139 results about "Incremental learning algorithm" patented technology

The invention relates to the technical field of devices for adjusting, controlling or stabilizing power or frequency in a power grid, in particular to a rapid frequency modulation method for a wind turbine generator based on disturbance adaptive compensation, which comprises the following steps of: constructing a dynamic disturbance sensing data set by collecting frequency deviation, a frequency deviation change rate and fan state parameters; a window length and a weight coefficient of a short-time window sliding mean algorithm are dynamically optimized by adopting a genetic algorithm, background noise interference of a power grid is suppressed, and high-precision disturbance characteristics are extracted. And reducing frequency interference of inertia identification by using a reverse test signal. And updating boundary layer parameters of the sliding mode control model through an online incremental learning algorithm, dynamically adjusting the output priority based on the disturbance energy entropy, and eliminating power conflicts. A multi-island genetic algorithm is introduced to optimize a mode switching threshold value, and frequency modulation safe exit is realized in combination with adaptive power ramp rate limitation. According to the method, the frequency response speed and the multi-source cooperation efficiency in the dynamic multi-disturbance and inertia time-varying scene are remarkably improved, and the frequency secondary drop risk is reduced.

The invention discloses an intelligent power grid optimal scheduling method and system based on multivariate energy storage cooperative scheduling, and relates to the technical field of power grid optimal scheduling, and the method comprises the following steps: building a prediction model based on first data, generating prediction data, coupling energy storage characteristic parameters of different types of energy storage equipment with the prediction data, and obtaining a prediction model; establishing a multi-energy collaborative scheduling model; dynamically screening the energy storage scheduling strategy set based on a preset real-time performance evaluation index to generate an optimal strategy subset; according to the optimal strategy subset, performing differentiated charging and discharging control instructions on the energy storage equipment cluster; and collecting second data in the charge and discharge control process, calculating a deviation value between the second data and the prediction data, converting the deviation value into a feature vector, inputting the feature vector into a preset incremental learning algorithm, and optimizing parameters of the multi-energy collaborative scheduling model. Layered screening is implemented in combination with real-time performance evaluation indexes, and it is ensured that the optimal scheduling scheme can be rapidly selected in different time periods and under the uncertain disturbance condition.

The invention relates to the technical field of precise injection molding control, and discloses an injection molding process parameter self-adaptive compensation system which comprises a multi-source sensing module used for collecting mold cavity pressure, melt temperature and screw displacement information in the injection molding process in real time; the data fusion module is used for performing time sequence synchronization, space registration and noise suppression on the multi-source sensing data; the dynamic modeling module is used for constructing a dynamic model of an injection molding process state based on the fused process data and outputting a state estimation value; and the stability analysis module is used for carrying out system stability analysis based on the dynamic model and generating a control compensation strategy. Cooperative control of precise positioning and high-pressure response is realized through a composite driving framework and a dynamic disturbance compensation technology; an incremental learning algorithm and a multi-dimensional quality evaluation system are combined, a process parameter self-evolution mechanism is constructed, material characteristic drift and complex working condition disturbance are effectively dealt with, and closed-loop optimization from process control to quality feedback is formed.

The invention relates to the technical field of new energy vehicle-mounted data processing, and discloses a new energy vehicle vehicle-mounted data processing method and system based on intelligent networking, and the method comprises the steps: collecting and preprocessing data through a multi-source sensor, carrying out the slicing fusion of the data based on time-space correlation, and extracting features through a lightweight convolutional neural network. Constructing an inter-vehicle communication topology model to realize collaborative feature learning, adopting a federal learning framework to train the model and protect privacy, applying a reinforcement learning algorithm to make a decision, establishing a multi-objective optimization model, deploying a dynamic priority scheduling module to manage resources, and optimizing the model based on an incremental learning algorithm. The system covers a plurality of modules such as data acquisition and preprocessing, data dynamic slicing and fusion and the like. According to the method, the data processing efficiency and accuracy are improved, intelligent decision and multi-objective optimization are realized, data privacy is guaranteed, computing resources are reasonably distributed, and the overall performance of the new energy automobile in an intelligent network connection environment is improved.

