312 results about "Predictive power" patented technology

A method for multivariate time-series long-term forecasting based on multi-scale temporal feature enhancements, includes a time-series forcasting model TFEformer. The model utilizes a multi-branch structure and a patch-series attention mechanism to extract global and local time-series features at multiple temporal scales, and designs an adaptive feature fusion mechanism to achieve adaptive fusion of multi-scale temporal features. It employs an variate-wise attention mechanism and a redesigned gated feedforward network to perform feature fusion among multivariate variables and within the time-series, respectively. The time-series forcasting model TFEformer proposed by the present invention significantly improves the prediction of long-term trends in time-series and enhances the fitting ability for short-term local fluctuations, comprehensively increasing prediction accuracy across different prediction time lengths in multivariate time-series forcasting tasks.

The invention relates to the technical field of ship power systems and energy management. The invention provides an energy optimization management method and system for an extended-range hybrid power ship. The method comprises the following steps: collecting ship multi-source heterogeneous data in real time, and constructing a multi-source heterogeneous data set; based on the multi-source heterogeneous data set, constructing a load prediction model based on a time sequence convolutional network and long and short term memory fusion, and dynamically outputting propulsive power demand probability distribution in a future navigation period; a multi-objective optimization model is established, and an improved multi-objective genetic algorithm is adopted to generate a Pareto frontier solution set; and on the basis of a rolling time domain optimization framework, in combination with real-time navigation situation awareness data, carrying out online correction on the Pareto solution set, and generating and executing an optimal energy distribution instruction set. The problems of an existing extended-range hybrid power ship energy management technology in the aspects of working condition adaptability, multi-energy coupling efficiency, real-time state sensing and predicting capacity and emission and economical efficiency balance are solved.

The invention relates to the technical field of power electronics, in particular to a photovoltaic off-grid inverter control method and system, and the method comprises the following steps: applying a test signal to identify an output interaction characteristic parameter, predicting the power generation power through combining with weather and attenuation, calculating the power fluctuation, and adjusting a current control parameter set. And calculating voltage and reactive power compensation parameters to generate an inverter control instruction, and combining a temperature load prediction trend to adjust a power allowable value to obtain a predictive power calibration coefficient. According to the invention, by analyzing the output interaction characteristics, enhancing the perception capability of the inverter to the actual operation environment, combining with the prediction of the photovoltaic power generation amount, realizing the prediction and influence evaluation of the power fluctuation, improving the adaptive adjustment capability of the current control parameters, improving the output voltage and reactive power control precision by utilizing the interaction characteristics, and optimizing the electric energy quality; and through thermal management of multi-point temperature and load prediction, the operation safety is improved, the service life of equipment is prolonged, and the operation performance of the photovoltaic off-grid system in a complex environment is improved.

The invention discloses a unified framework for solving automatic driving track prediction and planning consistency based on a world model. According to the method, through cooperative work of the automatic driving domain controller and the vehicle-mounted sensing system, end-to-end joint optimization of track prediction and planning in a complex traffic scene is realized, time sequence dependence and interaction dynamics among intelligent agents are accurately captured, and the prediction capability and robustness of a model are remarkably improved. The method comprises the following specific steps: firstly, constructing a generative world model, and generating potential future state representation by utilizing a behavior conditional and backtracking expansion technology; secondly, in combination with global modeling and a local convolutional network, multi-scale features are extracted, adaptive fusion is carried out, and a multi-modal prediction trajectory is generated; then, a multi-target planning model is adopted to integrate various driving indexes, and a track with the minimum loss function is generated; finally, path planning parameters are dynamically optimized through real-time environment perception and decision feedback, and the problems of prediction uncertainty and planning consistency of the automatic driving track are effectively solved.

The invention discloses an ablation impedance dynamic prediction method based on multi-modal time sequence fusion and a related device. The method comprises the following steps: acquiring multi-modal time sequence data of an ablation electrode in real time, and constructing a multi-dimensional input vector containing a temperature change rate, an ultrasonic echo gradient and a standardized timestamp; performing parameter fine tuning by using a pre-trained two-channel long-short-term memory neural network, respectively extracting feature vectors of the physical parameters and the image parameters, fusing the feature vectors, and outputting an impedance predicted value in a future preset time window; and dynamically adjusting the ablation output power by adopting a proportional control strategy based on the deviation between the predicted impedance and the target impedance. Through time sequence fusion of the multi-modal data and the prediction capability of the deep learning model, the real-time monitoring and power adjustment precision of the ablation process is improved, and the safety and effect stability of ablation treatment are effectively improved.

