984 results about "Learning unit" patented technology

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.

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

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 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 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.

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

The invention discloses a medical image analysis method and system based on a visual language model, and belongs to the technical field of medical image intelligent diagnosis, and the system comprises an image preprocessing unit which carries out the down-sampling of an original retina OCT image to 256 * 256 and carries out the normalization of the original retina OCT image; the feature encoding unit comprises an image encoder based on RET Found in combination with LoRA optimization and a text encoder based on BioClinicalBERT; the class balance comparison learning unit is used for adjusting loss through class balance coefficients so as to relieve the class imbalance problem; the uncertainty estimation unit is used for calculating confidence quality and uncertainty scores based on Dirichlet distribution, and determining a threshold value in combination with an improved Youden index; and the model training unit adopts a total loss function of class balance loss and uncertainty loss, outputs a diagnosis result and an uncertainty score through transfer learning, and further comprises an image input module, a result display module and a data storage module. Rare disease classification performance and reliability are improved, training efficiency is improved through LoRA optimization, and an accurate and reliable scheme is provided for detection of the rare retina diseases.

The purpose is to more reliably manage abnormal communications. [Solution] The anomalous communication detection device 1 includes a learning unit 11 that constructs a multivariate probability model using information on the presence state indicating whether the communication terminal 2 was present in the communication area of ​​each base station 3 during each time period, based on a history of presence information regarding the communication area of ​​the base station 3 in which the communication terminal 2 identified by the subscriber identifier was present, estimates parameters of the multivariate probability model, and generates a matrix indicating conditional dependencies between presence states, and a determination unit 12 that determines that an inconsistency exists in the presence information of the communication terminal when the value of a component in the matrix indicating the conditional dependencies between presence states exceeds a predetermined threshold.

A feature value generation device includes a generator configured to generate vectors whose elements are feature values of data items collected at a plurality of timings from a target of anomaly detection, so as to normalize or standardize the vectors based on a set of predetermined vectors; a learning unit configured to learn the predetermined vectors so as to output a learning result; and a detector configured to detect, for each of the vectors normalized or standardized by the generator, an anomaly based on said each of the vectors and the learning result. The set of predetermined vectors is a set of vectors with which no anomaly is detected by the detector, and the set of vectors is updated in accordance with no anomaly being detected by the detector.

In a learning phase, a communication unit (203) acquires communication data that includes a parameter value from which an operation state of a monitored system can be estimated, and that is to be communicated in the monitored system, as learning phase communication data. In the learning phase, a state input unit (204) acquires a learning phase operation state value that indicates a learning phase operation state which is an operation state of the monitored system. In the learning phase, a learning unit (210) performs learning using the learning phase operation state value and a learning phase parameter value included in the learning phase communication data, and generates a learning model (215) for estimating from an attack detection phase parameter value included in attack detection phase communication data which is communication data that is to be communicated in the monitored system in the attack detection phase, an attack detection phase operation state which is an operation state of the monitored system.

The invention provides a self-driven humidity detection system based on artificial intelligence. The self-driven humidity detection system comprises a humidity sensing converter module, an LED bulb, a self-driven module and a background processing module. The humidity sensing converter module comprises a humidity sensing module and an indication module; the self-driving module comprises a water storage device, a TENG, an instillation device and an integrated circuit; and the background processing module comprises a visual monitoring acquisition unit, a data analysis unit, a machine learning unit and a display interface unit. The system can continuously detect the humidity change in the environment, accurately measure the real-time humidity and realize the continuous visualization of the humidity signal. The humidity detection function is based on a pointer suite which deforms along with the change of environment humidity, a pointer of the indication module deforms, and an LED bulb fixed on the pointer of the indication module shifts along with the pointer. In the working state, water drops collected by the water storage device drop on the TENG through the instillation device, friction current is generated through impact, the LED emits light through a connecting circuit of the TENG and the LED, the position and relative displacement of the bulb are recognized and analyzed through the background processing module, and the relation between the position and the environment humidity of the bulb which are input in advance is compared. Therefore, the humidity signal is visualized and the environment humidity is accurately measured. Remote visualization of humidity detection is realized based on image identification and processing of artificial intelligence, sustainability and accuracy of humidity detection are guaranteed, and efficiency and convenience of humidity detection are improved.

