43 results about "Behavior learning" patented technology

The invention relates to the technical field of voice recognition, in particular to a voice interaction system and method for customer service based on artificial intelligence, and the system comprises a voice input processing module, an intention classification and routing module, a context dynamic adjustment module, a user behavior learning module, a multi-level intention fusion module and a final result module. According to the method, a multi-dimensional feature system is constructed by extracting tone intensity, speech speed frequency and emotional fluctuation amplitude, intention categories, priority weights and confidence scores are generated to realize accurate acquisition of appeals, and dialogue history, context and emotional change dynamic reconstruction path nodes, switching rules and response time sequences are tracked during interaction. Historical behavior mining preference features, habit fusion intention relevance, emergency calculation of an optimal strategy, construction of service steps, resource allocation schemes and execution timelines, adjustment of an interactive interface, a service process and a feedback mechanism according to multi-dimensional analysis, guarantee of differentiated service experience, and improvement of response accuracy and user satisfaction.

The invention relates to the technical field of textbook construction and optimization, in particular to an automatic textbook construction and optimization method and device.The method comprises the following steps that original resources of multiple types of textbooks are collected, and core knowledge elements in the original resources are recognized; constructing a structured mapping relationship between the knowledge elements and the teaching resources based on the core knowledge elements; on the basis of the structured mapping relationship, constructing a knowledge graph covering multidisciplinary knowledge points and a preposition dependency relationship, and automatically identifying knowledge faults and complementing missing association so as to form a knowledge network with self-evolution ability; based on the knowledge network, utilizing a hybrid generation model to cooperatively work, and quickly generating a structured textbook framework containing knowledge veins, teaching cases and exercise resources; learning condition data including classroom behaviors, learning feedback and knowledge mastering conditions are obtained, and a quantity and chemical condition report is obtained after data processing; and updating data based on the quantitative chemical situation report and the knowledge graph.

The invention relates to the technical field of intelligent indoor illumination, and discloses a sunshade and illumination linkage intelligent adjustment method based on environment self-learning, and the method comprises the following steps: constructing an environment feature model representing external light environment features based on collected environment illumination data; based on the intervention behavior of the user, learning to obtain a user preference model reflecting the comfort preference of the user; in combination with the environment feature model and the user preference model, determining a linkage control strategy for the sunshade equipment and the lighting equipment; and according to the linkage control strategy, the sunshade equipment and the lighting equipment are controlled. By adopting a reverse modeling strategy, an environment characteristic model is reversely deduced from actually acquired illumination data, adaptive learning of an external light environment is realized, the implementation threshold and cost of the system are remarkably reduced, and the system can automatically adapt to long-term or temporary changes of a surrounding environment of a building, so that the system is more intelligent and more convenient to use. And the environmental adaptability and robustness of the system are improved.

The invention discloses a robot application secondary development method supporting user behavior pattern self-learning, and relates to the field of robot application development, and the method specifically comprises the following steps: S1, data collection and treatment; s2, performing behavior abstraction; s3, mode learning; s4, performing double-domain scheduling during operation; s5, performing mode capitalization; and S6, low code reuse. According to the robot application secondary development method supporting user behavior mode self-learning, during operation, a double-domain isolation framework physically isolates a safety domain from a learning domain, and the situation that key operation of a robot is disturbed in the learning process is avoided; the scheduling algorithm combining the SMP and the EDF and a real-time monitoring mechanism can accurately control core indexes such as thread scheduling delay and interrupt response time, ensure that key tasks such as robot motion control and emergency fault processing are executed preferentially, meanwhile, parallel operation of behavior learning and execution is achieved, the strict requirement for real-time performance of an industrial scene is met, and the real-time performance of the industrial scene is improved. And the operation safety of the robot is ensured.

