2348 results about "Real time prediction" patented technology

The invention provides a computing resource optimization method and system for analysis tasks, and belongs to the technical field of computers.The computing resource optimization method comprises the steps that the priority of each analysis task is determined according to feature information of each analysis task and current available computing resource state information of the system, and a priority queue is generated; obtaining a task load prediction value according to the historical task load data and the real-time system state data; according to the type of the analysis task, the data scale and the data source position, the analysis task with the real-time requirement higher than a preset standard is allocated to an edge node to be executed, and resource allocation of the edge node is dynamically adjusted according to a task load predicted value; and dynamically allocating available resources from the computing resource pool according to the priority queue and the task load prediction value so as to execute the plurality of parallel analysis tasks. According to the resource management method based on task priority dynamic adjustment, task load prediction and edge computing optimization scheduling, real-time prediction and dynamic self-adaptive scheduling of computing resources are achieved.

The invention belongs to the field of intelligent traffic systems, and relates to a multi-mode space-time traffic flow modeling method supporting large-scale road network real-time prediction, and the method comprises the steps: firstly designing a space-time prediction framework facing a dynamic traffic network, and then carrying out the training to obtain a final prediction model; the space-time prediction framework comprises a data embedding layer, a space-time coding module and a deep modeling and output module based on an expert hybrid mechanism; the data embedding layer comprises two parallel channels of time embedding and spectral domain space embedding and a time-space data fusion module; the space-time coding module comprises a block-level sparse time attention module, a space attention-message passing module and a weighted fusion layer which are parallel; the deep modeling and output module based on the expert hybrid mechanism comprises an MoE dynamic expert modeling module, a full-connection mapping module, a jump connection layer and an output layer; according to the design, the response speed, the prediction precision and the cross-regional adaptive capacity of the model in a high-heterogeneity scene are improved.

The invention provides an electromagnetic-assisted nickel-based alloy laser melting deposition crack inhibition method and system, relates to the technical field of automatic control, constructs an intelligent control system, carries out deep fusion and real-time prediction on multi-source heterogeneous sensing data, generates a prediction result, sets a reward function and an action-value network, and improves the performance of the nickel-based alloy laser melting deposition crack inhibition system. Solving to obtain an optimization control strategy and analyzing and outputting the optimization control strategy; and constructing a three-dimensional electromagnetic field distribution matrix. In combination with real-time feedback of the shape of the molten pool, the molten pool is stimulated to flow directionally and oscillate non-linearly according to the resonance effect, oscillation parameters are obtained, thermodynamic behaviors corresponding to the molten pool are predicted according to the oscillation parameters, and a thermodynamic prediction result is fed back to the central control unit; the method comprises the following steps: detecting the surface appearance of a part, extracting defect characteristic parameter vectors, inverting defect influence factors, generating a dynamic regulation and control curve, dispersing the dynamic regulation and control curve into multi-dimensional control instruction vectors, updating a preset regulation and control model, outputting optimized regulation and control model parameters, and carrying out crack suppression operation again.

The invention discloses a multi-modal diagnosis and treatment data analysis and auxiliary diagnosis method for internet medical treatment, which comprises the following steps: S1, performing feature extraction and fusion on multi-modal diagnosis and treatment data through a nonlinear mapping algorithm based on quantum state optimization; s2, constructing a self-adaptive dynamic relation network, and capturing a complex interaction relation between cross-domain features in the diagnosis and treatment data; s3, fusing implicit features in the sparse medical data with semantic association; s4, optimizing the diagnosis and treatment decision process by applying a causal inference technology; s5, processing anomalies in the diagnosis and treatment data by using a fusion generative adversarial network and sparse tensor decomposition technology; s6, constructing a decentralized encrypted federated learning network to protect the privacy of the patient; s7, predicting a disease evolution path in real time to form dynamic decision support; and S8, performing accelerated analysis on the high-dimensional feature mode in the diagnosis and treatment data in combination with quantum calculation. The method has the advantages of high data fusion capability, high diagnosis precision and high real-time decision support capability.

