3569 results about "Model parameters" patented technology

The invention relates to the technical field of information, in particular to a multi-task data analysis method and device and a storage medium, and the method comprises the steps: constructing a multi-dimensional task state feature vector as the input of a task loss prediction model by collecting a task execution progress, a resource use condition and an abnormal log index in a task operation process; constructing a task dependency graph and a resource conflict graph according to the dependency relationship between the tasks and the resource occupation condition, learning the relevance between the tasks and a resource competition mode through a graph neural network algorithm, and obtaining an influence factor and a propagation path of task loss; the performance of a task loss prediction model is continuously tracked, model parameters are dynamically updated through an incremental learning mechanism, the method adapts to changes of a multi-task environment, prediction accuracy and real-time performance are improved, and a reliable decision basis is provided for task scheduling and resource optimization.

The invention relates to the field of power equipment data processing, in particular to a biomass power generation combustion parameter deep learning method and system, and aims to solve the problem of phase mismatch caused by sampling frequency difference and clock reference offset of multi-source heterogeneous time sequence data. A parallel multi-scale convolution and bidirectional long-short-term memory network hybrid model is constructed, transient fluctuation and long-period trend features are extracted, and combustion stage feature weights are dynamically distributed through a gating attention mechanism. The optimization control module generates a multi-target constraint condition, an operation instruction is output in combination with a fuzzy inference engine, and a digital twin platform simulates an extreme working condition to enhance model robustness. A closed-loop feedback mechanism dynamically adjusts model parameters through combustion efficiency monitoring data and simulation results, and a two-stage fault-tolerant strategy realizes sensor abnormity compensation and historical control strategy backtracking. The problem of asynchronous data stream feature misalignment is effectively solved, and the combustion efficiency prediction precision and the control decision reliability are improved.

The invention relates to the technical field of flow measurement calibration, in particular to a flow instrument intelligent calibration system based on multi-sensor fusion, which comprises a data acquisition unit, a deep coupling compensation unit and a closed loop verification unit, a data acquisition unit obtains a differential pressure value, an environment temperature, a pipeline pressure, a vibration frequency spectrum and sensor accumulated working time, a depth coupling compensation unit constructs an aging prediction model based on a Weibull distribution life model, and a temperature-pressure coupling equation and a vibration compensation mechanism are combined to obtain an aging real-time value and a time sequence deviation. Multi-parameter coupling characteristics are extracted through a neural network, an environment disturbance compensation coefficient matrix is constructed, a joint compensation amount is generated through dynamic weight distribution, a closed-loop verification unit optimizes model parameters, the problems that multi-source disturbance coupling analysis is insufficient and calibration precision is low in the prior art are solved, accurate calibration of a flow instrument under complex working conditions is achieved, and the calibration precision is improved. The metering stability is improved.

The invention discloses a labeling task assignment method and device based on artificial intelligence, and the method comprises the steps: obtaining historical behavior data, and constructing a multi-dimensional user portrait; receiving a task description document, a data sample and a quality requirement document to obtain a multi-dimensional task feature vector; based on the multi-dimensional user portraits and the multi-dimensional task feature vectors, a matching degree score is calculated through a multi-objective optimization algorithm, and an optimal task allocation scheme is generated; optimizing the task structure through a fireworks algorithm based on student t distribution, and generating an optimized task unit structure; real-time monitoring is carried out through the anomaly detection model and the quality prediction model, and quality control measures are triggered; model parameters are updated through a reinforcement learning algorithm, and a personalized feedback and capability improvement strategy is generated. According to the method, accurate matching between the annotators and the tasks is realized, the processing efficiency of complex tasks is improved, the annotation quality is improved, the expansibility and the response speed of a platform are enhanced, and an effective solution is provided for large-scale and high-quality data annotation.

