system

Abstract

We provide the system. [Solution] Means of obtaining weather information, A vehicle device that scans the surrounding environment in real time, A computing device that analyzes acquired data to predict snowfall, A control device that calculates the optimal route based on the analysis results, A machine control device that operates automatically based on the generated path, A communication device that receives feedback and adjusts the work, A system that includes monitoring devices to monitor the vehicle's condition and report any abnormalities.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Unexamined Patent Application Publication No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 Snow removal work is essential for maintaining the safety and quality of life of residents in snowy areas. However, due to the recent labor shortage and aging population, there is a shortage of snow removal workers, resulting in delays and inadequate responses in snow removal. This problem is not only serious in urban areas but also in rural areas, and a sustainable solution for realizing efficient snow removal work throughout the region is required. 【Means for Solving the Problems】 【0005】 This invention provides a system that enables efficient and flexible snow removal operations by linking weather information and field data in real time, based on autonomous driving technology. Specifically, it provides a system that includes means for acquiring weather information, a vehicle device for scanning the surrounding conditions of the vehicle, a computing device for analyzing past and present data to predict snowfall, a control device for calculating the optimal route based on the analysis results, a machine control device that operates automatically based on the generated route, a communication device for adjusting operations in response to user feedback, and a monitoring device for monitoring the vehicle status and reporting abnormalities. This system ensures efficient snow removal operations and traffic safety, and improves the living environment in the region. 【0006】 "Weather information" refers to data about current weather conditions and predicted weather conditions. 【0007】 "Scanning the surrounding environment in real time" refers to observation techniques used by snow removal vehicles to instantly understand their surrounding environment. 【0008】 A "vehicle-type device" is a mobile machine designed for snow removal operations. 【0009】 "Analyzing data to predict snowfall" is the process of predicting future snow conditions based on collected information. 【0010】 A "computing device" is a computer system that is responsible for data processing and performing calculations. 【0011】 "Calculating the optimal route" refers to the process of determining the most efficient and safest route for snow removal. 【0012】 A "control device" is a device used to operate machinery based on calculated information. 【0013】 "Operating automatically based on a generated route" refers to snow removal vehicles moving autonomously based on a pre-calculated route. 【0014】 A "mechanical control device" is a mechanical device for automatically controlling functions. 【0015】 "Receiving feedback and adjusting operations" refers to the process of reviewing the operation content and sequence according to the reactions and instructions from the user. 【0016】 A "communication device" is a device for transmitting and receiving data. 【0017】 "Monitoring the vehicle's state and reporting abnormalities" refers to the means of checking the vehicle's operation status and reporting any problems found immediately. 【0018】 A "monitoring device" is a device used to constantly check the operation and state of a vehicle. 【Brief Description of Drawings】 【0019】 [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which multiple emotions are mapped. [Figure 10] Shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an 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 an emotion engine is combined. 【Mode for Carrying Out the Invention】 【0020】 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. 【0021】 First, the language used in the following description will be described. 【0022】 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. 【0023】 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. 【0024】 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. 【0025】 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). 【0026】 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." 【0027】 [First Embodiment] 【0028】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0029】 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. 【0030】 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). 【0031】 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. 【0032】 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. 【0033】 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. 【0034】 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. 【0035】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0036】 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. 【0037】 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. 【0038】 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. 【0039】 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". 【0040】 This invention relates to an automated snow removal system designed to support efficient snow removal operations in snowy regions. This system acquires weather information, analyzes snow conditions in real time to determine the optimal route, and automates the actual snow removal work. 【0041】 System Configuration 【0042】 1. Data Collection 【0043】 The server periodically retrieves weather data from weather information services. This allows it to understand weather conditions that affect snow removal operations, such as snowfall and temperature. 【0044】 The terminal (snowplow) uses on-board sensors and cameras to scan the surrounding snow conditions and transmits that information to the server. 【0045】 2. Data Analysis and Path Calculation 【0046】 The server analyzes the received data and uses an AI algorithm to predict the depth and density of the snow. Based on these results, it calculates an efficient and safe snow removal route. 【0047】 By referring to past data, we can understand the trends in snow accumulation patterns in specific regions and generate the optimal work plan. 【0048】 3. Execute snow removal work 【0049】 The terminal operates automatically based on route information received from the server, performing snow removal tasks. This utilizes autonomous driving technology, allowing operations to be carried out day and night. 【0050】 During operation, a real-time monitoring function is used to detect and avoid obstacles in the surrounding area. 【0051】 4. Feedback and adjustments 【0052】 Users can check the current snow removal status and future forecasts via their smart devices. If necessary, they can provide feedback to the server regarding snow removal priorities and priority areas. 【0053】 The server receives feedback from the user, replans the work plan as needed, and sends new instructions to the terminal. 【0054】 5. Vehicle status monitoring 【0055】 The terminal constantly checks the vehicle's status using its self-diagnostic function and immediately reports any abnormalities to the server. 【0056】 By detecting early signs of malfunction, maintenance efficiency and work safety can be improved. 【0057】 Specific example 【0058】 When heavy snowfall is forecast in a particular area, this system operates as follows: First, the server analyzes weather data, and then the terminal scans for snow accumulation. Based on the information obtained, the server calculates the optimal route, and the terminal automatically follows the snow removal route. If the user specifies areas where snow removal should be prioritized along the way, the server incorporates that information and re-evaluates the route. The terminal also continues to check for safety during the operation and reports any abnormalities to the server. This entire process allows for efficient and rapid snow removal, maintaining the flow of traffic in the area. 【0059】 The following describes the processing flow. 【0060】 Step 1: 【0061】 The server accesses weather information services to retrieve current weather data. This data includes snowfall forecasts, temperature, wind speed, and other information. Based on this data, it analyzes future snowfall trends. 【0062】 Step 2: 【0063】 The terminal uses on-board sensors and cameras to scan the surrounding environment and understand the snow conditions in real time. This information is immediately transmitted to the server. 【0064】 Step 3: 【0065】 The server analyzes snow depth data received from terminals using an AI algorithm and maps the snow depth across the entire region. It also compares this data with past data to predict snow accumulation patterns. 【0066】 Step 4: 【0067】 The server calculates the optimal snow removal route based on the analysis results. This calculation takes into account factors such as the importance of each road, traffic volume, safety, and fuel efficiency. 【0068】 Step 5: 【0069】 The terminal receives the route and work pattern instructed by the server and autonomously begins snow removal work. Utilizing its autonomous driving function, it safely removes snow along the designated route. 【0070】 Step 6: 【0071】 Users can monitor snow removal status and check progress via smartphones or computers. If users request changes, they can specify priority areas or enter additional work instructions. 【0072】 Step 7: 【0073】 The server receives input from the user, adjusts the work plan based on that information, and sends new instructions to the terminal. Real-time feedback optimizes snow removal operations. 【0074】 Step 8: 【0075】 The terminal continuously monitors the vehicle's status even while work is in progress. If it detects any signs of malfunction or abnormalities, it immediately reports to the server and adjusts the driving mode as needed. 【0076】 Step 9: 【0077】 The server automatically notifies maintenance personnel based on reports from terminals. This enables smooth maintenance arrangements. 【0078】 (Example 1) 【0079】 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." 【0080】 Snow removal in snowy regions often relies heavily on manual labor, presenting challenges in ensuring efficiency and safety. Furthermore, the inability to respond quickly to changing weather conditions can disrupt traffic. Vehicle breakdowns and the selection of efficient routes also posed challenges. 【0081】 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. 【0082】 In this invention, the server includes means for acquiring weather conditions, a vehicle mechanism for scanning the surrounding environment in real time, and a computing module for analyzing the acquired information and predicting snow accumulation conditions. This enables efficient and rapid snow removal work. 【0083】 "Weather conditions" refer to information about the weather, including factors that affect snow removal operations, such as snowfall, temperature, and wind speed. 【0084】 "Surrounding environment" refers to the physical conditions and circumstances surrounding the vehicle, specifically including the depth of snow accumulation and the presence or absence of obstacles. 【0085】 A "vehicle mechanism" refers to hardware equipment installed on a snow removal vehicle that uses sensors to detect the surrounding conditions and collect information. 【0086】 A "calculation module" is a software program that analyzes collected data to determine and predict snow accumulation patterns. 【0087】 A "control unit" is a device that formulates an optimal operation plan based on analyzed data and instructs the vehicle's movements. 【0088】 A "technical device" is a combination of hardware and software that autonomously performs operations according to a specified plan. 【0089】 A "communication module" is a device that sends and receives data and instructions between elements within a system, and also plays a role in receiving feedback from external sources. 【0090】 A "monitoring module" is a mechanism that continuously checks the internal condition of a vehicle and reports any abnormalities that occur. 【0091】 The autonomous snow removal system of this invention enables efficient snow removal in snowy regions and mainly consists of a server, terminals, and users. This system functions effectively through the acquisition of weather conditions, scanning of the surrounding environment, data analysis, route calculation, and execution of snow removal by autonomous driving. 【0092】 The server retrieves data from weather information services via APIs. Specifically, it obtains information such as snowfall amount, temperature, and wind speed, and stores it in a database. The server also utilizes machine learning algorithms (e.g., open-source machine learning libraries) to analyze the collected data. This program has the ability to learn from past data and predict future snow conditions. 【0093】 The terminal uses sensors (e.g., LiDAR sensors) and cameras mounted on the vehicle to scan the surrounding environment in real time. This allows the terminal to detect snow depth and the location of obstacles, and transmit this information to a server via a communication module. Using autonomous driving technology (e.g., autonomous driving platform technology), the terminal autonomously performs snow removal operations according to the route information transmitted from the server. 【0094】 Users can interact with the system using smart devices. This includes checking the progress and location of snowplows and issuing priority snow removal instructions for specific areas. Instructions from users are sent to the server via a communication module, which then replans the work schedule and sends new route instructions to the terminal as needed. 【0095】 As a concrete example, in areas where heavy snowfall is forecast, the server analyzes the latest weather data, and the terminal scans for snow accumulation. Based on this information, the server calculates the optimal snow removal route, and the terminal automatically performs snow removal work. If the user has areas they would like prioritized for snow removal, the route is re-evaluated taking that information into account. 【0096】 Using the generated AI model, an example of a prompt message could be, "Please explain in detail the steps to calculate an efficient snow removal route in city A in preparation for the next heavy snowfall." 【0097】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0098】 Step 1: 【0099】 The server initiates the data collection process to obtain weather information. Specifically, it retrieves information on snowfall and temperature through the weather information service's API. This data is received in JSON format and stored in the database. The input for this step is real-time weather data from the weather information service, and the output is storage in the database in a data format suitable for analysis. 【0100】 Step 2: 【0101】 The terminal begins scanning the surrounding environment in real time. Using LiDAR sensors and cameras mounted on the vehicle, it detects road snow conditions and obstacles. This information is initially analyzed on the terminal's processor and then transmitted to the server using a communication module. The input for this step is raw data from the sensors and cameras, and the output is a processed dataset of snow information. 【0102】 Step 3: 【0103】 The server integrates and analyzes received weather and snow depth data. Here, machine learning algorithms are used to predict snow depth patterns and patterns. The generated AI model references past data to predict future snowfall. The results of this analysis are used to generate new snow removal routes. The input for this step is real-time snow depth data sent to the server, and the output is the analyzed snow depth prediction information. 【0104】 Step 4: 【0105】 The server calculates the optimal snow removal route based on the analysis results. Using an optimization algorithm, it generates a safe and efficient route. This route information is transmitted to the terminal and serves as a guideline for the automated driving system. The input for this step is snow depth analysis information, and the output is detailed snow removal route data. 【0106】 Step 5: 【0107】 The terminal operates automatically based on the snow removal route received from the server. Using autonomous driving technology, the terminal travels along the designated route and performs snow removal. During travel, it detects and avoids surrounding obstacles in real time. The input for this step is calculated route information, and the output is the actual snow removal operation. 【0108】 Step 6: 【0109】 Users can use smart devices to check the progress of snowplows and predict the completion date of the work. If necessary, they can send feedback to instruct priority snow removal in specific areas. This information is transmitted to the server and used to re-evaluate the work plan. The input for this step is the user's requests and instructions, and the output is the updated snow removal work plan. 【0110】 (Application Example 1) 【0111】 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." 【0112】 In snowy regions, heavy snowfall causes traffic disruptions and reduces the efficiency of infrastructure, which are significant problems. Furthermore, effectively carrying out snow removal using limited resources remains a challenge. It is difficult for users to understand the snow removal situation in real time and select the optimal route. There is also a need for easy feedback on the locations and priorities where snow removal is needed. Improving these conditions and enhancing the efficiency and safety of snow removal operations in urban areas is crucial. 【0113】 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. 【0114】 In this invention, the server includes a device for acquiring weather information, a mobile unit for observing the surrounding conditions in real time, and a processing unit for analyzing the acquired data and predicting snowfall. This makes it possible for users to understand the snow removal situation through their smart devices and easily make requests for snow removal according to priority. 【0115】 A "weather information acquisition device" is a device that collects weather conditions and snowfall data and provides it for analysis. 【0116】 A "mobile device for real-time observation of surrounding conditions" is a mobile device that can measure the surrounding snow conditions in real time using sensors and cameras and supply that data to a system. 【0117】 A "processing device that analyzes acquired data and performs snow depth prediction" is a device that uses data acquired from sensors and weather information to perform calculations and analyses to predict snow depth and density. 【0118】 A "control device for calculating the optimal route" is a device that calculates the optimal snow removal route according to the snow conditions and terrain, and controls the snow removal machinery based on that calculation. 【0119】 An "automatically operating machine control device" is a device that operates autonomously and performs snow removal work according to a calculated snow removal route. 【0120】 A "communication device that receives feedback and adjusts operations" is a device that can receive instructions and feedback from users and dynamically adjust the snow removal operation plan accordingly. 【0121】 A "monitoring device that monitors the status of a mobile object and reports abnormalities" is a device that constantly monitors the operating status of a mobile object used for snow removal and reports any abnormalities detected to the system. 【0122】 A "digital terminal" is an electronic device that can receive data from multiple external devices and display information such as analysis results and snow removal progress to the user. 【0123】 In this invention, the server periodically collects weather data using a weather information acquisition device. This allows for real-time monitoring of snowfall and temperature changes, providing the information necessary for snow removal operations. A mobile unit that observes the surrounding conditions in real time scans the surrounding snow conditions using on-board sensors and cameras and transmits the data to the server. This allows for the precise identification of specific areas where snow removal operations are required. 