1932 results about "Smart technology" patented technology

An AI-based Cybersecurity System and Method enable real-time detection, analysis, and mitigation of cyber threats within computing networks using adaptive artificial intelligence. The system continuously monitors network traffic, extracts behavioral and contextual attributes, and applies deep learning-based inference to identify anomalous activities indicating security breaches. The method integrates several computational units, including a network monitoring unit, feature extraction unit, artificial intelligence processor, contextual reasoning processor, and decision synthesis unit, to compute a composite risk index quantifying threat likelihood and severity. A classification processor categorizes detected threats into types such as ransomware, phishing, or unauthorized access, while a mitigation control processor initiates automated response actions to isolate compromised nodes and restore network integrity. An adaptive learning processor updates AI models using feedback from confirmed incidents. This provides a scalable, self-evolving cybersecurity framework that minimizes human intervention and enhances resilience against dynamic and zero-day threats.

The invention belongs to the technical field of industrial equipment monitoring, and discloses an industrial production equipment monitoring and early warning system based on the Internet of Things and edge intelligence, and the system collects multi-modal data in real time through a distributed sensor network, and carries out the real-time processing and feature extraction through the edge intelligence technology. Data fusion and optimization are carried out through a cloud deep learning model, a self-adaptive early warning and decision module dynamically adjusts a threshold value and optimizes an operation strategy, an environmental adaptability optimization module ensures the stability of equipment under extreme conditions, and an equipment health management and collaborative maintenance module realizes cross-equipment collaborative maintenance. The remote monitoring and interaction module provides man-machine interaction type control, and the cross-device collaborative learning module improves the model performance through federal learning. According to the system, the accuracy of equipment monitoring, the timeliness of early warning and the intelligent level of maintenance are remarkably improved.

The invention provides a large model cue word design method, system and device in an industrial scene and a medium, and belongs to the technical field of artificial intelligence. The method comprises the following steps: constructing a private cue word template library, and forming a domain special knowledge unit set through multi-source industrial data collection, intelligent semantic analysis and classified storage; customizing a four-layer structured template, sequentially establishing a task overview layer, a step disassembly layer, an instruction refining layer and an output specification layer, and constructing a layered mapping model from task definition to output execution; template library management and iterative optimization are carried out, and dynamic updating and performance improvement of a cue word system are realized through centralized system management, multi-source feedback acquisition and data-driven optimization. According to the method, a cue word design and optimization system based on a hierarchical structure is constructed for complex task requirements in an industrial scene, and the task processing performance of a large model in scenes of industrial production, quality detection, equipment operation and maintenance and the like is improved.

The invention discloses a task processing method and device, equipment, a storage medium and a product, and relates to the technical field of artificial intelligence, and the method comprises the steps: responding to a to-be-executed task input by a user, inputting the to-be-executed task and a preset cue word into a large language model, and obtaining a task analysis result output by the large language model; distributing each sub-task corresponding to the to-be-executed task to a corresponding service agent based on the task analysis result; a technical agent corresponding to the subtask is scheduled through the service agent to execute the subtask, and subtask execution results are summarized; and determining a task execution result corresponding to the to-be-executed task based on the sub-task execution result. Compared with the mode that an existing automatic robot can only execute the set task according to the fixed processing flow, the method has the advantages that the to-be-executed task can be fully understood, the to-be-executed task can be split, the to-be-executed task is executed in a graded mode through the corresponding service agent scheduling technology agent, human intervention can be reduced, and the user satisfaction degree can be improved.

A novel avatar-based conversational AI platform that integrates large language models (LLMs) with realistic avatar visualization is disclosed. The platform employs advanced AI technologies to create lifelike avatars capable of personalized and emotionally responsive communication with users. avatar creation tools enable users to generate unique avatar configurations, while animation machine learning subsystems automatically generate the avatar's speech, movements, and emotional responses in real-time. personality machine learning subsystems direct the avatar's voice, personality, and emotions, enhancing the realism of the interaction. The platform supports integration with third-party applications and provides comprehensive deployment and asset management tools. A two-way messaging architecture enables real-time interactions between avatars and users. Authorization and licensing controls protect intellectual property rights, while the avatar's personality dynamically evolves based on user interactions. By integrating LLMs, real-time animation, and emotional intelligence, the platform offers a groundbreaking solution for creating immersive and personalized conversational experiences across various applications.

