1599 results about "Learning unit" patented technology

To facilitate, when a generative AI being used is changed to another generative AI on the way, causing another generative AI after the change to take over an experience of conversations with the generative AI being used.SOLUTION: A generative AI management system according to the present invention is connected to servers of a plurality of generative AIs to provide user terminals with a function for utilizing the plurality of generative AIs and comprises: a recording control unit which records the plurality of generative AIs; an input information acquisition unit which acquires information to be given to one generative AI out of the plurality of generative AIs, as input information; an answer information acquisition unit which acquires an answer of the one generative AI corresponding to the input information, as answer information from a server; a conversation data storage unit in which the input information and the answer information corresponding to the input information are stored in association with each other as conversation data; and a training unit which, when the one generative AI is changed to another generative AI, trains this generative AI by using the conversation data which is stored in the conversation data storage unit and is obtained by conversation with the one generative AI.SELECTED DRAWING: Figure 4

The present invention discloses an online evolutionary algorithm and training platform for intelligent agents based on self-game, including: a task and environment module, an intelligent agent training module, a strategy evaluation module, a data management module and a user interaction module. The task and environment module dynamically defines training tasks and environmental parameters through a task generation unit and an environment simulation unit. The intelligent agent training module includes an initial strategy generation unit, a course learning unit and a self-game optimization unit to work together to strengthen the learning framework. The strategy evaluation module tests the advantages and disadvantages of intelligent agent strategies through performance evaluation, diversity analysis and comparative analysis. The data management module records and analyzes training data to manage the historical strategy library. The user interaction module provides task configuration, training monitoring and result visualization functions. The present invention improves the adaptability and robustness of intelligent agents by introducing a self-game mechanism and dynamic opponent generation, and is suitable for strategy optimization and training in complex collaboration and confrontation scenarios.

The invention discloses a UE4-based digital twin model construction system and method, and relates to the technical field of computer vision, and the system comprises a geometric modeling unit which generates three-dimensional model data based on geometric modeling data of a target physical entity, and achieves dynamic LOD rendering level switching through virtualization processing; the real-time data interaction unit is used for analyzing the real-time sensor data flow and generating equipment state data in combination with a transmission operation and maintenance instruction; the simulation engine unit is used for executing rigid body motion and collision simulation based on the equipment state data and outputting a collision event sequence and state transition data by utilizing an event trigger; the machine learning unit is used for training an LSTM (Long Short Term Memory) model based on the collision event sequence and the state transition data, and optimizing state covariance parameters through adaptive Kalman filtering; and the visual interaction unit is used for realizing data synchronization of the command large screen and the mobile terminal through multi-screen collaboration. According to the method, the digital model capable of accurately reflecting physical entity behaviors and characteristics is constructed.

The embodiment of the invention provides an intelligent learning path recommendation method and system based on a dynamic state. The method is applied to the technical field of intelligent learning recommendation, and comprises the following steps: calculating a skill adaptation weight, a load weight and an emergency degree weight in real time based on a dynamic state of a target employee, including a current skill improvement condition, a workload and a task emergency degree; combining the skill matching degree, the learning strength and the task association degree of each learning unit in the candidate learning path, and performing comprehensive evaluation by using a multi-weight scoring function; and sorting the paths according to the comprehensive score, generating a personalized recommended learning path set and sending the personalized recommended learning path set to an employee terminal, thereby realizing intelligent learning path recommendation with dynamic adaptation and accurate matching. According to the scheme, personalized learning path pushing aiming at actual post requirements and working states of the employees can be realized, correlation, urgency and acceptability of learning contents are improved, learning efficiency and task adaptability are remarkably enhanced, and the employees are helped to quickly compete with post targets.