The invention provides an intelligent power distribution comprehensive monitoring system integrating the Internet of Things and cloud computing. The system is applied to the technical field of power distribution monitoring and comprises a communication network and a cloud computing platform module, the data acquisition and transmission module is used for acquiring equipment operation parameters, environment parameters and the like of key point locations of a transformer substation or a power distribution substation in real time, and uploading the data to the cloud computing platform after data protocol conversion; the data analysis and processing module is used for carrying out time sequence modeling and fusion by adopting intrinsic mode modeling and warp dynamic linear modeling respectively to obtain a target parameter comprehensive predicted value; the fault prediction module is used for performing fault prediction based on the closed continuous neural network and a Bayesian estimator, fusing prediction results and outputting a final fault prediction result; and the intelligent control module is used for optimizing the equipment operation environment and early warning, and realizing integrated supervision of the transformer substation or the power distribution substation. According to the invention, the operation monitoring and fault prediction capabilities of the substation (distribution substation) equipment are improved.

The invention relates to the technical field of biodiversity assessment, and discloses a dynamic assessment method and system for the influence of engineering construction on biodiversity, and the method comprises the steps: obtaining a basic ecological data set, and carrying out the construction of an ecological patch graph structure and the edge building of hydrological connectivity according to the basic ecological data set, and obtaining a composite ecological graph model; performing plaque function heterogeneity aggregation and disturbance link simulation processing to obtain an ecological propagation path and a sensitivity pedigree of each node; performing spectral domain decomposition and key propagation node identification processing to obtain ecological intermediary nodes and propagation weights; performing reversible modeling and path entropy backtracking analysis processing to obtain a community reconstruction trend index and a function stability attenuation curve; and carrying out ecological response persistence analysis processing to obtain a time sequence biodiversity influence index. According to the method, the problems of static state, splitting, low coupling and the like in the prior art are solved, and the precision, timeliness and predictive capacity of ecological disturbance dynamic response evaluation are remarkably improved.

The invention discloses a multivariate time sequence prediction method based on timestamps and multi-scale modeling, and the method comprises the steps: extracting features through multi-scale feature fusion, carrying out the convolution operation of an input time sequence through employing convolution kernels of different scales, extracting and splicing short, medium and long-term features, and carrying out the fusion through employing a multi-head attention mechanism. Therefore, local details and global trends in the time sequence are captured, and the modeling capability of the model on a complex time mode is improved; timestamps are coded into high-dimensional vectors, the high-dimensional vectors are mapped through an encoder, the complex relation between the timestamps is captured, and the prediction capacity of the model for periodic and tendency changes is enhanced; a self-defined loss function is adopted to carry out model training, so that the model robustness is improved, and meanwhile, relatively high prediction precision is kept; and applying the trained model weight to a test set. Through combination of multi-scale feature fusion, timestamp feature extraction and a self-defined loss function, the precision and robustness of multivariate time sequence prediction are remarkably improved.

The invention relates to the technical field of hydraulic systems, in particular to a hydraulic and thermal fluid dynamic lubrication calculation model and an optimization method, comprising the following steps: S1, modeling a multi-scale heterogeneous system; s2, carrying out heat-friction-viscosity synergistic effect modeling; and S3, dynamic boundary lubrication and lubricant behavior prediction. According to the method, by integrating nanoscale, mesoscale and macroscopic level lubrication system modeling, physical phenomena on different scales and interaction of the physical phenomena are comprehensively captured, and the step covers multiple aspects of multi-scale heterogeneous system modeling, nanoscale lubrication behavior modeling, macroscopic fluid field and structure field coupling and the like; high-precision predictive capacity and systematic thermal management are achieved by combining multiple physical fields such as heat, friction, viscosity and fluid dynamics, through collaborative modeling of multiple scales and multiple physical fields, the accuracy and stability of a lubricating system are improved, a solid theoretical basis is provided for optimization design, and the method is suitable for popularization and application. And stable operation in high-load and high-temperature environments is ensured.