The invention is suitable for the technical field of graphic processing, and provides a high-density element loading system and method applied to large-scale scene rendering, and the system comprises a scene data analysis module which is used for constructing a scene graph with a spatial index; the multi-level-of-detail management module is used for generating multi-level-of-detail (LOD) resources for the elements; the visual attention degree analysis module comprises a static feature analysis unit and a dynamic behavior learning unit and is used for generating and dynamically correcting an attention degree thermodynamic diagram; the rendering scheduling module is used for dynamically formulating a rendering strategy based on the thermodynamic diagram, the current window state and the LOD resources; and the synthesis module is used for synthesizing and outputting the cache bitmap and the dynamic element to the display canvas. Therefore, according to the method, the rendering performance, the memory efficiency, the interaction fluency, the convenience and other dimensions are all improved.

The invention relates to the technical field of automobile cabin environment control, and discloses an intelligent cabin environment adaptive adjustment system based on an AI large model, which comprises a sensing module, an AI large model decision module, an execution module and a feedback module. According to the intelligent cabin environment self-adaptive adjustment technology based on the AI large model, a mixed decision sub-model of an AI large model decision-making module adopts an MPC-TD3 fusion algorithm, and a user behavior learning sub-model of the AI large model decision-making module learns user physiological parameters and operation records collected by a sensing module; the multi-physics field prediction sub-model predicts a cabin environment and a power battery state, and corrects a decision result in combination with comfort parameters fed back by a PMV-PPD evaluation model and an SET evaluation model of the feedback module in real time, so that the problems that a single MPC model is insufficient in robustness under complex working conditions and a single reinforcement learning model is difficult to quantify the comfort feeling of a user are solved, and the user experience is improved. And the dynamic response speed and the adjustment precision are considered.

This invention relates to the field of parcel delivery volume prediction technology, and discloses a method for predicting logistics parcel delivery volume based on a multi-level network architecture. The method includes: acquiring raw data and standardizing the data; constructing a multi-level network hierarchy and a multi-dimensional weighted ranking model; displaying the data in multiple dimensions; calculating the confidence interval based on the prediction model and attaching a confidence level label to each predicted value; introducing a user behavior learning model to record user click habits and query preferences, and optimizing the network display order and recommendation logic; using an integrated prediction model to fuse time series analysis, regression analysis, and machine learning prediction methods to form a combined prediction result; establishing a prediction error feedback mechanism to compare the actual delivery volume with the predicted delivery volume and optimize model parameters; and adopting a hierarchical prediction strategy. This invention provides reliable support for logistics delivery planning and resource allocation, reduces operating costs, and improves service quality.

The invention relates to the technical field of telecommunication, in particular to an eSIM card security encryption data transmission system which comprises a social simulation management module, a social behavior learning and strategy evolution module, a self-adaptive encryption strategy module, a trust collaborative verification module and an anomaly detection and self-healing module. According to the method, historical experience is precipitated through a digital twin memory bank, an optimization strategy is generated through reinforcement learning training, and a closed-loop mechanism of real-time calling and dynamic adjustment is adopted, so that autonomous collaborative optimization of the concealment and the transmission efficiency in a specific social situation is realized, and comprehensive risk quantitative evaluation based on multi-dimensional situation awareness and dynamic weighting is realized. And the dynamic matching is adaptive to encryption algorithms of different risk levels, so that the accurate balance among the security strength, the communication efficiency and the equipment resource consumption is realized.

The invention relates to a robot task level emotion feature extraction method and system based on multi-evaluator distribution modeling, and belongs to the technical field of artificial intelligence and robots. The method comprises the following steps: collecting emotion score data of multiple evaluators; calculating a plurality of emotion statistical characteristics; constructing an emotion prediction model and selecting an optimal feature subset based on the evaluation indexes; and outputting a structured emotion interface for a robot behavior learning system to call. According to the method, statistical modeling and feature selection mechanisms are introduced, so that the problem that an existing emotion recognition method lacks generalization and task-level coupling capability is solved, and the naturalness and decision-making flexibility of robot emotion interaction are remarkably improved.