The invention relates to the technical field of artificial intelligence, in particular to an agent-based question and answer method and device, equipment and a medium. The main agent obtains to-be-processed data input by a user for the to-be-executed task in the first dialogue round; determining a target intention of the to-be-processed data, determining a target response agent corresponding to the target intention in a plurality of response agents according to a pre-stored corresponding relationship between different intentions and the response agents, and sending the to-be-processed data to the target response agent; the target response agent processes the to-be-processed data based on a pre-configured large model of the target response agent, obtains a processing result and feeds back the processing result to the main agent; and the main agent receives and outputs the processing result, and the data to be processed are processed by using the large model, so that the question and answer accuracy is improved.

The invention provides an implementation method, system and equipment of a commercial intelligent system based on a large model ReAct, and belongs to the technical field of artificial intelligence. The method is applied to a ChatBI platform. Comprising the steps that a main agent receives query request information from a user terminal and guides an LLM to conduct cyclic reasoning on the query request information according to a preset ReAct normal form through a preset Prompt project, and the cyclic reasoning process comprises the steps that 1, input information from an upstream task is analyzed through the LLM, and a to-be-executed action corresponding to a downstream task is determined; step 2, according to each action task corresponding to the action to be executed and the action task type thereof, executing a reasoning action; 3, receiving an inference action feedback result, updating a loop inference context, and determining whether to trigger a next round of loop inference or not until a preset loop termination condition is met; and based on each decision analysis result obtained by loop reasoning, generating a query result and sending the query result to the user terminal.

The invention relates to the technical field of environment monitoring, in particular to a forest fire monitoring method based on edge intelligence, which comprises the following steps: S1, deploying edge nodes and sensing equipment; s2, collecting data acquired by the sensing equipment in real time, and performing primary processing on the data; s3, fire risk intelligent identification and decision making are carried out through the edge nodes; s4, emergency response and event management; and S5, user interaction and feedback. The edge intelligence technology is introduced, the architecture of the forest fire monitoring system is optimized, and local processing and real-time analysis of data are realized, so that the response speed and the detection precision of fire monitoring are improved, and the method is suitable for further popularization and application.

The embodiment of the invention relates to the technical field of artificial intelligence, and provides a linkage method and system of an apartment network and intelligent equipment, and the method comprises the steps: collecting network operation data and equipment operation data of a target apartment in real time; based on the equipment operation data, determining currently achievable candidate service scenes of the target apartment; fusing the network operation data and the equipment operation data into a joint operation map of the target apartment through the space-time diagram attention network; a multi-agent depth deterministic strategy gradient algorithm is adopted to carry out network resource allocation and equipment control decision making on the joint operation map, scene adaptive optimization is realized in combination with candidate service scenes, and a linkage strategy of a target apartment is obtained; and generating a decision instruction based on the linkage strategy, and adopting the decision instruction to realize remote control of the network equipment and the intelligent equipment. Through deep fusion of the space-time diagram attention network and multi-agent reinforcement learning, dynamic collaborative management and control of the network and equipment are realized, and the resource utilization rate and the service response speed are improved.

The invention provides a medical record data processing method and system based on multiple agents, and the method comprises the steps: collecting medical record data in a hospital information system, and carrying out the preprocessing; a plurality of agents are designed, a multi-agent reinforcement learning algorithm is adopted to train the agents, and through mutual cooperation and competition among the agents, an optimal strategy for classifying and processing medical record data is autonomously learned; introducing an attention mechanism to construct a self-adaptive pre-training model based on a bidirectional long-short-term memory network to intelligently extract the medical record data, and extracting key information; enhancing and repairing the medical record data by adopting a generative adversarial network, and generating an enhanced medical record data sample; the updated knowledge graph is monitored in real time, the knowledge graph is optimized in real time according to the monitoring result, and the medical record data is input and dynamically updated. According to the medical record data processing method and system, through combination of multiple frontier artificial intelligence technologies, the efficient, accurate and flexible medical record data processing method and system are provided.

