Data processing system

Abstract

The present application relates to a crisis management system which collects data from various information sources such as news, SNS, weather information, market data, disaster information, IoT sensors, etc. inside and outside the company in real time, uses generative AI to extract risk factors, predict occurrence probability and impact, generate composite risk scenarios, automatically determine risk levels, refer to past cases, company internal regulations, external guidelines to automatically develop the best response strategy, automatically generate and send reports and alert notifications optimized for each relevant person, automatically execute approved countermeasures with relevant systems, and also has progress monitoring and learning·evolution functions.

Description

Technical Field

[0001] The technology disclosed herein relates to a system. Background Technology

[0002] Patent Document 1 discloses a personalized chatbot control method executed by at least one processor, the method comprising: receiving user speech; adding the user speech to a prompt containing instructions related to a chatbot role; encoding the prompt; and inputting the encoded prompt into a language model to generate chatbot speech in response to the user speech.

[0003] Patent document 1: Japanese Patent Application Publication No. 2022-180282.

[0004] The problem this invention aims to solve is to comprehensively and fundamentally address a series of issues that threaten business continuity and social reputation. Specifically, in today's rapidly complex and diverse environment where business activities take place, and with the real-time flow of vast and diverse information such as news, social media, weather, market data, and disaster information both inside and outside the company, information overload leads to the omission of truly important risk information. Furthermore, delays and errors occur in initial responses and decisions when risks occur. Insufficient inter-departmental collaboration or inefficient information transmission further delays and inefficiencies in crisis response. Existing crisis management systems struggle to fully utilize massive amounts of data for advanced risk prediction and the automatic formulation and execution of optimal response strategies, and cannot achieve optimized information transmission and rapid countermeasure execution for management, responsible departments, and other relevant personnel. Summary of the Invention

[0005] This invention addresses the problem by providing a crisis management system comprising an information collection department, a risk analysis department, and a response strategy formulation and execution department. The information collection department automatically acquires data in real time from various information sources, including news, social media, weather information, market data, and disaster information from both inside and outside the company. It then categorizes and organizes the acquired data according to attributes such as time series, location of occurrence, relevant departments, and impact. The risk analysis department uses generative AI, employing natural language processing, pattern recognition, comparison with past cases, and correlation analysis with external data, to analyze the vast amount of data obtained from the information collection department. It extracts risk factors, quantitatively predicts the probability and impact of risks, and automatically classifies risks as major or moderate based on their severity. The system includes functions for assessing risk levels such as high risk and minor risk. The response strategy formulation and execution department, based on the risk assessment results from the risk analysis department, uses generative AI to automatically generate multiple optimal response strategies corresponding to each risk level, while referencing past response cases, internal company regulations, and external best practices. It automatically creates reports and alerts optimized for management, responsible departments, and other relevant personnel. Furthermore, for approved response plans, it integrates with relevant internal company systems to automatically execute specific countermeasures such as inventory adjustments, system switching, backup startup, and personnel assignment. This comprehensively and fundamentally addresses issues such as information overload leading to missed risk information and delayed responses, inefficient inter-departmental collaboration, and limitations of existing systems. Attached Figure Description

[0006] Figure 1 This is a conceptual diagram illustrating an example of the configuration of a data processing system according to the first embodiment.

[0007] Figure 2 This is a conceptual diagram illustrating an example of the main functions of the data processing apparatus and smart device according to the first embodiment.

[0008] Figure 3 This is a conceptual diagram illustrating an example of the data processing system configuration in the second embodiment.

[0009] Figure 4 This is a conceptual diagram illustrating an example of the main functions of the data processing device and smart glasses according to the second embodiment.

[0010] Figure 5 This is a conceptual diagram illustrating an example of the data processing system configuration in the third embodiment.

[0011] Figure 6 This is a conceptual diagram illustrating an example of the main functions of the data processing device and head-mounted terminal according to the third embodiment.

[0012] Figure 7This is a conceptual diagram illustrating an example of the data processing system configuration in the fourth embodiment.

[0013] Figure 8 This is a conceptual diagram illustrating an example of the functions of the main parts of the data processing device and robot according to the fourth embodiment.

[0014] Figure 9 It represents an emotion graph that maps multiple emotions.

[0015] Figure 10 It represents an emotion graph that maps multiple emotions.

[0016] Explanation of reference numerals in the attached figures Data processing systems 10, 210, 310, and 410 12 Data processing device 14 Smart devices 214 Smart Glasses 314 Head-mounted terminal 414 Robot. Detailed Implementation

[0017] Hereinafter, an example of an implementation of the system involved in this disclosure will be described with reference to the accompanying drawings.

[0018] First, let's explain the terms used in the following description.

[0019] In the following embodiments, the processor (hereinafter referred to as "processor") can be a single computing device or a combination of multiple computing devices. Furthermore, a processor can be a single computing device or a combination of multiple computing devices. Examples of computing devices include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc.

[0020] In the following implementation, the labeled RAM (Random Access Memory) is a memory that temporarily stores information and is used by the processor as working memory.

[0021] In the following embodiments, the labeled memory is one or more non-volatile storage devices used to store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), disk (e.g., hard disk) or magnetic tape, etc.

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

[0023] In the following implementation, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it can be only A, only B, or a combination of A and B. Furthermore, in this specification, when "and / or" connects more than three items, the same approach as "A and / or B" applies.

[0024] [First Implementation] Figure 1 An example of the configuration of the data processing system 10 according to the first embodiment is shown.

[0025] like Figure 1 As shown, the data processing system 10 includes a data processing device 12 and an intelligent device 14. An example of the data processing device 12 is a server.

[0026] The data processing apparatus 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" as understood in this disclosure. The computer 22 includes a processor 28, RAM 30, and memory 32. The processor 28, RAM 30, and memory 32 are connected to a bus 34. Furthermore, the database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. An example of the network 54 includes a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 includes a computer 36, a receiver 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, RAM 48, and memory 50. The processor 46, RAM 48, and memory 50 are connected to a bus 52. Furthermore, the receiver 38, output device 40, camera 42, and communication I / F 44 are also connected to the bus 52.

[0028] The receiving device 38 includes a touchscreen 38A and a microphone 38B, etc., for receiving user input. The touchscreen 38A receives user input generated by contact with an indicator (e.g., a pen or finger). The microphone 38B receives user input generated by sound by detecting the user's voice. The control unit 46A in the processor 46 sends data representing user input received through the touchscreen 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data representing the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting data in a form perceptible to the user 20 (e.g., sound and / or text). The display 40A displays visual information such as text and images according to instructions from the processor 46. The speaker 40B outputs sound 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 imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication I / F 44 is connected to network 54. Communication I / F 44 and 26 are responsible for sending and receiving various information between processor 46 and processor 28 via network 54.

[0031] Figure 2 An example of the main functions of the data processing device 12 and the smart device 14 is shown.

[0032] like Figure 2 As shown, in the data processing apparatus 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the memory 32. The specific processing program 56 is an example of a "program" as understood in this disclosure. The processor 28 reads the specific processing program 56 from the memory 32 and executes the read specific processing program 56 on the RAM 30. Specific processing is implemented by the processor 28 operating as a specific processing unit 290 based on the specific processing program 56 executed on the RAM 30.

[0033] The memory 32 stores a data generation model 58 and an emotion-specific model 59. The data generation model 58 and the emotion-specific model 59 are used by the specific processing unit 290. The specific processing unit 290 can use the emotion-specific model 59 to infer the user's emotions and perform specific processing based on the user's emotions. The emotion inference function using the emotion-specific model 59 (emotion-specific function) includes various inferences and predictions related to the user's emotions, but is not limited to this example. Furthermore, emotion inference and prediction also include, for example, emotion analysis (analysis).

[0034] In the smart device 14, the processor 46 performs the receive / output processing. The memory 50 stores the receive / output program 60. The receive / output program 60 is used in conjunction with the data processing system 10 and the specific processing program 56. The processor 46 reads the receive / output program 60 from the memory 50 and executes the read receive / output program 60 on the RAM 48. The receive / output processing is implemented by the processor 46 acting as a control unit 46A based on the receive / output program 60 executed on the RAM 48. Furthermore, the smart device 14 may also have the same data generation model and emotion-specific model as the data generation model 58 and the emotion-specific model 59, and use these models to perform the same processing as the specific processing unit 290. The receive / output processing is implemented by the processor 46 acting as a control unit 46A based on the receive / output program 60 executed on the RAM 48.

[0035] In addition, other devices besides the data processing device 12 may also have a data generation model 58.

[0036] For example, a server device (e.g., a generation server) may have a data generation model 58.

[0037] In this case, the data processing device 12 obtains the processing results (prediction results, etc.) using the data generation model 58 by communicating with a server device that has the data generation model 58. Furthermore, the data processing device 12 can be a server device or a user-owned terminal device (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of the processing performed by the data processing system 10 of the first embodiment will be described.

[0038] (Example 1) The specific processing flow in Example 1 will be described below. Each part of the system described below is implemented by the data processing device 12 and the smart device 14. Furthermore, the data processing device 12 is referred to as the "server" and the smart device 14 is referred to as the "terminal".

[0039] (Implementation methods for carrying out the present invention) The embodiments of the present invention will be described in more specific and detailed manner. The crisis management system of the present invention consists of a server and a terminal, and includes an information collection unit, a risk analysis unit, and a response strategy formulation and execution unit. Hereinafter, a comprehensive description will be provided from all perspectives, including the composition and function of each unit, the method of utilizing generative AI, the role division between the server and the terminal, specific application examples, scalability, security, user interface, external linkage, and learning and evolution functions. Specifically, this system is constructed using a cluster of computing servers equipped with multiple processors, large-capacity memory, and high-speed communication interfaces. This system deploys a large-scale neural network model with an attention mechanism on memory. This system performs massive matrix operations at high speed using a parallel computing cluster of graphics processing units. The data input to this system is a multi-dimensional numerical tensor. Elements of the input data include text embedding vectors and time-series data from sensors. This system performs preprocessing on the input data, such as normalization and noise removal. For example, the input data includes text data from news reports, temperature sensor data from factories, and image data from surveillance cameras. This system integrates these input data to perform inference processing. The output data from this system is a floating-point array representing the probability of risk occurrence. Each value in the output data represents the probability of a specific risk scenario becoming a reality. For example, the output data includes values ​​such as a device failure probability of 0.95%, a supply chain delay probability of 0.82%, and a cyberattack probability of 0.15. This system uses this output result for threshold determination in subsequent processing. If the output value exceeds a pre-set threshold, the system sends a start signal to the response strategy formulation module. This system does not simply mimic human mental activity; instead, it extracts subtle correlations that are indistinguishable to humans through complex nonlinear operations in a high-dimensional vector space. This allows the system to achieve overwhelmingly high-speed information processing and a leap in risk detection accuracy. This effect is achieved through a mechanism that maps multimodal data to a common latent space and performs integrated analysis. This system is extremely useful in the specific application area of ​​supply chain monitoring in the global manufacturing industry. Furthermore, as an alternative, this system can also be configured with a lightweight model in an edge computing environment.

[0040] First, the information collection department is primarily implemented on servers. It automatically collects data from a wide variety of sources, including news websites and social networking sites both inside and outside the company, weather agencies and disaster information providers, financial market data providers, EDI systems of trading partners, customer information management systems, IoT sensor networks, surveillance camera systems, employee business reporting applications, open data APIs from autonomous and public institutions, and alert dissemination services from industry groups. For example, the system automatically collects breaking news reports such as "bankruptcy of major trading partners," "outbreak of a new infectious disease," and "large-scale power outage" from news websites; it gathers submissions such as "product defect reports," "disaster scene photos from on-site employees," and "competitor's new service launch" from social media; it acquires real-time data such as "typhoon path forecasts," "heavy rain warnings," and "lightning strike information" from meteorological information; it obtains indicators such as "rapid exchange rate fluctuations," "stock price crashes," and "rising raw material prices" from market data; it acquires real-time data such as "abnormal vibrations of factory equipment," "rapid temperature increases in warehouses," and "location information of logistics vehicles" from IoT sensors; it obtains image analysis results such as "suspicious intrusion" and "smoke detection of fires" from surveillance cameras; and it gathers qualitative information such as "disaster situation on-site" and "delivery delays from suppliers" from employee reports. This information is automatically collected through various means, including API linkage, RSS subscriptions, email reception, file transfer, chatbots, web scraping, IoT gateways, and image recognition AI. Specifically, this information collection department uses asynchronous communication protocols to execute the data collection process in a multi-threaded manner. The department periodically polls the communication interfaces of each information source. The data input to this information collection unit consists of text streams, time-series numerical arrays, and image tensors. Elements of the input data include arrays of character codes, continuous floating-point values, and pixel brightness values. This information collection unit performs preprocessing on the input data, such as character code unification and interpolation of missing values. For example, the input data may include JSON-formatted meteorological information text, numerical arrays of vibration sensors sampled at 100-millisecond intervals, and full HD resolution surveillance camera images. This information collection unit temporarily stores this input data in a buffer area on memory. The output data from this information collection unit is structured data based on a unified data model. Each value in the output data represents a timestamp, information source identifier, and payload subject. For example, the output data may include a timestamp of October 1, 2023, a sensor identification number of 11, and a temperature of 35 degrees Celsius. This output result is forwarded to the data preprocessing module in subsequent processing. This information collection unit leverages the technical effects of efficient network bandwidth utilization and reduced data collection latency. This effect is achieved through minimizing processor standby time based on asynchronous input / output processing.This information gathering unit is indispensable in the specific application area of ​​wide-area disaster detection systems, where real-time performance is extremely important. Alternatively, this information gathering unit can also employ a publish-subscribe messaging model.

[0041] The collected data is stored in a database on the server and automatically categorized and tagged according to various attributes such as time series, location of occurrence, relevant departments, impact, information reliability, information source, data type (text, numerical, image, audio, video, etc.), urgency, related business areas, transaction object name, and customer name. For example, in SNS posts, posts containing "Company Name + Fault + On-site Photo" are tagged with "Fault Risk," "On-site," and "High Urgency." Anomalies from IoT sensors are categorized as "Equipment Risk," "Factory," and "Requires Immediate Response." The information collection department also automatically performs preprocessing such as duplicate filtering, noise removal, reliability scoring, time-series correction, geographic information assignment, and automatic caption generation for image data. Specifically, this data preprocessing department uses a distributed vector database to efficiently store massive amounts of data. This data preprocessing department executes a Transformer-based neural network for natural language processing. The data input to this neural network is a tokenized integer array of text. The dimension of the input data is, for example, fixed at 512 dimensions. This data preprocessing department performs preprocessing on the input data such as stop word removal and stemming. For example, the input data includes customer complaint articles, news reports, and daily employee reports. This neural network generates context-aware embedding vectors from the input data. The output data from this neural network is a score array representing the probability of matching each label. The values ​​in the output data are continuous values ​​from zero to one, signifying the confidence level of belonging to a specific attribute. For example, the output data includes values ​​such as a fault risk score of 0.9, a high urgency score of 0.85, and an equipment risk score of 0.1. This data preprocessing unit uses the output results for metadata assignment in subsequent processing. When the output value exceeds a preset threshold of 0.8, the data preprocessing unit registers the matching labels as indexes in the database. This data preprocessing unit achieves a significant improvement in retrieval speed and optimization of storage capacity. This effect is achieved through high-speed clustering processing using similarity calculations in a high-dimensional vector space. This data preprocessing unit is extremely useful in the specific application area of ​​building large-scale event management databases. In addition, as an alternative, this data preprocessing unit can also employ auxiliary combinations of rule-based regular expression matching.

[0042] The risk analysis department is also primarily implemented on servers. It uses generative AI to analyze the massive amounts of data obtained from the information gathering department from multiple perspectives. First, it uses natural language processing based on generative AI (such as large-scale language models) to extract keywords, phrases, sentiment expressions, causal relationships, time-series changes, and the intentions of relevant personnel from unstructured text data such as news reports, social media posts, internal company reports, and customer inquiries. For example, it automatically detects phrases like "earthquake occurred," "tsunami warning," "fire," "fault," "public outrage," "market crash," and "alarm." The system analyzes all risk-related terms such as "deterioration," "surge," "delivery delay," "cyberattack," "data outflow," "changes in laws and regulations," and "spread of infectious diseases," and infers the type, location, and scope of the risk based on context. Generative AI also performs sentiment analysis and topic modeling, quantitatively assessing signs of public outrage on social media, rising customer dissatisfaction, and increased employee anxiety. Specifically, the risk analysis department segments and loads a large-scale language model with billions of parameters into multiple graphics processing units. This department uses a self-attention mechanism to calculate dependencies between words within the text. The input data to this large-scale language model is the preprocessed text's tag embedding vector. The input data has a dimension of, for example, 768 dimensions. The risk analysis department performs preprocessing on the input data, adding positional encoding. For example, the input data includes short posts on social media, long economic news reports, and internal company fault reports containing technical jargon. This large-scale language model performs forward propagation operations based on the input data. The output data from this large-scale language model is an array of named entity recognition tags and a sentiment score. The values ​​in the output data represent the category identifiers of the extracted risk factors and numerical values ​​indicating emotional polarity. For example, the output data includes the event label "fire," the location label "Tokyo," and an emotional score of 0.92 indicating anger. This risk analysis unit then inputs the output results into the risk level determination module. If the emotional score exceeds a predetermined threshold, the risk analysis unit writes a high-risk flag to a shared area of ​​memory. This risk analysis unit achieves the technical effect of completely eliminating omissions caused by human visual observation and overwhelmingly speeding up the parsing process. This effect is achieved through an attention mechanism that mathematically models long-term dependencies in the context. This risk analysis unit is extremely useful in the specific application area of ​​early detection of brand damage risks on social media. Furthermore, as an alternative, this risk analysis unit can also employ a configuration that allows small-scale models fine-tuned for specific industry terminology to run in parallel.

[0043] Furthermore, using generative AI-based pattern recognition and time series analysis, outliers and sharp changes are detected from numerical data such as IoT sensor data and market data. For example, when vibration data of factory equipment deviates from its normal range, GPS data of logistics vehicles deviates from its usual route, or stock prices and exchange rates fluctuate significantly in a short period of time, risk signals are automatically generated through anomaly detection algorithms. Regarding image data, a generative AI-based image recognition and object detection model is used to automatically identify smoke from fires, intrusions by suspicious persons, and the extent of damage at a scene from surveillance camera footage. Regarding voice data, a generative AI-based speech recognition and sentiment analysis model is used to assess the severity and urgency of complaints from customer call center conversations. Specifically, this risk analysis department executes a one-dimensional convolutional neural network for time series analysis and a two-dimensional convolutional neural network for image recognition. The data input to this time series model is a multi-dimensional array consisting of sensor values ​​from the past 100 steps. The dimension of the input data is, for example, a matrix of 100 rows and 6 columns. This risk analysis department performs smoothing preprocessing on the input data using a moving average filter. For example, the input data includes triaxial acceleration data of the motor, exchange rate fluctuations per second, and vehicle latitude and longitude coordinates. This time series model predicts the value of the next step from the input data. The output data from this time series model is the reconstruction error score between the predicted value and the actual observation value. Each value in the output data indicates the degree of deviation from the normal pattern. For example, the output data includes a vibration anomaly score of 0.88 and a route deviation score of 0.95. On the other hand, the data input to this image model is a 224-pixel square three-channel color image tensor. The output data from this image model is an array of bounding box coordinates and class probabilities. For example, the output data includes a smoke detection probability of 0.97 and a suspicious person detection probability of 0.89. This risk analysis department sends these output results to the integrated alarm generation module in subsequent processing. If the error score or class probability exceeds the threshold, this risk analysis department sends an interrupt signal to the processor requiring immediate action. This risk analysis department utilizes the technical effect of comprehensive and real-time anomaly detection based on multimodal data. This effect is achieved through a mechanism that mathematically defines complex anomalies by projecting different data formats onto a common latent feature space. This risk analysis department is extremely useful in the specific application area of ​​predictive maintenance and preventative systems in smart factories. Furthermore, as an alternative, this risk analysis department can also employ time series forecasting using long short-term memory networks.

[0044] Furthermore, generative AI combines past risk occurrence cases, external databases (such as cross-industry disaster databases, cyberattack event sets, legal and regulatory amendment records, supply chain failure case sets, etc.) with currently acquired data and performs correlation analysis to quantitatively predict the probability and impact of risk occurrence. For example, by comparing past cases such as logistics disruptions in specific areas during typhoons, public outrage on social media leading to stock price crashes, and the cascading effects of system failures during cyberattacks with the current situation, multiple risk scenarios are generated. Generative AI automatically determines risk levels such as major, moderate, and minor risks based on their severity, prioritizes them by risk type, and performs composite risk assessments when multiple risks occur simultaneously. Specifically, this risk analysis department executes a graph neural network to model complex causal relationships. The data input to this graph neural network consists of node feature vectors representing the current situation and edge connection matrices extracted from the past case database. The elements of the input data include the occurrence time of each event, geographical coordinates, and the strength of the correlation between events. This risk analysis department performs graph structure normalization and isolated node removal preprocessing on the input data. As a specific example, the input data includes current typhoon pressure data, disaster scale data of past logistics disruption cases, and similarity edges connecting them. This graph neural network performs message passing processing to propagate feature vectors between adjacent nodes. The output data from this graph neural network is an array of the probability of occurrence and impact score for each risk scenario. The values ​​in the output data are probability values ​​from zero to one and integer values ​​from one to ten, representing the future disaster scale. As a specific example, the output data includes a logistics disruption probability of 0.85, an impact score of nine, a system failure cascading probability of 0.72, and an impact score of eight. This risk analysis department uses this output result to calculate the comprehensive risk score in subsequent processing. This risk analysis department calculates the product of the probability of occurrence and the impact score, and sorts the risk scenarios in descending order based on this result. This risk analysis department demonstrates the technical effect of highly accurate prediction of cascading risks that are difficult to predict from a single event. This effect is achieved through a learning mechanism of nonlinear relationships in a high-dimensional graph space. This risk analysis department is extremely useful in the specific application area of ​​simulating the impact of compound disasters in the global supply chain. In addition, as an alternative, this risk analysis department can also use probabilistic inference using Bayesian networks.