The invention relates to the technical field of robot control, in particular to an energy consumption optimization and automatic charging control system for an intelligent pet robot. The system comprises a robot data acquisition module, an energy consumption state evaluation module, an energy efficiency optimization module, an automatic charging scheduling module and a battery health management module which are in communication connection in sequence. The system is driven by multi-dimensional sensing data, energy consumption characteristics are extracted in combination with potential energy field mapping and sparse modeling, and an energy consumption influence map is constructed; joint optimization of the path and the servo strategy is realized by using a quantum behavior particle swarm algorithm; scheduling a charging path through a graph attention network; and the battery health state is dynamically predicted based on an incremental learning algorithm, and the operation efficiency, the cruising ability and the system intelligence of the robot are improved. According to the method, a closed-loop control process is constructed from energy consumption evaluation to energy efficiency optimization, automatic charging and battery health management, good system integration and intelligent response capability are achieved, and the cruising ability and operation stability of the pet robot in a complex application scene are remarkably improved.

The invention provides a financial analysis method and system based on big data, and the method comprises the steps: analyzing the dependency relationship between a non-standard code combination and a fuzzy text mode in a material purchasing scene based on a credible rule library, extracting a new attribute combination feature, and forming an optimization rule feature set if the new attribute combination feature can improve the matching accuracy; performing rule-based key field and attribute set verification matching on the real-time non-standard transaction data according to the optimization rule feature set in combination with a dependency relationship between a non-standard code combination and a fuzzy text mode, and outputting a matched transaction record set; and comparing the difference between the matched transaction record set and the historical manual matching record, extracting and judging whether the newly added non-standard attribute combination is applicable to the non-standard transaction data which is not covered or not after a matching rule is adjusted by adopting an incremental learning algorithm, and if so, forming a dynamic matching table.

The invention provides a classical music cross-modal high-reliability experimental data set construction method for full-scene teaching application, and the method comprises the steps: constructing an original data set through collecting audio, video, music score and other multi-modal data, carrying out the standardization of each modal data through a preprocessing technology, and obtaining a multi-modal data set in a unified format; according to the semantic feature vectors, a knowledge graph is constructed, nodes represent semantic relations between music elements, edges represent semantic relations between the elements, a graph convolutional network is adopted to encode the knowledge graph, and semantic representation of the teaching content is obtained; a virtual reality rendering technology is adopted, multi-modal teaching resources are integrated into an immersive teaching scene, a content sequence is presented in real time, and dynamic teaching experience is obtained; and continuously collecting interaction data of the learner, updating the knowledge graph and the semantic feature vector, and optimizing the cross-modal fusion model by adopting an incremental learning algorithm to obtain a self-adaptive teaching data set.

The invention discloses a water conservancy knowledge graph intelligent question-answering system and method based on a large language model, and relates to the technical field of water conservancy information, and the method comprises the following steps: carrying out the fusion processing of multi-source water conservancy data in advance, constructing a triple knowledge graph containing a water conservancy field entity type and a relation system, and dynamic updating of the knowledge graph is realized through an incremental learning algorithm. According to the method, the defects of a traditional method in semantic understanding are effectively overcome, deep semantic association of professional query can be accurately captured, and answer deviation caused by keyword matching limitation is avoided. Meanwhile, a dynamic updating mechanism of the knowledge graph can integrate new knowledge such as new projects and industry standard updating in real time through an incremental learning algorithm and a time decay function, obsolete out-of-time information synchronously, ensure that a knowledge system of the system is synchronous with development of the water conservancy industry, and solve the problems that a traditional system is high in updating cost and long in period.

