0463】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0464】 This invention includes a server that generates logistics information by integrating land transport information and sea transport information, a server that automatically generates optimal logistics routes and tasks using an artificial intelligence agent, and a server that monitors the logistics status in real time using a mobile terminal, receives change information, and immediately reflects it in the plan. This enables more efficient and sustainable logistics operations. 【0465】 An "information processing device" is a device that has the function of integrating logistics information and generating the optimal transportation route. 【0466】 An "artificial intelligence agent" is a program that evaluates transportation options and automatically generates the optimal logistics route by applying complex algorithms. 【0467】 "Logistics information" is information generated by integrating data related to land and sea transport, and it forms the basis of logistics planning. 【0468】 A "display device" is a device that visually shows generated logistics routes and tasks, and provides an interface for users to check and input information. 【0469】 A "mobile terminal" is a portable electronic device used by managers at logistics centers and other locations to monitor the situation in real time and immediately input any changes. 【0470】 "Real-time monitoring" is a process that instantly observes the progress of logistics and immediately detects any signs of delays or other anomalies. 【0471】 "Environmental impact" refers to the effects that logistics activities have on the natural environment, and minimizing this impact is required. 【0472】 To implement this invention, a server, acting as an information processing device, plays a central role. The server collects data related to land and sea transport in order to aggregate logistics information. This involves obtaining data from sources such as internal company databases and APIs for providing official transport information. This collected data is then processed and standardized using Python libraries such as Pandas and Requests. 【0473】 Next, the server generates the optimal logistics route based on integrated logistics information through an artificial intelligence agent. It utilizes generative AI models such as Scikit-learn and TensorFlow, applying complex algorithms that take into account minimizing transportation costs, time, and environmental impact. 【0474】 Meanwhile, the terminal provides a user-friendly interface developed with React Native. Users can check the logistics status in real time via the mobile terminal and easily provide necessary feedback and make changes to plans through the terminal. Real-time monitoring of logistics status is achieved using WebSocket technology, and the system is equipped with a function that immediately notifies the user of alerts if delays or anomalies occur. 【0475】 For example, if a logistics center manager wants to optimize cargo transport from Tokyo to Osaka, they can use the app to check the current status and see the most efficient route suggested by AI. If delays are predicted during transport, an alert will be sent to the manager's terminal, and an alternative route will be presented. 【0476】 An example of a prompt message might be: "Please propose the optimal logistics route from Tokyo to Osaka, minimizing environmental impact, cost, and time." 【0477】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0478】 Step 1: 【0479】 The server collects land and sea transport information from APIs and internal corporate databases. Its inputs are various forms of logistics data, and its output is integrated logistics information. The server uses Pandas to standardize and manage the data. 【0480】 Step 2: 【0481】 The server passes integrated logistics information as input to an artificial intelligence agent. Based on this input data, the agent uses TensorFlow, a generative AI model, to apply logic that minimizes transportation costs, time, and environmental impact, and outputs the optimal logistics route. The AI ​​agent utilizes complex algorithms to generate an efficient plan from multiple options. 【0482】 Step 3: 【0483】 The terminal visually presents the user with optimized transportation route information transmitted from the server. The input is optimized logistics route information from the server, and the output is visual information displayed on the user's screen. The interface, developed with React Native, allows users to easily access and review the information. 【0484】 Step 4: 【0485】 The user inputs any necessary changes or feedback based on the information presented. This input is sent to the server via the terminal and reflected in the logistics plan as new conditions. Here, the user's feedback acts as output for revising the plan. 【0486】 Step 5: 【0487】 The terminal uses WebSocket to perform real-time monitoring during the logistics process. Its input is progress data during logistics, and its output is an alert generated when an anomaly is detected. This immediately notifies the user. 【0488】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0489】 This invention combines an emotion engine with a logistics optimization system to provide an interface experience that takes user emotions into account, thereby achieving more efficient logistics management. This system is implemented by information processing devices, namely servers, terminals, and the users who use them. 【0490】 The server first collects land and sea transport information from the internet and internal corporate databases, and integrates this information to generate logistics data. Based on this logistics data, an artificial intelligence agent automatically generates the optimal logistics route and tasks. The generated plan is then optimized based on pre-configured conditions within the server. 【0491】 Here, the present invention further incorporates an emotion engine into the terminal to analyze the user's facial expressions, voice, input patterns, etc., when viewing plans, and recognizes the user's emotions. This emotion information is reflected in the display content and style of the user interface. For example, if the user is feeling anxious, detailed information can be presented in a clearer manner, or if the user is satisfied with the choices, an animation can be displayed to encourage smooth execution. 【0492】 As a concrete example, consider a scenario where a user is planning transportation from Tokyo to Fukuoka using the system. In this case, the server generates the optimal transportation route from existing data, and the terminal presents the details to the user. If the emotion engine detects stress from the user's facial expressions and voice while viewing the information, the terminal provides detailed support information and guided navigation. Furthermore, if the user expresses satisfaction with the proposed plan, the execution confirmation process is simplified to reduce response time. 【0493】 Users review the presented plan and provide emotion-based feedback through input on a terminal. Based on this feedback, the server adjusts the final logistics plan and moves to the execution phase. As a result, users can manage their logistics more efficiently and comfortably through an emotion-sensitive system operation. 【0494】 The following describes the processing flow. 【0495】 Step 1: 【0496】 The server collects data on land and sea transport from multiple sources. It obtains real-time data via APIs and generates logistics information by standardizing and centralizing different data formats. 【0497】 Step 2: 【0498】 The server inputs the generated logistics information into an artificial intelligence agent, which automatically generates the optimal logistics route and tasks. The algorithm creates the plan while taking cost, time, and environmental impact into consideration. 【0499】 Step 3: 【0500】 The terminal presents the generated logistics plan to the user. At this time, the emotion engine is activated and analyzes the user's voice, facial expressions, and input patterns to recognize the user's emotions. 【0501】 Step 4: 【0502】 Based on the analysis results of the emotion engine, the device adjusts the user interface. For example, if the user shows anxiety, it displays additional information or guides to reassure them. Also, if the user is satisfied with the plan, the confirmation process is simplified. 