The invention belongs to the technical field of vehicle control, and particularly relates to a vehicle energy-saving navigation method and system based on historical behavior learning, and the method comprises the steps: obtaining the travel data of a target vehicle in a historical time period, generating a historical path sequence composed of standard road segment identifiers through map matching, mining a high-frequency passing path of the target vehicle, and obtaining the high-frequency passing path of the target vehicle; according to the speed sequence, track points meeting a preset low-speed condition are identified, and the track points are clustered and verified as intersection waiting points; for each intersection waiting point, establishing a time-dependent waiting probability model according to historical passing records of the intersection waiting point under different time slices; when the target vehicle runs in real time and is matched with a certain high-frequency passing path, inquiring a waiting probability model of a waiting point of a next intersection in front according to the current position, the current time and the vehicle speed; and according to the queried waiting probability value, generating and outputting a hierarchical driving guide instruction to a vehicle end human-computer interaction interface. And a personalized intersection passing memory bank is established for vehicle owners, so that the mileage anxiety is relieved.

The application relates to the field of robots and discloses a skill simulation method, device and equipment of a robot and a storage medium. The method comprises the following steps: acquiring the gripper state when the robot completes an operation task and inputting the gripper state into a gripper simulation environment to obtain a first joint action position through simulation simulation; inputting the first joint action position into a joint simulation environment to obtain a second joint action position and a visual image through simulation simulation, taking the second joint action position and the visual image as input data, training and optimizing a preset initial prediction formula through a preset loss function, obtaining a prediction formula, inputting the initial joint action position and the initial visual image of the robot in the joint simulation environment into the prediction formula, obtaining a target joint action sequence, and driving the robot to perform corresponding actions. According to the scheme, the joint action information and the visual information are learned, the action sequence of the robot is predicted, the technical difficulty of the robot in operation behavior learning is reduced, and the intelligent degree and the generalization ability of skill simulation of the robot are improved.

The invention discloses an industrial control security isolation device and method suitable for an industrial control network, and relates to the technical field of industrial control system network security. The objective of the invention is to solve the problems of complex deployment, insufficient detection depth and lack of intelligent behavior learning ability of an existing general protection product at an industrial control terminal. The method is deployed between an operation host and a terminal controller, and a port sniffing and protocol identification module is used for automatically identifying a controller protocol and loading an analysis method; analyzing message characteristics through a communication learning and trust value modeling module, and establishing a dynamic trust value model; the risk identification agent module takes over communication in an agent mode, carries out grading processing on messages according to a trust value model, and carries out application layer deep protocol analysis and risk identification on low-trust-value messages. According to the technical scheme, targeted and intelligent safety isolation and protection of the industrial control terminal are realized.

The invention relates to the technical field of automobile artificial intelligence, and provides a vehicle-mounted interaction system and service prediction method based on cross-scene behavior learning, and the system comprises a data layer which collects behavior data of a user in a vehicle, constructs a four-dimensional behavior modeling engine, and compares the collected behavior data with behavior data stored in a cloud knowledge base; the learning layer runs a federal learning framework in a vehicle-mounted edge computing unit, generates a situation model of the current comprehensive state of the user and sends the situation model to the interaction layer; the interaction layer is used for establishing a service execution grading mechanism based on credibility, generating a prediction result, sending the prediction result to the execution layer, and determining a service execution mode according to prediction accuracy; and the execution layer is used for establishing a prediction service matrix and executing a corresponding service action according to the prediction result. According to the method, single-dimensional association is broken through; the dynamic situation is fused, so that the position prediction is more accurate; and a progressive trust mechanism is adopted.

Systems and methods for learning behavioral activity correlations. A method includes intercepting a plurality of requests, wherein each of the plurality of requests is directed to a respective destination entity of a plurality of destination entities; creating a request queue by queueing the plurality of requests; inspecting contents of the plurality of requests; separately forwarding each intercepted request to its respective destination entity based on the request queue; monitoring runtime output of each of the plurality of destination entities, wherein the runtime output includes behavioral activities of the plurality of destination entities; and training a machine learning model based on the contents of the plurality of requests the runtime output of each of the plurality of destination entities, wherein the machine learning model is trained to output request-output correlations between groups of requests and subsequent behavioral activities.

