2129 results about "Data prediction" patented technology

A data-driven dynamic energy management system for the adaptive coordination of renewable and conventional energy sources, consisting of: a processing unit configured to perform real-time calculations to optimize the generation, storage, and distribution of electrical energy by continuously analyzing operational data, forecasting future energy demand, and generating control instructions to match available generation resources with forecasted consumption demand; a storage unit connected to the processing unit, configured to store records of historical energy production and consumption, environmental data, operating thresholds and learned model parameters, and to provide said data as input for the forecasting and optimization routines performed by the processing unit; a multitude of IoT-based monitoring units, each comprising at least one sensor configured to measure instantaneous parameters of generation, storage level, consumption rate and environmental conditions, with each monitoring unit being configured to periodically transmit measurement packets to the processing unit via a secure communication network; a forecasting unit implemented in the processing unit, configured to process historical and real-time data to create forecast curves for demand and generation using statistical and probabilistic forecasting techniques, and to dynamically update the weights of the forecasting model in response to observed deviations between forecasted and actual output; an optimization control unit implemented in the processing unit and configured to evaluate the outputs of the forecasting unit together with current operational data to determine a set of optimized control variables representing the target generation contribution of each energy source, and to pass these targets to a lower-level controller for execution; a controller that is communicatively connected to the processing unit and the multiple energy generation sources and is configured to regulate the operation of each source by adjusting the activation state, output level and operating priority based on the control signals received from the processing unit; an energy storage management unit comprising at least one battery array and a power conditioning circuit, configured to receive control instructions from the processing unit, store excess generated energy, release stored energy when forecasted demand exceeds available generation, and report charging and discharging characteristics in real time to the processing unit for continuous recalibration; an alarm and notification control unit connected to the processing unit, configured to continuously compare storage levels and generation reserves with stored operating thresholds, trigger predefined responses when critical or abnormal conditions are detected, and transmit acoustic, visual, and digital remote alerts to designated operators; a user interface terminal connected to the processing unit, configured to display real-time generation statistics, demand forecasts, energy storage status, and system alerts, and to accept operator-defined parameter inputs that are transmitted to the processing unit for recalibration of forecast or optimization parameters; and a secure server interface configured to synchronize operational logs, learning data, and performance indicators with a remote monitoring or analysis server for centralized monitoring, long-term data analysis, and distributed decision support.

The invention discloses a battery abnormal data prediction and state evaluation method, and relates to the technical field of battery state prediction, and the method mainly comprises the steps: carrying out the preprocessing of an experiment data set, and obtaining multi-dimensional time series data; a combined feature encoder, a pre-response encoder and a memory analysis module are constructed to realize a battery abnormal data fault prediction model; training the model by using the multi-dimensional time sequence data to obtain a trained model, and predicting the to-be-predicted data to obtain a prediction result; and calculating a reconstruction error between a prediction result and original data, constructing an AUROC evaluation model, and evaluating the battery abnormal data fault prediction model. By implementing the battery abnormal data prediction and state evaluation method provided by the invention, the feature extraction efficiency, the abnormal recognition precision, the detection stability and the generalization ability can be improved.

The invention relates to a spare part service life prediction method, device and equipment and a computer readable medium. The method comprises the steps of collecting multi-mode state data of a target spare part; verifying the historical consistency of the multi-modal state data; and under the condition that the historical consistency verification of the multi-modal state data is passed, inputting the multi-modal state data into a target residual life prediction model so as to predict a degradation track of the target spare part based on the multi-modal state data by using the target residual life prediction model, the target residual life prediction model is a neural network model obtained by training by taking a physics degradation mechanism of the spare part as priori knowledge; and determining the predicted remaining life of the target spare part based on the degradation trajectory. According to the method, the evaluation one-sidedness caused by insufficient single data dimension is avoided, the prediction credibility is improved through a data verification and physical mechanism constraint model, and the technical problem of low life prediction accuracy caused by spare part life counterfeiting is effectively solved.

