229 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 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 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 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 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 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 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.

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 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.

The invention discloses a network construction type energy storage inverter control method based on complex network space-time optimization, and the method comprises the steps: positioning a historical timestamp on a time axis, constructing a function dependence graph of the historical timestamp, enabling the function dependence graph to comprise K nodes representing a power grid function unit, and enabling the K nodes to represent the power grid function unit according to a preset time step length, a function dependency graph of L historical timestamps is constructed in a sliding mode, a dependency graph sequence is generated on the basis of sequential arrangement of the historical timestamps, the dependency graph sequence is cut into M space-time dependency samples, iteration supervision training is conducted on a graph neural network model on the basis of the M space-time dependency samples, a space-time state prediction model is obtained, and a space-time state prediction model is obtained on the basis of the space-time state prediction model. According to the method, the space-time dependency degree is determined as the edge weight, the prediction capability of the model on the future state of the power grid is enhanced, and the network-building type energy storage inverter is supported to generate the prediction-based control instruction.

The invention discloses a dam safety monitoring and crack prediction method and system based on vibration sensing data, relates to the technical field of data processing, and solves the problems of incomplete data acquisition, data processing lag and insufficient prediction capability in existing dam safety monitoring. Adaptive normalization processing based on a dynamic window is carried out on the dam vibration data; a prediction model is constructed, an LSTM network captures time dependence of dam vibration data, an attention mechanism network fuses LSTM network output and external environment variables to extract key time steps, and an output layer predicts the probability of crack generation according to the key time steps; inputting the preprocessed dam vibration data into a prediction model to obtain a crack generation probability; calculating a dynamic threshold according to the external environment factor, and comparing the crack probability to obtain a crack prediction result; by collecting dam vibration data at multiple positions, the crack risk is predicted in advance based on the prediction model.

One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.

The invention relates to a shaft ECD intelligent regulation and control method and system based on machine learning and closed-loop control, and the method comprises the steps: obtaining original data of drilling parameters, and constructing an ECD prediction model based on the original data; acquiring real-time input data; inputting the real-time input data into the ECD prediction model for prediction to obtain an ECD prediction value; calculating according to the ECD predicted value and the target ECD value to obtain an ECD error value; and selecting a model intelligent regulation and control mode or a PID intelligent regulation and control mode according to the ECD error value to carry out ECD regulation and control. According to the method, the shaft ECD is predicted through the machine learning model, and the drilling parameters are adjusted in real time through the PID controller, so that the ECD is kept within the preset range. The double-layer control system combines the real-time feedback advantage of PID and the predictive capacity of machine learning, it is ensured that variables are accurately regulated and controlled in the drilling process, the ECD is maintained within the safety range, the risks such as stratum fracture and blowout are reduced, and the drilling efficiency and safety are improved.

The invention relates to an intelligent thunderstorm weather forecasting method and system and a medium, belongs to the field of thunderstorm forecasting, and provides the intelligent thunderstorm weather forecasting method for solving the problem that an existing mode is insufficient in forecasting capacity, and the intelligent thunderstorm weather forecasting method comprises the following steps: collecting and processing initial data, and obtaining a multi-source fusion initial field of a unified format; constructing a short-time forecasting model based on a Swin Transform backbone network, and outputting a thunderstorm forecasting field in the future 6 hours by inputting a multi-source fusion initial field and underlying surface information; and a short-term forecasting model based on a FuXi large model is constructed, and by inputting the multi-source fusion initial field and underlying surface information obtained in the step 1, possible thunderstorm occurrence areas and intensity distribution data in more than six hours in the future are output. By introducing a Swin Transform backbone network and combining an autoregression prediction strategy and a multi-task loss function, the capturing precision and real-time performance of the thunderstorm time-space evolution process are effectively improved.