A self-adaptive dynamic stratum recognition method based on multi-source drilling parameters comprises the steps that firstly, the drilling rate, the rotating speed and oil pressure parameters are collected through a sensor set, a drilling depth-time curve is constructed, time sequence data are segmented through a sliding window, and filtering, standardization and abnormal value processing are carried out; calculating a mean value, a standard deviation and skewness statistical characteristic of each parameter in the window, and constructing a nine-dimensional characteristic vector in combination with an energy ratio and a covariant coefficient; updating a stratum feature library based on an incremental learning algorithm, and dynamically optimizing a clustering center to realize autonomous evolution; a confidence coefficient threshold value is set according to the statistical characteristics and the clustering distance, and stratum type dual discrimination is completed; by comparing a real-time judgment result with manual sampling data, the generalization of a learning rate parameter optimization model is dynamically adjusted; and outputting a stratum type corresponding to the current drilling depth and a confidence degree evaluation result based on the optimized clustering model. According to the invention, by improving a data processing mechanism and an adaptive decision rule, high-precision and strong-generalization real-time stratum identification is realized.

The invention discloses a method and a system for detecting the health state of a vehicle battery based on multi-source data analysis, and relates to the technical field of big data analysis, and the method comprises the steps: collecting multi-source data and historical capacity attenuation data of the vehicle battery, carrying out the space-time correlation modeling of the multi-source data, generating a space-time correlation matrix, and carrying out the detection of the health state of the vehicle battery based on the space-time correlation matrix. The method comprises the following steps: extracting key features by adopting an attention mechanism, establishing a capacity fading prediction model based on the key features and historical capacity fading data, calculating a health state index of a current vehicle battery, judging whether the change rate of the health state index exceeds a preset fluctuation range or not, and if the change rate exceeds the preset fluctuation range, judging whether the change rate exceeds the preset fluctuation range. And if not, performing real-time processing on the newly collected multi-source data by adopting an incremental learning algorithm and updating model parameters of the capacity attenuation prediction model. And a reliable basis is provided for battery life prediction, fault diagnosis and maintenance decision making.

The invention discloses a lithium battery recession track dynamic prediction system based on deep learning, and the system comprises a data collection module which is used for collecting the related data of a lithium battery; the feature extraction module is used for performing multi-dimensional feature fusion on the related data of the lithium battery, obtaining a fusion feature vector and sending the fusion feature vector to the trajectory prediction module; the trajectory prediction module is used for receiving the fusion feature vector, inputting the fusion feature vector into a prediction model, and performing decline trajectory prediction on the lithium battery through the prediction model; the updating module is used for dynamically updating prediction model parameters by utilizing an incremental learning algorithm; and the visualization module is used for carrying out dynamic mapping based on decline trajectory prediction and lithium battery related parameters, and generating an interactive decline trajectory thermodynamic cloud picture and a residual service life probability distribution curved surface. According to the method, the residual service life probability distribution is output by capturing the nonlinear dynamic change and the long and short term dependency relationship in the lithium battery recession process, and quantitative analysis of prediction uncertainty is realized.

The invention relates to the technical field of numerically-controlled machine tool diagnosis, and discloses a numerically-controlled machine tool fault diagnosis system based on machine learning. The system comprises a multi-source sensing data acquisition module for acquiring multi-dimensional sensing data such as vibration spectrum, spindle current waveform, temperature distribution, servo motor encoder feedback and the like; the operation feature coding module receives the multi-dimensional sensing data, extracts time domain statistical features and frequency domain energy distribution features, and generates a multi-source feature coding result; the incremental learning analysis module dynamically updates the feature weight through an incremental learning algorithm, and constructs an incremental training data set; the genetic optimization module optimizes the network structure and hyper-parameter configuration of the fault diagnosis model according to the incremental training data set, and generates optimized network structure parameters; and the integrated diagnosis decision module receives the current operation state data and the optimized network structure parameters, fuses diagnosis results of a plurality of base classifiers through an integrated learning algorithm, and outputs fault type classification signals.

