725 results about "System identification" patented technology

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.

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.

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 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 an end-side cloud collaborative approximate redundancy removal system and method for multi-camera video analysis, and belongs to the technical field of end-side cloud collaborative optimization of an internet application layer. According to the invention, an end-side cloud collaborative cache architecture is provided, the cloud cache provides more storage resources with lower cost, the edge cache is constructed through a maximum coverage cache selection algorithm, the edge cache is located at a place closer to the camera, and the delay is shorter. The camera is connected to the edge server and continuously transmits captured video streams to the edge server, and the system quickly and accurately identifies a target result by combining the advantages of the two caches. The same target is only identified once by the system, and then the features and the identification result of the target are stored in the cache. And when the target appears again, the system can query the corresponding identification result from the cache without calling the model for repeated calculation, so that the calculation resources are greatly saved.

A system and method for multi-level compaction of floating-point numbers and associated metadata within datasets. The system identifies floating-point numbers and their associated metadata, pre-encodes numbers into binary string representations, and encodes metadata into compact binary form. These encoded elements are linked together and indexed to indicate they represent floating-point numbers with metadata. The dataset is organized into multiple compaction levels based on semantic relationships between metadata elements. The system creates specialized indices for metadata-based retrieval, maintains relationship maps, and implements inheritance policies across hierarchical levels. During retrieval, the system reconstructs both the original floating-point values and their associated metadata, preserving hierarchical relationships. This approach enables more intelligent data organization, richer contextual information preservation, and more powerful query capabilities while maintaining efficient floating-point compaction.

In a general aspect, benchmarking for data quality monitoring is described. In some embodiments, a system identifies a base data set to be used as input to a machine learning (ML) model. The system generates a modified base data set by causing synthetic anomaly injection operations to be performed on data of the base data set. The system causes the ML model to run, using the base data set as input, to determine a first output of the ML model, and to run, using the modified base data set as input, to determine a second output of the ML model. The system determines a set of performance metrics representing performance of the ML model at detecting data anomalies and outputs a representation of the set of performance metrics.

The invention discloses a sperm library identity verification system and method based on face recognition, belongs to the technical field of information management, and solves the problems that a three-dimensional face recognition device of an existing system cannot complete identity verification at different angles and postures, and the recognition speed of the system and the waiting time of a user are delayed. The method comprises the steps that facial feature information is collected in real time, an identity recognition model is executed, and the identity recognition model recognizes and analyzes the facial feature information with sperm library associated personnel information as prior information; iterative training is carried out on the identity recognition model through the training set and the test set, the identity recognition model recognizes and analyzes the facial feature information by taking the sperm library associated personnel information as prior information, and the verification accuracy is not influenced by taking the sperm library associated personnel information as the prior information. And feature attention analysis is carried out on the facial feature information under the multi-view attitude, so that the identity verification accuracy of the sperm bank is improved, and the model calculation amount is reduced at the same time.

The invention discloses a construction method, a construction system, a recognition method and a recognition system for a fine tree species recognition model, belongs to the field of forest management and ecological protection, and solves the problems that the overall contribution of the whole group of time sequence features to model performance cannot be comprehensively examined, and due to the resolution limitation of remote sensing images and the randomness of forest species distribution space and quantity, the model performance cannot be comprehensively examined in the prior art. And the performance of the remote sensing tree species classification model is obviously reduced. The method comprises the steps of 1, constructing a high-dimensional multi-source feature data set; 2, performing feature optimization based on the high-dimensional multi-source feature data set to obtain an optimal classification feature set; 3, collecting actual measurement sample data, and constructing a deep learning sample library in combination with the optimal classification feature set; and step 4, presetting a fine tree species identification model, and inputting the deep learning sample library to train the preset fine tree species identification model to obtain the fine tree species identification model. The method is used for obtaining the accurate and newest tree species distribution map.