The invention discloses a multi-scene timer control method and system based on offline voice wake-up. The method comprises the following steps: S1, establishing a trigger starting point of natural interaction through offline voice wake-up; s2, determining the content and intention of the timed task through voice recognition and semantic understanding; s3, determining an applicable scene according to the voice content and the environmental condition, and generating a corresponding control strategy; s4, executing a timing process according to the control strategy and performing staged energy consumption management; s5, completing a task closed loop through diversified reminding and continuous task connection; and S6, the recognition effect and task response logic are optimized through voice behavior learning. According to the multi-scene timer control method and system based on off-line voice wake-up, the comprehensive control strategy table is generated in combination with environment characteristics after voice recognition, and a proper timing mode, a proper reminding mode and a proper feedback strategy are selected according to the use scene.

The invention discloses a KNX smart home adaptive control method and system based on user behavior learning, relates to the field of smart home control, and aims to solve the problem of insufficient flexibility of traditional KNX smart home control. The method comprises the following steps: collecting and storing historical behavior data of a user through a multi-source sensor, identifying and marking abnormal data through an anomaly detection algorithm, generating a training data set through time sequence interpolation correction, and training an LSTM model to obtain a dynamic trigger threshold value; and collecting real-time behavior data, processing the real-time behavior data by the same method, inputting the processed data into the model, comparing the real-time data with a threshold value, and generating a KNX standard control instruction to drive equipment linkage if a condition is met. The system supports the implementation of the method, can adaptively match user habits, and improves the intelligence and convenience of smart home control.

The invention discloses a file sending method, electronic equipment and a computer storage medium, and is applied to terminal equipment, the method comprises the following steps: obtaining a file operation of a user acting on a preset application, carrying out operation analysis on the file operation based on a preset user behavior learning program, and determining an operation identification result; if the operation identification result is that the file operation needs to be intercepted, a first file operation entry provided by a preset file sending application is called to replace a second file operation entry of the preset application so as to select files through the first file operation entry, and the first file operation entry comprises options of a predicted folder corresponding to the file operation; and the selected target file is transmitted to the preset application, so that the sending of the target file is executed through the preset application, and the file sending efficiency and experience of the user are improved.

The invention provides a step adjusting method and electronic equipment, and the method comprises the steps: an intelligent agent executes a first task, and triggers a behavior learning event; obtaining a first execution parameter of the target step, wherein the first execution parameter comprises first data representing a user operation behavior and second data representing a picture change; determining a matching result based on an association relationship between the first data and the second data; and adjusting the target step based on the matching result.

The invention relates to the technical field of intelligent control, in particular to a control method, system and device for an intelligent air conditioner, and the method comprises the steps that firstly, a data set is obtained, and the data set is preprocessed; then, based on the preprocessed data set, a user behavior learning algorithm is adopted, long-term use data of the user is analyzed, and an optimization control strategy is generated; next, based on the preprocessed data set and the optimization control strategy, an environment self-adaptive adjustment algorithm is adopted to carry out environment self-adaptive adjustment; and finally, based on event-driven and rule engine technologies, realizing linkage and collaboration with the smart home equipment by adopting an equipment linkage collaboration algorithm. According to the control method of the intelligent air conditioner, through multi-sensor fusion and an intelligent control algorithm, the intelligent air conditioner can accurately sense user requirements and environment changes, the running state is automatically adjusted, and a more comfortable and personalized indoor environment is provided for a user.

This invention relates to the field of lithium battery management technology, and in particular to a method for accurate localization of micro-short-circuit faults in lithium battery protection boards based on deep learning. The method includes the following steps: S1, collecting voltage, current, and temperature time-series data of each individual cell in the battery pack within a continuous time window, and obtaining the estimated health status, discharge rate, and state of charge of each individual cell; S2, inputting the time-series data into a pre-trained benchmark behavior learning network. In this invention, the benchmark behavior learning network predicts the normal voltage behavior of the battery, and a dynamic threshold generation network is introduced. Based on the real-time health status, temperature, discharge rate, state of charge, and prediction uncertainty estimation of the battery, the dynamic positive and negative deviation thresholds for each individual cell are adaptively output. Compared to traditional fixed threshold or simple lookup table methods, this invention can automatically adapt to battery aging and changes in operating conditions.