It aims to appropriately manage the bias of the signal processing amount. 【Solution means】 The abnormality management device 1 uses, as teacher data, normal data indicating the number of normal packets distributed to each device for each time zone, and learns, by maximum likelihood estimation, the parameters of a probability model that outputs the posterior probability that the number of packets distributed to each device for each time zone is normal. A learning unit 11, the posterior probability for each device for each time zone estimated by the learned probability model, the probability distribution of the normal data for each device estimated based on the normal data, and the prior probability of being normal. A derivation unit 12 that derives the probability distribution of abnormal data indicating the number of abnormal packets distributed to each device in the time zone, and the probability distribution of the normal data and the probability distribution of the abnormal data for each device for each time zone are equal. And a setting unit 13 that sets, as a threshold value for abnormality determination of the number of packets distributed to each device, the number of packets corresponding to the value of the probability distribution that becomes the value.

Processing circuitry of a learning platform may be configured to maintain a graph database describing student learners. Processing circuitry may obtain new student learner data and load the data into the graph database. Processing circuitry may receive an engagement or interaction from the new student learner and responsively extract new learnings about the new student learner which are loaded into the graph database. Processing circuitry may receive an inquiry from the new student learner and in response, extract the new student learner data and the new learnings from the graph database and contextualize, using a large language model, a learning unit from the educational content provided by the learning platform as a response to the inquiry using the new student learner data and the new learnings. Processing circuitry may further return the learning unit contextualized by the large language model to the new student learner.

The invention relates to a motion trail planning system and method for interventional puncture surgery, and the system comprises a camera module which is used for collecting the motion trail of an instrument in an external environment and generating a first image; the image equipment is used for acquiring the movement track of the instrument in the internal environment and generating a second image; the preprocessing module is used for generating an external three-dimensional coordinate and an internal three-dimensional coordinate based on the first image and the second image, and forming a data pair of the movement track of the instrument based on the external three-dimensional coordinate and the internal three-dimensional coordinate; the control module comprises a machine learning unit and a trajectory planning unit; the machine learning unit is used for training a deep learning model based on the data of the instrument movement track and generating a prediction path based on the trained deep learning model; and the trajectory planning unit is used for optimizing and adjusting the predicted path. According to the method, the movement track of the interventional puncture operation of the specific target part can be accurately predicted and planned.

An additive manufacturing condition search device includes a defect database that accumulates a material, shape information, an additive manufacturing condition, monitoring information during modeling, and defect information in association with each other, a first machine learning unit that outputs an additive manufacturing condition corresponding to material information and device information, and outputs a new additive manufacturing condition from a combination of a plurality of the additive manufacturing conditions and the defect information, a specification unit that causes an additive manufacturing apparatus to perform modeling by the additive manufacturing condition, acquires the monitoring information during modeling, and acquires the shape information and the defect information by inspection of a modeled object, a second machine learning unit in which a model trained by using the defect database as train data estimates defect information of the modeled object from the monitoring information and stores the defect information in the defect database, and a determination unit that determines whether or not the defect information of the modeled object has achieved an evaluation target value.

To increase the amount of information to be utilized.SOLUTION: An information processing system includes an information processing apparatus, a first terminal device that holds first user characteristic information, and a second terminal device that holds second user characteristic information. The information processing apparatus includes: an information collection unit which collects, from the first terminal device, first anonymized information generated by anonymizing the first user characteristic information, and collects, from the second terminal device, second anonymized information generated by anonymizing the second user characteristic information; a learning unit which generates a model configured to learn, by machine learning, a relationship between the first user characteristic information and the second user characteristic information, using the first anonymized information and the second anonymized information, as learning data, and output, on receipt of the first user characteristic information, estimated user characteristic information estimated from the relationship between the first user characteristic information and the second user characteristic information; and a model output unit which outputs the model to the first terminal device.SELECTED DRAWING: Figure 3

A heavy traffic abnormal event detection and state discrimination system belongs to the field of intelligent traffic and comprises a data acquisition and labeling module, an image processing module, a traffic state analysis module, a state determination and abnormal event identification module and a transfer learning module. The data acquisition and labeling module comprises a data acquisition unit and a data labeling unit; the image processing module comprises an image enhancement unit and an image compression and reconstruction unit; the traffic state analysis module comprises a traffic bottleneck identification unit and a traffic flow wave analysis unit; the state judgment and abnormal event recognition module comprises a vehicle statistical unit, a congestion state recognition unit, a state transition analysis unit and a compressed sensing model establishment unit; and the transfer learning module comprises a transfer learning unit. According to the method, the false detection rate of the traffic flow abnormal events in bad weather can be reduced, so that the traffic abnormal events can be detected all day long, and the occurrence probability of the traffic events in bad weather conditions can be reduced.