The invention discloses an edge computing collaborative distributed computing power scheduling method and device, and relates to the technical field of distributed computing and edge computing. The method comprises the following steps: collecting real-time operation state data of each edge node in a distributed edge node group; processing the real-time operation state data through a preset time sequence analysis operation, and predicting a predicted user load of each edge node in a preset future time period; combining the real-time operation state data with the predicted user load, and constructing a joint state vector; inputting the joint state vector into a preset reinforcement learning algorithm, and outputting a GPU resource dynamic allocation strategy; and when an AI reasoning request input by a user is received, determining a target edge node for the AI reasoning request from the distributed edge node group according to the GPU resource dynamic allocation strategy, and assigning the AI reasoning request to the target edge node. By implementing the technical scheme provided by the invention, the real-time performance and the stability of the distributed computing power system in a high-concurrency scene are improved.

The invention relates to the technical field of geological disaster monitoring, and discloses a landslide dynamic simulation monitoring and early warning method based on multi-source sensing fusion, and the method comprises the steps: collecting multi-source data of a landslide body through a plurality of heterogeneous sensors, and enabling the multi-source data to be in space-time alignment after preprocessing; building a multi-parameter fusion model fusing a displacement field, a mechanical field and an environment field based on the preprocessed data, enabling the multi-parameter fusion model to output a deformation rate and a stability coefficient, and dynamically adjusting the weights of the displacement field, the mechanical field and the environment field according to a landslide evolution stage; predicting a future deformation trend of the landslide mass in combination with a geological structure and historical data, comparing a stability coefficient with a dynamic safety threshold to judge a risk level, and generating early warning information; and dynamically correcting a reference weight coefficient in the model based on the deviation between the monitoring data and the model output, so that the model is adaptively optimized. The problems of single monitoring dimension and static model solidification in the prior art are solved, and accurate and adaptive monitoring and early warning of the risk state of the landslide mass are realized.

The invention relates to the technical field of water supply pipeline detection, and discloses an underground water supply pipeline health grade evaluation and risk prediction method, which comprises the following steps: sensor arrangement: arranging a flow sensor, a pressure sensor and a sonic sensor at key positions of an underground water supply pipeline; and data acquisition: acquiring signals of the sensor in real time through a data acquisition module, wherein the signals comprise flow, pressure and sound wave signals. Preprocessing the data: carrying out preprocessing such as denoising and normalization on the collected signals; and multi-source data fusion: inputting flow, pressure and sound wave signals into a deep neural network model, and performing feature extraction and fusion analysis. And health level assessment: assessing the health level of the pipeline based on the output result of the deep neural network model. And risk prediction: predicting abnormal working conditions possibly occurring in the future and risk levels of the abnormal working conditions by analyzing the current pipeline state and historical data. And abnormal positioning: accurately positioning an abnormal position in combination with the propagation time of the sensor signal and a positioning model of the deep neural network.

The invention provides a method and a device for predicting influence of sea wave fluctuation on signal intensity based on ship data. The method provided by the invention comprises the following steps: acquiring multi-attitude angle data of a ship based on a nine-axis inertial measurement unit; the multi-attitude angle data comprises a rolling angle, a pitch angle and a yawing angle; establishing a Cartesian coordinate system taking the gravity center of the ship as an original point; based on the multi-attitude angle data, calculating spatial transformation of the antenna position in the Cartesian coordinate system through an automatic spatial transformation algorithm to obtain the transformed antenna position; determining the direction of the transformed antenna through vector operation by combining the initial orientation of the antenna and the position of the transformed antenna; and determining a corresponding angle through an angle resolving algorithm based on the initial antenna direction and the transformed antenna direction, matching a corresponding antenna gain in an antenna gain database based on the angle, and determining a signal strength change prediction value based on the antenna gain.

