874 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 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 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 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 provides a man-machine interaction method, device, system and equipment and a medium, and relates to the technical field of artificial intelligence, in particular to the technical field of large language models, intelligent assistants and the like. According to the implementation scheme, in response to a received user instruction, an operation instruction generation request is obtained based on the user instruction and function description information of multiple functions; an operation instruction generation request is sent to the server, and the operation instruction generation request is used for triggering the server to apply the cloud large model so as to select at least one function from the multiple functions based on the user instruction and the function description information, and an operation instruction used for completing the user instruction is generated based on the at least one function; and in response to a received operation instruction sent by the server, executing a calling operation for each function in the at least one function based on the operation instruction so as to obtain an execution result of the user instruction.

The invention discloses a logistics vehicle path planning method and a motion control system, and belongs to the artificial intelligence technology, and the method comprises the steps: initializing map information, and obtaining a starting node coordinate, an obstacle node coordinate and a target node coordinate of a logistics vehicle at present; circularly executing the following steps until a target node is found, and generating an optimal path from the starting node to the target node; executing a self-adaptive search strategy guided based on a target direction from the current node to search candidate nodes; when a jump point exists in the candidate nodes, executing a key jump point security policy improved based on a JPS algorithm to screen security nodes; and through an A * algorithm cost function improved based on environment information and a reward function, an optimal node is screened out from candidate nodes or security nodes to serve as a current node for next search. The method can solve the problems that an A * algorithm of an existing path planning method is not high in search efficiency, and a JPS algorithm is not high enough in safety.

The invention relates to the technical field of performance evaluation, and provides an intelligent project performance evaluation and optimization platform which comprises a multi-source heterogeneous data fusion module, an association rule deep mining engine, a self-adaptive dynamic performance evaluation model, a closed-loop optimization decision module and an intelligent interaction system. According to the invention, an intelligent, dynamic and closed-loop project performance evaluation and optimization platform is constructed by fusing an advanced artificial intelligence technology and a project management theory, and the scientificity, refinement and intelligent level of project management is greatly improved.

The embodiment of the invention discloses a task processing method and device, electronic equipment and a storage medium, relates to the technical field of artificial intelligence, and can realize efficient processing of multi-link complex tasks and improve the reliability of task processing results. The method comprises the steps of receiving a target task processing request input by a user; based on the target task processing request, a thinking chain skill matched with the target task is determined, and the thinking chain skill describes a solution of the target task; the execution process of the target task is arranged based on a solution described by the thinking chain skill by applying the thinking chain skill, the execution process comprises at least one step needing to be executed by applying an atomic skill, and executable logic codes used for achieving a preset function are packaged in the atomic skill; when the atomic skills are applied, the executable logic codes are called to achieve a preset function; and according to the arranged execution process, executing the steps by using the atomic skills to obtain a processing result of the target task. The method and the device are suitable for complex task processing scenes of intelligent agents.

The invention belongs to the technical field of artificial intelligence, and particularly relates to a knowledge graph updating method, electronic equipment and a storage medium, and the method comprises the steps: obtaining a target new literature, and extracting a candidate triple; performing multi-dimensional conflict detection on the candidate triple; when the candidate triad does not pass the multi-dimensional conflict detection, determining a corrected triad based on the large model, the external knowledge base and the candidate triad; determining a target triple based on the corrected triple and an existing triple in the knowledge graph to be updated; and updating the to-be-updated knowledge graph based on the target triple. According to the technical scheme provided by the invention, stream processing, multi-modal verification and dynamic weight technologies are fused, triples are extracted in real time through an RAG model, a triple verification mechanism of semantic embedding, an aging range and an external knowledge base is combined, conflicts are accurately recognized, a correction strategy is generated by utilizing a large model, and confidence coefficients of new and old knowledge are balanced by adopting a weighted fusion algorithm, so that the reliability of the new and old knowledge is improved. Second-level updating is achieved, and the efficiency and accuracy of knowledge graph updating are improved.

