862 results about "System identification" patented technology

The invention relates to the technical field of charging pile power distribution, and discloses a charging pile dynamic power distribution method and device based on load prediction, and the method comprises the steps: carrying out the system identification analysis of the real-time operation data of a charging pile, and constructing a charging pile system small signal model; training to obtain a low-sensitivity load prediction model and a high-sensitivity load prediction model, and generating charging load prediction data; performing control optimization calculation to obtain a dynamic power compensation control parameter; genetic optimization and multi-stage power distribution are carried out, and a multi-stage power distribution scheme including a conventional load distribution mode and a peak load distribution mode is obtained; power limitation is carried out by reducing charging power of non-priority users, load transfer is carried out through power coordination of adjacent charging piles, an emergency power adjustment control instruction is obtained, a closed-loop optimization mechanism is adopted, and stable operation of a charging pile system under various working conditions is guaranteed through parameter online optimization and dynamic adjustment of a control strategy.

The invention relates to the technical field of intelligent lock recognition, in particular to an intelligent lock image feature recognition method and system based on deep learning. The recognition method comprises the following steps: constructing a multi-branch deep neural network model, wherein the multi-branch deep neural network model comprises a backbone network, a feature extraction branch and a feature fusion branch; a framework training model is used to learn identity recognition, attitude estimation and environment perception tasks; and identifying the comprehensive feature representation by using a trained framework training model, and generating a control instruction of the intelligent lock according to an identification result. According to the intelligent lock image feature recognition method based on deep learning provided by the invention, comprehensive analysis of multi-dimensional information such as identity, posture and environment is realized by constructing the multi-branch deep neural network model.

The invention discloses an aero-engine control system and method based on self-adaptive specified interference cancellation, and belongs to the field of aero-engine control. A disturbance affine form nonlinear mathematical model of an aero-engine is obtained through system identification, a fixed-time disturbance observer is designed to estimate the lumped interference of the system in real time, and the disturbance of the aero-engine is estimated in real time; and a self-adaptive specified interference elimination composite controller is constructed based on a nonlinear backstepping method and a dynamic inverse method, so that accurate control on the rotating speed of the engine is realized. The system adjusts the control quantity in real time based on a backstepping method and a dynamic inverse method, and the controller adopts a non-smooth instruction filter and a self-adaptive dynamic weight distribution mechanism, so that the uncertainty of the system is effectively compensated. The method can effectively cope with uncertainty caused by system parameter perturbation, unmodeled dynamics and external load fluctuation, gives consideration to dynamic and static performance, has high control precision, good anti-interference capability and closed-loop stability, and is suitable for aeroengine rotating speed control under complex working conditions.

Methods and systems are presented for facilitating a context-aware dialogue with a user. A chat system identifies a particular domain associated with a query received from the user. Contents are retrieved from one or more data sources that are associated with the particular domain. A semantic-based algorithm and a term-based algorithm are used on tandem to rank the contents, where a selective subset of contents is identified based on the ranking. An enriched prompt is generated for an artificial intelligence (AI) model by integrating the subset of contents into the query. The enriched prompt is then provided to the AI model. The AI model is configured to generate a response to the query based on the enriched prompt.

A computer system uses a machine-learned language model to generate an SQL query for a user query. The system receives a user query comprising a task for performing a database query. The system identifies an embedding for the user query to represent the user query. The system generates a prompt for input to a machine-learned language model, and the prompt specifies the user query, metadata associated with the identified data table and a request to generate one or more SQL statements for performing the database query on the data table. The system provides the prompt to a model serving system and receives an output generated that includes the requested SQL statements for performing the database query. The system presents a response to the user query using the received SQL statements.