A wireless transmit / receive unit (WTRU) may receive predictive buffer status report (BSR) configuration information. The predictive BSR configuration information may include information indicating an associated time frame and at least one triggering condition. The WTRU may predict an amount of uplink (UL) traffic corresponding to the associated time frame. The WTRU may predict BSR configuration information. The WTRU may determine that at least one triggering conditions has been met. The WTRU may send a predictive BSR, the predictive BSR comprising an indication of the predicted amount of UL traffic.

Disclosed in the present invention are a load-prediction-based control method and apparatus for an energy storage apparatus, and a terminal device and a storage medium. The method comprises: using a preset load prediction model to perform prediction on the basis of real-time operation data, so as to obtain a predicted load value of a micro-grid within a future time period; then, on the basis of a power generation capacity and the predicted load value, determining whether there is surplus electricity after the current power generation capacity of the micro-grid satisfies a future load demand; if so, controlling an energy storage apparatus to be charged, so as to avoid the waste of electric energy; and if not, controlling the energy storage apparatus to supply power to the micro-grid, so as to ensure that the micro-grid can satisfy the future load demand, thereby effectively ensuring the stable power supply of the micro-grid.

The invention provides an inference service instance expansion and contraction method, electronic equipment and a storage medium, and belongs to the technical field of artificial intelligence, and the method comprises the steps: predicting a future business load based on historical operation data of a target inference service to generate an active expansion and contraction instance decision; evaluating the current operation state based on the real-time operation data of the target inference service to generate a passive scaling instance decision; and performing collaborative decision-making on the active expansion and contraction instance decision and the passive expansion and contraction instance decision to determine a final expansion and contraction instance instruction, and adjusting the instance number of the target inference service according to the final expansion and contraction instance instruction. According to the method, a double-engine cooperation mechanism combining active prediction and passive response is established, while prospective capacity expansion and contraction are realized by using historical data to reduce time delay, bottom correction is carried out by using real-time data to cope with burst load, the problem of response lag or resource waste of a single capacity expansion and contraction mode is effectively solved, and the method is suitable for large-scale popularization and application. And the resource utilization rate and the service quality stability of the inference service are obviously improved.

The invention relates to the technical field of thermal field modeling and control, and particularly provides a molecular beam epitaxy substrate temperature field control method and system based on artificial intelligence, and the method comprises the following steps: obtaining the all-region temperature data of the surface of a substrate, and the heating power and environment parameters of each region of a heating cavity; inputting the heating power and the environmental parameters into the temperature field model, and outputting a temperature distribution matrix and temperature gradient data of the surface of the substrate; predicting temperature prediction data according to the temperature distribution matrix, the heating power and the sequence data of the environmental parameters in the historical time period; and inputting a state vector formed by the temperature distribution matrix, the temperature gradient data, the heating power data and the temperature prediction data into a deep reinforcement learning model, and then outputting the heating power adjusting quantity of each area to control a temperature field. According to the invention, the problems of insufficient prediction precision and real-time performance of the substrate temperature in the prior art are solved, the influence of environmental factors and heating power on the substrate temperature is considered, and the temperature field control capability is improved.

The invention discloses a power grid dispatching scheme generation method and system based on load optimization, and the method comprises the steps: predicting a reference load change curve and a confidence interval of each node in a future set time period according to the load data and meteorological data of each node in a power grid topological graph in a corresponding time period; continuously determining a risk area based on the risk assessment model and determining a predicted load offset and a standard deviation so as to correspondingly optimize and broaden a reference load change curve and a confidence interval of each node in the risk area; and constructing an uncertain scene set based on the reference load change curve and the confidence interval, then constructing an objective function, and solving the objective function based on the uncertain scene set to generate an elastic scheduling scheme with the minimum power generation cost and the minimum load vacancy in the worst load scene. According to the invention, through supplementing the prediction load offset and the standard deviation, the prediction precision of the short-term load change under the condition of sudden power consumption peak or extreme weather is obviously enhanced, and the power grid dispatching level is improved.