The invention relates to the technical field of medical rehabilitation, in particular to an artificial intelligence-based orthopedic full-cycle rehabilitation management system, which comprises a rehabilitation demand evaluation module, a three-dimensional pathological modeling module, a multi-modal training generation module, a rehabilitation effect prediction module, a dynamic parameter adjustment module and a full-cycle management module. Multi-dimensional data such as bone mineral density, joint motion range, gait mechanics and psychological state of a patient are collected, an artificial intelligence algorithm is combined for analysis, a personalized rehabilitation scheme is generated, and rehabilitation parameters are predicted and adjusted in real time. The system can dynamically correct the training intensity, the nutritional supplement dosage, the psychological intervention mode and the rehabilitation schedule, and the safety and reliability of data are guaranteed through the block chain technology; according to the system, biomechanics, nutriology, psychology and environmental factors are comprehensively fused, the system has strong adaptive ability and full-period management ability, real-time optimization can be performed according to the rehabilitation process of a patient, and the rehabilitation effect of the orthopedics department and the satisfaction degree of the patient are remarkably improved.

The invention provides an optical energy storage regulation and control method and system based on multi-time scale rolling optimization in the technical field of energy storage regulation and control, and the method comprises the steps: S1, inputting a 24-hour electricity price prediction curve, a 24-hour photovoltaic output prediction curve, a 24-hour load prediction curve and an initial SOC into a day-ahead prediction model, a 24-hour SOC reference trajectory and a standard charging and discharging plan are obtained; s2, collecting feedback data, and inputting the 24-hour SOC reference trajectory, the standard charging and discharging plan and the feedback data into the intra-day prediction model to obtain a corrected charging and discharging plan; and S3, setting an SOC dynamic limit value, collecting a real-time electricity price and real-time photovoltaic output, and inputting the corrected charging and discharging plan, the SOC dynamic limit value, the real-time electricity price, the real-time photovoltaic output and the actual SOC into the real-time prediction model to obtain a charging and discharging instruction and frequency modulation output power. The method has the advantages that the precision, economical efficiency and power grid adaptability of energy storage regulation and control are greatly improved, and the attenuation speed of the service life of the battery is greatly delayed.

The invention discloses a sewage plant total nitrogen concentration real-time prediction and process regulation and control method based on an optimization integration algorithm, and belongs to the technical field of environment monitoring and treatment. According to the method, sewage plant data are monitored and collected, a sliding window and a time sequence are combined to analyze and clean the data and reconstruct features, total nitrogen concentration strong correlation variables are screened, data quality is standardized and optimized, a plurality of machine learning algorithms are adopted to construct a prediction model, and an optimal model is optimized through cross validation and performance evaluation. The robustness is improved by global parameter adjustment in combination with optimization algorithms such as a particle swarm, process schemes such as aeration intensity and carbon source adding are generated through multi-objective optimization after containerization deployment, and a whole-process intelligent management and control system is constructed by integrating virtual verification, graded early warning and a self-adaptive feedback mechanism. According to the method, the problems of detection lag, insufficient model generalization ability, regulation response delay and the like of a traditional method are solved, and the operation energy consumption and the medicament cost are remarkably reduced while it is guaranteed that the effluent quality stably reaches the standard.

The invention discloses a ship arrival and departure prediction method based on a multi-modal neural network and an adaptive LSTM, and the method comprises the following steps: S1, collecting data, including ship position data, environment data and historical arrival and departure data; s2, data preprocessing: cleaning and standardizing the data; s3, feature extraction optimization is carried out on various types of data; s4, performing multi-modal data fusion, including data splicing fusion, data weighted fusion and data deep fusion; s5, training and constructing a self-adaptive LSTM model, wherein the self-adaptive LSTM model predicts the port entering and leaving behaviors and time of the ship; s6, evaluating and optimizing a self-adaptive LSTM (Long Short Term Memory) model; s7, performing real-time prediction and scheduling; and S8, dynamically predicting and providing scheduling suggestions. Based on the ship position data, the environment data and the historical port entering and leaving data, the multi-mode neural network is used for data fusion, the port entering and leaving prediction model based on the self-adaptive LSTM is constructed, and the prediction accuracy and real-time performance are improved.