The invention discloses a combined type coaxial high-speed helicopter flight / turboshaft engine comprehensive control system. The system comprises the following steps that according to control mechanism parameters output by a flight control system and a combined type coaxial high-speed helicopter fuzzy torque prediction model, a flight required torque advanced prediction value of a combined type coaxial high-speed helicopter system is output; the flight required torque advanced prediction value is input into a turboshaft engine system identification model after being superposed by a variable transmission ratio proportionality coefficient to obtain a fuel prediction value, and the fuel prediction value is further superposed with the output of an engine control system to obtain the fuel input of the turboshaft engine system; calculating a flight required torque advanced prediction value error, and if the error value is greater than a threshold value, updating the fuzzy torque prediction model by using the flight state parameters of the composite coaxial high-speed helicopter system; the relative error value of the current power turbine rotating speed is calculated, and if the relative error value is larger than a threshold value, primary parameter updating is conducted on the identification model of the turboshaft engine system through a recursive least square algorithm according to the real-time and historical torque input of the turboshaft engine system and the power turbine rotating speed. According to the method, under the condition that the constant rotating speed control target of the turboshaft engine is considered, the stability of a coaxial high-speed helicopter flight / turboshaft engine comprehensive control system is enhanced, the response speed of the turboshaft engine is increased, and the overshoot and droop amount of the rotating speed of a power turboshaft is reduced.

The invention discloses an intelligent analysis and management method and system for an integrated controller, relates to the field of integrated control, and aims to complete complex air conditioning system identification model training and off-line optimization of an optimal PID parameter strategy by using historical data at a cloud end with sufficient computing power. And generating an expert strategy library containing various working conditions and corresponding optimal control parameters thereof. Then, a lightweight proxy model with small calculation amount and high reasoning speed is trained based on the strategy library; the proxy model is deployed on a resource-constrained edge controller. Therefore, the edge end does not need to execute complex online optimization calculation, and only needs to input the real-time working condition data into the agent model, so that the current optimal PID parameter can be quickly deduced. According to the cloud training and edge reasoning architecture, online, real-time and intelligent self-tuning of controller parameters is realized, and a gap between an advanced algorithm and industrial landing is effectively filled up.

The invention discloses an attitude-elasticity-pneumatic coupling flutter control method of a flying wing layout aircraft, and relates to the technical fields of mechanics, aeroelasticity, control and the like. The control method comprises the steps that a Lagrangian equation under a general body shafting is synthesized, a full-quantity coupling dynamic model is established, and a disturbance motion dynamic equation used for flight control system design is established through small disturbance linearization; correcting the disturbance motion kinetic equation based on a system identification flight test; designing an attitude-elastic-pneumatic coupling flutter suppression controller based on a traditional or modern control theory, and embedding the attitude-elastic-pneumatic coupling flutter suppression controller into a flight control system; the effectiveness of the flutter suppression controller is verified through a flight test. The problems that existing numerical analysis and wind tunnel experiments cannot accurately reflect the complex coupling effect in flight, flutter cannot be effectively restrained in flight, and the flight envelope cannot be expanded are solved, and the flight safety and stability of the flying wing layout aircraft can be effectively improved.

The invention discloses a vibration control method for an offshore wind turbine structure, which comprises the following steps of: a, implementing real-time multi-source data acquisition which is realized by deploying a series of sensors on the offshore wind turbine structure and in the surrounding environment so as to obtain environmental load parameters, structural response parameters and running state parameters of the offshore wind turbine; and b, inputting the acquired data into a composite environment and structure state decoupling and feature extraction module, preprocessing the acquired data by the module, and identifying an environment load spectrum in real time. According to the vibration control method for the offshore wind turbine structure, through multi-source information fusion, online system identification and self-adaptive multi-target control, broadband, multi-mode and self-adaptive suppression of vibration of the offshore wind turbine structure is achieved, and the limitation of a traditional fixed parameter damper in a complex dynamic marine environment is overcome; complex excitation sources such as wind, waves and ocean currents and the coupling effect of the excitation sources are effectively handled.

Techniques for receiving, storing, analyzing, and utilizing raw content are disclosed. The system receives raw text from a user, including a first tag and second tags. The system identifies and removes formatting attributes in the raw text, including whitespace characters, to generate a normalized input. The system stores the normalized input in target dataset(s) and parses the normalized input for the first tag and the second tags. In this case, the first tag corresponds to one or more topics, and the second tags represent a computing system associated with corresponding portions of the normalized input. The system analyses the first tag and the second tags to identify the topics. The system generates one or more topic maps for the target dataset(s) based on the first tag and the one or more second tags. The topic map(s) include one or more references to content items within the target dataset(s).

Techniques for identifying a document layout are disclosed. In one embodiment, attribute data associated with an electronic document is accessed. A machine learning model is then applied to the attribute data. The machine learning model is configured to classify the electronic document based on the attribute data and feature sets of a plurality of document classes. Based on the document class predicted by the machine learning model, the system identifies a layout associated with the document class. The layout specifies layout elements and content types associated with the layout elements. The system extracts and stores information from the electronic document according to its content type.