The embodiment of the invention relates to the technical field of artificial intelligence, and provides an intelligent recommendation method and system based on the plasticizing industry, and the method comprises the steps: obtaining multi-source heterogeneous data from a plurality of third-party data sources of the plasticizing industry; constructing the multi-source heterogeneous data into a dynamic knowledge graph in combination with a plasticizing industry knowledge base; performing demand prediction on the dynamic knowledge graph based on a knowledge-guided hybrid particle swarm algorithm to obtain a first plasticizing recommendation strategy; enhancing a recommendation model through causal reasoning, and performing anti-fact prediction on the dynamic knowledge graph in combination with industry real-time change data to obtain a second plasticizing recommendation strategy; the first plasticizing recommendation strategy and the second plasticizing recommendation strategy are dynamically fused based on Bayesian optimization, a plasticizing industry recommendation report is generated, the plasticizing industry recommendation report comprises the industry flow relation change trend and the corresponding industry hotspot recommendation, and a user is assisted in marketing decision making. The method can shorten the time consumption of the whole supply-demand docking process, effectively alleviates the information asymmetry problem, and remarkably improves the industry operation efficiency.

The invention belongs to the technical field of wireless household electrical appliance intelligence, and discloses a wireless household electrical appliance intelligent identification and analysis method and system based on multi-protocol fusion, which can automatically extract new equipment protocol features through centralized middleware scheduling and adaptive learning mechanism of a protocol adaptation engine unit in combination with dynamic model training of an AI identification center unit. Generating a temporary adaptive interface and updating the protocol template; the dynamic protocol library supports OTA updating, a novel protocol and unknown equipment can be adapted without manual configuration, and the defects that the number of traditional system equipment is increased, and protocol type expansion is blocked are overcome; the intelligent optimization module dynamically adjusts the bandwidth priority and the data reporting frequency based on reinforcement learning and behavior prediction, guarantees smooth communication of key equipment in a high-concurrency scene, reduces standby power consumption of idle equipment, solves the problems of high-concurrency delay and high energy consumption of a traditional system, and improves the system reliability. And double improvement of identification reliability and system energy efficiency is realized.

The invention discloses a body intelligent controller architecture based on an end side intelligent model and an operation method, and belongs to the technical field of industrial body intelligence. According to the architecture, a CPU, a GPU and an FPGA are integrated by adopting a heterogeneous multi-core processor, and high-speed interconnection is realized through a star-type + NoC hybrid topology; a multi-level task scheduling mechanism is constructed based on a real-time operating system, and it is ensured that control tasks are executed preferentially. End-side visual model lightweight (YOLOv5s model parameters are reduced to 3.5 M) and FPGA hardware acceleration (control instruction time delay is 10ns) are innovatively realized on an algorithm framework layer, and a transmission bottleneck is eliminated through a zero-copy data path. The system supports model hot switching less than or equal to 10ms and FPGA bit stream online updating less than or equal to 50ms, and has MD5 check and an automatic rollback mechanism. Compared with a traditional separation type scheme, the processing time delay is reduced by 25 times, the hardware cost is reduced, the size is reduced, and the real-time performance, reliability and deployment flexibility of the industrial robot and other intelligent agents with the body are remarkably improved.

The invention discloses a delivery task cooperative processing method, device and equipment based on a quadruped robot and a medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: analyzing a delivery task through an upper computer, combining the real-time dynamic information of an office scene, and employing an improved binary particle swarm optimization algorithm to optimize the task distribution of multiple robots, and efficient collaborative distribution in an intelligent office environment is realized. The robot receives a distribution scheme sent by the upper computer, a multi-level navigation strategy is started by utilizing environmental perception data, a topological map and a D * Lite algorithm are fused to carry out path planning, and dynamic obstacle avoidance and efficient distribution are ensured. The target path planning information is monitored in real time through the upper computer, dynamic analysis is carried out through the scene perception algorithm, the scene relevance result is obtained, the collaborative distribution strategy is executed to complete the distribution task, the task response speed and the distribution efficiency are improved, interference to the office environment is reduced, and the work efficiency is improved. And automation and intelligentization of robot distribution in an intelligent office scene are realized.

The invention discloses an intelligent agent optimization method and device, equipment, a storage medium and a product, relates to the technical field of artificial intelligence, and discloses a method for recording a task execution track of an intelligent agent in a process of executing a task based on a target large model in the intelligent agent; extracting execution empirical data from the task execution track, wherein the execution empirical data comprises problems existing in the task execution process, generation reasons of the problems and solutions; and generating a target training sample based on the execution experience data, the target training sample being used for training the target large model to optimize the agent. According to the method, the efficiency of obtaining the training sample can be remarkably improved, so that the optimization efficiency of the intelligent agent is improved, and the ability of the intelligent agent to deal with rapidly changing task requirements and complex application scenes is enhanced.