The invention provides a power prediction precision improvement method and system for a wind and light station, and relates to the technical field of new energy power generation, and the method comprises the steps: obtaining the historical power, weather and equipment operation data of the wind and light station through a data preprocessing module, and carrying out the time sequence decomposition, spatial interpolation, abnormal value detection and correction, and obtaining the fusion feature data. And constructing a deep learning prediction model based on the fused feature data, extracting spatial features by using a convolutional neural network, processing the spatial features by using a long-short-term memory network to obtain an initial prediction result, introducing an attention mechanism to perform weight allocation to obtain weighted features, and combining to obtain power prediction data. And judging the weather type according to the real-time meteorological condition, and correcting by adopting the matched correction model when the extreme weather is identified. And finally, an error compensation model is constructed based on historical prediction errors for compensation, a final power prediction result is obtained, error correction is performed according to actual power data, and compensation model parameters are updated.

The invention discloses an electrical equipment fault diagnosis and prediction analysis system, which relates to the field of intelligent operation and maintenance of a power system and comprises an acquisition and preprocessing module, an extraction fusion module, a fault diagnosis modeling module, a prediction evaluation module and an update feedback module. According to the invention, through fusion of structured sensing data and unstructured image data, multi-modal depth feature joint representation is realized, and the accuracy and robustness of fault identification are significantly improved; a fusion time sequence prediction model is introduced, and a health degree scoring system is combined, so that accurate prediction of key parameter trends and quantitative estimation of the residual life of equipment are realized; a transfer learning and incremental learning mechanism is adopted, when a new fault or small sample data appears, model parameters can be quickly updated, and efficient adaptation to a new scene is achieved; a data alignment mechanism with time-space synchronization and an auto-encoder anomaly detection algorithm are constructed, and the multi-source heterogeneous data processing capacity and the real-time fault early warning capacity are remarkably improved.

The invention discloses a hemispherical resonator gyroscope inertial navigation system error online calibration method, device and system and a readable storage medium. The method comprises the following steps: establishing a dynamic observation equation of a gain error and a misalignment angle error; adaptively extracting error features of different time scales, and constructing a high-dimensional feature vector; decoupling the gain error and the misalignment angle error to obtain an error estimation value; constructing a dynamic compensation matrix, and correcting and measuring the angular velocity in real time; an online self-correction mechanism of the error compensation model is designed, and a compensation matrix is dynamically adjusted to adapt to dynamic changes of errors; an adaptive time sequence regression model is introduced to predict an error long-term drift trend, and a nonlinear compensation method is combined to actively counteract an error accumulation effect; dynamically adjusting the optimization rate of the compensation matrix through stability analysis; and dynamically optimizing error model parameters based on convergence evaluation of error residual errors. According to the invention, the limitation of a traditional method in a dynamic environment, especially the coupling problem of a gain error and a misalignment angle error, is solved.

The invention discloses a BIM-based electromechanical installation project progress planning and resource scheduling method and system, and belongs to the technical field of intelligent construction. The method comprises the following steps: 1) collecting multi-dimensional parameters (real-time construction parameters, prediction model parameters and external constraint parameters) in construction in a classified manner, and constructing a dynamic knowledge graph; 2) detecting progress and resource deviation based on a preset threshold value of the BIM model, and dynamically correcting a resource demand curve through the model; 3) in combination with constraint conditions such as policies and weather, optimizing an equipment scheduling path by adopting an algorithm, and screening compliance candidate schemes; 4) performing multi-objective optimization (minimizing progress deviation, maximizing resource utilization rate and controlling supply chain risk) on the scheme by using a genetic algorithm, and verifying through simulation iteration; and 5) outputting the optimal scheme and synchronizing the optimal scheme to a visual interface. According to the invention, real-time closed loop of data is realized through hardware-algorithm cooperation, and an efficient, dynamic and extensible intelligent management scheme is provided for electromechanical engineering.