【0124】 The processing unit uses a generated AI model based on the acquired data to perform snowfall prediction. The analyzed data is supplied to the control unit to calculate the optimal snow removal route. The control unit utilizes AI algorithms, particularly to determine the most effective route, to formulate a plan that enables efficient snow removal. 【0125】 The automated machine control system mounted on the mobile unit operates autonomously based on received route information, enabling rapid snow removal. This system can operate day and night and also has the function to recognize and avoid surrounding obstacles. Furthermore, an observation device continuously monitors the status of the mobile unit and immediately reports any abnormalities to the server, ensuring effective maintenance and safety. 【0126】 The communication device receives feedback and replans operations as needed based on priority instructions from users. This enables a rapid response to areas with high urgency. Users can visually check the current snow removal progress and forecasts via digital terminals and send new requests as needed. 【0127】 As a concrete example, in a region where heavy snowfall is forecast, a server analyzes weather data and a mobile vehicle scans for snow accumulation. When a user issues a command via a smart device to prioritize snow removal in a specific area, the communication device receives that information and generates a new route plan. Furthermore, it would be possible to input prompts such as, "Heavy snowfall is forecast. I would like to check the progress of snow removal work on major roads and in specific areas, as well as suggestions for the best detour route." 【0128】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0129】 Step 1: 【0130】 The server retrieves weather data from weather information services. The input is online weather data, and the output is organized weather condition information. This information includes wind speed, temperature, snowfall, etc., and is used in subsequent processing. 【0131】 Step 2: 【0132】 The terminal uses on-board sensors and cameras to scan the surrounding snow conditions in real time. Inputs are sensor data and video data, and output is digital scan data indicating snow depth and density. This data is transmitted to a server. 【0133】 Step 3: 【0134】 The server uses the data acquired in Steps 1 and 2 to perform snow depth prediction using a generative AI model. The input is weather condition information and digital scan data, and the output is a predicted value for short-term snow depth. This value is used to formulate an optimal snow removal plan. 【0135】 Step 4: 【0136】 The server uses an AI algorithm to calculate the optimal snow removal route based on the analysis results. The input is snow depth prediction values, and the output is specific snow removal route data. The calculated route is optimized considering efficiency and safety. 【0137】 Step 5: 【0138】 The terminal automatically performs snow removal based on snow removal route data received from the server. The input is snow removal route data, and the output is the condition of the cleared road. The terminal moves along the route and takes avoidance actions when it detects obstacles. 【0139】 Step 6: 【0140】 Users can check the current snow removal status and forecasts via a digital terminal. Input is snow removal progress information sent from the server, and output is the snow removal status displayed on the user's screen. New requests can be submitted as needed. 【0141】 Step 7: 【0142】 The communication device receives priority instructions from the user and replans the work schedule. The input is user feedback, and the output is the updated snow removal plan. This plan is fed back to the server and reflected in the next snow removal route. 【0143】 Step 8: 【0144】 The terminal's observation device monitors the status of the moving object and reports any detected abnormalities to the server. The input is real-time machine status data, and the output is an alert when an abnormality is detected. This enables early response. 【0145】 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. 【0146】 This invention provides an automated snow removal system that takes user emotions into consideration in order to carry out snow removal work efficiently and in a human-centered manner. In addition to conventional snow removal functions, this system dynamically adjusts the work plan by recognizing the user's emotions, thereby reducing user stress and improving work efficiency. 【0147】 System Configuration 【0148】 1. Data Collection and Analysis 【0149】 The server retrieves data from external weather information services and analyzes local weather and snow conditions. Based on this data, it identifies areas where snowfall is expected. 【0150】 The terminal uses sensors mounted on the vehicle to monitor the surrounding snow conditions in real time and transmits that information to the server. 【0151】 2. Applications of the Emotional Engine 【0152】 Users interact with the system using voice commands from their smart devices. The emotion engine recognizes emotions such as stress and tension from this voice data. 【0153】 Based on feedback from the emotion engine, the server revises the work plan according to the user's emotional state. For example, if the user is feeling stressed, the settings will be changed to allow for faster work. 【0154】 3. Coordination of automated snow removal operations and plans 【0155】 The terminal automatically proceeds with snow removal work, following the optimal snow removal route instructed by the server. It adjusts its operation according to the situation during the work, ensuring safe and efficient execution. 【0156】 The server stores user emotional changes as historical data and executes an algorithm that optimizes future work plans by reflecting past responses. 【0157】 4. User Feedback and Response 【0158】 Users can check the progress and predictions of their work via smart devices, and if anxiety or dissatisfaction arises, that information is captured through an emotion engine. 【0159】 The server receives user feedback in real time, replans snow removal operations as needed, and sends new instructions to the terminal. This allows for flexible responses that meet user expectations. 【0160】 Specific example 【0161】 Consider a scenario where an unexpected heavy snowfall occurs during a snow removal project. This system can solve the problem through the following steps: First, the server analyzes weather data and predicts the increase in snowfall. Terminals provide on-site snow data, which is used to determine the optimal snow removal route. Then, the user reports the on-site situation to the system via voice commands. If the user expresses anxiety, an emotion engine analyzes their emotions, and the server updates the plan to provide more detailed information and take a quicker response. This allows for efficient snow removal while maintaining the user's sense of security. 【0162】 The following describes the processing flow. 【0163】 Step 1: 【0164】 The server accesses weather information services to obtain current weather data such as snowfall forecasts, temperature, and wind speed. Based on this information, it analyzes areas where snowfall is expected and predicts how much snow removal will be necessary. 【0165】 Step 2: 【0166】 The terminal uses sensors and cameras mounted on the vehicle to scan the surrounding snow conditions and transmits the data to the server in real time. Machine learning algorithms are used to measure the depth and density of the snow. 【0167】 Step 3: 【0168】 The server uses an AI algorithm to calculate the optimal snow removal route based on the received snow depth data. This calculation includes factors such as traffic volume, snow removal priority, and safety. 【0169】 Step 4: 【0170】 Users send voice commands to the system from a dedicated smart device to check the progress of snow removal. During this process, an emotion engine analyzes the voice input to detect stress and anxiety. 【0171】 Step 5: 【0172】 Based on emotional state feedback from the emotion engine, the server re-evaluates the work plan as needed. For example, if the user indicates stress, it adjusts the speed and frequency of snow removal work accordingly. 【0173】 Step 6: 【0174】 The terminal automatically performs snow removal work based on instructions sent from the server. During operation, it avoids obstacles and operates efficiently while ensuring safety. 【0175】 Step 7: 【0176】 The server records user emotional changes and work results as a history, which is then used to plan future snow removal operations. In this process, past data is analyzed by AI to identify areas for improvement in subsequent operations. 【0177】 Step 8: 【0178】 Users can continuously monitor snow removal status and forecast data, and send feedback to the system as needed, enabling them to receive prompt responses and appropriate work support. 【0179】 (Example 2) 【0180】 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." 【0181】 This invention aims to improve efficiency in snow removal operations and reduce the psychological burden on users, and its objective is to provide a system that dynamically adjusts the work plan according to weather conditions and the user's emotional state. 【0182】 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. 【0183】 In this invention, the server includes a device for acquiring weather information, a mobile device for monitoring the surrounding conditions in real time, and an information processing device for analyzing the acquired data and performing snowfall predictions. This enables flexible work adjustments based on weather data and the user's emotional state. 【0184】 A "weather information acquisition device" is a device that automatically acquires weather-related data from an external weather database or information service. 【0185】 A "mobile device that monitors the surrounding environment in real time" is a device that uses sensors mounted on mobile hardware to continuously observe the surrounding environment and conditions. 【0186】 An "information processing device" is a computer system that analyzes acquired data and performs necessary calculations and decisions. 【0187】 A "planning device for designing the optimal route" is a device that calculates the most efficient and safest travel route based on analyzed data. 【0188】 An "automatic motion control system" is a system that controls machinery and equipment based on specified routes and motion instructions to perform actions. 【0189】 A "dialogue device that analyzes the user's emotional state and adjusts the work" is a device that analyzes the user's emotions based on their input and dynamically changes the work plan. 【0190】 A "communication device that receives feedback and replans its actions" is a device that receives information from users and external factors and has the function of replanning its action plan based on that information. 【0191】 A "monitoring device" is a device used to check the operating status and any abnormalities of a mobile object and to report them as necessary. 【0192】 This invention provides an embodiment of an automated driving system that streamlines snow removal operations while reducing the psychological burden on the user. 【0193】 The server uses equipment to acquire weather information and analyzes data obtained from external weather data services. For example, the server uses an API to acquire weather data and uses machine learning algorithms to predict snowfall in a region. This allows the server to understand snowfall trends in real time and issue instructions for snow removal work at the appropriate time. 【0194】 The terminal functions as a mobile device that monitors the surrounding environment in real time. Using LiDAR sensors and cameras mounted on the vehicle, the terminal detects snow accumulation and road surface conditions, and transmits this information to a server. This allows the terminal to constantly understand the accurate situation on site. 【0195】 Users interact with the system through smart devices. By issuing voice commands, users' emotional states are reported to the system in real time. These emotional states are analyzed by an emotion analysis engine; for example, if the analysis indicates the user is feeling anxious or stressed, the server dynamically adjusts the work schedule. This process minimizes the user's psychological burden. 【0196】 As a concrete example, if a server uses a weather forecast API to collect snow forecast data and a user gives a voice command saying, "Tell me the progress of the snow removal work," the emotion engine will read the user's emotions from the voice, and the server will make adjustments to the work plan accordingly. 【0197】 An example of a prompt message is, "Collect weather data necessary to plan this morning's snow removal, interpret the user's emotions from their voice commands, and adjust the work accordingly." Using such prompts makes it possible to carry out snow removal work efficiently and in a human-centered manner. 【0198】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0199】 Step 1: 【0200】 The server obtains weather data via a weather information service API. It receives data such as snowfall forecasts, temperature, and wind speed from this API as input. Based on this data, the server runs a machine learning algorithm and outputs a snow depth forecast. This analysis result is then used to plan snow removal operations. 【0201】 Step 2: 【0202】 The terminal uses LiDAR sensors and cameras mounted on the vehicle to monitor surrounding snow and road conditions in real time. It receives raw data from the sensors as input and sends it to the server. Based on this data, the terminal creates output to support situational judgment for autonomous driving. 【0203】 Step 3: 【0204】 The user sends commands to the system via the voice input function of their smart device. The voice data is passed as input to the emotion analysis engine. The analysis engine determines the user's emotional state (stress and anxiety) and outputs this to the server. Based on this feedback, the server adjusts the work plan to suit the user's mental state. 【0205】 Step 4: 【0206】 The server generates an optimized work plan based on weather data, on-site sensor information, and user sentiment data. In this step, this data is integrated to produce a route and schedule output for fast and safe snow removal. 【0207】 Step 5: 【0208】 The terminal automatically performs snow removal based on the optimal route generated, following instructions from the server. It uses GPS data and a pre-programmed road map as input and outputs the vehicle's movements using a motion control algorithm. 【0209】 Step 6: 【0210】 When a user performs a confirmation action from a smart device, that feedback information is sent as input to the emotion engine. The server analyzes the received feedback and obtains output to revise the snow removal plan as needed. 【0211】 (Application Example 2) 【0212】 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". 【0213】 In snow removal operations, conventional techniques prioritize efficiency to such an extent that they often neglect the user's mental state, leading to stress and anxiety. Furthermore, the fixed nature of work plans makes real-time adjustments difficult, resulting in an inability to respond to sudden weather changes or shifts in user emotions. 【0214】 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. 【0215】 In this invention, the server includes means for acquiring weather information, means for scanning the surrounding environment in real time, and means for sentiment analysis that recognizes the user's emotions and reflects them in the work plan. This enables flexible responses to weather changes and changes in the user's emotions, reducing stress and allowing for efficient snow removal work. 【0216】 "Means for acquiring weather information" refers to a device or software that has the function of acquiring external weather data and providing it to the system. 【0217】 A "vehicle device that scans the surrounding environment in real time" is a device mounted on a vehicle that detects the surrounding physical conditions and environment in real time. 【0218】 An "information processing device" is a computer or program used to analyze acquired data and perform snowfall predictions and other necessary calculations. 【0219】 A "control device" is a system that calculates the optimal route based on analysis results and issues commands to operate machinery. 【0220】 A "machine control device" is a device that automatically operates a machine based on generated path information. 【0221】 A "communication device" is a device used to receive feedback and send and receive information. 【0222】 A "monitoring device" is a system that constantly monitors the vehicle's condition and issues a warning if an abnormality is detected. 【0223】 An "emotion analysis device" is an engine or algorithm that recognizes a user's emotions and dynamically modifies the work plan based on that data. 【0224】 An "information provision device" is a system equipped with an interface for providing users with necessary information and feedback. 【0225】 The system implementing this invention mainly consists of three components: a server, a terminal, and a user. 【0226】 The server communicates with external data sources to obtain weather information and collects the latest weather data. Based on the acquired information, it predicts snowfall and calculates the optimal snow removal route. Furthermore, it processes emotional data from users and modifies the work plan according to the situation. For this purpose, the server uses an information processing device and an emotion analysis device. Emotion analysis is performed from voice data, and a generative AI model is used to identify mental states. 【0227】 The terminal uses sensors mounted on the vehicle to scan the surrounding environment in real time and transmits the data to the server. It also uses mechanical control devices to automatically perform snow removal work according to a designated route. Furthermore, it receives instructions from the server via a communication device and adjusts its operation based on the situation during the work. 【0228】 Users can interact with the system via smart devices. An information provider allows them to receive real-time information on work progress and weather changes, as well as provide their emotional state as voice input. An emotion analysis device analyzes this voice data to identify emotions such as stress. This information is fed back into the system and used to adjust the snow removal plan. 【0229】 For example, if the server receives a forecast of unexpected heavy snowfall, it can quickly instruct vehicles to change their routes via terminals, thereby alleviating the anxiety users might feel through prompt information provision. This system aims to streamline snow removal operations by considering user emotions and providing a greater sense of security. 【0230】 An example of a prompt to the generating AI model is, "If the user feels uneasy about the snow removal situation, how should the snow removal plan be changed?" 【0231】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0232】 Step 1: 【0233】 The server obtains the latest weather data from an external weather information service. The input is data obtained from the weather service's API, and the output is weather information for analysis. Based on this data, the server uses an information processing device to perform data analysis in order to predict snowfall and weather changes. 【0234】 Step 2: 【0235】 The terminal uses its built-in sensors to scan the snow conditions around the vehicle in real time and transmit the data to the server. The input is ambient environmental data from the sensors, and the output is the scan information received by the server. By doing this, the terminal provides detailed environmental data based on the current snow conditions. 【0236】 Step 3: 【0237】 Users interact with the system by issuing voice commands via a smart device. The input is the user's voice, and the output is voice data processed on a server. The user's commands are analyzed by an emotion analysis device, and the data is used to extract their emotional state. 