The invention provides a biodiversity influence comprehensive evaluation method and system based on multi-source ecological factors, and relates to the technical field of biodiversity analysis, and the method comprises the steps: obtaining multi-source ecological factor data of a geological disaster control influence region, and carrying out the maximum likelihood estimation and kernel function learning processing of the multi-source ecological factor data to obtain a space-time intensity field; performing adaptive time-frequency decomposition processing based on the time-space intensity field and the time sequence of the community functional traits to obtain a multi-scale modal set; performing low-rank dynamic recognition processing on the multi-scale modal set to obtain an approximate operator and control gain pair; performing sparse regression modeling processing to obtain a nonlinear state equation set; and carrying out distribution displacement measurement processing based on the nonlinear state equation set and preset quadrat baseline distribution to obtain a comprehensive evaluation result of the biodiversity influence. According to the method, the problems of single index, rough modeling and lack of multi-scene prediction capability in the prior art are solved.

The invention provides a filling process prediction method and device based on a discrete element method and data driving, and relates to the technical field of bulk material forming, and the method combines the physical modeling advantage of the discrete element method and the powerful prediction capability of a data driving method. The filling process of the granular material under different process parameters is simulated through a discrete element method, key quality indexes are obtained to serve as input of a data driving model, and the process parameters serve as output for training. The model not only can accurately predict the filling quality, but also can adjust the process parameters in real time according to the target quality index, so that the intelligent optimization of the filling process is realized. According to the method, the calculation efficiency is greatly improved, the generalization ability of the model is enhanced, the dependence on empirical data is reduced, a more efficient and accurate solution is provided for the filling process in the industries of building materials, pharmacy, powder metallurgy and the like, the product quality is improved, the production cost is reduced, and the research, development and application of novel materials and equipment are accelerated.

The invention relates to the technical field of intelligent operation and maintenance of ship power equipment, in particular to a DLinear-Transform-based anomaly detection method for a marine power generation steam turbine system, and the method comprises the steps: S1, carrying out the collection and processing of non-stationary time series data of the marine power generation steam turbine system; s2, constructing a DLinear-Transform model framework, and carrying out the construction of the DLinear-Transform model framework; s3, carrying out anomaly detection on the marine power generation steam turbine system on the basis of DLinear-Transform; and S4, verifying the performance of the model public data set. According to the method, a DLinear-Transform fusion framework is constructed, and anomaly detection of the key parameters of the ship power generation steam turbine system under the non-stable working condition is realized based on the DLinear linear characteristic and the Transform context modeling capability. Through SMD, SWaT and actual marine power generation steam turbine data set verification, the method is superior to existing optimal models such as TransAD and the like. Compared with an existing ship intelligent operation and maintenance technology, the system equipment anomaly detection and fault prediction capability is improved, and a new technical architecture is provided for predictive maintenance of ship power equipment.

The invention relates to the technical field of concrete quality control, in particular to a concrete strength prediction system and method based on SVR. According to the invention, multi-dimensional data of rebound test, compressive strength test and carbonization depth value measurement are collected through the data acquisition module, rich input characteristics are provided for the model, and the prediction capability of the model is enhanced; and abnormal data is eliminated by adopting a Grubbs criterion, so that the quality and consistency of input data are ensured, and the influence of noise on model training is reduced. According to the method, a concrete rebound curved surface regression equation and a support vector regression model are established respectively and serve as prediction criteria, data fusion is carried out through a Kalman filter, and the prediction precision and reliability are further improved. Through automatic and standardized data processing procedures including data acquisition, preprocessing, regression prediction, SVR prediction, data fusion and the like, rapid prediction of the concrete strength is realized, and the prediction efficiency is improved.

The invention discloses an asset use analysis method and system based on an intelligent prediction and optimization algorithm, and aims to solve the problems of low efficiency, insufficient prediction capability and unscientific purchase decision in existing asset management. According to the invention, asset use records, maintenance information and purchase history are obtained through the data acquisition module; constructing an asset use prediction model by using time sequence prediction, regression analysis and machine learning algorithms, and predicting future asset demand quantity and use trend; generating an optimized purchase plan and an asset allocation scheme in combination with an inventory state, an asset life cycle and a budget constraint; and the prediction result and the optimization suggestion are displayed through a visual tool, and support is provided for decision making. According to the invention, efficient planning and purchase optimization of asset use are realized, and the method can be widely applied to the field of enterprise asset management and supply chain optimization.