The invention relates to a thermal power plant fault early warning and diagnosis method based on a nebula system, and the method comprises the steps: collecting the multi-dimensional operation time sequence data of a thermal power plant, carrying out the feature extraction and lexical element processing of the multi-dimensional operation time sequence data through an encoder, and obtaining a unified equipment state lexical element sequence; based on the equipment state lexical element sequence, constructing a dynamic star map representing the operation state of the whole power plant; inputting the dynamic star map into a space-time fusion backbone network; the space-time fusion backbone network performs iterative processing on the dynamic star map and generates a health degree attenuation trajectory; when the slope of the health degree attenuation trajectory exceeds a preset threshold value, dynamic early warning and system diagnosis are triggered, and a natural language diagnosis report containing a causal reasoning chain is generated; new multi-dimensional operation time sequence data are collected in real time, and an incremental learning algorithm is used to update the encoder and the space-time fusion backbone network online; compared with the prior art, the system can continuously adapt to working condition changes and has high self-optimization capacity.

The invention relates to the technical field of injection mold monitoring, and discloses an injection mold cavity state monitoring method, system, equipment and medium, the injection mold cavity state monitoring method comprises the following steps: multiple sensors collect mold state data in real time, and the mold state data are preprocessed and fused to generate a comprehensive data set; training a machine learning model based on historical samples, and updating model parameters in real time through an incremental learning algorithm; analyzing current data by adopting dynamic fuzzy logic reasoning, and outputting a state evaluation result; real-time data and results are uploaded to the cloud for deep analysis and distributed storage; a user feedback mechanism is integrated, and model parameters and a fuzzy inference rule base are optimized. According to the method, model parameters are updated in real time through an incremental learning algorithm, a multi-parameter nonlinear coupling relation is analyzed in combination with dynamic fuzzy logic reasoning, and based on cloud collaboration and a user feedback closed-loop mechanism, self-adaptive monitoring and continuous optimization of the injection mold state are achieved, and the anomaly detection precision and the system robustness are improved.

The invention relates to the technical field of large models, in particular to an urban intelligent collaborative decision-making system and method based on a large model, and the system comprises a multi-source heterogeneous data fusion perception layer, a deep learning cognitive engine, a self-adaptive regulation and control decision-making layer and a system efficiency optimization and safety guarantee system. The method has the advantages that multi-modal data of a traffic camera, a geomagnetic sensor, a vehicle-mounted terminal and the like are integrated, space-time correlation characteristics are extracted by using a multi-layer attention mechanism of a large model, and traffic flow prediction and signal timing optimization linkage regulation and control are realized in combination with an incremental learning algorithm. Compared with the prior art, the platform solves the problems that a traditional method is poor in dynamic adaptability to a complex road network and low in prediction precision, the passing efficiency of the urban road network can be improved by 15%-22%, and the abnormal congestion response time is shortened to be within 3 minutes.

The invention discloses a generative image forgery detection method based on attention guidance and incremental learning, and the method comprises the steps: constructing an end-to-end detection model which comprises a multi-modal feature coding module, an A-DTG module, an incremental learning module and a classification module; the A-DTG module generates a domain label by using a self-attention mechanism and a multi-modal attention fusion technology; the incremental learning module is combined with an online incremental learning algorithm, knowledge distillation and a transfer learning technology to realize real-time updating of the model; and optimizing model training through a classification loss function, a distillation loss function and a total loss function. The method effectively solves the problems that in the prior art, the detection capacity of a novel forgery technology is insufficient, multi-modal data processing is weak, and a model cannot be updated in real time, and can remarkably improve the accuracy, generalization and timeliness of image forgery detection in the scenes of news media, judicial evidence obtaining, social media and the like.

This invention presents a multisensor-connected, AI-enabled telehealth system for assisting healthcare providers with differential diagnosis and patients with early health concern detection. The system comprises a multi-sensor medical device with at least seven sensors, a secure cloud-based platform, and an interactive telehealth module. The device preprocesses and securely transmits patient information to the cloud platform, where an ensemble of deep learning models analyzes the data to generate ranked potential diagnoses with likelihood scores. The telehealth module facilitates communication between providers, patients, and the cloud platform, presenting visualizations and receiving feedback. The system continuously updates and fine-tunes its models using incremental learning algorithms, adapting to new data while retaining previous knowledge. It also generates alerts for providers and patients when deviations from normal physiological patterns are detected, accompanied by explainable AI visualizations.