The invention discloses an intelligent scheduling method based on task optimization. According to the method, firstly, the task load of each time period is predicted through historical data, and then an M / M / c queuing model is established to quantify a task queuing duration expected value as an optimization target. A two-stage optimization strategy is adopted: in the first stage, the optimal number of service desks in each time period is determined by applying dynamic planning, and accurate distribution of human resources is realized; in the second stage, specific employees are distributed to shifts through constraint planning, and meanwhile, complex constraints such as total working time fixing, continuous working duration, shift intervals and skill matching are met. According to the system, a queuing theory, constraint optimization and a prediction model can be combined, and on the premise of ensuring the feasibility of a scheduling scheme, the task processing waiting time is effectively shortened, and the service quality and the human resource utilization rate are improved.

The invention discloses a mountain cloud sea landscape universalization forecasting method based on a vertical layering meteorological factor, and relates to the technical field of landscape forecasting, and the method comprises the following steps: obtaining forecast meteorological data of a target mountain, the meteorological data comprising a meteorological profile; based on the forecast meteorological data, predicting the dynamic evolution trend of the thermal inversion layer to obtain a thermal inversion layer evolution prediction result; correcting the initial meteorological profile based on a thermal inversion layer evolution prediction result; based on the corrected meteorological profile, analyzing and judging a space matching relationship among a thermal inversion layer, a wet layer and a wind field, and forming an ornamental index set of the cloud sea landscape; and comprehensively judging the ornamental value index set, and outputting a cloud sea landscape observability result of the target mountain land in the forecasting time period. According to the method, the dynamic evolution trend of the thermal inversion layer is identified, and the cloud sea landscape ornamental index is generated and predicted in combination with terrain disturbance correction, so that the problems of inaccurate generation and elimination depiction of the thermal inversion layer and difficulty in quantitative prediction of the cloud sea landscape in the prior art are solved.

The invention discloses a speed reducer self-adaptive torque control method and system based on load identification, and relates to the technical field of speed reducer control. The method comprises the steps of collecting multi-source time sequence sensing data in real time when the speed reducer operates; performing time domain and frequency domain feature extraction on the data, and identifying current load features including load levels and types; predicting a future load change trend based on the load characteristics and historical data; a self-adaptive torque control instruction is generated according to the load characteristics, the change trend and the health state parameters, and the output torque is dynamically adjusted; and recording instruction execution efficiency data, and optimizing a load identification and torque control strategy by adopting a strategy gradient reinforcement learning algorithm. The system realizes the functions through cooperation of multiple modules. The problem that torque control of a traditional speed reducer cannot adapt to dynamic load changes is solved, precise self-adaptive adjustment of torque output is achieved, the operation efficiency and equipment safety are improved, and the service life is prolonged.

The invention discloses a cross-cloud task arrangement optimization method based on multi-cloud resource scheduling, and the method comprises the steps: constructing a task topological weighted graph, and recognizing a parallelizable node set; constructing a task node preliminary scheduling feasibility table according to task topological weighted hierarchy information; constructing a granularity feature vector, and updating the task structure matrix; identifying granularity collaborative attenuation abnormal nodes, and performing cross-cloud task path repair and structure update; generating a cross-cloud scheduling time matrix based on each cloud platform resource supply capability index; predicting resource competition conflicts in combination with task execution index historical data, and generating a conflict prediction table; identifying granularity strategy deviation, and determining a repair trigger point; and generating a cross-cloud task granularity adaptive arrangement configuration file based on the repair trigger point information. According to the method, high reliability, high flexibility and high adaptability of task scheduling can be realized in a complex multi-cloud heterogeneous environment, and the stability, the resource utilization rate and the overall cooperation efficiency of cross-cloud task execution are remarkably improved.