The invention provides a multi-modal large model construction and operation method and system for reservoir dam safety. The method comprises the following steps: constructing a training set, wherein the training set comprises a single sample; multi-modal data as model input: sensor data arranged on the dam, historical hidden danger records, engineering technology documents formed in various stages of planning, design, construction and operation of the dam, information resources related to dam safety, geometric and material information of the BIM model, timestamps and spatial position information; the probability value of occurrence of the hidden danger event serving as the training label is obtained; the training set is adopted to train a multi-mode pre-training model based on a Transform framework; performing real-time prediction and early warning on potential safety hazards of the dam; and calculating the actual influence degree, the comprehensive risk score, the dynamically set early warning threshold and the emergency measure suggestion of the hidden danger event according to the probability value of whether the hidden danger event occurs or not output by the model. The scientificity and effectiveness of dam safety monitoring are comprehensively improved.

The invention relates to the technical field of welding, in particular to a corrosion steel welding cooperative control method and system. Comprising the following steps that welding seam geometric parameters, molten pool dynamic characteristic parameters and welding heat input parameters in the corrosion steel welding process are collected in real time through a multi-dimensional sensor array; constructing a corroded steel welding seam feature space model based on the welding seam geometric parameters, and determining material corrosion grade distribution and mechanical property parameters of a welding seam area in combination with a preset corroded steel material database; a molten pool form evolution prediction model is established through an adaptive Kalman filtering algorithm by utilizing the dynamic characteristic parameters of the molten pool and the welding heat input parameters, and the solidification behavior and the welding seam forming trend of the molten pool are predicted in real time; according to the material corrosion grade distribution, the mechanical property parameters and the molten pool forming trend, a dynamic adjustment strategy of the welding process parameters is generated through a multi-objective optimization algorithm; the reliability and safety of the corrosion steel welding joint can be improved.

The invention belongs to the technical field of electromechanical equipment intelligent monitoring and operation and maintenance management, and discloses an electromechanical system health monitoring and optimization method based on artificial intelligence, and the method comprises the steps: building a digital twin architecture based on a simulation model, reasonably arranging a multi-source sensor, and carrying out the optimization of the health of an electromechanical system; key equipment is fully covered; the system acquires operation data at high frequency and synchronously transmits the operation data to the cloud through multiple channels to realize real-time monitoring; the collected data is cleaned and preprocessed to remove abnormities so as to ensure the data quality; inputting the cleaned data into a neural network, extracting features, and identifying an operation trend and a fault mode; establishing a prediction model in combination with historical data, and constructing a maintenance knowledge base; the equipment state is predicted in real time, and early warning is dynamically generated; through virtual-real synchronization, health and carbon emission states are mapped in real time, equipment operation is intelligently optimized, and cloud closed-loop monitoring is continuously iterated.

The invention discloses a safety monitoring system of a liquid cooling over-charging pile, and relates to the technical field of over-charging pile monitoring, the system comprises a data acquisition module, a data processing and analysis module, a dynamic model construction module, a temperature prediction module and a safety early warning module; according to the method, the dynamic model is constructed through the long short-term memory network LSTM, the complex nonlinear relation and the time sequence dependence of the multi-dimensional data are mined by using the gating mechanism of the dynamic model, the accurate characterization of the operation state of the liquid cooling over-charging pile is realized, the actual operation state of the equipment can be accurately described, the temperature data time sequence modeling is performed through the LSTM, and the accuracy of the temperature data time sequence modeling is improved. Parameters such as multi-source temperature and cooling liquid flow are fused, real-time prediction of the temperature change trend is achieved, the defect that a traditional algorithm is insufficient in temperature time sequence dependence capture is overcome, temperature abnormity can be recognized in advance, a safety threshold value is dynamically adjusted through a fuzzy logic algorithm, and self-adaptive threshold value adjustment is achieved in combination with parameters such as charging power. The problem that a traditional fixed threshold value is poor in adaptability is solved, and the early warning accuracy is improved.

The invention relates to a switching power supply control method, system and device and a storage medium, and the method comprises the steps: monitoring the voltage and current of the output end of a switching power supply, and the rate and amplitude of load change, and obtaining the real-time data of the load change; according to the real-time data of the load change, the change of the current load is identified, and a load change mode is obtained; dynamically adjusting operation parameters of the controller according to the load change mode; based on the real-time data of the load change, predicting the load change trend of the switching power supply in real time by using a pre-trained load prediction model to obtain a prediction result, and generating a disturbance compensation signal based on the prediction result; and adjusting the switching state of a switching circuit of the switching power supply according to the control parameter adjusted by the controller and the disturbance compensation signal. The switching power supply has the effect of improving the stability of the switching power supply.

