105 results about "Incremental learning algorithm" patented technology

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 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.

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 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 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.

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 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 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.

The invention discloses a complete-cycle health maintenance method, system and device for secondary circuit equipment and a medium, and the method comprises the steps: obtaining the operation data of the secondary circuit equipment, and dividing the operation data into an initial data set and an incremental data set; constructing an initial health prediction model by using the initial data set, and predicting the residual service life of the secondary circuit equipment; according to an incremental learning algorithm, updating the initial health prediction model by using an incremental data set to obtain an updated health prediction model; and performing health state evaluation and residual service life prediction on the secondary circuit equipment by using the updated health prediction model, thereby realizing complete-cycle health maintenance of the secondary circuit equipment, automatically adjusting an incremental learning strategy according to the change of an equipment operation environment, and improving the adaptability of the system.

The invention discloses a cross-brand elevator fault prediction method and system based on transfer learning. The method comprises the steps that elevator data of multiple brands are collected, a multi-source elevator data set is constructed, and the multi-source elevator data set comprises sensor data and fault labels of the multiple brands of elevators; performing standardization processing on the multi-source elevator data set, and performing cross-brand feature alignment by adopting domain adaptation so as to minimize the data distribution difference between a source domain and a target domain; a deep transfer learning model is constructed, the model comprises a shared feature extraction layer and a domain adaptation module, the shared feature extraction layer extracts cross-brand common features through a pre-trained convolutional neural network, and the domain adaptation module aligns source domain and target domain feature spaces through an adversarial training strategy; and dynamically optimizing the model based on sample data, realizing online updating of model parameters in combination with an incremental learning algorithm, and outputting a fault type and a corresponding prediction probability. The cross-brand migration cost is low, the small sample scene adaptability is good, and the dynamic updating capacity is high.

The invention discloses a bedridden patient sign measurement and medication assisting system based on multi-sensor fusion, which belongs to the technical field of medical instruments and comprises a multi-sensor fusion measurement module, a self-adaptive calibration compensation module, an intelligent evaluation decision module, a medication assisting calculation module and a closed-loop feedback optimization module. A pressure sensor array, an ultrasonic sensor and an acceleration sensor are used for cooperatively measuring the height and weight of a patient, a deformation compensation model and a body position correction model are adopted for accurate calibration, a BMI index is automatically calculated, a VTE risk score, a falling risk score and a self-care ability score are generated, and the medication dosage is intelligently recommended according to medicine characteristics and patient physique. And continuously optimizing system parameters through an incremental learning algorithm to form a measurement-evaluation-medication-optimization deep coupling closed-loop system, so that the physical sign measurement precision, clinical evaluation efficiency and medication safety of the bedridden patient are remarkably improved, and the nursing labor intensity and the medical error risk are reduced.

The invention provides a power grid anti-bird multichannel twitter sound source positioning and intervention method, which comprises the following steps: acquiring sound signals from a power grid area through a multichannel microphone array equipped with a laser radar calibration module, optimizing a separation threshold by adopting Fourier transform and combining a preset bird voiceprint feature library, separating twitter components and noise components, and performing interference on the twitter components and the noise components; pure birdsong signals are obtained; according to the pure birdsong signal, calculating the time difference between channels by adopting a time delay estimation method, dynamically correcting a preset threshold value by combining a real-time environment sensor, if the time difference exceeds the corrected threshold value, marking as an effective birdsong event, and adopting a centimeter-level differential GPS module to assist in determining the position coordinates of the birds; interference execution confirmation is received through an edge computing node and returned to a system log, a D-S evidence theory data fusion method is adopted to integrate the positioning coordinates and the risk level, if it is judged that the fusion result is consistent in a preset confidence interval, the bird damage monitoring model is updated based on an incremental learning algorithm, and real-time closed-loop feedback is obtained.

The invention provides an automobile foot mat multi-style mixed flow production scheduling method based on customer order data, and the method comprises the steps: carrying out the standardization and feature vector generation of order process parameters, combining with the real-time state collection of production equipment, building a static process similarity and dynamic equipment response cost evaluation model, and fusing multi-source data to analyze the dynamic switching cost. The method comprises the following steps: establishing a scheduling sequence, realizing scheduling sequence optimization by using an improved NSGA-II multi-target genetic algorithm, comprehensively considering switching cost, equipment utilization rate and production line rhythm, finally dynamically selecting an optimal scheduling scheme through a TOPSIS decision method and a workshop load rate, and continuously correcting and evaluating model parameters by using an online incremental learning algorithm, thereby realizing scheduling sequence optimization. According to the method, the production scheduling efficiency and the resource utilization rate are improved, the equipment switching loss is remarkably reduced, and flexible production self-adaptive optimization is realized.