The invention provides a hydropower station fusion computing power intelligent server system, and relates to the technical field of carbon emission. The method comprises the following steps: an acquisition correction module acquires hydropower station generation power data and server cluster instantaneous energy consumption data in real time, establishes a time sequence corresponding relation between a power generation side and a power utilization side, and obtains clean energy supply quantity data; the prediction scheduling module generates a non-tampering carbon footprint tracking chain number, and predicts a clean energy supply fluctuation trend sequence by adopting a long short-term memory network algorithm; training a random forest model to predict a computing power demand trend sequence; optimizing task scheduling according to a carbon emission minimization target, and generating dynamic balance configuration data of a computing power task and clean energy supply; and the monitoring evaluation module outputs computing power task level carbon emission and a clean energy use ratio in real time through an application programming interface according to the dynamic balance configuration data, and generates quantitative evaluation data of a computing power service carbon neutralization target by adopting a data visualization technology monitoring technology.

The invention provides a traffic flow prediction method combining a dynamic graph neural network and a Mama mechanism. The future short-term traffic flow is predicted by using historical traffic data. According to the method, firstly, normalization preprocessing is carried out on traffic state data collected by multiple sensors, a training sample is generated by adopting a sliding window, and a traffic flow value in the next one hour is predicted according to data in the past 24 hours. On the basis of the model structure, a time modeling module composed of multiple layers of MambaBlocks is constructed and used for capturing historical time sequence dependence; constructing a spatial modeling module of dynamic graph convolution, and combining a static adjacency matrix of the road network with a learnable adaptive adjacency structure to extract spatial association; and finally, the outputs of the modules are fused, and a prediction result is obtained through a prediction output module. In the training process, a Huber loss function is used as an optimization target, and evaluation indexes such as a mean absolute error (MAE), a root mean square error (RMSE) and a mean absolute percentage error (MAPE) are used for evaluating the performance of the model. According to the method, the traffic space-time dynamic characteristics are effectively mined, and the long-range dependence modeling capability and the prediction precision are improved.

Systems and methods iteratively train, using training data, a natural language processing (NLP) model to interpret conversational input during a conversation between an agent and a user by predicting key statements to be used in the prediction of a user intent, the training comparing outputs to a target variable during each iteration and adjusting parameters of the NLP model during each iteration to improve predictability of the user intent from the conversational input. Real-time conversational data is transmitted to the NLP model and the trained NLP algorithm derives key statements predicted to indicate intents and predicts one or more user intents based on the data from the conversation. One or more pre-filled forms predicted to effectuate the one or more user intents is generated, the pre-filled forms including generated text derived from information from the data of the conversation, and the form is transmitted to an agent device.

The invention discloses a GFRP durability evaluation method based on a neural network, and belongs to the technical field of composite material performance evaluation. The method comprises the following steps: constructing a training data set containing working condition parameters, macroscopic performance data and microscopic mechanism data; a multi-head physical guidance neural network model is constructed, and the model is provided with a main output head for outputting a macroscopic performance data predicted value and an auxiliary output head which is connected to an internal physical mechanism characterization layer of the model and is used for outputting a microscopic mechanism data predicted value; performing multi-task cooperative training on the model through a composite loss function; and finally, synchronously outputting a macroscopic performance prediction result and a microcosmic mechanism diagnosis result by utilizing the trained model. According to the method, physical mechanism knowledge is fused into the neural network, and a traditional black box model is improved into an interpretable grey box model through middle layer supervision and multi-task cooperative training, so that the accuracy of macroscopic prediction is improved, and quantitative diagnosis of an internal degradation mechanism is realized.

The invention discloses a plasticizing industry intelligent logistics scheduling method based on order prediction, and relates to the field of plasticizing product logistics, and the method comprises the steps: predicting the order demands of a plurality of clients for plasticizing products in a preset time window based on historical data, and constructing a knowledge graph based on client nodes, order nodes and mark nodes corresponding to orders; in the knowledge graph, attribute parameters are added to each mark node, and the attribute parameters comprise the melt flow rate, the color grade and the additive grade; constructing a switching scoring function based on the attribute parameters; on the premise of ensuring that all orders are delivered in a preset time window, constructing a total objective function which comprehensively considers the total driving mileage, the vehicle cleaning cost score and the customer cleaning cost score; and by taking minimization of the total objective function as an objective, generating a scheduling scheme by adopting an optimization algorithm. According to the invention, the problem of cross contamination caused by neglect of trademark replacement in a traditional scheduling method is effectively solved.