The invention discloses a new energy medium-voltage switch intelligent control method based on multi-data interaction, and particularly relates to the field of switch intelligent control. According to the method, electrical, mechanical and temperature multi-dimensional operation data are collected in real time through a multi-source sensor, fault diagnosis is performed by adopting a hybrid reasoning architecture, features are extracted in combination with Hilbert-Huang transform and Mel cepstrum coefficient algorithms, and a three-level reasoning mechanism is constructed to realize accurate classification; based on a diagnosis result, a system dynamically optimizes a control strategy, the control strategy optimizes a switch-on and switch-off time sequence based on phase-space reconstruction and LSTM prediction, plasma impedance closed-loop adjustment and a quantum genetic algorithm are combined to realize arc suppression and mechanical compensation, and a six-dimensional compensation vector is introduced to correct mechanical operation deviation; the self-learning unit is based on a depth deterministic strategy gradient and an online knowledge distillation technology, verifies the validity of parameter update by using digital twin simulation, and forms a fault feature-control strategy-maintenance scheme triple knowledge base.

It aims to more reliably manage abnormal communication. 【Solution means】 The abnormality management device 1 is configured such that a first learning unit 12 treats each observed value of a normal presence time series as discrete values independent of each other, and estimates probability parameters of a probability model representing the normal presence time after quantization based on the appearance frequency of each observed value; and a second learning unit 13 configured to fix the generator parameters of a generator 131 that generates pseudo-normal data sufficiently deviated from the distribution of true normal data, with each observed value of the normal presence time series being true normal data, and update only the discriminator parameters of a discriminator 132 that discriminates between true normal data and pseudo-normal data in a direction to maximize the discrimination accuracy, based on the probability parameters of the normal presence time estimated by the first learning unit 12.

The invention relates to the technical field of intelligent education, in particular to a history self-learning system based on virtual intelligent tutoring, which comprises a screening classification unit, a recommendation learning module, a learning unit, a deepening module, a feedback module and a secondary deepening module. Wherein the screening and classifying unit is used for classifying learners into a function and benefit oriented type and an emotional resonance type by adopting a learner classifying algorithm based on POS (Point of Sale) annotation and a random forest; the recommendation learning module is used for recommending different historical learning contents to the learner according to the classification result and a self-learning mechanism; the learning unit is used for providing various learning modes for the learner; the deepening module is used for deepening the learning of the learner in an intelligent tutoring mode; the feedback module is used for feeding back a learning condition to the learner and optimizing a self-learning mechanism according to a feedback result; and the secondary deepening module is used for reminding the learner to review and customizing review questions for the learner. Therefore, the problems that in the prior art, courses lack personalized adaptation, learning interaction is not deep, feedback lags and the like are solved.

The purpose is to easily manage abnormal program operation. [Solution] The abnormality management device 1 includes a first learning unit 11 that uses normal data indicating processing resource usage data in which the processing resource usage corresponding to each process ID is normal as training data from among a plurality of processing resource usage data each including the usage of processing resources used in executing a process corresponding to each process ID, and learns parameters of a probability model that outputs a posterior probability that the processing resource usage corresponding to each process ID is normal by maximum likelihood estimation, and a derivation unit 12 that derives a probability distribution of abnormal data indicating processing resource usage data including abnormal processing resource usage based on the posterior probability estimated by the learned probability model, the probability distribution of the normal data, and the prior probability of normality.

Processing circuitry of a learning platform may be configured to maintain a graph database describing student learners. Processing circuitry may obtain new student learner data and load the data into the graph database. Processing circuitry may receive an engagement or interaction from the new student learner and responsively extract new learnings about the new student learner which are loaded into the graph database. Processing circuitry may receive an inquiry from the new student learner and in response, extract the new student learner data and the new learnings from the graph database and contextualize, using a large language model, a learning unit from the educational content provided by the learning platform as a response to the inquiry using the new student learner data and the new learnings. Processing circuitry may further return the learning unit contextualized by the large language model to the new student learner.