[0045] The Response Strategy Formulation and Execution Department is primarily implemented on the server, but also has some functionality on the client side. Based on the risk assessment results from the Risk Analysis Department, the Response Strategy Formulation and Execution Department uses generative AI to automatically generate multiple optimal response strategies corresponding to the risk level and type, while referencing past response cases, internal company regulations, external best practices, industry guidelines, laws, contract terms, agreements with customers, and contract terms with trading partners. For example, for earthquake risk, it automatically generates strategies such as "ensuring employee safety," "emergency shutdown of equipment," "supply chain restructuring," "notifying customers of the impact," "ensuring evacuation routes," "investigating the disaster situation at local branches," and "coordinating with local authorities." For cyberattack risk, it automatically generates strategies such as "system shutdown," "backup activation," "contacting relevant departments," "legal response preparation," "contacting external experts," and "temporarily cutting off customer information." For the risk of inciting public outrage, it automatically generates specific action plans such as "issuing an official statement by the Public Relations Department," "strengthening SNS monitoring," "adding a customer response window," "instant updates to FAQs," "automatic generation of apologies," and "determining the scope of impact." Generative AI automatically creates reports and alerts optimized for various stakeholders, including management, responsible departments, on-site managers, trading partners, and customers. These are rapidly disseminated through management screens, mobile devices, email, chat systems, voice calls, and push notifications. For example, reports summarizing the overall risk and key decision-making points are provided to management; on-site managers receive specific operational instructions and emergency contact information; the crisis management department has a dashboard providing real-time visualization of overall progress and response status; and customers receive concise notifications summarizing the scope of impact and response strategies. Specifically, the response strategy formulation department assembles a large-scale language model using retrieval-enhanced generative technology. The input data for this language model consists of a comprehensive risk score, risk category vectors, and internally defined contextual text retrieved from a vector database, all from the risk analysis department. The input data is a lengthy sequence of thousands of labels. The response strategy formulation department preprocesses the input data by embedding it into prompt templates. For instance, the input data includes risk information about an earthquake of intensity six or higher and a manual outlining emergency shutdown procedures for the factory. This language model generates text through autoregressive decoding. The output data from this language model consists of a text sequence for each step of the response strategy, along with an execution priority score for each step. Each value in the output data signifies the specific action content and an indicator of whether the action should be executed immediately. For example, the output data might include the text "network disconnection from the main server" with a priority score of 1, and the text "sending the first report to the customer" with a priority score of 2, etc.In subsequent processing, the response strategy formulation department uses the output for filtering based on the permission levels of relevant personnel. The department converts the generated text sequence into structured data in JSON format and sends it to the display modules of each terminal. This response strategy formulation department achieves the technical effect of instantly generating dynamic and optimal resource allocation plans based on the situation. This effect is achieved through an attention mechanism that combines a large historical knowledge base with the current context in the latent space. This response strategy formulation department is extremely useful in the specific application area of ​​automating incident response during large-scale cyberattacks. Alternatively, the response strategy formulation department can also employ a policy gradient method that uses reinforcement learning to maximize the success probability of the response strategy.

[0046] Furthermore, the Response Strategy Formulation and Implementation Department automatically executes approved countermeasures by integrating with relevant internal company systems. For example, it integrates with the inventory management system to automatically adjust inventory based on disaster risk, with the information systems department to automate system switchover and backup startup, with the employee management system to automatically assign responsibilities, with the logistics system to automatically change delivery routes, or with the customer management system to automate notifications to affected customers. Generative AI also automatically verifies the effectiveness and progress of countermeasures after implementation and automatically generates additional countermeasures as needed. For example, if new risks arise after the initial response or if the response is delayed, the generative AI reassesses the situation and automatically notifies relevant personnel of additional instructions and warnings. Specifically, this Implementation Management Department automatically issues control commands to various external systems via an application programming interface gateway. The data input to this Implementation Management Department is structured data of the approved countermeasures. The elements of the input data include the system identifier of the object to be executed, the name of the function to be called, and the parameter set as parameters. This execution management unit performs preprocessing on the input data, including data format conversion and authentication token assignment. For example, the input data includes the inventory management system identifier, inventory movement commands, and the parameter for the number of items to be moved. This execution management unit sends these commands asynchronously via a network interface. The output data received by this execution management unit consists of execution completion status codes and execution log data from each system. The values ​​in the output data represent integers indicating whether a command was processed correctly and detailed processing history. For example, the output data includes a status code indicating normal completion and a timestamp indicating the time of processing completion. This execution management unit uses this output result for writing to a time-series database for progress monitoring in subsequent processing. In cases of incorrect status codes, this execution management unit automatically schedules retry processing using an exponential backoff algorithm. This execution management unit achieves the technical effect of drastically reducing response time and completely eliminating human error through inter-system collaboration without human intervention. This effect is achieved through the causal relationship of state consistency guarantees based on a distributed transaction management mechanism. This execution management unit is particularly useful in the specific application areas of dynamic routing of logistics networks and automatic inventory reconfiguration during natural disasters. In addition, as an alternative, this execution management department can also adopt a lower latency communication protocol using remote procedure calls.

[0047] On the server side, the main processing functions of the information collection department, risk analysis department, and response strategy formulation and execution department are centrally executed. This includes large-scale data analysis based on generative AI, scenario generation, automatic response strategy formulation, automatic report generation, internal system linkage, progress monitoring, history management, access control management, security monitoring, and log management. On the terminal side, the system provides dashboard displays for relevant personnel, alarm notification reception, status reports and response input from on-site personnel, decision-making and approval operations by management, confirmation of on-site emergency response instructions, voice input and image upload, automatic parsing of on-site photos, automatic text transcription of on-site voice, and feedback transmission from the site. For example, an on-site personnel report the disaster situation with photos from a mobile terminal, or management presses the approval button for a response plan on a terminal, thus initiating automated processing on the server side. Specifically, this system adopts a distributed architecture that clearly separates the roles of edge computing and cloud computing. This server-side system uses a parallel computing cluster of graphics processing units to perform high-load tensor operations in batch and stream processing. On the other hand, this terminal-side system deploys an optimized lightweight inference model on a mobile processor. The data input to this terminal-side model consists of raw image data from a camera sensor and raw speech waveform data from a microphone. The input data is a one-dimensional array of millions of pixels or tens of thousands of samples. This terminal-side system performs preprocessing on the input data, including downsampling and noise reduction. For example, the input data includes high-resolution images of rubble at a disaster site and voice reports from on-site workers including ambient noise. This terminal-side model extracts features from this input data. The output data from this terminal-side model consists of compressed vector data after feature extraction and an initial judgment label. Each value in the output data represents a low-dimensional numerical array representing the essential characteristics of the data. For example, the output data includes a 256-dimensional image feature vector and the text label "fire occurred." This terminal-side system uses this output as a payload to send to the server in subsequent processing. This terminal-side system sends the compressed vector data to the server via a secure communication channel, significantly reducing the server's load. This system achieves the technical benefits of saving communication bandwidth and distributing the computational load on the server side. This effect is achieved through a causal relationship between edge-based data compression and minimizing initial inferred network traffic. This system is particularly useful in the specific application of efficient condition reporting from disaster sites with unstable communication infrastructure. Furthermore, as an alternative, this system can also employ edge-based federated learning to update the model while protecting privacy.

[0048] The application of generative AI is at the core of this invention. Examples include risk factor extraction based on natural language processing, anomaly detection based on pattern recognition, risk prediction based on comparison with past cases, prompting multiple risk patterns generated from scenarios, automatic formulation of response strategies, automatic generation of reports and notifications, automatic monitoring and reassessment of progress, automatic parsing of feedback from the field, automatic response to user inquiries, automatic parsing of voice, image, and video data, automatic linkage with external databases, and automatic learning of new risk patterns both within and outside the company. Generative AI is essential in all aspects of the system. For instance, upon detecting signs of public outrage on social media, generative AI analyzes the submitted content, compares it with past cases of public outrage to predict the impact, automatically generates the best public relations response strategy, and immediately notifies the public relations department and management. Similarly, upon detecting an approaching typhoon from weather information, generative AI compares past typhoon damage data with current inventory levels, automatically generating instructions for inventory movement and delivery route changes for the logistics department. For example, when market data detects sharp exchange rate fluctuations, generative AI, referencing past exchange rate response strategies, automatically generates hedging and fund movement proposals for the finance department. Similarly, when IoT sensors detect abnormal vibrations in factory equipment, generative AI compares this data with past equipment failure data and automatically generates instructions for emergency inspections and preventative maintenance for the maintenance department. Specifically, this system adopts a holistic multi-agent AI architecture. This system enables task-specific sub-models, such as language models, visual models, and temporal models, to interact via a shared latent vector space. The input data to this integrated model consists of feature vector groups output from each sub-model. Input data elements include text context vectors, image spatial feature vectors, and numerical trend vectors. The system performs vector combination and dimensionality reduction preprocessing on the input data. For example, the input data might include angry sentiment vectors from SNS posts, typhoon path prediction vectors, and abnormal vibration vectors from the factory. This integrated model integrates these input data to infer the optimal course of action for the entire system. The output data from this integrated model is the integrated control vector that maximizes the action value function. The values ​​in the output data represent weighted scores for commands issued to various departments. For example, the output data includes a score of 0.9 for a notification to the public relations department and 0.85 for a route change to the logistics department. In subsequent processing, this output is input into a decoder network. The system then converts the integrated control vector into specific control commands and natural language text messages, which are sent to the terminals of each department. This system achieves the technical effect of unifying siloed information to achieve optimal overall decision-making. This effect is achieved through a cross-attention mechanism that learns relationships across multiple modalities.This system is extremely useful in the specific application area of ​​integrated risk management across the entire multinational corporation. Furthermore, as an alternative, this system can also employ a distributed multi-agent system where each agent independently performs reinforcement learning.

[0049] Furthermore, in terms of security, communication between the server and the terminal is encrypted, and access permissions are strictly managed on a per-user basis. Generative AI also automatically parses access logs and operation history, sending immediate alerts to system administrators upon detecting signs of unauthorized access or internal violations. The system supports multiple languages ​​and can be used in overseas branches and multinational corporations. The user interface can be flexibly customized based on the type of terminal (PC, smartphone, tablet, wearable device, etc.) and the user's role (management, field manager, crisis management department, public relations department, IT department, customer, trading partner, etc.). Specifically, this security management department uses transport layer security protocols to encrypt all communication paths and builds a stateless authentication foundation using JSON Web Tokens. This security management department implements a recurrent neural network for user behavior detection. The data input to this neural network is a time-series vector representing the user's operation history. Elements of the input data include login time, Internet Protocol address, accessed resource identifier, and encoded values ​​of the operation content. This security management department performs preprocessing on the input data, serializing and normalizing it for each session. For example, the input data includes access records at 2 AM, connections from unknown overseas IP addresses, and a history of numerous queries to confidential databases. This neural network calculates the deviation of the current input data from past normal behavior patterns. The output data from this neural network is an anomaly score representing deviations from normal behavior. The output values ​​are continuous values ​​from zero to one, signifying the probability of unauthorized access. For example, the output data includes values ​​such as an account hijacking probability of 0.95 and an insider information smuggling probability of 0.88. In subsequent processing, this security management department sends the output result to the access control module. If the anomaly score exceeds a pre-set threshold of 0.8, the security management department forcibly terminates the session and immediately sends a warning notification to the administrator's terminal. This security management department achieves the technical effect of implementing a zero-trust architecture and real-time blocking of insider threats. This effect is not achieved by a static, rule-based mechanism, but by an anomaly detection mechanism based on learning dynamic time-series patterns. This system is extremely useful in the specific application area of ​​providing a secure remote working environment in financial institutions that process confidential information. Furthermore, this system can also employ multimodal authentication combined with biometric authentication data as an alternative.

[0050] Furthermore, the system possesses self-learning and evolutionary capabilities. Generative AI continuously learns new risk patterns and response strategies, improving the overall accuracy and response capabilities of the system. For example, for new types of cyberattacks, unprecedented natural disasters, and newly established laws and regulations, the generative AI automatically expands its knowledge base and reflects this in future risk predictions and response strategy formulation. The system also offers flexible integration with external systems. Through API integration, it can exchange information in real time with other companies' crisis management systems, cross-industry information sharing platforms, autonomous system disaster prevention systems, supply chain management systems of trading partners, and customer CRM systems. Specifically, this learning and evolutionary department implements incremental learning and continuous fine-tuning mechanisms. This department regularly feeds new data collected during system operation into the learning pipeline. The data input to this learning model consists of historical data on responses to newly occurring events and labels representing the correct answers. The elements of the input data include the event's feature vector, the sequence of executed response strategies, and binary values ​​representing the success or failure of the response. This learning and evolutionary department performs preprocessing on the input data, including data augmentation and small-batch creation. As a specific example, the input data includes attack logs of unknown malware, network disconnect commands executed against it, and a label indicating the correct answer that successfully prevented the spread of the attack. This learning and evolution unit uses cross-entropy error as the loss function and optimizes the weight parameters of the neural network using gradient descent. The output data from this learning and evolution unit is a set of updated weight matrices and bias vectors. Each value in the output data represents the connection strength of the network reflecting the new knowledge. As a specific example, the output data includes a parameter file consisting of hundreds of millions of floating-point numbers. This learning and evolution unit uses this output in subsequent processing for the dynamic deployment of the inference model to the production environment. This learning and evolution unit seamlessly updates the model parameters in the background without causing system downtime. This system effectively improves the system's adaptability to environmental changes and prevents model staleness. This effect is achieved through the causal relationship of parameter optimization using continuous error backpropagation from real-world application data. This system is extremely useful in the specific application area of ​​adaptive cybersecurity systems targeting increasingly sophisticated malware. Furthermore, as an alternative, this system can also employ algorithms that prevent catastrophic forgetting by learning new data while retaining important past data.

[0051] In this way, the crisis management system of the present invention is designed with comprehensive considerations, including server and terminal coordination, full utilization of generative AI, real-time data collection from multiple information sources, automation of risk analysis, prediction, response strategy formulation, execution, monitoring, and learning, optimal communication among relevant personnel, external collaboration, security, scalability, and user interface flexibility. This design significantly improves the risk management level of enterprises in today's information-overloaded world. Specifically, this system uses a microservice architecture and container orchestration technology to build the overall system. The system deploys information collection, risk analysis, and response strategy formulation functions as independent containers. The data input to the system is metric data representing the overall system load. Elements of the input data include processor utilization, memory consumption, and network traffic for each container. The system performs a moving average preprocessing operation on the input data. For example, the input data includes a 90% processor utilization for the risk analysis container and 8 gigabytes of memory consumption for the information collection container. The system determines dynamic resource allocation based on this input data. The output data from the system is scaling commands specifying the number of replicas for each microservice. The values ​​in the output data indicate the number of container instances that should be started. For example, the output data might include a command to increase the number of replicas of the risk analysis container from five to ten. In subsequent processing, the system sends this output to the control interface of the virtualization infrastructure. Upon detecting a sharp increase in traffic, the system starts new containers in milliseconds and automatically updates the load balancer settings. This system effectively improves overall system availability, eliminates single points of failure, and prevents processing latency during sharp traffic increases. This effect is due to the loosely coupled nature of the architecture, allowing modules to scale independently horizontally. This system is particularly useful in the specific application of robust social infrastructure management systems capable of withstanding large-scale disasters and centralized access. Alternatively, this system can be used with a serverless computing environment to completely abstract infrastructure management.

[0052] (System Composition) The system described in this implementation includes an information collection department, a data preprocessing department, a risk analysis department, a response strategy formulation department, an execution management department, a progress monitoring department, a user interface department, an external linkage department, a learning and evolution department, and a security management department. The information collection department has the function of automatically acquiring data in real time from various information sources, including news websites, social networking sites (SNS), meteorological information providers, market data suppliers, electronic data interchange systems of trading partners, customer consultation management systems, IoT sensor networks, surveillance camera systems, employee business reporting applications, open data APIs from autonomous and public institutions, and alert dissemination services from industry groups. For example, it can obtain breaking news reports such as "bankruptcy of major trading partners" or "emergency outbreaks of new infectious diseases" from news websites; collect submissions such as "poor performance reports of the company's products" or "disaster scene photos taken by on-site employees" from SNS; obtain real-time data such as "typhoon path forecasts" or "heavy rain warnings" from meteorological information; and collect data such as "abnormal vibrations of factory equipment" or "rapid temperature increases in warehouses" from IoT sensors. In addition, image analysis results such as "suspicious intrusion" or "smoke detection of fire" are obtained from surveillance cameras, and qualitative information such as "disaster situation on site" or "delivery delay contact from supplier" is obtained from employee reports. Specifically, this information collection department uses a message queue system to implement asynchronous communication between modules. This information collection department performs parallel data acquisition based on multiple processes. The data input to this information collection department consists of the endpoints of the application programming interfaces of each data source and authentication tokens. Elements of the input data include a Uniform Resource Locator (URI) string and an encrypted access key. This information collection department performs preprocessing on the input data, setting connection timeouts or defining the number of retries. As a specific example, the input data includes the open data access address of the Japan Meteorological Agency and credentials for connecting to the electronic data interchange system of the trading partner. This information collection department opens a network socket and sends a request via Hypertext Transfer Protocol (HTTP). The output data from this information collection department is a byte stream of the acquired raw data and a timestamp. The values ​​of the output data represent the raw binary data and the precise time of data acquisition. As a specific example, the output data includes a byte array of a compressed JSON file and a UNIX timestamp with millisecond precision. In subsequent processing, this information gathering unit transmits the output to the input buffer of the data preprocessing unit. This information gathering unit maximizes throughput by hiding input / output latency. This effect is achieved through a mechanism that avoids processor blocking via an event-driven asynchronous input / output architecture. This system is particularly useful in the specific application of millisecond-level market data collection systems in financial institutions. Furthermore, as an alternative, this system can also employ a bidirectional, persistent connection stream using WebSockets.

[0053] The data preprocessing department automatically categorizes and tags the data obtained from the information collection department based on attributes such as time series, location of occurrence, relevant departments, impact, information reliability, information release source, data category, urgency, relevant business areas, transaction object name, and customer name. It also performs preprocessing such as duplicate removal, noise removal, reliability scoring, time-series correction of information, assignment of geographic information, and automatic caption generation for image data. For example, in SNS submissions, submissions containing "Company Name + Fault + On-site Photo" are tagged with "Fault Risk," "On-site," and "High Urgency." Outliers from IoT sensors are categorized as "Equipment Risk," "Factory," and "Requires Immediate Response." The reliability of news reports is scored based on the release source and past performance, and automatic captions are assigned to surveillance camera images. Specifically, this data preprocessing department constructs a pipeline that executes natural language processing models and image caption generation models in parallel. The data input to this data preprocessing department consists of raw string data containing noise and raw image tensors from surveillance cameras. The elements of the input data include text containing special characters and HTML tags, and pixel arrays containing compressed noise. This data preprocessing unit performs preprocessing on the input data, such as removing unwanted characters using regular expressions or resizing images through bilinear interpolation. For example, the input data includes social media posts using numerous emojis and hashtags, as well as low-light surveillance camera images taken at night. This data preprocessing unit feeds these input data into their respective dedicated neural networks. The output data from this data preprocessing unit consists of a normalized array of word vectors and a text string describing the image features. The values ​​in the output data represent coordinates in the semantic space and a description of the situation in natural language. For example, the output data includes a 768-dimensional text embedding vector and the caption string "White smoke was produced in a factory." This data preprocessing unit uses this output for conversion to a common JSON format in subsequent processing. This data preprocessing unit integrates the features obtained from the text and images and sends them to the risk analysis unit. This data preprocessing unit improves the inference accuracy of the subsequent artificial intelligence model and reduces the computational load. This effect is achieved through the mechanism of pre-processing dimensionality reduction and semantic feature extraction of the input data. This data preprocessing unit is extremely useful in the specific application of data cleaning in integrated monitoring centers where data of various formats are mixed. Furthermore, as an alternative, this data preprocessing unit can also employ a structure that adds a speech recognition model to the pipeline to extract text from speech data.