The invention relates to a data processing method for glaze and rime spatial modeling by fusing live and weather forecast. The method comprises the following steps: acquiring topographic data, weather forecast data and weather state data of each grid region of a target region; wherein the meteorological state data comprises the occurrence of glaze, the occurrence of rime, the non-occurrence of glaze and rime; according to the weather forecast data, predicting a first probability that the temperature of water drops in each grid region is lower than a first threshold value and a second probability that the temperature of a target object is lower than a second threshold value; determining initial spatial distribution according to the first probability and the second probability; correcting the initial spatial distribution according to the difference between the initial spatial distribution and meteorological state data to obtain intermediate spatial distribution; and according to the topographic data and the intermediate space distribution, performing interpolation processing on each grid region of a target region to obtain target space distribution. By adopting the method, the accuracy of predicting the spatial distribution of the glaze and the rime can be improved.

The invention discloses a detection system of an absorption spectrum water quality multi-parameter analyzer, and relates to the field of water quality detection. Comprising a water sample acquisition and self-adaptive preprocessing module, a full-spectrum dynamic acquisition and waveband optimization module, a multi-dimensional data fusion processing module, a dynamic evolutionary model training and optimization module, an intelligent pollution traceability and parameter inversion module and a real-time decision and early warning response module. According to the detection system of the absorption spectrum water quality multi-parameter analyzer, through multi-dimensional data fusion, a dynamic evolution model and a spectrum fingerprint traceability technology, the pollutant concentration can be accurately inverted, the pollution source can be rapidly positioned, and the diffusion trend is predicted in combination with hydrological-meteorological data; graded early warning and customized disposal suggestions are synchronously generated, and the environmental protection supervision platform and the treatment terminal are linked through the Internet of Things, so that closed-loop response of detection, source tracing, early warning and disposal is realized.

The invention provides a gestational diabetes risk prediction system and method based on multi-modal data fusion, and the system comprises a data collection module which is used for integrating clinical indexes and medical record text data; the data preprocessing module converts the multimode data into numerical values and text variables which can be used for modeling; the variable screening module is used for extracting data features by adopting LASSO regression in combination with a recursive feature elimination algorithm and a Clinical-BERT model; the data prediction module is used for constructing a GDM risk prediction model through a dual-channel calculation unit and a fusion unit, generating an accurate risk probability and providing an interpretable clinical index in combination with an SHAP value; and a prediction result is output through the output module in the forms of a dynamic column diagram, a webpage calculator and an API interface, so that clinical operation and application are facilitated. According to the method, the limitation of a traditional prediction method is broken through, the prediction precision and the real-time monitoring capability are improved, and the development of precise medical treatment is promoted.

The invention provides a real-time prediction method and system for milling deformation of a thin-wall part and electronic equipment. The method comprises the steps that a partial differential equation set used for reflecting the deflection and stress coupling relation in the milling machining process of the thin-walled workpiece is constructed based on a von Karman control equation set; constructing a deformation prediction model based on a depth operator network; according to the partial differential equation set, constructing a loss function comprising a partial differential equation residual term and a boundary condition term; training the deformation prediction model according to the loss function and two-dimensional coordinate points and load function sampling points used for training the deformation prediction model; and deploying the trained model in a machine tool system, inputting real-time load data and real-time coordinate data of the thin-wall part in the milling process, predicting deformation response of the thin-wall part in the milling process, and obtaining a target prediction result. The method can quickly and accurately predict the deformation response of the thin-wall part under the complex load working condition, and improves the machining quality and efficiency of the thin-wall part.

A time series data prediction method and apparatus, and a storage medium are provided. The method includes: obtaining current time series data collected in a current time window that is adjacent to and precedes a prediction time window in a current time period, and obtaining a plurality of groups of historical time series data separately collected in a same target time window of a plurality of historical time periods; encoding the plurality of groups of historical time series data by using a plurality of encoders respectively, to obtain a plurality of historical time series features, where each historical time series feature represents relative location information and change trend information of each group of historical time series data in the target time window; and determining, predicted time series data corresponding to a target object in the prediction time window.