The invention discloses an integrated power distribution cabinet energy efficiency dynamic optimization method based on multi-modal data fusion, and relates to the technical field of intelligent power grids. The problems of failure of an energy efficiency optimization model and poor long-term operation stability caused by data time sequence misalignment and error accumulation of a multi-source sensor in the prior art are solved. According to the scheme, the time offset is dynamically corrected through the adaptive time sequence deviation prediction model; compensating missing data by adopting a non-uniform time step reconstruction algorithm and Kalman filtering; detecting an error drift trend through an exponentially weighted moving average model, updating a feature weight, and inhibiting long-term error accumulation; constructing a self-adaptive time sequence attention fusion network model, and fusing physical constraints and a data driving mechanism to generate an optimization decision; bayesian optimization is utilized to quantify parameter uncertainty, closed-loop feedback execution data is carried out, and model parameters are dynamically updated; according to the invention, the precision, long-term stability and equipment safety of energy efficiency optimization of the power distribution cabinet are remarkably improved, and efficient and reliable operation under multi-physics field coupling constraint is ensured.

The invention discloses a computing power scheduling method and system based on dynamic load prediction and resource priority ranking. The computing power scheduling method comprises the following steps: collecting historical load data, task submission data and resource state data of each node in a computing power cluster; on the basis of the preprocessed multi-dimensional load feature data set, constructing an improved hybrid prediction model, optimizing model parameters through training, and predicting the load change trend of each computing power node in a future preset time period by using the trained model to obtain a node load prediction result; extracting a service level protocol parameter, a resource demand type and historical execution efficiency data of a to-be-scheduled task, and establishing a multi-dimensional resource priority evaluation index system; according to the computing power scheduling method, the problems of low resource utilization rate and high task response delay caused by low load prediction precision and mismatching of resource allocation and task priority in a traditional computing power scheduling method are solved, and the overall operation efficiency and service quality of a computing power cluster are improved.

The invention relates to the technical field of wind turbine generator data analysis, and discloses an offshore wind turbine generator operation data analysis and prediction system. The system comprises a marine environment data integration module for collecting data to generate a multi-source time-space synchronization data set; the multi-modal feature fusion module is used for extracting cross-modal correlation features to generate a high-dimensional fusion feature tensor; the dynamic fault prediction module is used for constructing a two-way gating circulation network model to predict the degradation probability and the residual life of key components of the equipment; and the self-adaptive optimization control module is used for constructing a multi-target dynamic programming model to optimize a fan operation strategy. In addition, the system is further provided with a feedback correction module for correcting prediction model parameters, and a virtual sensor module based on a physical information neural network is used for monitoring tower stress and diagnosing sensor faults. According to the system, comprehensive monitoring, accurate fault prediction and optimal control of the offshore wind turbine generator are realized, the operation efficiency, reliability and safety of the wind turbine generator are effectively improved, and the operation and maintenance cost is reduced.

The invention discloses a solar radiation space-time prediction method and system based on physical information constraint and a neural network, and the method comprises the steps: collecting multi-dimensional time sequence meteorological data, extracting high-dimensional time sequence dynamic features, converting geographic space data into a fuzzy set, and carrying out the defuzzification of the fuzzy set through an inference rule, thereby obtaining geographic space features; and a gating mechanism is adopted to realize deep fusion of the space-time features to generate high-dimensional space-time fusion features. In a model training stage, an energy conservation equation is introduced into an optimization process, a physical residual error is constructed by calculating a time derivative and a space derivative of a predicted value, a physical constraint total loss function is formed in combination with a data loss item, and model parameters are updated by using a gradient descent method. According to the method, the accuracy and reliability of a prediction result are remarkably improved while the calculation efficiency is ensured, and the method is particularly suitable for solar radiation prediction under complex meteorological conditions; according to the method, abnormal prediction caused by data noise can be effectively corrected, and a solution with physical rationality and data adaptability is provided for the fields of solar resource evaluation, photovoltaic power generation power prediction and the like.