The invention relates to a photovoltaic power generation intelligent scheduling method and device based on dynamic supply and demand prediction. The photovoltaic power generation intelligent scheduling method based on dynamic supply and demand prediction comprises the following steps: obtaining position information of a current cloud cluster, combining the position information with wind speed data, and carrying out gridding prediction by adopting Kalman filtering to obtain a cloud cluster coverage state; performing shielding calculation on the cloud cluster coverage state by adopting a shielding probability function to obtain a cloud cluster shielding probability; and performing joint calculation on the cloud cluster coverage state and the cloud cluster shielding probability by adopting an illumination intensity attenuation model and a photoelectric power linear model to obtain a photovoltaic power generation power prediction value, performing difference calculation on the photovoltaic power generation power prediction value and an electrical load prediction curve to obtain predicted supply and demand power, and performing scheduling distribution on the power storage system according to the predicted supply and demand power. And completing predictive power supply scheduling of power utilization. The photovoltaic power generation intelligent scheduling method based on dynamic supply and demand prediction has the advantage of remarkably improving the real-time utilization rate of photovoltaic electric energy in severe weather.

The invention provides a semantic enhancement pre-training method for intention understanding. Comprising a semantic enhancement pre-training framework and an intention understanding fine tuning framework, and a trajectory prediction intention understanding model is trained for a model for processing trajectory prediction information: the semantic enhancement pre-training framework firstly needs to design a fine-grained sequence reconstruction task and a coarse-grained intention comparison task; the fine-grained sequence reconstruction task adopts a mask strategy of a time dimension, the coarse-grained intention comparison task adds loss based on similarity, and the intention understanding fine tuning framework generates a multi-modal prediction intention of a target subject by using a multi-modal future decoder. Therefore, the prediction performance of the model on the future intention of the subject in different scenes, especially the prediction capability in a long-tail scene, is improved.

The invention discloses a project cost data visualization monitoring method and system, and the method comprises the steps: building a causal conduction path of a project digital twinborn dynamic simulation risk event, quantifying the increment influence on a cost chain, and combining the prediction capability of a machine learning model for the overall cost trend, thereby achieving the visual monitoring of the project cost data. And carrying out fusion calculation on the local cost increment caused by the sudden risk and the global predicted value to generate a final total completion cost predicted value which reflects the macroscopic trend and contains the impact of the sudden event, and finally dynamically rendering a fusion prediction result in a three-dimensional model space through a unified visualization engine. Thus, the defect that static BIM association lacks risk conduction analysis is overcome, the problem that a traditional prediction model and construction logic are unhooked is solved, a decision view with the overall trend control and emergency traceability is provided for engineering managers, engineering cost management is fundamentally promoted to be converted from post-event accounting to pre-control, and the engineering cost management efficiency is improved. And the cost anti-risk capability of the complex engineering project is obviously improved.

The invention relates to the field of medical artificial intelligence, in particular to a clinical scoring system combining large language model multi-agents and machine learning and a self-evolution method of the clinical scoring system, and provides the clinical scoring system based on the large language model multi-agents and the self-evolution method of the clinical scoring system. The objective of the invention is to overcome the defects of data missing sensitivity, insufficient interpretability, weak dynamic updating capability and the like of an existing clinical scoring system. According to the system, through a multi-agent collaboration architecture, the natural language interaction capability of a large language model (LLM) and the precise prediction capability of a machine learning model are deeply fused, and a self-evolution mechanism is introduced to realize dynamic optimization, so that the reliability, flexibility and transparency of clinical decisions are remarkably improved.

The invention discloses a regional wind-solar combined power interval prediction method and system based on multi-task learning and a hybrid neural network, and relates to the technical field of combined prediction. Meteorological data and wind-solar historical power data of a to-be-researched region are collected, and a historical data set is constructed; preprocessing the historical data set, and performing feature selection by adopting a predictive ability scoring algorithm to obtain screened meteorological data and historical power data; constructing a joint power interval prediction model; and performing combined power generation prediction based on the trained combined power interval prediction model. According to the method, the space-time correlation and the complementary relation of wind energy and photovoltaic energy are excavated, the joint power interval prediction model is constructed, and the power prediction precision is improved.