The invention provides a cross-domain equipment fault diagnosis method and system based on large and small model cooperation, and relates to the technical field of equipment fault diagnosis. According to the method, the causal field generalization structure is introduced into the small model, explicit decomposition is carried out on the stable causal law and the field specific difference, and meanwhile, the causal field generalization structure is corrected by using the large model, so that the small model can automatically identify and retain the causal relationship which is universally applicable to each device and each field; therefore, the influence of inter-domain distribution difference is effectively eliminated. Theoretical analysis shows that the generalization error of the model mainly depends on the accuracy of the stable causal item, and the structure can minimize error drift caused by distribution drift. Therefore, the robustness of health state evaluation and fault prediction can be remarkably improved in a cross-domain scene, and the fault diagnosis model can still keep the prediction capability close to the training domain level under the condition of no target domain annotation data.

The invention belongs to the technical field of power equipment monitoring, and particularly relates to a transformer load remote monitoring and detecting system. Comprising a data acquisition module, an edge calculation module, a load adjustment module, a load prediction module and a load analysis module. The data acquisition module acquires transformer secondary side load power, current, vibration signals and winding temperature data; the edge calculation module processes load early warning including data denoising and the like; the load adjusting module calculates and adjusts the primary side load power; the load prediction module constructs a model to predict future primary side load power; the load analysis module analyzes the future load condition according to the prediction result. The system realizes remote real-time monitoring, improves data processing precision, load adjustment accuracy and prediction capability, and ensures safe and efficient operation of the transformer.

The invention discloses an electric power meteorological prediction method and device based on adaptive enhancement, and relates to the technical field of electric power meteorological prediction, and the method comprises the following steps: collecting historical meteorological data and electric power data, and cleaning the collected data; according to the method, a model reasoning path tracing module is constructed based on Chain-of-Though logic, a prediction result is decomposed into subtasks such as meteorological factor contribution degree and power system response logic, interpretability of the prediction process is achieved, reliability of a generated result is evaluated by adopting sequence probability confidence, a visual interpretation report is dynamically generated, and the prediction efficiency is improved. According to the method, operation and maintenance personnel can visually acquire key information, sensitivity of the model to key meteorological events is optimized through adversarial training, prediction capability of the model to extreme meteorological conditions is improved, the trust problem of the black box model in power decision is finally effectively solved, a visual and reliable decision basis is provided for the operation and maintenance personnel, and the operation and maintenance efficiency is improved. And the operation safety and stability of the power system under the complex meteorological condition are improved.

The invention discloses a carbon emission prediction method and device based on the relation between capture variables and a medium, and relates to the technical field of traffic carbon emission prediction. The method comprises the following steps: based on a preset traffic carbon emission influence factor set, calculating the correlation between the traffic carbon emission influence factor set and the traffic carbon emission, and carrying out preliminary screening to obtain a preliminary variable candidate pool; performing multi-collinearity detection on the preliminary variable candidate pool and updating the preliminary variable candidate pool to generate a screened variable candidate pool; performing nonlinear relation analysis on the screened variable candidate pool through an XGBoost algorithm, and screening to obtain a key variable sequence; inputting the key variable sequence and the traffic carbon emission into a Transform model, carrying out feature embedding and position coding, and generating a time sequence vector; and respectively predicting traffic carbon emission levels under different levels for the time sequence vectors. According to the method, the scientificity of the variable screening process and the reliability of model construction are ensured, and then the prediction capability of the model is enhanced.

The invention belongs to the technical field of predictive analysis in a water treatment process, and particularly discloses a sewage quality predictive method based on a multi-model fusion and stacking pyramid-shaped three-dimensional model, which comprises the following steps of: performing deformation expansion on wastewater quality index monitoring data, expanding a data set and enhancing feature engineering information amount; the multiple models LSTM, RFR, CNN and RBF are used for predicting a target array in parallel, and then the multiple models are connected with the meta-model BPNN in series to construct a three-dimensional prediction model based on multi-model fusion stacking; and inputting wastewater quality monitoring data into the three-dimensional prediction model, and outputting to obtain a final COD predicted value and a final NH3-N predicted value, thereby completing wastewater quality prediction. According to the method, prediction results of different models are effectively integrated, the prediction capability of each model is fully excavated, a more accurate prediction result is obtained, and the problem that in the water treatment process, an existing prediction analysis method is difficult to accurately reflect the real-time change condition of the sewage quality is solved.