The invention provides a rebalance dynamic adjustment method applied to a multi-source system and a service system, and relates to the technical field of smart energy, and the method comprises the steps: collecting a real-time operation data set of the multi-source energy system, covering energy supply, load demand and system operation state features, carrying out the dynamic supply and demand deviation feature extraction, and carrying out the dynamic adjustment of the dynamic supply and demand deviation features; a feature set containing supply, demand fluctuation and system stability deviation is generated, then a pre-trained multi-source collaborative prediction model is called, and a multi-source collaborative prediction result containing energy supply and load demand trend prediction features is generated based on the feature set; and dynamic adjustment strategy matching is carried out according to a multi-source collaborative prediction result and real-time operation data, and a strategy set of energy output adjustment, energy storage charging and discharging, load priority distribution and the like is obtained. And finally, executing an adjustment strategy and obtaining feedback data, optimizing parameters of the multi-source collaborative prediction model based on the adjustment strategy, triggering closed-loop adjustment operation, and realizing rebalance dynamic adjustment of the multi-source system.

A dynamic optimization system for energy consumption of a refrigeration house based on digital twinning is characterized by comprising a data acquisition module used for acquiring basic structure data of the refrigeration house, technical parameters of a refrigeration system, real-time operation data and historical operation data, preprocessing the data and then outputting a standardized multi-dimensional real-time data stream; the model construction module is used for constructing a 3D geometric model, a thermodynamic transfer model and a refrigeration system mathematical model according to the multi-dimensional real-time data flow, performing machine learning calibration on model parameters through historical operation data, and performing fusion to construct a refrigeration house digital twin model; the prediction analysis module is used for predicting future energy consumption demand and load change according to the refrigeration house digital twin model and the real-time operation data, and outputting an energy consumption prediction result and a load analysis report; a strategy generation module; an execution feedback module; and a learning optimization module. Overall energy consumption of the refrigeration house is reduced, energy utilization efficiency is remarkably improved, and goods storage safety is guaranteed.

The invention provides a vehicle driving track prediction method, system and device and a medium, and belongs to the technical field of vehicle track prediction.The method comprises the steps that vehicle state information and environment information of each track point of a vehicle are collected to serve as track data, and a deep learning denoising algorithm is used for preprocessing and feature sequence extraction; constructing a trajectory prediction model by using a Transform in combination with a graph neural network, constructing a training set by using the extracted feature sequence to train the trajectory prediction model, and adjusting model parameters by using a multi-task loss function constructed by trajectory prediction precision and uncertainty estimation in the training process; and using the trajectory prediction model to predict the vehicle trajectory, using the Bayesian optimization method to optimize the trajectory parameters with the goal of minimizing the prediction error, and adjusting the optimization parameters with the vehicle dynamics constraint and the road constraint as constraint conditions in the optimization process. According to the invention, through multi-fusion and multi-stage optimization, high-precision prediction of the vehicle track in a complex traffic environment is realized.

The invention relates to an electrical load prediction and optimization regulation and control method and system for high-energy-consumption equipment, and solves the problems of inaccurate load prediction, single regulation and control means and difficulty in dynamic adaptation of the high-energy-consumption equipment, and the method comprises the steps: collecting multi-source data of the high-energy-consumption equipment in real time, constructing a dynamic equipment collaborative causal graph after preprocessing, and extracting key constraints; inputting the data and the constraints into the dynamic digital sample model to obtain a system state simulation result; based on the result, a multi-objective optimization regulation and control strategy is generated and executed by using a meta-learning + reinforcement learning decision framework; and collecting actual data comparison deviation, starting hierarchical federated learning when a threshold value is exceeded, grouping and aggregating similar experiences according to a causal graph topology, and dynamically calibrating model parameters and a decision framework. The method has the following effects that accurate load prediction and multi-target cooperative regulation and control of the high-energy-consumption equipment are achieved, working condition changes are dynamically adapted, the cost is reduced, and continuous production and the service life of the equipment are guaranteed.

