519 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 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 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 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 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 an intelligent examination and approval method, system and device based on a multi-modal large model and a medium, and belongs to the technical field of artificial intelligence. The method comprises the following steps: analyzing a multi-modal examination and approval material through a visual large model to obtain multi-modal feature information; performing semantic understanding by using the language large model to generate structured semantic data; performing entity matching, relationship verification and logical reasoning based on the knowledge graph to generate a research and judgment report; intelligent decision support and risk early warning are realized; approval instruction conversion, service evaluation and self-adaptive interaction are realized through a multi-mode interaction technology; and continuously optimizing the model and the knowledge base by using the approval process data. According to the invention, by constructing a technical framework of deep fusion of vision, language and knowledge, comprehensive intelligentization of the approval process is realized, and the beneficial effects of improving the approval efficiency, guaranteeing decision fairness, enhancing the complex material processing capability, improving the user experience and realizing continuous evolution of the system are achieved.

The embodiment of the invention discloses an agent skill construction method and device, electronic equipment and a storage medium, relates to the technical field of artificial intelligence, and can effectively improve the efficiency and accuracy of skill evaluation evolution. The method comprises the following steps: acquiring intelligent agent skill construction information, wherein the intelligent agent skill construction information comprises question and answer data; based on the agent skill construction information, constructing a target skill to generate a target skill file package, the target skill file package comprising a test case written based on the question and answer data; and based on the test case, testing the constructed target skill. The method is suitable for large models and intelligent agents.

The invention relates to the technical field of data processing, in particular to an intelligent technical supervision analysis decision method and system, and the method comprises the steps: obtaining a natural language supervision demand of a user, extracting a supervision target, a supervision dimension and a constraint condition through semantic analysis, and generating a structured supervision demand expression; the method comprises the following steps: generating a multi-source data query instruction set by adopting a model for converting a natural language to a database query statement on the basis of a structured supervision demand expression, obtaining a structured query result, an unstructured document fragment and external dynamic data in combination with a retrieval enhancement architecture, and generating an enhanced decision data set after fusion filtering; constructing a supervision analysis model, inputting the enhanced decision data set, and outputting a preliminary supervision analysis result; and verifying the preliminary supervision analysis result, if yes, outputting a final supervision decision result, and if not, returning to adjust the multi-source data query instruction set, and repeating the subsequent steps. The method can improve the efficiency and reliability of supervision decision, is adaptive to multi-source heterogeneous data, and achieves the dynamic optimization of a whole process.

The invention relates to the technical field of intelligent routing inspection, and provides an unmanned aerial vehicle automatic intelligent routing inspection method and system, and the system comprises a routing inspection planning terminal which is used for constructing a three-dimensional digital twin model of a routing inspection region, and planning a routing inspection path covering the whole region and differentiated routing inspection task parameters; the unmanned aerial vehicle execution terminal carries a multi-mode sensor group and can autonomously complete flight, hovering and multi-dimensional data acquisition actions according to a preset inspection path; and the data processing terminal is used for carrying out defect identification, classification and equipment state quantitative evaluation on the acquired data through an intelligent algorithm. According to the invention, through fusion of multi-terminal cooperative work and intelligent technology, the automation and intelligence level of inspection is improved, the three-dimensional digital twinborn model constructed by the inspection planning terminal and the optimized path realize comprehensive coverage and differentiated task configuration of the inspection area, and path planning and task setting can be completed without manual intervention.