【0503】 Step 5: 【0504】 The user operates the device to review the generated plan and enter feedback. This feedback, including emotional aspects, is reflected on the device and sent to the server. 【0505】 Step 6: 【0506】 The server takes feedback into account and makes final adjustments to the plan. The final version of the plan is then deployed throughout the entire logistics system. 【0507】 Step 7: 【0508】 The terminal monitors the logistics process in real time during the execution phase and alerts the user if problems or delays occur. It also uses the functionality of an emotion engine to prompt the user to take appropriate action. 【0509】 (Example 2) 【0510】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0511】 In logistics systems, existing route and task optimization methods often fail to consider the psychological state of users, which can lead to stress and frustration that hinders management efficiency. Therefore, there is a need to achieve efficient and comfortable logistics management that takes users' emotional states into account. Furthermore, systems that lack environmental considerations can, conversely, cause social problems. 【0512】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0513】 In this invention, the server includes means for generating logistics information by integrating land transport information and sea transport information using an information processing device, means for automatically generating optimal logistics routes and operations using an artificial intelligence agent, and means for recognizing the user's emotions using an emotion analysis engine and dynamically adjusting the interface. This makes it possible to provide an interface that takes the user's emotions into consideration and to realize more efficient and comfortable logistics management. 【0514】 An "information processing device" refers to a device that has the ability to collect, integrate, and analyze data related to transportation. 【0515】 "Information on land transport" refers to data related to transport conducted using roads. 【0516】 "Maritime transport information" refers to data related to transportation conducted via the ocean using ships. 【0517】 "Logistics information" refers to information obtained as a result of integrating data from multiple transportation-related sources. 【0518】 An "artificial intelligence agent" refers to artificial intelligence technology that analyzes data and automatically generates optimal transportation plans and tasks. 【0519】 A "logistics route" refers to the path that goods are scheduled to take when they are transported. 【0520】 "Work" refers to a series of activities and processes related to logistics. 【0521】 A "display device" refers to a device used to visually present information to users. 【0522】 "User" refers to a person who operates the system to check and issue instructions regarding logistics information. 【0523】 An "emotion analysis engine" refers to a program that recognizes the user's emotional state and adjusts the information displayed accordingly. 【0524】 An "interface" refers to the operating environment that allows users to interact with a system. 【0525】 "Logistics planning" refers to the final plan for the transportation of goods. 【0526】 "Related logistics systems" refers to a set of systems involved in the execution of logistics plans. 【0527】 "Environmental impact" refers to the impact that logistics activities have on the natural environment. 【0528】 This invention combines a logistics optimization system with an emotion analysis engine, providing users with an emotionally sensitive user experience while achieving efficient logistics management. The system is primarily implemented by servers, terminals, and the users who utilize them. 【0529】 The server first collects information on land and sea transport from the internet and internal corporate databases. This is done using commonly used database management system software. This collected data is processed, integrated, and stored on the server as logistics information. Based on this logistics information, the server utilizes generative AI models to automatically generate optimal transport routes and related logistics tasks. Machine learning algorithms based on Python or R are sometimes used in this process. 【0530】 The terminal is equipped with an emotion analysis engine that uses a camera and microphone to analyze the user's facial expressions, voice, and input patterns as they view logistics plans through the system. Software libraries such as OpenCV and TensorFlow are utilized for this analysis. The resulting user emotions are reflected in the interface in real time. For example, if the user is feeling anxious, the terminal will provide additional information and support guides to aid understanding. 【0531】 Users review the plan presented by the logistics system and input feedback into a terminal to reflect their own feelings. This user input is immediately transmitted to the server, contributing to the final adjustment of the logistics plan. The final plan becomes more efficient and user-friendly, and then proceeds to the execution phase. 【0532】 As a concrete example, a user might input a prompt message into the system saying, "Tell me the optimal transportation plan from Tokyo to Fukuoka." In response to this prompt, the server quickly collects data and uses an AI model to formulate the optimal plan. The terminal then analyzes the user's response and provides an optimal interface. Through this process, the user can manage their logistics efficiently and with minimal stress. 【0533】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0534】 Step 1: 【0535】 The server collects information on land and sea transport from the internet and internal databases. As input, it uses internet and database queries to gather transport-related data (e.g., weather information, traffic conditions, ship location information, etc.). This data is organized and integrated by a database management system and output as a single logistics information dataset. Specifically, a Python script retrieves information from an API, and this data is stored in an SQL database. 【0536】 Step 2: 【0537】 The server receives integrated logistics information as input and uses a generative AI model to generate optimal logistics routes and tasks. In this process, the AI ​​algorithm analyzes the transportation information and creates an optimized route plan and schedule. As output, several recommended transportation routes and their detailed information are generated. Specifically, the model uses the machine learning framework TensorFlow to make predictions based on the training data. 【0538】 Step 3: 【0539】 The terminal receives the logistics plan from the server and displays it on the user interface. It collects data such as facial expressions and voice, as well as data entered by the user during prompting. The emotion analysis engine analyzes the user's emotional state and adapts the displayed content based on the results. The output is a customized interface designed to be easily understood by the user. Specifically, this involves facial recognition using OpenCV and identification of emotion patterns using a local database. 【0540】 Step 4: 【0541】 Users review the displayed logistics plan and input their opinions and feedback on the system. The submitted sentiment feedback is sent to the server and used to fine-tune the logistics plan. The final, adjusted logistics plan is then generated as output. Specifically, the user clicks on options in the GUI and inputs their feedback in text format. 【0542】 Step 5: 【0543】 The server deploys the final logistics plan to the relevant logistics systems and arranges for its execution. It receives final feedback and sentiment data from users as input and sends data to each relevant system in an executable format based on this. As output, newly updated logistics network data is distributed to the relevant facilities, and operations begin. Specifically, automated system calls are generated and delivered to logistics partners via corresponding APIs. 【0544】 (Application Example 2) 【0545】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0546】 In logistics management, there is a need for systems that can efficiently carry out operations while taking into account the mental state of users. Conventional logistics systems have struggled to provide interfaces that consider the emotional state of users, failing to alleviate the stress and anxiety they experience. Furthermore, there is a desire for further optimization of efficient logistics route generation through new technologies. 