2749 results about "Prediction system" patented technology

The invention relates to the technical field of exception prediction, in particular to a storage cabinet exception trend prediction system based on time series data analysis, which comprises a state monitoring module, an interval sensing module, a path reconstruction module, a symptom activation module and an evolution prediction module. According to the method, the state vectors including the temperature, the voltage, the current and the door lock state are constructed and combined with the timestamp information to form the time sequence data sequence, and the dynamic expression mode of state change is established; a jump characteristic is analyzed by using a ratio of a time interval to a state change amplitude, a short-time disturbance path and a trend evolution path are distinguished by combining a jump rate statistical index, and an evolution activation signal is identified based on trend maintenance and non-fallback characteristics. On the basis, a neural network structure with long-time dependent learning ability is introduced to capture an aperiodic thermal anomaly trend in a state sequence, and the accuracy and timeliness of anomaly recognition are improved through multi-dimensional parameter cooperative processing and path construction logic.

The invention relates to the technical field of mining industry, and discloses an intelligent prediction method for gold ore beneficiation process parameters based on cloud and edge fusion, which realizes space-time correlation modeling of beneficiation process parameters and accurately depicts dynamic interaction influence among equipment. The cloud edge collaborative architecture considers global optimization and real-time response requirements, and the prediction stability under complex working conditions is effectively improved. The introduction of physical constraints enhances the applicability of the model in an actual production environment, a bidirectional feedback mechanism ensures the adaptive ability of the system in a dynamic change environment, and through the joint reasoning of a knowledge graph and a neural network, the consistency of a prediction result and a process principle is enhanced, and the risk of misjudgment under an abnormal working condition is reduced; the man-machine cooperation mechanism significantly improves the labeling efficiency of high-value samples, shortens the model iteration period, and ensures the continuous optimization capability of the prediction system in the actual production environment.

The invention relates to the technical field of electric power engineering purchase demand prediction, in particular to an electric power engineering purchase demand prediction system based on machine learning, and the system comprises the steps: obtaining historical purchase data, construction progress information and electric power engineering design parameters, carrying out the standard stage division and time alignment, and constructing a stage sequence model reflecting the material use rhythm; and a coupling factor matrix is generated based on the material co-occurrence frequency and the stage position relationship, and the modeling capability of the model for the material cooperation relationship is enhanced. And the stage time sequence features, the coupling information and the structured engineering parameter vectors are fused and input into a regression prediction model, so that accurate mapping of material demands and multi-dimensional engineering features is realized, and the purchase prediction precision in a target period is improved. A deviation sequence is constructed based on historical prediction errors, and error correction is performed through a feedforward neural network, so that prediction accuracy and response capability are effectively improved, and resource waste and construction delay are reduced.

The invention relates to the field of energy consumption analysis, and discloses an AI-based energy consumption data analysis and prediction system, which comprises the steps of collecting environmental parameters and running states of equipment, dynamically identifying the current system working condition by using a working condition identification algorithm combining incremental clustering and historical mode matching, and predicting the energy consumption data. Collected data is divided according to time, space and working condition dimensions, multi-scale features are extracted, normalization parameters can be dynamically adjusted along with changes of working conditions, a drift index is calculated through comparison of a drift threshold value and historical distribution, an optimal normalization updating strategy is selected according to the drift index, the normalization parameters are dynamically updated, and energy consumption trend prediction is conducted through a statistical model. A prediction result is combined with a working condition label to carry out weighted correction, error analysis and deviation detection are carried out in combination with a drift index, working condition prediction and historical error data, and an analysis result is fed back to a working condition sensing module, a feature adaptive module and a normalization control module. The method has the advantage of improving the stability and reliability in a dynamic environment.