The invention provides a port mobile machinery intelligent decision-making method, device and equipment based on multiple modes, and relates to the technical field of artificial intelligence. Comprising the steps of capturing a causal chain of wind speed sudden change-lifting appliance swing amplitude increase-AGV path offset through a multi-modal causal embedding layer, mapping heterogeneous data into uniform potential space representation, eliminating data modal difference, and adaptively enhancing the response capability to key risks by a model by dynamically adjusting a causal edge weight, so as to improve the stability of the AGV path. And furthermore, an AGV speed limiting or lifting appliance braking instruction is triggered through a preset condition. Therefore, according to the embodiment of the invention, the problems of multi-modal data fusion and dynamic causal modeling can be solved, the decision-making speed and accuracy are improved, and the collision risk is greatly reduced.

The invention discloses a cross-domain knowledge migration method and device, electronic equipment, a medium and a product, and relates to the technical field of artificial intelligence. Through the technical scheme of combining data acquisition preprocessing, cross-domain semantic alignment and retrieval enhancement generation technologies, the problems that a traditional development mode is low in efficiency and insufficient in model generalization ability are effectively solved, and the method has the advantages that the development efficiency is improved, the labor cost is reduced, the model generalization ability is improved, and the dynamic environment adaptability is enhanced.

The invention provides a large model evaluation method, a large model optimization method and device, electronic equipment, a storage medium, an intelligent agent and a program product, and relates to the technical field of artificial intelligence, in particular to the technical field of large models, the technical field of evaluation analysis, the technical field of optimization and the like. According to the specific implementation scheme, a dialogue log is obtained, wherein the dialogue log comprises dialogue content and an intermediate result; based on the dialogue content features of the dialogue content, determining evaluation nodes and a plurality of evaluation indexes belonging to the evaluation nodes, the evaluation nodes being used for evaluating sub-processes executed by the large model; based on the dialogue log, performing evaluation analysis on the large model, and determining respective evaluation results of the plurality of evaluation indexes; and based on the respective evaluation results of the plurality of evaluation indexes, determining a target evaluation result for the large model.

The invention relates to an intelligent nursing management system based on artificial intelligence, in particular to the field of intelligent nursing services, and realizes efficient and personalized nursing services by integrating multi-source heterogeneous data and an advanced artificial intelligence technology. According to the system, wearable equipment, physiological signals, environment data and emotion data are fused into low-dimensional spatiotemporal features by using a dynamic graph convolutional network, the data processing efficiency is greatly improved, an LSTM model is trained locally through a federated learning framework and a gradient distillation mechanism, the communication burden is reduced while privacy protection is optimized, and the user experience is improved. A dynamic path generation module analyzes user behaviors, generates personalized nursing paths and marks potential risks, and finally, a meta-reinforcement learning mechanism dynamically adjusts a feature weighting coefficient to ensure that a new user is adaptive within 15 minutes, so that the intelligence, accuracy and efficiency of nursing services are remarkably improved.

The invention discloses an advertisement creative real-time optimization and putting control method and system based on artificial intelligence, electronic equipment and a computer readable medium, and the method achieves the precise matching, effect prediction and dynamic regulation and control of the advertisement creative through an intelligent technology, thereby improving the efficiency and effect of advertisement putting. The core process of the method comprises the following steps: firstly, carrying out fusion and joint modeling on multi-source features such as user portraits, advertisement creative contents and real-time context environments so as to realize high-matching-degree real-time optimization of creativity, users and scenes; and secondly, a deep cross network (DCN) model is adopted to capture a complex high-order interaction relationship among the user, the creativity and the environment, so that key indexes such as a click rate (CTR) and a conversion rate (CVR) are predicted with high precision. Besides, the system establishes a real-time monitoring mechanism based on a sliding time window and a change slope, creative fatigue is actively identified through the change trend of data such as exposure, clicking and conversion, and intervention and adjustment are carried out in time.