[0054] The Risk Analysis Department uses generative AI to analyze the massive amounts of data obtained from the Data Preprocessing Department from multiple perspectives. Through natural language processing, the generative AI extracts keywords, phrases, sentiment expressions, causal relationships, time-series changes, and the intentions of relevant personnel from unstructured text data such as news reports, social media posts, internal company reports, and customer inquiries, identifying potential risk factors. For example, it automatically detects risk-related terms such as "earthquake," "tsunami warning," "fire," "failure," "public outrage," "market crash," "alarm," "deterioration," "surge," "delivery delay," "cyberattack," "data outflow," "changes in laws and regulations," and "spread of infectious diseases," and infers the type, location, and scope of impact of the risk based on context. The generative AI also performs sentiment analysis and topic modeling, quantitatively assessing signs of public outrage on social media, rising customer dissatisfaction, and increased employee anxiety. Furthermore, using pattern recognition and time series analysis, outliers and sharp changes are detected from numerical data such as IoT sensor data and market data. For example, when vibration data from factory equipment deviates from its normal range, or when stock prices and exchange rates fluctuate significantly in a short period, risk signals are automatically generated through anomaly detection algorithms. Regarding image data, image recognition AI is used to automatically identify smoke from fires, intrusions by suspicious persons, and the extent of damage at a scene from surveillance camera footage. Regarding voice data, voice recognition AI is used to assess the severity and urgency of complaints from customer call center conversations. In addition, past risk occurrence cases, external databases, and currently acquired data are combined and correlation analysis is performed to quantitatively predict the probability and impact of risk occurrences and to conduct composite risk assessments when multiple risks occur simultaneously. Specifically, this risk analysis department executes a multimodal integrated model that maps text, images, and voice into a common latent space. The data input to this integrated model consists of preprocessed text vectors, image feature tensors, and Mel-frequency cepstral coefficient arrays of the voice. The elements of the input data include high-dimensional numerical arrays extracted from each modality. The Risk Analysis Department performs a preprocessing step on the input data: a linear transformation to unify the dimensionality of each modality. Specifically, the input data includes embedding vectors from news reports, two-dimensional feature maps from surveillance camera images, and speech features representing angry customer voices. This integrated model uses a cross-attention mechanism to calculate the correlations between different modalities. The output data from this integrated model is a high-dimensional context vector that integrates the features of each modality, and a composite risk score based on it. The values ​​in the output data, ranging from zero to one hundred, represent the severity of the overall crisis faced by the system. Specifically, the output data includes a 124-dimensional context vector and a composite risk score of eighty-five. The Risk Analysis Department uses this output in subsequent processing to generate emergency response indicators.When the composite risk score exceeds a predetermined threshold of 80, this risk analysis department generates an emergency response indicator and immediately sends it to the response strategy formulation department. This risk analysis department leverages the technical effectiveness of combining subtle signs that might be missed in a single data source to achieve high-precision risk detection. This effectiveness stems from an architecture that learns nonlinear relationships by integrating multimodal data. This risk analysis department is particularly useful in the specific application area of ​​smart city platforms that integrate and manage the entire city's prevention and disaster mitigation efforts. Alternatively, this risk analysis department can also employ a structure that integrates the judgment results of various modalities through ensemble learning.

[0055] Based on the risk assessment results from the Risk Analysis Department, the Response Strategy Formulation Department uses generative AI to automatically generate multiple optimal response strategies corresponding to the risk level and type. This is achieved by referencing past response cases, internal company regulations, external best practices, industry guidelines, laws, contract terms, agreements with customers, and contract terms with trading partners. For example, for earthquake risk, the system automatically generates strategies such as "ensuring employee safety," "emergency equipment shutdown," "rebuilding the supply chain," "notifying customers of the impact," "ensuring evacuation routes," "investigating the damage to on-site locations," and "coordinating with local authorities." For cyberattack risk, the system automatically generates strategies such as "system shutdown," "backup activation," "contacting relevant departments," "preparing legal responses," "contacting external experts," and "temporarily cutting off customer information." For the risk of triggering public outrage, the system automatically generates specific action plans such as "issue an official statement through the Public Relations Department," "strengthen SNS monitoring," "add a customer response window," "update the FAQ in real time," "automatically generate an apology," and "determine the scope of impact." Generative AI automatically creates reports and alerts optimized for various stakeholders, including management, responsible departments, on-site supervisors, trading partners, and customers. These reports are rapidly disseminated through management screens, mobile devices, email, chat systems, voice calls, and push notifications. Specifically, the Response Strategy Formulation Department uses retrieval-enhanced generative technology to perform fact-based, accurate text generation. The input data to this department consists of emergency response indicators and context vectors from the Risk Analysis Department. The elements of the input data include high-dimensional numerical arrays representing the current crisis situation. The Response Strategy Formulation Department performs a preprocessing step of querying and transforming the input data into a vector database. For example, the input data includes indicators representing a cyberattack and vectors representing attack methods. The Response Strategy Formulation Department retrieves text embedding vectors of similar past response cases and relevant laws from the vector database and inputs them as prompts into a large-scale language model. The output data from this language model is a list of specific action items corresponding to the roles of each stakeholder, along with explanatory text in natural language. The values ​​in the output data represent a set of tasks structured in JSON array format. As a specific example, the output data includes server shutdown tasks for the Information Systems Department, reporting tasks to authorities for the Legal Department, and text recording their execution steps. The Response Strategy Formulation Department then uses this output for registration with the Task Queue of the Execution Management Department. The Response Strategy Formulation Department parses the generated JSON array and assigns each task to an asynchronous worker. The Response Strategy Formulation Department effectively suppresses unfounded output and automatically generates executable response strategies that strictly adhere to internal company regulations and laws. This effect is achieved through an architecture that combines a dynamic retrieval and generation model with an external knowledge base.This response strategy development department is particularly useful in the specific application area of ​​incident response in healthcare institutions and financial institutions with extremely stringent compliance requirements. Furthermore, as an alternative, this response strategy development department can also employ and validate the structure of the generated results using a decision tree-based rule engine.

[0056] The Execution Management Department automatically executes countermeasures approved by the Response Strategy Formulation Department in conjunction with relevant internal company systems. For example, it integrates with the inventory management system to automatically adjust inventory accordingly to disaster risks; with the information systems department to automate system switchover and backup startup; with the employee management system to automatically assign responsible personnel; with the logistics system to automatically change delivery routes; and with the customer management system to automatically notify affected customers. Furthermore, the Execution Management Department automatically verifies the effectiveness and progress of countermeasures after implementation and automatically generates additional response strategies as needed. Specifically, the Execution Management Department uses a workflow engine to manage the reliable execution of distributed transactions. The data input to the Execution Management Department consists of action items registered in the task queue from the Response Strategy Formulation Department. Elements of the input data include the endpoints of the target system, the methods to be executed, and the payload data. The Execution Management Department performs preprocessing on the input data, including dependency resolution and execution order scheduling. For example, the input data includes database backup startup commands and subsequent main server shutdown commands. The Execution Management Department sends requests to various external systems via Remote Procedure Calls or Hypertext Transfer Protocols. The output data from this execution management unit consists of JSON data containing response codes and execution results from various systems. Each value in the output data indicates whether the processing was successful or failed, and the time taken. For example, the output data includes a status code indicating success and a message string indicating backup completion. This execution management unit uses this output for status updates and error handling in subsequent processing. In the event of a timeout or error, this execution management unit uses an exponential backoff algorithm to perform retry processing, initiating an alternative process after a predetermined number of failures. This execution management unit effectively enables the reliable execution of complex response processes across multiple systems and achieves automatic recovery in the event of failures. This effect is achieved through a distributed transaction management mechanism that persists state transitions and guarantees rollback in case of failures. This execution management unit is particularly useful in the specific application area of ​​automatic failover during server downtime in large-scale e-commerce websites and automatic compensation to customers. Furthermore, as an alternative, this execution management unit can also employ an event sourcing model to fully track system state changes.

[0057] The Progress Monitoring Department monitors the progress of all risk response processes in real time and visualizes the implementation status of each response strategy, unresolved issues, delayed locations, feedback from the field, and the occurrence of additional risks in a dashboard format. For example, when a field manager reports the disaster situation with photos attached from a mobile terminal, this information is immediately reflected in the overall system, allowing management and the crisis management department to grasp the latest situation. Upon detecting delays or unresolved risks, the Progress Monitoring Department sends automatic alerts to relevant personnel, prompting rapid follow-up responses. Specifically, this Progress Monitoring Department uses a stream processing engine to build a real-time data pipeline. The data input to this Progress Monitoring Department consists of status update event streams from the Execution Management Department and feedback data from field terminals. Input data elements include numerical values ​​indicating the progress of tasks, text reports from the field, and binary images. This Progress Monitoring Department performs preprocessing on the input data, including time-window-based aggregation and filtering. For example, the input data includes a status update indicating an 80% evacuation completion rate and photographic data showing the flooding situation at the site. This Progress Monitoring Department caches this data in an in-memory database, enabling high-speed retrieval. The output data from this progress monitoring department consists of summary data and alarm trigger signals used to depict the dashboard. The values ​​in the output data represent the percentage of task completion, a list of delayed tasks, and warning levels. For example, the output data includes an overall progress rate of 75%, a flag indicating delays in specific departments, and a red alarm signal. In subsequent processing, this progress monitoring department uses the output results to push notifications to client terminals. This progress monitoring department dynamically updates the client terminal screen in milliseconds via WebSocket communication. This progress monitoring department leverages the technology of rapid decision-making and early bottleneck detection through zero-latency information sharing. This effect is achieved through memory processing that eliminates disk I / O and an asynchronous data publishing mechanism based on a publish-subscribe model. This progress monitoring department is particularly useful in the specific application area of ​​dynamic management of security personnel and overall event response in large-scale event venues. Alternatively, this progress monitoring department can also employ batch updates via periodic polling to reduce server load.

[0058] The user interface (UI) is primarily implemented on the terminal side, providing dashboard displays for relevant personnel, receiving alarm notifications, status reports and response inputs from on-site supervisors, decision-making and approval operations by management, confirmation of on-site emergency response instructions, voice input and image uploads, automatic parsing of on-site photos, automatic text-to-speech conversion of on-site speech, and feedback transmission from the field, as well as other UI elements required for on-site response. For example, an on-site supervisor reports the disaster situation with accompanying photos from a mobile terminal, or management presses an approval button for a countermeasure plan on the terminal, thus initiating automated processing on the server side. Specifically, this UI utilizes edge AI in the front-end application to achieve asynchronous communication. The data input to this UI includes user touch events, voice waveform data from the microphone, and image frames from the camera. Elements of the input data include coordinate values ​​on the screen, sampled sound pressure level arrays, and pixel RGB values. This UI performs preprocessing on the input data, such as noise removal and resolution adjustment, on the local device. As a specific example, the input data includes the click coordinates of the approval button, voice data describing the disaster situation, and on-site photos taken by the smartphone's camera. This user interface unit uses a built-in neural network processing unit to perform inference. The output data from this user interface unit includes text conversion results based on a speech recognition model, initial judgment labels based on an image classification model, and a request payload sent to the server. The values ​​in the output data represent the converted text string, the category of the object captured in the image, and structured data in JSON format. For example, the output data includes the text "There is a crack in the wall," the label "damaged," and the data storing these. This user interface unit uses this output result for data synchronization with the server in subsequent processing. In offline environments, this user interface unit temporarily stores the output data in local encrypted storage and automatically synchronizes it to the server via a background process when the network connection is restored. This user interface unit provides a seamless user experience independent of the communication environment and reduces the load on the server side. This effect is achieved through high-level information processing at the edge and an asynchronous queuing mechanism for monitoring network status. This user interface unit is particularly useful in the specific application area of ​​supporting infrastructure inspection operators in blind spots such as underground facilities and mountainous areas with low radio waves. Alternatively, this user interface can also be configured to provide the same functionality as native applications in a browser using progressive web application technology.

[0059] The external linkage department, through API integration, exchanges information in real time with other companies' crisis management systems, cross-industry information sharing platforms, autonomous system disaster prevention systems, supply chain management systems of trading partners, and customer CRM systems. For example, it automatically acquires disaster information from autonomous systems, automatically receives delivery delay information from trading partners, automatically responds to alerts from industry groups, and automatically parses customer inquiries. Specifically, the external linkage department executes an asynchronous communication protocol. This linkage department sends and receives serialized data. It converts received external data into multidimensional tensors. The converted tensors are fed into the input layer of a large-scale language model. This linkage department parses disaster information in Extensible Markup Language (XML) format from autonomous system systems. The parsed information is input to a natural language processing model. This model receives text strings representing the type and location of the disaster as input data. The model outputs a continuous score from 0 to 1 representing the severity of the disaster. When the score exceeds a predetermined threshold, the system initiates emergency alert processing. This reduces communication overhead between systems. This configuration is applied to real-time risk detection in supply chain management.

[0060] The Learning & Evolution Department uses generative AI to continuously learn new risk patterns and response strategies, improving the overall accuracy and response capability of the system. For example, for new types of cyberattacks previously dealt with, unprecedented natural disasters, and newly established laws and regulations, the generative AI automatically expands the knowledge base and reflects this in future risk predictions and response strategy formulation. Specifically, the Learning & Evolution Department of this system has a dedicated computing processor cluster for performing reinforcement learning and fine-tuning. The Learning & Evolution Department vectorizes past risk response history. The Learning & Evolution Department uses this vectorized history data as teacher data. The Learning & Evolution Department updates the weight parameters of the large-scale language model using the error backpropagation method. The Learning & Evolution Department applies gradient descent to minimize the cross-entropy error function. The Learning & Evolution Department receives a 512-dimensional text embedding vector and a time-series array of response actions as input data. The model outputs a Softmax vector representing the probability distribution of the best response strategy for new risks. The system registers the response strategy with the highest probability as the initial response template for the next time in the database. This improves the system's adaptability to unknown cyberattacks. This structure is used in unauthorized access detection systems of financial institutions.

[0061] The Security Management Department performs tasks such as encrypting communication between servers and terminals, strictly managing access permissions, automatically parsing access logs and operation history, detecting signs of unauthorized access and internal violations, and sending immediate alerts to system administrators. For example, if access from a time or location different from the usual one is detected, the department automatically notifies the system administrator and implements access restrictions as needed. Specifically, the Security Management Department uses a hardware security module to perform encryption and decryption of communication data packets. The department applies Advanced Encryption Standard (AES) algorithms. The department converts access logs into multi-dimensional time-series vectors. These vectors are then input to an autoencoder network for anomaly detection. This network receives feature vectors containing the user's identifier, access time, and IP address as input data. The network compresses the input vectors into a low-dimensional latent space. The network reconstructs the original vectors from the latent space. The network outputs the reconstructed feature vectors. The network calculates the reconstruction error between the input and output vectors. The department determines if the reconstruction error exceeds a specified threshold as a sign of unauthorized access. Based on the determination, the department automatically blocks communication from the corresponding IP address via a firewall. This results in a dramatic increase in the precision of defense against internal breaches and zero-day attacks. This approach is applied to database protection for government agencies that handle confidential information.

[0062] Specific examples of prompts read by generative AI in implementing this invention include: "Extract risk factors related to our company from the following SNS submission groups and predict their probability of occurrence and impact," "Detect signs of equipment anomalies from the following time-series changes in IoT sensor data and propose three recommended initial response strategies," "Compare the following news reports and past disaster cases to generate composite risk scenarios that may occur within the next 24 hours," "Automatically generate specific action instructions for on-site managers based on the following on-site reports and company internal regulations," and "Analyze the following progress data to list unaddressed risks and additional response strategies." These prompts are used to instruct specific parsing and generation tasks when generative AI is utilized in various parts of the system. Specifically, this system has a prompt engineering module that dynamically generates these prompts. This module converts the input raw data into a tokenized sequence that can be interpreted by a natural language processing model. This module tokenizes the text data through byte-pair encoding. This module feeds the tokenized data as a 768-dimensional input tensor to a large-scale language model. This model calculates the dependencies between the tokens within the input tensor through a self-attention mechanism. This model outputs risk factor classification labels, floating-point numbers representing the probability of occurrence from 0.0 to 1.0, and classification data representing the degree of impact. When the output probability value is above 0.8, the system automatically initiates the subsequent response strategy formulation module. This eliminates the need for manual input prompts from human operators. This configuration is applied to the automated triage of complaints in large-scale customer support centers.

[0063] (Implementation steps) Step 1: Real-time collection of diverse information In this step, data is automatically collected in real time from a wide variety of information sources, including news websites, social networking sites (SNS), weather information providers, market data suppliers, electronic data interchange systems of trading partners, customer service management systems, IoT sensor networks, surveillance camera systems, employee business reporting applications, open data APIs from autonomous and public institutions, and alert dissemination services from industry groups. This is achieved through API integration, RSS subscriptions, email reception, file transfers, chatbots, web scraping, IoT gateways, and AI-powered image recognition. For example, data such as "problem reports of our company's products" and "disaster scene photos from on-site employees" are collected from SNS; "typhoon path forecasts" and "heavy rain warnings" are obtained from weather information; and "abnormal vibrations of factory equipment" and "rapid temperature increases in warehouses" are collected from IoT sensors. Specifically, in this step, the information collection department uses a distributed processing framework to process multiple data streams in parallel. This department parses the Document Object Model (DOM) tree from Hypertext Markup Language (HAML) data obtained through web scraping. This department extracts text nodes from the parsing results. This department then inputs the extracted text into a Natural Language Processing (NLP) model. This data collection unit buffers the time-series values ​​acquired from sensors as a one-dimensional array into memory. It inputs high-resolution pixel value tensors acquired from surveillance cameras into the image recognition model. This image recognition model uses a convolutional neural network to extract feature maps within the image. The model outputs the bounding box coordinates and class probabilities of objects contained within the image. This system asynchronously writes this collected data into a unified database. This eliminates input / output bottlenecks in the collection of heterogeneous data formats. This configuration is applied to wide-area disaster monitoring networks in smart cities.

[0064] Step 2: Data preprocessing and attribute assignment The collected data is stored in a database on the server and automatically categorized and tagged according to various attributes such as time series, location of occurrence, relevant departments, impact, information reliability, information source, data category, urgency, relevant business areas, transaction object name, and customer name. It also automatically performs preprocessing such as duplicate removal, noise removal, reliability scoring, time series correction of information, geographic information assignment, and automatic caption generation for image data. For example, in SNS submissions, submissions containing "Company Name + Fault + On-site Photo" are tagged with "Fault Risk," "On-site," and "High Urgency." Specifically, in this step, the data preprocessing department performs normalization and standardization operations on the collected data. This preprocessing department removes stop words and performs stemming on the text data. This preprocessing department vectorizes the processed text using algorithms based on word frequency and inverse document frequency. This preprocessing department inputs the vectorized data into a support vector machine. This preprocessing department applies a pre-learned image captioning generation model to the image data. This model outputs a natural language text string describing the image content from the input image tensor. This preprocessing unit applies a moving average filter to the time series data to remove high-frequency noise. It also calculates a reliability score for each data point based on the past accuracy of the information source. Data with reliability scores below a benchmark value are excluded from subsequent processing. This ensures the quality of the data input to the risk analysis model and improves inference accuracy. This component is used for signal generation in algorithmic trading in financial markets.

[0065] Step 3: Risk Factor Extraction and Analysis Based on Generative AI Generative AI is used in this step. Generative AI extracts keywords, phrases, sentiment expressions, causal relationships, time-series changes, and the intentions of relevant personnel from unstructured text data such as news reports, SNS posts, internal company reports, and customer inquiries using natural language processing. It uses pattern recognition and time-series analysis to detect outliers and sharp changes in numerical data such as IoT sensor data and market data. Image recognition AI automatically identifies smoke from fires, suspicious intrusions, and the extent of damage at the scene from surveillance camera footage. Speech recognition AI assesses the severity and urgency of complaints from customer call center conversations. Specific examples of alert messages could include: "Extract risk factors related to our company from the following SNS post groups and predict their probability of occurrence and impact," "Detect signs of equipment anomalies from the following time-series changes in IoT sensor data and propose three recommended initial response strategies," and "Compare the following news reports with past disaster cases to generate composite risk scenarios that may occur within the next 24 hours." Specifically, in this step, the risk analysis department executes a large-scale language model with multiple stacked Transformer layers on a parallel computing cluster. This analysis department inputs preprocessed text vectors, time-series numerical arrays, and image feature tensors into a multimodal integration layer. This integration layer maps data from different modalities to the same latent space. The model, as input data, receives a time-series array of 1024 elements representing vibration data from factory equipment. This model uses a self-attention mechanism to learn the long-term dependencies of the time-series data. The model outputs a one-hot vector representing the category of the risk factor and the output value of a Sigmoid function representing the probability of anomaly occurrence. Based on the output probability values, the system determines the necessity of subsequent risk scenario generation processing. This enables the detection of subtle abnormal patterns that cannot be visually confirmed by humans. This configuration is applied to a predictive maintenance system in a chemical plant.

[0066] Step 4: Risk scenario generation and risk level determination Generative AI combines past risk occurrence cases, external databases, and currently acquired data, performing correlation analysis to quantitatively predict the probability and impact of risks, and also assesses compound risks when multiple risks occur simultaneously. It automatically classifies risks into major, moderate, and minor levels based on their severity and prioritizes them by type. Specific examples of prompts could include: "Based on the following data, generate multiple possible risk scenarios and quantitatively assess their respective probabilities and impacts," or "Analyze the compound risks when multiple risk factors occur simultaneously and list them by priority." Specifically, in this step, the scenario generation unit uses the extracted risk factor vector as the initial state to execute the Markov chain Monte Carlo method. This generation unit uses a transition probability matrix extracted from a past event database to simulate future state transitions. The generation unit inputs a list of currently occurring risk factors and tensors of environmental variables into a large-scale language model. The model outputs text descriptions of the multiple possible risk scenarios, floating-point numbers representing the probability of each scenario, and scores representing the impact. This system calculates the product of the probability of occurrence and the impact of the output to determine a comprehensive risk score. The system compares this comprehensive risk score with a defined threshold group and assigns it a category label: major, moderate, or minor. Scenarios identified as major risks are registered in a priority queue. This improves the computational efficiency of quantitatively assessing the cascading effects of compound risks. This system is applied to dynamic path control in airline operations management systems.