The invention relates to the technical field of agricultural production data prediction, and discloses a tobacco plant number prediction method based on deep learning, and the method comprises the steps: collecting tobacco field environment data through a soil humidity sensor, a weather sensor, a laser radar point cloud sensor, a multispectral sensor and other multi-source agricultural sensors, carrying out the multi-source feature fusion through a dynamic feature selection network, and generating an environment feature vector. And inputting the model into a multi-modal time sequence prediction model adopting a space-time attention mechanism, and modeling a tobacco plant growth trend. And constructing an adaptive weighted regression model according to a prediction result, and dynamically adjusting the weight by using an improved particle swarm optimization algorithm. And constructing a hierarchical correction model to optimize prediction data. A real-time prediction updating method is provided, and newly-added data can be processed. The multispectral sensor data is subjected to dimension reduction, correlation modeling and interpolation fusion processing. According to the method, multi-source data is fully utilized, the accuracy, real-time performance and stability of tobacco plant number prediction are improved, and powerful support is provided for tobacco planting management.

The invention discloses a meta-learning Bayesian optimization prediction method for multi-modal displacement of a tank body of a photo-thermal power station, and the method comprises the steps: collecting the data of displacement, temperature, vibration and the like through a multi-modal sensor, separating a displacement sequence into trend, season and residual components through STL decomposition, and carrying out the fusion with the data of the sensor, thereby constructing a 6-dimensional spatial-temporal characteristic matrix; a two-way LSTM-attention mechanism model is adopted, time sequence dependence is captured in a two-way mode, and key cross-modal features are dynamically weighted. And introducing meta-learning-guided working condition adaptive Bayesian optimization: pre-training a meta-model by using a historical working condition to establish a mapping relationship between working condition characteristics and hyper-parameters, dynamically dividing working conditions by real-time data, then activating a corresponding Gaussian sub-model, initializing a search space through meta-learning prior, and optimizing hyper-parameters in combination with an adaptive acquisition function. The test set evaluates the performance of the model through RMSE and MAPE, and finally three-way displacement real-time prediction and safety early warning are achieved. The prediction precision and the dynamic adaptability of the tank body of the photo-thermal power station under the complex working condition are remarkably improved.

The invention provides a weather forecast learning system based on an artificial intelligence algorithm, and the system comprises a data access collection module which collects a multi-source data set; the data fusion and processing module is used for carrying out data fusion and processing to obtain a multi-source fusion data set; the AI model design module is used for constructing a multi-model collaborative architecture and carrying out multi-model parallel training and optimization; the model fusion module is used for carrying out multi-model weighted fusion to obtain an AI model; the real-time prediction module is used for updating a prediction result according to the real-time data; the visualization and interpretability module is used for designing a visualization interface and carrying out interpretability verification; and the evaluation and iteration module is used for carrying out comprehensive evaluation and continuous improvement on a prediction result in combination with evaluation indexes. According to the method, accurate and reliable observation data can be obtained, massive meteorological data are efficiently processed by combining an artificial intelligence algorithm, trend analysis and prediction are automatically carried out, and a reliable prediction result is generated.

The invention discloses a lower limb motion data real-time analysis method and system applied to rehabilitation guidance, and the method comprises the steps: calling a personalized model according to user information, converting real-time motion data into historical motion feature vectors, inputting the historical motion feature vectors into a real-time prediction module, and generating multiple frames of future motion feature vectors through coding and decoding; calculating a deviation value between each future motion feature vector and a preset threshold range to obtain a motion deviation value, calculating a motion risk proportion of each future motion feature vector, performing weighted fusion to obtain a motion risk probability, and generating an equipment parameter adjustment value and pose adjustment information through reverse mapping; and finally, according to risk probability assessment training stage promotion, synchronously displaying correction information and adjusting equipment operation parameters. According to the method, the dynamic model is constructed through pre-training, real-time prediction of future motion features is realized in combination with the lightweight LSTM, and the predictability and personalized adaptation ability of rehabilitation guidance are improved.