【0238】 Step 4: 【0239】 The server calculates the optimal snow removal route based on analyzed weather information, scan data, and the user's emotional state. Inputs are weather data, scan information, and user emotional data, while output is optimized route information. The server uses a computational algorithm to integrate this data and dynamically generate optimal action instructions. 【0240】 Step 5: 【0241】 The terminal receives the latest route instructions from the server and automatically controls the vehicle. The input is route information from the server, and the output is the physical operation of the vehicle. The terminal uses a mechanical control device to follow the instructions and perform snow removal work safely and efficiently. 【0242】 Step 6: 【0243】 The server receives user feedback on ongoing tasks and readjusts the work plan as needed. The input is emotional feedback from the user, and the output is the adjusted work plan. Using a generative AI model enables flexible responses that meet user expectations. 【0244】 Step 7: 【0245】 The user receives current work status and additional feedback through an information provision device, and provides reactions to the system based on their own emotions. The input is work information from the server, and the output is data that enhances the user's sense of security and provides additional emotional data. Through this process, it is ensured that snow removal work is performed in a user-centric manner. 【0246】 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. 【0247】 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. 【0248】 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. 【0249】 [Second Embodiment] 【0250】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0251】 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. 【0252】 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). 【0253】 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. 【0254】 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. 【0255】 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). 【0256】 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. 【0257】 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. 【0258】 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. 【0259】 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. 【0260】 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. 【0261】 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". 【0262】 This invention relates to an automated snow removal system designed to support efficient snow removal operations in snowy regions. This system acquires weather information, analyzes snow conditions in real time to determine the optimal route, and automates the actual snow removal work. 【0263】 System Configuration 【0264】 1. Data Collection 【0265】 The server periodically retrieves weather data from weather information services. This allows it to understand weather conditions that affect snow removal operations, such as snowfall and temperature. 【0266】 The terminal (snowplow) uses on-board sensors and cameras to scan the surrounding snow conditions and transmits that information to the server. 【0267】 2. Data Analysis and Path Calculation 【0268】 The server analyzes the received data and uses an AI algorithm to predict the depth and density of the snow. Based on these results, it calculates an efficient and safe snow removal route. 【0269】 By referring to past data, we can understand the trends in snow accumulation patterns in specific regions and generate the optimal work plan. 【0270】 3. Execute snow removal work 【0271】 The terminal operates automatically based on route information received from the server, performing snow removal tasks. This utilizes autonomous driving technology, allowing operations to be carried out day and night. 【0272】 During operation, a real-time monitoring function is used to detect and avoid obstacles in the surrounding area. 【0273】 4. Feedback and adjustments 【0274】 Users can check the current snow removal status and future forecasts via their smart devices. If necessary, they can provide feedback to the server regarding snow removal priorities and priority areas. 【0275】 The server receives feedback from the user, replans the work plan as needed, and sends new instructions to the terminal. 【0276】 5. Vehicle status monitoring 【0277】 The terminal constantly checks the vehicle's status using its self-diagnostic function and immediately reports any abnormalities to the server. 【0278】 By detecting early signs of malfunctions, the efficiency of maintenance and the safety of operations are enhanced. 【0279】 Specific example 【0280】 When heavy snow is forecast in a certain area, this system operates in the following procedure. First, the server analyzes meteorological data, and then the terminal scans the snow accumulation situation. Then, the server calculates the optimal route based on the obtained information, and the terminal automatically follows the snow removal route. During the process, if the user designates an area where snow removal is required with priority, the server incorporates that information and re-evaluates the route. The terminal also continues to confirm safety during operation and reports any abnormalities to the server. Through this series of processes, snow removal operations can be carried out efficiently and quickly, maintaining the traffic flow in the area. 【0281】 The following explains the processing flow. 【0282】 Step 1: 【0283】 The server accesses the meteorological information service to obtain current weather data. This data includes snowfall forecasts, temperature, wind speed, etc. Based on this data, future snowfall trends are analyzed. 【0284】 Step ²: 【0285】 The terminal scans the surrounding environment using on-vehicle sensors and cameras to grasp the snow accumulation situation in real time. This information is immediately sent to the server. 【0286】 Step 3: 【0287】 The server analyzes the snow accumulation data received from the terminal using AI algorithms to map the snow depth across the region. It also compares with past data to predict the progress pattern of snow accumulation. 【0288】 Step 4: 【0289】 The server calculates the optimal snow removal route based on the analysis results. This calculation takes into account factors such as the importance of each road, traffic volume, safety, and fuel efficiency. 【0290】 Step 5: 【0291】 The terminal receives the route and work pattern instructed by the server and autonomously begins snow removal work. Utilizing its autonomous driving function, it safely removes snow along the designated route. 【0292】 Step 6: 【0293】 Users can monitor snow removal status and check progress via smartphones or computers. If users request changes, they can specify priority areas or enter additional work instructions. 【0294】 Step 7: 【0295】 The server receives input from the user, adjusts the work plan based on that information, and sends new instructions to the terminal. Real-time feedback optimizes snow removal operations. 【0296】 Step 8: 【0297】 The terminal continuously monitors the vehicle's status even while work is in progress. If it detects any signs of malfunction or abnormalities, it immediately reports to the server and adjusts the driving mode as needed. 【0298】 Step 9: 【0299】 The server automatically notifies maintenance personnel based on reports from terminals. This enables smooth maintenance arrangements. 【0300】 (Example 1) 【0301】 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." 【0302】 Snow removal operations in snowfall areas often rely on manual labor, and there are problems in ensuring efficiency and safety. In addition, it was a problem that it was impossible to quickly respond to changes in weather and it hindered traffic. Furthermore, vehicle failures and the selection of efficient routes also existed as problems. 【0303】 The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0304】 In this invention, the server includes means for acquiring weather conditions, a vehicle mechanism for scanning the surrounding environment in real time, and a calculation module for analyzing the acquired information and predicting the snow accumulation situation. Thereby, efficient and rapid snow removal operations become possible. 【0305】 "Weather conditions" are information related to the weather and are factors that affect snow removal operations such as snowfall amount, temperature, wind speed, etc. 【0306】 "Surrounding environment" refers to the physical state and situation existing around the vehicle, and specifically includes the depth of snow accumulation and the presence or absence of obstacles. 【0307】 "Vehicle mechanism" is a hardware device mounted on a snow removal vehicle and is for detecting the surrounding situation with sensors and collecting information. 【0308】 "Calculation module" is a software program for analyzing the collected data and performing snow accumulation pattern and prediction. 【0309】 "Control unit" is a device that formulates an optimal operation plan based on the analyzed data and instructs the operation of the vehicle.​​​​​​​A "communication module" is a device that sends and receives data and instructions between elements within a system, and also plays a role in receiving feedback from external sources. 【0312】 A "monitoring module" is a mechanism that continuously checks the internal condition of a vehicle and reports any abnormalities that occur. 【0313】 The autonomous snow removal system of this invention enables efficient snow removal in snowy regions and mainly consists of a server, terminals, and users. This system functions effectively through the acquisition of weather conditions, scanning of the surrounding environment, data analysis, route calculation, and execution of snow removal by autonomous driving. 【0314】 The server retrieves data from weather information services via APIs. Specifically, it obtains information such as snowfall amount, temperature, and wind speed, and stores it in a database. The server also utilizes machine learning algorithms (e.g., open-source machine learning libraries) to analyze the collected data. This program has the ability to learn from past data and predict future snow conditions. 【0315】 The terminal uses sensors (e.g., LiDAR sensors) and cameras mounted on the vehicle to scan the surrounding environment in real time. This allows the terminal to detect snow depth and the location of obstacles, and transmit this information to a server via a communication module. Using autonomous driving technology (e.g., autonomous driving platform technology), the terminal autonomously performs snow removal operations according to the route information transmitted from the server. 【0316】 Users can interact with the system using smart devices. This includes checking the progress and location of snowplows and issuing priority snow removal instructions for specific areas. Instructions from users are sent to the server via a communication module, which then replans the work schedule and sends new route instructions to the terminal as needed. 【0317】 As a concrete example, in areas where heavy snowfall is forecast, the server analyzes the latest weather data, and the terminal scans for snow accumulation. Based on this information, the server calculates the optimal snow removal route, and the terminal automatically performs snow removal work. If the user has areas they would like prioritized for snow removal, the route is re-evaluated taking that information into account. 【0318】 Using the generated AI model, an example of a prompt message could be, "Please explain in detail the steps to calculate an efficient snow removal route in city A in preparation for the next heavy snowfall." 【0319】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0320】 Step 1: 【0321】 The server initiates the data collection process to obtain weather information. Specifically, it retrieves information on snowfall and temperature through the weather information service's API. This data is received in JSON format and stored in the database. The input for this step is real-time weather data from the weather information service, and the output is storage in the database in a data format suitable for analysis. 【0322】 Step 2: 【0323】 The terminal begins scanning the surrounding environment in real time. Using LiDAR sensors and cameras mounted on the vehicle, it detects road snow conditions and obstacles. This information is initially analyzed on the terminal's processor and then transmitted to the server using a communication module. The input for this step is raw data from the sensors and cameras, and the output is a processed dataset of snow information. 【0324】 Step 3: 【0325】 The server integrates and analyzes received weather and snow depth data. Here, machine learning algorithms are used to predict snow depth patterns and patterns. The generated AI model references past data to predict future snowfall. The results of this analysis are used to generate new snow removal routes. The input for this step is real-time snow depth data sent to the server, and the output is the analyzed snow depth prediction information. 【0326】 Step 4: 【0327】 The server calculates the optimal snow removal route based on the analysis results. Using an optimization algorithm, it generates a safe and efficient route. This route information is transmitted to the terminal and serves as a guideline for the automated driving system. The input for this step is snow depth analysis information, and the output is detailed snow removal route data. 【0328】 Step 5: 【0329】 The terminal operates automatically based on the snow removal route received from the server. Using autonomous driving technology, the terminal travels along the designated route and performs snow removal. During travel, it detects and avoids surrounding obstacles in real time. The input for this step is calculated route information, and the output is the actual snow removal operation. 【0330】 Step 6: 【0331】 Users can use smart devices to check the progress of snowplows and predict the completion date of the work. If necessary, they can send feedback to instruct priority snow removal in specific areas. This information is transmitted to the server and used to re-evaluate the work plan. The input for this step is the user's requests and instructions, and the output is the updated snow removal work plan. 【0332】 (Application Example 1) 【0333】 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." 【0334】 In snowy regions, heavy snowfall causes traffic disruptions and reduces the efficiency of infrastructure, which are significant problems. Furthermore, effectively carrying out snow removal using limited resources remains a challenge. It is difficult for users to understand the snow removal situation in real time and select the optimal route. There is also a need for easy feedback on the locations and priorities where snow removal is needed. Improving these conditions and enhancing the efficiency and safety of snow removal operations in urban areas is crucial. 【0335】 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. 【0336】 In this invention, the server includes a device for acquiring weather information, a mobile unit for observing the surrounding conditions in real time, and a processing unit for analyzing the acquired data and predicting snowfall. This makes it possible for users to understand the snow removal situation through their smart devices and easily make requests for snow removal according to priority. 【0337】 A "weather information acquisition device" is a device that collects weather conditions and snowfall data and provides it for analysis. 【0338】 A "mobile device for real-time observation of surrounding conditions" is a mobile device that can measure the surrounding snow conditions in real time using sensors and cameras and supply that data to a system. 【0339】 A "processing device that analyzes acquired data and performs snow depth prediction" is a device that uses data acquired from sensors and weather information to perform calculations and analyses to predict snow depth and density. 【0340】 A "control device for calculating the optimal route" is a device that calculates the optimal snow removal route according to the snow conditions and terrain, and controls the snow removal machinery based on that calculation. 【0341】 An "automatically operating machine control device" is a device that operates autonomously and performs snow removal work according to a calculated snow removal route. 【0342】 A "communication device that receives feedback and adjusts operations" is a device that can receive instructions and feedback from users and dynamically adjust the snow removal operation plan accordingly. 【0343】 A "monitoring device that monitors the status of a mobile object and reports abnormalities" is a device that constantly monitors the operating status of a mobile object used for snow removal and reports any abnormalities detected to the system. 【0344】 A "digital terminal" is an electronic device that can receive data from multiple external devices and display information such as analysis results and snow removal progress to the user. 【0345】 In this invention, the server periodically collects weather data using a weather information acquisition device. This allows for real-time monitoring of snowfall and temperature changes, providing the information necessary for snow removal operations. A mobile unit that observes the surrounding conditions in real time scans the surrounding snow conditions using on-board sensors and cameras and transmits the data to the server. This allows for the precise identification of specific areas where snow removal operations are required. 【0346】 The processing unit uses a generated AI model based on the acquired data to perform snowfall prediction. The analyzed data is supplied to the control unit to calculate the optimal snow removal route. The control unit utilizes AI algorithms, particularly to determine the most effective route, to formulate a plan that enables efficient snow removal. 【0347】 The automated machine control system mounted on the mobile unit operates autonomously based on received route information, enabling rapid snow removal. This system can operate day and night and also has the function to recognize and avoid surrounding obstacles. Furthermore, an observation device continuously monitors the status of the mobile unit and immediately reports any abnormalities to the server, ensuring effective maintenance and safety. 【0348】 The communication device receives feedback and replans operations as needed based on priority instructions from users. This enables a rapid response to areas with high urgency. Users can visually check the current snow removal progress and forecasts via digital terminals and send new requests as needed. 【0349】 As a concrete example, in a region where heavy snowfall is forecast, a server analyzes weather data and a mobile vehicle scans for snow accumulation. When a user issues a command via a smart device to prioritize snow removal in a specific area, the communication device receives that information and generates a new route plan. Furthermore, it would be possible to input prompts such as, "Heavy snowfall is forecast. I would like to check the progress of snow removal work on major roads and in specific areas, as well as suggestions for the best detour route." 【0350】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0351】 Step 1: 【0352】 The server retrieves weather data from weather information services. The input is online weather data, and the output is organized weather condition information. This information includes wind speed, temperature, snowfall, etc., and is used in subsequent processing. 【0353】 Step 2: 【0354】 The terminal uses on-board sensors and cameras to scan the surrounding snow conditions in real time. Inputs are sensor data and video data, and output is digital scan data indicating snow depth and density. This data is transmitted to a server. 【0355】 Step 3: 【0356】 The server uses the data acquired in Steps 1 and 2 to perform snow depth prediction using a generative AI model. The input is weather condition information and digital scan data, and the output is a predicted value for short-term snow depth. This value is used to formulate an optimal snow removal plan. 【0357】 Step 4: 【0358】 The server uses an AI algorithm to calculate the optimal snow removal route based on the analysis results. The input is snow depth prediction values, and the output is specific snow removal route data. The calculated route is optimized considering efficiency and safety. 