The invention provides an intelligent water conservancy monitoring method and system based on the Internet of Things. The method comprises the steps of obtaining heterogeneous water conservancy data of various sources; preprocessing and standardizing the heterogeneous water conservancy data from various sources; constructing a water conservancy domain knowledge graph, and performing multi-dimensional data fusion to generate comprehensive water conservancy situation data; based on the comprehensive water conservancy situation data, constructing a water conservancy digital twinborn body, and realizing real-time driving and hydrological and hydraulic process simulation of the water conservancy digital twinborn body; and multi-modal deep analysis is performed on the comprehensive water conservancy situation data and the water conservancy digital twin, and interpretable intelligent decision support information is generated by using an artificial intelligence technology. According to the method, the problems of scattered data sources, high isomerism, relatively low intelligent degree and insufficient information presentation modes of a water conservancy management system are solved by acquiring and preprocessing the multi-source heterogeneous water conservancy data, fusing and cognizing the water conservancy data, twinning and simulating the water conservancy system and performing analysis and decision intelligent support.

The invention provides a lightweight design method, device and equipment for an intelligent robot with a body and a storage medium, and relates to the technical field of artificial intelligence. According to the method, the scene simulation environment is constructed by collecting the task scene parameters, the robot three-dimensional model is constructed based on the lightweight design scheme, the test cases are generated according to the task requirements, the model test parameters are collected, and comprehensive evaluation and diagnosis of the initial lightweight design scheme of the robot are achieved. Through collection, evaluation and diagnosis of model test parameters, a design scheme is continuously optimized, and the reliability of robot performance is improved; through repeated testing and optimization evaluation, iterative optimization of a lightweight design scheme is realized, so that an optimal optimization design scheme is obtained, the lightweight design level of the intelligent robot with the body is remarkably improved, the convenience and adaptability of the specific intelligent robot are enhanced, and the intelligent robot with the body is more intelligent. And powerful technical support is provided for efficient application of the robot in complex task scenes in the financial and medical fields.

The invention discloses a multi-Agent cooperation AADL model AGREE verification statement automatic generation and verification method. The method comprises the following steps: S1, constructing a domain knowledge base; s2, the Agent is analyzed by the AADL model to extract key information; s3, the demand analysis Agent splits atomic propositions from the natural language demand; s4, the AGREE generator Agent generates an AGREE attachment code; s5, the model fusion Agent automatically embeds the AGREE specification into the target AADL component; s6, the AADL verification Agent performs iterative verification and automatic repair, and a multi-dimensional verification system is constructed; and S7, executing AGREE-based AADL architecture formalized verification, and performing logic attribute verification on the grammar compliance model. According to the invention, innovative fusion of a formalized verification method and an intelligent technology is realized, and the core problem that formalized verification of the AADL architecture is difficult to implement is effectively solved.

The invention provides a steelmaking process intelligent agent method and system based on multi-model collaboration, and belongs to the technical field of industrial intelligence. The method comprises the following steps: constructing and vectorizing a domain knowledge system containing a knowledge graph and a special corpus; a large language model optimized by domain data is adopted as a core; when the task is received, information is retrieved from the knowledge system through a retrieval enhancement generation mechanism to generate an enhanced context; autonomously invoking an external dedicated model or algorithm based on the context, and integrating their outputs to generate a decision scheme; and finally, feeding back and updating the knowledge graph according to an actual execution result, and adaptively adjusting model parameters. The invention further provides a corresponding system. According to the method, the utilization of domain knowledge is improved, active decision making is supported, and a knowledge updating and model optimizing mechanism is provided to integrate an automatic process.

The embodiment of the invention provides a model training method and device, electronic equipment and a storage medium, and belongs to the technical field of artificial intelligence. The method comprises the steps of obtaining preset question and answer training data, wherein the question and answer training data comprises input question data and a model output result corresponding to the input question data; reasoning trajectory generation is carried out based on each piece of input question data and the corresponding model output result, and a plurality of candidate reasoning trajectories are obtained; performing confusion degree evaluation based on the plurality of candidate reasoning trajectories to obtain a confusion degree score corresponding to each candidate reasoning trajectory; a target reasoning trajectory is determined from the plurality of candidate reasoning trajectories based on the confusion score to guide model training. According to the embodiment of the invention, stable model training can be carried out on a large language model used for business scenes such as intelligent investment advisers or insurance claims and the like in a low-cost and high-efficiency manner, the logic stability of the large language model during business processing is improved, and business services with high professionality and depth are provided.