The invention relates to a dynamic cooperative path planning and autonomous landing control method for an unmanned aerial vehicle and an unmanned ship, and the method comprises the following steps: collecting the state data of the unmanned ship and the unmanned aerial vehicle and the high sea condition environment data, and carrying out the data fusion processing; an improved fast extended random tree algorithm is adopted to plan a safe navigation path for the unmanned ship, the generated path of the unmanned ship is optimized based on an unmanned ship motion model considering environmental interference, and a model prediction control algorithm is adopted to plan a path for the unmanned ship to fly to a predicted landing point of the unmanned ship. Dynamic cooperation between the unmanned ship and the unmanned aerial vehicle is realized through information interaction between the unmanned ship and the unmanned aerial vehicle; the unmanned aerial vehicle recognizes an unmanned ship landing identifier through a visual servo guide system, calculates the position and attitude deviation relative to a landing point, adjusts flight control parameters according to interference conditions by using an adaptive control algorithm, and performs trajectory prediction compensation in combination with an unmanned ship motion model to realize autonomous landing. Compared with the prior art, safe landing of the unmanned aerial vehicle on the unmanned ship under the high sea condition is achieved.

The invention discloses an intelligent warehousing system based on supply chain management, and particularly relates to the technical field of warehousing identification and supply chain information processing, and the system comprises an information collection module, a trigger judgment module, a data verification module, a risk assessment module and a state grading module. Collecting target carrier identification data through a multi-source device, performing credibility scoring according to stability, consistency and a historical confidence level, and judging whether to enter a verification process or not; in the verification stage, an identification consistency index and a supply chain synchronization credibility index are generated, and a risk score is output by the model; implementing credible marking, image checking or multi-source reconstruction according to the scoring level, and realizing identification result hierarchical control and operation closed loop; according to the method, the identification vector is constructed through multi-source acquisition, so that the state of the target carrier is accurately acquired; the identification processing efficiency is improved by adopting a hierarchical judgment mechanism; a differential response strategy is executed based on the risk score, a recognition credibility closed loop is constructed, and the recognition accuracy and operation safety of the system are effectively improved.

The invention relates to the technical field of food processing and intelligent control, and discloses a pear juice production system and an intelligent control method thereof.The system comprises a raw material processing module used for recognizing, sorting and cleaning pears entering a production line based on a visual sensor, image recognition equipment and automatic cleaning equipment; the juicing control module is used for controlling the working state of the juicer according to the real-time data; the invention further discloses a method which comprises the following steps: acquiring raw material information: acquiring physical characteristic data of types, maturity and hardness of pears entering a production line through a sensor and an image recognition technology, and classifying and preprocessing the pears; by combining an optimal control theory, a robust control algorithm, a system identification and adaptive adjustment algorithm and a deep learning and big data analysis technology, real-time optimization and adjustment of operating parameters of the juicer are realized, changes of different pear varieties and maturity are automatically adapted, potential problems are found in advance, and early warning is provided.

Data digitization via custom integrated machine learning ensembles is provided. For example, a system integrates multiple trained machine learning ensembles to identify, extract, and map data. The system receives a data set from sources. The system identifies ensembles can include machine learning models that can determine an outcome. The system filters a subset of data from the data set. The system identifies a layout for the data set based on a vendor type, data type, and the data set. The system executes a block detection module to identify blocks of the layout. The system executes a header detection module. The system executes a policy detection module to identify the headers as policies. The system transforms, based on the headers, the layout, the blocks, and the policies, the data set into a second file type, and presents the transformed data set for integration into a capital management system.

The invention belongs to the technical field of plastic part quality detection, and discloses a plastic part gold stamping mark accurate positioning method and system based on visual identification, and the method comprises the steps: collecting environment illumination parameters, equipment operation parameters and image data, and processing the image data to obtain regional image data; performing edge detection and fracture repair operation on the regional image data to obtain identification edge feature data; calculating to obtain a position deviation quantized value according to the identification edge feature data; based on the position deviation quantized value, in combination with preset inclination direction information, adjusting an image visual angle and correcting an angle deviation to obtain corrected image data; executing edge path tracking on the corrected image data to obtain path complete data; obtaining an identifier positioning result based on the path complete data in combination with the environment illumination parameters and the equipment operation parameters; and carrying out stability evaluation on the identification positioning result, and obtaining a final system identification stability result in combination with preset detection fluctuation characteristics.