The invention belongs to the technical field of honey analysis, and particularly relates to a method for analyzing characteristic chemical components of Qinling Mountain medium-peak honey. The analysis method comprises the following steps: obtaining different extraction layers, and carrying out mass spectrometry on each layer of sample; constructing a molecular network by mass spectrum data through a GNPS platform, and comparing the mass spectrum data with a database to obtain primary identification; unmatched nodes predict the skeleton by using an NAP tool to obtain a secondary result; then fractions are separated, and an unknown structure is deduced by combining an SMART technology with nuclear magnetism; and finally, synthesizing the three types of results to determine the characteristic chemical components of the honey. Compared with a single chromatography-mass spectrometry technology, the analysis method provided by the invention not only realizes data visualization, but also reduces the dependence of chemical component identification on a database to a certain extent, and provides reference for rapid characterization of chemical components in a honey crude extract or fraction.

The invention discloses an intelligent agent planning method, system and device based on context awareness and a medium, and belongs to the technical field of artificial intelligence. The method comprises the steps that firstly, user input is analyzed, structured task requirements are generated, and meanwhile, external tool function attributes and historical interaction data are collected; according to the method, an ordered execution plan is generated in combination with task requirements and tool attributes, subsequent plans are optimized by utilizing historical interaction contexts, in an execution stage, parameters required by a target function can be accurately extracted from the task requirements and context information, corresponding tools are called to complete a task, and finally, an execution result is fed back for display, so that the execution efficiency is improved. And performing execution control based on the function calling state and the result. According to the method, more intelligent task planning and execution are realized through context sensing, and the method is particularly suitable for complex task scenes needing multi-step and multi-tool cooperation.

The invention provides a model training method and device, electronic equipment, a storage medium and a program product, and relates to the technical field of artificial intelligence. The method comprises the steps of obtaining a model training task and a model structure feature corresponding to the model training task; acquiring a current resource state; dividing the model training task into a plurality of sub-tasks; selecting a target resource scheduling strategy based on the model training task, the model structure feature and the current resource state; and distributing the subtasks to corresponding computing nodes based on the target resource scheduling strategy, wherein the computing nodes are used for executing the corresponding subtasks. According to the embodiment, the target resource scheduling strategy is selected based on the model training task, the model structure feature and the current resource state, the sub-tasks are efficiently allocated to the computing nodes through the target resource scheduling strategy, and the distributed training efficiency and the resource utilization rate are improved.

The invention discloses a model reasoning method, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence. In the model reasoning process, various information is integrated, and the logarithm probability of candidate lexical elements to be generated is corrected, so that thinking switching of a model in the later stage of reasoning is avoided, and the reasoning efficiency is improved. In this way, the situation that the reasoning process is too long due to frequent switching of reasoning ideas is avoided, and waste of model reasoning resources is avoided while the accuracy of the model reasoning result is guaranteed.

The invention belongs to the technical field of medical treatment, and discloses a wearable intelligent method and device for monitoring the mental health of community old people, and the method comprises the steps: collecting the physiological indexes of the community old people in real time through an intelligent chest card, and achieving the dynamic collection of multi-dimensional data; data are transmitted to the cloud platform through Bluetooth or Wi-Fi, the equipment supports a data caching mechanism, and key data are temporarily stored when connection is abnormal; a dynamic evaluation model is constructed based on AI learning, and a multi-factor decision tree model and a natural language processing NLP algorithm are combined; a group psychological health trend and an environment risk thermodynamic diagram are presented in real time through a chest card APP, main factors influencing psychological health of the old are analyzed, and community managers are assisted in making an intervention plan. By making and applying the community old people mental health monitoring and early warning model, a unified mental health assessment system is provided for old people in different states, the applicability, universality and compliance of mental assessment are improved, home rehabilitation is achieved, and the overall medical cost is reduced.

The invention provides an intelligent agent-based information processing method and device and a storage medium, and relates to the technical field of artificial intelligence. The method comprises the following steps: in response to acquired session information currently input by a user, constructing a first cue word according to the session information by a planning module in an intelligent agent, and calling a first large language model according to the first cue word to perform task planning so as to obtain description information of a target task; an execution module in the intelligent agent constructs a second cue word according to the description information of the target task, calls a second large language model according to the second cue word to determine tool information required for executing the target task, and executes the target task according to the tool information; and a reply module in the intelligent agent constructs a third prompt word according to an execution result of the target task, and calls a third language model according to the third prompt word to generate a reply corresponding to the session information. By means of the method, the accuracy and efficiency of the reply output to the user can be improved, and the user experience is improved.