【0359】 Step 5: 【0360】 The terminal automatically performs snow removal based on snow removal route data received from the server. The input is snow removal route data, and the output is the condition of the cleared road. The terminal moves along the route and takes avoidance actions when it detects obstacles. 【0361】 Step 6: 【0362】 Users can check the current snow removal status and forecasts via a digital terminal. Input is snow removal progress information sent from the server, and output is the snow removal status displayed on the user's screen. New requests can be submitted as needed. 【0363】 Step 7: 【0364】 The communication device receives priority instructions from the user and replans the work schedule. The input is user feedback, and the output is the updated snow removal plan. This plan is fed back to the server and reflected in the next snow removal route. 【0365】 Step 8: 【0366】 The terminal's observation device monitors the status of the moving object and reports any detected abnormalities to the server. The input is real-time machine status data, and the output is an alert when an abnormality is detected. This enables early response. 【0367】 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. 【0368】 This invention provides an automated snow removal system that takes user emotions into consideration in order to carry out snow removal work efficiently and in a human-centered manner. In addition to conventional snow removal functions, this system dynamically adjusts the work plan by recognizing the user's emotions, thereby reducing user stress and improving work efficiency. 【0369】 System Configuration 【0370】 1. Data Collection and Analysis 【0371】 The server retrieves data from external weather information services and analyzes local weather and snow conditions. Based on this data, it identifies areas where snowfall is expected. 【0372】 The terminal uses sensors mounted on the vehicle to monitor the surrounding snow conditions in real time and transmits that information to the server. 【0373】 2. Applications of the Emotional Engine 【0374】 Users interact with the system using voice commands from their smart devices. The emotion engine recognizes emotions such as stress and tension from this voice data. 【0375】 Based on feedback from the emotion engine, the server revises the work plan according to the user's emotional state. For example, if the user is feeling stressed, the settings will be changed to allow for faster work. 【0376】 3. Coordination of automated snow removal operations and plans 【0377】 The terminal automatically proceeds with snow removal work, following the optimal snow removal route instructed by the server. It adjusts its operation according to the situation during the work, ensuring safe and efficient execution. 【0378】 The server stores user emotional changes as historical data and executes an algorithm that optimizes future work plans by reflecting past responses. 【0379】 4. User Feedback and Response 【0380】 Users can check the progress and predictions of their work via smart devices, and if anxiety or dissatisfaction arises, that information is captured through an emotion engine. 【0381】 The server receives user feedback in real time, replans snow removal operations as needed, and sends new instructions to the terminal. This allows for flexible responses that meet user expectations. 【0382】 Specific example 【0383】 Consider a scenario where an unexpected heavy snowfall occurs during a snow removal project. This system can solve the problem through the following steps: First, the server analyzes weather data and predicts the increase in snowfall. Terminals provide on-site snow data, which is used to determine the optimal snow removal route. Then, the user reports the on-site situation to the system via voice commands. If the user expresses anxiety, an emotion engine analyzes their emotions, and the server updates the plan to provide more detailed information and take a quicker response. This allows for efficient snow removal while maintaining the user's sense of security. 【0384】 The following describes the processing flow. 【0385】 Step 1: 【0386】 The server accesses weather information services to obtain current weather data such as snowfall forecasts, temperature, and wind speed. Based on this information, it analyzes areas where snowfall is expected and predicts how much snow removal will be necessary. 【0387】 Step 2: 【0388】 The terminal uses sensors and cameras mounted on the vehicle to scan the surrounding snow conditions and transmits the data to the server in real time. Machine learning algorithms are used to measure the depth and density of the snow. 【0389】 Step 3: 【0390】 The server uses an AI algorithm to calculate the optimal snow removal route based on the received snow depth data. This calculation includes factors such as traffic volume, snow removal priority, and safety. 【0391】 Step 4: 【0392】 Users send voice commands to the system from a dedicated smart device to check the progress of snow removal. During this process, an emotion engine analyzes the voice input to detect stress and anxiety. 【0393】 Step 5: 【0394】 Based on emotional state feedback from the emotion engine, the server re-evaluates the work plan as needed. For example, if the user indicates stress, it adjusts the speed and frequency of snow removal work accordingly. 【0395】 Step 6: 【0396】 The terminal automatically performs snow removal work based on instructions sent from the server. During operation, it avoids obstacles and operates efficiently while ensuring safety. 【0397】 Step 7: 【0398】 The server records user emotional changes and work results as a history, which is then used to plan future snow removal operations. In this process, past data is analyzed by AI to identify areas for improvement in subsequent operations. 【0399】 Step 8: 【0400】 Users can continuously monitor snow removal status and forecast data, and send feedback to the system as needed, enabling them to receive prompt responses and appropriate work support. 【0401】 (Example 2) 【0402】 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". 【0403】 This invention aims to improve efficiency in snow removal operations and reduce the psychological burden on users, and its objective is to provide a system that dynamically adjusts the work plan according to weather conditions and the user's emotional state. 【0404】 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. 【0405】 In this invention, the server includes a device for acquiring weather information, a mobile device for monitoring the surrounding conditions in real time, and an information processing device for analyzing the acquired data and performing snowfall predictions. This enables flexible work adjustments based on weather data and the user's emotional state. 【0406】 A "weather information acquisition device" is a device that automatically acquires weather-related data from an external weather database or information service. 【0407】 A "mobile device that monitors the surrounding environment in real time" is a device that uses sensors mounted on mobile hardware to continuously observe the surrounding environment and conditions. 【0408】 An "information processing device" is a computer system that analyzes acquired data and performs necessary calculations and decisions. 【0409】 A "planning device for designing the optimal route" is a device that calculates the most efficient and safest travel route based on analyzed data. 【0410】 An "automatic motion control system" is a system that controls machinery and equipment based on specified routes and motion instructions to perform actions. 【0411】 A "dialogue device that analyzes the user's emotional state and adjusts the work" is a device that analyzes the user's emotions based on their input and dynamically changes the work plan. 【0412】 A "communication device that receives feedback and replans its actions" is a device that receives information from users and external factors and has the function of replanning its action plan based on that information. 【0413】 A "monitoring device" is a device used to check the operating status and any abnormalities of a mobile object and to report them as necessary. 【0414】 This invention provides an embodiment of an automated driving system that streamlines snow removal operations while reducing the psychological burden on the user. 【0415】 The server uses equipment to acquire weather information and analyzes data obtained from external weather data services. For example, the server uses an API to acquire weather data and uses machine learning algorithms to predict snowfall in a region. This allows the server to understand snowfall trends in real time and issue instructions for snow removal work at the appropriate time. 【0416】 The terminal functions as a mobile device that monitors the surrounding environment in real time. Using LiDAR sensors and cameras mounted on the vehicle, the terminal detects snow accumulation and road surface conditions, and transmits this information to a server. This allows the terminal to constantly understand the accurate situation on site. 【0417】 Users interact with the system through smart devices. By issuing voice commands, users' emotional states are reported to the system in real time. These emotional states are analyzed by an emotion analysis engine; for example, if the analysis indicates the user is feeling anxious or stressed, the server dynamically adjusts the work schedule. This process minimizes the user's psychological burden. 【0418】 As a concrete example, if a server uses a weather forecast API to collect snow forecast data and a user gives a voice command saying, "Tell me the progress of the snow removal work," the emotion engine will read the user's emotions from the voice, and the server will make adjustments to the work plan accordingly. 【0419】 An example of a prompt message is, "Collect weather data necessary to plan this morning's snow removal, interpret the user's emotions from their voice commands, and adjust the work accordingly." Using such prompts makes it possible to carry out snow removal work efficiently and in a human-centered manner. 【0420】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0421】 Step 1: 【0422】 The server obtains weather data via a weather information service API. It receives data such as snowfall forecasts, temperature, and wind speed from this API as input. Based on this data, the server runs a machine learning algorithm and outputs a snow depth forecast. This analysis result is then used to plan snow removal operations. 【0423】 Step 2: 【0424】 The terminal uses LiDAR sensors and cameras mounted on the vehicle to monitor surrounding snow and road conditions in real time. It receives raw data from the sensors as input and sends it to the server. Based on this data, the terminal creates output to support situational judgment for autonomous driving. 【0425】 Step 3: 【0426】 The user sends commands to the system via the voice input function of their smart device. The voice data is passed as input to the emotion analysis engine. The analysis engine determines the user's emotional state (stress and anxiety) and outputs this to the server. Based on this feedback, the server adjusts the work plan to suit the user's mental state. 【0427】 Step 4: 【0428】 The server generates an optimized work plan based on weather data, on-site sensor information, and user sentiment data. In this step, this data is integrated to produce a route and schedule output for fast and safe snow removal. 【0429】 Step 5: 【0430】 The terminal automatically performs snow removal based on the optimal route generated, following instructions from the server. It uses GPS data and a pre-programmed road map as input and outputs the vehicle's movements using a motion control algorithm. 【0431】 Step 6: 【0432】 When a user performs a confirmation action from a smart device, that feedback information is sent as input to the emotion engine. The server analyzes the received feedback and obtains output to revise the snow removal plan as needed. 【0433】 (Application Example 2) 【0434】 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." 【0435】 In snow removal operations, conventional techniques prioritize efficiency to such an extent that they often neglect the user's mental state, leading to stress and anxiety. Furthermore, the fixed nature of work plans makes real-time adjustments difficult, resulting in an inability to respond to sudden weather changes or shifts in user emotions. 【0436】 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. 【0437】 In this invention, the server includes means for acquiring weather information, means for scanning the surrounding environment in real time, and means for sentiment analysis that recognizes the user's emotions and reflects them in the work plan. This enables flexible responses to weather changes and changes in the user's emotions, reducing stress and allowing for efficient snow removal work. 【0438】 "Means for acquiring weather information" refers to a device or software that has the function of acquiring external weather data and providing it to the system. 【0439】 A "vehicle device that scans the surrounding environment in real time" is a device mounted on a vehicle that detects the surrounding physical conditions and environment in real time. 【0440】 An "information processing device" is a computer or program used to analyze acquired data and perform snowfall predictions and other necessary calculations. 【0441】 A "control device" is a system that calculates the optimal route based on analysis results and issues commands to operate machinery. 【0442】 A "machine control device" is a device that automatically operates a machine based on generated path information. 【0443】 A "communication device" is a device used to receive feedback and send and receive information. 【0444】 A "monitoring device" is a system that constantly monitors the vehicle's condition and issues a warning if an abnormality is detected. 【0445】 An "emotion analysis device" is an engine or algorithm that recognizes a user's emotions and dynamically modifies the work plan based on that data. 【0446】 An "information provision device" is a system equipped with an interface for providing users with necessary information and feedback. 【0447】 The system implementing this invention mainly consists of three components: a server, a terminal, and a user. 【0448】 The server communicates with external data sources to obtain weather information and collects the latest weather data. Based on the acquired information, it predicts snowfall and calculates the optimal snow removal route. Furthermore, it processes emotional data from users and modifies the work plan according to the situation. For this purpose, the server uses an information processing device and an emotion analysis device. Emotion analysis is performed from voice data, and a generative AI model is used to identify mental states. 【0449】 The terminal uses sensors mounted on the vehicle to scan the surrounding environment in real time and transmits the data to the server. It also uses mechanical control devices to automatically perform snow removal work according to a designated route. Furthermore, it receives instructions from the server via a communication device and adjusts its operation based on the situation during the work. 【0450】 Users can interact with the system via smart devices. An information provider allows them to receive real-time information on work progress and weather changes, as well as provide their emotional state as voice input. An emotion analysis device analyzes this voice data to identify emotions such as stress. This information is fed back into the system and used to adjust the snow removal plan. 【0451】 For example, if the server receives a forecast of unexpected heavy snowfall, it can quickly instruct vehicles to change their routes via terminals, thereby alleviating the anxiety users might feel through prompt information provision. This system aims to streamline snow removal operations by considering user emotions and providing a greater sense of security. 【0452】 An example of a prompt to the generating AI model is, "If the user feels uneasy about the snow removal situation, how should the snow removal plan be changed?" 【0453】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0454】 Step 1: 【0455】 The server obtains the latest weather data from an external weather information service. The input is data obtained from the weather service's API, and the output is weather information for analysis. Based on this data, the server uses an information processing device to perform data analysis in order to predict snowfall and weather changes. 【0456】 Step 2: 【0457】 The terminal uses its built-in sensors to scan the snow conditions around the vehicle in real time and transmit the data to the server. The input is ambient environmental data from the sensors, and the output is the scan information received by the server. By doing this, the terminal provides detailed environmental data based on the current snow conditions. 【0458】 Step 3: 【0459】 Users interact with the system by issuing voice commands via a smart device. The input is the user's voice, and the output is voice data processed on a server. The user's commands are analyzed by an emotion analysis device, and the data is used to extract their emotional state. 【0460】 Step 4: 【0461】 The server calculates the optimal snow removal route based on analyzed weather information, scan data, and the user's emotional state. Inputs are weather data, scan information, and user emotional data, while output is optimized route information. The server uses a computational algorithm to integrate this data and dynamically generate optimal action instructions. 【0462】 Step 5: 【0463】 The terminal receives the latest route instructions from the server and automatically controls the vehicle. The input is route information from the server, and the output is the physical operation of the vehicle. The terminal uses a mechanical control device to follow the instructions and perform snow removal work safely and efficiently. 【0464】 Step 6: 【0465】 The server receives user feedback on ongoing tasks and readjusts the work plan as needed. The input is emotional feedback from the user, and the output is the adjusted work plan. Using a generative AI model enables flexible responses that meet user expectations. 【0466】 Step 7: 【0467】 The user receives current work status and additional feedback through an information provision device, and provides reactions to the system based on their own emotions. The input is work information from the server, and the output is data that enhances the user's sense of security and provides additional emotional data. Through this process, it is ensured that snow removal work is performed in a user-centric manner. 【0468】 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. 【0469】 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. 【0470】 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. 【0471】 [Third Embodiment] 【0472】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0473】 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. 【0474】 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). 【0475】 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. 【0476】 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. 【0477】 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). 【0478】 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. 【0479】 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. 【0480】 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. 【0481】 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. 【0482】 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. 【0483】 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". 【0484】 This invention relates to an automated snow removal system designed to support efficient snow removal operations in snowy regions. This system acquires weather information, analyzes snow conditions in real time to determine the optimal route, and automates the actual snow removal work. 【0485】 System Configuration 【0486】 1. Data Collection 【0487】 The server periodically retrieves weather data from weather information services. This allows it to understand weather conditions that affect snow removal operations, such as snowfall and temperature. 【0488】 The terminal (snowplow) uses on-board sensors and cameras to scan the surrounding snow conditions and transmits that information to the server. 