The invention discloses a medical oncology specimen collection and management system, and relates to the technical field of specimen management, and the system comprises a collection and detection unit which is used for collecting biopsy specimens and corresponding specimen data; the tracking quality control unit is used for storing full-process information of the biopsy specimens by utilizing a block chain and dynamically evaluating and tracing quality information of the biopsy specimens; the integrated learning unit is used for constructing a multi-omics database, generating a tumor multi-omics knowledge graph and carrying out dynamic knowledge updating through data mining; and the decision management unit is used for sharing resources and monitoring authorized use records of the biopsy specimens. By integrating the block chain, federal learning and process reconstruction, full-chain upgrading of medical oncology specimens from'collection 'to'application' is realized, tumor diagnosis and treatment are promoted to step forward from'experience-driven 'to'data-driven', digitization, standardization and traceable management of biopsy specimen full-process information are realized, and the efficiency of medical oncology diagnosis and treatment is improved. And data support and decision support are provided for clinical practice and scientific research.

The invention discloses an intelligent ward patrol robot, and relates to the technical field of intelligent medical robots, the intelligent ward patrol robot comprises a robot body, and the robot body is internally provided with a multi-mode sensing unit, an edge calculation unit, a federal learning unit and a decision execution unit. According to the multi-mode sensing unit, a polarization type 3D camera, a 60 GHz millimeter wave radar and an environment sensor array are integrated in a robot body. By installing the multi-mode sensing unit, the non-contact emotion recognition function is achieved, in combination with contact detection of the intelligent bracelet on the signs of the patient, the bottleneck of non-contact emotion and sign cooperative monitoring is broken through, the emotion sensing ability of the patient is improved, and the medical service quality is improved.

The invention discloses a pi RNA and disease association prediction method, device and equipment based on comparative learning. The method comprises the following steps: step S1, constructing a p RNA-disease heterogeneous graph network; s2, fusing the multi-level semantic information; step S3, according to the node representation, introducing a Transform model, and enhancing the global association modeling capability of the piRNA and the disease node; s4, constructing a topological graph and a semantic graph; step S5, obtaining an accurate p iRNA-disease association prediction score; the prediction device comprises a heterogeneous graph construction unit, a node embedding learning unit and a node embedding learning unit. An input and output unit, a storage unit, a communication unit, an RAM unit, an ROM unit and a GPU of the electronic equipment are connected with one another through a bus, and the requirements for complex calculation and data interaction of a p-RNA and disease associated prediction task are met. The method has the characteristic of high prediction accuracy.

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 purpose is to more easily detect abnormalities in program operation. [Solution] The anomaly detection device 1 comprises a learning unit 11 configured to construct a multivariate probabilistic model using each of a plurality of processing resource usages used in the execution of processes corresponding to a plurality of process IDs observed in each time period within an observation period, and to generate a matrix representing the conditional dependency between a plurality of processing resource usages corresponding to a plurality of process IDs by estimating parameters of the constructed multivariate probabilistic model; and a judgment unit 12 configured to judge an operational anomaly of processes corresponding to two process IDs based on values ​​indicating the conditional dependency between the processing resource usages used in the execution of processes corresponding to any two of the plurality of process IDs in the matrix generated by the learning unit 11.

The invention discloses a teaching resource intelligent management system and method based on big data, and relates to the technical field of teaching resource intelligent management. Constructing a teaching resource arrangement model based on the mastery index of the micro knowledge points; solving the teaching resource arrangement model by adopting a two-stage heuristic decomposition algorithm to generate an optimized learning arrangement sequence; sequentially pushing the assembled micro learning units to a user terminal in a streaming manner according to the sequence of the optimized learning arrangement sequence, and obtaining process interaction data of a user; extracting process interaction data to analyze answer accuracy and response time stability; updating the total mastery index according to the two-dimensional normal probability model; and triggering re-optimization of the subsequent optimization learning arrangement sequence based on the updated total mastery index. Instant refreshing of the state is realized; the analysis efficiency is improved.