The invention discloses a concrete filled steel tube service life prediction method and system based on machine learning. According to the method, sensor data, external environment data, unmanned aerial vehicle images and satellite remote sensing data are collected to serve as original data and real-time monitoring data, fusion, denoising, normalization and missing value filling are conducted on the original data, and a feature matrix is generated. And training the feature matrix by using a machine learning model to obtain an initial life prediction model, and continuously receiving real-time data through an incremental learning algorithm for dynamic updating to obtain a dynamically optimized life prediction model. According to the structure aging characteristics, life prediction is divided into an initial aging stage, a middle-term damage stage and an advanced-age stage, and accurate prediction is carried out by applying customized models. And finally, integrating the prediction result to an intelligent decision support platform, and displaying the prediction result, the structure health state and the maintenance suggestion through a visual tool. The precision and real-time performance of service life prediction of the concrete-filled steel tube structure are remarkably improved, and the method has a wide application prospect.

The invention relates to the technical field of intelligent detection, in particular to a cage guide anomaly detection method based on audio signal analysis, which comprises the following steps: S1, acquiring audio signals of equipment in different running states in real time; s2, the collected audio signals are preprocessed, and audio features related to the running state of the cage guide are extracted; s3, establishing a dynamic feature change model; s4, applying the dynamic feature change model established in the S3 to analysis of real-time audio signals, and judging whether abnormity occurs or not; and S5, if the audio signal is judged to be abnormal in the step S4, carrying out classification processing on the abnormal signal through a pre-trained classification model. According to the method, the dynamic feature change model and the incremental learning algorithm are combined, real-time and accurate analysis of the audio signals of the cage guide system is achieved, and various abnormal types can be effectively recognized and automatically classified.

The invention discloses a power robot abnormal target detection method based on improved YOLOX. According to the method, image data shot by an electric power robot are acquired, an improved YOLOX model which introduces a multi-scale cross-level local network MS-CSPNet and a small target decoupling detection head is used for processing an image, and accurate detection and positioning of an abnormal target are realized. The MS-CSPNet enhances the feature expression ability of a small target through multi-path convolution, a decoupling detection head is combined with deep convolution, expansion convolution and 1 * 1 convolution, context and long-range dependency information is effectively extracted, classification and regression task separation is achieved, and the detection precision is improved. Each power robot model is continuously optimized based on a federated online incremental learning algorithm, multi-terminal cooperative training is supported, adaptive updating of the model is realized while data privacy is guaranteed, and the method is widely applied to power equipment state monitoring and fault early warning scenes.

The invention relates to the field of display voice interaction, in particular to a display voice interaction system. The method comprises the following steps: acquiring voice data in a real-time environment through a microphone array, converting the voice data into text data by using an improved RNN-T voice recognition model, and marking a timestamp and confidence to obtain an initial control instruction; acquiring historical dialogue data, associating the initial control instruction with the historical dialogue data, dynamically adjusting a context weight through an incremental learning algorithm, taking a display as a main node, establishing a semantic protocol with the smart home equipment, analyzing the target control instruction, distributing the target control instruction to associated equipment, and synchronizing a context; and analyzing feedback logs in the display and the smart home equipment. The setting of the equipment can be automatically adjusted, personalized intelligent control of the display and the equipment is realized, and the use experience of a user is improved.