The invention discloses a cerebral apoplexy risk and prognosis prediction system and method, and relates to the field of intelligent medical treatment, and the system comprises a data processing and knowledge construction layer which is used for extracting, cleaning and constructing a space-time multi-modal knowledge graph and structured clinical features from multi-source heterogeneous medical data; the feature engineering and fusion layer is used for deeply fusing dynamic semantic information in the space-time multi-modal knowledge graph and the structured clinical features through a graph embedding and attention mechanism to generate a fusion feature vector for a cerebral apoplexy prediction task; and the prediction model and output layer is used for performing cerebral apoplexy risk and prognosis prediction based on the fusion feature vector to obtain a prediction result, and generating a decision result for assisting a doctor in understanding the model through an interpretable mechanism. The method provided by the invention can improve the accuracy of stroke recurrence, bleeding transformation or function prognosis prediction, and provides a new way for accurate stroke management.

The invention discloses a multi-modal data fusion air conditioner optimization control method and system, and belongs to the technical field of intelligent building equipment control. According to the method, temperature, humidity, energy consumption, user behaviors and meteorological data are collected through a multi-source sensor, the data are subjected to dynamic window standardization processing and then input into a gated convolution LSTM network to predict the system state, an optimization strategy is generated in combination with an online reinforcement learning algorithm, multi-modal instructions are dynamically weighted and fused, and execution parameters are adjusted in real time through a feedback correction mechanism. The system comprises a multi-source sensing array, an edge computing unit, a strategy optimization engine and an intelligent execution controller. According to the method, through collaborative optimization of multi-modal spatial-temporal feature fusion and deep reinforcement learning, the problem of unbalance of energy efficiency and comfort is solved, the energy efficiency level and the user comfort of the air conditioning system are remarkably improved, and the method has the characteristics of real-time response and high stability, is suitable for intelligent air conditioning control of modern buildings and has wide application prospects.

The invention relates to the technical field of combustion monitoring, and discloses a combustion instability boundary prediction system based on complex environment condition analysis, and the system comprises a multi-source sensing module, a data fusion module, an anti-interference processing module, an instability feature library, an intelligent decision engine and a digital twinborn body. A dynamic environment interference compensation mechanism is constructed, spectrum correction parameters are configured according to different environment working conditions during real-time prediction of a combustion instability boundary, the problem of spectrum distortion caused by environment disturbance on combustion dynamic parameters is eliminated in real time, spectrum interference deviation of a combustion measurement position can be automatically sensed and corrected, and real-time prediction of the combustion instability boundary is realized. The accuracy of combustion instability sensitive feature extraction is guaranteed, the instability early warning error is reduced, a flame pose real-time tracking system is deployed, fuel control parameter self-adaptive adjustment is triggered immediately when it is detected that the pose is abnormal, autonomous correction of the combustion instability feature recognition position is achieved, and it is guaranteed that the feature capture space is accurately positioned.

The invention discloses a medical risk intelligent prediction system based on time series data mining. The system comprises a multi-dimensional time sequence data acquisition and preprocessing module, a time sequence mode deep mining engine, a multi-dimensional risk assessment engine, an intelligent intervention decision support system and a real-time monitoring feedback module. A time sequence mode mining engine adopts a layered architecture, and short, medium and long-term time sequence modes are respectively analyzed through a bidirectional LSTM-attention network, a wavelet transform-convolutional network and a seasonal decomposition-gating circulation network. The risk assessment engine integrates an isolated forest, an auto-encoder, a Transform multi-task network and knowledge graph reasoning, and realizes all-around risk quantification. The decision support system generates a personalized intervention strategy based on deep Q network reinforcement learning and case reasoning. According to the system, early prediction and accurate intervention of medical risks are realized, and the prediction accuracy and the medical safety level are remarkably improved.