【0489】 2. Data Analysis and Path Calculation 【0490】 The server analyzes the received data and uses an AI algorithm to predict the depth and density of the snow. Based on these results, it calculates an efficient and safe snow removal route. 【0491】 By referring to past data, we can understand the trends in snow accumulation patterns in specific regions and generate the optimal work plan. 【0492】 3. Execute snow removal work 【0493】 The terminal operates automatically based on route information received from the server, performing snow removal tasks. This utilizes autonomous driving technology, allowing operations to be carried out day and night. 【0494】 During operation, a real-time monitoring function is used to detect and avoid obstacles in the surrounding area. 【0495】 4. Feedback and adjustments 【0496】 Users can check the current snow removal status and future forecasts via their smart devices. If necessary, they can provide feedback to the server regarding snow removal priorities and priority areas. 【0497】 The server receives feedback from the user, replans the work plan as needed, and sends new instructions to the terminal. 【0498】 5. Vehicle status monitoring 【0499】 The terminal constantly checks the vehicle's status using its self-diagnostic function and immediately reports any abnormalities to the server. 【0500】 By detecting early signs of malfunction, maintenance efficiency and work safety can be improved. 【0501】 Specific example 【0502】 When heavy snowfall is forecast in a particular area, this system operates as follows: First, the server analyzes weather data, and then the terminal scans for snow accumulation. Based on the information obtained, the server calculates the optimal route, and the terminal automatically follows the snow removal route. If the user specifies areas where snow removal should be prioritized along the way, the server incorporates that information and re-evaluates the route. The terminal also continues to check for safety during the operation and reports any abnormalities to the server. This entire process allows for efficient and rapid snow removal, maintaining the flow of traffic in the area. 【0503】 The following describes the processing flow. 【0504】 Step 1: 【0505】 The server accesses weather information services to retrieve current weather data. This data includes snowfall forecasts, temperature, wind speed, and other information. Based on this data, it analyzes future snowfall trends. 【0506】 Step 2: 【0507】 The terminal uses on-board sensors and cameras to scan the surrounding environment and understand the snow conditions in real time. This information is immediately transmitted to the server. 【0508】 Step 3: 【0509】 The server analyzes snow depth data received from terminals using an AI algorithm and maps the snow depth across the entire region. It also compares this data with past data to predict snow accumulation patterns. 【0510】 Step 4: 【0511】 The server calculates the optimal snow removal route based on the analysis results. This calculation takes into account factors such as the importance of each road, traffic volume, safety, and fuel efficiency. 【0512】 Step 5: 【0513】 The terminal receives the route and work pattern instructed by the server and autonomously begins snow removal work. Utilizing its autonomous driving function, it safely removes snow along the designated route. 【0514】 Step 6: 【0515】 Users can monitor snow removal status and check progress via smartphones or computers. If users request changes, they can specify priority areas or enter additional work instructions. 【0516】 Step 7: 【0517】 The server receives input from the user, adjusts the work plan based on that information, and sends new instructions to the terminal. Real-time feedback optimizes snow removal operations. 【0518】 Step 8: 【0519】 The terminal continuously monitors the vehicle's status even while work is in progress. If it detects any signs of malfunction or abnormalities, it immediately reports to the server and adjusts the driving mode as needed. 【0520】 Step 9: 【0521】 The server automatically notifies maintenance personnel based on reports from terminals. This enables smooth maintenance arrangements. 【0522】 (Example 1) 【0523】 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." 【0524】 Snow removal in snowy regions often relies heavily on manual labor, presenting challenges in ensuring efficiency and safety. Furthermore, the inability to respond quickly to changing weather conditions can disrupt traffic. Vehicle breakdowns and the selection of efficient routes also posed challenges. 【0525】 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. 【0526】 In this invention, the server includes means for acquiring weather conditions, a vehicle mechanism for scanning the surrounding environment in real time, and a computing module for analyzing the acquired information and predicting snow accumulation conditions. This enables efficient and rapid snow removal work. 【0527】 "Weather conditions" refer to information about the weather, including factors that affect snow removal operations, such as snowfall, temperature, and wind speed. 【0528】 "Surrounding environment" refers to the physical conditions and circumstances surrounding the vehicle, specifically including the depth of snow accumulation and the presence or absence of obstacles. 【0529】 A "vehicle mechanism" refers to hardware equipment installed on a snow removal vehicle that uses sensors to detect the surrounding conditions and collect information. 【0530】 A "calculation module" is a software program that analyzes collected data to determine and predict snow accumulation patterns. 【0531】 A "control unit" is a device that formulates an optimal operation plan based on analyzed data and instructs the vehicle's movements. 【0532】 A "technical device" is a combination of hardware and software that autonomously performs operations according to a specified plan. 【0533】 A "communication module" is a device that sends and receives data and instructions between elements within a system, and also plays a role in receiving feedback from external sources. 【0534】 A "monitoring module" is a mechanism that continuously checks the internal condition of a vehicle and reports any abnormalities that occur. 【0535】 The autonomous snow removal system of this invention enables efficient snow removal in snowy regions and mainly consists of a server, terminals, and users. This system functions effectively through the acquisition of weather conditions, scanning of the surrounding environment, data analysis, route calculation, and execution of snow removal by autonomous driving. 【0536】 The server retrieves data from weather information services via APIs. Specifically, it obtains information such as snowfall amount, temperature, and wind speed, and stores it in a database. The server also utilizes machine learning algorithms (e.g., open-source machine learning libraries) to analyze the collected data. This program has the ability to learn from past data and predict future snow conditions. 【0537】 The terminal uses sensors (e.g., LiDAR sensors) and cameras mounted on the vehicle to scan the surrounding environment in real time. This allows the terminal to detect snow depth and the location of obstacles, and transmit this information to a server via a communication module. Using autonomous driving technology (e.g., autonomous driving platform technology), the terminal autonomously performs snow removal operations according to the route information transmitted from the server. 【0538】 Users can interact with the system using smart devices. This includes checking the progress and location of snowplows and issuing priority snow removal instructions for specific areas. Instructions from users are sent to the server via a communication module, which then replans the work schedule and sends new route instructions to the terminal as needed. 【0539】 As a concrete example, in areas where heavy snowfall is forecast, the server analyzes the latest weather data, and the terminal scans for snow accumulation. Based on this information, the server calculates the optimal snow removal route, and the terminal automatically performs snow removal work. If the user has areas they would like prioritized for snow removal, the route is re-evaluated taking that information into account. 【0540】 Using the generated AI model, an example of a prompt message could be, "Please explain in detail the steps to calculate an efficient snow removal route in city A in preparation for the next heavy snowfall." 【0541】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0542】 Step 1: 【0543】 The server initiates the data collection process to obtain weather information. Specifically, it retrieves information on snowfall and temperature through the weather information service's API. This data is received in JSON format and stored in the database. The input for this step is real-time weather data from the weather information service, and the output is storage in the database in a data format suitable for analysis. 【0544】 Step 2: 【0545】 The terminal begins scanning the surrounding environment in real time. Using LiDAR sensors and cameras mounted on the vehicle, it detects road snow conditions and obstacles. This information is initially analyzed on the terminal's processor and then transmitted to the server using a communication module. The input for this step is raw data from the sensors and cameras, and the output is a processed dataset of snow information. 【0546】 Step 3: 【0547】 The server integrates and analyzes received weather and snow depth data. Here, machine learning algorithms are used to predict snow depth patterns and patterns. The generated AI model references past data to predict future snowfall. The results of this analysis are used to generate new snow removal routes. The input for this step is real-time snow depth data sent to the server, and the output is the analyzed snow depth prediction information. 【0548】 Step 4: 【0549】 The server calculates the optimal snow removal route based on the analysis results. Using an optimization algorithm, it generates a safe and efficient route. This route information is transmitted to the terminal and serves as a guideline for the automated driving system. The input for this step is snow depth analysis information, and the output is detailed snow removal route data. 【0550】 Step 5: 【0551】 The terminal operates automatically based on the snow removal route received from the server. Using autonomous driving technology, the terminal travels along the designated route and performs snow removal. During travel, it detects and avoids surrounding obstacles in real time. The input for this step is calculated route information, and the output is the actual snow removal operation. 【0552】 Step 6: 【0553】 Users can use smart devices to check the progress of snowplows and predict the completion date of the work. If necessary, they can send feedback to instruct priority snow removal in specific areas. This information is transmitted to the server and used to re-evaluate the work plan. The input for this step is the user's requests and instructions, and the output is the updated snow removal work plan. 【0554】 (Application Example 1) 【0555】 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." 【0556】 In snowy regions, heavy snowfall causes traffic disruptions and reduces the efficiency of infrastructure, which are significant problems. Furthermore, effectively carrying out snow removal using limited resources remains a challenge. It is difficult for users to understand the snow removal situation in real time and select the optimal route. There is also a need for easy feedback on the locations and priorities where snow removal is needed. Improving these conditions and enhancing the efficiency and safety of snow removal operations in urban areas is crucial. 【0557】 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. 【0558】 In this invention, the server includes a device for acquiring weather information, a mobile unit for observing the surrounding conditions in real time, and a processing unit for analyzing the acquired data and predicting snowfall. This makes it possible for users to understand the snow removal situation through their smart devices and easily make requests for snow removal according to priority. 【0559】 A "weather information acquisition device" is a device that collects weather conditions and snowfall data and provides it for analysis. 【0560】 A "mobile device for real-time observation of surrounding conditions" is a mobile device that can measure the surrounding snow conditions in real time using sensors and cameras and supply that data to a system. 【0561】 A "processing device that analyzes acquired data and performs snow depth prediction" is a device that uses data acquired from sensors and weather information to perform calculations and analyses to predict snow depth and density. 【0562】 A "control device for calculating the optimal route" is a device that calculates the optimal snow removal route according to the snow conditions and terrain, and controls the snow removal machinery based on that calculation. 【0563】 An "automatically operating machine control device" is a device that operates autonomously and performs snow removal work according to a calculated snow removal route. 【0564】 A "communication device that receives feedback and adjusts operations" is a device that can receive instructions and feedback from users and dynamically adjust the snow removal operation plan accordingly. 【0565】 A "monitoring device that monitors the status of a mobile object and reports abnormalities" is a device that constantly monitors the operating status of a mobile object used for snow removal and reports any abnormalities detected to the system. 【0566】 A "digital terminal" is an electronic device that can receive data from multiple external devices and display information such as analysis results and snow removal progress to the user. 【0567】 In this invention, the server periodically collects weather data using a weather information acquisition device. This allows for real-time monitoring of snowfall and temperature changes, providing the information necessary for snow removal operations. A mobile unit that observes the surrounding conditions in real time scans the surrounding snow conditions using on-board sensors and cameras and transmits the data to the server. This allows for the precise identification of specific areas where snow removal operations are required. 【0568】 The processing unit uses a generated AI model based on the acquired data to perform snowfall prediction. The analyzed data is supplied to the control unit to calculate the optimal snow removal route. The control unit utilizes AI algorithms, particularly to determine the most effective route, to formulate a plan that enables efficient snow removal. 【0569】 The automated machine control system mounted on the mobile unit operates autonomously based on received route information, enabling rapid snow removal. This system can operate day and night and also has the function to recognize and avoid surrounding obstacles. Furthermore, an observation device continuously monitors the status of the mobile unit and immediately reports any abnormalities to the server, ensuring effective maintenance and safety. 【0570】 The communication device receives feedback and replans operations as needed based on priority instructions from users. This enables a rapid response to areas with high urgency. Users can visually check the current snow removal progress and forecasts via digital terminals and send new requests as needed. 【0571】 As a concrete example, in a region where heavy snowfall is forecast, a server analyzes weather data and a mobile vehicle scans for snow accumulation. When a user issues a command via a smart device to prioritize snow removal in a specific area, the communication device receives that information and generates a new route plan. Furthermore, it would be possible to input prompts such as, "Heavy snowfall is forecast. I would like to check the progress of snow removal work on major roads and in specific areas, as well as suggestions for the best detour route." 【0572】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0573】 Step 1: 【0574】 The server retrieves weather data from weather information services. The input is online weather data, and the output is organized weather condition information. This information includes wind speed, temperature, snowfall, etc., and is used in subsequent processing. 【0575】 Step 2: 【0576】 The terminal uses on-board sensors and cameras to scan the surrounding snow conditions in real time. Inputs are sensor data and video data, and output is digital scan data indicating snow depth and density. This data is transmitted to a server. 【0577】 Step 3: 【0578】 The server uses the data acquired in Steps 1 and 2 to perform snow depth prediction using a generative AI model. The input is weather condition information and digital scan data, and the output is a predicted value for short-term snow depth. This value is used to formulate an optimal snow removal plan. 【0579】 Step 4: 【0580】 The server uses an AI algorithm to calculate the optimal snow removal route based on the analysis results. The input is snow depth prediction values, and the output is specific snow removal route data. The calculated route is optimized considering efficiency and safety. 【0581】 Step 5: 【0582】 The terminal automatically performs snow removal based on snow removal route data received from the server. The input is snow removal route data, and the output is the condition of the cleared road. The terminal moves along the route and takes avoidance actions when it detects obstacles. 【0583】 Step 6: 【0584】 Users can check the current snow removal status and forecasts via a digital terminal. Input is snow removal progress information sent from the server, and output is the snow removal status displayed on the user's screen. New requests can be submitted as needed. 【0585】 Step 7: 【0586】 The communication device receives priority instructions from the user and replans the work schedule. The input is user feedback, and the output is the updated snow removal plan. This plan is fed back to the server and reflected in the next snow removal route. 【0587】 Step 8: 【0588】 The terminal's observation device monitors the status of the moving object and reports any detected abnormalities to the server. The input is real-time machine status data, and the output is an alert when an abnormality is detected. This enables early response. 【0589】 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. 【0590】 This invention provides an automated snow removal system that takes user emotions into consideration in order to carry out snow removal work efficiently and in a human-centered manner. In addition to conventional snow removal functions, this system dynamically adjusts the work plan by recognizing the user's emotions, thereby reducing user stress and improving work efficiency. 【0591】 System Configuration 【0592】 1. Data Collection and Analysis 【0593】 The server retrieves data from external weather information services and analyzes local weather and snow conditions. Based on this data, it identifies areas where snowfall is expected. 【0594】 The terminal uses sensors mounted on the vehicle to monitor the surrounding snow conditions in real time and transmits that information to the server. 【0595】 2. Applications of the Emotional Engine 【0596】 Users interact with the system using voice commands from their smart devices. The emotion engine recognizes emotions such as stress and tension from this voice data. 【0597】 Based on feedback from the emotion engine, the server revises the work plan according to the user's emotional state. For example, if the user is feeling stressed, the settings will be changed to allow for faster work. 【0598】 3. Coordination of automated snow removal operations and plans 【0599】 The terminal automatically proceeds with snow removal work, following the optimal snow removal route instructed by the server. It adjusts its operation according to the situation during the work, ensuring safe and efficient execution. 【0600】 The server stores user emotional changes as historical data and executes an algorithm that optimizes future work plans by reflecting past responses. 