【0547】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0548】 In this invention, the server includes means for integrating land transport information and sea transport information in order to generate logistics information in an information processing device, means for automatically generating the optimal logistics route and operations using a machine learning system, and means for adjusting the display content of the interface using an emotion analysis engine to analyze the emotional state of the user. This enables flexible logistics management in accordance with the emotional state of the user. 【0549】 An "information processing device" is a device that has the function of integrating information on land transport and sea transport in order to generate logistics information. 【0550】 A "machine learning system" is a technology that automatically generates optimal logistics routes and operations based on logistics information. It is a system that learns using algorithms based on data and can derive optimal results. 【0551】 An "emotion analysis engine" is a system that analyzes the emotional state of a user. It is a technology that determines the user's psychological state based on facial expressions, voice, and input patterns, and adjusts the content displayed on the interface accordingly. 【0552】 A "logistics route" refers to the optimized travel path for products and services from the supplier to the consumer, designed to ensure efficient movement. 【0553】 "Work" refers to various tasks and procedures that must be performed in logistics management, and includes specific tasks such as picking, shipping, and delivery at a logistics center. 【0554】 This invention realizes a system that optimizes logistics management through collaboration between a server, terminals, and users. The server generates logistics information using an information processing device. Specifically, it integrates land transport information and sea transport information to grasp the overall picture of logistics. This enables real-time data processing. 【0555】 The server further uses a machine learning system to automatically generate the optimal logistics routes and operations. It utilizes algorithms to calculate the optimal plan, taking into account economic efficiency and speed. The machine learning system used here learns from historical data and makes highly accurate predictions even for new datasets. 【0556】 The device is equipped with an emotion analysis engine that analyzes the user's emotional state. It analyzes facial expressions, voice, and input patterns in real time and dynamically adjusts the content and style of the interface display. For example, if the user is feeling anxious, it will provide clear information and visual support. 【0557】 Users can review logistics plans and provide necessary feedback through the terminal. The terminal's interface is customized according to the user's emotional state, allowing for smooth and stress-free operation. 【0558】 As a concrete example, smart glasses are used in a logistics center. The glasses analyze the worker's facial expressions and voice, and visually present work instructions. This improves work efficiency. In addition, if a worker is confused, the glasses provide additional support information to assist them in their work. 【0559】 In this way, the user experience can be improved throughout the entire logistics management process by utilizing sentiment analysis. An example of a prompt is: "Explain how to automatically display support information that takes into account the emotional state of workers during picking operations at a logistics center." 【0560】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0561】 Step 1: 【0562】 The server collects land and sea transport information from the company's internal database and the internet. This allows the server to prepare the basic data necessary for generating logistics information. The input is a dataset of land and sea transport data, and the output is logistics information that integrates this data. Data processing includes formatting standardization and reduction of duplicate data. 【0563】 Step 2: 【0564】 The server automatically generates optimal logistics routes and work plans using a machine learning system based on the generated logistics information. Here, the input is integrated logistics information, and the output is an optimized logistics route and work plan. Data calculations include prediction and optimization using algorithms based on historical data. The generative AI model is used to calculate the optimal route under various conditions. 【0565】 Step 3: 【0566】 The terminal visually displays the generated logistics routes and work plans and accepts feedback from users. Input is the logistics route and work plan from the server, and output is user feedback data. Feedback is entered directly by the user through the terminal's interface. 【0567】 Step 4: 【0568】 The device analyzes the user's facial expressions and voice in real time using an emotion analysis engine and adjusts the interface display accordingly. Input is the user's facial expressions and voice data, and output is an interface display corresponding to the user's emotional state. Data processing includes extracting emotional features and determining the emotional state. 【0569】 Step 5: 【0570】 Users review the logistics plan provided on the terminal and provide feedback as needed. Input is displayed via the terminal's interface, while output is the user's evaluation and opinions. Specifically, users provide feedback using touchscreens or voice input. 【0571】 Step 6: 【0572】 The server determines the final logistics plan, incorporating user feedback, and deploys it to the relevant logistics systems. The input is user feedback data, and the output is the final logistics plan that takes feedback into account. Data processing includes feedback analysis and adaptive adjustment of the plan. 【0573】 The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0574】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0575】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314. 【0576】 [Fourth Embodiment] 【0577】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0578】 As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server. 【0579】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0580】 The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52. 【0581】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0582】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0583】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0584】 The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes. 【0585】 Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0586】 The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30. 【0587】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0588】 In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0589】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0590】 This invention is a logistics optimization system implemented by an information processing device, namely a server, a terminal, and a user operating it, and aims to realize more efficient and sustainable logistics operations. 【0591】 The server first collects land and sea transport information from various data sources on the internet and internal corporate databases. This data includes diverse information related to logistics, such as truck schedules and port usage. The collected data is unified from different formats and centrally managed in a standardized form. 【0592】 Based on this integrated data, the server uses artificial intelligence agents to evaluate multiple transportation options and automatically generate the optimal logistics route, reduce tasks, and efficiently allocate resources. This generation process applies complex algorithms to minimize transportation costs, time, and environmental impact. 【0593】 The generated transportation plan is displayed visually to the user via a terminal. An intuitive and easy-to-understand user interface is used, allowing users to quickly review the plan details. Users can provide feedback and modify conditions via the terminal as needed, and this information is immediately reflected on the server. 