[0067] Step 5: Automatic formulation of response strategies based on generative AI Based on the risk assessment results from the Risk Analysis Department, generative AI is used to automatically generate multiple optimal response strategies corresponding to risk levels and types, while referencing past response cases, internal company regulations, external best practices, industry guidelines, laws, contract terms, agreements reached with customers, and contract terms with trading partners. For example, for earthquake risk, it automatically generates "ensuring employee safety," "emergency shutdown of equipment," "supply chain restructuring," and "notification of impact on customers." For cyberattack risk, it automatically generates "system shutdown," "backup activation," "contacting relevant departments," and "legal response preparation." For the risk of inciting public outrage, it automatically generates specific action plans such as "issue an official statement by the Public Relations Department," "strengthen SNS monitoring," and "add a customer response window." Specific examples of prompts could include "Based on the following risk analysis results and past response cases, multiple optimal response strategies are proposed for each risk level," and "For the following risk scenarios, referencing internal company regulations and external guidelines, specific action plans are automatically generated." Specifically, in this step, the Response Strategy Formulation Department executes a recommendation algorithm based on reinforcement learning. This formulation department defines the state vector representing the risk level, risk category, and current resource status as environmental information. This formulation department inputs the state vector into a neural network using the policy gradient method. The network outputs a probability distribution of multiple possible response strategies. The formulation department then inputs the identifier of the selected action and the relevant internally defined text embedding vector into a large-scale language model. This model outputs phased text data in natural language representing the specific action steps. The system converts the output action steps into a structured data format and sends it to the execution management module. This allows for the generation of the optimal response strategy corresponding to the situation in milliseconds. This configuration is applied to crowd guidance and counter-terrorism systems in large-scale event venues.

[0068] Step 6: Automatic generation and issuance of reports and alerts categorized by relevant personnel. Generative AI automatically creates reports and alerts optimized for various stakeholders, including management, responsible departments, on-site managers, trading partners, and customers. These are rapidly disseminated through management screens, mobile devices, email, chat systems, voice calls, and push notifications. For example, reports summarizing the overall risk and key decision-making points are provided to management; specific operational instructions and emergency contact information are provided to on-site managers; a dashboard providing real-time visualization of overall progress and response status is provided to the crisis management department; and concise notifications summarizing the scope of impact and response strategies are provided to customers. Specific examples of prompts include: "Automatically generate a summary report for management based on the following response strategies," "Automatically generate specific action instructions for on-site managers," and "Create a concise notification summarizing the scope of impact and response strategies for customers." Specifically, in this step, the report generation department uses a natural language generation model to perform text summarization and style transformation based on the target audience. This generation department receives structured data of the generated response strategies, detailed text of the risk scenario, and profile vectors representing recipient attributes as input data. This model employs an attention mechanism, assigning weights to information most relevant to the recipient's attributes. As output, it generates high-level summary text for management, itemized instructions for field managers, and polite notification text for customers. The system converts the generated text into email format or a payload format for push notifications. Using a message queuing protocol, the system asynchronously sends data to various distribution channels. This minimizes information delivery latency and optimizes network bandwidth consumption. This architecture is applied to crisis management and public relations platforms for global enterprises.

[0069] Step 7: Automated Implementation and Progress Monitoring Regarding approved countermeasures, the Execution Management Department integrates with relevant internal company systems to automate their implementation. For example, it integrates with the inventory management system to automatically adjust inventory based on disaster risk; with the information systems department to automate system switchover and backup startup; with the employee management system to automatically assign responsible personnel; with the logistics system to automatically change delivery routes; and with the customer management system to automate mass notifications to affected customers. The Progress Monitoring Department monitors the progress of all risk response processes in real time, visualizing the implementation status of each response strategy, unaddressed issues, areas of delay, feedback from the field, and the occurrence of additional risks in a dashboard format. Upon detecting delays or unaddressed risks, it sends automatic alerts to relevant personnel, prompting rapid additional responses. Specifically, in this step, the Execution Management Department uses an orchestration engine to control calls to the application programming interfaces (APIs) of multiple external systems. This department parses executable command sets from the generated response strategy data. This department sends requests to the inventory management system instructing inventory movement. The Progress Monitoring Department receives callbacks from each system and parses status codes. This monitoring department maintains an array of Boolean values ​​in memory representing the completion status of each task. This monitoring unit sets a delay flag if the task execution time exceeds the specified timeout value. Upon detecting the delay flag, the monitoring unit initiates an escalation process and sends a trigger signal to the response strategy formulation unit to recalculate the alternative response strategy. This prevents omissions and delays in the execution of countermeasures at the system level. This configuration is applied to emergency avoidance control in fleet management of autonomous vehicles.

[0070] Step 8: Improve system accuracy through learning and evolution The Learning and Evolution Department uses generative AI to continuously learn new risk patterns and coping strategies, improving the overall accuracy and response capabilities of the system. For example, for new types of cyberattacks, unprecedented natural disasters, and newly established laws and regulations, the generative AI automatically expands the knowledge base and reflects this in future risk predictions and response strategy formulation. Specific examples of prompts include "Learn the following new risk cases and reflect them in future risk prediction models" and "Evaluate the effectiveness of past coping strategies and update the best coping strategy model." Specifically, in this step, the Learning and Evolution Department dynamically updates the parameters of the inference model using online learning algorithms. This department collects feedback data representing the results of executed coping strategies and subsequent changes in risk states. This department converts this feedback data into reward signals. This department updates the action value function of the reinforcement learning agent. This department vectorizes the text data of newly observed risk cases and appends it to the vector database. This department inputs prompts containing new cases into a large-scale language model, performing model fine-tuning. This department uses contrastive loss as the loss function, adjusting the weights to make the embedding vectors of similar risk patterns closer together in the latent space. Thus, the system autonomously acquires the ability to respond to unknown threats without human intervention. This configuration forms an autonomous security operations center applied to cloud infrastructure.

[0071] (specific use case) For example, the implementation of the crisis management system of this invention by a company with manufacturing and logistics sites in multiple countries and regions will be described. The information collection unit automatically collects data in real time from local news websites, social networking sites (SNS), meteorological information providers, disaster information APIs of local governments, electronic data exchange systems of trading partners, IoT sensor networks, surveillance camera systems, and employee business reporting applications at each site. For example, it obtains submissions from SNS such as "Large-scale road flooding occurred around the local logistics center," forecasts of "Typhoon paths passing near the logistics site" from meteorological information, and data such as "Rapid temperature rise in the warehouse" and "Abnormal vibration values ​​displayed by equipment" from IoT sensors. The data preprocessing unit automatically classifies and tags this information according to attributes such as time series, location of occurrence, relevant departments, impact, information reliability, information source, data category, urgency, relevant business area, and trading partner name, and performs noise removal and duplicate elimination, reliability scoring, and geographic information assignment. Specifically, in this embodiment, the information collection unit includes a geospatial data processing module. This collection unit receives a payload containing GPS coordinate data sent from each site. This data collection unit uses a multilingual natural language processing model to parse news reports and submissions written in local languages. The model converts the input multilingual text into a language-independent common semantic space vector. The data preprocessing unit applies a Kalman filter to the time-series array of acquired sensor data to impute missing values ​​and smooth noise. This preprocessing unit assigns spatial indices based on timestamps and geographic coordinates to each data point. This preprocessing unit stores these data in a distributed graph database and defines the relationships between nodes as edges. This significantly reduces the latency of retrieval and aggregation processing of massive global data streams. This configuration is applied to logistics monitoring in manufacturing industries with international supply chain networks.

[0072] The Risk Analysis Department uses generative AI to extract multiple risk factors such as "road flooding around logistics hubs," "approaching typhoons," "abnormal temperatures in warehouses," and "abnormal equipment vibrations." These factors are compared with past disaster cases, logistics failure cases, equipment failure cases, and meteorological databases to quantitatively predict the probability and impact of risk occurrence. The generative AI generates composite risk scenarios where multiple risks occur simultaneously, providing warnings such as "the possibility of delays exceeding 48 hours in shipments from logistics hubs when a typhoon approaches and road flooding overlap," and "the risk of deterioration in inventory quality and equipment downtime increases when abnormal temperatures and abnormal equipment vibrations occur simultaneously." Specific examples of these warnings include: "Based on SNS submissions, meteorological information, IoT sensor data, and past disaster cases, generate risk scenarios related to logistics hubs and assess their respective probabilities and impacts," and "Analyze composite risks when multiple risk factors occur simultaneously and prioritize them." Specifically, in this implementation, the Risk Analysis Department uses graph neural networks to simulate the propagation of composite risks. This analysis department extracts subgraphs centered on specific logistics hubs from the graph database built in the preprocessing department. This analysis unit inputs the adjacency matrix and feature matrix of the extracted subgraph into a graph convolutional network. This network performs message passing between nodes and calculates the interactions of risk factors. The analysis unit inputs the output vector of the graph network and the time-series tensor of meteorological data into a large-scale language model. This model, as output, represents a continuous value from 0 to 1 indicating the probability of a specific location experiencing functional shutdown, along with a list of other affected locations. Based on this output, the system calculates the vulnerability score of the entire supply chain. Thus, it can accurately predict cascading failures caused by a single event based on a mathematical model. This configuration is applied to the optimization of global procurement networks for multinational corporations.

[0073] The response strategy development department uses generative AI to automatically generate multiple specific response strategies corresponding to risk levels and types, such as "determination to stop shipments from logistics locations," "automatic selection of alternative routes," "temporary relocation of inventory," "ensuring the safety of on-site personnel," "emergency shutdown of equipment," "notification of delivery delays to customers," "contacting trading partners," and "coordination with local authorities." Furthermore, it automatically generates optimized reports and alerts for relevant personnel, such as a report summarizing the overall risk and key decision points for management, alternative route options and criteria for restarting shipments for the logistics department, a list of on-site safety assurance procedures and emergency contact methods for on-site managers, and a concise notification summarizing anticipated delivery delays and response strategies for customers. These are then distributed via terminals, email, and chat systems. Specific examples of alert messages include: "Based on the following risk analysis results and past response cases, automatically generate specific action instructions for on-site managers at logistics locations," and "For customers, create a concise notification summarizing anticipated delivery delays and response strategies." Specifically, in this embodiment, the strategy formulation department combines a constrained optimization solver and a generative model. This formulation department formulates a linear programming problem with variables such as inventory levels at each location, transportation costs, and delivery cycles. The department inputs the location stoppage probabilities output from the risk analysis department as constraints into the solver. The solver outputs a numerical solution for the optimal alternative route that maximizes the benefits of the entire supply chain or minimizes losses, along with the inventory movement amount. The formulation department inputs an array of these numerical solutions and a configuration file vector representing contextual information for relevant personnel into a large-scale language model. This model outputs specific instruction and notification texts that translate the numerical data into natural language. The system then publishes these texts via a module that automatically translates them into the official languages ​​of various countries. Thus, the results of complex mathematical optimization are converted into a format that on-site personnel can immediately execute. This configuration is applied to the dynamic reconfiguration of vessel allocation plans in the international shipping industry.

[0074] Regarding the approved countermeasures, the Execution Management Department collaborates with the Inventory Management System to automatically implement temporary movements of inventory, with the Logistics System to automatically set alternative delivery routes, with the Employee Management System to automatically send local employee scheduling adjustments and safety assurance instructions, and with the Customer Management System to automatically send mass notifications to affected customers. The Progress Monitoring Department monitors the progress of all risk response processes in real time, visualizing the implementation status of each response strategy, unresolved issues, areas of delay, feedback from the field, and the occurrence of additional risks in a dashboard format. Upon detecting delays or unresolved risks, it sends automatic alerts to relevant personnel, prompting rapid additional responses. Specifically, in this implementation, the Execution Management Department executes distributed transaction control based on a microservices architecture. This department divides the formulated response strategies into multiple independent tasks and assigns each task to containerized worker processes. The department issues idempotent requests to the Inventory Management System and the Logistics System. The Progress Monitoring Department receives survival confirmation signals and status update data periodically sent from each worker process. The monitoring department stores the received data in an in-memory data grid and aggregates it using a real-time stream processing engine. This monitoring department triggers an anomaly detection event when the completion of a specific task is delayed by more than a specified threshold. The system then uses this event as a trigger to automatically initiate an alternative workflow. This improves fault tolerance to communication and system failures across the global network. This configuration is applied to the automation of order fulfillment on cross-border e-commerce platforms.

[0075] The Learning and Evolution Department uses generative AI to accumulate learning data such as "the entire process of handling logistics disruptions caused by this typhoon," "initial responses and their effects when equipment malfunctions," and "responses to customer delivery delay notifications." This data is then reflected in improving the accuracy of future risk predictions and response strategy formulation. Specific examples of notification messages include "Learning the historical records of the following risk response processes to update the best response strategy for future logistics disruptions" and "Analyzing past customer notification messages and customer reactions to build a more effective notification message generation model." Specifically, in this implementation, the Learning and Evolution Department uses a federated learning framework to comprehensively learn data from various locations. This learning department performs local model learning on edge servers configured at each location. The learning department only sends the updated differences of the weight parameters of the local model to the central aggregation server. The aggregation server averages the received multiple updated differences and updates the parameters of the global model. The learning department inputs the historical response history texts and the resulting customer satisfaction scores into a large-scale language model as teacher data. This model undergoes supervised fine-tuning to adjust its internal weights, increasing the probability of generating notification messages that lead to higher customer satisfaction. As a result, the overall predictive accuracy and response quality of the system are improved without transmitting confidential data from each location to external parties. This configuration is applied to a network of healthcare institutions that share global insights while complying with data protection regulations in various countries.

[0076] As described above, the crisis management system of the present invention, even in situations involving multiple locations, various information sources, and complex risk factors, utilizes generative AI as its core to automate the entire process from information collection, risk analysis, scenario generation, response strategy formulation, notification by relevant personnel, countermeasure execution, progress monitoring, to learning and evolution. This enhances business continuity and social reputation for enterprises. Specifically, in this embodiment, the system has a central control processor that integrates these functional modules. This processor adopts an event-driven architecture, controlling the data flow between modules via asynchronous message queues. The system executes a series of processes, from the input multidimensional sensor data tensor to the final set of execution commands for the response strategy, as an autonomous pipeline that requires no human intervention. The system records the computation results of each processing step as hash values ​​in a distributed ledger on a blockchain network. This cryptographically guarantees the transparency and tamper resistance of the entire crisis management process. This configuration is applicable to compliance management systems of financial institutions that require strict management of audit trails.

[0077] (Application Example 1) The specific processing flow corresponding to Use Case 1 will be described below. The various parts of the system described below are implemented by the data processing device 12 and the intelligent device 14. Furthermore, the data processing device 12 is referred to as the "server" and the intelligent device 14 is referred to as the "terminal".

[0078] (Implementation methods for carrying out the present invention) In this embodiment of the invention, the data center crisis management system comprises multiple functional departments, including a data collection department, a risk analysis department, a response strategy formulation and execution department, a progress monitoring department, a learning and evolution department, an external linkage department, a user interface department, and a security management department. The following provides a detailed explanation from all perspectives, including the specific structure and function of each department, the utilization of generative AI, the role sharing between servers and terminals, practical application examples, scalability, security, user interface, external system linkage, and self-learning capabilities. Specifically, in this embodiment, the data center crisis management system adopts a hybrid architecture that coordinates the actions of edge computing nodes and cloud server clusters. Each functional department is deployed as an independent microservice on a container orchestration platform. In the data collection department, a time-series database runs in memory to process millions of index data points per second. In the risk analysis department, dedicated hardware with tensor processing units is used to accelerate the inference of large-scale language models. In the response strategy formulation and execution department, the system collaborates with the controller of a software-defined network, enabling dynamic changes to the network topology. This achieves the system's self-healing capability against physical and logical faults within the data center. This configuration is applied to the autonomous operation of infrastructure for large-scale cloud providers.

[0079] First, the data collection department automatically acquires real-time data from various sensors and monitoring devices within the data center, including the power system, cooling units, security monitoring, fire detection, personnel monitoring, and network monitoring. For example, from the power system, it acquires data on the operating status of the main power supply, backup power supply, and emergency generator; voltage, current, frequency, power switching status; UPS battery remaining capacity; historical records of power failures; and trends in power consumption. From the cooling units, it acquires data on cooling water pressure, refrigerant temperature, fan speed, cooling tower operating status, abnormal alarms, maintenance history of the cooling units, and trends in cooling capacity. From security monitoring, it acquires entry and exit records, suspicious visits, surveillance camera images, biometric authentication logs, security guard patrol records, door opening and closing history, and alarm device operation history. From fire detection, it acquires data on smoke detection, temperature rise, fire alarms, the operational status of fire extinguishing equipment, on-site images of the fire, remaining extinguishing agent levels, and fire detector maintenance history. From personnel monitoring, it acquires data on operator entry and exit, work history, suspicious person detection, operator health status, adherence to work procedures, operator qualification information, and operator location information. The system monitors network activity to obtain data such as communication latency, packet loss, bandwidth utilization, external connectivity status, network attack signs, network device operational status, detection of unauthorized communication, and network configuration change history. Specifically, in this embodiment, the data collection unit includes a multi-protocol gateway that interprets multiple industrial communication protocols. The unit acquires waveform data of three-phase AC voltage and current from smart meters in the power system as a high-sampling-rate time-series array. It reads temperature and pressure values ​​as 16-bit integers from the programmable logic controller of a cooling device. The unit segments video streams from security surveillance cameras into frame-level image tensors. It serializes this heterogeneous data into structured objects based on a common data pattern. The unit transmits the serialized data to the subsequent risk analysis unit via a distributed messaging system. The unit applies data compression algorithms during data transmission to minimize network bandwidth consumption. This maximizes throughput in data collection from a large number of IoT devices. This configuration is applied to the monitoring of core network equipment in large-scale telecommunications operators.

[0080] In addition, external meteorological information, news, SNS, and disaster information are automatically acquired through APIs, RSS, web scraping, and external collaborative services. For example, meteorological information includes typhoon warnings, heavy rain warnings, lightning strike warnings, high temperature warnings, snowfall forecasts, strong wind warnings, earthquake alerts, and tsunami warnings. News includes information on fires, road flooding, power outages, traffic disruptions, accidents at nearby facilities, social unrest, and terrorism. SNS includes rumors of power outages, topics about cyberattacks, photos of disaster scenes, employee reports, customer complaints, public opinion, and the data center's reputation. Disaster information includes earthquake alerts, tsunami warnings, evacuation instructions, emergency contact information from the municipality, disaster response headquarters announcements, evacuation information, and traffic control information. Specifically, this data collection department periodically sends requests to external servers using asynchronous communication protocols. This data collection department parses the received structured text-formatted data. This data collection department uses natural language processing models to extract named entities from the text. This data collection department maps the extracted information to a multi-dimensional vector space. This data collection unit acquires meteorological information by treating numerical data such as precipitation, wind speed, and temperature as floating-point arrays. It also converts news text into word embedding vectors. Furthermore, it inputs image data from social media into a convolutional neural network. Finally, it extracts image feature tensors from the image data. Through these processes, the system manages various types of unstructured data in a unified vector format. By parallelizing data processing, the system reduces information collection latency to milliseconds. It eliminates the need for manual information collection. This system fundamentally improves the efficiency of data acquisition via computer networks. It is applicable to use cases such as data center physical protection and supply chain management.

[0081] The data collection department automatically categorizes and tags this diverse data based on attributes such as time series, location, equipment type, anomaly level, reliability, information source, data type (text, numerical, image, audio, video), urgency, relevant business area, transaction object name, and customer name. Preprocessing includes deduplication, noise removal, reliability scoring, time-series correction, geographic information assignment, automatic captioning for image data, and automatic transcription of audio data. For example, in the event of an abnormal alarm from a cooling device, tags such as "cooling risk," "high urgency," and "equipment system" are added. In SNS submissions, submissions containing "data center name + power outage + on-site photo" are categorized as "power risk," "on-site," and "high reliability." The reliability of news reports is scored based on the source and past performance, and automatic captions are assigned to surveillance camera images. Specifically, the data collection department inputs the acquired multidimensional vector data into a clustering algorithm. It uses a support vector machine to calculate the reliability score for text data. The reliability score is output as a floating-point number between 0 and 1. A pre-learned image captioning generation model is applied to image data. This image caption generation model receives a 3D image tensor as input. It outputs a sequence of text tags. The data collection unit performs speech recognition processing on the speech data using a recurrent neural network. This unit converts the speech waveform data into acoustic feature vectors. It applies noise removal filters to improve the signal-to-noise ratio. The preprocessed data is stored in a tensor database in shared memory. Through these preprocessing steps, the system significantly improves the inference accuracy of the subsequent artificial intelligence model. This system has the technical effect of transforming noisy raw data into a clean data structure suitable for machine learning. This system is applicable to use cases such as anomaly detection systems and automated monitoring systems.