【0601】 4. User Feedback and Response 【0602】 Users can check the progress and predictions of their work via smart devices, and if anxiety or dissatisfaction arises, that information is captured through an emotion engine. 【0603】 The server receives user feedback in real time, replans snow removal operations as needed, and sends new instructions to the terminal. This allows for flexible responses that meet user expectations. 【0604】 Specific example 【0605】 Consider a scenario where an unexpected heavy snowfall occurs during a snow removal project. This system can solve the problem through the following steps: First, the server analyzes weather data and predicts the increase in snowfall. Terminals provide on-site snow data, which is used to determine the optimal snow removal route. Then, the user reports the on-site situation to the system via voice commands. If the user expresses anxiety, an emotion engine analyzes their emotions, and the server updates the plan to provide more detailed information and take a quicker response. This allows for efficient snow removal while maintaining the user's sense of security. 【0606】 The following describes the processing flow. 【0607】 Step 1: 【0608】 The server accesses weather information services to obtain current weather data such as snowfall forecasts, temperature, and wind speed. Based on this information, it analyzes areas where snowfall is expected and predicts how much snow removal will be necessary. 【0609】 Step 2: 【0610】 The terminal uses sensors and cameras mounted on the vehicle to scan the surrounding snow conditions and transmits the data to the server in real time. Machine learning algorithms are used to measure the depth and density of the snow. 【0611】 Step 3: 【0612】 The server uses an AI algorithm to calculate the optimal snow removal route based on the received snow depth data. This calculation includes factors such as traffic volume, snow removal priority, and safety. 【0613】 Step 4: 【0614】 Users send voice commands to the system from a dedicated smart device to check the progress of snow removal. During this process, an emotion engine analyzes the voice input to detect stress and anxiety. 【0615】 Step 5: 【0616】 Based on emotional state feedback from the emotion engine, the server re-evaluates the work plan as needed. For example, if the user indicates stress, it adjusts the speed and frequency of snow removal work accordingly. 【0617】 Step 6: 【0618】 The terminal automatically performs snow removal work based on instructions sent from the server. During operation, it avoids obstacles and operates efficiently while ensuring safety. 【0619】 Step 7: 【0620】 The server records user emotional changes and work results as a history, which is then used to plan future snow removal operations. In this process, past data is analyzed by AI to identify areas for improvement in subsequent operations. 【0621】 Step 8: 【0622】 Users can continuously monitor snow removal status and forecast data, and send feedback to the system as needed, enabling them to receive prompt responses and appropriate work support. 【0623】 (Example 2) 【0624】 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." 【0625】 This invention aims to improve efficiency in snow removal operations and reduce the psychological burden on users, and its objective is to provide a system that dynamically adjusts the work plan according to weather conditions and the user's emotional state. 【0626】 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. 【0627】 In this invention, the server includes a device for acquiring weather information, a mobile device for monitoring the surrounding conditions in real time, and an information processing device for analyzing the acquired data and performing snowfall predictions. This enables flexible work adjustments based on weather data and the user's emotional state. 【0628】 A "weather information acquisition device" is a device that automatically acquires weather-related data from an external weather database or information service. 【0629】 A "mobile device that monitors the surrounding environment in real time" is a device that uses sensors mounted on mobile hardware to continuously observe the surrounding environment and conditions. 【0630】 An "information processing device" is a computer system that analyzes acquired data and performs necessary calculations and decisions. 【0631】 A "planning device for designing the optimal route" is a device that calculates the most efficient and safest travel route based on analyzed data. 【0632】 An "automatic motion control system" is a system that controls machinery and equipment based on specified routes and motion instructions to perform actions. 【0633】 A "dialogue device that analyzes the user's emotional state and adjusts the work" is a device that analyzes the user's emotions based on their input and dynamically changes the work plan. 【0634】 A "communication device that receives feedback and replans its actions" is a device that receives information from users and external factors and has the function of replanning its action plan based on that information. 【0635】 A "monitoring device" is a device used to check the operating status and any abnormalities of a mobile object and to report them as necessary. 【0636】 This invention provides an embodiment of an automated driving system that streamlines snow removal operations while reducing the psychological burden on the user. 【0637】 The server uses equipment to acquire weather information and analyzes data obtained from external weather data services. For example, the server uses an API to acquire weather data and uses machine learning algorithms to predict snowfall in a region. This allows the server to understand snowfall trends in real time and issue instructions for snow removal work at the appropriate time. 【0638】 The terminal functions as a mobile device that monitors the surrounding environment in real time. Using LiDAR sensors and cameras mounted on the vehicle, the terminal detects snow accumulation and road surface conditions, and transmits this information to a server. This allows the terminal to constantly understand the accurate situation on site. 【0639】 Users interact with the system through smart devices. By issuing voice commands, users' emotional states are reported to the system in real time. These emotional states are analyzed by an emotion analysis engine; for example, if the analysis indicates the user is feeling anxious or stressed, the server dynamically adjusts the work schedule. This process minimizes the user's psychological burden. 【0640】 As a concrete example, if a server uses a weather forecast API to collect snow forecast data and a user gives a voice command saying, "Tell me the progress of the snow removal work," the emotion engine will read the user's emotions from the voice, and the server will make adjustments to the work plan accordingly. 【0641】 An example of a prompt message is, "Collect weather data necessary to plan this morning's snow removal, interpret the user's emotions from their voice commands, and adjust the work accordingly." Using such prompts makes it possible to carry out snow removal work efficiently and in a human-centered manner. 【0642】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0643】 Step 1: 【0644】 The server obtains weather data via a weather information service API. It receives data such as snowfall forecasts, temperature, and wind speed from this API as input. Based on this data, the server runs a machine learning algorithm and outputs a snow depth forecast. This analysis result is then used to plan snow removal operations. 【0645】 Step 2: 【0646】 The terminal uses LiDAR sensors and cameras mounted on the vehicle to monitor surrounding snow and road conditions in real time. It receives raw data from the sensors as input and sends it to the server. Based on this data, the terminal creates output to support situational judgment for autonomous driving. 【0647】 Step 3: 【0648】 The user sends commands to the system via the voice input function of their smart device. The voice data is passed as input to the emotion analysis engine. The analysis engine determines the user's emotional state (stress and anxiety) and outputs this to the server. Based on this feedback, the server adjusts the work plan to suit the user's mental state. 【0649】 Step 4: 【0650】 The server generates an optimized work plan based on weather data, on-site sensor information, and user sentiment data. In this step, this data is integrated to produce a route and schedule output for fast and safe snow removal. 【0651】 Step 5: 【0652】 The terminal automatically performs snow removal based on the optimal route generated, following instructions from the server. It uses GPS data and a pre-programmed road map as input and outputs the vehicle's movements using a motion control algorithm. 【0653】 Step 6: 【0654】 When a user performs a confirmation action from a smart device, that feedback information is sent as input to the emotion engine. The server analyzes the received feedback and obtains output to revise the snow removal plan as needed. 【0655】 (Application Example 2) 【0656】 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." 【0657】 In snow removal operations, conventional techniques prioritize efficiency to such an extent that they often neglect the user's mental state, leading to stress and anxiety. Furthermore, the fixed nature of work plans makes real-time adjustments difficult, resulting in an inability to respond to sudden weather changes or shifts in user emotions. 【0658】 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. 【0659】 In this invention, the server includes means for acquiring weather information, means for scanning the surrounding environment in real time, and means for sentiment analysis that recognizes the user's emotions and reflects them in the work plan. This enables flexible responses to weather changes and changes in the user's emotions, reducing stress and allowing for efficient snow removal work. 【0660】 "Means for acquiring weather information" refers to a device or software that has the function of acquiring external weather data and providing it to the system. 【0661】 A "vehicle device that scans the surrounding environment in real time" is a device mounted on a vehicle that detects the surrounding physical conditions and environment in real time. 【0662】 An "information processing device" is a computer or program used to analyze acquired data and perform snowfall predictions and other necessary calculations. 【0663】 A "control device" is a system that calculates the optimal route based on analysis results and issues commands to operate machinery. 【0664】 A "machine control device" is a device that automatically operates a machine based on generated path information. 【0665】 A "communication device" is a device used to receive feedback and send and receive information. 【0666】 A "monitoring device" is a system that constantly monitors the vehicle's condition and issues a warning if an abnormality is detected. 【0667】 An "emotion analysis device" is an engine or algorithm that recognizes a user's emotions and dynamically modifies the work plan based on that data. 【0668】 An "information provision device" is a system equipped with an interface for providing users with necessary information and feedback. 【0669】 The system implementing this invention mainly consists of three components: a server, a terminal, and a user. 【0670】 The server communicates with external data sources to obtain weather information and collects the latest weather data. Based on the acquired information, it predicts snowfall and calculates the optimal snow removal route. Furthermore, it processes emotional data from users and modifies the work plan according to the situation. For this purpose, the server uses an information processing device and an emotion analysis device. Emotion analysis is performed from voice data, and a generative AI model is used to identify mental states. 【0671】 The terminal uses sensors mounted on the vehicle to scan the surrounding environment in real time and transmits the data to the server. It also uses mechanical control devices to automatically perform snow removal work according to a designated route. Furthermore, it receives instructions from the server via a communication device and adjusts its operation based on the situation during the work. 【0672】 Users can interact with the system via smart devices. An information provider allows them to receive real-time information on work progress and weather changes, as well as provide their emotional state as voice input. An emotion analysis device analyzes this voice data to identify emotions such as stress. This information is fed back into the system and used to adjust the snow removal plan. 【0673】 For example, if the server receives a forecast of unexpected heavy snowfall, it can quickly instruct vehicles to change their routes via terminals, thereby alleviating the anxiety users might feel through prompt information provision. This system aims to streamline snow removal operations by considering user emotions and providing a greater sense of security. 【0674】 An example of a prompt to the generating AI model is, "If the user feels uneasy about the snow removal situation, how should the snow removal plan be changed?" 【0675】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0676】 Step 1: 【0677】 The server obtains the latest weather data from an external weather information service. The input is data obtained from the weather service's API, and the output is weather information for analysis. Based on this data, the server uses an information processing device to perform data analysis in order to predict snowfall and weather changes. 【0678】 Step 2: 【0679】 The terminal uses its built-in sensors to scan the snow conditions around the vehicle in real time and transmit the data to the server. The input is ambient environmental data from the sensors, and the output is the scan information received by the server. By doing this, the terminal provides detailed environmental data based on the current snow conditions. 【0680】 Step 3: 【0681】 Users interact with the system by issuing voice commands via a smart device. The input is the user's voice, and the output is voice data processed on a server. The user's commands are analyzed by an emotion analysis device, and the data is used to extract their emotional state. 【0682】 Step 4: 【0683】 The server calculates the optimal snow removal route based on analyzed weather information, scan data, and the user's emotional state. Inputs are weather data, scan information, and user emotional data, while output is optimized route information. The server uses a computational algorithm to integrate this data and dynamically generate optimal action instructions. 【0684】 Step 5: 【0685】 The terminal receives the latest route instructions from the server and automatically controls the vehicle. The input is route information from the server, and the output is the physical operation of the vehicle. The terminal uses a mechanical control device to follow the instructions and perform snow removal work safely and efficiently. 【0686】 Step 6: 【0687】 The server receives user feedback on ongoing tasks and readjusts the work plan as needed. The input is emotional feedback from the user, and the output is the adjusted work plan. Using a generative AI model enables flexible responses that meet user expectations. 【0688】 Step 7: 【0689】 The user receives current work status and additional feedback through an information provision device, and provides reactions to the system based on their own emotions. The input is work information from the server, and the output is data that enhances the user's sense of security and provides additional emotional data. Through this process, it is ensured that snow removal work is performed in a user-centric manner. 【0690】 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. 【0691】 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. 【0692】 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. 【0693】 [Fourth Embodiment] 【0694】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0695】 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. 【0696】 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). 【0697】 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. 【0698】 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. 【0699】 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). 【0700】 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. 【0701】 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. 【0702】 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. 【0703】 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. 【0704】 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. 【0705】 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. 【0706】 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". 【0707】 This invention relates to an automated snow removal system designed to support efficient snow removal operations in snowy regions. This system acquires weather information, analyzes snow conditions in real time to determine the optimal route, and automates the actual snow removal work. 【0708】 System Configuration 【0709】 1. Data Collection 【0710】 The server periodically retrieves weather data from weather information services. This allows it to understand weather conditions that affect snow removal operations, such as snowfall and temperature. 【0711】 The terminal (snowplow) uses on-board sensors and cameras to scan the surrounding snow conditions and transmits that information to the server. 【0712】 2. Data Analysis and Path Calculation 【0713】 The server analyzes the received data and uses an AI algorithm to predict the depth and density of the snow. Based on these results, it calculates an efficient and safe snow removal route. 【0714】 By referring to past data, we can understand the trends in snow accumulation patterns in specific regions and generate the optimal work plan. 【0715】 3. Execute snow removal work 【0716】 The terminal operates automatically based on route information received from the server, performing snow removal tasks. This utilizes autonomous driving technology, allowing operations to be carried out day and night. 【0717】 During operation, a real-time monitoring function is used to detect and avoid obstacles in the surrounding area. 【0718】 4. Feedback and adjustments 【0719】 Users can check the current snow removal status and future forecasts via their smart devices. If necessary, they can provide feedback to the server regarding snow removal priorities and priority areas. 【0720】 The server receives feedback from the user, replans the work plan as needed, and sends new instructions to the terminal. 【0721】 5. Vehicle status monitoring 【0722】 The terminal constantly checks the vehicle's status using its self-diagnostic function and immediately reports any abnormalities to the server. 【0723】 By detecting early signs of malfunction, maintenance efficiency and work safety can be improved. 【0724】 Specific example 【0725】 When heavy snowfall is forecast in a particular area, this system operates as follows: First, the server analyzes weather data, and then the terminal scans for snow accumulation. Based on the information obtained, the server calculates the optimal route, and the terminal automatically follows the snow removal route. If the user specifies areas where snow removal should be prioritized along the way, the server incorporates that information and re-evaluates the route. The terminal also continues to check for safety during the operation and reports any abnormalities to the server. This entire process allows for efficient and rapid snow removal, maintaining the flow of traffic in the area. 【0726】 The following describes the processing flow. 【0727】 Step 1: 【0728】 The server accesses weather information services to retrieve current weather data. This data includes snowfall forecasts, temperature, wind speed, and other information. Based on this data, it analyzes future snowfall trends. 【0729】 Step 2: 【0730】 The terminal uses on-board sensors and cameras to scan the surrounding environment and understand the snow conditions in real time. This information is immediately transmitted to the server. 【0731】 Step 3: 【0732】 The server analyzes snow depth data received from terminals using an AI algorithm and maps the snow depth across the entire region. It also compares this data with past data to predict snow accumulation patterns. 