The invention discloses a power equipment fault prediction system based on big data analysis. The method comprises the following steps: acquiring initial equipment multi-dimensional data; constructing a dynamic topology network of the power equipment, including a dependency relationship between the equipment and a fault propagation path, performing embedded learning on the dynamic topology network by using a GNN graph neural network, and extracting equipment collaboration features in the initial equipment multi-dimensional data; a multi-task learning framework is constructed in combination with the equipment cooperation features to predict the equipment fault probability and the remaining service life, and an equipment health index is obtained; and acquiring environmental parameters, dynamically adjusting a fault judgment threshold based on the equipment health index and the environmental parameters, generating a prediction result, integrating the prediction result with an SCADA system, and triggering graded early warning. And the influence of environmental factors on the operation state of the equipment is fully considered. Under different environmental conditions, the equipment fault risk can be judged timely and accurately.

The invention relates to the field of medical image analysis, in particular to a deep learning prediction system and method based on multi-modal thyroid cancer lymph node metastasis, and the system comprises a data collection module, a preprocessing module, a nodule segmentation module, a feature extraction module, a feature fusion module, a metastasis prediction module, an interpretability analysis module and a result display module. An ultrasonic image, an elastic imaging image, an ultra-micro blood flow image and clinical index data of a patient are integrated, an improved U-Net algorithm is used for precise segmentation of a thyroid nodule region, a multi-branch deep network is used for extracting multi-modal features, a dynamic weight fusion algorithm is used for integrating the features, and the accuracy of the thyroid nodule region is improved. According to the method, the thyroid cancer lymph node metastasis state (non-metastasis, central region metastasis or lateral neck metastasis) is predicted, meanwhile, a two-dimensional interpretability framework of Grad-CAM activation diagram and SHAP value contribution degree analysis is introduced, an intuitive prediction basis is provided for doctors, and the thyroid cancer lymph node metastasis prediction accuracy is remarkably improved.

The invention discloses a road congestion prediction system and method based on spatial-temporal feature extraction, and belongs to the field of intelligent traffic. The system adopts a layered distributed architecture, and comprises a multi-source data acquisition module, a data preprocessing unit, a double-flow spatio-temporal feature extraction network, a two-stage spatio-temporal attention mechanism module, a congestion prediction model and a result feedback interface. Multi-modal data such as a vehicle-mounted GPS track, checkpoint flow, video monitoring and meteorological data are integrated, a double-flow feature extraction network is constructed by adopting a graph convolutional network and a bidirectional gating circulation unit, and a key space-time region is dynamically focused in combination with a multi-head self-attention and time weighted dot product attention mechanism; and finally, optimizing the generalization ability of the model through a composite loss function. According to the method, a dynamic adaptive learning framework and multi-source data combined modeling mode is adopted for urban road traffic flow characteristics, the space-time precision and the real-time response capability of road network congestion prediction are remarkably improved, and reliable decision support is provided for intelligent traffic control.

The invention belongs to the technical field of medical data processing and artificial intelligence, and particularly relates to a cardiovascular disease risk prediction system based on multi-modal fusion, which comprises a multi-modal data acquisition and preprocessing module, a cross-modal association graph construction module, a dynamic fusion and prediction module based on a graph neural network and an interpretability analysis module. By constructing a heterogeneous graph fusing prior knowledge and data driving and utilizing a graph attention network to perform multi-level dynamic feature fusion, deep integration and interaction of multi-modal data such as genomes, iconography, clinical and intestinal flora metabolism are realized, so that the accuracy and interpretability of cardiovascular disease risk prediction are improved.

The invention relates to the technical field related to automatic driving, in particular to a trajectory prediction method based on adaptive interaction and dynamic intention, which comprises the following steps: firstly, constructing a heterogeneous interaction map, dividing a traffic scene into a vehicle grid, an environment grid and a non-driving area grid, and embedding multi-dimensional dynamic features; then dynamically adjusting a region of interest based on the behavior intention of the target vehicle, and extracting a high-correlation interaction subnet; modeling an interaction relationship by adopting a heterogeneous graph convolutional network, and processing the motion characteristics of the target vehicle and the neighbor vehicle through a sub-channel coding strategy; further realizing dynamic intention perception through a double-branch parallel attention architecture, and fusing macroscopic intention and dynamic intention information; and finally, iteratively generating a future trajectory prediction result based on a decoding architecture of a message passing mechanism. The method can effectively improve the long-term prediction performance in a lane changing scene, adaptively captures a dynamic interaction relationship, and improves the adaptability of a prediction system to the behavior intention change of a driver.