[0082] The Risk Analysis Department utilizes generative AI to analyze the massive amounts of data obtained from the Data Collection Department from multiple perspectives. Through natural language processing, the generative AI extracts keywords, phrases, sentiment expressions, causal relationships, time-series changes, and the intentions of relevant personnel from unstructured text data such as news reports, social media posts, internal company reports, and worker comments, identifying potential risk factors. For example, it automatically detects risk-related terms such as "power outage risk," "cooling anomaly," "fire alarm," "cyberattack," "communication interruption," "equipment failure," "human error," and "external disaster," and infers the type, location, and scope of impact of the risk based on context. The generative AI also performs sentiment analysis and topic modeling, quantitatively assessing signs of public outrage on social media, increased anxiety among on-site workers, increased customer complaints, and social reputation risks. Furthermore, using pattern recognition and time-series analysis, it detects outliers and abrupt changes from numerical data such as sensor data and equipment logs, extracting abnormal signals such as voltage drops in the power system, temperature increases in cooling devices, increased network latency, and smoke detection from fire detection systems. Regarding image data, image recognition AI is used to automatically identify fire smoke, suspicious intrusions, on-site damage, equipment damage, and operator misconduct from surveillance camera footage. Regarding voice data, voice recognition AI is used to assess urgency, abnormal signs, violations of operating procedures, and on-site chaos from the content of on-site operator conversations. Specifically, this risk analysis department deploys a large-scale language model with a self-attention mechanism in memory. This risk analysis department inputs preprocessed text tag sequences into the large-scale language model. This large-scale language model receives string vectors such as submissions about power outages and text representations from equipment logs as input. This large-scale language model outputs classification labels representing risk types and Softmax values ​​representing their probability distributions as outputs. This risk analysis department applies a time series prediction model using a Long Short-Term Memory (LSTM) network to numerical data. This time series prediction model receives a time series array of past sensor data as input. This time series prediction model outputs future predicted values ​​and anomaly scores. This risk analysis department applies an object detection model using a residual network to image data. This object detection model receives color image tensors as input. This object detection model outputs the coordinates of the bounding box and the class probability. This risk analysis department integrates these outputs to calculate a comprehensive risk score. This system uses feature synthesis in high-dimensional space to detect subtle anomalies that are imperceptible to humans at an early stage. This system effectively reduces the false alarm rate by integrating and analyzing multiple modalities. This system is applicable to use cases such as preventative maintenance and security monitoring in data centers.

[0083] Furthermore, generative AI combines past failure cases, external databases (such as cross-industry failure case sets, disaster databases, cyberattack event sets, equipment failure history records, meteorological disaster history records, and legal and regulatory amendment history records) with currently acquired data and performs correlation analysis to quantitatively predict the probability and impact of risks. For example, by comparing past cases such as cooling devices stopping when a typhoon approaches, network failures cascading during a cyberattack, power outages occurring simultaneously with fire alarms, increased failure risk due to equipment aging and abnormal vibrations, and large-scale failures caused by human error and system cascading failures with the current situation, multiple risk scenarios are generated. Generative AI automatically determines risk levels such as major, moderate, and minor risks based on their severity, prioritizes them by risk type, and performs composite risk assessments and cascading risk assessments when multiple risks occur simultaneously. Specifically, this risk analysis department uses graph neural networks to perform correlation analysis. This risk analysis department constructs a knowledge graph with past failure cases and information from external databases as nodes and their relationships as edges. This risk analysis department extracts feature vectors from the current input data and maps them to node features on the knowledge graph. The graph neural network updates these features through message passing between adjacent nodes. It receives multi-dimensional arrays as input, including current equipment anomaly vectors and past meteorological disaster vectors. The output of the graph neural network consists of floating-point numbers representing the probability of occurrence for each risk scenario and categorical variables representing the degree of impact. If the output probability exceeds a predetermined threshold, the risk analysis department sends the risk scenario to subsequent response strategy formulation. This system models complex causal relationships as a graph structure. This system has a significant technical effect in improving the prediction accuracy of cascading compound risks. This system is applied to use cases such as infrastructure protection and supply chain disruption prediction during wide-area disasters.

[0084] Based on the risk assessment results from the Risk Analysis Department, the Response Strategy Formulation and Implementation Department uses generative AI to automatically generate multiple optimal response strategies corresponding to risk levels and types. This is achieved by referencing past response cases, operational regulations, external guidelines, laws, contract terms, agreements with customers, contract terms with trading partners, industry best practices, local disaster prevention manuals, and recommended procedures from equipment manufacturers. For example, for fire risk, it automatically generates strategies such as "automatic activation of the fire extinguishing system," "building-wide evacuation instruction," "power cut-off," "emergency contact with on-site supervisor," "automatic notification to fire department," "automatic photography and recording of the disaster situation," and "automatic monitoring of remaining extinguishing agent levels." For power failure risk, it automatically generates strategies such as "emergency generator activation," "load balancing," "priority operation of critical servers," "power switching," "automatic contact with the power company," "UPS battery level monitoring," and "power consumption suppression instruction." For cooling anomaly risk, it automatically generates strategies such as "backup activation of cooling devices," "enhanced temperature monitoring," "on-site inspection instruction," "switching of cooling water supply paths," "automatic cleaning of cooling towers," and "recalculation of cooling capacity." For network attack risk, it automatically generates strategies such as "disconnection of external connections," "emergency notification to administrator," "enhanced monitoring," and "daily..." The system automatically generates action plans for communication failure risks, including automatic saving of logs, system isolation, cutting off unauthorized communications, and automatic application of security patches. For communication failure risks, it automatically generates plans such as automatic switching to backup lines, automatic contact with communication operators, network equipment restart, communication load balancing, automatic changes to network structure, and automatic resetting of communication paths. For human error risks, it automatically generates plans such as sending reminders to on-site supervisors, automatic distribution of work procedure manuals, contacting on-site supervisors, automatic recording of work history, automatic warnings for violations of work procedures, and monitoring of operator health status. For external disaster risks, it automatically generates specific action plans such as on-site evacuation instructions, suspension of critical equipment, automatic monitoring of on-site conditions, linkage with autonomous communities, automatic guidance of evacuation routes, and automatic reporting to the disaster response headquarters. Generative AI automatically creates optimized reports and alarm notifications for management, on-site supervisors, operations departments, customers, trading partners, and autonomous communities, and rapidly disseminates them through terminal management screens, mobile terminals, email, chat systems, voice calls, push notifications, and wearable devices.For example, reports summarizing the overall risk profile and key decision-making points are provided to management; specific work instructions and emergency contact lists are provided to site managers; dashboards providing real-time visualization of overall progress and response status are provided to operations departments; concise notifications summarizing the scope of impact and response strategies are provided to customers; notifications communicating expected delivery delays and service impacts are provided to trading partners; and reports summarizing site conditions and response strategies are provided to autonomous organizations. Specifically, the Response Strategy Formulation and Implementation Department utilizes a generative model embedded with reinforcement learning algorithms. This department receives risk scenario vectors and risk scores from the Risk Analysis Department as its state space. The generative model explores action sequences that maximize expected returns from combinations of various response strategies defined as an action space. The model receives numerical values ​​representing fire risk probabilities and numerical vectors representing impact levels as inputs. The model outputs structured data such as fire suppression system activation commands and evacuation instruction texts. The Response Strategy Formulation and Implementation Department inputs the generated response strategies into a natural language generation model to generate report texts for relevant personnel. This natural language generation model receives conditional vectors representing object attributes as input. This natural language generation model outputs text sequences with adjusted terminology difficulty and information granularity. The system performs these processes on parallel processors, deriving optimal response strategies in milliseconds. This system effectively eliminates human judgment delays and minimizes the escalation of damage. Applications include automated control in smart buildings and emergency stop systems in factories.

[0085] Furthermore, the Response Strategy Formulation and Implementation Department automatically executes approved countermeasures in conjunction with relevant equipment control systems and network management systems. For example, it automatically activates the fire suppression system upon fire detection, automatically starts the emergency generator during a power failure, automatically activates the backup cooling system when cooling is abnormal, automatically disconnects external network connections during a network attack, automatically switches to backup lines during communication failures, sends automatic alerts to the site supervisor in case of human error, and automatically performs remote equipment operation and automatic checks, automatic camera capture, operator location information acquisition, and on-site voice recording. These processes are achieved through API integration with building management systems, power control systems, cooling system management systems, network management systems, operator management systems, on-site camera systems, and voice recording systems. Specifically, the Response Strategy Formulation and Implementation Department converts the generated countermeasures into a set of machine-readable control commands. This set of control commands is sent to each equipment control system via a secure communication protocol. The department outputs digital signals to the fire suppression system to drive relay circuits. It also sends pulse signals to the emergency generator to initiate the startup sequence. The Response Strategy Formulation and Implementation Department sends a request to the network management system to rewrite the routing table. This department receives the execution results of these control commands as status codes. In the event of an incorrect status code, the department automatically executes predefined backup procedures. This system closely links software-based logical judgments with hardware-based physical control. This system achieves fully automated closed-loop control without human intervention, dramatically improving response speed. This system is applicable to use cases such as autonomous operation of unmanned data centers and management of communication base stations in remote areas.

[0086] The Progress Monitoring Department monitors the progress of all risk response processes in real time, visualizing the implementation status of each response strategy, unaddressed issues, delayed locations, feedback from the field, occurrence of additional risks, on-site photos and voice recordings, operator location information, and equipment operating status in a dashboard format. Upon detecting delays or unaddressed risks, it sends automatic alerts to relevant personnel, prompting rapid follow-up responses. For example, if a field manager reports a disaster situation with accompanying photos via a mobile terminal, this information is immediately reflected in the entire system, enabling management and operations departments to grasp the latest situation. On-site voice recordings and operator location information are also visualized in real time, allowing for immediate understanding of the chaos and safety status on-site. Specifically, this Progress Monitoring Department receives status update events from various devices and terminals via an asynchronous message queue. The department stores the received event data in an in-memory database, enabling high-speed retrieval and aggregation. The department compares the estimated completion time of each response strategy with the actual progress. The department calculates delay times in milliseconds. If a delay exceeds an acceptable threshold, the department initiates an exception handling procedure. This progress monitoring department inputs image data sent by the on-site supervisor into a convolutional neural network. It then quantifies and outputs the recovery progress as a percentage. The department also acquires operator location information as coordinate data and maps it onto a 3D facility map. The system uses a two-way communication protocol to push and publish this dynamic data to client terminals. This system achieves the technical effect of enabling real-time situation monitoring while minimizing image redrawing. This system is applicable to use cases such as security systems for large-scale events and wide-area rescue command during disasters.

[0087] The Learning & Evolution Department uses generative AI to continuously learn new risk patterns and response strategies, improving the overall accuracy and response capabilities of the system. For example, it automatically expands its knowledge base to address previously encountered new types of cyberattacks, unprecedented natural disasters, newly established laws and regulations, new failure modes due to equipment aging, changes in human error tendencies, and the expansion of social risk perception. This knowledge is reflected in future risk predictions and response strategy formulation. The department also offers flexible integration with external systems. Through API integration, it can exchange information in real time with other companies' crisis management systems, cross-industry information sharing platforms, autonomous system disaster prevention systems, supply chain management systems of trading partners, customer CRM systems, equipment manufacturers' remote monitoring systems, and insurance companies' risk assessment systems. Specifically, the Learning & Evolution Department uses online learning algorithms to dynamically update the weight parameters of the neural network. It calculates the loss function using the error between actual failure data and the system's predicted risk score. It uses backpropagation to calculate the gradient and adjusts the parameters through optimization algorithms. The department receives data packet feature vectors from new types of cyberattacks and sensor time-series data from unknown equipment failures as input. The Learning & Evolution Department outputs the updated model's weight matrix and bias vector. To prevent overfitting, it employs Dropout and data augmentation techniques. It utilizes event data from other companies obtained from external systems as source data for transfer learning. Through continuous model updates, the system autonomously acquires the ability to adapt to environmental changes. This system has the technical effect of improving the prediction accuracy of unknown threats over time. Applications include improper detection systems in financial institutions and hazard prediction systems for autonomous vehicles.

[0088] The user interface (UI) is primarily implemented on the terminal side, providing dashboard displays for relevant personnel, alarm notification reception, status reports and response input from on-site supervisors, decision-making and approval operations by management, confirmation of on-site emergency response instructions, voice input and image uploading, automatic parsing of on-site photos, automatic text-to-speech conversion of on-site voice, feedback transmission from the field, and safety monitoring of on-site personnel via wearable devices, as well as other UI elements required for on-site response. For example, an on-site supervisor reports the disaster situation with accompanying photos from a mobile terminal, or management presses an approval button for a countermeasure plan on the terminal, initiating automated processing on the server side. Specifically, this UI adopts a single-page application architecture. It uses a virtual document object model to perform high-speed differential updates of the screen. It encodes image data acquired from the mobile terminal's camera module into text and sends it to the server. It converts voice waveform data acquired from the microphone into digital signals. It streams the voice data to the server-side speech recognition model. It detects click events on the approval button displayed on the management terminal screen. Using the click event as a trigger, it sends an encrypted approval token to the server. This system reduces network bandwidth consumption by combining client-side data preprocessing and asynchronous communication. It minimizes user operation latency and supports rapid decision-making in emergencies. Applications include electronic medical record input systems in healthcare settings and monitoring terminals on factory production lines.

[0089] The Security Management Department performs tasks such as encrypting communication between servers and terminals, strictly managing access permissions, automatically parsing access logs and operation history, detecting signs of unauthorized access and internal misconduct, sending immediate alerts to system administrators, multi-factor authentication of users, encrypted data storage, system vulnerability diagnosis, and automatic generation of audit trails. For example, when access from a time or location different from the usual one is detected, the department automatically notifies the system administrator and implements access restrictions as needed. Specifically, the Security Management Department uses transport layer security protocols to encrypt all communication paths. It employs a hybrid cryptographic approach combining public-key cryptography and private-key cryptography. The department inputs the time-series data of the access logs into an autoencoder-type neural network. This autoencoder learns the characteristics of normal access patterns, compresses the input data into a low-dimensional latent space, and then restores it to its original dimension. The department calculates the reconstruction error between the input data and the restored data. If the reconstruction error exceeds a predetermined threshold, the department detects the access as abnormal. The department receives data vectorized with attributes such as IP address, access time, and request URL as input. This security management department outputs a score between 0 and 1, representing the probability of an anomaly. This system mathematically detects unknown network attacks and internal misconduct that rule-based detection methods cannot handle, treating them as geometric anomalies in a high-dimensional data space. This system effectively minimizes the risk of information leakage. Applications include confidential information management systems in government agencies and transaction monitoring in financial institutions.

[0090] Thus, the data center crisis management system of this invention has undergone a comprehensive design from all perspectives, including data collection, risk analysis, response strategy formulation and execution, progress monitoring, learning and evolution, external linkage, user interface, and security management. By utilizing generative AI throughout the entire process, it can detect and predict various risks in data center operations in real time, automating optimal responses and significantly improving business continuity and security. Specifically, this system operates in a distributed computing environment composed of multiple computing nodes connected by a high-speed network. Each processing unit is deployed independently as a microservice architecture. The system uses a container orchestration platform to dynamically scale the number of replicas of each service based on load. The system constructs an end-to-end processing pipeline from data collection to response strategy execution. The system transfers tensor-form data at high speed between pipelines via shared memory. The system uses quantization and pruning techniques to lightweight the inference processing of generative models, enabling them to execute on edge devices. The system processes massive data streams exceeding the limits of human cognition in real time. This system not only improves business efficiency but also fundamentally enhances the autonomous fault recovery and adaptability of computer systems. This system achieves the technical benefits of minimizing downtime and drastically reducing operating costs. It is applied in use cases such as the integrated management infrastructure of smart cities and the autonomous operation systems of space stations.

[0091] (System Composition) The system involved in this embodiment includes a data collection department, a risk analysis department, a response strategy formulation and execution department, a progress monitoring department, a learning and evolution department, an external linkage department, a user interface department, and a security management department. The data collection department has the function of automatically acquiring real-time data from various sensors and monitoring devices within the data center, such as the power system, cooling units, security monitoring, fire detection, personnel monitoring, and network monitoring. For example, from the power system, it can acquire the operating status of the main power supply and emergency generator, voltage, current, frequency, power switching status, UPS battery remaining capacity, and power consumption trends. From the cooling unit, it can acquire cooling water pressure, refrigerant temperature, fan speed, cooling tower operating status, abnormal alarms, and maintenance history of the cooling unit. From security monitoring, it can acquire entry and exit records, suspicious visits, surveillance camera images, biometric authentication logs, guard patrol records, and door opening and closing history. From fire detection, it can acquire smoke detection, temperature rise, fire alarms, the operational status of fire extinguishing equipment, on-site images of the fire, and remaining extinguishing agent. From personnel monitoring, it can acquire personnel entry and exit records, work history, suspicious personnel detection, personnel health status, compliance with work procedures, and personnel qualification information. From network monitoring, it can acquire communication latency, packet loss, bandwidth utilization, external connection status, network attack signs, network equipment operating status, and detection of improper communication. Furthermore, external meteorological information, news, SNS, and disaster information are automatically acquired through APIs, RSS, web scraping, and external collaborative services. For example, meteorological information includes typhoon warnings, heavy rain alerts, lightning strike warnings, high temperature warnings, and earthquake alerts; news includes information on fires, road flooding, power outages, and traffic disruptions around the data center; SNS includes rumors of power outages, topics about cyberattacks, local disaster photos, and on-site reports from employees; and disaster information includes earthquake alerts, tsunami warnings, evacuation instructions, and emergency contacts from the local government. The data collection department automatically categorizes and tags this diverse data according to attributes such as time series, location, equipment type, anomaly level, reliability, information source, data category, urgency, related business areas, transaction object name, and customer name. Preprocessing includes duplicate removal, noise removal, reliability scoring, time-series correction, geographic information assignment, automatic captioning of image data, and automatic text conversion of voice data. Specifically, this data collection department connects to a programmable logic controller and remote terminal units via industrial communication protocols. This data collection unit acquires the voltage and current values ​​of the sensors as digital data at a sampling rate of milliseconds. The unit normalizes the acquired raw data, converting it into floating-point numbers within a predetermined range. This unit sends a secure request using a token-based authorization protocol to an external Web API to obtain disaster information in structured text format. The acquired text data is then input into a morphological parsing engine, which segments it into a sequence of tokenized words.This data collection unit multiplies the labeled sequence with a word embedding matrix to generate a high-dimensional text feature vector. It also applies a neural network with alternating convolutional and pooling layers to image data to extract spatial feature maps. Furthermore, it utilizes a multimodal learning model to project these heterogeneous data into the same latent space. This multimodal learning model receives numerical arrays from sensors, text vectors, and image tensors as input. It outputs a unified context vector. This system effectively absorbs differences in data formats and generates a unified data structure that can be processed by the subsequent AI model. This system is applicable to use cases such as integrated monitoring systems in large-scale factories and predictive maintenance systems for wide-area infrastructure.

[0092] The Risk Analysis Department has the capability to use generative AI to analyze the massive amounts of data obtained from the Data Collection Department from multiple perspectives. Through natural language processing, it extracts keywords, phrases, sentiment expressions, causal relationships, time series changes, and the intentions of relevant personnel from unstructured text data such as news reports, SNS submissions, internal company reports, and worker comments that could be considered risk factors. For example, it can automatically detect risk-related terms such as "power outage risk," "abnormal cooling," "fire alarm," "cyber attack," "communication interruption," "equipment failure," "human error," and "external disaster," and infer the type, location, and scope of impact of the risk based on the context. Generative AI also performs sentiment analysis and topic modeling, quantitatively assessing signs of public outrage on social media, increased anxiety among on-site workers, and a rise in customer complaints. Using pattern recognition and time series analysis, it detects outliers and abrupt changes from numerical data such as sensor data and equipment logs. For example, it extracts abnormal signals such as voltage drops in the power system, temperature increases in cooling devices, increased network latency, and smoke detection from fire detection systems. Regarding image data, it uses image recognition AI to automatically identify smoke from fires, intrusions by suspicious persons, the extent of damage on-site, and equipment damage from surveillance camera footage. Regarding voice data, it uses voice recognition AI to assess urgency, abnormal signs, and violations of operating procedures from the content of on-site workers' conversations. Furthermore, generative AI combines past failure cases, external databases, and currently acquired data with correlation analysis to quantitatively predict the probability and impact of risks. For example, it compares past cases such as cooling system shutdowns during typhoons, cascading network failures during cyberattacks, and power outages coinciding with fire alarms with the current situation to generate multiple risk scenarios. It automatically categorizes risks as major, moderate, or minor based on severity, prioritizes them by type, and performs composite and cascading risk assessments when multiple risks occur simultaneously. Specific examples of prompts read by generative AI include: "Based on the following sensor data and external information, extract risk factors related to the data center and predict their probability and impact," and "Based on the following SNS submissions, weather information, and equipment logs, generate composite risk scenarios and prioritize them." Specifically, this risk analysis department executes a large-scale Transformer-based language model with billions of parameters on a parallel computing processor cluster. This risk analysis department embeds the context vectors output from the data collection department into the prompt template. This risk analysis department receives a sequence of tags as input, combining numerical data such as sensor data arrays and text vectors with indicator text. This large-scale language model calculates the probability of the next tag appearing through autoregressive decoding.This large-scale language model outputs structured text containing risk factors, probabilities of occurrence, and impact levels. The risk analysis unit applies a recurrent neural network with an attention mechanism to time-series data. This recurrent neural network receives past time-series windows as input and outputs a predicted distribution of future data points. When actual observations deviate from the confidence interval of the predicted distribution, the risk analysis unit calculates the deviation as an anomaly score. This system specializes a general language model for specific domains through combined cueing engineering and fine-tuning. This system possesses the technical ability to mathematically derive hidden causal relationships between complex events and achieve objective risk assessment independent of human intuition. This system is applied to use cases such as aircraft flight data analysis and systemic risk prediction in financial markets.