The invention provides an intelligent question and answer method and device, electronic equipment and a storage medium, and belongs to the technical field of artificial intelligence. The method comprises the steps that an input question is analyzed, and a query intention and entity relation graph is constructed based on an analysis result; based on the query intention and an entity relation graph, querying a knowledge graph library to obtain first retrieval information; vectorizing the input question, and retrieving a vector library based on a vectorization result to obtain second retrieval information; the first retrieval information, the second retrieval information and the input question are integrated and then input into a large language model, and a question answer output by the large language model is obtained. The scheme can be applied to different fields and scenes, the intention of the user can be accurately captured in both daily consultation and professional questions, and related answers can be retrieved from a huge knowledge graph library.

The invention discloses a product recommendation method and device based on an intelligent agent and a medium, and belongs to the technical field of artificial intelligence, and the method comprises the steps: obtaining a user portrait of a target user in response to a product recommendation request for the target user; if the target user is a new user, the user portrait is an initial portrait constructed by performing question and answer interaction with the target user by using an interaction agent in the large model agent; based on an analysis agent in the large model agent, determining a matching degree between the user portrait vector of the target user and each product vector in a product vector library of the to-be-recommended product set, and generating a product recommendation result for the target user according to the determined matching degree; under the condition of insufficient user data, the analysis of the recommendation capability of the agent is formed by simulating feedback of different users on recommendation results by using a large model and training based on simulated data. The product recommendation technology is optimized, and the performance of the recommendation system and the user experience are improved.

The invention discloses an air purification system for a clean workshop. The air purification system comprises a self-adaptive control unit, an air quality monitoring module, an air flow control assembly, an environmental parameter adjusting assembly, a self-optimization filtering assembly, a human-computer interaction interface and predictive maintenance system, an energy efficiency optimization module and a remote diagnosis and early warning system. On the basis of intelligent technologies such as machine learning, reinforcement learning and a data fusion algorithm, real-time monitoring, dynamic optimization and energy efficiency management of the air quality of the clean workshop are achieved. Based on real-time data and historical data, an algorithm is used for carrying out self-adaptive optimization on the air purification system, and manual intervention is reduced. And the running state of the system is dynamically adjusted through the energy efficiency optimization module, so that the energy consumption is reduced while the cleanliness is ensured. The system can diagnose equipment faults in advance, reduce downtime and prolong the service life of equipment. And due to a man-machine interaction interface and a remote monitoring function, an operator can know the state of the system at any time and carry out regulation and control.

The invention discloses a large language model-based situational problem reasoning method and related equipment, and relates to the technical field of artificial intelligence. The method comprises the following steps of: obtaining a situational problem; the situational question is input into an intelligent agent, a first answer output by the intelligent agent is obtained, and the first answer is obtained through reasoning of a large language model integrated in the intelligent agent based on the situational question; if it is determined that the first answer is wrong, the first answer is input to the intelligent agent, a second answer output by the intelligent agent is obtained, and the second answer is obtained by reasoning of the large language model after the intelligent agent conducts reasoning reflection or knowledge supplementation on the large language model based on the first answer; wherein the intelligent agent is obtained based on sample situation problem training. By implementing the technical scheme provided by the invention, a mechanism can be supplemented to the reflection and knowledge of the intelligent agent, so that the accuracy and reliability of answering the situation question are improved.

The invention discloses a multi-robot task scheduling method, system and device and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the role definition of a robot, collecting environment information in real time, and constructing a dynamic environment model; according to the dynamic environment model, identifying current tasks to be executed, evaluating urgency, complexity and resource requirements of each task, and forming a priority list; disassembling a high-priority task in the priority list into sub-tasks, and assigning a role and a task allocation scheme of the robot; a biologically inspired self-organizing algorithm is adopted, the robot exchanges information according to the state of the robot and task requirements, and a task allocation scheme is dynamically adjusted; the robot exchanges information according to the own state and the task demand, and dynamically adjusts the task allocation scheme, so that the flexibility and adaptability of task allocation are realized, the change of environment and state can be responded in time, and task delay caused by insufficient resources is avoided.