The invention relates to the technical field of constructional engineering informatization management, and discloses a constructional engineering full-period collaborative management and risk prediction system, which comprises a resource token definition module, a resource token generation module, a resource management module and a risk prediction module, and is characterized in that physical elements are mapped into discrete resource tokens configured with unit time delay charge rates; the process transactional modeling module is used for packaging the process into an atomic transaction unit containing a request and a release instruction; the discrete event rehearsal simulation engine executes circulation in the virtual time axis and records the hanging duration; the deadlock detection module monitors the occupancy topology in real time to identify a loop waiting closed loop; according to the dynamic priority arbitration logic, an accumulated lag weight value is calculated according to the product of the rate and the duration, resources are forcibly allocated accordingly to eliminate deadlock, the dynamic arbitration algorithm based on the time value gradient is constructed, the physical lag cost is converted into the calculation weight, and the capacity of the system for automatically converging to the optimal solution under complex constraints is improved.

The invention relates to the technical field of health management of optical fiber communication equipment, and discloses an optical fiber connector life prediction system based on digital twinning and multi-parameter fusion, which comprises a data acquisition module, a data preprocessing module, a feature extraction module, a model construction module, a fusion analysis module and a life prediction module. According to the optical fiber connector service life prediction system based on digital twinborn and multi-parameter fusion, multi-dimensional data is formed through the data acquisition module, complete and associated data support is provided for subsequent feature extraction and service life analysis, and a digital twinborn model is constructed based on three-dimensional structure parameters and material attributes of the optical fiber connector, so that the service life of the optical fiber connector is predicted. The virtual model can accurately reflect the structural state change of a physical entity in real time, in addition, the fusion feature vector and the structural state parameter are subjected to joint analysis, a comprehensive evaluation index capable of reflecting the association of the fusion feature vector, the structural state parameter and the comprehensive evaluation index is output, an accurate multi-dimensional evaluation basis is provided for life prediction, and then the life condition of the optical fiber connector is accurately predicted.

The invention discloses a multi-base-station AOA cooperative low-altitude target rapid positioning system. The system comprises an AOA measurement base station network, an AOA data preprocessing module, an adaptive weighted intersection positioning module, an extended Kalman filtering state estimation module and a data fusion and system integration module. The system synchronously measures the arrival angle of a target signal through multiple base stations, removes noise in combination with smoothing filtering and an anomaly rejection algorithm, and then solves the initial position of a target by using a self-adaptive weighted intersection algorithm based on measurement quality and geometric distribution. And then, fusing the target motion model and the measurement model by adopting an extended Kalman filtering algorithm to realize dynamic estimation and prediction of the position, the speed and the course. The system can realize high-precision and real-time positioning and continuous tracking of targets such as low-altitude unmanned aerial vehicles, small aircrafts and the like in a complex electromagnetic environment and a sight distance limited scene, and has visual display and regional alarm functions.

The invention relates to the technical field of intelligent operation and maintenance management of urban water supply pipe networks, in particular to a double-model urban water supply pipe explosion risk prediction method based on multi-dimensional dynamic indexes. The method comprises the following steps: collecting multi-dimensional data of a water supply pipe network, and combining with gas-containing water hammer simulation analysis to form a standardized index feature set; establishing a dual-model prediction system comprising a subjective and objective weight fusion model and a machine learning model, and dynamically adjusting and setting a risk assessment weight proportion of the model according to the change of model prediction precision in the dual-model prediction system; and calculating the risk probability score of the pipe section according to the dynamically adjusted weight ratio, marking the pipe section when the prediction result difference of the double-model prediction system exceeds a preset threshold value, and further dividing the risk level of the pipe network. According to the method, closed-loop management from prediction to decision making is realized based on multi-dimensional data, dual-model prediction and operation efficiency evaluation, and the scientificity and efficiency of operation and maintenance of the water supply network are improved.