The invention discloses a temperature dynamic control system for optical fiber optical wand processing. The temperature dynamic control system comprises an acquisition unit, a control unit, a learning unit and an integration unit. The acquisition unit captures production line material imaging data through a multi-macro camera array, acquires temperature, tension, humidity and speed data in real time by using a production line sensor network, and forms production state comprehensive data after edge computing node fusion preprocessing. The control unit establishes a dynamic mathematical prediction model based on the comprehensive data, predicts future temperature states of a plurality of temperature zones and generates an optimization control input instruction; the learning unit receives an optimization control input instruction and historical temperature data, and generates a self-adaptive control strategy by adopting a reinforcement learning algorithm; the integration unit integrates the control input instruction and the self-adaptive control strategy to form a final control instruction, the temperature of each temperature zone is adjusted, and the temperature control precision and the system stability are ensured.

The invention relates to an automatic image segmentation and recognition system for the radiology department, and relates to the technical field of intelligent medical treatment. Comprising the following steps: an image preprocessing module provides a high-quality image for subsequent analysis by applying a multi-scale transformation and adaptive filtering technology; the feature extraction and fusion unit accurately excavates features by means of a CNN and an attention mechanism; the disease diagnosis reasoning engine is combined with the RNN and a medical knowledge base to deduce disease details; and the personalized treatment scheme recommendation module customizes a scheme by using reinforcement learning according to diagnosis and patient information. In addition, the system also comprises a virtual case generation unit, a model interpretability analysis unit, a self-supervised pre-training unit, a continuous learning unit, a distributed cooperative training architecture and a cross-platform adaptive interface. The method comprises the steps of collecting and standardizing image data, inputting the image data into a system to obtain diagnosis and schemes, and visually displaying auxiliary decision-making. The system efficiently processes radiology department images, improves diagnosis accuracy, optimizes a treatment scheme, adapts to multi-center and multi-platform requirements, and is expected to promote intelligent development of radiology department diagnosis and treatment.

The invention discloses a laser accurate positioning system and method based on an improved PID control algorithm, and relates to the technical field of laser positioning, a holder laser module is constructed and comprises a laser emission unit and a holder execution unit and is used for executing a control strategy, and a multi-sensor array is constructed to collect parameter data of the holder laser module; a control module is constructed and comprises a dynamic bimodal PID unit, a space-time decoupling learning unit and a frequency domain anti-interference unit and is used for analyzing parameter data to generate a control strategy of a holder execution device, a fault processing module is constructed and is used for detecting whether a fault occurs in the process of a laser precise positioning system, and when the fault occurs, the fault processing module is used for processing the fault. Executing a three-stage shutdown process; according to the invention, by improving the PID control algorithm and the frequency domain anti-interference design, the anti-interference capability and the system stability of the laser positioning system in a complex dynamic environment are significantly improved.

The invention relates to the technical field of electric power asset management, and discloses an electric power equipment full life cycle management system based on a digital twinning technology, and the system comprises a causal knowledge graph construction unit, a strategy rule learning unit, a decision option value quantification unit, a combined decision optimization unit, and a maintenance decision voucher management unit. The method comprises the following steps: constructing a knowledge graph containing a causal relationship among equipment, environment and management activities; carrying out anti-fact deduction based on the atlas to dynamically learn strategy rules; quantizing the value of the potential maintenance decision by adopting a physical option model; performing combination optimization under resource constraints of budget, spare parts and the like to generate an optimal decision combination; and finally, generating a standardized maintenance decision voucher for the decision in the combination. According to the method, decision-making flexibility and environment uncertainty can be quantified into specific economic values, future-oriented and globally optimal resource allocation is realized, and a decision-making process and a decision-making result are solidified into traceable and manageable digital assets.