The invention relates to the technical field of intelligent manufacturing, in particular to a rolling method, system and equipment in a full-closed mode and a storage medium, and the method comprises the steps that plate thickness and temperature data are collected in real time through a thickness measuring device integrated with a temperature sensor; predicting a future thickness change trend by using a temperature-sensitive deep learning model and combining historical working condition parameters; calculating a dynamic compensation gap sequence based on the prediction result, the target thickness and the compression roller characteristics, and driving a compression roller control system to adjust the gap; and feedback data after rolling are synchronously collected, model parameters are updated in a self-adaptive mode, and closed-loop control is formed. According to the full-closed mode rolling method, high-precision rolling is achieved through closed-loop control, and the quality and efficiency are improved.

The invention discloses a runoff prediction method and terminal based on a space-time heterogeneous graph neural network, and the method comprises the steps: obtaining hydrological time series data and space station information data collected by each station, and carrying out the preprocessing of the hydrological time series data and the space station information data; performing weighted fusion according to the features of the preprocessed hydrological time series data and the space station information data, generating a feature embedding vector representing a global variable time-space relationship through a graph attention network, and inputting the feature embedding vector into a full connection layer to obtain an output multi-station runoff prediction sequence. Constructing a space-time heterogeneous graph flood forecasting model; performing model parameter optimization on the space-time heterogeneous graph flood forecasting model; and carrying out runoff prediction on the hydrological time sequence data and the space station information data which are received in real time according to the optimized space-time heterogeneous graph flood forecasting model. In this way, hydrological space-time correlation characteristics can be effectively captured in forecasting, so that the accuracy and reliability of forecasting are improved.

The invention discloses an interpretability prediction analysis system for multivariable time series data. According to the method, the prediction precision and the decision support capability of the complex time series data are remarkably improved through multi-module cooperation. Firstly, an adaptive learning optimization module dynamically adjusts model parameters and a prediction strategy, so that the model can quickly adapt to time-varying characteristics of data distribution, for example, when a causal relationship between variables suddenly changes, the weight of latest data is automatically enhanced, and historical noise interference is reduced. The dynamic causal interpretation engine tracks the influence intensity and hysteresis effect of key variables in real time, converts traditional black box prediction into a traceable causal relationship chain, and helps a user to intuitively understand driving factors of a prediction result, for example, it is identified that prediction value sudden increase in a certain period is mainly derived from hysteresis effect accumulation of an upstream variable A.

The invention is suitable for the technical field of life evaluation, and provides an enhanced high-voltage circuit breaker life evaluation method, which comprises the following steps: constructing a dynamic evolution model of a contact material wear rate; constructing a nozzle degradation dynamic prediction model; constructing residual life probability distribution of the insulating material; according to the dynamic evolution model of the wear rate of the contact material, the dynamic prediction model of nozzle degradation and the residual life probability distribution of the insulating material, constructing a comprehensive life evaluation index; inputting the comprehensive life evaluation index into a preset layered prediction architecture for evaluation; wherein the first layer generates basic life distribution through a Bayesian network, the second layer outputs posterior life distribution correction parameters through a convolutional neural network, and the third layer optimizes posterior distribution through a KL divergence minimization algorithm to obtain a probability density function and a confidence interval of residual electrical life; according to the method, the error of life evaluation can be reduced on the basis of complex working conditions.

The invention discloses a gait sub-phase prediction method and device based on deep learning model fusion. The method comprises the following steps: synchronously collecting multi-source gait data of a wearer walking at various speeds; preprocessing the collected multi-source gait data, dividing a time window by adopting a sliding overlapping window technology, and constructing a supervised learning training data set based on double tags; hierarchically fusing CNN, Bi-LSTM and MHA, and constructing a gait sub-phase prediction model; automatically searching an optimal hyper-parameter combination by using a Bayesian optimization algorithm; a stable prediction time (Pstable) index is defined, and model performance is comprehensively evaluated through one-by-one subject rejection cross validation and a speed migration robustness experiment in combination with a classical index; a modular gait sub-phase prediction system architecture is constructed based on a Simulink platform, and a multi-thread buffer mechanism is integrated to process data streams in real time; and solidifying model parameters to an online reasoning module, and dynamically outputting a prediction result.