The purpose is to appropriately manage abnormal communications with a simpler configuration. [Solution] The abnormality management device 1 includes a learning unit 12 that uses the characteristic directions of normal data as training data to learn, by maximum likelihood estimation, parameters of a probability model that outputs the posterior probability that the number of packets received in each time period corresponding to each of the characteristic directions of the normal data is normal; a derivation unit 13 that derives a probability distribution for the characteristic directions of abnormal data that indicates the number of abnormal packets received in each time period that deviates from the range of normal packet numbers, based on the posterior probability estimated by the probability model learned by the learning unit 12, the probability distribution for the characteristic directions of the normal data, and the prior probability of normality; and a calculation unit 14 that calculates a first index value that indicates the degree of spatial agreement formed by the probability distribution for the characteristic directions of the abnormal data derived by the derivation unit 13 and the probability distribution for the characteristic directions of the normal data.

The object is to manage signal abnormalities even when there is little measurement data of the abnormal signal. [Solution] The abnormality management device 1 includes a learning unit 12 that uses the characteristic directions of normal data as training data to learn, by maximum likelihood estimation, parameters of a probability model that outputs the posterior probability that the intensity of each frequency component corresponding to each of the characteristic directions of the normal data is normal; a derivation unit 13 that derives a probability distribution for the characteristic directions of abnormal data that shows abnormal intensities of frequency components that deviate from a normal intensity range, based on the posterior probability estimated by the probability model learned by the learning unit 12, the probability distribution for the characteristic directions of the normal data, and the prior probability of normality; and a calculation unit 14 that calculates a first index value that indicates the degree of spatial agreement formed by the probability distribution for the characteristic directions of the abnormal data derived by the derivation unit 13 and the probability distribution for the characteristic directions of the normal data.

To control a group behavior while reducing the complexity of reward design.SOLUTION: A control system includes: a second learning unit 12 that performs adversarial learning of a generative model having a generator 121 that generates pseudo-policy data similar to true policy data, using a policy of a course that the first UAV2a should take sequentially from its current position obtained by learning by a first learning unit 11 as the true policy data, and a classifier 122 that distinguishes between the pseudo-policy data generated by the generator 121 and the true policy data; a generation unit 14 that generates the pseudo-policy data similar to the true policy data by using a learned generator 121' obtained by the adversarial learning of the second learning unit 12; and a setting unit 15 that sets information including the pseudo-policy data generated by the generation unit 14 to each moving body UAV2 as control information for controlling the courses of multiple UAV2s.SELECTED DRAWING: Figure 1

A machine learning device includes an image set acquiring unit to acquire an image set including images, and an image set selecting unit to select an image set similar to the acquired image set from a plurality of image sets different from the acquired image set. In addition, the machine learning device includes a performance comparison unit, and a preprocessing acquisition unit to select a learning model from a plurality of machine-learned learning models based on a performance comparison result by the performance comparison unit and to acquire preprocessing performed on an image set used for machine learning of the learning model selected. Furthermore, the machine learning device includes a model learning unit to perform the acquired preprocessing on the acquired image set and to cause a learning model that has not yet trained to perform machine learning using the preprocessed image set.

In one aspect, the inventions include a system for control of a software defined computer network state system. First, an application plane layer is adapted to receive instructions regarding operation of the state system. Preferably, the application plane layer is coupled to an application plane layer interface. Second, a control plane layer includes an adaptive control unit, such as a cognitive computing unit, an artificial intelligence unit or a machine-learning unit. Third, a data plane layer includes an input interface to receive data input from one or more data sources. A title transfer network element is provided to transfer digital assets via a blockchain. The system may use domain transformations and difference engines.