The invention discloses a power grid time frequency change trend prediction method and system based on intelligent electric meter data fusion, and the method comprises the steps: measuring the network transmission delay and time deviation between a master clock and a slave clock of a power grid, obtaining an initial time frequency deviation sequence, carrying out the error calibration, carrying out the data fusion after time domain alignment, and carrying out the prediction of the time frequency change trend. Generating a fusion time sequence; performing recursive estimation on the fusion time sequence, outputting an optimal time frequency deviation sequence, and performing synchronous estimation to obtain a clock phase error, a frequency error and frequency drift as state characteristic quantities; and training a preset machine learning prediction model by taking the optimal time frequency deviation sequence as a supervision signal and taking the state characteristic quantity of the corresponding moment as input, and obtaining a prediction result of the power grid time frequency change trend in the future period based on the trained time frequency change trend prediction model. According to the method, the power grid time synchronization precision and prediction capability are remarkably improved, and reliable technical guarantee is provided for stable operation of a power system.

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 agricultural harvesting machine based on the predictive value of the power characteristic.

The invention provides a hybrid energy scheduling method and system. The method comprises the following steps: acquiring predicted meteorological information and predicted power generation demand in a preset future time period; adopting a multi-target mixed integer programming algorithm to obtain a planning scheme; starting preliminary power generation by each energy unit according to a preset power generation priority, acquiring actual output data in real time, and generating an updated power generation demand according to a preset condition; acquiring real-time meteorological information; dynamically adjusting the operation state of each energy unit by adopting a model prediction control algorithm, and adjusting the actual output according to a surplus scene / gap scene / normal scene subdivision adjustment strategy; calculating a deviation ratio between the adjusted actual output and the updated generating capacity demand, and judging whether the deviation ratio exceeds a preset threshold by adopting a dynamic trigger mechanism; and if the deviation rate exceeds a preset threshold value, starting graded energy complementation. The scheme has the characteristics of high-precision prediction capability, dynamic planning mechanism, subdivision scene adjustment strategy and flexible expansion.

The invention is suitable for the technical field of electrical equipment detection, and provides a ceramic insulator intelligent evaluation method based on spectral feature analysis, and the method comprises the steps: obtaining spectral data through 120 GHz high-frequency laser, synchronously collecting environment and electrical parameters, extracting 10-dimensional spectral features, and fusing the 10-dimensional spectral features into a multi-modal feature vector; training is carried out by using an integrated learning model fused by a random forest and XGBoost and an LSTM time sequence model, and a dynamic weight updating mechanism is combined to adapt to environmental changes; outputting aging grade probability distribution, aging trend prediction in the future 6 months and a risk value gt; and 3.0, early warning is triggered. According to the scheme, evaluation precision, dynamic adaptability and prediction capability are improved, and reliable support is provided for operation and maintenance of a power system.

The invention discloses a predictive maintenance method for an implicit state of power equipment, and belongs to the technical field of intelligent operation and maintenance of the power equipment. According to the method, multi-mode information such as SCADA data, acoustic vibration data, infrared thermal image data and partial discharge data is collected, space-time alignment and attention mechanism fusion are carried out, and a hidden state code representing the internal health state of equipment is extracted; a dynamic state deduction model combining physical constraint and data driving is constructed, and prediction of the future state evolution trajectory of the equipment is achieved; and performing a virtual maintenance experiment in the digital twin based on a prediction result, and generating an optimal maintenance strategy through multi-objective optimization. According to the method, the problems that the hidden state of the equipment cannot be sensed and the prediction capability is lacked in the prior art are solved, the conversion from passive maintenance to predictive maintenance is realized, and the accuracy and foresight of operation and maintenance of the power equipment are improved.