The invention relates to a safety monitoring method for an irrigation and drainage project operation state and an intelligent system, and belongs to the technical field of artificial intelligence. The method comprises the following steps: collecting and marking irrigation and drainage project operation monitoring data through a sensor, and constructing a training data set; completing data normalization by combining quantile and median robust scaling with adaptive nonlinear transformation, and mining and screening high-order interaction features by combining a mutual information theory and a gradient boosting decision tree; constructing a deep classification network fusing physical prior and adaptive feature interaction, introducing physical constraint and multi-scale feature fusion, and optimizing a model through adaptive marginal classification loss and physical feature manifold alignment loss; real-time data is preprocessed and then input into the model, and operation state grade classification and graded alarm are achieved. According to the method, data noise can be inhibited, a multi-index coupling relationship can be mined, the interpretability and robustness of the model can be improved by integrating a physical rule, irrigation and drainage project abnormity can be accurately identified and early warned, and the method is suitable for intelligent safety monitoring of an irrigation area.

The invention relates to the technical field of geotechnical engineering intelligence, in particular to a multi-field coupling intelligent triaxial test method and system combined with machine learning, and the method comprises the following steps: initializing a test device, and establishing a material parameter prior distribution function; defining a target test path and obtaining a target control sequence; acquiring real-time test data through a multi-source fusion sensing array, and preprocessing the real-time test data to obtain multi-dimensional feature fusion data; establishing an online parameter identification module to obtain real-time correction material parameters; establishing a heat-water-force multi-field coupling prediction model to obtain an optimal control sequence; acquiring latest test data, and performing rolling optimization on the model to establish a lightweight control model; and generating an intelligent control instruction to realize self-adaptive intelligent control of the triaxial test. According to the invention, through combination with machine learning, the spanning of the triaxial test from experience control to intelligent control is realized, and an intelligent technical means is provided for the research of the multi-field coupling characteristic of the geotechnical material.

The invention discloses a model routing method, electronic equipment and a readable storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: receiving an interaction task request of a user, and obtaining capability portrait data of a plurality of candidate models, the ability portrait data describes the ability of the candidate model at least from two dimensions of static ability, dynamic performance, economic cost and historical performance; based on the ability portrait data, evaluating competency evaluation values and competency values of the candidate models for the interaction task request; and selecting a target candidate model from the candidate models according to the competency evaluation values and the competency values, and routing the interaction task request to the target candidate model to perform interaction processing. According to the method, the accuracy of model routing is improved, so that the processing effect of the interaction task is improved.

The invention provides a large model training method and device, a function recommendation method and device, equipment and a medium, and relates to the technical field of artificial intelligence, in particular to the technical fields of natural language processing, deep learning, large language models, information recommendation, intelligent agents, intelligent agents and the like. According to the implementation scheme, first sample data are obtained, the first sample data comprise sample dialogue information and corresponding positive sample labels and negative sample labels, and the positive sample labels indicate target function recommendation results determined according to the sample dialogue information; the negative sample label is a function recommendation result which is inconsistent with the positive sample label and is obtained by inputting sample dialogue information into an initial large model to execute sampling operation; and training the initial large model based on the first sample data to obtain a target large model.

The invention relates to the technical field of artificial intelligence, in particular to a load balancing method and device, electronic equipment, a storage medium and a program product, and the load balancing method comprises the steps: obtaining traffic characteristics of communication links between different nodes in a data block migration process of a distributed storage system; determining a load distribution mode of each communication link based on the flow characteristics of each communication link; based on the flow characteristics and the load distribution mode of each communication link, determining hotspot nodes and corresponding service priorities thereof; determining a target hotspot node based on the access frequencies, the data transmission quantities, the node loads and the corresponding service priorities of all the hotspot nodes; and based on the processing performance, the network state and the storage capacity of the node, screening out a target low-load node meeting a preset condition from the distributed storage system, and migrating data of the target hotspot node to the target low-load node. According to the invention, the network flow distribution and node load balancing state can be dynamically optimized.