The invention provides a trajectory prediction method, device and equipment for a target object. The method comprises the following steps: acquiring actual trajectory data of the target object in a preset time period; performing mask extraction processing on time information in the actual track data by adopting a first mask strategy to obtain first extraction data; performing mask extraction processing on interaction relation data between the target object and other objects in the actual track data by adopting a second mask strategy to obtain second extraction data; performing feature alignment processing on the first extraction data and the second extraction data; and respectively inputting the first extraction data and the second extraction data after feature alignment and the data of the lane where the target object is located into an embedding layer for feature extraction processing, inputting the data into a trained trajectory prediction model for trajectory prediction processing, obtaining a trajectory prediction result of the target object, and outputting the trajectory prediction result. According to the invention, the prediction capability of the trajectory prediction method can be enhanced.

The invention discloses a safety production standardized comprehensive management system and method, and relates to the technical field of safety production management, the system comprises the following components: a data acquisition module, a data preprocessing module, a model construction and training module, a prediction analysis module and a maintenance management module; according to the method, the time sequence data in the full life cycle of the equipment are continuously collected, the time sequence data comprise key parameters such as operation duration, start-stop times and maintenance records, the improved LSTM neural network is utilized to construct the equipment safety life prediction model, and the model can not only predict the overall life of the equipment, but also can predict the service life of the equipment. The method can accurately predict the residual safe use cycle of the easily-worn part, the accurate prediction capability enables an enterprise to plan a maintenance plan in advance, production interruption and safety accidents caused by sudden equipment faults are avoided, and the accuracy and foresight of equipment safety management are remarkably improved.

The invention discloses a mine water inflow prediction method and system based on adaptive graph convolution, and relates to the field of coal mine safety. Comprising the steps of making a clustering data set based on historical data; training a water burst scene classifier by using the clustering data set; using the trained water burst scene classifier to identify historical data to obtain different water burst types corresponding to mine water burst; based on different water inflow types and the clustering data set, respectively training to obtain a water inflow prediction model; identifying the new water inflow association index data by using the trained water inflow scene classifier to obtain a target water inflow type; and predicting the new water inflow data by using the target water inflow prediction model to obtain the water inflow. According to the method, a unique adaptive graph convolution formula and a hydrogeological attention mechanism are introduced, the prediction capability and scene adaptability of the prediction model are improved, working face water inflow prediction under different hydrogeological conditions is considered, and important technical support is expected to be brought to coal mine water disaster prevention and control.

The invention provides a power distribution communication network fault management process optimization method, which is based on a fault evolution reverse deduction technology of historical repair knowledge, and realizes efficient fault root cause positioning and repair recommendation by combining a dynamic space-time atlas and a graph database technology. The method comprises the following steps: constructing a dynamic space-time atlas, and storing power distribution communication network topology and fault data; constructing a fault causal relationship model, and describing a fault state transition probability and an evolution causal chain; multi-path hypothesis testing is executed, possible fault sources and evolution paths are generated, and confidence scores are distributed; similar fault modes and repair measures are retrieved from a historical repair knowledge base, and a directional repair strategy is recommended; and space-time backtracking analysis is realized, and the whole fault evolution process is visually displayed. According to the method, the root cause positioning efficiency and accuracy are remarkably improved, the fault processing flow is improved, the fault prediction capability is improved, the preventive maintenance level is enhanced, and continuous accumulation and optimized application of maintenance knowledge are realized.

Disclosed embodiments provide methods, systems, and computer program products for implementing predictive three-phase power distribution unit (PDU) output load switching for input phase balancing. Disclosed embodiments measure PDU output port power values for powering a plurality of loads over time, perform pattern recognition on the PDU output port power values over time to produce recognized power patterns; and create switching rules based on the recognized power patterns to provide input phase load balancing, wherein the switching rules include at least one of (i) predicted output port power values for a periodic time interval, or (ii) predicted output port power values based on the measured PDU output port power values associated with the loads. Disclosed embodiments switch input phase connection to one or more PDU outputs based on the switching rules.