This invention relates to a vehicle sunshade control method and device based on user behavior learning, belonging to the field of vehicle manufacturing technology. The method includes acquiring historical operation records of user control of the vehicle sunshade and multi-dimensional environmental data corresponding to the historical operation records; training a machine learning model based on the historical operation records and the multi-dimensional environmental data to obtain a user behavior prediction model; in response to the fulfillment of the sunshade function activation conditions, acquiring current environmental data and inputting the current environmental data into the user behavior prediction model to obtain the user's control intention for the vehicle sunshade in the current scenario; and executing a control strategy for the windshield sunshade and linked vehicle equipment corresponding to the control intention, thereby improving the intelligence of sunshade control.

This invention relates to the field of medical information security technology, and discloses a blockchain-based method and system for auditing and detecting unauthorized access to medical data. The method includes: structurally modeling medical data access behavior to construct access behavior feature vectors; performing multi-source fusion risk assessment based on an access control policy model and a behavior learning model to obtain a comprehensive risk value; executing a graded processing strategy according to the risk level and generating corresponding audit logs; structurally organizing the audit logs and constructing a Merkle tree to generate data fingerprints; writing the data fingerprints and summary information into the main chain, storing the complete audit logs in a side chain or off-chain storage, and achieving consistency verification through Merkle proofs; using smart contracts to verify the audit data and automatically execute access control responses, while simultaneously feeding back the audit results for model updates. This invention enables real-time detection, graded processing, and trusted auditing of medical data access behavior.

According to the drawing assisting system and method based on intelligent composition, personalized and dynamic drawing assisting is achieved through cooperation of multiple modules, the stroke position, pressure and speed are monitored in real time, composition complexity and a pressure thermodynamic diagram are calculated, a user concerned area is marked, the user stroke is compared with an art rule base, a three-dimensional lookup table is established, and the drawing assisting effect is achieved. The method comprises the following steps: storing user preferences, historical analysis and general rule coefficients, dynamically switching a local lightweight AI and a cloud deep optimization model, identifying a creation environment through multi-sensor fusion, calculating a fatigue index in combination with stroke pressure, speed and path curvature, providing strength adjustment suggestions, and storing user data by adopting a time sequence database. Through K-means clustering and LSTM behavior prediction, a model is updated in a weekly incremental manner, preference changes are captured, suggestions are optimized through testing, and through multi-dimensional data fusion, dynamic model adaptation and user behavior learning, creation efficiency and an artistic effect are remarkably improved.

The invention discloses a double-heat-source hot water system with a water using behavior learning function, and belongs to the technical field of domestic hot water supply. The system comprises a core control module, a temperature and flow sensing module, an actuator module and a network communication module. The core innovation lies in that the core control module is configured to call an adaptive learning model, record the relationship between the flow of a user water consumption event and the delay time of a main heat source and establish a historical event library, so that an optimal heat source switching strategy can be predicted based on the real-time flow during subsequent water consumption; meanwhile, a state machine model is adopted to control system operation, and when faults such as heating overtime and sensor abnormity are detected, a dual protection mechanism combining software heating stopping and hardware watchdog reset / mechanical temperature control switch is triggered. The problems that a far-end gas water heater is long in waiting time and wastes water resources are effectively solved, and unification of instant heating and high reliability is achieved.