【0733】 Step 4: 【0734】 The server calculates the optimal snow removal route based on the analysis results. This calculation takes into account factors such as the importance of each road, traffic volume, safety, and fuel efficiency. 【0735】 Step 5: 【0736】 The terminal receives the route and work pattern instructed by the server and autonomously begins snow removal work. Utilizing its autonomous driving function, it safely removes snow along the designated route. 【0737】 Step 6: 【0738】 Users can monitor snow removal status and check progress via smartphones or computers. If users request changes, they can specify priority areas or enter additional work instructions. 【0739】 Step 7: 【0740】 The server receives input from the user, adjusts the work plan based on that information, and sends new instructions to the terminal. Real-time feedback optimizes snow removal operations. 【0741】 Step 8: 【0742】 The terminal continuously monitors the vehicle's status even while work is in progress. If it detects any signs of malfunction or abnormalities, it immediately reports to the server and adjusts the driving mode as needed. 【0743】 Step 9: 【0744】 The server automatically notifies maintenance personnel based on reports from terminals. This enables smooth maintenance arrangements. 【0745】 (Example 1) 【0746】 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". 【0747】 Snow removal in snowy regions often relies heavily on manual labor, presenting challenges in ensuring efficiency and safety. Furthermore, the inability to respond quickly to changing weather conditions can disrupt traffic. Vehicle breakdowns and the selection of efficient routes also posed challenges. 【0748】 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. 【0749】 In this invention, the server includes means for acquiring weather conditions, a vehicle mechanism for scanning the surrounding environment in real time, and a computing module for analyzing the acquired information and predicting snow accumulation conditions. This enables efficient and rapid snow removal work. 【0750】 "Weather conditions" refer to information about the weather, including factors that affect snow removal operations, such as snowfall, temperature, and wind speed. 【0751】 "Surrounding environment" refers to the physical conditions and circumstances surrounding the vehicle, specifically including the depth of snow accumulation and the presence or absence of obstacles. 【0752】 A "vehicle mechanism" refers to hardware equipment installed on a snow removal vehicle that uses sensors to detect the surrounding conditions and collect information. 【0753】 A "calculation module" is a software program that analyzes collected data to determine and predict snow accumulation patterns. 【0754】 A "control unit" is a device that formulates an optimal operation plan based on analyzed data and instructs the vehicle's movements. 【0755】 A "technical device" is a combination of hardware and software that autonomously performs operations according to a specified plan. 【0756】 A "communication module" is a device that sends and receives data and instructions between elements within a system, and also plays a role in receiving feedback from external sources. 【0757】 A "monitoring module" is a mechanism that continuously checks the internal condition of a vehicle and reports any abnormalities that occur. 【0758】 The autonomous snow removal system of this invention enables efficient snow removal in snowy regions and mainly consists of a server, terminals, and users. This system functions effectively through the acquisition of weather conditions, scanning of the surrounding environment, data analysis, route calculation, and execution of snow removal by autonomous driving. 【0759】 The server retrieves data from weather information services via APIs. Specifically, it obtains information such as snowfall amount, temperature, and wind speed, and stores it in a database. The server also utilizes machine learning algorithms (e.g., open-source machine learning libraries) to analyze the collected data. This program has the ability to learn from past data and predict future snow conditions. 【0760】 The terminal uses sensors (e.g., LiDAR sensors) and cameras mounted on the vehicle to scan the surrounding environment in real time. This allows the terminal to detect snow depth and the location of obstacles, and transmit this information to a server via a communication module. Using autonomous driving technology (e.g., autonomous driving platform technology), the terminal autonomously performs snow removal operations according to the route information transmitted from the server. 【0761】 Users can interact with the system using smart devices. This includes checking the progress and location of snowplows and issuing priority snow removal instructions for specific areas. Instructions from users are sent to the server via a communication module, which then replans the work schedule and sends new route instructions to the terminal as needed. 【0762】 As a concrete example, in areas where heavy snowfall is forecast, the server analyzes the latest weather data, and the terminal scans for snow accumulation. Based on this information, the server calculates the optimal snow removal route, and the terminal automatically performs snow removal work. If the user has areas they would like prioritized for snow removal, the route is re-evaluated taking that information into account. 【0763】 Using the generated AI model, an example of a prompt message could be, "Please explain in detail the steps to calculate an efficient snow removal route in city A in preparation for the next heavy snowfall." 【0764】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0765】 Step 1: 【0766】 The server initiates the data collection process to obtain weather information. Specifically, it retrieves information on snowfall and temperature through the weather information service's API. This data is received in JSON format and stored in the database. The input for this step is real-time weather data from the weather information service, and the output is storage in the database in a data format suitable for analysis. 【0767】 Step 2: 【0768】 The terminal begins scanning the surrounding environment in real time. Using LiDAR sensors and cameras mounted on the vehicle, it detects road snow conditions and obstacles. This information is initially analyzed on the terminal's processor and then transmitted to the server using a communication module. The input for this step is raw data from the sensors and cameras, and the output is a processed dataset of snow information. 【0769】 Step 3: 【0770】 The server integrates and analyzes received weather and snow depth data. Here, machine learning algorithms are used to predict snow depth patterns and patterns. The generated AI model references past data to predict future snowfall. The results of this analysis are used to generate new snow removal routes. The input for this step is real-time snow depth data sent to the server, and the output is the analyzed snow depth prediction information. 【0771】 Step 4: 【0772】 The server calculates the optimal snow removal route based on the analysis results. Using an optimization algorithm, it generates a safe and efficient route. This route information is transmitted to the terminal and serves as a guideline for the automated driving system. The input for this step is snow depth analysis information, and the output is detailed snow removal route data. 【0773】 Step 5: 【0774】 The terminal operates automatically based on the snow removal route received from the server. Using autonomous driving technology, the terminal travels along the designated route and performs snow removal. During travel, it detects and avoids surrounding obstacles in real time. The input for this step is calculated route information, and the output is the actual snow removal operation. 【0775】 Step 6: 【0776】 Users can use smart devices to check the progress of snowplows and predict the completion date of the work. If necessary, they can send feedback to instruct priority snow removal in specific areas. This information is transmitted to the server and used to re-evaluate the work plan. The input for this step is the user's requests and instructions, and the output is the updated snow removal work plan. 【0777】 (Application Example 1) 【0778】 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". 【0779】 In snowy regions, heavy snowfall causes traffic disruptions and reduces the efficiency of infrastructure, which are significant problems. Furthermore, effectively carrying out snow removal using limited resources remains a challenge. It is difficult for users to understand the snow removal situation in real time and select the optimal route. There is also a need for easy feedback on the locations and priorities where snow removal is needed. Improving these conditions and enhancing the efficiency and safety of snow removal operations in urban areas is crucial. 【0780】 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. 【0781】 In this invention, the server includes a device for acquiring weather information, a mobile unit for observing the surrounding conditions in real time, and a processing unit for analyzing the acquired data and predicting snowfall. This makes it possible for users to understand the snow removal situation through their smart devices and easily make requests for snow removal according to priority. 【0782】 A "weather information acquisition device" is a device that collects weather conditions and snowfall data and provides it for analysis. 【0783】 A "mobile device for real-time observation of surrounding conditions" is a mobile device that can measure the surrounding snow conditions in real time using sensors and cameras and supply that data to a system. 【0784】 A "processing device that analyzes acquired data and performs snow depth prediction" is a device that uses data acquired from sensors and weather information to perform calculations and analyses to predict snow depth and density. 【0785】 A "control device for calculating the optimal route" is a device that calculates the optimal snow removal route according to the snow conditions and terrain, and controls the snow removal machinery based on that calculation. 【0786】 An "automatically operating machine control device" is a device that operates autonomously and performs snow removal work according to a calculated snow removal route. 【0787】 A "communication device that receives feedback and adjusts operations" is a device that can receive instructions and feedback from users and dynamically adjust the snow removal operation plan accordingly. 【0788】 A "monitoring device that monitors the status of a mobile object and reports abnormalities" is a device that constantly monitors the operating status of a mobile object used for snow removal and reports any abnormalities detected to the system. 【0789】 A "digital terminal" is an electronic device that can receive data from multiple external devices and display information such as analysis results and snow removal progress to the user. 【0790】 In this invention, the server periodically collects weather data using a weather information acquisition device. This allows for real-time monitoring of snowfall and temperature changes, providing the information necessary for snow removal operations. A mobile unit that observes the surrounding conditions in real time scans the surrounding snow conditions using on-board sensors and cameras and transmits the data to the server. This allows for the precise identification of specific areas where snow removal operations are required. 【0791】 The processing unit uses a generated AI model based on the acquired data to perform snowfall prediction. The analyzed data is supplied to the control unit to calculate the optimal snow removal route. The control unit utilizes AI algorithms, particularly to determine the most effective route, to formulate a plan that enables efficient snow removal. 【0792】 The automated machine control system mounted on the mobile unit operates autonomously based on received route information, enabling rapid snow removal. This system can operate day and night and also has the function to recognize and avoid surrounding obstacles. Furthermore, an observation device continuously monitors the status of the mobile unit and immediately reports any abnormalities to the server, ensuring effective maintenance and safety. 【0793】 The communication device receives feedback and replans operations as needed based on priority instructions from users. This enables a rapid response to areas with high urgency. Users can visually check the current snow removal progress and forecasts via digital terminals and send new requests as needed. 【0794】 As a concrete example, in a region where heavy snowfall is forecast, a server analyzes weather data and a mobile vehicle scans for snow accumulation. When a user issues a command via a smart device to prioritize snow removal in a specific area, the communication device receives that information and generates a new route plan. Furthermore, it would be possible to input prompts such as, "Heavy snowfall is forecast. I would like to check the progress of snow removal work on major roads and in specific areas, as well as suggestions for the best detour route." 【0795】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0796】 Step 1: 【0797】 The server retrieves weather data from weather information services. The input is online weather data, and the output is organized weather condition information. This information includes wind speed, temperature, snowfall, etc., and is used in subsequent processing. 【0798】 Step 2: 【0799】 The terminal uses on-board sensors and cameras to scan the surrounding snow conditions in real time. Inputs are sensor data and video data, and output is digital scan data indicating snow depth and density. This data is transmitted to a server. 【0800】 Step 3: 【0801】 The server uses the data acquired in Steps 1 and 2 to perform snow depth prediction using a generative AI model. The input is weather condition information and digital scan data, and the output is a predicted value for short-term snow depth. This value is used to formulate an optimal snow removal plan. 【0802】 Step 4: 【0803】 The server uses an AI algorithm to calculate the optimal snow removal route based on the analysis results. The input is snow depth prediction values, and the output is specific snow removal route data. The calculated route is optimized considering efficiency and safety. 【0804】 Step 5: 【0805】 The terminal automatically performs snow removal based on snow removal route data received from the server. The input is snow removal route data, and the output is the condition of the cleared road. The terminal moves along the route and takes avoidance actions when it detects obstacles. 【0806】 Step 6: 【0807】 Users can check the current snow removal status and forecasts via a digital terminal. Input is snow removal progress information sent from the server, and output is the snow removal status displayed on the user's screen. New requests can be submitted as needed. 【0808】 Step 7: 【0809】 The communication device receives priority instructions from the user and replans the work schedule. The input is user feedback, and the output is the updated snow removal plan. This plan is fed back to the server and reflected in the next snow removal route. 【0810】 Step 8: 【0811】 The terminal's observation device monitors the status of the moving object and reports any detected abnormalities to the server. The input is real-time machine status data, and the output is an alert when an abnormality is detected. This enables early response. 【0812】 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. 【0813】 This invention provides an automated snow removal system that takes user emotions into consideration in order to carry out snow removal work efficiently and in a human-centered manner. In addition to conventional snow removal functions, this system dynamically adjusts the work plan by recognizing the user's emotions, thereby reducing user stress and improving work efficiency. 【0814】 System Configuration 【0815】 1. Data Collection and Analysis 【0816】 The server retrieves data from external weather information services and analyzes local weather and snow conditions. Based on this data, it identifies areas where snowfall is expected. 【0817】 The terminal uses sensors mounted on the vehicle to monitor the surrounding snow conditions in real time and transmits that information to the server. 【0818】 2. Applications of the Emotional Engine 【0819】 Users interact with the system using voice commands from their smart devices. The emotion engine recognizes emotions such as stress and tension from this voice data. 【0820】 Based on feedback from the emotion engine, the server revises the work plan according to the user's emotional state. For example, if the user is feeling stressed, the settings will be changed to allow for faster work. 【0821】 3. Coordination of automated snow removal operations and plans 【0822】 The terminal automatically proceeds with snow removal work, following the optimal snow removal route instructed by the server. It adjusts its operation according to the situation during the work, ensuring safe and efficient execution. 【0823】 The server stores user emotional changes as historical data and executes an algorithm that optimizes future work plans by reflecting past responses. 【0824】 4. User Feedback and Response 【0825】 Users can check the progress and predictions of their work via smart devices, and if anxiety or dissatisfaction arises, that information is captured through an emotion engine. 【0826】 The server receives user feedback in real time, replans snow removal operations as needed, and sends new instructions to the terminal. This allows for flexible responses that meet user expectations. 【0827】 Specific example 【0828】 Consider a scenario where an unexpected heavy snowfall occurs during a snow removal project. This system can solve the problem through the following steps: First, the server analyzes weather data and predicts the increase in snowfall. Terminals provide on-site snow data, which is used to determine the optimal snow removal route. Then, the user reports the on-site situation to the system via voice commands. If the user expresses anxiety, an emotion engine analyzes their emotions, and the server updates the plan to provide more detailed information and take a quicker response. This allows for efficient snow removal while maintaining the user's sense of security. 【0829】 The following describes the processing flow. 【0830】 Step 1: 【0831】 The server accesses weather information services to obtain current weather data such as snowfall forecasts, temperature, and wind speed. Based on this information, it analyzes areas where snowfall is expected and predicts how much snow removal will be necessary. 【0832】 Step 2: 【0833】 The terminal uses sensors and cameras mounted on the vehicle to scan the surrounding snow conditions and transmits the data to the server in real time. Machine learning algorithms are used to measure the depth and density of the snow. 【0834】 Step 3: 【0835】 The server uses an AI algorithm to calculate the optimal snow removal route based on the received snow depth data. This calculation includes factors such as traffic volume, snow removal priority, and safety. 【0836】 Step 4: 【0837】 Users send voice commands to the system from a dedicated smart device to check the progress of snow removal. During this process, an emotion engine analyzes the voice input to detect stress and anxiety. 【0838】 Step 5: 【0839】 Based on emotional state feedback from the emotion engine, the server re-evaluates the work plan as needed. For example, if the user indicates stress, it adjusts the speed and frequency of snow removal work accordingly. 【0840】 Step 6: 【0841】 The terminal automatically performs snow removal work based on instructions sent from the server. During operation, it avoids obstacles and operates efficiently while ensuring safety. 【0842】 Step 7: 【0843】 The server records user emotional changes and work results as a history, which is then used to plan future snow removal operations. In this process, past data is analyzed by AI to identify areas for improvement in subsequent operations. 