The invention discloses an automobile service demand prediction method based on big data analysis, and the method comprises the steps: an image collection module which carries out the data cleaning and preprocessing through collecting the multi-dimensional data, such as vehicle owner behaviors, vehicle sensors, maintenance history, market trends, social media, weather and policy changes, and carries out the data cleaning and preprocessing through a machine learning algorithm; key features influencing automobile service requirements are identified and adjusted through deep learning, and new factors such as new automobile types, consumer behaviors and policy changes are dynamically tracked; in combination with various models such as regression analysis, a decision tree, a support vector machine and a deep neural network, the prediction accuracy is improved through integrated learning, and online learning and real-time updating are realized by using an incremental learning algorithm; an external event sensing module is introduced to monitor market, economic and policy changes in real time, and a prediction model is automatically adjusted; the model is continuously optimized through a feedback mechanism, demand changes are analyzed in combination with an interpretability analysis tool and causal inference, and simple decision support information is provided.

The invention discloses a photographing, meter reading and power saving method based on AI, and belongs to the technical field of meter reading, the photographing, meter reading and power saving method comprises the following steps: deploying an embedded meter reading robot at a water meter end, automatically photographing a water meter reading picture, and uploading the collected picture to a cloud end; deploying an AI reckoning module at the cloud end, and reckoning a predicted meter reading value interval of the next meter reading period; the meter reading robot accurately shoots a water meter dial plate according to a preset meter reading period, a local data comparison engine is called, and deviation verification is carried out on current meter reading data and a locally cached predicted value interval; when the meter reading data is consistent with the predicted value, the meter reading robot does not upload the collected meter reading picture to the cloud, and when the meter reading data is not consistent with the predicted value, the current photographed picture is uploaded to a cloud data storage library; the cloud AI calculation module is dynamically updated based on an incremental learning algorithm; the photographing, meter reading and power saving method realizes a power saving target through algorithm optimization and cloud collaboration, and is high in practicability and wide in application range.

This invention belongs to the field of campus management and discloses a method for monitoring campus personnel behavior trajectories based on multi-source data fusion, including the following steps: acquiring multi-source data within the campus; preprocessing the multi-source data using a data weighted fusion algorithm; performing differentiated scene optimization processing on the collected facial images to output clear facial image frames and effective feature data; constructing real-time behavior trajectories of campus personnel based on a temporal modeling model using clear facial image frames, effective feature data, and preprocessed multi-source data; analyzing the real-time behavior trajectories through an anomaly detection mechanism to determine the trajectory anomaly status and classify the anomaly level; executing multi-channel alarm push according to the anomaly level; visually restoring the real-time behavior trajectories and historical behavior trajectories based on a campus spatial model; and absorbing new data and anomaly judgment results through an incremental learning algorithm to update the parameters of the temporal modeling model, anomaly detection mechanism, and differentiated scene optimization processing related models.

The application relates to the field of display voice interaction, in particular to a display voice interaction system. Voice data in a real-time environment is acquired through a microphone array, the voice data is converted into text data by using an improved RNN-T voice recognition model, a timestamp and a confidence level are marked, and an initial operation instruction is obtained; historical dialogue data is acquired, the initial operation instruction and the historical dialogue data are associated, a context weight is dynamically adjusted through an incremental learning algorithm, a display is taken as a master node, a semantic protocol is established with smart home equipment, the target operation instruction is analyzed, is distributed to associated equipment and is synchronized with a context; and feedback logs in the display and the smart home equipment are analyzed. Settings of the equipment can be automatically adjusted, the display and the equipment are individually and intelligently controlled, and the use experience of a user is improved.

This invention relates to a self-learning method and system for home environment regulation based on multimodal feedback and scene awareness. The method collects the user's physiological characteristics and body movement signals through non-contact sensing devices to identify the user's current activity scene. The system integrates the user's explicit intervention operations with implicit comfort feedback based on physiological stability, dynamically assigning fusion weights for explicit and implicit feedback according to the activity scene to generate target feedback labels. An online incremental learning algorithm is used to update the local environmental control model, and a privacy-preserving model aggregation is performed in the cloud through a federated learning mechanism. This invention solves the problems of traditional home control relying on single commands and lacking physiological feedback mechanisms, achieving intelligent closed-loop control that balances user privacy and personalized comfort.