The invention provides a task processing method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence, in particular to the technical fields of cloud terminals, agents, large models, natural language processing, deep learning and the like. According to the specific implementation scheme, a target task is received through an intelligent agent service, and a target application needed for executing the target task is determined; calling a task scheduling service through a model context protocol (MCP) service so as to select a target cloud terminal through the task scheduling service; and controlling a target application in the target cloud terminal through the cloud terminal control service to execute the target task.

The invention discloses a task scheduling method and device, equipment, a storage medium and a product, and relates to the technical field of artificial intelligence. According to the method, a large model is divided into a plurality of sub-tasks, the computing power demand is quantified in real time, and the matching score of the schedulable task and each neural network processor node is calculated in combination with the computing power evaluation information of each neural network processor node; the schedulable task with the highest priority in the scheduling queue is allocated to the neural network processor node with the highest matching score, and all subtasks of different large models are reasonably allocated through loop execution; besides, priorities are distributed to the to-be-scheduled sub-tasks in the scheduling queue based on the scheduling strategy, scheduling is carried out through a greedy algorithm, it can be guaranteed that each scheduling behavior is a real-time optimal scheduling scheme, and therefore the problem how to schedule large model tasks more efficiently is solved, and the scheduling efficiency is improved. And the computing power resource of the neural network processor is efficiently utilized to reduce the technical problem of computing power waste.

The invention discloses a preschool education field knowledge base construction method fusing a knowledge graph and a large language model, and relates to the technical field of preschool education method optimization. Multi-channel collection of preschool education data and cleaning preprocessing; utilizing a customized model to extract entities and relationships, and constructing a structured knowledge graph; performing fine tuning on the big language model in the preschool education field; fusing atlas and model knowledge through a gating mechanism, and performing dual verification; establishing a monthly updating mechanism to realize incremental learning; in addition, personalized knowledge recommendation is realized based on a user portrait, interactive questions and answers are optimized through combination of retrieval and a model, and data security is guaranteed by adopting anonymization and authority control. Through multi-source data integration and intelligent technology fusion, an accurate and dynamic preschool education knowledge base is constructed, requirements of children are more accurately understood, content fitting cognition is generated, personalized learning recommendation is realized, interaction experience and knowledge service quality are improved, data security is guaranteed, and digital upgrading of the preschool education field is promoted.

The invention provides an intelligent dialogue method and system, and belongs to the technical field of intelligence, the intelligent dialogue method and system comprise an intelligent dialogue system method, the intelligent dialogue system method comprises an intelligent dialogue module, and the intelligent dialogue system is used for processing natural language interaction between a player and an AI assistant (space-time secrete). Comprising functional modules of semantic comprehension, sentiment analysis, context memory and the like; due to the addition of the emotion interaction unit, the system can identify the emotion of the player, such as happiness, disappointment or anger, and dynamically adjust a reply strategy through an emotion mapping strategy, such as giving comfort or encouragement at proper time, so that the reality sense and interestingness of interaction are greatly increased, the player feels understood and concerned, and the user experience is improved. Therefore, the viscosity and the satisfaction degree of the game are improved.

The invention discloses a question and answer large model training and question and answer method and device, related equipment and a program product, and relates to the technical field of artificial intelligence. The question and answer large model is trained by adopting a reinforcement learning mode, and the question sample is sent into the question and answer large model to obtain the output result of the question and answer large model. The quality of the answers is determined, the matching degree of the length of the thinking chain and the requirement for setting the length of the thinking chain is determined, the reward value of the output result is determined according to the quality of the answers and the matching degree of the length of the thinking chain, and the reward value is in positive correlation with the quality of the answers and the matching degree; and performing reinforcement learning training on the question and answer large model according to the reward value of the output result, enhancing the control capability of the question and answer large model on the output thinking chain length, and improving the quality of answers output by the question and answer large model.