【0844】 Step 8: 【0845】 Users can continuously monitor snow removal status and forecast data, and send feedback to the system as needed, enabling them to receive prompt responses and appropriate work support. 【0846】 (Example 2) 【0847】 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". 【0848】 This invention aims to improve efficiency in snow removal operations and reduce the psychological burden on users, and its objective is to provide a system that dynamically adjusts the work plan according to weather conditions and the user's emotional state. 【0849】 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. 【0850】 In this invention, the server includes a device for acquiring weather information, a mobile device for monitoring the surrounding conditions in real time, and an information processing device for analyzing the acquired data and performing snowfall predictions. This enables flexible work adjustments based on weather data and the user's emotional state. 【0851】 A "weather information acquisition device" is a device that automatically acquires weather-related data from an external weather database or information service. 【0852】 A "mobile device that monitors the surrounding environment in real time" is a device that uses sensors mounted on mobile hardware to continuously observe the surrounding environment and conditions. 【0853】 An "information processing device" is a computer system that analyzes acquired data and performs necessary calculations and decisions. 【0854】 A "planning device for designing the optimal route" is a device that calculates the most efficient and safest travel route based on analyzed data. 【0855】 An "automatic motion control system" is a system that controls machinery and equipment based on specified routes and motion instructions to perform actions. 【0856】 A "dialogue device that analyzes the user's emotional state and adjusts the work" is a device that analyzes the user's emotions based on their input and dynamically changes the work plan. 【0857】 A "communication device that receives feedback and replans its actions" is a device that receives information from users and external factors and has the function of replanning its action plan based on that information. 【0858】 A "monitoring device" is a device used to check the operating status and any abnormalities of a mobile object and to report them as necessary. 【0859】 This invention provides an embodiment of an automated driving system that streamlines snow removal operations while reducing the psychological burden on the user. 【0860】 The server uses equipment to acquire weather information and analyzes data obtained from external weather data services. For example, the server uses an API to acquire weather data and uses machine learning algorithms to predict snowfall in a region. This allows the server to understand snowfall trends in real time and issue instructions for snow removal work at the appropriate time. 【0861】 The terminal functions as a mobile device that monitors the surrounding environment in real time. Using LiDAR sensors and cameras mounted on the vehicle, the terminal detects snow accumulation and road surface conditions, and transmits this information to a server. This allows the terminal to constantly understand the accurate situation on site. 【0862】 Users interact with the system through smart devices. By issuing voice commands, users' emotional states are reported to the system in real time. These emotional states are analyzed by an emotion analysis engine; for example, if the analysis indicates the user is feeling anxious or stressed, the server dynamically adjusts the work schedule. This process minimizes the user's psychological burden. 【0863】 As a concrete example, if a server uses a weather forecast API to collect snow forecast data and a user gives a voice command saying, "Tell me the progress of the snow removal work," the emotion engine will read the user's emotions from the voice, and the server will make adjustments to the work plan accordingly. 【0864】 An example of a prompt message is, "Collect weather data necessary to plan this morning's snow removal, interpret the user's emotions from their voice commands, and adjust the work accordingly." Using such prompts makes it possible to carry out snow removal work efficiently and in a human-centered manner. 【0865】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0866】 Step 1: 【0867】 The server obtains weather data via a weather information service API. It receives data such as snowfall forecasts, temperature, and wind speed from this API as input. Based on this data, the server runs a machine learning algorithm and outputs a snow depth forecast. This analysis result is then used to plan snow removal operations. 【0868】 Step 2: 【0869】 The terminal uses LiDAR sensors and cameras mounted on the vehicle to monitor surrounding snow and road conditions in real time. It receives raw data from the sensors as input and sends it to the server. Based on this data, the terminal creates output to support situational judgment for autonomous driving. 【0870】 Step 3: 【0871】 The user sends commands to the system via the voice input function of their smart device. The voice data is passed as input to the emotion analysis engine. The analysis engine determines the user's emotional state (stress and anxiety) and outputs this to the server. Based on this feedback, the server adjusts the work plan to suit the user's mental state. 【0872】 Step 4: 【0873】 The server generates an optimized work plan based on weather data, on-site sensor information, and user sentiment data. In this step, this data is integrated to produce a route and schedule output for fast and safe snow removal. 【0874】 Step 5: 【0875】 The terminal automatically performs snow removal based on the optimal route generated, following instructions from the server. It uses GPS data and a pre-programmed road map as input and outputs the vehicle's movements using a motion control algorithm. 【0876】 Step 6: 【0877】 When a user performs a confirmation action from a smart device, that feedback information is sent as input to the emotion engine. The server analyzes the received feedback and obtains output to revise the snow removal plan as needed. 【0878】 (Application Example 2) 【0879】 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". 【0880】 In snow removal operations, conventional techniques prioritize efficiency to such an extent that they often neglect the user's mental state, leading to stress and anxiety. Furthermore, the fixed nature of work plans makes real-time adjustments difficult, resulting in an inability to respond to sudden weather changes or shifts in user emotions. 【0881】 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. 【0882】 In this invention, the server includes means for acquiring weather information, means for scanning the surrounding environment in real time, and means for sentiment analysis that recognizes the user's emotions and reflects them in the work plan. This enables flexible responses to weather changes and changes in the user's emotions, reducing stress and allowing for efficient snow removal work. 【0883】 "Means for acquiring weather information" refers to a device or software that has the function of acquiring external weather data and providing it to the system. 【0884】 A "vehicle device that scans the surrounding environment in real time" is a device mounted on a vehicle that detects the surrounding physical conditions and environment in real time. 【0885】 An "information processing device" is a computer or program used to analyze acquired data and perform snowfall predictions and other necessary calculations. 【0886】 A "control device" is a system that calculates the optimal route based on analysis results and issues commands to operate machinery. 【0887】 A "machine control device" is a device that automatically operates a machine based on generated path information. 【0888】 A "communication device" is a device used to receive feedback and send and receive information. 【0889】 A "monitoring device" is a system that constantly monitors the vehicle's condition and issues a warning if an abnormality is detected. 【0890】 An "emotion analysis device" is an engine or algorithm that recognizes a user's emotions and dynamically modifies the work plan based on that data. 【0891】 An "information provision device" is a system equipped with an interface for providing users with necessary information and feedback. 【0892】 The system implementing this invention mainly consists of three components: a server, a terminal, and a user. 【0893】 The server communicates with external data sources to obtain weather information and collects the latest weather data. Based on the acquired information, it predicts snowfall and calculates the optimal snow removal route. Furthermore, it processes emotional data from users and modifies the work plan according to the situation. For this purpose, the server uses an information processing device and an emotion analysis device. Emotion analysis is performed from voice data, and a generative AI model is used to identify mental states. 【0894】 The terminal uses sensors mounted on the vehicle to scan the surrounding environment in real time and transmits the data to the server. It also uses mechanical control devices to automatically perform snow removal work according to a designated route. Furthermore, it receives instructions from the server via a communication device and adjusts its operation based on the situation during the work. 【0895】 Users can interact with the system via smart devices. An information provider allows them to receive real-time information on work progress and weather changes, as well as provide their emotional state as voice input. An emotion analysis device analyzes this voice data to identify emotions such as stress. This information is fed back into the system and used to adjust the snow removal plan. 【0896】 For example, if the server receives a forecast of unexpected heavy snowfall, it can quickly instruct vehicles to change their routes via terminals, thereby alleviating the anxiety users might feel through prompt information provision. This system aims to streamline snow removal operations by considering user emotions and providing a greater sense of security. 【0897】 An example of a prompt to the generating AI model is, "If the user feels uneasy about the snow removal situation, how should the snow removal plan be changed?" 【0898】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0899】 Step 1: 【0900】 The server obtains the latest weather data from an external weather information service. The input is data obtained from the weather service's API, and the output is weather information for analysis. Based on this data, the server uses an information processing device to perform data analysis in order to predict snowfall and weather changes. 【0901】 Step 2: 【0902】 The terminal uses its built-in sensors to scan the snow conditions around the vehicle in real time and transmit the data to the server. The input is ambient environmental data from the sensors, and the output is the scan information received by the server. By doing this, the terminal provides detailed environmental data based on the current snow conditions. 【0903】 Step 3: 【0904】 Users interact with the system by issuing voice commands via a smart device. The input is the user's voice, and the output is voice data processed on a server. The user's commands are analyzed by an emotion analysis device, and the data is used to extract their emotional state. 【0905】 Step 4: 【0906】 The server calculates the optimal snow removal route based on analyzed weather information, scan data, and the user's emotional state. Inputs are weather data, scan information, and user emotional data, while output is optimized route information. The server uses a computational algorithm to integrate this data and dynamically generate optimal action instructions. 【0907】 Step 5: 【0908】 The terminal receives the latest route instructions from the server and automatically controls the vehicle. The input is route information from the server, and the output is the physical operation of the vehicle. The terminal uses a mechanical control device to follow the instructions and perform snow removal work safely and efficiently. 【0909】 Step 6: 【0910】 The server receives user feedback on ongoing tasks and readjusts the work plan as needed. The input is emotional feedback from the user, and the output is the adjusted work plan. Using a generative AI model enables flexible responses that meet user expectations. 【0911】 Step 7: 【0912】 The user receives current work status and additional feedback through an information provision device, and provides reactions to the system based on their own emotions. The input is work information from the server, and the output is data that enhances the user's sense of security and provides additional emotional data. Through this process, it is ensured that snow removal work is performed in a user-centric manner. 【0913】 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. 【0914】 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. 【0915】 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. 【0916】 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. 【0917】 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. 【0918】 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. 【0919】 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. 【0920】 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. 【0921】 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." 【0922】 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. 【0923】 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. 【0924】 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. 【0925】 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. 【0926】 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. 【0927】 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. 【0928】 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. 【0929】 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. 【0930】 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. 【0931】 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. 【0932】 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. 【0933】 All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference. 【0934】 The following is further disclosed regarding the embodiments described above. 【0935】 (Claim 1) 【0936】 Means of obtaining weather information, 【0937】 A vehicle device that scans the surrounding environment in real time, 【0938】 A computing device that analyzes acquired data to predict snowfall, 【0939】 A control device that calculates the optimal route based on the analysis results, 【0940】 A machine control device that operates automatically based on the generated path, 【0941】 A communication device that receives feedback and adjusts the work, 【0942】 A system that includes monitoring devices to monitor the vehicle's condition and report any abnormalities. 【0943】 (Claim 2) 【0944】 The system according to claim 1, wherein the computing device performs snowfall prediction using a machine learning model. 【0945】 (Claim 3) 【0946】 The system according to claim 1, wherein the communication device replans the work schedule based on priority instructions from the user. 【0947】 "Example 1" 【0948】 (Claim 1) 【0949】 Means of obtaining weather conditions, 【0950】 A vehicle mechanism that scans the surrounding environment in real time, 【0951】 A calculation module that analyzes acquired information to predict snow conditions, 【0952】 A control unit that calculates the optimal path based on the analysis results, 【0953】 A technological device that operates autonomously based on a specified route, 【0954】 A communication module that receives feedback and adjusts the work, 【0955】 A system that includes a monitoring module that monitors the vehicle's status and notifies of any abnormalities. 【0956】 (Claim 2) 【0957】 The system according to claim 1, wherein the calculation module predicts snow conditions using a machine learning algorithm. 【0958】 (Claim 3) 【0959】 The system according to claim 1, wherein the communication module reconfigures the work plan based on priority settings from the user. 【0960】 "Application Example 1" 【0961】 (Claim 1) 【0962】 A device for acquiring weather information, 【0963】 A mobile device that observes the surrounding situation in real time, 【0964】 A processing device that analyzes acquired data and performs snow depth prediction, 【0965】 A control device that calculates the optimal route based on the analysis results, 【0966】 A machine operating device that operates automatically based on the generated path, 【0967】 A communication device that receives feedback and adjusts the work, 【0968】 An observation device that monitors the status of a moving object and reports any abnormalities, 【0969】 A system that shares data with multiple external devices and includes a digital terminal that displays information to users. 【0970】 (Claim 2) 【0971】 The system according to claim 1, wherein the processing device performs snowfall prediction using machine learning technology. 【0972】 (Claim 3) 【0973】 The system according to claim 1, wherein the communication device replans the work schedule based on priority instructions from the user. 【0974】 "Example 2 of combining an emotion engine" 【0975】 (Claim 1) 【0976】 A device for acquiring weather information, 【0977】 A mobile device that monitors the surrounding situation in real time, 【0978】 An information processing device that analyzes acquired data and performs snow depth prediction, 【0979】 A planning device that designs the optimal route based on the analysis results, 【0980】 A motion control device that operates automatically based on the generated path, 【0981】 A dialogue device that analyzes the user's emotional state and adjusts the task accordingly, 【0982】 A communication device that receives feedback and replans its actions, 【0983】 A system including a monitoring device that monitors the status of a moving object and reports any abnormalities. 【0984】 (Claim 2) 【0985】 The system according to claim 1, wherein the information processing device performs snowfall prediction using a machine learning model. 【0986】 (Claim 3) 【0987】 The system according to claim 1, wherein the dialogue device dynamically replans the work plan based on the user's emotional information. 【0988】 "Application example 2 when combining with an emotional engine" 【0989】 (Claim 1) 【0990】 Means of obtaining weather information, 【0991】 A vehicle device that scans the surrounding environment in real time, 【0992】 An information processing device that analyzes acquired data and performs snow depth prediction, 【0993】 A control device that calculates the optimal route based on the analysis results, 【0994】 A machine control device that operates automatically based on the generated path, 【0995】 A communication device that receives feedback and adjusts the work, 【0996】 A monitoring device that monitors the vehicle's condition and reports any abnormalities, 【0997】 An emotion analysis device that recognizes the user's emotions and reflects them in the work plan, 【0998】 A system including an information provision device that provides feedback according to the user's status. 【0999】 (Claim 2) 【1000】 The system according to claim 1, wherein the information processing device performs snowfall prediction using a machine learning algorithm. 【1001】 (Claim 3) 【1002】 The system according to claim 1, wherein the communication device readjusts the work plan based on emotional feedback from the user. [Explanation of Symbols] 【1003】 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] Means of obtaining weather information, A vehicle device that scans the surrounding environment in real time, A computing device that analyzes acquired data to predict snowfall, A control device that calculates the optimal route based on the analysis results, A machine control device that operates automatically based on the generated path, A communication device that receives feedback and adjusts the work, A system that includes monitoring devices to monitor the vehicle's condition and report any abnormalities. [Claim 2] The system according to claim 1, wherein the computing device performs snowfall prediction using a machine learning model. [Claim 3] The system according to claim 1, wherein the communication device replans the work schedule based on priority instructions from the user.

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