[0093] The Response Strategy Formulation and Implementation Department, based on the risk assessment results from the Risk Analysis Department, uses generative AI to automatically generate multiple optimal response strategies corresponding to risk levels and types. This is achieved by referencing past response cases, operational procedures, external guidelines, laws, contract terms, agreements with customers, contract terms with trading partners, industry best practices, local disaster prevention manuals, and recommended procedures from equipment manufacturers. For example, for fire risks, it automatically generates strategies such as "automatic activation of fire suppression systems," "building-wide evacuation instructions," "power cut-off," "emergency contact with on-site supervisor," and "automatic notification to fire authorities." For power failure risks, it automatically generates strategies such as "emergency generator activation," "load balancing," "prioritizing critical server operation," "power switching," and "automatic contact with the power company." For cooling anomaly risks, it automatically generates strategies such as... The system automatically generates actions such as "backup startup of cooling devices," "enhanced temperature monitoring," "on-site inspection instructions," and "switching of cooling water supply paths." For network attack risks, it automatically generates actions such as "internal connection disconnection," "emergency notification to management," "enhanced monitoring," and "automatic log saving." For communication failure risks, it automatically generates actions such as "automatic switching to backup lines," "automatic contact with communication operators," "restart of network equipment," and "communication load balancing." For human error risks, it automatically generates actions such as "attracting attention to on-site supervisors," "automatic release of work procedures," "contacting on-site supervisors," and "automatic recording of work history." For external disaster risks, it automatically generates specific action plans such as "local evacuation instructions," "suspension of critical equipment," "automatic monitoring of on-site conditions," and "coordination with autonomous communities." The generative AI automatically creates reports and alarm notifications optimized for management, on-site supervisors, operations departments, customers, trading partners, and autonomous communities, and rapidly disseminates them through management screens, mobile devices, email, chat systems, voice calls, and push notifications. Specific examples of prompts that can be read by generative AI include, "Based on the following risk analysis results and past response cases, automatically generate action instructions for the on-site manager in the event of a fire risk," and "Create a summary report for management for the following risk scenarios." Furthermore, for approved countermeasures, they are automatically executed in conjunction with relevant equipment control systems and network management systems. For example, automatically activating the fire suppression system during fire detection, automatically starting the emergency generator during a power failure, automatically activating backup cooling devices during cooling anomalies, automatically disconnecting external network connections during a network attack, automatically switching to backup lines during communication failures, and automatically sending an alert to the on-site manager in case of human error. Specifically, the response strategy formulation and execution department employs a retrieval-enhanced generative architecture. The response strategy formulation and execution department uses the risk scenario vectors output by the risk analysis department as queries to retrieve data from the vector database.In this vector database, past response cases and operational regulations are stored as dense vectors. The response strategy formulation and execution department extracts highly relevant document vectors based on cosine similarity. This department combines the extracted document text and risk scenarios to construct input prompts for a large-scale language model. The large-scale language model receives text-tagged sequences of risk scenarios and relevant regulations as input. It outputs specific command sequences and notification texts for relevant personnel. The response strategy formulation and execution department performs syntactic parsing on the output command sequences, converting them into communication payloads for various equipment control systems. This department then sends these payloads to the fire suppression system and power control system via communication protocols. By dynamically introducing external knowledge and suppressing model illusions, this system effectively generates accurate, fact-based response strategies. This system is applied to use cases such as emergency avoidance systems in autonomous vehicles and triage support systems in medical institutions.

[0094] The Progress Monitoring Department monitors the progress of all risk response processes in real time, visualizing the implementation status of each response strategy, unresolved issues, locations of delays, feedback from the field, occurrence of additional risks, on-site photos and voice recordings, personnel location information, and equipment operating status in a dashboard format. Upon detecting delays or unresolved risks, it sends automatic alerts to relevant personnel, prompting rapid follow-up responses. For example, when a field manager reports a disaster situation with photos attached from a mobile terminal, this information is immediately reflected in the overall system, enabling management and operations departments to grasp the latest situation. On-site voice recordings and personnel location information are also visualized in real time, allowing for immediate assessment of on-site chaos and safety assurance. Specifically, this Progress Monitoring Department adopts an event-driven architecture, utilizing a distributed messaging system. It subscribes to system status logs published from each subsystem in real time. It uses a stream processing engine to aggregate and filter events within a moving window. It compares the timeout values ​​set in each step of the response strategy with the current time. Steps that exceed the timeout period are identified and their identifiers and delay times are output as error logs. This progress monitoring department acquires GPS coordinates (latitude and longitude) data sent from the mobile terminal of the on-site supervisor. This data is then compared with the facility's 3D building information model (BIM) data to determine the precise spatial location of the workers. These processing results are then sent as a structured data payload to the front-end application. This system offers the technical advantage of processing a large number of asynchronous events without delay and maintaining the overall consistency of the system's state. This system is applicable to use cases such as robot control and monitoring in large-scale logistics centers and distribution network monitoring in smart grids.

[0095] The Learning & Evolution Department utilizes generative AI to continuously learn new risk patterns and response strategies, improving the overall accuracy and response capabilities of the system. For example, it automatically expands its knowledge base to address new types of cyberattacks, unprecedented natural disasters, newly established laws and regulations, new failure modes due to equipment aging, changes in human error tendencies, and the expansion of social reputation risks, reflecting these changes in future risk predictions and response strategy formulation. Specific examples of prompts for the generative AI include: "Learn the following failure response history to update the best response strategy pattern for future cooling device malfunctions," and "Analyze past cyberattack cases and response strategies to automatically generate response strategies for new attack patterns." Specifically, the Learning & Evolution Department employs a continuous learning framework. It accumulates newly collected event data paired with response results as a learning dataset. To prevent catastrophic forgetting, it maintains important past learning data in a replay buffer. The department creates small batches of data that combine new data and data from the replay buffer. This learning and evolutionary unit inputs small batches of data into a neural network, performs forward propagation calculations, and outputs predicted values. It calculates the cross-entropy error between the predicted values ​​and the correct answer labels. The unit then calculates the weight gradients of each layer using backpropagation and updates the weights using a gradient descent-based optimization algorithm. The unit receives log text of novel network attacks and binary labels representing the success or failure of countermeasures as input. Finally, it outputs a checkpoint file containing the updated model parameters. This system exhibits the technical effect of increasing inference accuracy with continued model use and enhancing the system's self-healing capabilities against unknown threats. This system is applicable to use cases such as automatic updates of malware detection engines and motion optimization for industrial robots.

[0096] The External Linkage Department has the capability to exchange information in real time with other companies' crisis management systems, cross-industry information sharing platforms, autonomous region disaster prevention systems, trading partners' supply chain management systems, customer CRM systems, equipment manufacturers' remote monitoring systems, and insurance companies' risk assessment systems via API. For example, it can automatically acquire disaster information from autonomous regions, automatically receive delivery delay information from trading partners, automatically respond to alerts from industry groups, and automatically analyze customer inquiries. Specifically, this External Linkage Department functions as an API gateway for inter-service communication. It supports multiple communication protocols with external systems. It performs data mapping processing, converting comma-separated text data received from external systems into a standardized structured text format within the system. To ensure communication reliability, it is equipped with a retry mechanism with exponential backoff. It acquires numerical and textual data such as seismic intensity and epicenter location from autonomous region disaster prevention systems. It acquires integer data such as component inventory and delivery delay times from trading partners' supply chain management systems. This acquired data is stored in a distributed cache on memory, enabling high-speed retrieval from other processing units. This system effectively eliminates data silos in heterogeneous environments and enables seamless data integration across organizational boundaries. It is applied in use cases such as dynamic optimization of intelligent supply chains and wide-area healthcare networks.

[0097] The user interface (UI) is primarily implemented on the terminal side, providing dashboard displays for relevant personnel, alarm notification reception, situation reports and response input for on-site supervisors, management decision-making and approval operations, confirmation of on-site emergency response instructions, voice input and image upload, automatic parsing of on-site photos, automatic text transcription of on-site voice, feedback transmission from the field, and safety monitoring of on-site workers based on wearable devices, as well as other UI functions required for on-site response. For example, an on-site supervisor can report the disaster situation with photos attached from a mobile terminal, or management can press the approval button for a countermeasure plan on the terminal, thereby initiating automated processing on the server side. Specifically, this UI uses a browser to perform high-speed data processing on the client side using binary instruction format. This UI uses a parallel processing processor on the client side to render a large number of time-series data points received from the server, achieving smooth chart drawing. This UI acquires raw data from the accelerometer and heart rate sensor built into the wearable device via a short-range wireless communication protocol. This UI applies a low-pass filter to the acquired raw data to generate a noise-removed feature vector. This user interface unit inputs feature vectors into the edge AI model to locally determine worker falls and abnormal stress states. Upon detecting an anomaly, the user interface unit immediately sends an alarm payload to the server. It also inputs on-site voice data into a local lightweight speech recognition model, converts it into text tags, and then sends it to the server, thus saving communication bandwidth. This system optimally distributes edge computing and cloud computing loads, improving overall system response speed and scalability. This system is applicable to use cases such as construction site safety management systems and remote medical patient monitoring.

[0098] The Security Management Department performs tasks such as encrypting communication between servers and terminals, strictly managing access permissions, automatically parsing access logs and operation history, detecting signs of unauthorized access and internal violations, sending immediate alerts to system administrators, multi-factor authentication of users, encrypted data storage, system vulnerability diagnosis, and automatic generation of audit trails. For example, when access from a time or location different from the usual one is detected, the system administrator is automatically notified, and access restrictions are implemented as needed. Specifically, based on a zero-trust architecture, this Security Management Department performs dynamic context evaluation on all access requests. This department inputs not only user authentication information but also multi-dimensional attribute vectors such as device security status, network location, and access time into the evaluation engine. This evaluation engine uses a random forest algorithm to classify the attribute vectors and outputs a binary value indicating whether access is allowed. This Security Management Department uses Advanced Encryption Standard (AES) algorithms to encrypt confidential data stored in the database in blocks. This Security Management Department securely manages the lifecycle of encryption keys within the hardware security module. As a system vulnerability diagnostic tool, this Security Management Department regularly executes automated penetration testing tools. This security management department inputs the output from testing tools into a natural language processing model to automatically calculate a score representing the severity of vulnerabilities. This system effectively minimizes the risk of information leakage mathematically by implementing multi-layered security controls independent of perimeter defenses as internal computer processing. This system is applicable to use cases such as the protection of cloud-native applications and the defense of military communication networks.

[0099] Thus, the system described in this implementation possesses a robust structure encompassing data collection, risk analysis, response strategy formulation and execution, progress monitoring, learning and evolution, external collaboration, user interface, and security management. By leveraging generative AI throughout the entire process, it can detect and predict various risks in data center operations in real time, automating optimal responses and significantly improving business continuity and security. Specifically, this system is configured as a distributed system where each processing unit is containerized and autonomously managed on a container orchestration platform. The system controls the data flow from data collection to response strategy execution through a workflow engine defined as a directed acyclic graph. The system asynchronously transmits the processing results from each node to the next node in tensor form. In the inference processing of the generative AI model, the system utilizes dedicated hardware for tensor operations to parallelize matrix multiplication operations, maximizing throughput. The system automatically performs continuous integration for periodically updating model weight parameters and a continuous delivery pipeline. This system requires no human intervention, seamlessly integrating the physical control of the hardware and the logical inference of the software. This system offers a technological improvement by deriving optimal solutions based on probabilistic inference in high-dimensional space in milliseconds for handling unknown, complex risks that existing rule-based systems cannot address. This system has applications in advanced use cases such as next-generation fully unmanned data centers and autonomous control systems for deep space probes.

[0100] (Implementation steps) Step 1: Real-time collection of diverse information. In this step, real-time data is automatically acquired from various sensors and monitoring devices within the data center, including the power system, cooling devices, security monitoring, fire detection, personnel monitoring, and network monitoring. This data includes the operating status of the main power supply and emergency generator, voltage, current, frequency, power switching status, remaining UPS battery power, cooling water pressure, refrigerant temperature, fan speed, entry and exit records, suspicious visits, surveillance camera images, biometric authentication logs, smoke detection, temperature rise, fire alarms, fire suppression system operation status, personnel entry and exit records, work history, health status, communication latency, packet loss, bandwidth utilization, and network attack signs. Furthermore, external weather information, news, SNS information, and disaster information are automatically acquired via APIs, RSS, web scraping, and external linkage services. This diverse data is then automatically categorized and tagged according to attributes such as time series, location, equipment type, anomaly level, reliability, information source, data category, urgency, and relevant business areas. Preprocessing includes duplicate removal, noise removal, reliability scoring, time-series correction of information, geographic information assignment, automatic captioning of image data, and automatic text transcription of voice data. Specifically, this system uses a cluster of computing servers with multiple processors and large-capacity memory to construct the data collection module. The system receives streaming data asynchronously from thousands of IoT sensors configured within the data center. The received data is temporarily stored in a circular buffer on memory. The system obtains voltage and current values ​​from power system sensors as one-dimensional floating-point arrays. The system obtains refrigerant temperature and fan speed from cooling device sensors as time-series numerical vectors. The system segments image data from surveillance cameras frame by frame. The system converts the segmented image frames into 224-pixel vertical and horizontal, 3-channel color image tensors. The system obtains external weather information and news text via API. The system inputs the acquired text data into a natural language processing engine. The system segments the text data into word tags. The system converts the segmented tags into 768-dimensional embedding vectors. The system synchronizes these heterogeneous data based on a common timestamp. The system integrates the synchronized data as a multimodal feature matrix. The system performs missing value imputation algorithms as part of the data preprocessing. This system uses a Kalman filter to remove noise from sensor data. It applies contrast enhancement to image data. It performs a Fast Fourier Transform on speech data to generate a frequency spectrum. These preprocessing steps homogenize the data quality. The preprocessed high-dimensional tensor data is then transmitted to the subsequent risk analysis module. Through this data integration and preprocessing, the system significantly improves the inference accuracy of subsequent artificial intelligence models. By discarding unnecessary noisy data early on, the system substantially reduces its overall computational load. Finally, the system optimizes the bandwidth consumption of the communication network.This system completely eliminates manual data collection tasks by humans. It processes massive amounts of data at speeds exceeding the limits of human cognition. This system demonstrates exceptional technical effectiveness in use cases such as data center infrastructure monitoring and anomaly detection in large-scale manufacturing plants.

[0101] Step 2: Risk Factor Extraction and Analysis Based on Generative AI. Generative AI is used in this step. Through natural language processing, it extracts keywords, phrases, sentiment expressions, causal relationships, time-series changes, and the intentions of relevant personnel from unstructured text data such as news reports, SNS posts, internal company reports, and worker comments, identifying potential risk factors. It also performs sentiment analysis and topic modeling, quantitatively assessing signs of public outrage on SNS, increased anxiety among on-site workers, and a rise in customer complaints. Pattern recognition and time-series analysis are used to detect outliers and sharp changes in numerical data such as sensor data and equipment logs. Image recognition AI automatically identifies smoke from fires, intrusions by suspicious individuals, the extent of damage on-site, and equipment damage from surveillance camera footage. Speech recognition AI assesses urgency, abnormal signs, and violations of operating procedures from on-site worker conversations. Specific examples of prompts for generative AI to read include: "Extract risk factors about the data center from the following sensor data and external information, and predict the probability and impact of occurrence," and "Generate composite risk scenarios based on the following SNS posts, weather information, and equipment logs, and list them with priorities." Specifically, this system deploys a large-scale language model with an attention mechanism and a convolutional neural network in memory. The system inputs preprocessed multimodal feature matrices into the AI ​​model. For text data, such as typhoon approach warnings from the meteorological agency, power outage posts on SNS, and equipment inspection reports from within the company, the system inputs these texts as thousands of-dimensional labeled embedding vectors into the model. For image data, such as smoke images captured by surveillance cameras, images of suspicious intrusions, and images of damaged equipment, the system inputs these images as multidimensional pixel value tensors into the model. For numerical data, such as temperature changes in server racks, cooling water pressure changes, and network packet loss rates, the system inputs these numerical values ​​as time-series floating-point arrays into the model. This system performs inference processing using an artificial intelligence model. As output of the model, the system generates risk scores, probability distributions for risk categories, and extracted feature vectors. Risk scores are output as floating-point numbers such as 0.95, 0.82, and 0.45. Risk categories are output as probability distributions such as an 80% probability of fire, a 15% probability of a network attack, and a 5% probability of equipment failure. Extracted features include vectors for smoke symptom, abnormal vibration, and unauthorized access. These outputs are then sent to the subsequent scene generation module. The system triggers advanced analytical processing only when the output risk score exceeds a predetermined threshold. This threshold prevents unnecessary consumption of computational resources. Through feature extraction in high-dimensional space, the system can detect subtle anomalies that are imperceptible to humans at an early stage.This system specifically addresses unknown risk factors that existing rule-based anomaly detection systems cannot handle. Through this processing, the system dramatically reduces data center failure rates. It has demonstrated exceptional technical effectiveness in use cases such as system monitoring in financial institutions and the protection of critical infrastructure.

[0102] Step 3: Risk Scenario Generation and Risk Level Determination. Generative AI combines past failure cases, external databases, and currently acquired data, performing correlation analysis to quantitatively predict the probability and impact of risks. It generates multiple risk scenarios by comparing them to past cases such as cooling device shutdowns during approaching typhoons, cascading network failures during cyberattacks, and power outages coinciding with fire alarms. Based on the severity of the risks, it automatically determines risk levels (major, moderate, minor, etc.), prioritizes them by type, and performs composite and cascading risk assessments when multiple risks occur simultaneously. Specific examples of prompts for the generative AI to read include: "Based on the following sensor data, external information, and past failure cases, generate composite risk scenarios and assess their respective probabilities and impacts," or "Analyze the composite risks when multiple risk factors occur simultaneously and list them by priority." Specifically, this system uses a graph neural network correlation analysis module deployed in memory. The system inputs the extracted risk feature vector group and case embedding vectors obtained from the past failure case database into the model. This system takes as input data features, such as feature vectors representing the current temperature rise, vectors of past cooling device failures, and feature vectors representing current network latency. It arranges these vectors in a multi-dimensional space. The system calculates the cosine similarity and inner product between the vectors. It uses a cross-modal attention mechanism to learn the hidden correlations between different data sources. As output, the system generates generation probability scores and impact matrices for multiple risk scenarios. Generation probability scores are output as floating-point numbers, such as 0.88 for scenario A, 0.65 for scenario B, and 0.32 for scenario C. Impact matrices are output as structured data, such as a system downtime of 120 minutes, data loss of 50 gigabytes, and recovery cost of 1 million yen. Based on these outputs, the system automatically classifies risk levels. For example, it classifies a situation where the product of the generation probability score and the impact score exceeds a predetermined threshold as a major risk. The system transmits the determined risk levels and generated scenario data to the subsequent response strategy formulation module. Through this correlation analysis, the system accurately predicts complex cascading risks that cannot be detected by a single sensor. This system eliminates judgments based on human rules of thumb. Through vector operations in high-dimensional space, it compares data with a vast number of past cases in milliseconds. This increased processing speed enables proactive responses before failures occur. This system demonstrates exceptional technical effectiveness in use cases such as the management of large-scale cloud infrastructure and disaster prevention systems for smart cities.

[0103] Step 4: Automatic Formulation and Optimization of Response Strategies Based on Generative AI. Based on the risk assessment results from the Risk Analysis Department, generative AI is used to automatically generate multiple optimal response strategies corresponding to the risk level and type, while referring to past response cases, operating procedures, external guidelines, laws and regulations, contract terms, agreements with customers, contract terms with trading partners, industry best practices, the disaster prevention manual of the autonomous region, and recommended procedures from equipment manufacturers. For example, in response to fire risks, the system automatically generates actions such as "automatic activation of the fire extinguishing system," "evacuation instructions for the entire building," "power cut-off," "emergency contact with the on-site supervisor," and "automatic notification to the fire department." In response to power failure risks, it automatically generates actions such as "emergency generator activation," "load balancing," "priority operation of important servers," "power switching," and "automatic contact with the power company." In response to cooling anomaly risks, it automatically generates actions such as "backup activation of cooling devices," "enhanced temperature monitoring," "on-site inspection instructions," and "switching of cooling water supply paths." In response to network attack risks, it automatically generates actions such as "internal connection disconnection," "emergency notification to the administrator," "enhanced monitoring," and "automatic log saving." In response to communication failure risks, it automatically generates actions such as "automatic switching to backup lines," "automatic contact with communication operators," "restart of network equipment," and "communication load balancing." In response to human error risks, it automatically generates actions such as "reminding the on-site supervisor," "automatic distribution of work procedure manuals," "contact with on-site supervisors," and "automatic recording of work history." In response to external disaster risks, it automatically generates specific action plans such as "on-site evacuation instructions," "suspension of important equipment," "automatic monitoring of on-site conditions," and "coordination with the autonomous community." As specific examples of prompts that generative AI can read, examples include "Automatically generate action instructions for on-site personnel when a fire risk occurs, based on the following risk analysis results and past response cases," and "Create a summary report for management for the following risk scenarios." Specifically, this system deploys a response strategy formulation module that combines reinforcement learning algorithms and constrained generative models on memory. The system inputs risk level vectors, scenario embedding vectors, and resource constraint matrices into the model. Input data includes, for example, a unique heat vector representing major risks, a 768-dimensional vector representing a combined fire and power outage scenario, and a numerical matrix representing the number of currently operational emergency generators and the number of standby personnel. The system uses a cluster search algorithm to explore the optimal steps from a large combination of response strategies. Outputs from the model generate a list of execution steps for each response strategy and a success probability score for each strategy. The system outputs a list of execution steps, such as an array of commands for steps like starting an emergency generator (step 1), shutting down a specific server (step 2), and activating fire suppression equipment (step 3). This system outputs success probability scores, such as floating-point numbers like 0.92, 0.85, and 0.78.This system sends these outputs to subsequent report generation and automatic execution modules. It converts the output command array into a machine-readable format. Through this optimization process, it minimizes recovery time under limited resource conditions. Even in emergencies such as human panic, it calmly and logically derives the optimal action plan. It achieves optimal allocation of computer resources. This system demonstrates exceptional technical effectiveness in use cases such as handling anomalies in aircraft autopilot systems and emergency control in nuclear power plants.