The invention discloses an L-shaped conveyor belt path dynamic planning and congestion prediction system, which relates to the technical field of automatic control, and constructs a conveying path node diagram by collecting material weight, speed and spacing information of each node on a conveying path, and calculates a congestion index of each path section; the method comprises the following steps: combining a light-mass material jumping sensitivity model, identifying a high-risk node and marking the high-risk node as a congestion prediction area, constructing a dynamic optimization model based on path risk distribution and a material flow direction, generating and evaluating a plurality of path adjustment strategies, executing an optimal strategy according to priority, and realizing path speed adjustment, standby path switching and feeding rhythm control; through a feedback mechanism, a strategy effect is evaluated in real time, a path state is updated, operation data is synchronized to a historical database, a learnable conveying operation file is formed, the stability and the intelligent response capability of a conveying system can be effectively improved, and the method is suitable for high-speed sorting and automatic packaging scenes.

The invention discloses a heavy traffic asphalt pavement damage evolution prediction system, and relates to the technical field of road engineering, and the system comprises a data acquisition module which is used for obtaining continuous multi-frame time sequence images of pavement cracks, a vehicle-mounted axle load spectrum and environment temperature and humidity data; the image processing and space-time modeling module is used for performing multi-scale fractal processing and dynamic time warping on the time sequence image and calculating a cross-scale fractal dimension sequence; the material performance association module is used for establishing and updating a probability association model of the fractal dimension and the shear modulus in real time according to the test data of the dynamic shear rheometer; the multi-physics field data fusion module is used for performing multi-physics field feature fusion through the fractal attention network to generate a multi-physics field collaborative damage correction factor; and the damage prediction and early warning module is used for predicting a fractal dimension growth trend through a fractal domain adaptive transfer learning algorithm, and outputting damage evolution early warning based on a viscoelasticity phase consistency verification result.

The invention discloses a drainage basin water body heavy metal pollution prediction system based on multi-modal attention, and the system comprises a data collection module which is used for collecting multi-modal data related to drainage basin water body heavy metal pollution, and a preprocessing module which is used for carrying out the standardization processing of the obtained multi-modal data. The multi-modal attention fusion module is used for performing feature extraction and cross-modal interaction on the preprocessed multi-modal data to generate fusion features; the dynamic prediction module is used for outputting a spatial-temporal distribution prediction result of the heavy metal pollutant concentration in the drainage basin by constructing a spatial-temporal coupled prediction model; according to the method, the cross-modal interaction accuracy is improved by dynamically focusing the key association information of the multi-modal data through the intra-modal and inter-modal attention mechanism, meanwhile, the data-driven prediction model is constructed, the prediction precision of the high-risk area is optimized through the weighted loss function, the prediction error is effectively reduced, and the prediction efficiency is improved. And high-precision dynamic prediction of heavy metal pollution under the watershed scale is realized.

The invention discloses a system and a method for predicting postoperative recurrence risk after triple negative breast cancer neoadjuvant therapy based on multi-modal time sequence medical image data, and belongs to the field of medical image analysis. The system comprises a data processing module used for constructing a multi-modal data set; the multi-modal feature extraction and screening module is used for extracting deep learning, radiomics and tumor habitat features from the region and carrying out feature screening; the model training module is used for constructing a time sequence model based on a Transform architecture and carrying out training through a multi-task learning strategy integrated with time consistency constraint and gene association auxiliary loss; and the recurrence risk prediction module is used for loading the trained model and outputting a recurrence probability and a risk level. According to the method, the multi-modal time sequence image and gene information are fused, so that the recurrence risk of the triple negative breast cancer patient is dynamically and accurately quantified, and support is provided for clinical individualized treatment decision.