【0594】 As a concrete example, consider a case where a user needs to transport a large amount of cargo from Tokyo to Osaka. In this case, the server takes into account the shortage of truck drivers and congestion in sea transport, and proposes a route that combines sea transport from Tokyo to Kobe Port, followed by land transport from Kobe to Osaka. This proposal is designed to reduce environmental impact and minimize overall costs. 【0595】 Even after the plan is implemented, real-time monitoring of the logistics status is performed via terminals, and alerts are sent to users if any anticipated problems or delays occur. This information allows users to make quick decisions and ensure that the logistics flow is maintained without interruption. 【0596】 This system can provide sustainable solutions to the driver shortage and environmental problems facing the logistics industry. Furthermore, by effectively utilizing various modes of transport, it can significantly improve overall logistics efficiency. 【0597】 The following describes the processing flow. 【0598】 Step 1: 【0599】 The server collects data related to land and sea transport from the internet and internal corporate databases. Specifically, it obtains truck operational status, port utilization rates, weather information, etc., via APIs, and utilizes scraping techniques as needed. 【0600】 Step 2: 【0601】 The server standardizes the collected data and stores it in an integrated database. Here, different data formats are converted into a unified format, and missing or inconsistent data is automatically identified, supplemented, and corrected. 【0602】 Step 3: 【0603】 The server runs an artificial intelligence agent that uses integrated data to generate optimal logistics routes and tasks. In this process, the best plan is selected from multiple route options based on conditions such as transportation costs, time, and environmental impact. 【0604】 Step 4: 【0605】 The generated plan is visualized on the device. The user interface graphically displays route details, anticipated problems, and cost analysis. Users can make quick decisions based on this information. 【0606】 Step 5: 【0607】 Users review the logistics plan displayed on their device and enter any necessary changes or additional conditions. For example, they can add specific time limits or prioritize certain modes of transport. 【0608】 Step 6: 【0609】 The server takes user input, reconstructs the plan, and finalizes the adjusted route. The final plan is then deployed to the logistics system and moves into the execution phase. 【0610】 Step 7: 【0611】 The terminal monitors the ongoing logistics process in real time and reports progress and anticipated problems to the user. As soon as an anomaly is detected, an alert is sent to the user, prompting a quick response. 【0612】 (Example 1) 【0613】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0614】 Modern logistics systems face challenges of complexity and inefficiency. In particular, finding the optimal transportation route is extremely difficult amidst the diversification of transport methods. Furthermore, increasing environmental impact has become a social issue, demanding sustainable logistics operations. The lack of systems capable of real-time monitoring of logistics status and rapid alert notifications is also a problem. 【0615】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0616】 This invention includes a server that collects transportation information using a data processing device, converts it into a unified format, and centrally manages it; a server that automatically generates the optimal transportation route and resource allocation using a generation AI model; and a server that monitors the logistics status in real time and immediately notifies the user of any problems if they are detected. This enables the construction of optimal logistics routes and flexible real-time responses even under complex transportation conditions. 【0617】 A "data processing device" is a device consisting of hardware and software for collecting, transforming, storing, and managing information. 【0618】 "Transportation information" refers to operational schedules, traffic conditions, port usage, and related data concerning various modes of transport, including land and sea. 【0619】 A "unified format" is a standardized data format used to convert data in different formats into a consistent format, enabling cross-platform data management. 【0620】 A "generative AI model" is an algorithm that uses machine learning and data analysis techniques to extract patterns and insights from data and propose the optimal logistics route and resource allocation. 【0621】 "Transportation route" is a term that refers to the optimal path or route selected for sending goods or cargo, and may include a combination of different modes of transport. 【0622】 "Resource allocation" is the process of appropriately allocating and managing human, material, and financial resources available for efficient logistics operations. 【0623】 "Real-time monitoring" is a process of continuously observing the current situation without time delay and updating information and taking action as needed. 【0624】 "Notifying a warning" is an action taken to immediately inform users when an anomaly or malfunction is detected, in order to encourage a quick response. 【0625】 This invention relates to an information processing device for the purpose of optimizing logistics. It primarily consists of a server, terminals, and users who operate them. The server functions as a data processing device, collecting data from multiple transportation-related information sources. These information sources include operational schedules, traffic conditions, and port usage related to land and sea transport. This data collection process is performed in real time via the internet. 【0626】 The server processes the collected data, converting different formats into a unified format for centralized management. This ensures data integrity and enables efficient analysis in the next stage. The server then uses a generative AI model to automatically generate optimal transportation routes and resource allocations based on the collected data. This AI model includes complex algorithms that assess challenges within the logistics network and provide scenarios that minimize cost, time, and environmental impact. 【0627】 The generated transportation plan is presented visually to the user via a terminal. The terminal features an intuitive user interface, allowing users to easily understand the plan and provide feedback as needed. User feedback is immediately reflected on the server, and the plan is re-evaluated. 【0628】 For example, in a scenario where a user needs to transport a large volume of cargo from Tokyo to Osaka, the server will propose a combination of sea transport from Tokyo to Kobe Port and land transport from Kobe to Osaka. This is done to reduce overall costs while taking into account congestion and driver shortages, and while mitigating environmental impact. 【0629】 Even after the plan's execution begins, real-time monitoring of the logistics status is performed using terminals. If any anticipated problems or delays are detected, the terminals immediately notify the user and encourage them to ensure the smooth continuation of the logistics flow. 【0630】 An example of a prompt to the generating AI model is, "Optimize the transportation route from Tokyo to Osaka. Propose the best route and cost reduction measures, taking into account the shortage of truck drivers and congestion." In this way, this invention achieves optimization that meets the complex needs of logistics, enabling more sustainable and efficient logistics operations. 【0631】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0632】 Step 1: 【0633】 The server collects transportation-related information from multiple data sources on the internet. The server receives input data such as land transport schedules, traffic information, and port usage data for sea transport. Since the collected data is provided in various formats, the server converts them into a unified format. This process involves data cleaning to remove noise and inconsistencies. 【0634】 Step 2: 【0635】 The server stores the data, converted to a unified format, in a database for centralized management. This database is indexed to enable efficient data retrieval. After storage in the database, the server queries the stored data to extract the necessary information. This process ensures data integrity while enabling rapid access. 