The invention relates to the technical field of data processing, and discloses a secondary equipment hidden danger mining method and system based on a wave recording file and monitoring data. The method comprises the steps of calculating a conditional probability and intervention probability difference identification causal relationship by analyzing a recording file in combination with equipment topology and protection logic to construct a correlation graph, and performing Fourier transform and wavelet decomposition on a sampling sequence to extract multi-scale features; small signal test excitation is injected, a system identification estimation transfer function is fused with monitoring data to form a health feature vector to judge a health state, and a correlation map and the health state are input into a map neural network to calculate weighted attention coefficients and aggregate neighbor information to predict a fault propagation path; and establishing a degradation model, correcting the failure rate, predicting the residual life and generating a graded early warning report. According to the method and the device, the transformation from passive post analysis to active predictive maintenance is realized, and the timeliness, accuracy and systematicness of hidden danger identification of the secondary equipment are improved.

The invention discloses a weather system identification method based on multi-source meteorological information. The method comprises the following steps: S1, acquiring historical multi-source meteorological data of a target area; s2, cleaning and complementing the original meteorological data; s3, marking the type and position of a weather system, and constructing a training set; s4, training a single-source meteorological feature extraction model; s5, fusing the single-source features, and training an integrated recognition model; s6, deploying the trained model to a reasoning environment; and S7, inputting real-time meteorological data, and outputting an identification result. According to the invention, efficient fusion and accurate identification of multi-source weather information are realized, and the real-time performance and accuracy of weather system identification are significantly improved.

A system leverages a multimodal model to predict peripheral arterial disease (PAD) risk. The system provides a dynamic questionnaire leveraging a language model to generate questions in response to user input. From the dynamic questionnaire, the system identifies user-specific risk factor(s) for PAD. The system also receives sensor data recorded by health sensor(s) including biometric signals of the user. The system applies a sensor classification model to the sensor data to output clinical data associated with a health state of the user. The system applies a multimodal PAD risk prediction model to the identified user-specific risk factors and the clinical data to output a PAD risk prediction indicating whether the user is at risk for PAD. Based on the outputs, the system generates and transmits one or more personalized recommendations for the user for treating or mitigating PAD risk and / or control instructions for controlling operation of the health sensor(s) and / or the medical device(s).

The invention relates to the technical field of data processing, and discloses a child autism behavior recognition method and system based on data analysis and a medium. The method comprises the steps of collecting and preprocessing child multi-modal behavior data; extracting nonlinear features to obtain a time sequence feature matrix; identifying a repeated behavior mode through a three-dimensional convolutional network; analyzing behavior time sequence change by using a time convolutional network; fusing a plurality of feature representations to obtain a comprehensive feature vector; a multi-classifier system is applied to identify autism behavior types and evaluate severity. By extracting the nonlinear time sequence features, constructing the deep spatial-temporal feature extraction network and designing a feature fusion mechanism and a multi-classifier integration system, the method can overcome the limitations of a single mode, a linear feature and a single algorithm in the prior art, and improves the accuracy and interpretability of autism behavior recognition.

The present disclosure relates to systems, methods, and non-transitory computer-readable media that modifies parameters of a fused feature extractor. In particular, the disclosed systems generate inferred masks from digital images and digital text prompts using a fused feature extractor. Furthermore, the disclosed systems identify a subset of the inferred masks that satisfy a validity threshold. Moreover, the disclosed systems generate an augmented training set by combining the subset of the inferred masks with a training set that includes the ground truth masks. Further, the disclosed systems generate object mask predictions from the augmented training set and determine ground truth and pseudo measures of loss by comparing the object mask predictions with the inferred masks and the ground truth masks. From the ground truth and pseudo measures of loss, the disclosed systems modify parameters of the fused feature extractors.