The invention relates to an intelligent continuous rigid frame bridge construction period deformation prediction system. The system comprises a construction data acquisition module, a data primary processing module, a feature extraction model construction module, a mode special model construction module and a deformation prediction module. According to the invention, original data is obtained through data acquisition; a data primary processing method of data standardization, weighted time sequence alignment, effect decoupling and data set segmentation is adopted; a multi-branch deep learning model is adopted as a feature extraction model, the time dynamics, the spatial topological relation and the physical mechanics priori of bridge structure response are integrated, and the completeness and the discrimination ability of feature expression are remarkably improved; a progressive strategy of firstly extracting and clustering features and then grouping and independently modeling is adopted, so that not only is the specificity of different construction stages fully considered, but also the prediction precision is improved through targeted model optimization, and the problem of mode diversity in a complex construction system is solved.

The invention relates to the field of building construction safety, in particular to a risk prediction method and system for building construction. Aiming at the defects of multi-source data isolated analysis, dynamic risk response lagging, insufficient prediction precision and the like in the prior art, a unified analysis base is formed by constructing a space-time fusion data space and integrating multi-dimensional dynamic data such as structure micro-deformation monitoring, environmental parameters, three-dimensional live-action scanning, personnel positioning, a building information model and the like; based on a deep neural network architecture, designing a multi-modal feature extraction mechanism to quantify the coupling risk, and generating a partition risk probability distribution diagram; and in combination with a construction stage characteristic matching security policy library, implementing a three-level early warning mechanism and an automatic avoidance instruction. A closed-loop optimization mechanism is introduced, model parameters and decision threshold values are dynamically adjusted through actual accident feedback, and continuous evolution of a prediction system is achieved. According to the method, the active prevention and control capacity of compound accidents such as collapse and high-altitude falling is remarkably improved, and a self-adaptive intelligent protection system is constructed for a construction site.

The invention belongs to the technical field of office park electric load prediction, and relates to an office park electric load prediction and scheduling method for a novel electric power system. The method comprises the steps that S1, multi-source data fusion and panoramic view construction are carried out, historical load data of a park are collected, and park operation data, meteorological data and a special event calendar are obtained; s2, data preprocessing and feature directional extraction; s3, building and cooperating short-term, medium-term and long-term prediction models on the basis of multi-time scale prediction of model cooperation to form a hierarchical prediction system; s4, optimizing and evaluating a prediction result, and performing uncertainty evaluation on the prediction result in the step S3; and S5, generating and executing a hierarchical scheduling instruction based on hierarchical scheduling decision and execution of multi-objective optimization. According to the method, the problem of full-chain link splitting is solved, panoramic data view, collaborative prediction model, quantitative risk assessment and multi-target optimization scheduling are realized, prediction accuracy and scheduling flexibility are improved, and active participation in energy management of the office park is supported.

The invention discloses a power grid photovoltaic output and load sequence modeling method, system and device and a storage medium, and the method comprises the steps: comprehensively utilizing the multi-scale feature extraction capability of a time-frequency decomposition technology, the time sequence dependence modeling capability of a long and short-term memory network, and the global hyper-parameter optimization capability of a Bayesian optimization algorithm; and carrying out collaborative modeling and prediction on the photovoltaic output and the power load under a unified framework. By introducing a source load time-delay correlation analysis and probability interval construction mechanism, point prediction results and uncertainty intervals of photovoltaic, load and net load can be output at the same time, and a set of source load integrated prediction system with high prediction precision, strong robustness and reliable interval characterization capability is constructed. The method can improve the precision and reliability of photovoltaic power and load prediction, also can reduce the risk in power system scheduling, optimizes the energy storage configuration strategy, and especially has wide popularization potential and application prospects in the scenes of new energy grid-connected operation, intelligent micro-grid and virtual power plant management and the like.