【0636】 Step 3: 【0637】 The server utilizes a generative AI model to analyze optimal transportation routes and resource allocation from centrally managed data. The inputs provided are factors related to economic efficiency, time, and environmental impact. Based on these factors, the server applies complex algorithms to perform optimization. The output proposes the most efficient route selection and resource allocation. 【0638】 Step 4: 【0639】 The server generates a transportation plan, which is then provided to the user via a terminal. The terminal displays the plan data in a visually easy-to-understand format. Through the user interface, the user can review the plan details and provide feedback. Upon receiving feedback, the terminal sends the input to the server. This process allows the user to easily react to the plan. 【0640】 Step 5: 【0641】 Upon receiving user feedback, the server re-evaluates the plan and updates it as needed. The updated plan is quickly reflected on the device, allowing users to reconfirm it. The server also considers the new parameters and runs the AI ​​model again to further optimize it. 【0642】 Step 6: 【0643】 Once the plan is executed, the terminal monitors the delivery status in real time. The real-time data acquired as input is used to detect delays and anomalies. If an anomaly is detected, the terminal immediately alerts the user and provides a notification that includes a suggested solution. 【0644】 This series of processes improves the efficiency and sustainability of logistics, enabling users to achieve flexible logistics operations. 【0645】 (Application Example 1) 【0646】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0647】 Modern logistics demands optimized transportation routes and efficient task management, with real-time situation monitoring and rapid response being particularly challenging. Furthermore, it's necessary to effectively integrate various transportation methods while minimizing environmental impact, costs, and transit times. Traditional systems have struggled to meet all these requirements simultaneously. 【0648】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0649】 This invention includes a server that generates logistics information by integrating land transport information and sea transport information, a server that automatically generates optimal logistics routes and tasks using an artificial intelligence agent, and a server that monitors the logistics status in real time using a mobile terminal, receives change information, and immediately reflects it in the plan. This enables more efficient and sustainable logistics operations. 【0650】 An "information processing device" is a device that has the function of integrating logistics information and generating the optimal transportation route. 【0651】 An "artificial intelligence agent" is a program that evaluates transportation options and automatically generates the optimal logistics route by applying complex algorithms. 【0652】 "Logistics information" is information generated by integrating data related to land and sea transport, and it forms the basis of logistics planning. 【0653】 A "display device" is a device that visually shows generated logistics routes and tasks, and provides an interface for users to check and input information. 【0654】 A "mobile terminal" is a portable electronic device used by managers at logistics centers and other locations to monitor the situation in real time and immediately input any changes. 【0655】 "Real-time monitoring" is a process that instantly observes the progress of logistics and immediately detects any signs of delays or other anomalies. 【0656】 "Environmental impact" refers to the effects that logistics activities have on the natural environment, and minimizing this impact is required. 【0657】 To implement this invention, a server, acting as an information processing device, plays a central role. The server collects data related to land and sea transport in order to aggregate logistics information. This involves obtaining data from sources such as internal company databases and APIs for providing official transport information. This collected data is then processed and standardized using Python libraries such as Pandas and Requests. 【0658】 Next, the server generates the optimal logistics route based on integrated logistics information through an artificial intelligence agent. It utilizes generative AI models such as Scikit-learn and TensorFlow, applying complex algorithms that take into account minimizing transportation costs, time, and environmental impact. 【0659】 Meanwhile, the terminal provides a user-friendly interface developed with React Native. Users can check the logistics status in real time via the mobile terminal and easily provide necessary feedback and make changes to plans through the terminal. Real-time monitoring of logistics status is achieved using WebSocket technology, and the system is equipped with a function that immediately notifies the user of alerts if delays or anomalies occur. 【0660】 For example, if a logistics center manager wants to optimize cargo transport from Tokyo to Osaka, they can use the app to check the current status and see the most efficient route suggested by AI. If delays are predicted during transport, an alert will be sent to the manager's terminal, and an alternative route will be presented. 【0661】 An example of a prompt message might be: "Please propose the optimal logistics route from Tokyo to Osaka, minimizing environmental impact, cost, and time." 【0662】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0663】 Step 1: 【0664】 The server collects land and sea transport information from APIs and internal corporate databases. Its inputs are various forms of logistics data, and its output is integrated logistics information. The server uses Pandas to standardize and manage the data. 【0665】 Step 2: 【0666】 The server passes integrated logistics information as input to an artificial intelligence agent. Based on this input data, the agent uses TensorFlow, a generative AI model, to apply logic that minimizes transportation costs, time, and environmental impact, and outputs the optimal logistics route. The AI ​​agent utilizes complex algorithms to generate an efficient plan from multiple options. 【0667】 Step 3: 【0668】 The terminal visually presents the user with optimized transportation route information transmitted from the server. The input is optimized logistics route information from the server, and the output is visual information displayed on the user's screen. The interface, developed with React Native, allows users to easily access and review the information. 【0669】 Step 4: 【0670】 The user inputs any necessary changes or feedback based on the information presented. This input is sent to the server via the terminal and reflected in the logistics plan as new conditions. Here, the user's feedback acts as output for revising the plan. 【0671】 Step 5: 【0672】 The terminal uses WebSocket to perform real-time monitoring during the logistics process. Its input is progress data during logistics, and its output is an alert generated when an anomaly is detected. This immediately notifies the user. 【0673】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0674】 This invention combines an emotion engine with a logistics optimization system to provide an interface experience that takes user emotions into account, thereby achieving more efficient logistics management. This system is implemented by information processing devices, namely servers, terminals, and the users who use them. 【0675】 The server first collects land and sea transport information from the internet and internal corporate databases, and integrates this information to generate logistics data. Based on this logistics data, an artificial intelligence agent automatically generates the optimal logistics route and tasks. The generated plan is then optimized based on pre-configured conditions within the server. 【0676】 Here, the present invention further incorporates an emotion engine into the terminal to analyze the user's facial expressions, voice, input patterns, etc., when viewing plans, and recognizes the user's emotions. This emotion information is reflected in the display content and style of the user interface. For example, if the user is feeling anxious, detailed information can be presented in a clearer manner, or if the user is satisfied with the choices, an animation can be displayed to encourage smooth execution. 