The invention relates to the technical field of intelligent tea garden management, and provides an intelligent tea garden service collaborative management system, which comprises a tourist picking reservation module used for receiving a reservation picking request of a tourist through a terminal and intelligently arranging autonomous picking time and region of the tourist; the picking behavior processing module is used for analyzing, processing and prompting picking behaviors of tourists; and the comprehensive management docking module is used for integrating all module data, generating a multi-dimensional report to be docked with the tea garden enterprise ERP system, generating a collaborative scheme of management personnel, planting personnel and customer service personnel, and completing collaborative management of data integration-role adaptation-decision landing. A full-chain tea garden management system from data acquisition and integration, growth and variety analysis to visual decision making is constructed, comprehensive improvement of yield increase, quality improvement, cost reduction and efficiency improvement of the tea garden is achieved by means of multi-source data fusion and intelligent technologies, economic benefits are enhanced, and the resource utilization efficiency and ecological sustainability are improved.

The embodiment of the invention relates to the technical field of artificial intelligence, in particular to a generation system of an intelligent scoring model, and the generation system of the intelligent scoring model provided by the embodiment of the invention identifies and classifies the professional field of the intelligent scoring model by using a configuration management module, and manages and stores the configuration information of the intelligent scoring model. And calling the large language model by using a model generation module to generate original data about the intelligent scoring model, processing the original data by using a data processing module, outputting tree structure data with an association relationship, and finally obtaining the intelligent scoring model. The professional field and standardized intelligent scoring model can be quickly generated by using the large language model, time and labor are saved, and the accuracy of the intelligent scoring model in professional field knowledge is enhanced.

The embodiment of the invention relates to the technical field of artificial intelligence, and provides a federated learning-based ship water gauge intelligent identification model online optimization method and system. The method comprises the following steps: a central server distributes an initial global model to an edge node and establishes an encryption channel; and the edge node monitors scene change in real time, triggers a local model updating process when the new scene data accumulation exceeds a threshold value or the recognition accuracy is lower than an alarm value, enhances the anti-interference capability of the model by generating an adversarial sample, and encrypts and uploads the optimized local model. The central server optimizes the model uploaded by each edge node by using a secure aggregation algorithm, generates a new global model and distributes the new global model, the edge nodes compare the performance of the new and old models through parallel reasoning, the new model is switched when a preset condition is met, the model performance and scene change are continuously monitored, a trigger threshold value and training parameters are dynamically adjusted, and an optimization closed loop is maintained; and on the premise of protecting data privacy, a novel model optimization mechanism continuously adapts to diversified and dynamic recognition scenes.

The invention belongs to the technical field of electric power artificial intelligence, and discloses an electric power knowledge question-answering method based on a knowledge reasoning thinking chain embedded large model and a related device. The method comprises the steps of obtaining a power problem text; and inputting the power question text into a pre-trained knowledge reasoning thinking chain embedding large model, and outputting a text answer of the power question. According to the method, a supervised learning and preference optimization mechanism is fused, and collaborative optimization of large-model professional knowledge learning and thinking path modeling is realized by constructing an electric power knowledge question and answer and analyzing a thinking chain data set; the technical problems that an existing large model is weak in knowledge, inconsistent in thinking process and insufficient in generalization ability in power professional text questions and answers are solved.

The invention discloses an agent construction method and device based on a large model, a storage medium and equipment, and belongs to the technical field of artificial intelligence. Selecting a dialogue scene according to the current dialogue information and the scene description information; obtaining tool calling data from an intelligent agent corresponding to the dialogue scene; generating a short-term memory context according to the historical dialogue information, generating a tool context according to tool calling data, generating a file context according to a file, generating a long-term memory context according to the current dialogue information and the historical dialogue information, and forming a scene context; generating a candidate tool list sorted according to preferences according to the current dialogue information or the tool calling data; generating a system prompt word according to the scene context and the candidate tool list; and optimizing the intelligent agent according to the system cue word and the candidate tool list to obtain the intelligent agent based on user preference and context awareness. The intention and preference of the user can be recognized, answering is carried out according to the intention and preference of the user, and the accuracy is improved.