The application discloses a multi-element energy storage collaborative scheduling-based intelligent power grid optimal scheduling method and system, relates to the technical field of power grid optimal scheduling, and comprises the following steps: constructing a prediction model based on first data, generating prediction data, coupling energy storage characteristic parameters of different types of energy storage devices with the prediction data, and establishing a multi-energy collaborative scheduling model; dynamically screening an energy storage scheduling strategy set based on preset real-time efficiency evaluation indexes, and generating an optimal strategy subset; performing differentiated charging and discharging control instructions on an energy storage device cluster according to the optimal strategy subset; collecting second data in a charging and discharging control process, calculating deviation values of the second data and the prediction data, converting the deviation values into feature vectors, inputting the feature vectors into a preset incremental learning algorithm, and optimizing parameters of the multi-energy collaborative scheduling model. The application implements hierarchical screening in combination with real-time efficiency evaluation indexes, and ensures that optimal scheduling schemes can be quickly selected under different time periods and uncertain disturbance conditions.

This invention discloses a system for simultaneous acquisition of apparent spectra and inversion of water quality parameters based on an unmanned surface vessel (USV), belonging to the field of intelligent sensing technology. The system includes: acquiring environmental parameters from the USV; constructing a deep reinforcement learning (PLSR) model and inputting the environmental parameters, outputting phase control commands; constructing a water quality parameter inversion model and inputting noise-free spectral data, outputting water quality parameters; when the turbidity in the water quality parameters exceeds a dynamic turbidity mutation threshold, triggering the acquisition of laboratory water quality verification data and comparing it with the water quality parameters to output turbidity error; when the turbidity error exceeds an inversion verification error threshold, updating the weights of the water quality parameter inversion model; performing spatial interpolation calculations based on the updated water quality parameter inversion model, outputting the water quality parameter distribution, and displaying a water quality parameter heatmap and tracing pollution source paths via an electronic map. This invention uses an incremental learning algorithm to update the PLSR weights online, improving the model's adaptation speed.

The application relates to the technical field of work order management, in particular to a work order closed-loop management system based on PDCA-SDCA double circulation, which comprises a data acquisition module, a hidden danger analysis unit, a rule reasoning module, a work order distribution module, a verification feedback module and a knowledge optimization module. Through the structured feature deconstruction algorithm of the 5M1E, i.e. man (Man), machine (Machine), material (Material), method (Method), environment (Environment) and measurement (Measurement), and the safety rule knowledge base of graph structure storage, accurate identification and classification of hidden dangers are realized; the unmanned aerial vehicle rephotographing and image comparison technology is used to complete automatic verification of rectification effect; and the safety rule knowledge base is dynamically optimized through an incremental learning algorithm, for example, an online random forest algorithm is used to realize dynamic updating of the knowledge base. The application can improve the comprehensiveness and accuracy of hidden danger identification, ensure dynamic adjustment and closed-loop management of rectification task priority, and effectively improve the efficiency and reliability of safety management of a railway engineering construction site.

The invention relates to the technical field of model predictive control, and provides a decomposing furnace outlet temperature model predictive control method comprising the following steps: S1, carrying out data standardization preprocessing on a cement plant data set; s2, performing principal component analysis processing on the preprocessed cement plant data, and reducing the number of obtained observation variables from K to F; s3, training a width learning network model, if the verified R2 decision coefficient is greater than a preset value, keeping the trained width learning network model and then ending training, otherwise, training by using an incremental learning algorithm; s4, taking the predicted output of the model as a predicted value of the outlet temperature of the decomposition furnace in the future, and constructing a model prediction control MPC optimization problem; s5, solving a model predictive control MPC optimization problem by adopting a rolling optimization strategy; and S6, inputting the optimal control quantity into an actual control system of the decomposing furnace, executing corresponding adjustment operation, and obtaining newest observation data again at the next sampling moment. And a reliable technical support is provided for daily operation regulation and control and production decision making of a cement plant.

The invention relates to the technical field of bladder internal pressure monitoring and management, and provides a neurogenic bladder internal pressure monitoring and management system based on incremental learning, which comprises a data acquisition module, an embedded hardware platform and a user side prompt module. The data acquisition module is responsible for acquiring physiological parameter information such as bladder internal pressure, abdominal pressure and body position angle in real time. The bladder internal pressure prediction module integrated by the embedded hardware platform establishes a pressure-time relationship in a piecewise linear modeling mode, continuously adjusts model parameters by applying an incremental learning algorithm, and generates prediction data in a future time window; and the multi-modal state identification module receives the prediction data and the current multi-source physiological information, performs comprehensive analysis by applying a fuzzy logic reasoning mechanism, identifies and eliminates the influence of instantaneous interference factors, and outputs the grade evaluation of the bladder state. And the user side prompting module executes a grading early warning strategy according to the state grade and sends intervention prompting information of different emergency degrees to the patient.