1486 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.

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 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 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 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 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 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.

The invention belongs to the technical field of artificial intelligence, and particularly relates to a voice question-answering intention recognition method based on large-model multi-agent, which comprises the following specific steps: voice data preprocessing and intention labeling; reinforcement learning multi-agent training is carried out; obtaining a dialogue state through a dialogue state tracking technology; and updating the model through a near-end strategy optimization algorithm. According to the method, the key problems of data quality and consistency, intelligent agent collaboration and learning ability, dialogue state tracking, stability and high efficiency of strategy updating and the like are solved, the user experience and the overall performance of the system are remarkably enhanced, and it is ensured that the system can quickly and accurately respond to user requirements.

The invention discloses a health monitoring system and method based on multi-sensor fusion. The system comprises a health index acquisition system used for acquiring various health index data through various sensors; the multi-modal data analyzing and processing system is used for analyzing and processing the obtained multi-modal health index data to generate sensing information and analyzing and processing different data to obtain corresponding health indexes; and the health index evaluation report system is used for generating a health index evaluation report according to the perception information. According to the system, multiple data sources including images, sign signals, voice interaction and the like are fused through multi-modal data, and comprehensive health assessment is achieved. Different from a traditional health monitoring system which only depends on a single signal, the system fuses various data and can provide a more comprehensive and detailed health analysis result. Four diagnosis methods of watching, smelling, asking and cutting in traditional Chinese medicine are creatively combined with a modern intelligent technology, and modern application of health monitoring is achieved.

The invention provides an intelligent question and answer 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 large models, intelligent question and answer and the like. According to the historical question and answer information, matching a target dialogue skill from a pre-constructed dialogue skill library; and in response to the matched target dialogue skill, generating the target reply information according to the target dialogue skill. Therefore, the target reply information is generated according to the target dialogue skill, the professionality and persuasion of the target reply information are improved, the user requirements are better met, the accuracy of the target reply information is improved in an intelligent question and answer scene, and the user experience is improved.

The invention provides a digital human video generation method and device based on a large model, an intelligent agent, electronic equipment and a storage medium, relates to the technical field of artificial intelligence, in particular to the technical fields of deep learning, the large model, computer vision and the like, and can be applied to scenes such as video live broadcast, advertisement production, e-commerce sales and the like. The digital human video generation method based on the large model comprises the steps that demand information is acquired, the demand information comprises action description information used for describing a specified action video clip, and the action video clip represents a specified action of a target object; processing the demand information by utilizing a language large model to obtain a target script which comprises a target oral playing fragment text matched with the action description information; and processing the target script and the action video clip by using a visual large model to obtain a target video which is used for displaying oral playing of the target object based on the target oral playing clip text in the process of executing the specified action.

The invention provides a DDD architecture service code review and evaluation method based on a multi-expert LLM, and belongs to the technical field of industry intelligence and the field of software process quality assurance. Multi-expert large model cooperation, token polling transmission and specific scene domain drive design (DDD) architecture analysis are adopted; in the same submitted code group, different dominant large models can be tried to serve as dominant large models, and final code review is performed to optimize an access condition of a suggested number. And through a multi-expert large model cooperation mechanism, the environment problem easily generated by a single large model is avoided, and the review stability is improved. The cooperation efficiency between large models is improved through a token polling mechanism of a large model expert group, fair speaking of each large model is ensured, an online state detection mechanism of a subordinate large model can ensure that tokens are transmitted to currently available large models, the situation that the overall review process is affected by individual large models is avoided, and the review efficiency is improved. And the business code review efficiency of the system is effectively improved.

The invention relates to the technical field of power plant operation monitoring, in particular to an intelligent power plant operation data monitoring method based on artificial intelligence assistance. The method comprises the following steps: extracting multi-source power plant operation data, detecting an abnormal value, obtaining optimized power plant operation characteristics through interpolation optimization processing, analyzing the multi-source data by using an artificial intelligence technology, predicting aging state characteristics of equipment, carrying out difference comparison, identifying potential hardware operation faults, and carrying out fault diagnosis. The method comprises the following steps: identifying a cooperative connection path of equipment through operation data, predicting a cooperative fault risk node, mapping a risk physical connection relationship of the equipment, performing fault transfer simulation based on the risk physical connection relationship, generating hardware fault transfer data, and determining hidden danger data when each hardware has an operation fault; and integrating the hardware operation fault information and the corresponding data into operation fault monitoring data, and carrying out fault alarm. According to the invention, intelligent and systematized power plant operation monitoring is realized, and construction and development of an intelligent power plant are promoted.

The invention discloses an interactive access method, device and equipment of an intelligent agent and a storage medium, and relates to the technical field of artificial intelligence, and the interactive access method of the intelligent agent comprises the following steps: receiving an initial service session request initiated by a user, carrying out request interception on the initial service session request based on session request information, determining a target service session request; and determining a service scene to which the target service session request belongs according to a preset intention recognition model, calling an intelligent agent in the service scene to respond to the target service session request, and returning a standard service answer corresponding to the target service session request. According to the technical scheme, the threshold of accessing the intelligent agent by the user is reduced, and the processing efficiency of the digitization capability of each service scene is improved.

The invention belongs to the technical field of artificial intelligence, and particularly relates to a large model voice question and answer method based on historical dialogue information, which comprises the following specific steps: selecting a large language model GPT for pre-training; taking the historical dialogue information of the user as a current state; learning by using a deep Q network; setting a reward index based on the user satisfaction and the dialogue round; historical information is stored and utilized in combination with a long-term and short-term memory network; and guiding the updating of the large language model by using a DQN network. The method has the advantages that personalized response can be provided, the efficiency is improved, the user experience is enhanced, and high adaptability and accuracy are achieved; through the combined action of the characteristics, the overall performance of the voice question-answering system is remarkably improved, and the voice question-answering system is more effective and user-friendly in practical application.

The invention discloses a document generation method based on fusion of a large model and a knowledge graph. On the basis of text generation completely based on a large model at present, the large model and the knowledge graph are fused, a technical scheme of a document generation technology based on fusion of the large model and the knowledge graph is provided, and the technical scheme is innovative application of an artificial intelligence technology and a service scene. According to the method, the knowledge graph is constructed with the assistance of the large model, the law enforcement document is generated with the assistance of the knowledge graph, traditional manual writing of the document is assisted through the document generation technology, and the problems that a traditional mode document is long in writing period, non-standard in content, difficult in information tracing and the like are solved. According to the method, the accuracy and the normalization of law enforcement document generation are further improved, meanwhile, the document writing effect is improved, and intelligent law enforcement whole-course electronization in a real sense is achieved.