The invention provides an electroslag remelting smelting endpoint element dynamic forecasting and process decision optimization method and system, and the method comprises the steps: obtaining a historical data set and a real-time data flow in a smelting process, and carrying out the preprocessing of data; an online stochastic gradient descent and incremental learning algorithm is introduced, an element dynamic prediction model is constructed, sensor data in the smelting process are received in real time, and an element concentration prediction model is dynamically optimized; on the basis of a depth deterministic strategy gradient algorithm, an optimization decision model is constructed, and a smelting operation strategy is adjusted in real time according to element components, smelting working conditions and other environment parameters predicted online by the element dynamic forecasting model; and a real-time feedback mechanism is set, so that the element dynamic prediction model and the optimization decision model work cooperatively, and element concentration prediction and real-time synchronous adjustment of a control strategy are realized. The smelting parameters can be adjusted in real time, element prediction precision is improved, energy consumption is reduced, manual intervention is reduced, and smelting efficiency and quality stability are remarkably improved.

The invention discloses a water body apparent spectrum synchronous acquisition and water quality parameter inversion system based on an unmanned ship, and relates to the technical field of intelligent sensing, and the system comprises the following steps: acquiring environmental parameters of the unmanned ship, constructing a deep reinforcement learning model, inputting the environmental parameters, and outputting a phase control instruction; constructing a water quality parameter inversion model, inputting noise-free spectrum data, and outputting water quality parameters; when the water quality turbidity in the water quality parameters exceeds a dynamic turbidity sudden change threshold value, triggering a laboratory to collect water quality verification data, comparing the water quality verification data with the water quality parameters to output a turbidity error, and when the turbidity error exceeds an inversion verification error threshold value, updating the weight of the water quality parameter inversion model; and performing spatial interpolation calculation based on the updated water quality parameter inversion model, outputting water quality parameter distribution, and displaying a water quality parameter thermodynamic diagram and a traceability pollution path through an electronic map. According to the method, the PLSR weight is updated online through the incremental learning algorithm, and the model adaptation speed is increased.

The invention provides a three-coordinate measuring machine adaptive dynamic error compensation method, and relates to the field of three-coordinate measuring machines, and the method comprises the steps: obtaining multi-source state data of a three-coordinate measuring machine, and calculating a real-time dynamic error, the state data comprising a motion state, a dynamic response and environment disturbance data; based on the real-time dynamic error, using a recurrent neural network to construct a virtual measuring machine model, and performing offline training to obtain an initial error prediction model; and acquiring real-time error feedback data, and performing fine adjustment on the initial error prediction model by using an incremental learning algorithm and a sliding time window mechanism to obtain a prediction model capable of dynamically evolving and aging adaptive parameters. The method is used for overcoming the defect that in the prior art, a linear model or a fixed compensation parameter is difficult to accurately describe and compensate all dynamic errors sometimes.

The invention provides a decision supervision support method based on evidence-based medicine knowledge management, and relates to the technical field of evidence-based medicine, and the method comprises the following steps: obtaining an evidence set for describing an entity relationship between exposure factors and / or intervention measures and an outcome index, using the evidence set to construct a first entity relationship graph as quantitative representation of the entity relationship; when the new evidence appears, updating the edge weight according to the quality score of the new evidence by adopting an incremental learning algorithm, and generating a second entity relation graph; the hospital system uses the second entity relation graph to carry out diagnosis and treatment to obtain real-time clinical feedback information; and adjusting the edge weight of the second entity relation graph according to real-time clinical feedback information of the multi-party hospital system to obtain a comprehensive decision support quantitative relation. According to the method, the problem that the medical decision supervision support is lagged due to the fact that the relation between the exposure factor and / or the intervention measure and the outcome index is difficult to dynamically update according to the retrieved new evidence in the prior art is solved.

The invention provides a centralized heating optimal scheduling method and system based on multi-heat-source dynamic matching, and the method comprises the steps: constructing a thermal load prediction model, and fusing various types of data to generate a thermal load demand dynamic curve in the next 24 hours; establishing a dynamic priority ranking model, and generating a heat source priority sequence; generating a multi-heat-source cooperative operation strategy; constructing a multi-heat-source dynamic matching optimization model to obtain an optimal scheduling strategy; heat sources are adjusted in real time through the central control system, and dynamic correction is conducted by combining pipe network feedback data; and periodically updating the thermal load prediction and dynamic priority ranking model. According to the method, a bidirectional LSTM neural network is fused with data to construct a high-precision thermal load prediction model, and a curve is generated in combination with a feature pyramid network; meanwhile, related data are periodically combined, the weight of the LSTM neural network is updated through an incremental learning algorithm, the weight of the priority ranking model is adjusted through a gradient descent method, the adaptability of the prediction model is continuously optimized, and the prediction precision is improved.