The invention discloses a server and an equipment monitoring system and method thereof, and relates to the technical field of servers, the server comprises a first controller and a second controller, the first controller in an interconnection resource module interacts with firmware of an exchange component connected with an acceleration card in the interconnection resource module, and the first controller is connected with the second controller. The method comprises the following steps: acquiring identification information of acceleration cards connected under a plurality of switching ports of a switching component by a second controller, and determining monitoring modes of all local acceleration cards in a first computing resource module where the second controller is located by the second controller according to the identification information of all acceleration cards acquired by the first controller, the local acceleration cards are respectively monitored according to the corresponding monitoring modes, so that the technical problems that the undistributed acceleration cards cannot be identified by the system and the monitoring link cannot be established are solved; the technical effects of dynamically identifying the types of the acceleration cards under different heterogeneous acceleration card resource pool configurations and adopting corresponding monitoring logic to realize out-of-band monitoring are achieved.

The invention discloses an incomplete multi-mode crisis event detection method based on a memory pool and a mode perception expert system, and relates to the field of machine learning, and the method specifically comprises the steps: building a prompt strongly related to an event instance based on the memory pool, and complementing a missing mode through a large model; a CNN encoder, a ResNet encoder and a CLIP encoder are utilized to construct a double-flow encoder, and text and visual feature extraction is achieved; capturing fine-grained event information and clues; the distribution difference between a complemented sample and a complete sample is identified based on a modal perception expert system, and the quality of fusion representation is improved; and inputting the mixed features into the classification head to complete a crisis event detection task. According to the invention, through the prompt construction module based on the memory pool, a large model is introduced to realize careful completion of a missing mode, and the distribution difference between a completion sample and a complete sample is accurately identified based on a mode perception expert system, so that a multi-mode data missing scene can be effectively coped with, and the accuracy and timeliness of crisis event detection are improved.

The invention relates to the technical field of thermal management control, and particularly discloses a thermal management controller regulation and control method and system based on dynamic thermal inertia prediction. According to the method, the dynamic thermal inertia model is constructed through system identification, and the future temperature field evolution and thermal accumulation risks of the system are accurately predicted. On the basis, a multi-layer nested compensation strategy composed of pre-compensation, main compensation and residual compensation is generated in three dimensions of a compensation angle, a compensation order and a compensation domain, and prospective accurate intervention is achieved. In order to evaluate the control effect, an artifact intensity entropy value is introduced as a quantitative index, and a model and a strategy are corrected in real time through an outer ring optimization mechanism to form self-correction. When continuous thermal unbalance caused by hardware design defects is detected, the system can further start hardware cooperative regulation and control, local heat capacity and a heat dissipation path are optimized fundamentally, cross-level cooperative optimization from a control algorithm to a physical structure is achieved, and finally high-precision, low-energy-consumption and self-adaptive system-level thermal management is achieved.

The invention relates to the technical field of electrical equipment temperature intelligent on-line monitoring and control, and discloses an electrical equipment temperature intelligent on-line monitoring system, which comprises a data acquisition module used for acquiring temperature, heat flow and environmental data of equipment in real time through a plurality of sensors; the data preprocessing and fusion module is used for carrying out noise removal and standardization processing on the collected data and fusing the multi-source data into a comprehensive data set; and the system identification module is used for updating the thermodynamic model of the equipment according to the real-time data and the historical data. Through cooperative work of the self-adaptive nonlinear optimization control module, the multi-level feedback control module and the energy optimization and scheduling module, optimal balance between temperature control precision and energy consumption is realized, a cooling system can be dynamically adjusted according to real-time temperature of equipment and environment change, energy waste is reduced to the greatest extent, and the service life of the cooling system is prolonged. And the response speed, the energy efficiency and the equipment stability of the system are improved.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing a card-not-present machine learning model to generate a fraud prediction for a network transaction where a credit card used for the transaction is not present. In particular, in one or more embodiments, the disclosed systems identify features associated with the network transaction and generate, utilizing the card-not-present machine learning model, a fraud prediction based on the identified features. The disclosed systems can also apply transaction logic based on the fraud prediction to process the network transaction by performing an authorizing, declining, or other action with regard to the network transaction.