【0677】 As a concrete example, consider a scenario where a user is planning transportation from Tokyo to Fukuoka using the system. In this case, the server generates the optimal transportation route from existing data, and the terminal presents the details to the user. If the emotion engine detects stress from the user's facial expressions and voice while viewing the information, the terminal provides detailed support information and guided navigation. Furthermore, if the user expresses satisfaction with the proposed plan, the execution confirmation process is simplified to reduce response time. 【0678】 Users review the presented plan and provide emotion-based feedback through input on a terminal. Based on this feedback, the server adjusts the final logistics plan and moves to the execution phase. As a result, users can manage their logistics more efficiently and comfortably through an emotion-sensitive system operation. 【0679】 The following describes the processing flow. 【0680】 Step 1: 【0681】 The server collects data on land and sea transport from multiple sources. It obtains real-time data via APIs and generates logistics information by standardizing and centralizing different data formats. 【0682】 Step 2: 【0683】 The server inputs the generated logistics information into an artificial intelligence agent, which automatically generates the optimal logistics route and tasks. The algorithm creates the plan while taking cost, time, and environmental impact into consideration. 【0684】 Step 3: 【0685】 The terminal presents the generated logistics plan to the user. At this time, the emotion engine is activated and analyzes the user's voice, facial expressions, and input patterns to recognize the user's emotions. 【0686】 Step 4: 【0687】 Based on the analysis results of the emotion engine, the device adjusts the user interface. For example, if the user shows anxiety, it displays additional information or guides to reassure them. Also, if the user is satisfied with the plan, the confirmation process is simplified. 【0688】 Step 5: 【0689】 The user operates the device to review the generated plan and enter feedback. This feedback, including emotional aspects, is reflected on the device and sent to the server. 【0690】 Step 6: 【0691】 The server takes feedback into account and makes final adjustments to the plan. The final version of the plan is then deployed throughout the entire logistics system. 【0692】 Step 7: 【0693】 The terminal monitors the logistics process in real time during the execution phase and alerts the user if problems or delays occur. It also uses the functionality of an emotion engine to prompt the user to take appropriate action. 【0694】 (Example 2) 【0695】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0696】 In logistics systems, existing route and task optimization methods often fail to consider the psychological state of users, which can lead to stress and frustration that hinders management efficiency. Therefore, there is a need to achieve efficient and comfortable logistics management that takes users' emotional states into account. Furthermore, systems that lack environmental considerations can, conversely, cause social problems. 【0697】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0698】 In this invention, the server includes means for generating logistics information by integrating land transport information and sea transport information using an information processing device, means for automatically generating optimal logistics routes and operations using an artificial intelligence agent, and means for recognizing the user's emotions using an emotion analysis engine and dynamically adjusting the interface. This makes it possible to provide an interface that takes the user's emotions into consideration and to realize more efficient and comfortable logistics management. 【0699】 An "information processing device" refers to a device that has the ability to collect, integrate, and analyze data related to transportation. 【0700】 "Information on land transport" refers to data related to transport conducted using roads. 【0701】 "Maritime transport information" refers to data related to transportation conducted via the ocean using ships. 【0702】 "Logistics information" refers to information obtained as a result of integrating data from multiple transportation-related sources. 【0703】 An "artificial intelligence agent" refers to artificial intelligence technology that analyzes data and automatically generates optimal transportation plans and tasks. 【0704】 A "logistics route" refers to the path that goods are scheduled to take when they are transported. 【0705】 "Work" refers to a series of activities and processes related to logistics. 【0706】 A "display device" refers to a device used to visually present information to users. 【0707】 "User" refers to a person who operates the system to check and issue instructions regarding logistics information. 【0708】 An "emotion analysis engine" refers to a program that recognizes the user's emotional state and adjusts the information displayed accordingly. 【0709】 An "interface" refers to the operating environment that allows users to interact with a system. 【0710】 "Logistics planning" refers to the final plan for the transportation of goods. 【0711】 "Related logistics systems" refers to a set of systems involved in the execution of logistics plans. 【0712】 "Environmental impact" refers to the impact that logistics activities have on the natural environment. 【0713】 This invention combines a logistics optimization system with an emotion analysis engine, providing users with an emotionally sensitive user experience while achieving efficient logistics management. The system is primarily implemented by servers, terminals, and the users who utilize them. 【0714】 The server first collects information on land and sea transport from the internet and internal corporate databases. This is done using commonly used database management system software. This collected data is processed, integrated, and stored on the server as logistics information. Based on this logistics information, the server utilizes generative AI models to automatically generate optimal transport routes and related logistics tasks. Machine learning algorithms based on Python or R are sometimes used in this process. 【0715】 The terminal is equipped with an emotion analysis engine that uses a camera and microphone to analyze the user's facial expressions, voice, and input patterns as they view logistics plans through the system. Software libraries such as OpenCV and TensorFlow are utilized for this analysis. The resulting user emotions are reflected in the interface in real time. For example, if the user is feeling anxious, the terminal will provide additional information and support guides to aid understanding. 【0716】 Users review the plan presented by the logistics system and input feedback into a terminal to reflect their own feelings. This user input is immediately transmitted to the server, contributing to the final adjustment of the logistics plan. The final plan becomes more efficient and user-friendly, and then proceeds to the execution phase. 【0717】 As a concrete example, a user might input a prompt message into the system saying, "Tell me the optimal transportation plan from Tokyo to Fukuoka." In response to this prompt, the server quickly collects data and uses an AI model to formulate the optimal plan. The terminal then analyzes the user's response and provides an optimal interface. Through this process, the user can manage their logistics efficiently and with minimal stress. 【0718】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0719】 Step 1: 【0720】 The server collects information on land and sea transport from the internet and internal databases. As input, it uses internet and database queries to gather transport-related data (e.g., weather information, traffic conditions, ship location information, etc.). This data is organized and integrated by a database management system and output as a single logistics information dataset. Specifically, a Python script retrieves information from an API, and this data is stored in an SQL database. 【0721】 Step 2: 【0722】 The server receives integrated logistics information as input and uses a generative AI model to generate optimal logistics routes and tasks. In this process, the AI ​​algorithm analyzes the transportation information and creates an optimized route plan and schedule. As output, several recommended transportation routes and their detailed information are generated. Specifically, the model uses the machine learning framework TensorFlow to make predictions based on the training data. 