[0104] Step 5: Automatically generate and publish reports and alerts based on relevant personnel. Generative AI automatically creates optimized reports and alerts for various relevant personnel, including management, field managers, operations departments, customers, trading partners, and autonomous systems. These are rapidly published via management screens, mobile devices, email, chat systems, voice calls, and push notifications. For example, reports summarizing the overall risk and key points for decision-making are provided to management; specific work instructions and emergency contact information are provided to field managers; dashboards providing real-time visualization of overall progress and response status are provided to operations departments; concise notifications summarizing the scope of impact and response strategies are provided to customers; notifications communicating anticipated delivery delays and service impacts are provided to trading partners; and reports summarizing the on-site situation and response strategies are provided to autonomous systems. Specific examples of prompts for the generative AI to read include: "Automatically generate a summary report for management based on the following response strategies," "Automatically generate specific action instructions for field managers," and "Create a concise notification summarizing the scope of impact and response strategies for customers." Specifically, this system deploys a report generation module with a natural language generation model and template synthesis engine on memory. The system inputs an array of response strategy commands and a vector of target configuration files into the model. Input data includes, for example, an array of commands to start an emergency generator, a vector representing the authority level of management, and a vector representing the skill set of on-site personnel. The system dynamically adjusts the level and information content of technical terms in the output text based on the attributes of the target. Output data includes text data, importance markers, and data specified by publishing protocol. For example, the system outputs a summary text containing the predicted total disaster amount to management and detailed text containing specific valve operation steps to on-site personnel. Importance markers include, for example, outputting categorized data such as critical, high, medium, and low. Publishing protocol specifications include, for example, outputting communication protocol identifiers such as SMTP, HTTPS, and MQTT. The system sends these outputs to subsequent communication interfaces. When the importance marker is critical, the system allocates the highest priority communication bandwidth to send data packets. Through this dynamic generation process, the system minimizes information transmission latency. This system reduces the load on communication networks by suppressing unnecessary information transmission. It completely replaces manual report creation and network operation based on human intervention. This system demonstrates exceptional technical effectiveness in use cases such as wide-area evacuation guidance systems during large-scale disasters and global crisis management networks for multinational corporations.

[0105] Step 6: Automated Countermeasure Execution and Progress Monitoring. For approved countermeasure plans, the Countermeasure Formulation and Execution Department collaborates with relevant equipment control systems and network management systems to automatically execute countermeasures. For example, it automatically activates the fire suppression system upon detecting a fire, automatically starts the emergency generator in case of a power failure, automatically activates the backup cooling device in case of abnormal cooling, automatically disconnects external network connections during a network attack, automatically switches to the backup line in case of a communication failure, and automatically sends an alert to the on-site supervisor in case of human error. It also automatically performs remote equipment operation and automatic inspection, automatic on-site camera capture, acquisition of operator location information, and on-site voice recording. The Progress Monitoring Department monitors the progress of all risk response processes in real time, visualizing the implementation status of each countermeasure strategy, unaddressed issues, delayed locations, feedback from the site, occurrence of additional risks, on-site photos and voice recordings, operator location information, and equipment operating status in a dashboard format. Upon detecting delays or unaddressed risks, it sends automatic alarm notifications to relevant personnel, prompting rapid additional responses. Specifically, this system deploys the execution management module, which is linked to the distributed control system, on the memory. This system inputs an array of response strategy commands and real-time device state vectors into its modules. Input data includes, for example, emergency generator start commands, floating-point numbers representing the current mains power voltage, and binary arrays representing network switch port states. Based on these inputs, the system generates specific control signal data packets for each hardware device. Outputs include control data packets for programmable logic controllers (PLCs), API requests for IoT gateways, and progress status codes. Control data packets may output hexadecimal binary data for opening specific valves. API requests may output JSON-formatted payloads for changing router routing settings. Progress status codes may output status transition data such as in progress, completed, and error. These outputs are then sent to the target devices. The system receives response signals from the devices. If an error is indicated by the response signal, the system immediately recalculates an alternative response strategy command. This feedback control ensures reliable hardware operation. By directly controlling physical devices without human intervention, the system reduces response time to milliseconds. The system achieves autonomous stabilization of the entire system. This system demonstrates exceptional technical effectiveness in use cases such as autonomous operation of unmanned data centers and automatic recovery systems in smart factories.

[0106] Step 7: Improve System Accuracy Through Learning and Evolution. The Learning and Evolution Department uses generative AI to continuously learn new risk patterns and response strategies, improving the overall system's accuracy and response capabilities. For example, for new types of cyberattacks previously dealt with, unprecedented natural disasters, new laws and regulations, new failure modes caused by equipment aging, changes in the tendency of human error, and the expansion of social reputation risks, the generative AI automatically expands its knowledge base and reflects this in future risk predictions and response strategy formulation. Specific examples of prompts for the generative AI to read include "Learn the following failure response history to update the best response strategy pattern for future cooling device malfunctions," and "Analyze past cyberattack cases and response strategies to automatically generate response strategies for new attack patterns." Specifically, this system deploys a learning module with online learning algorithms and an experience replay buffer on memory. The system inputs feedback data of the execution results and the state tensor at the time of prediction into the model. Input data for this system includes, for example, binary labels indicating the success or failure of the response strategy, numerical data indicating the recovery time, and the multimodal feature matrix used in the prediction. The system calculates the difference between the predicted and actual results. This system defines cross-entropy error or mean squared error as the loss function. It uses backpropagation to calculate the gradient of the neural network. As output of the model, it generates updated weight parameter matrices and bias vectors. The weight parameter matrix is ​​output as a multidimensional tensor consisting of millions of floating-point numbers. The bias vector is an array of values ​​used to adjust the activation thresholds of each neuron. Using these outputs, the system dynamically updates the model parameters for the production environment. The updated parameters are applied immediately from the start of the next inference cycle. Through this continuous learning process, the system dramatically improves its adaptability to environmental changes and unknown threats over time. It continuously reduces inference errors during long-term operation. It completely eliminates manual model tuning by humans. This system demonstrates significant technical effectiveness in use cases such as threat intelligence systems for cybersecurity and learning from driving data in autonomous vehicles.

[0107] (specific use case) For example, suppose that in a data center operation, multiple risk factors overlap and occur simultaneously as a typhoon approaches. The data collection department automatically obtains typhoon path predictions and heavy rain warnings from meteorological information; automatically obtains voltage drops in the main power supply and the operating status of emergency generators from the power system; automatically obtains rising refrigerant temperatures and falling cooling water pressures from cooling devices; automatically obtains abnormal values ​​from smoke sensors from fire detection; automatically obtains increased communication latency and packet loss from network monitoring; automatically obtains increased suspicious access from security monitoring; automatically obtains abnormal health status and violation of work procedures by on-site personnel from personnel monitoring; automatically obtains submissions such as "rumors of data center power outages" and "photos of flooding" from social networking sites; and simultaneously obtains information such as "large-scale power outages in surrounding areas" and "traffic disruptions caused by road flooding" from news outlets. This diverse data is automatically classified and tagged according to attributes such as time series, location, equipment type, anomaly level, reliability, information source, and urgency, and noise removal and reliability scoring are performed. Specifically, this system will use a high-speed data collection module with multi-threaded processing deployed on memory. This system receives asynchronous multimodal data streams in parallel. Input data includes, for example, JSON-formatted path prediction data from a meteorological API, voltage value arrays with 100-millisecond cycles from power sensors, and multidimensional vectors of temperature and pressure from cooling devices. Further, text data obtained from social networking sites (SNS) is input into a natural language processing model and converted into a 768-dimensional embedding vector. Flooding images obtained from SNS are input into a convolutional neural network and converted into a 2048-dimensional image feature vector. This system synchronizes these heterogeneous data using a common timestamp. The synchronized data is then integrated into a massive multimodal feature matrix. The system outputs normalized feature tensors and reliability scores for each data point. Feature tensors may output high-dimensional floating-point arrays with dimensions exceeding 10,000. Reliability scores may be output as 0.99 for meteorological data and 0.45 for anonymous SNS submissions. These outputs are then transmitted to the subsequent risk analysis department in batch or streaming form. Data with reliability scores below a predetermined threshold is filtered out. This system dramatically improves the computational efficiency of subsequent analytical processing through data integration and filtering. It also prevents false detections caused by noisy data. This system demonstrates exceptional technical effectiveness in use cases such as complex disaster monitoring in large-scale factories and real-time monitoring of wide-area infrastructure.

[0108] The Risk Analysis Department uses generative AI to extract risk factors such as "power outages," "flooding," "traffic disruptions," and "equipment malfunctions" from social media submissions and news reports via natural language processing. It uses pattern recognition and time series analysis to detect anomalies such as voltage drops, cooling anomalies, smoke detection, and communication delays from sensor data. Image recognition AI automatically identifies signs of flooding and equipment damage from on-site camera images, and speech recognition AI assesses the urgency and chaos of the situation from conversations among on-site personnel. Furthermore, it compares past typhoon disaster failure cases, cooling system shutdown cases, emergency generator switching failure cases, and on-site response delays caused by traffic disruptions with external databases to generate composite risk scenarios. For example, it might suggest a scenario with a high risk of a chain reaction of emergency generator switching delays, cooling system shutdowns, communication failures, and on-site personnel evacuation delays when a typhoon approaches and the main power supply voltage drops simultaneously. The system then classifies these risks as major, moderate, or minor and prioritizes them accordingly. Specifically, this system deploys a deep learning model with a cross-modal attention mechanism on memory. This system inputs normalized feature tensors and embedding vectors from past cases into the model. Input data includes vectors such as power outage risk extracted from SNS text, voltage drop vectors extracted from sensor data, and flooding symptom vectors extracted from camera images. The system calculates the inner product of these vectors to generate an attention weight matrix. This weight matrix is ​​used to quantify the hidden correlations between different modalities. Outputs include a fused risk performance vector and a probability score for each scenario. The risk performance vector outputs a 512-dimensional high-density floating-point array. Probability scores output values ​​such as a cascading failure scenario probability of 0.87 and a single device failure scenario probability of 0.34. These outputs are then sent to the subsequent response strategy formulation department. Based on the probability scores, the system automatically classifies risk levels into three stages: major, moderate, and minor. Through feature fusion in this high-dimensional space, the system accurately predicts complex risks that might be missed by a single data source. This system eliminates intuitive human judgment, achieving objective risk assessment based on pure mathematical calculations. This system, through a significant leap in predictive capabilities, prevents catastrophic system downtime. It has demonstrated exceptional technical effectiveness in use cases such as fault prediction in financial trading systems and anomaly detection in traffic control systems.

[0109] The Response Strategy Formulation and Implementation Department utilizes generative AI to automatically generate multiple specific response strategies, including "immediate start-up of emergency generators," "automatic start-up of cooling system backup," "building-wide evacuation instructions," "fire suppression system switch to standby mode," "emergency contact with on-site personnel," "alternate route guidance in case of traffic obstruction," "service impact notification to customers," and "automatic on-site status report to the municipality," while referencing past typhoon disaster response cases, equipment manufacturer's recommended procedures, the municipality's disaster prevention manual, operating regulations, and contract terms. Furthermore, it automatically generates optimized reports and alerts for relevant personnel, such as a report summarizing overall risks and decision points for management, a list of on-site safety assurance procedures and emergency contact methods for on-site managers, a dashboard showing progress and unresolved issues for operations departments, notifications regarding service impact and expected recovery for customers, and a report on on-site status and response policies for the municipality. These reports are then disseminated via terminals, email, chat systems, and push notifications. Specifically, this system deploys optimization algorithms and natural language generation models for solving constraint satisfaction problems on memory. This system inputs the fused risk performance vector and resource constraint matrix into the model. Input data includes, for example, a 512-dimensional vector representing cascading failure risk, numerical data representing currently available backup power capacity, and a coordinate matrix representing the on-site personnel configuration. Based on these inputs, the system evaluates combinations of executable response strategies within the search space. Output data includes optimized response strategy sequence data and object-specific notification text data. Response strategy sequence data includes, for example, a sorted array of commands such as step 1 (generator start), step 2 (cooling device switching), and step 3 (evacuation instructions). Notification text data includes, for example, a summary string for management and a specific step string for the field. These outputs are then sent to the subsequent execution management department and communication interfaces. The system datapackets the generated text data according to a specified protocol. Through this optimization process, the system rapidly derives the optimal solution under limited resources. This system significantly reduces system recovery time. By automating the communication of information to relevant personnel, the system completely eliminates human communication errors. This system has demonstrated exceptional technical effectiveness in use cases such as emergency reconstruction of disaster response systems and logistics networks in large-scale hospitals.

[0110] The Execution Management Department, in conjunction with the equipment control system, automatically executes approved countermeasures, including the automatic startup of emergency generators and cooling devices, the switching of fire suppression systems to standby mode, and the automatic switching of communication paths, automatic image capture by on-site cameras, acquisition of personnel location information, and on-site voice recording. The Progress Monitoring Department visualizes the progress of all risk response processes, unresolved issues, delayed locations, feedback from the field, occurrence of additional risks, on-site photos and voice recordings, personnel location information, and equipment operating status on a dashboard. Upon detecting delays or unresolved risks, the department sends automatic alarm notifications to relevant personnel, prompting rapid additional responses. Specifically, this system deploys an execution control module with distributed transaction management capabilities on memory. The system inputs optimized response strategy sequence data and the current state vectors of each device into the module. Input data includes, for example, an array of generator start-up commands, a binary vector representing the current operating state of the cooling device, and the current routing table of the network router. The system uses a state transition model to simulate the impact of each command execution on the entire system. This system, as the output of the model, generates device-specific control protocol data packets and progress status update data. As control protocol data packets, it outputs hexadecimal control commands conforming to the Modbus protocol. As progress status update data, it outputs integer values ​​from 0 to 100 representing the completion rate of each task. This system sends these outputs to the target hardware devices and dashboard rendering engines. The system receives Ack signals from devices and performs transaction commits or rollbacks. Through this distributed control, the system ensures reliable execution of commands across multiple devices. It distributes network load, avoiding uncontrolled states due to single points of failure. Through real-time visualization, the system dramatically improves the speed at which system administrators can grasp the situation. This system demonstrates significant technical effectiveness in use cases such as grid control in smart grids and robot control in automated large warehouses.

[0111] The Learning & Evolution Department uses generative AI to accumulate learning data, including the entire process of responding to this typhoon disaster, the initial response and its effectiveness when equipment malfunctions, the response to customer service impact notifications, and the evacuation history of on-site personnel. This data is then reflected in improving the accuracy of future risk predictions and response strategy formulation. Specifically, this system expands the reward function calculation module and experience replay buffer from reinforcement learning onto memory. The system inputs a series of response process data and final result indicators into the model. Input data includes vector representations of executed response strategy sequences, numerical data representing the final system recovery time, and floating-point numbers representing the total disaster amount. Based on these inputs, the system calculates the contribution of each response step. The system uses a reward function to calculate a scalar reward value that is higher for shorter recovery times and lower disaster amounts. Output data generates updated policy network weight parameters and gradient vectors of the value function. Weight parameters are output as, for example, multi-dimensional floating-point tensors. Gradient vectors are output as numerical arrays representing the correction direction and magnitude of each parameter. This system uses these outputs to relearn the neural network of its inference engine. The updated parameters are then applied to the next inference cycle. Through this autonomous learning cycle, the system gains self-organizing capabilities against unknown complex disasters. With each accumulated experience, the system mathematically minimizes the inference error. This system fully automates post-analysis and manual revision tasks previously performed by humans. This system demonstrates exceptional technical effectiveness in use cases such as the autonomous operation systems of space probes and the strategy updates for high-frequency algorithmic trading.

[0112] As specific examples of prompts that generative AI can read, examples include: "Based on the following sensor data, SNS submissions, meteorological information, and past failure cases, generate a composite risk scenario in the data center when a typhoon approaches, and assess the probability of occurrence and impact of each scenario"; "Based on the following risk analysis results and past response cases, automatically generate action instructions for on-site personnel when emergency generator switching fails"; "Analyze the following progress data to list unaddressed risks and additional response strategies"; "Learn from the following failure response history to update the best response strategy model for future typhoon disasters," etc. Specifically, this system will deploy the context optimization module that automates the prompting process on memory. This system inputs the natural language prompt string and the accompanying context data tensor into the model. The system uses input data such as the aforementioned instruction string, a time series array of sensor data, and embedding vectors of past cases. The system segments these inputs using a tokenizer and truncates them to the optimal length according to the model's context window size. The system outputs the internal hidden state vector and the generated text tag sequence. The system outputs the hidden state vector, for example, the thousands of-dimensional activation value tensors in each layer. This system outputs a sequence of text tags, such as arrays of word IDs that constitute the generated scene and action instructions. These outputs are then sent to a decoder module. The system parses the tag sequence into human-readable strings or structured JSON data. Through vectorization and context optimization of the prompts, the system stabilizes the inference accuracy of large-scale language models. Mathematically, the system suppresses the illusion of the model generating information that differs from reality. The system achieves maximum information extraction efficiency while minimizing computational resource consumption. This system demonstrates significant technical effectiveness in use cases such as what-see-is-growth in medical diagnostic support systems and automated review systems for legal documents.

[0113] Thus, even in situations where multiple anomalies and external risks occur simultaneously, the data center crisis management system of this invention, with generative AI at its core, automates information collection, risk analysis, scenario generation, response strategy formulation, notification by relevant personnel, countermeasure execution, progress monitoring, and learning / evolution in a seamless process, maximizing business continuity and security of the data center. Specifically, this system deploys an orchestration engine for a containerized AI model group based on a microservice architecture on memory. The system continuously inputs multimodal data streams flowing throughout the system into each module. Input data includes, for example, tens of thousands of sensor logs per second, asynchronous data from external APIs, and status responses from devices. This system uses high-speed bus communication to transmit this data between microservices. Output data generates autonomous system control actions and optimized resource allocation mappings. Control actions include, for example, outputting direct hardware control commands and dynamic network reconfiguration commands. Resource allocation mappings include, for example, outputting numerical matrices representing the dynamic allocation of CPU and memory to each container. The system uses these outputs to maintain continuous monitoring and feedback loops throughout the system. This system, through its comprehensive architecture, significantly enhances the scalability of the entire system. It achieves a high degree of fault tolerance, ensuring the entire system continues to function even if some modules fail. This system enables fully autonomous infrastructure operation without human intervention. It demonstrates exceptional technical effectiveness in use cases such as next-generation smart grid management and the integrated control of fully automated super factories.

[0114] The specific processing unit 290 sends the result of the specific processing obtained in Embodiment 1 or Application Example 1 described above 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 voice representing the user's input on the result of the specific processing. The control unit 46A sends the voice data representing the user's 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 voice data.

[0115] Data generation model 58 is what is known as generative AI (Artificial Intelligence).

[0116] One example of data generation model 58 includes ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Generative AI, etc.

[0117] The data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing instructions is input to the data generation model 58, along with inference data such as speech data representing speech, text data representing text, and image data representing images (e.g., data from still images or moving images). The data generation model 58 infers the input inference data based on the instructions shown in the prompt and outputs the inference result in one or more data forms, such as speech data, text data, and image data. The data generation model 58 may include, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the aforementioned specific processing while using the data generation model 58. The data generation model 58 may also be a fine-tuned model to output inference results from prompts without instructions; in this case, the data generation model 58 can output inference results from prompts without instructions. The data processing device 12, etc., includes multiple data generation models 58, including AI other than generative AI. AI other than generative AI includes, but is not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or Naive Bayes. Furthermore, AI can also act as an AI agent. Additionally, when the processing described above is performed by AI, this processing may be partially or entirely performed by AI, but is not limited to this example. Moreover, processing performed by AI, including generative AI, can be replaced by rule-based processing, and vice versa.

[0118] Furthermore, the processing performed by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it can also be executed jointly by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or external devices, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or external devices.

[0119] In the above embodiments, examples of specific processing performed by the data processing device 12 are given, but the technology disclosed herein is not limited to this, and specific processing may also be performed by the smart device 14.

[0120] [Second Implementation] Figure 3 An example of the configuration of the data processing system 210 according to the second embodiment is shown.

[0121] like Figure 3 As shown, the data processing system 210 includes a data processing device 12 and smart glasses 214. One example of the data processing device 12 is a server.

[0122] The data processing apparatus 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" as understood in this disclosure. The computer 22 includes a processor 28, RAM 30, and memory 32. The processor 28, RAM 30, and memory 32 are connected to a bus 34. Furthermore, the database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. An example of the network 54 includes a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0123] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, RAM 48, and memory 50. The processor 46, RAM 48, and memory 50 are connected to a bus 52. Furthermore, the microphone 238, speaker 240, camera 42, and communication I / F 44 are also connected to the bus 52.

[0124] Microphone 238 receives instructions from user 20 by receiving the sounds emitted by user 20. Microphone 238 captures the sounds emitted by user 20, converts the captured sounds into speech data, and outputs it to processor 46. Speaker 240 outputs sound according to the instructions from processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system such as a lens, aperture and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, used to capture the area around user 20 (e.g., the shooting range defined by an angle of view equivalent to the field of vision of an average healthy person).