The invention discloses a multi-modal perception and agent collaborative urban operation event handling method, which belongs to the technical field of artificial intelligence, and adopts the technical scheme that multi-modal front-end equipment is deployed to collect data, a fusion confidence coefficient is generated based on multi-source data, and an event work order is generated when the fusion confidence coefficient reaches a threshold value; the method comprises the following steps: classifying work orders by utilizing an urban management knowledge graph, determining responsibility subjects and disposal guidance, and determining priorities by integrating risks and regions through a weight model; abstracting various resources as disposal resources, and realizing work order and resource matching assignment under the constraint of response time limit; when a complex event condition is met, disassembling into sub-work orders according to a knowledge graph and carrying out parallel processing; the processing resources upload processing feedback, automatic verification is carried out based on computer vision and multi-source evidence, closed loop is qualified, if not, processing or evidence supplementation is carried out, and a closed loop result is archived and used for continuously optimizing the model and scheduling. The beneficial effect of the invention is that the urban operation event handling method based on multi-modal perception and intelligent agent cooperation is provided.

The embodiment of the invention provides a multi-round dialogue complexity assessment method and device, electronic equipment and a computer readable storage medium, and relates to the technical field of artificial intelligence. According to the method, by fusing the trend of the session state, the semantic turning of the abstract and the correlation of the historical dialogues, the evaluation accuracy of the real complexity of the request in multiple rounds of dialogues is improved, and misjudgment caused by only depending on the original text length or keywords is avoided; and meanwhile, the context length transmitted to the large language model is effectively compressed by using the abstract containing intention transformation and the related dialogue, so that the Token cost is directly saved, the calculation overhead brought by redundant context transmission is reduced, and a more efficient and more intelligent model routing decision is supported. Accurate context compression and routing decision reduce the average duration of request processing, and the user experience can be improved.

The application provides a training method and device of an AI model, a computing device and a storage medium, and belongs to the technical field of artificial intelligence. The method is applied to an AI platform, the AI platform is associated with a computing resource pool, the computing resource pool includes computing nodes for model training, and the method comprises the following steps: providing a training configuration interface to a user, the training configuration interface comprises multiple training modes selected by the user, each training mode represents a distribution strategy of the computing nodes required for training an initial AI model, at least one training task is generated according to the selection of the user on the training configuration interface, the at least one training task is executed to train the initial AI model, an AI model is obtained, and the obtained AI model is downloaded or used by the user. According to the application, distributed training can be more flexibly performed.

The invention relates to the technical field of artificial intelligence, discloses a human outsourcing intelligent customer service body based on a large model and reinforcement learning and a service method, and aims to solve the problems that an existing customer service system is rigid in service process, broken in customer experience and incapable of learning evolution from artificial expert experience. The intelligent customer service body comprises an intention analysis unit, a dynamic strategy generation unit, a closed-loop learning unit and the like. Wherein the dynamic strategy generation unit is used for generating and executing a service strategy by utilizing a large language model; and the closed-loop learning unit realizes continuous learning and capability self-optimization of the intelligent customer service body through explicit reasoning chain monitoring, correction instruction analysis, multi-dimensional reward calculation and double-track system model iteration. Through adoption of the technical scheme, the service flow flexibility, continuity and customer experience can be improved, and the technical problems of insufficient system rigidity and learning evolution are effectively solved.