The invention provides an agent evaluation method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence, in particular to the technical field of agents, large language models and the like. The intelligent evaluation method comprises the steps that a first agent and a second agent are called to execute a simulation task to obtain dialogue data, the simulation task is used for at least one simulation dialogue between multiple first objects and a second object generated by the second agent, and the multiple first objects are generated by the first agent and have different dialogue styles; and inputting the dialogue data into at least one evaluation large model of the third agent to obtain a target dialogue feature, and outputting an evaluation result of the second agent based on the target dialogue feature.

The invention provides a virtual expert creation method and device, equipment and a medium, and relates to the technical field of artificial intelligence, and the method specifically comprises the steps: receiving an expert knowledge base uploaded by an artificial expert, and carrying out the preprocessing of the expert knowledge base; performing text splitting and extraction on the preprocessed expert knowledge base on the basis of a large language model, and constructing an initial knowledge graph on the basis of extracted knowledge points and semantic relationships; receiving a modification operation instruction of the artificial expert on the initial knowledge graph through a man-machine interaction interface of a large language model; and in response to the operation instruction, dynamically adjusting and optimizing the initial knowledge graph, and generating a virtual expert with a professional domain knowledge graph. The method is used for replacing a traditional rule-based virtual expert system, and is more intelligent and efficient.

The invention discloses an intelligent question answering method and device, equipment, a storage medium and a program product, and relates to the technical field of business intelligence, and the intelligent question answering method comprises the steps: receiving a question input by a user; according to the problem, prompt words are generated through a preset multi-scene knowledge base, and the multi-scene knowledge base is generated based on a preset knowledge configuration file; inputting the cue word into a preset large language model to obtain a query code output by the large language model; and according to the query code, performing data query on a target database to obtain an answer of the question. According to the method, intelligent question answering of the configurable multi-scene knowledge base is realized, the problem that the existing intelligent question answering cannot effectively adapt to different scenes is solved, and the adaptive capacity of the intelligent question answering to different scenes is improved.

The invention discloses a training sample data generation method and device, a readable storage medium and a program product, and relates to the technical field of artificial intelligence. The method comprises the following steps: inputting a seed topic and a cue word of a programming language processing task into a sample data generation model; and modifying the seed topic in the modification range boundary condition by using the cue word based on the same algorithm thinking, and generating a test case set covering the constraint condition of the new topic according to the type of the new topic and the case generation condition. Answering the new question passing the verification, and outputting an answer passing the verification of the test case set; taking the new question, the test case set and the answer as training sample data; and adjusting and modifying the range boundary condition and / or the cue word and / or the use case generation condition, and generating new training sample data by utilizing the new question. The problem that training data generated by related technologies cannot meet model training requirements can be solved, and high-quality training sample data can be efficiently generated for programming language processing tasks.

The invention relates to an intelligent customer service question-answering system answer generation method and device and a storage medium. Belongs to the technical field of artificial intelligence. The method comprises the steps of obtaining text information input by a user; inputting the text information into the created knowledge base for retrieval recall, and determining a knowledge base slice recall result matched with the text information; inputting the text information and a knowledge base slice recall result into a large language model to obtain an information illusion cognition result; if the information illusion cognition result indicates that the illusion exists, not feeding back the question and answer based on retrieval to the user; if the information illusion cognition result indicates that illusion does not exist, the text information and the knowledge base slice recall result are input into a large language model, and a final answer result is obtained. The information illusion confirmation is performed based on the user input text and the knowledge base recall result, and the final reply result is output based on the confirmation result, so that the overall accuracy is improved, and the overall effect of the intelligent customer service question-answering system is improved.