The invention discloses a frequency response modeling method, system and device based on power grid planning and a storage medium, and relates to the field of power system planning operation, and the method comprises the steps: employing an improved K-means algorithm based on data intensity, and carrying out the clustering of water, fire, wind and light units according to characteristic parameters; predicting a long-sequence meteorological scene based on historical meteorological data by using a three-layer BP neural network and reducing the long-sequence meteorological scene to obtain a typical operation scene; establishing an optimization model to obtain a power supply total output plan by combining unit clustering, a typical scene and a power grid boundary condition; a mixed integer optimization model is constructed to determine a synchronous unit starting scheme, equivalent inertia is calculated, new energy and stored energy equivalent inertia is calculated, and system frequency response model identification parameters are established and simplified; the method can improve the frequency stability of the power grid, reduce the operation risk caused by new energy access, optimize the power supply and net rack configuration in the planning stage, reduce the frequency out-of-limit and load loss during a fault, and enhance the safety and economy of the power grid.

The invention provides a hybrid vehicle energy management system and method based on a traffic state, a storage medium and a computer program product, and the system comprises the steps: constructing a congestion prediction model based on a deep learning model, the congestion prediction is used for predicting the traffic flow and the driving speed of a future time period according to the historical traffic flow data and the historical driving speed data; calculating a first congestion index according to the predicted traffic flow in the future period; calculating a second congestion index according to the predicted driving vehicle speed sequence of the future time period; performing weighted calculation on the first congestion index and the second congestion index to obtain a comprehensive traffic congestion index in a future time period; traffic jam types are divided according to the interval where the comprehensive traffic jam index is located; and determining an energy management mode and / or a target SOC of the hybrid vehicle based on the traffic jam type. According to the invention, multi-source data are collected and analyzed in real time, congestion is accurately quantified by using a deep learning algorithm, an energy distribution strategy of vehicles is dynamically adjusted, and the energy utilization efficiency is improved.

The invention discloses a method and device for predicting carbon emission data of a gas turbine of a thermal power plant, and the method comprises the steps: carrying out the standardization processing of a trend term through reversible instance normalization, and enabling m variable channels to be clustered into n channel clusters through employing a K-Means algorithm for the trend term after the standardization processing; through a cluster-driven prediction module, applying an exclusive independent prediction model for each channel cluster to carry out parallel prediction, and carrying out inverse standardization on a prediction result to obtain a final trend prediction value, and carrying out structured multi-scale wavelet decomposition on a seasonal item to obtain a final approximate component and a plurality of high-frequency detail components; performing collaborative coding on the final approximate component to obtain depth context representation; performing dynamic fusion on the depth context representation and the plurality of high-frequency detail components by adopting an attention cross-scale fusion module to obtain fusion representation; and reconstructing a seasonal component according to the fusion representation and the plurality of high-frequency detail components through inverse wavelet transform to obtain a seasonal predicted value and a trend predicted value to form a final long-term time sequence prediction result, and outputting the result.

The invention discloses a battery state prediction method and device and electronic equipment. The method relates to the field of artificial intelligence, and comprises the following steps: obtaining operation data of a target battery in a first historical time period, and obtaining N groups of first historical operation data; inputting the N groups of first historical operation data into an operation data prediction model to obtain an operation data set of the target battery at H target moments in the target time period; m operation data sequences of the target battery in the target time period are generated; under the condition that an abnormal fluctuation index exists in the fluctuation indexes of the M operation data sequences, determining that the target battery is abnormal; and under the condition that no abnormal fluctuation index exists in the fluctuation indexes of the M operation data sequences, obtaining an initial residual capacity value of the target battery in the first historical time period, and determining the operation state of the target battery according to the initial residual capacity value. Through the method and the device, the problem of low accuracy of determining the battery state according to the use duration in related technologies is solved.