[0126] Communication I / F 44 is connected to network 54. Communication I / F 44 and 26 are responsible for the transmission and reception of various information between processor 46 and processor 28 via network 54. The transmission and reception of various information between processor 46 and processor 28 using communication I / F 44 and 26 is performed in a secure state.

[0127] Figure 4 An example of the main functions of the data processing device 12 and the smart glasses 214 is shown. Figure 4As shown, in the data processing device 12, specific processing is performed by the processor 28. The memory 32 stores a specific processing program 56.

[0128] The specific processing program 56 is an example of a "program" as disclosed herein. The processor 28 reads the specific processing program 56 from the memory 32 and executes the read specific processing program 56 on the RAM 30. Specific processing is implemented by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0129] The memory 32 stores a data generation model 58 and an emotion-specific model 59. The data generation model 58 and the emotion-specific model 59 are used by the specific processing unit 290. The specific processing unit 290 can use the emotion-specific model 59 to infer the user's emotions and perform specific processing based on the user's emotions. The emotion inference function using the emotion-specific model 59 (emotion-specific function) includes various inferences and predictions related to the user's emotions, but is not limited to this example. Furthermore, emotion inference and prediction also include, for example, emotion analysis (analysis).

[0130] In the smart glasses 214, the processor 46 performs the receive / output processing. The memory 50 stores the receive / output program 60. The processor 46 reads the receive / output program 60 from the memory 50 and executes the read receive / output program 60 on the RAM 48. The receive / output processing is implemented by the processor 46 operating as a control unit 46A based on the receive / output program 60 executed on the RAM 48. Furthermore, the smart glasses 214 may also have the same data generation model and emotion-specific model as the data generation model 58 and the emotion-specific model 59, and use these models to perform the same processing as the specific processing unit 290.

[0131] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. Each part of the system described below is implemented by the data processing device 12 and the smart glasses 214. 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".

[0132] (Example 1) Since the specific processing flow is the same as that in Embodiment 1 described in the first embodiment above, the description is omitted.

[0133] (Application Example 1) Since the specific processing flow is the same as that in Application Example 1 described in the first embodiment above, the description is omitted.

[0134] The specific processing unit 290 sends the result of the specific processing obtained in Embodiment 1 or Application Example 1 described above 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 voice representing the user's input on the specific processing result. The control unit 46A sends the voice data representing the user's input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0135] Furthermore, the processing performed by the aforementioned data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it can also be executed jointly by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Additionally, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or external devices, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or external devices.

[0136] In the above embodiments, examples of specific processing performed by the data processing device 12 are given, but the technology disclosed herein is not limited to this, and specific processing may also be performed by the smart glasses 214.

[0137] [Third Implementation] Figure 5 An example of the configuration of the data processing system 310 according to the third embodiment is shown.

[0138] like Figure 5 As shown, the data processing system 310 includes a data processing device 12 and a head-mounted terminal 314. An example of the data processing device 12 is a server.

[0139] The data processing apparatus 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" as understood in this disclosure. The computer 22 includes a processor 28, RAM 30, and memory 32. The processor 28, RAM 30, and memory 32 are connected to a bus 34. Furthermore, the database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. An example of the network 54 includes a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0140] The head-mounted terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and memory 50. The processor 46, RAM 48, and memory 50 are connected to a bus 52. Furthermore, the microphone 238, speaker 240, camera 42, display 343, and communication I / F 44 are also connected to the bus 52.

[0141] Microphone 238 receives instructions from user 20 by receiving the sounds emitted by user 20. Microphone 238 captures the sounds emitted by user 20, converts the captured sounds into speech data, and outputs it to processor 46. Speaker 240 outputs sound according to the instructions from processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system such as a lens, aperture and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, used to capture the area around user 20 (e.g., the shooting range defined by an angle of view equivalent to the field of vision of an average healthy person).

[0143] Communication I / F 44 is connected to network 54. Communication I / F 44 and 26 are responsible for the transmission and reception of various information between processor 46 and processor 28 via network 54. The transmission and reception of various information between processor 46 and processor 28 using communication I / F 44 and 26 is performed in a secure state.

[0144] Figure 6 An example of the main functions of the data processing device 12 and the head-mounted terminal 314 is shown. Figure 6 As shown, in the data processing device 12, specific processing is performed by the processor 28. The memory 32 stores a specific processing program 56.

[0145] The specific processing program 56 is an example of a "program" as disclosed herein. The processor 28 reads the specific processing program 56 from the memory 32 and executes the read specific processing program 56 on the RAM 30. Specific processing is implemented by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] The memory 32 stores a data generation model 58 and an emotion-specific model 59. The data generation model 58 and the emotion-specific model 59 are used by the specific processing unit 290.

[0147] In the head-mounted terminal 314, the processor 46 performs the receiving and output processing.

[0148] The memory 50 stores a receive / output program 60. The processor 46 reads the receive / output program 60 from the memory 50 and executes the read receive / output program 60 on the RAM 48. The receive / output processing is implemented by the processor 46 operating the control unit 46A based on the receive / output program 60 executed on the RAM 48.

[0149] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. Each part of the system described below is implemented by the data processing device 12 and the head-mounted terminal 314. In the following description, the data processing device 12 will be referred to as the "server" and the head-mounted terminal 314 will be referred to as the "terminal".

[0150] (Example 1) Since the specific processing flow is the same as that in Example 1 described in the first embodiment above, the description is omitted.

[0151] (Application Example 1) Since the specific processing flow is the same as that in Application Example 1 described in the first embodiment above, the description is omitted.

[0152] The specific processing unit 290 sends the result of the specific processing obtained in Embodiment 1 or Application Example 1 described above to the head-mounted terminal 314. In the head-mounted terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice representing the user's input on the specific processing result. The control unit 46A sends the voice data representing the user's input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0153] Furthermore, the processing performed by the aforementioned data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the head-mounted terminal 314, but it can also be executed jointly by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the head-mounted terminal 314. Additionally, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the head-mounted terminal 314 or external devices, and the head-mounted terminal 314 acquires or collects information required for processing from the data processing device 12 or external devices.

[0154] In the above embodiments, examples of specific processing performed by the data processing device 12 are given, but the technology disclosed herein is not limited to this, and specific processing may also be performed by the head-mounted terminal 314.

[0155] [Fourth Implementation] Figure 7 An example of the configuration of the data processing system 410 according to the fourth embodiment is shown.

[0156] like Figure 7 As shown, 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.

[0157] The data processing apparatus 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" as understood in this disclosure. The computer 22 includes a processor 28, RAM 30, and memory 32. The processor 28, RAM 30, and memory 32 are connected to a bus 34. Furthermore, the database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. An example of the network 54 includes a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0158] Robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control object 443. Computer 36 includes a processor 46, RAM 48, and memory 50. The processor 46, RAM 48, and memory 50 are connected to a bus 52. Furthermore, the microphone 238, speaker 240, camera 42, control object 443, and communication I / F 44 are also connected to the bus 52.

[0159] Microphone 238 receives instructions from user 20 by receiving the sounds emitted by user 20. Microphone 238 captures the sounds emitted by user 20, converts the captured sounds into speech data, and outputs it to processor 46. Speaker 240 outputs sound according to the instructions from processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system such as a lens, aperture and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, used to capture the area around user 20 (e.g., the shooting range defined by an angle of view equivalent to the field of vision of an average healthy person).

[0161] Communication I / F 44 is connected to network 54. Communication I / F 44 and 26 are responsible for the transmission and reception of various information between processor 46 and processor 28 via network 54. The transmission and reception of various information between processor 46 and processor 28 using communication I / F 44 and 26 is performed in a secure state.

[0162] The controlled object 443 includes a display device, LEDs for the eyes, and motors for driving the arms, hands, and feet. The posture and movements of the robot 414 are controlled by controlling the motors for the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. In addition, facial expressions of the robot 414 can also be expressed by controlling the illumination state of the LEDs for the robot 414's eyes.

[0163] Figure 8 An example of the main functions of the data processing device 12 and the robot 414 is shown. For example... Figure 8 As shown, in the data processing device 12, specific processing is performed by the processor 28. The memory 32 stores a specific processing program 56.

[0164] The specific processing program 56 is an example of a "program" as disclosed herein. The processor 28 reads the specific processing program 56 from the memory 32 and executes the read specific processing program 56 on the RAM 30. Specific processing is implemented by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0165] The memory 32 stores a data generation model 58 and an emotion-specific model 59. The data generation model 58 and the emotion-specific model 59 are used by the special processing unit 290.

[0166] In robot 414, the processor 46 performs the receive / output processing. The memory 50 stores the receive / output program 60. The processor 46 reads the receive / output program 60 from the memory 50 and executes the read receive / output program 60 on RAM 48. The receive / output processing is implemented by the processor 46 operating the control unit 46A based on the receive / output program 60 executed on RAM 48.

[0167] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. Each part of the system described below is implemented by the data processing device 12 and the robot 414. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 will be referred to as the "terminal".

[0168] (Example 1) Since the specific processing flow is the same as that in Example 1 described in the first embodiment above, the description is omitted.

[0169] (Application Example 1) Since the specific processing flow is the same as that in Application Example 1 described in the first embodiment above, the description is omitted.

[0170] The specific processing unit 290 sends the result of the specific processing obtained in Embodiment 1 or Application Example 1 described above 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 voice representing the user's input on the specific processing result. The control unit 46A sends the voice data representing the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0171] Furthermore, the processing performed by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it can also be executed jointly by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Additionally, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or external devices, and the robot 414 acquires or collects information required for processing from the data processing device 12 or external devices.

[0172] In the above embodiments, examples of specific processing performed by the data processing device 12 are given, but the technology disclosed herein is not limited to this, and specific processing may also be performed by the robot 414.

[0173] Furthermore, the emotion-specific model 59, serving as an emotion engine, can determine the user's emotion based on a specific mapping. Specifically, the emotion-specific model 59 can determine the user's emotion based on an emotion graph that serves as a specific mapping (see...). Figure 9 The system determines the user's emotions. In addition, the emotion-specific model 59 can also determine the robot's emotions in the same way, and the specific processing unit 290 can also perform specific processing using the robot's emotions.

[0174] Figure 9 This is a diagram representing an emotion map 400 that maps various emotions. In the emotion map 400, emotions are arranged radially from the center in concentric circles. The closer to the center of the concentric circles, the more primitive the emotion is. Further out on the concentric circles, emotions are arranged representing states or actions arising from mood. Emotion is a concept that includes both feelings and mental states. To the left of the concentric circles, emotions generated by reactions occurring in the brain are arranged roughly. To the right of the concentric circles, emotions guided by situational judgments are arranged roughly. Above and below the concentric circles, emotions generated by reactions occurring in the brain and guided by situational judgments are arranged roughly. Furthermore, the emotion of "pleasure" is arranged above the concentric circles, and the emotion of "unpleasantness" is arranged below. Thus, in the emotion map 400, various emotions are mapped according to the structure of emotion generation, while easily generated emotions are mapped nearby.

[0175] These emotions are distributed at the 3 o'clock position on the Emotion Chart 400, and usually fluctuate between peace and unease. In the right half of the Emotion Chart 400, because situational awareness is more dominant than internal feelings, it gives a sense of calm.

[0176] The inner side of the emotion diagram 400 represents the mind, and the outer side of the emotion diagram 400 represents actions. Therefore, the further you go to the outer side of the emotion diagram 400, the more the emotion can be seen (manifested in actions).

[0177] Here, human emotions are based on a balance of various factors such as posture and blood sugar levels. When these balances deviate from the ideal, it indicates unhappiness; when they approach the ideal, it indicates pleasure. In robots, cars, and motorcycles, emotions can also be created based on a balance of factors such as posture and remaining battery power. When these balances deviate from the ideal, it indicates unhappiness; when they approach the ideal, it indicates pleasure. Emotion maps can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Speech Emotion Recognition and Brain Physiological Signal Analysis Systems for Emotion, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). In the left half of the emotion map, emotions belonging to the "Reaction" domain, where sensation is dominant, are arranged. Furthermore, in the right half of the emotion map, emotions belonging to the "Situation" domain, where situational cognition is dominant, are arranged.

[0178] The emotion map defines two types of emotions that promote learning. One is a negative emotion located near the middle of "repentance" or "reflection" on the situation side. That is, when the robot experiences negative emotions such as "I never want to feel this way again" or "I never want to be scolded again." The other is a positive emotion located near "desire" on the response side. That is, when the robot experiences positive feelings such as "wanting more" or "wanting to know more."

[0179] The emotion-specific model 59 feeds user input into a pre-learned neural network to obtain emotion values ​​representing each emotion shown in the emotion graph 400, and determines the user's emotion. This neural network is pre-learned based on multiple learning data sets that combine user input with emotion values ​​representing each emotion shown in the emotion graph 400. Furthermore, this neural network is learned to... Figure 10 As shown in sentiment graph 900, nearby sentiment values ​​are similar to each other. Figure 10 Examples show how emotions such as "peace of mind," "stability," and "reassurance" can result in similar emotional values.

[0180] The above description primarily focuses on the functions of the data processing device 12 to illustrate the system disclosed herein. However, the system disclosed herein may not necessarily be implemented on a server. The system disclosed herein can be implemented as a general information processing system. For example, it can be implemented as a software program running on a personal computer, an application running on a smartphone, etc. The methods disclosed herein can be provided to users in the form of SaaS (Software as a Service).

[0181] In the above embodiments, an example of specific processing performed by a single computer 22 is given. However, the technology disclosed herein is not limited to this, and distributed processing of specific processes by multiple computers, including computer 22, is also possible. For example, the data generation model 58 may also be provided in an external device of the data processing apparatus 12, where data is generated based on input data.

[0182] In the above embodiments, an example of storing a specific processing program 56 in memory 32 is illustrated, but the technology disclosed herein is not limited thereto. For example, the specific processing program 56 may also be stored in a portable computer-readable non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 performs specific processing according to the specific processing program 56.

[0183] Alternatively, the specific processing program 56 can be stored in a storage device such as a server connected to the data processing device 12 via a network 54, and the specific processing program 56 can be downloaded and installed into the computer 22 upon request from the data processing device 12.

[0184] Furthermore, it is not necessary to store the entire specific process 56 in a storage device such as a server connected to the data processing device 12 via the network 54, nor is it necessary to store the entire specific process 56 in the memory 32; a portion of the specific process 56 may also be stored.

[0185] As a hardware resource for performing specific processing, various processors can be used. For example, a CPU can be listed as a processor; it is a general-purpose processor that functions as a hardware resource for performing specific processing by executing software, i.e., a program. Furthermore, as a processor, a dedicated circuit can be listed as a processor; it is a processor with a circuit structure specifically designed for performing specific processing, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit).

[0186] Every processor has built-in or connected memory, and every processor performs specific processes by using memory.

[0187] The hardware resources for performing a specific process can consist of one of these various processors, or a combination of two or more processors of the same or different types (e.g., a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Furthermore, the hardware resources for performing a specific process can also be a single processor.

[0188] As an example of a single processor, the first type consists of a combination of one or more CPUs and software, which functions as a hardware resource to perform specific processing. The second type uses a processor, such as a System-on-a-chip (SoC), which implements the entire system functionality, including multiple hardware resources performing specific processing, using a single IC chip. In this case, the specific processing is implemented using one or more of the aforementioned processors that serve as hardware resources.

[0189] Furthermore, as the hardware architecture of these various processors, more specifically, circuits combining semiconductor elements and other circuit components can be used. Moreover, the specific process described above is merely an example. Therefore, it goes without saying that, without departing from the main point, unnecessary steps can be removed, new steps can be added, or the processing order can be changed.

[0190] The foregoing descriptions and illustrations are detailed explanations of the parts covered by this disclosure and are merely one example of this disclosure. For instance, the descriptions of the above-described structure, function, role, and effect are just one example of the structure, function, role, and effect of the parts covered by this disclosure. Therefore, it goes without saying that, without departing from the spirit of this disclosure, unnecessary parts can be deleted, new elements can be added, or replacements can be made to the foregoing descriptions and illustrations. Furthermore, to avoid confusion and facilitate understanding of the parts covered by this disclosure, explanations of technical common sense that does not require special explanation for implementing this disclosure have been omitted from the foregoing descriptions and illustrations.

[0191] All documents, patent applications and technical standards described in this specification are incorporated herein by reference as if they were specifically and individually described as incorporated by reference.

[0192] In addition, the following notes further explain the above explanation.

[0193] (Note 1) A data center crisis management system, comprising a data collection department, a risk analysis department, and a response strategy formulation and execution department, is characterized in that... The data collection unit automatically collects real-time data from various sensors and monitoring devices within the data center, such as power systems, cooling devices, security monitoring, fire detection, personnel monitoring, and network monitoring, as well as data from external sources such as meteorological information, news, SNS, and disaster information. The collected data is then classified and organized according to attributes such as time series, location of occurrence, equipment type, anomaly degree, reliability, and information release source. The risk analysis department uses generative AI, employing techniques such as natural language processing, pattern recognition, anomaly detection, comparison with past failure cases, and correlation analysis with external data, to analyze the massive amounts of data obtained from the data collection department. It extracts risk factors such as power outages, cooling anomalies, fires, cyberattacks, communication failures, human error, and external disasters, quantitatively predicts the probability and impact of these risks, and performs composite risk assessments and prioritization when multiple risks occur simultaneously. Based on the risk assessment results from the Risk Analysis Department, the Response Strategy Formulation and Implementation Department uses generative AI to automatically generate multiple optimal response strategies corresponding to risk levels and types, while referencing past response cases, operational regulations, external guidelines, laws, and contractual conditions. It also automatically creates reports and alarm notifications optimized for management, on-site supervisors, and operational departments. Furthermore, for approved response plans, it coordinates with relevant equipment control systems and network management systems to automatically execute specific countermeasures such as automatic activation of the fire suppression system, emergency generator startup, external connection disconnection, load balancing, and sending instructions to on-site supervisors.

[0194] (Note 2) As described in Appendix 1, the data center crisis management system is characterized by the use of generative AI. The risk analysis department analyzes real-time data from various sensors and monitoring devices such as power systems, cooling devices, security monitoring, fire detection, personnel monitoring, and network monitoring, as well as various external data such as meteorological information, news, SNS, and disaster information, acquired by the data collection department through natural language processing, pattern recognition, anomaly detection, comparison with past failure cases, and correlation analysis with external data. It extracts risk factors, predicts the probability and impact of risk occurrence, conducts composite risk assessment, and prioritizes risks. The response strategy formulation and execution department automatically generates the best response strategy corresponding to the risk level and risk type, automatically creates optimized reports and alarm notifications for relevant personnel, and automatically executes the countermeasures based on the approved countermeasures and in conjunction with the equipment control system and network management system.

[0195] (Note 3) As described in Appendix 1, the data center crisis management system is characterized by using generative AI. The risk analysis department combines and analyzes past failure cases, external databases, and currently acquired data to generate multiple risk scenarios. The response strategy formulation and execution department automatically generates multiple optimal response strategies for each scenario, automatically creates reports and alarm notifications with optimized content for management, on-site personnel, operations departments, and other relevant personnel, and automatically executes specific countermeasures such as automatic activation of the fire extinguishing system, emergency generator activation, external connection disconnection, load balancing, and sending instructions to on-site personnel based on the approved countermeasures and in conjunction with the equipment control system and network management system.

Claims

1. A crisis management system, comprising an information collection department, a risk analysis department, and a response strategy formulation and execution department, characterized in that, The Information Collection Department automatically acquires data in real time from multiple information sources, including news, social media, weather information, market data, disaster information, and other sources that may affect the company's activities, both internally and externally. The acquired data is then categorized and organized according to attributes such as time series, location of occurrence, relevant departments, and degree of impact. The Risk Analysis Department uses generative AI, employing natural language processing, pattern recognition, comparison with past cases, and correlation analysis with external data, to analyze the massive amount of data obtained from the Information Collection Department. This process extracts risk factors, quantitatively predicts the probability and impact of risks, and automatically determines risk levels such as major, moderate, and minor risks based on their severity. The Response Strategy Formulation and Implementation Department, based on the risk assessment results from the Risk Analysis Department, uses generative AI to automatically generate multiple optimal response strategies corresponding to the risk level, while referencing past response cases, internal company regulations, and external best practices. It automatically creates reports and alerts optimized for management, responsible departments, and other relevant personnel. Furthermore, for approved response plans, it integrates with relevant internal company systems to automatically execute specific countermeasures such as inventory adjustments, system switching, backup startup, and personnel assignment.

2. The crisis management system as described in claim 1, characterized in that, Using generative AI, the risk analysis department analyzes various data, such as news, SNS, weather information, market data, and disaster information, acquired by the information collection department from within and outside the company through natural language processing and pattern recognition. It extracts risk factors, predicts the probability and impact of risk occurrence, and automatically determines the risk level. The response strategy formulation and execution department automatically generates the best response strategy corresponding to the risk level, automatically creates optimized reports and alarm notifications for management, responsible departments, and other relevant personnel, and automatically executes the response strategy based on the approved response plan and in conjunction with relevant systems.

3. The crisis management system as described in claim 1, characterized in that, Using generative AI, the risk analysis department combines and analyzes past risk occurrence cases, external data, and currently acquired data to generate multiple risk scenarios. The response strategy formulation and execution department automatically generates multiple optimal response strategies for each scenario, automatically creates reports with optimized content for management, responsible departments, and other relevant personnel, and automatically executes specific countermeasures such as inventory adjustments, system switching, backup startup, and personnel allocation based on approved countermeasures and related system linkages.

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