The invention discloses a dyeing process parameter optimization and recommendation system based on big data analysis, particularly relates to the field of dyeing process parameter optimization, and comprises a data interface unit, a hybrid modeling unit, a parameter recommendation unit and a feedback learning unit. The data interface unit is used for collecting and processing multi-source heterogeneous data and generating a historical data packet and a current order data packet; the hybrid modeling unit is used for establishing a mapping relation between process parameters and dyeing results on the basis of a historical data packet in combination with a mechanism constraint and data driving method; the parameter recommendation unit generates a plurality of groups of process parameter recommendation schemes according to the current order data in combination with a quality prediction model and a multi-objective optimization strategy; and the feedback learning unit analyzes a deviation source and updates the model in real time by comparing actual and predicted quality indexes. According to the system, intelligent recommendation and dynamic optimization of dyeing process parameters can be realized, the dyeing quality stability is remarkably improved, the energy consumption and the production cost are reduced, and the system has good self-adaption and continuous optimization capability.

The invention relates to the technical field of disease auxiliary diagnosis, in particular to a digital mammary gland artificial intelligence auxiliary diagnosis system based on multi-modal fusion, and the system comprises a multi-modal data collection module which is used for obtaining digital mammary gland image data and rehabilitation scheme data of a patient; the image quality correction module is used for carrying out acquisition quality evaluation and correction on the digital mammary gland image data; the feature modeling and evaluation module is used for performing feature extraction on the corrected digital mammary gland image data and rehabilitation scheme data, and comprises an image-drug action mechanism cooperation unit and an image-rehabilitation bimodal co-learning unit; and the rehabilitation effect prediction module is used for constructing a rehabilitation effect prediction model and outputting a rehabilitation prediction result under the influence of the scheme specificity of the current rehabilitation scheme of the patient. According to the method, the digital mammary gland image and the patient rehabilitation scheme are deeply fused, so that the accuracy and reliability of mammary gland tumor rehabilitation prediction are effectively improved.

In order to reduce a load on a user and risks regarding privacy by inhibiting acquisition of unnecessary personal characteristics in a behavior change assistance system, this behavior change assistance device has: a preference estimator which is pre-trained by using pre-collected learning data so as to estimate intervention preferences as a probability distribution from an answer to a question; a reception unit which receives inputs of an answer to a question posed to a user and an evaluation value with respect to an intervention on the user; an agent-training unit which performs reinforcement learning, by using the probability distribution which is estimated by the preference estimator from the answer to the question posed to the user, on an agent for predicting, from the answer to the question posed to the user, a behavior suitable for the user; and an output unit which presents, to the user, a question suitable for the user or an intervention suitable for the user on the basis of a behavior predicted, by using the agent, from the answer to the question posed to the user.

The invention discloses a safety equipment fault prediction and intelligent maintenance decision-making system based on an AI large model, which belongs to the technical field of computers and comprises a data acquisition and integration unit, an AI large model construction and training unit, a fault prediction and root cause positioning unit, an intelligent maintenance decision-making generation unit and a dynamic optimization and self-learning unit. According to the safety equipment fault prediction and intelligent maintenance decision-making system based on the AI large model, through data integration, model optimization and decision-making closed loop, the equipment reliability is remarkably improved, the operation and maintenance cost is reduced, in the future, along with deepening of model interpretability, industry customization and ecological cooperation, the technology will become a core infrastructure, and the technology has a wide application prospect. The manufacturing industry is promoted to advance towards predictive maintenance and zero-fault production, a deep learning model is constructed by integrating multi-source data, and precise prediction of the equipment health state and intelligent optimization of a maintenance strategy are realized in combination with industry knowledge.

Disclosed are an apparatus and method for predicting collision of a logistics robot, the apparatus comprising: a collection unit for collecting process state information, a learning unit for learning a collision prediction criterion for each collision type based on the process state information, a prediction unit for predicting the collision prediction criterion, and an output unit for outputting the collision prediction criterion; the prediction unit is used for predicting occurrence of collision of at least one of the plurality of logistics robots based on a collision prediction standard and the process state information collected after learning, and the output unit is used for outputting a control signal corresponding to a prediction result.