The invention relates to the technical field of medical image intelligent analysis and artificial intelligence auxiliary diagnosis, in particular to a medical image disease course prediction system based on an industrial neural network, and the system comprises a reference generation module which is used for obtaining static image data; processing the static image data by using a physical perception neural network to generate a pure ideal state reference; a perturbation simulation module; the industrial kinetic parameters are used as perturbation terms to be superposed to a pure ideal state reference, and a theoretical damaged state is generated; the projection verification module is used for acquiring real multi-modal observation data; generating a real residual error; generating a theoretical residual error based on the theoretical damaged state and the pure ideal state reference; calculating a manifold coupling confidence coefficient; the closed-loop correction module is used for performing inversion optimization on the industrial kinetic parameters; outputting a disease course prediction result according to the manifold coupling confidence coefficient; according to the method, the problem that a traditional medical model lacks physical consistency explanation is solved, and the credibility of artificial intelligence auxiliary diagnosis is remarkably improved.

The invention discloses a dynamic thermal management and risk pre-judgment system for a lithium battery energy storage system, and relates to the technical field of lithium battery energy storage, and the system comprises a thermal field monitoring module which generates a real-time three-dimensional thermal field distribution map of a battery pack; the dynamic regulation and control module is used for self-adaptively regulating the heat dissipation power and the refrigerant flow in combination with the charging and discharging states of the battery and the environmental parameters; the risk assessment module is used for constructing a thermal runaway risk grade assessment model and generating a risk probability value; the early warning response module triggers a multi-stage early warning mechanism according to the risk probability value, and is linked with the dynamic regulation and control module to execute an emergency heat dissipation strategy to generate a risk disposal scheme; and the data archiving module is used for storing data. According to the invention, the whole-process intelligent design of monitoring, regulation and control, evaluation, early warning and archiving is provided, the thermal management precision, the risk prevention and control capability and the operation economy of the lithium battery energy storage system are comprehensively improved, and a key technical guarantee is provided for large-scale energy storage application.

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 discloses a space-time adaptive power prediction system and method for distributed photovoltaic power generation in a mountainous area, and particularly relates to the technical field of fault prediction and health management. The method is used for solving the problem that the accuracy of power prediction and equipment health state evaluation is influenced by environment characteristic data reconstruction reference drift caused by equipment performance degradation in the prior art. A spatio-temporal data sequence is constructed by acquiring historical data of a photovoltaic unit, a spatio-temporal association network of generated power among equipment is constructed, performance stability of a reference equipment group is evaluated, and when the stability does not meet conditions, a performance attenuation spatio-temporal mode is analyzed to identify common features and personalized features. The propagation path of performance degradation on the space-time correlation network is analyzed based on the characteristics, a critical point is evaluated, reference correction is performed on the environment characteristic data by using the common characteristics and the critical point, and finally, the corrected environment characteristic data is used for executing power generation power prediction and equipment health state evaluation. Therefore, the prediction accuracy and the health management reliability are improved.

The invention discloses a numerically-controlled machine tool fault prediction system, and the system comprises a data collection and preprocessing module which is used for collecting and preprocessing multi-modal data of a numerically-controlled machine tool; the data set construction module is used for constructing a cross-modal index and generating a multi-modal data set; the feature extraction module is used for extracting continuous type, periodic type, environment type and image type modal features to generate a multi-modal feature set; the feature processing module is used for forming a sample-level multi-modal feature set; the multi-modal fusion modeling module is used for inputting the sample-level multi-modal feature set into the improved MGPR model and outputting fusion feature representation; the state prediction module is used for inputting the fusion feature representation into an HMM model and outputting a prediction result and a health index; and the early warning generation module is used for generating and storing fault early warning information entries, realizing fusion and modeling of multi-source heterogeneous signals, and improving the accuracy and robustness of fault prediction of the numerical control machine tool.