The embodiment of the invention provides a method and a system for identifying key environmental elements influencing experience. The recognition method comprises the following steps: collecting eye movement data of a plurality of testers, and collecting physiological feature data of the testers at the same time; analyzing and processing the eye movement data of each testee to obtain eye movement index data of each testee; determining an ontology experience feeling score of each tester based on the physiological feature data; and performing correlation analysis based on the ontology experience feeling score and the eye movement index data of each tester, and determining key environment elements influencing the experience feeling of the tester. Because the physiological feature data objectively reflects subconscious physiological feature changes when the testee observes the environment scene, the corresponding ontology experience feeling score obtained based on the physiological feature data also relatively objectively represents the feeling of the testee to the environment scene, so that the data in the method for determining the key environment elements in the scheme is more objective; and the corresponding identification result is more accurate.

The invention discloses a method and system capable of identifying medical waste stealing actions, and the method comprises the steps: firstly obtaining visual information of a loading and unloading region through an omnibearing real-time video, carrying out the preprocessing of video data, and removing interference factors to enhance the definition and accuracy of an image; and then distance estimation is carried out on the preprocessed video, the moving distance of the human body to the transfer trolley is analyzed, and whether an abnormal approaching behavior exists or not is judged. If the system recognizes the abnormal approaching behavior, the arm action of the human body is further analyzed, and whether the abnormal action exists or not is judged according to the angle change of the arm. If the arm angle is detected to be abnormal and the system recognizes that medical waste is possibly held in the hand, the system automatically triggers an early warning mechanism. The method effectively overcomes the defects that traditional manual monitoring is low in efficiency and poor in judgment accuracy, abnormal behaviors can be accurately found in real time, and intelligent support is provided for safety management of medical waste.

A system partitions files, including segments identified by fingerprints. into clusters. The system counts common fingerprints by counting fingerprints which correspond to both a file cluster and another file cluster. The system counts unique fingerprints by counting fingerprints which correspond to the file cluster and / or the other file cluster. The system uses the common and unique fingerprint counts to approximate the distance between the file cluster and the other file cluster. The system identifies the smallest of distances which are approximated between all file clusters. The system merges the file cluster and the other file cluster into a merged file cluster if the approximated distance is the smallest of distances. The system determines an index corresponding to the smallest and next smallest of distances. The system determines indexes which correspond to merges of all file clusters. The system uses the maximum of indexes to identify the optimal file clustering.

An aquatic cleaning system includes a pool cleaner having a housing, a drive system for moving the housing, and a debris basket associated with the housing. The debris basket is designed to receive debris from the aquatic environment. The system includes a data capture system including an imaging device operably coupled to the cleaner. The system also includes a detection system having a storage medium storing a trained model, and a processor designed to receive data elements from the data capture system and input the data elements into the trained model to identify a detected object within the aquatic environment as being debris or non-debris. The system also includes a control system in communication with the detection system and the drive system to move the pool cleaner toward the detected object identified by the detection system when the detected object is identified by the detection system as being debris.

The invention discloses a modal parameter identification method based on a long short-term memory neural network, belongs to the field of artificial intelligence, system identification and structural health monitoring, and aims to improve the precision of modal parameter identification by using the neural network. The method comprises the following steps: firstly, acquiring response data of a structure model after excitation, preprocessing the response data to obtain a sample set, and dividing the sample set into a training set and a test set; then, a neural network based on long and short term memory is constructed, wherein the neural network is mainly composed of an encoder (MLP), an LSTM feature extractor, a decoder (MLP) and a linear reconstructor; training the neural network by using the training set data until the loss function converges, identifying by using the test set data, and finally obtaining the natural frequency of the structure through the power spectral density analysis of the obtained data. According to the method, the modal frequency identification precision is remarkably improved, and an efficient solution is provided for intelligent health monitoring of a complex structure.

The invention relates to the technical field of intelligent lighting control, in particular to a multi-user sharing-oriented self-adaptive lighting intelligent identification control method, which comprises the following steps of: identifying user identities through a multi-user feature identification system, acquiring historical use habit data, generating personalized lighting parameters based on the data and current environment data, the group preference learning system constructs similar user clusters to optimize shared space illumination; the conflict adjustment system identifies and processes multi-user demand conflicts; the priority distribution system dynamically adjusts user control authority; the feedback learning system obtains user feedback, calculates satisfaction, updates a preference model and continuously optimizes an illumination control strategy; intelligent lighting control based on user features and group preferences is achieved, conflict processing and continuous learning capabilities are achieved, rapid and accurate recognition of users is achieved through a face recognition technology, lighting parameters are automatically adjusted in combination with historical use habit data, and personalized lighting experience is provided; dynamic changes of user demands are captured, and the accuracy of illumination scene recommendation is improved.