The invention discloses a thermal power generating unit fly ash quality control method, and belongs to the technical field of control optimization, and the method comprises the steps: collecting and integrating working condition data from a thermal power generating unit system to form a standardized data set, and building a prediction model based on the standardized data set to predict the key indexes of fly ash in the future, then, a multi-objective optimization engine containing an expert rule and a particle swarm optimization algorithm and a digital twinborn simulation technology are utilized to generate and optimize a control instruction covering electric field operation, boiler combustion organization, graded conveying path switching and flue gas conditioning; and finally, the model is updated through instruction execution and an online incremental learning algorithm to form closed-loop adaptive control. According to the method, the dynamic prediction model is constructed, the problems that a traditional control method depends on artificial experience and response lags behind are solved, the perspectiveness and initiative of quality control are improved, and meanwhile the contradiction that the high-quality fly ash output rate and the system operation energy consumption are difficult to consider at the same time is solved through multi-target collaborative optimization.

The invention discloses a physical examination suggestion intelligent adaptation and recommendation system based on a personalized health portrait. The method aims to solve the problems of static portraits, general suggestions, lack of continuous tracking, insufficient safety and the like in existing health management services. Through real-time acquisition and fusion of multi-source heterogeneous data, the incremental learning algorithm is constructed and adopted to dynamically update the health portrait of the user, and the timeliness of the portrait is ensured. Based on the dynamic portrait, the system carries out deep traceability and quantitative attribution on abnormal indexes through a multi-path reasoning decision tree, and carries out comprehensive health risk assessment. In a recommendation stage, the system comprehensively considers a user portrait, a risk assessment result, cost and feasibility preference, and performs strict medical suggestion conflict resolution by using a medical knowledge graph based on an OWL ontology and an SWRL rule, so that a highly personalized, safe and feasible physical examination suggestion and health management scheme is generated.

The invention relates to an intelligent power grid load demand dynamic monitoring and evaluation method, and the core scheme of the method comprises the steps: carrying out the data synchronous collection and structural processing of a multi-protocol sensing network, carrying out the normalization and anomaly filtering to enhance the data quality, and analyzing the influence of a dynamic quantification external factor on a load through a sliding window and a correlation coefficient. Dimensionality reduction and dynamic weight adjustment are carried out through principal component analysis, and self-adaptive modeling of multiple factors on load changes is achieved. The incremental learning algorithm supports real-time optimization and compression of model parameters, the online updating efficiency is improved, model self-correction is achieved in combination with a prediction deviation feedback and calibration mechanism, and follow-up initialization optimization is supported through a knowledge base. According to the method, the response capability of the load prediction model to external environment change, the prediction precision and the stability of engineering application are remarkably improved.

The invention discloses a fan nonlinear load self-adaptive control method and device and a storage medium. The fan nonlinear load self-adaptive control method and device are used for efficiently achieving dynamic tracking control over fan nonlinear loads. The method comprises the steps of collecting operation state data of a fan system; constructing a dynamic neural network model containing a long short-term memory network-attention mechanism mixed structure, inputting the operation state data into the dynamic neural network model, and outputting a dynamic predicted value of the fan nonlinear load; constructing an adaptive model prediction controller based on the dynamic prediction value, and generating a control sequence; calculating a prediction residual error of the dynamic neural network model, and when the prediction residual error exceeds a standard threshold value, triggering a model updating mechanism; performing parameter updating on the dynamic neural network model by adopting an incremental learning algorithm with gradient constraint to obtain updated parameters; and the safety control quantity is output to a fan execution mechanism, and dynamic tracking control over the nonlinear load of the fan is achieved.