The invention belongs to the technical field of industrial intelligence, and relates to a deep reinforcement learning-based non-correlation parallel machine scheduling method, which comprises the following steps of: constructing a mathematical model suitable for non-correlation parallel machine scheduling; the machining time of each workpiece on each heterogeneous machine is collected, and normalization processing is carried out; initializing a genetic algorithm scheduling population and a depth Q network; constructing a deep reinforcement learning training framework; expressing a state vector by using the average fitness, the optimal fitness and the optimal individual code; operating parameters of the genetic algorithm are controlled by using the action space, and parameters of the deep Q network are updated by using a reward function; and dynamically controlling operator selection in a genetic algorithm iteration process by using the trained deep reinforcement learning model to obtain an optimal scheduling solution and realize scheduling of the non-correlation parallel machine. According to the method, a deep reinforcement learning algorithm is provided for solving similar problems in the manufacturing industry production scheduling field by analyzing a non-correlation parallel machine scheduling problem data model, and the production efficiency is improved.

The invention relates to the technical field of artificial intelligence, and provides a large language model optimization method and device. According to the method, a candidate answer is obtained by inputting a question in a pre-trained large language model, a first reward value, a second reward value and a third reward value of the candidate answer are calculated, the first reward value is used for representing a basic reward of the candidate answer, the second reward value is used for representing the integrating degree of the candidate answer and the question, and the third reward value is used for representing the integrating degree of the candidate answer and the question. The third reward value is used for representing the alignment degree of the candidate answers and the user preferences, then performing multi-head fusion on the first reward value, the second reward value and the third reward value to obtain a target reward value, and finally training a pre-trained large language model based on the target reward value to obtain an optimized large language model. The importance coefficients of different levels of reward mechanisms of the large language model are adaptively learned in a multi-head layering manner, so that the model training efficiency is improved, and the generation quality of the trained model is improved.

The invention provides a method, system and device for achieving industry knowledge questions and answers based on a large model and a medium, and belongs to the technical field of artificial intelligence. The method comprises the steps that standard answers of industry experts are collected for questions with the occurrence frequency higher than a threshold value in a target industry, and a standard answer library is constructed; collecting professional knowledge related to the target industry, and constructing an industry knowledge base; responding to a target question, input by a user, of a target industry on a front-end interface, matching a standard answer library and a retrieval industry knowledge library according to the target question, when a return result exists, constructing a cue word according to the return result, and when the return result does not exist, constructing the cue word according to the retrieval industry knowledge library; according to historical multi-round dialogue information of the user on the front-end interface, cue words are constructed and provided for the general large model; and returning a knowledge question and answer result output by the general large model to the user through a front-end interface. By constructing the standard answer library and the industry knowledge library and combining the general large model, intelligent answering of the industry questions is achieved, and the accuracy and specialty of question answering are improved.

The invention discloses an AI-driven personalized foreign language learning content generation platform, and belongs to the technical field of artificial intelligence. Comprising the following modules: a data center and user portrait module used for comprehensively presenting personalized learning features and demands; the language ability evaluation module predicts the learning ability in combination with historical learning data and generates a standardized ability evaluation report; the intelligent content service module preferentially recommends the matched existing content, automatically generates personalized learning resources and dynamically adjusts the content difficulty; the learning path planning module continuously optimizes a path planning algorithm through a feedback closed loop to realize long-term formulation and dynamic adjustment of a learning strategy; the real-time monitoring and diagnosis module monitors and analyzes learning behaviors in real time, provides instant feedback and accurate error diagnosis, and continuously optimizes the learning effect based on data driving; and the multi-modal interaction module is used for providing scene-based adaptive interface display and ensuring interaction experience consistency and fluency among the functional modules.

The invention discloses a task scheduling method and device, a medium and a product, and relates to the technical field of artificial intelligence. According to the scheme, the task is received, the corresponding task information is determined, the agent modeling is carried out on the multiple existing agents to determine the capability portraits of the agents, various basic information of the to-be-executed task and the task execution capability of the agents in the system are clarified, and therefore the basis of multi-agent cooperation is formed; in consideration of resource competition and coordination problems in agent task allocation, when task scheduling is executed, tasks are sequenced to generate a task sequence, and the tasks are sequentially allocated to corresponding agents according to the task sequence, capability portraits and an operating system scheduling algorithm. The task allocation strategy can be dynamically adjusted according to the characteristics and task importance of the agents, the resource utilization rate is maximized, the task execution performance is optimized, and therefore the efficient and stable task scheduling requirement of the multi-agent system in the complex environment is met.