【0723】 Step 3: 【0724】 The terminal receives the logistics plan from the server and displays it on the user interface. It collects data such as facial expressions and voice, as well as data entered by the user during prompting. The emotion analysis engine analyzes the user's emotional state and adapts the displayed content based on the results. The output is a customized interface designed to be easily understood by the user. Specifically, this involves facial recognition using OpenCV and identification of emotion patterns using a local database. 【0725】 Step 4: 【0726】 Users review the displayed logistics plan and input their opinions and feedback on the system. The submitted sentiment feedback is sent to the server and used to fine-tune the logistics plan. The final, adjusted logistics plan is then generated as output. Specifically, the user clicks on options in the GUI and inputs their feedback in text format. 【0727】 Step 5: 【0728】 The server deploys the final logistics plan to the relevant logistics systems and arranges for its execution. It receives final feedback and sentiment data from users as input and sends data to each relevant system in an executable format based on this. As output, newly updated logistics network data is distributed to the relevant facilities, and operations begin. Specifically, automated system calls are generated and delivered to logistics partners via corresponding APIs. 【0729】 (Application Example 2) 【0730】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0731】 In logistics management, there is a need for systems that can efficiently carry out operations while taking into account the mental state of users. Conventional logistics systems have struggled to provide interfaces that consider the emotional state of users, failing to alleviate the stress and anxiety they experience. Furthermore, there is a desire for further optimization of efficient logistics route generation through new technologies. 【0732】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0733】 In this invention, the server includes means for integrating land transport information and sea transport information in order to generate logistics information in an information processing device, means for automatically generating the optimal logistics route and operations using a machine learning system, and means for adjusting the display content of the interface using an emotion analysis engine to analyze the emotional state of the user. This enables flexible logistics management in accordance with the emotional state of the user. 【0734】 An "information processing device" is a device that has the function of integrating information on land transport and sea transport in order to generate logistics information. 【0735】 A "machine learning system" is a technology that automatically generates optimal logistics routes and operations based on logistics information. It is a system that learns using algorithms based on data and can derive optimal results. 【0736】 An "emotion analysis engine" is a system that analyzes the emotional state of a user. It is a technology that determines the user's psychological state based on facial expressions, voice, and input patterns, and adjusts the content displayed on the interface accordingly. 【0737】 A "logistics route" refers to the optimized travel path for products and services from the supplier to the consumer, designed to ensure efficient movement. 【0738】 "Work" refers to various tasks and procedures that must be performed in logistics management, and includes specific tasks such as picking, shipping, and delivery at a logistics center. 【0739】 This invention realizes a system that optimizes logistics management through collaboration between a server, terminals, and users. The server generates logistics information using an information processing device. Specifically, it integrates land transport information and sea transport information to grasp the overall picture of logistics. This enables real-time data processing. 【0740】 The server further uses a machine learning system to automatically generate the optimal logistics routes and operations. It utilizes algorithms to calculate the optimal plan, taking into account economic efficiency and speed. The machine learning system used here learns from historical data and makes highly accurate predictions even for new datasets. 【0741】 The device is equipped with an emotion analysis engine that analyzes the user's emotional state. It analyzes facial expressions, voice, and input patterns in real time and dynamically adjusts the content and style of the interface display. For example, if the user is feeling anxious, it will provide clear information and visual support. 【0742】 Users can review logistics plans and provide necessary feedback through the terminal. The terminal's interface is customized according to the user's emotional state, allowing for smooth and stress-free operation. 【0743】 As a concrete example, smart glasses are used in a logistics center. The glasses analyze the worker's facial expressions and voice, and visually present work instructions. This improves work efficiency. In addition, if a worker is confused, the glasses provide additional support information to assist them in their work. 【0744】 In this way, the user experience can be improved throughout the entire logistics management process by utilizing sentiment analysis. An example of a prompt is: "Explain how to automatically display support information that takes into account the emotional state of workers during picking operations at a logistics center." 【0745】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0746】 Step 1: 【0747】 The server collects land and sea transport information from the company's internal database and the internet. This allows the server to prepare the basic data necessary for generating logistics information. The input is a dataset of land and sea transport data, and the output is logistics information that integrates this data. Data processing includes formatting standardization and reduction of duplicate data. 【0748】 Step 2: 【0749】 The server automatically generates optimal logistics routes and work plans using a machine learning system based on the generated logistics information. Here, the input is integrated logistics information, and the output is an optimized logistics route and work plan. Data calculations include prediction and optimization using algorithms based on historical data. The generative AI model is used to calculate the optimal route under various conditions. 【0750】 Step 3: 【0751】 The terminal visually displays the generated logistics routes and work plans and accepts feedback from users. Input is the logistics route and work plan from the server, and output is user feedback data. Feedback is entered directly by the user through the terminal's interface. 【0752】 Step 4: 【0753】 The device analyzes the user's facial expressions and voice in real time using an emotion analysis engine and adjusts the interface display accordingly. Input is the user's facial expressions and voice data, and output is an interface display corresponding to the user's emotional state. Data processing includes extracting emotional features and determining the emotional state. 【0754】 Step 5: 【0755】 Users review the logistics plan provided on the terminal and provide feedback as needed. Input is displayed via the terminal's interface, while output is the user's evaluation and opinions. Specifically, users provide feedback using touchscreens or voice input. 【0756】 Step 6: 【0757】 The server determines the final logistics plan, incorporating user feedback, and deploys it to the relevant logistics systems. The input is user feedback data, and the output is the final logistics plan that takes feedback into account. Data processing includes feedback analysis and adaptive adjustment of the plan. 【0758】 The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0759】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0760】 In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414. 【0761】 Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion. 【0762】 Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together. 【0763】 These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression. 【0764】 The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become. 【0765】 Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant. 