The invention discloses a knowledge integration decomposition network for long-term time sequence prediction, which comprises the following steps that: a multi-scale moving average module performs moving average on input time sequence data # imgabs0 # to obtain a trend sequence # imgabs1 # and a seasonal sequence # imgabs2 #, and connects # imgabs3 # and # imgabs4 # in parallel to obtain a multi-scale sequence # imgabs5 #; a season learning unit and a trend learning unit respectively learn a trend characteristic # imgabs7 # and a seasonal characteristic # imgabs8 # according to # imgabs6 #; a unit difference value # imgabs9 # is obtained according to the remainder items; the seasonal prediction unit is used for predicting a seasonal sequence # imgabs10 #; the trend prediction unit outputs a trend prediction sequence # imgabs11 #; the output unit is used for adding the # imgabs12 # and the # imgabs13 # to obtain a final prediction output # imgabs14 #; and the training module carries out joint training on the learning module and the prediction module.

The invention relates to the technical field of sleep environment regulation and control, and discloses a sleep environment self-adjustment system based on multi-source heterogeneous data. An environmental parameter acquisition module and a physiological feature acquisition module of the system respectively acquire sleep space environmental parameters and user physiological feature data in real time; after the master control system receives the two types of original data, a fusion calculation unit cleans original environment parameters to generate a standardized environment data stream, and extracts features from the original physiological feature data to generate a physiological feature time sequence; the dynamic evaluation module divides the two types of data into a plurality of sleep stage data segments according to a preset rule, and calculates an environmental parameter fluctuation index and a physiological feature deviation index; the label distribution mechanism combines the two types of indexes to generate a comprehensive comfort label of each sleep stage; an adjustment decision engine generates a global sleep environment adjustment strategy according to all labels, and an actuator control module drives environment adjustment equipment to execute; and the feedback learning unit receives the adjusted data and updates the calculation benchmark of the dynamic evaluation module.

The invention relates to the technical field of power utilization monitoring, and discloses a photovoltaic user power utilization abnormity monitoring method and system based on artificial intelligence. The photovoltaic user electricity consumption abnormity monitoring system based on artificial intelligence comprises a data acquisition module which is used for acquiring photovoltaic power generation data, electricity consumption data and environment data of a user; the data preprocessing and feature engineering module is used for cleaning, aligning and normalizing the original data acquired by the data acquisition module and constructing a feature data set for model training and reasoning; and the artificial intelligence analysis engine module comprises an unsupervised learning unit, a supervised learning unit and a deep learning unit. According to the invention, the false alarm rate and the missing report rate can be effectively reduced, the accurate diagnosis of the abnormal type can be realized, and the intelligent and accurate operation and maintenance requirements of power grid enterprises on the power utilization monitoring of photovoltaic users are met.

A state signal generation unit that detects the state of a mechanical apparatus as a state signal, a condition signal generation unit that generates a condition signal by detecting the operating status of the mechanical apparatus as an operating condition, a state feature generation unit that generates state features from the state signal, a condition feature generation unit that generates condition features from the condition signal, an initial state learning unit that executes learning based on initial learning state features and outputs initial state learning results, an initial condition learning unit that executes learning based on initial learning condition features and outputs initial condition learning results, an anomaly degree calculation unit that calculates the degree of anomaly based on state learning results and detection state features, and an unknownness degree calculation unit that calculates the degree of unknownness based on condition learning results and detection condition features are included.

An object of a system according to an embodiment is to automatically learn a behavior pattern of a target person, detect an abnormality, and issue an alert.SOLUTION: A system according to an embodiment includes a GPS device, a AI learning unit, an abnormality detection unit, and an alert generation unit. The GPS device keeps track of the current location of the subject. The AI learning unit automatically learns a daily behavior pattern based on the position information of the target person acquired by the GPS device. The abnormality detection unit detects an abnormality by comparing the behavior pattern learned by the AI learning unit with the current position information. The alert issuing unit issues an alert based on the abnormality detected by the abnormality detection unit.SELECTED DRAWING: Figure 1

In a learning device (10), a first evaluation unit (14) performs quantitative evaluation on a plurality of pieces of image data and thereby acquires a plurality of first evaluation results for each of the plurality of pieces of image data, a teacher data generation unit (15) selects a second parameter from among a plurality of first parameters having values different from each other on the basis of the plurality of first evaluation results and generates a first set of teacher data including the selected second parameter, and a first machine learning unit (17) performs machine learning using first sets of teacher data and thereby generates a learned model that outputs a third parameter used for processing of image data of a processing target.