The application relates to the cross field of artificial intelligence and robot technology, in particular to a humanoid robot operation behavior learning method and system based on natural language guidance and knowledge reasoning. The method first analyzes a natural language instruction, generates a task semantic graph containing action nodes, target object nodes and semantic association edges, constructs a causal knowledge graph based on multi-source perception data, fuses the task semantic graph and the causal knowledge graph to generate a unified reasoning graph structure, generates an operation path meeting semantic logic and physical feasibility through a graph search algorithm, selects an action primitive according to the operation path and constructs an initial action sequence, calculates strategy feasibility by using a deep evaluation model, receives natural language feedback and analyzes the same into a semantic vector, drives dynamic updating of a double-graph structure and weighted correction of a strategy loss function. The application improves the accuracy of task understanding, the causal consistency of operation logic and the adaptive optimization capability of an execution strategy.

The invention discloses an air purification control method and device based on an air conditioner, the air conditioner and a medium. The method comprises the steps that when an air quality detection value is larger than or equal to a first target threshold value, meanwhile, it is detected that people exist in a working area where the air conditioner is located, or the current moment reaches the target moment, an air purification mode is controlled, the target moment is used for indicating an advanced starting moment of the air purification mode; or responding to the air purification instruction sent by the remote control equipment, and controlling to enter the air purification mode. According to the invention, a control mechanism capable of actively sensing the air quality, deciding and executing the air purification mode is constructed by combining multiple sensors (the air quality detection module and the human body existence detection module) and the purification efficiency of the user behavior learning and purification module, so that the on-demand active execution of the air purification mode is realized, and the air purification effect is effectively improved.

The invention discloses a K-12 after-class homework tutoring system based on an intelligent AI assistant, and the system comprises the following steps: collecting student answering behaviors, learning time and knowledge point correlation information, and generating a cognitive state vector sequence; multi-point activation and adjacency mapping are realized by using a Kohonen topological mapping network in which a K-WTA sparse activation mechanism is introduced, a soft competition response cluster is formed, and a multi-dimensional cognitive state of a student is expressed; generating a sparse cognitive map by fusing student error annotations and knowledge point structure information, and constructing a homework task path according to a map structure; the system generates an adjusting signal according to the map change, and dynamically adjusts a sparseness control parameter K value in the K-WTA; and in combination with job completion condition feedback, the cognitive state is corrected, and accurate recommendation and intelligent feedback are realized. According to the invention, a personalized and structured K-12 after-class homework tutoring process is realized.

The invention discloses a turbocharger control method and device based on driving habits, a vehicle and a medium, and relates to the technical field of vehicle control. The method comprises the steps that accelerator operation parameters are collected, and a personalized coefficient and a gas path coupling coefficient are obtained based on the historical driving characteristics of a driver and the operation working condition of an engine; the accelerator operation parameters are input into the short-term driving behavior learning model and the long-term driving behavior learning model, the first prediction result and the second prediction result are dynamically fused, and the predicted accelerator opening degree at the future moment is determined; according to the predicted accelerator opening degree, the individuation coefficient, the gas path coupling coefficient and the engine operation parameters, the time compensation amount and the opening degree compensation amount of the waste gas valve are determined; the wastegate valve is controlled on the basis of the time compensation amount and the opening compensation amount. By the adoption of the method, self-adaptive adjustment of the turbocharger can be achieved according to operation habits of different drivers, supercharging pressure is established in advance, turbine lag is reduced, and the response speed is increased.

The application provides a dialogue behavior simulation method and device based on a large language model, equipment and a medium, relates to the technical field of artificial intelligence, and comprises the following steps: collecting real human-computer dialogue logs in a preset dialogue system; constructing a structured dialogue sample library comprising multiple human-computer dialogue samples according to the real human-computer dialogue logs; performing value evaluation on each human-computer dialogue sample by using a preset large language model to obtain a value evaluation result of each human-computer dialogue sample; determining a human-computer dialogue sample with user behavior learning value as a target human-computer dialogue sample according to the value evaluation result of each human-computer dialogue sample; constructing a user persona model corresponding to the target human-computer dialogue sample according to the target human-computer dialogue sample and a user behavior value point of the target human-computer dialogue sample; and performing simulated dialogue between the preset dialogue system and the preset large language model according to the user persona model. The application can improve the authenticity of user dialogue behavior simulation.