The invention relates to the technical field of soft soil area building engineering construction monitoring, and discloses a soft soil area static pressure pile soil squeezing characteristic time effect monitoring and analysis method, which comprises the following steps: arranging a multi-source sensor array in a construction area, and collecting signals such as soil displacement and pore water pressure; and after noise filtering, time sequence alignment and data normalization preprocessing, a layered soil body finite element model is established. A mathematical model with the soil squeezing effect associated with time is constructed in combination with time sequence analysis, a prediction model based on machine learning is used for predicting soil body response, and model parameters are adjusted through an optimization algorithm. And judging whether convergence occurs or not according to a model prediction error so as to dynamically adjust the sensor array and form a closed-loop iterative analysis process. The method can comprehensively and accurately monitor and analyze the soil squeezing characteristic time effect of the static pressure pile in the soft soil area, provides a scientific basis for construction decision making, and guarantees engineering safety and quality.

The invention provides a self-adaptive high-temperature damage model construction method and system. Belongs to the technical field of material science and engineering calculation. The method comprises the following steps: acquiring experimental data of a material in a high-temperature environment, preprocessing the collected data through a machine learning algorithm, and extracting key damage features; constructing a basic damage prediction model based on a deep learning framework, training the model by using the preprocessed data, and enabling the basic damage prediction model to adaptively adjust internal parameters according to prediction errors through a reinforcement learning mechanism; and carrying out global search on thermodynamic parameters in the model by utilizing a genetic algorithm, searching an optimal parameter combination, and dynamically adjusting model parameters in combination with real-time temperature monitoring data. Through integration of deep learning, reinforcement learning, a genetic algorithm and a molecular dynamics simulation technology, the damage evolution process of the material in a high-temperature environment can be predicted more accurately and comprehensively.

The invention belongs to the technical field of bearing fault diagnosis, and discloses a self-aligning roller bearing fault diagnosis method and system, and the method comprises the steps: obtaining operation data and sensor data, carrying out the simulation through a digital twin model, calculating the deviation between model prediction and actual measurement, and carrying out the mode recognition according to the deviation. Digital twin model parameters are dynamically calibrated in a normal mode, fault type identification and bearing positioning are carried out in combination with the calibrated model in an abnormal mode, accurate diagnosis of new installation and bearing faults in a running-in period is realized, dynamic calibration of the digital twin model parameters is realized, normal deviation and abnormal deviation are distinguished, and fault diagnosis accuracy is improved. The accuracy of fault diagnosis in the new installation and running-in period is improved, and fault type recognition and fault bearing positioning are achieved.

The invention discloses a metering error correction method and system based on electric power data acquisition, and the method comprises the steps: detecting an operation state through a self-inspection and data acquisition module, and obtaining environment data, electrical parameters and error information; key feature data are selected and standardized through a data preprocessing module; an LSTM model and an SVR model are constructed through a model establishment and fusion module, and prediction results are fused to generate error compensation output; real-time error compensation is carried out on a combined prediction result by utilizing an ARIMA model and error time sequence processing through a dynamic error compensation module; the data smoothing and evaluation module is used for smoothing the voltage data and evaluating the error compensation effect; an error compensation effect is monitored through a monitoring and optimizing module, and feature selection and model parameters are optimized regularly; through the fault diagnosis and emergency module, an abnormal condition is detected, and a corresponding standby scheme is triggered. According to the invention, the accuracy and anti-interference capability of electric power metering are effectively improved, and the method is suitable for complex and changeable metering environments.