【0766】 The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more." 【0767】 The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values. 【0768】 The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format. 【0769】 In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data. 【0770】 In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56. 【0771】 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. 【0772】 Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56. 【0773】 The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory. 【0774】 The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor. 【0775】 Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources. 【0776】 Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose. 【0777】 The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above. 【0778】 All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference. 【0779】 The following is further disclosed regarding the embodiments described above. 【0780】 (Claim 1) 【0781】 A means for generating logistics information by integrating land transport information and maritime transport information using an information processing device, 【0782】 A means for automatically generating the optimal logistics route and tasks using an artificial intelligence agent based on the aforementioned logistics information, 【0783】 A means for displaying the generated logistics routes and tasks on a display device and accepting input from the user, 【0784】 A means of determining the final logistics plan that reflects user input data and deploying it to the relevant logistics systems, 【0785】 A system that includes this. 【0786】 (Claim 2) 【0787】 The system according to claim 1, comprising means for monitoring logistics status in real time and generating an alert to notify the user when an anomaly is detected. 【0788】 (Claim 3) 【0789】 The system according to claim 1, comprising means for evaluating the environmental impact of the coexistence of multiple means of transport and adjusting logistics routes and tasks to minimize the environmental impact. 【0790】 "Example 1" 【0791】 (Claim 1) 【0792】 A data processing device is used to collect transportation information, convert it into a unified format, and manage it centrally. 【0793】 A means for automatically generating optimal transportation routes and resource allocations using a generated AI model based on the aforementioned unified data, 【0794】 A means for visually presenting the generated transportation plan on a display device and receiving input from the user, 【0795】 A means of receiving user feedback, updating plans based on conditions, and reflecting them in the logistics system, 【0796】 A system that includes this. 【0797】 (Claim 2) 【0798】 The system according to claim 1, comprising means for monitoring logistics status in real time and immediately notifying the user of a warning when a problem is detected. 【0799】 (Claim 3) 【0800】 The system according to claim 1, comprising means for evaluating the environmental impact of different combinations of means of transport and optimizing transport routes and operations to reduce environmental impact. 【0801】 "Application Example 1" 【0802】 (Claim 1) 【0803】 A means for generating logistics information by integrating land transport information and maritime transport information using an information processing device, 【0804】 A means for automatically generating the optimal logistics route and tasks using an artificial intelligence agent based on the aforementioned logistics information, 【0805】 A means for displaying the generated logistics routes and tasks on a display device and accepting input from the user, 【0806】 A means of determining the final logistics plan that reflects user input data and deploying it to the relevant logistics systems, 【0807】 A means of monitoring the logistics situation in real time using mobile terminals, receiving change information, and immediately reflecting it in the plan, 【0808】 ... 【0809】 A system that includes this. 【0810】 (Claim 2) 【0811】 The system according to claim 1, comprising means for monitoring logistics status in real time, generating alerts and notifying the user when an anomaly is detected, and means for re-proposing transportation routes while taking into consideration the minimization of environmental impact, cost, and time. 【0812】 (Claim 3) 【0813】 The system according to claim 1, comprising means for evaluating the environmental burden caused by the coexistence of multiple means of transport, adjusting logistics routes and tasks to minimize environmental impact, and efficiently presenting information through a visual device. 【0814】 "Example 2 of combining an emotion engine" 【0815】 (Claim 1) 【0816】 A means for generating logistics information by integrating land transport information and maritime transport information using an information processing device, 【0817】 A means for automatically generating the optimal logistics route and operations using an artificial intelligence agent based on the aforementioned logistics information, 【0818】 A means for displaying the generated logistics route and operations on a display device and accepting input from the user, 【0819】 A means of recognizing the user's emotions through a display device using an emotion analysis engine, 【0820】 Means for dynamically adjusting the interface of a display device based on recognized emotion information, 【0821】 A means of determining a final logistics plan that reflects user input data and emotional information, and deploying it to the relevant logistics systems. 【0822】 A system that includes this. 【0823】 (Claim 2) 【0824】 The system according to claim 1, comprising means for monitoring logistics status in real time and generating a warning and notifying the user if an anomaly is detected. 【0825】 (Claim 3) 【0826】 The system according to claim 1, comprising means for evaluating the environmental impact of the coexistence of multiple means of transport and adjusting logistics routes and operations to minimize the environmental impact. 【0827】 "Application example 2 when combining with an emotional engine" 【0828】 (Claim 1) 【0829】 The information processing device includes means for integrating land transport information and maritime transport information in order to generate logistics information. 【0830】 A means for automatically generating the optimal logistics route and operations using a machine learning system based on the aforementioned logistics information, 【0831】 A means for displaying the generated logistics route and operations on a display device and accepting input from the user, 【0832】 A means of adjusting the display content of the interface using an emotion analysis engine to analyze the emotional state of the user, 【0833】 A means of determining the final logistics plan that reflects user input data and deploying it to the relevant logistics systems, 【0834】 A system that includes this. 【0835】 (Claim 2) 【0836】 The system according to claim 1, comprising means for monitoring logistics status in real time and generating a warning and notifying the user if an anomaly is detected. 【0837】 (Claim 3) 【0838】 The system according to claim 1, comprising means for evaluating the environmental burden caused by the coexistence of multiple transportation methods and adjusting logistics routes and operations to minimize the environmental impact. [Explanation of symbols] 【0839】 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

[Claim 1] A means for generating logistics information by integrating land transport information and maritime transport information using an information processing device, A means for automatically generating the optimal logistics route and tasks using an artificial intelligence agent based on the aforementioned logistics information, A means for displaying the generated logistics routes and tasks on a display device and accepting input from the user, A means of determining the final logistics plan that reflects user input data and deploying it to the relevant logistics systems, A system that includes this. [Claim 2] The system according to claim 1, comprising means for monitoring logistics status in real time and generating an alert to notify the user when an anomaly is detected. [Claim 3] The system according to claim 1, comprising means for evaluating the environmental burden caused by the coexistence of multiple means of transport and adjusting logistics routes and tasks to minimize the environmental impact.

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