The invention relates to the technical field of electric power prediction, discloses a photovoltaic power generation prediction system based on a multi-source analysis model, and aims to solve the problems that an existing photovoltaic power generation prediction technology is insufficient in individual and isomerized node prediction capability, and prediction is limited due to dependence on historical data in a newly-accessed and data-missing node scene. The system comprises a multi-dimensional node feature quantification module, a virtual historical data synthesis module, a power prediction module based on an enhanced data set, a closed-loop deviation traceability and correction module and a compensation strategy execution module oriented to a specific scene. Through adoption of the technical scheme, high-confidence prediction can be provided for blank or data missing nodes on the premise of not depending on historical data of the target node, and the precision, the coverage rate and the dynamic adaptability of distributed photovoltaic prediction are remarkably improved.

The invention discloses a T cell receptor-epitope binding specificity prediction method based on multilevel knowledge distillation, and relates to a T cell receptor-epitope binding specificity prediction method. The method aims to solve the problem that an existing deep learning model is poor in performance when generalized to be free of epitopes. The feature extraction module is constructed by using the convolutional neural network, and the interaction mode between the TCR and the epitope is explored through a bilinear attention mechanism. Thirdly, internal multi-feature knowledge distillation and inter-domain knowledge distillation are carried out, knowledge is extracted from multiple teacher models, and the predictive ability of student models is enhanced; tCR-epitope pairs in a training set are input into the model, and the model is trained; after multiple iterations, a final prediction model is obtained; and inputting the TCR-epitope pair in the test set into the final prediction model to obtain a prediction result, and performing result analysis. The invention belongs to the technical field of biological information.

The invention discloses a fuel gas system management method and system based on data analysis, and belongs to the technical field of fuel gas system management.The fuel gas system management method comprises the steps that a wax deposition kinetic model and a deep neural network are combined, and a hybrid prediction model architecture is designed; the wax deposition kinetic model is used for describing physical and chemical processes of wax deposition and providing basic prediction capability; the deep neural network is used for capturing a non-linear relationship and a complex mode in the data and improving the accuracy and generalization ability of prediction; based on an improved particle swarm optimization algorithm (PSO-ADE), a self-adaptive pigging decision engine is designed; initializing the algorithm to generate an initial particle swarm; operating an algorithm, and evaluating and updating the particles according to a target function; through iterative optimization, an optimal pigging strategy is found; and feedback data is collected, and the algorithm is further adjusted and optimized, so that the accuracy and effectiveness of decision making are improved. The pipeline cleaning strategy of the oil pipeline can be optimized.

The invention discloses a wave field rapid prediction method based on an Encoder-Only Transform model, and the method comprises the following specific steps: collecting and preparing a data set, and carrying out the data preprocessing; the method comprises the following steps of: constructing an Encoder-Only Transform model; initializing model parameters and sliding window method parameters; inputting the preprocessed training set data and the verification set into a built network model through a sliding window method for model training; inputting test set data into the model through a sliding window method, and evaluating the prediction capability of the model by using a test set; the parameters of the Transform model and the parameters of the sliding window method are initialized again; and the prediction precision is optimized. According to the method, the original decoder layer is removed, the training and reasoning speed is greatly improved, and the sliding window method is introduced to update historical data in time, so that the rapid prediction of the wave field is realized.

The invention discloses a super-long pile foundation resistance prediction method and model, and the method comprises the steps: firstly, improving the adaptability of the model to complex input and the precise prediction capability of the model to bearing capacity through the strong nonlinear expression capability of a CatBoost algorithm and the characteristics of native support class variables; then six intelligent optimization algorithms are introduced to automatically search hyper-parameters of the model, manual parameter adjustment errors are avoided, the stability and generalization ability of the model are improved, and finally an optimal fusion model is determined for prediction. By constructing the fusion optimization strategy, the multi-optimization algorithm and the CatBoost are deeply fused, the robustness and applicability of the system in the multi-scene and multi-sample environment are improved, and the problems that in the prior art, the resistance prediction precision of the super-long pile foundation is insufficient, and the model stability is poor are solved.

An agricultural harvesting system includes a control system. The control system identifies a predictive value of a power characteristic based on a relationship between the power characteristic and a characteristic. The control system generates a control signal to control a controllable subsystem of a mobile hybrid agricultural harvesting machine based on the predictive value of the power characteristic.