The invention discloses an expressway traffic flow induction method and system based on an unmanned aerial vehicle. The system comprises a data acquisition and processing subsystem, a cloud traffic flow simulation subsystem and an induction subsystem. According to the method, control areas are divided for frequently-congested road sections (ramps and service areas) and upstream areas of the expressway, data acquisition is carried out, cloud simulation models of the corresponding control areas are constructed, and meanwhile, real-time updating of the models is carried out according to a certain time window; after the system recognizes congestion, a speed limiting strategy is calculated through the cloud model, the speed limiting strategy is sent to the airport, the unmanned aerial vehicle departs from the designated airport, arrives at the designated position of the road section and carries out speed limiting prompting to carry out traffic flow induction, the effect of relieving the traffic condition of the congested road section is achieved, and meanwhile the system can be used for accidental accident prompting.

The invention discloses an AI voice processing method and system based on intelligent equipment, and the method comprises the steps: obtaining the voice information of a user, and extracting the intention of the user through semantic recognition and natural language understanding; user identity verification is completed in combination with the voice information and the intention, a use scene is extracted, and identity information is obtained; performing text analysis based on the intention, the identity information and the scene, generating an operation command, extracting a multi-modal characteristic parameter, and uploading the multi-modal characteristic parameter to an Internet of Things platform; the platform generates an execution instruction by utilizing AI large model reasoning; user confirmation is carried out, and an independent database is established to store multi-modal characteristic parameters; security verification information is generated and sent to the mobile device, security authentication is completed, and a cloud data access address is obtained; and accessing cloud data through the address, obtaining intelligent equipment information, establishing connection with the intelligent equipment, and executing operation. According to the method, the user identity and the use scene can be finely distinguished, so that the recognition and response accuracy of the voice system of the intelligent equipment is improved.

The invention discloses a TITAN product thunderstorm forecast field correction method and system based on multi-scale ensemble learning. The method comprises the following steps: S1, forming a unified multi-source meteorological data set and a TITAN product set; s2, constructing a meteorological target dichotomy label; s3, preprocessing the multi-source meteorological data set, and matching meteorological target dichotomy tags to generate a sample data set; s4, calculating a multi-scale feature set reflecting macro and micro features of the thunderstorm system and the non-thunderstorm system based on the sample data set; s5, generating a comprehensive multi-scale feature expression; s6, forming a plurality of sub-models with differentiated scale sensing capability; s7, training to form a final thunderstorm system identification model; s8, constructing a TITAN product forecast field reconstruction module; and S9, generating a forecast result reflecting the spatial distribution, the development trend and the potential influence area of the thunderstorm system in real time. According to the method, the time-space evolution feature capturing precision of the thunderstorm system and the prediction stability and reliability are improved.

The invention provides a Hammerstein nonlinear dynamic system identification method and system based on an industrial process, and relates to the technical field of system identification, and the method comprises the steps: collecting the historical data of a Hammerstein nonlinear dynamic system as a data set; setting related parameters of the RBF neural network; determining a neuron center point of a hidden layer of the RBF neural network through a K-means algorithm; calculating a standard deviation of a neuron center point through a nearest neighbor Brute method; calculating a hidden layer output matrix of a hidden layer of the RBF neural network; system parameters of the Hammerstein nonlinear dynamic system are determined; an output estimation value of the Hammerstein nonlinear dynamic system is calculated; calculating a mean square error; judging whether the mean square error is smaller than a preset mean square error or whether the number of iterations reaches a preset number of iterations; and if yes, outputting the system parameters as the overall identification result of the Hammerstein nonlinear dynamic system, otherwise, updating the neuron center point, and restarting the calculation of the standard deviation. The method can improve the calculation efficiency and enhance the identification effect and stability of the algorithm.