The invention discloses a multivariable fusion power load prediction method and system, and relates to the technical field of load prediction, and the method comprises the following steps: obtaining first data, and synchronizing the first data into second data based on a physical constraint interpolation method; shielding the harmonic dominant frequency band based on the second data, and de-noising the load waveform by combining the real fluctuation of the filtering separation load; a load prediction model is constructed based on the denoised load waveform in combination with a harmonic distortion rate weighted double-flow network, prediction model parameters are corrected in real time, and a load prediction value is output; the real-time correction is a dynamic correction strategy based on wavelet transform. Through a dynamic selection interpolation method, the load data, the meteorological data and the new energy output data can be synchronized on a unified time scale, the synchronism and precision of different data sources can be ensured, high-quality input data is provided for a prediction model, and the reliability of a prediction result is improved.

The invention discloses a grouting method and system for enhancing strip mine slope stability, and belongs to the technical field of slope protection. The grouting method comprises the steps that early-stage data are pre-collected, and an analysis model is established; grouting is designed according to a model analysis result; a pressure-resistant grouting pipe is installed and connected with a grouting pump; grouting parameters are adjusted in real time according to feedback of the sensor; after grouting, all data in the grouting holes are analyzed; outputting a slurry permeation three-dimensional cloud picture, a stress change curve and a stability evaluation index; and storing all monitoring data and model parameters. A cyclic self-adaptive system is formed, it is ensured that the grouting process is efficient and accurate, the overall stability is improved for heterogeneous rock mass, dependence on a static geological report is reduced, and therefore the problem that grouting parameters depend on engineering experience or conservative of the static report is avoided.

The present invention relates to the technical field of coal mine water inrush disaster prevention and control, and relates to an artificial intelligence-based method for identifying a water inrush point in a mine and parameters of a simulation model, comprising the following steps: step 1: establishing a numerical model on the basis of observation data, and determining prior information of parameters to be identified including position coordinates of a water inrush point; step 2: on the basis of the numerical model and the prior information of the parameters, generating a training sample dataset and a test sample dataset for an alternative model; step 3: constructing and training an alternative model neural network; step 4: testing the accuracy of the alternative model; and step 5: executing a simulated annealing algorithm to identify the position of the water inrush point and parameters of a simulation model. By using the method provided by the present invention, the problem in accurate positioning of water inrush points in underground coal mines can be solved, which is of great significance in carrying out scientific disaster management and rescue efforts in a timely manner.

The invention relates to the field of energy management, and discloses an industrial park load prediction method based on artificial intelligence, and the method comprises the steps: collecting historical load, production plan, weather and holiday and festival information through multi-source data; through data preprocessing, a time sequence feature extraction module fusing an attention mechanism and LSTM and a deep learning model integrating a sudden change adaptation module are constructed, and high-precision load prediction is realized. Wherein the abrupt change adaptation module dynamically adjusts model parameters to cope with load abrupt change through sliding window statistical feature monitoring, incremental learning and GAN simulation abrupt change scenes; and a real-time feedback mechanism further optimizes the prediction result, and generates a power dispatching suggestion in combination with a dynamic electricity price strategy. According to the method, the problems of large prediction deviation and poor adaptability of a traditional model in a load sudden change scene are solved, and the efficiency and stability of industrial park energy management are remarkably improved.

The invention discloses an event early warning efficient management method and system based on park management, and relates to the technical field of safety management, and the method comprises the steps: constructing a hexagonal cellular unit layout, generating an equipment registry and a network topology structure, and forming a structured data set through data alignment and sliding window verification; the edge server loads an initial weight, calculates a dynamic weight matrix based on the LSTM network, and generates a high-order feature vector set in combination with the graph neural network; inputting the high-order feature vector set into a space-time prediction model to generate an environment evolution trend prediction value, fusing network topology and BIM model parameters to calculate a dynamic risk index, and triggering graded early warning; the command center analyzes the early warning signal to generate a fusion visual interface, historical cases are matched through federal learning, a resource allocation scheme is optimized, and an optimal disposal strategy is output through Monte Carlo simulation. By dynamically adjusting the weight of the sensor, the weight of the infrared sensor in the high-temperature-sensitive area is increased, and the abnormality capturing efficiency is improved.