The invention provides a learning process-oriented ternary second-order coupling dynamic cognitive diagnosis method, which comprises the following steps of: dividing learning units in a learning process into'knowledge, time sequence and behavior 'triples; knowledge-time sequence and behavior-time sequence first-order coupling analysis knowledge state and behavior state changes are obtained through a neural network model and a behavior feature extraction technology, meanwhile, a guess gate, a proficiency gate and a learning gate are set by adopting a GLU gating technology, complex modeling of cognitive state changes of learners in the learning process is achieved, and the learning efficiency is improved. Obtaining a potential ability state of the learner; finally, through multi-task prediction of behavior characteristics and reaction results, diagnosis of the cognitive state of the learner and prediction of future performance of the learner are achieved. According to the invention, the learning condition of the learner can be scientifically and comprehensively predicted, and the purpose of assisting the teacher in precise teaching is achieved.

Not only do we recommend items that are appropriate for the user, but we also recommend items in a way that allows them to experience serendipity. [Solution] The recommendation system 1 includes a learning unit 10 and an input / output unit 20. The learning unit 10 inputs a user and an item history, inputs the user and partially missing item history into a model, encodes the user and partially missing item history into a latent space, learns a model so that the original item history can be reconstructed from the user and partially missing item history encoded in the latent space, learns a model so that recommended items can be predicted from the user and partially missing item history encoded in the latent space, and learns the model so as to minimize an objective function that represents the rarity of the recommended item from the perspective of all users and the unexpectedness of the recommended item from the perspective of the users and item history. The input / output unit 20 inputs the user and item history into the learned model, and outputs recommended items to be recommended to the user.

The invention discloses a bipartite graph link prediction system based on edge weight guided comparative learning and a recommendation method thereof, and relates to the technical field of recommendation systems. The system comprises a data preprocessing unit, an embedding unit, a weight-guided side attention unit, a double-layer contrast learning unit, a fusion learning unit and a prediction unit. Based on the system, neighbor information is dynamically aggregated through a weight-guided side attention mechanism, so that the model can adaptively pay attention to an important connection relationship; a double-layer contrast learning framework is adopted, and local interaction modes and global structure similarity are comprehensively captured; a weight-degree balance negative sampling strategy is provided, high-quality negative samples are generated, and the negative samples are excellent in performance in the sparse network; a unified learning framework is provided, classification and comparison learning targets are fused, and optimization of local and global structures is realized. The method effectively improves the accuracy and efficiency of a recommendation system, is suitable for large-scale and sparse user-article interaction data, and has a wide application prospect.

To support objective and fair personnel evaluations that do not require comparison with other members. [Solution] The personnel evaluation support device 1 of the present invention comprises an information acquisition unit 111 that acquires workplace information and subject information related to the subject of personnel evaluation, a provisional goal providing unit 112 that provides the subject's supervisor with a provisional goal related to the subject's personnel evaluation, generated by a process of inputting the workplace information and the subject information into a first machine learning model, a final goal acquisition unit 113 that acquires a final goal related to the personnel evaluation provided by the supervisor, a provisional evaluation providing unit 114 that provides the supervisor with a provisional evaluation related to the subject's personnel evaluation, generated by a process of inputting the achievements related to the final goal and the final goal into a second machine learning model, a final evaluation acquisition unit 115 that acquires a final evaluation related to the personnel evaluation provided by the supervisor, and a machine learning unit 116 that performs machine learning related to the first machine learning model and the second machine learning model.

An information processing device (10) includes a learning unit (16b) that learns an action of a user on a game application (corresponding to an example of an “application”) by machine learning, and a gameplay assistance unit (16c) (corresponding to an example of an “assistance unit”) that executes, across one or more of the game applications, gameplay assistance processing (corresponding to an example of “operation assistance processing”) of assisting an input by the user on the game application by using a learning result of the learning unit (16b).