The invention relates to the technical field of automobile intelligence, in particular to a sun visor display screen control method and device and a vehicle. The method comprises the steps that the image collection view angle range of the vehicle is obtained; on the basis of the image acquisition view angle range, dynamic spatial features of at least one target object are obtained, and the dynamic spatial features are used for representing the instant state and the action trend of the target object; analyzing the dynamic spatial features, and determining an interaction intention of the target object; and determining interaction information for display based on the interaction intention, and controlling a sun shield of the vehicle to execute outward overturning so as to display the interaction information to the target object through a display screen arranged on the outer side of the sun shield. Information interaction between the vehicle and the target object can be achieved directly through the sun shield, the diversity of interaction between the vehicle and the outside is enriched, and meanwhile the driving safety can be further improved.

The invention provides a message processing system for digital copies, relates to the technical field of artificial intelligence, and is used for still ensuring continuous promotion of businesses under the condition that employees are not online. The system comprises a communication platform used for sending a knowledge matching request to an intelligent agent platform through a first digital branch of a first user account under the condition that a service request message sent to the first user account is obtained and the first user account is in an offline state, the knowledge matching request is used for requesting to process the service content in the service request message. And the agent platform is used for calling a knowledge base of the first agent through the first agent associated with the first digital cloned body, processing the knowledge matching request and obtaining a knowledge matching result of the knowledge matching request. And the intelligent agent platform sends the knowledge matching result to the first digital cloned body. And the communication platform is also used for sending the knowledge matching result to a second user account through the first digital branch, and the second user account is an account sending the service request.

The invention discloses an avoiding method, a robot and a computer storage medium, relates to the technical field of artificial intelligence, and is used for recognizing static / dynamic obstacles through low-cost computing power, effectively improving the intelligent degree of the low-cost mobile robot and reasonably avoiding the obstacles. The method comprises the steps that a grid map is updated periodically, obstacle states of cells in the updated grid map and time information of state switching are recorded, the obstacle states indicate whether the cells have obstacles or not, and the state switching indicates switching among different obstacle states; according to the obstacle states of the cells in the updated grid map and the time information of state switching, the obstacle types of obstacles in the updated grid map are determined, and the obstacle types are used for distinguishing dynamic obstacles from fixed obstacles; and performing path planning according to the obstacle types of the obstacles in the updated grid map, and controlling the robot to move according to the planned path so as to avoid different types of obstacles.

This invention relates to the field of intelligent technology for identifying hidden dangers on reservoir and river / lake embankments, specifically to a method for intelligent identification and risk assessment of embankment hidden dangers based on multimodal perception using a walking robot. The method involves a quadruped robot equipped with multiple sensor modules autonomously inspecting reservoir and river / lake embankment areas to collect multimodal data from the embankment surface. This multimodal data is then transmitted to a data processing center. Based on this data, the types and locations of hidden dangers on the embankment surface are identified. These hidden danger types include cracks, seepage or piping, settlement, and structural deformation. The types and locations of these hidden dangers are quantitatively analyzed to obtain quantitative results. Based on these results, a risk assessment report, a visual map, and an early warning are generated. This invention improves the spatial coverage accuracy and temporal synchronization of data acquisition, and possesses higher sensitivity and complementarity in identifying hidden danger types such as embankment cracks, seepage, and deformation, providing a perceptual foundation for subsequent analysis.

The invention discloses an action instruction generation method, computing equipment and a machine readable storage medium, and relates to the technical field of intelligent equipment. The method comprises the steps of querying a preset scene knowledge base based on an input task instruction to obtain background knowledge corresponding to the task instruction; generating an enhancement instruction based on the background knowledge and the task instruction; and inputting the enhancement instruction and the body state of the controlled equipment into a pre-trained action planning model to obtain an action instruction for driving the controlled equipment to execute a task. By introducing a scene knowledge base and an instruction enhancement mechanism, precise understanding and reliable planning of fuzzy or complex instructions by the controlled equipment are realized, and the adaptability and task success rate of the controlled equipment under strange environments and complex tasks are improved. And the action sequence which is safe and efficient and conforms to the context situation is generated, so that the capability of continuously learning and executing tasks in the open world of the controlled equipment is realized at lower cost.