The invention relates to the technical field of content pushing, and discloses a personalized content real-time pushing method based on a user portrait. The method comprises the steps of collecting a user behavior data set and historical interaction records, extracting real-time behavior characteristics and generating a behavior dynamic sequence; identifying behavior delay features and converting the behavior delay features into interest feedback lag; determining a dynamic influence weight in combination with an interaction relationship between the static attribute characteristics of the user and the real-time behavior characteristics; predicting the interest tendency of the user in real time by using the hysteresis and the dynamic influence weight, and generating a fluctuation deviation value; and when the deviation value exceeds a preset threshold value, triggering dynamic updating of the user portrait and adjusting a pushing strategy. According to the method, the user behavior dynamic change is captured, the behavior feedback hysteresis is considered, and the feature influence weight is dynamically adjusted, so that the user interest is accurately predicted in real time, the user portrait and the pushing strategy can be flexibly updated, the individuation and timeliness of content pushing are improved, and the real-time requirement of the user is met.

The invention belongs to the technical field of intelligent driving, and particularly relates to an intelligent driving method and system with a body, and the method comprises the steps: fusing the V2X data of a vehicle-mounted laser radar, a millimeter wave radar and a road side unit, and obtaining the vehicle driving information; constructing a centimeter-level dynamic environment model by adopting a space-time alignment algorithm, and realizing multi-source data space-time synchronization and obstacle real-time prediction; designing a hierarchical reward function including security, efficiency and comfort rewards based on a user risk cognition mechanism, and generating an end-to-end reinforcement learning decision in combination with a Transform architecture; optimizing the dynamic environment model through reinforcement learning decision; inputting vehicle driving information into the optimized centimeter-level dynamic environment model, and predicting obstacles and vehicle tracks in a vehicle driving path; an MPC-Hybrid feedforward-feedback control system is constructed, a feedforward module is adopted to calculate a front wheel turning angle and acceleration parameters according to obstacles in a vehicle driving path and a vehicle track, and a feedback module is adopted to optimize transverse and longitudinal errors; controlling the driving direction of the vehicle according to the front wheel turning angle and the acceleration parameter transverse and longitudinal errors; according to the invention, through deep fusion of vehicle-road collaborative perception and reinforcement learning decision, the reliability and adaptability of the automatic driving system are significantly improved.

The invention discloses a control method and device for heat dissipation equipment, and relates to the technical field of computers, and the method comprises the steps: employing a load dynamic perception and prediction model cooperative control technology, building a dynamic correlation model of a load and heat dissipation, and achieving the mode upgrade of a heat dissipation system from lagging regulation to advanced response. A control instruction is generated by predicting a load change trend in real time, and a dual-mode protection mechanism is combined, so that energy efficiency loss caused by traditional temperature control delay is avoided, system stability and energy efficiency performance are optimized, an efficient and energy-saving active heat dissipation solution is provided for high-load equipment, and the service life of the system is prolonged. The problems of low control efficiency of the heat dissipation equipment and energy efficiency loss caused by delayed adjustment in related technologies are solved, and the technical effects of improving the control efficiency of the heat dissipation equipment and reducing energy consumption are achieved.

The invention discloses a self-optimized injection molding prediction and monitoring method. The method comprises a data acquisition module, a data analysis module, a remote monitoring module, an appearance detection module, a fault processing module and a process adjustment module. And the data analysis module trains a process parameter prediction model and a defect prediction model by using historical production data to realize real-time prediction of key process parameters and early warning of defects. And the process adjustment module adaptively optimizes the process parameter setting of the injection molding machine according to the prediction result, so that the stability of the product quality is ensured, and human intervention is reduced. And the remote monitoring module is connected with a smart phone and a PC terminal of a user through the Internet, so that a technician can check the production state at any time and intervene in time when the system cannot be automatically adjusted, long-time shutdown of equipment and damage to the product quality are avoided, and the stability and efficiency of a production line are further improved.

The invention discloses an elevator taking optimization system and method based on artificial intelligence, and relates to an intelligent elevator control technology combining multi-mode perception, prediction model dynamic adjustment and reinforcement learning scheduling strategies. The method comprises the steps that firstly, elevator running states, environment information and passenger behavior data are collected through multiple types of sensors, and multi-modal scene perception vectors are generated; and secondly, elevator loads and floor requirements are predicted in real time through a dynamically-adjusted prediction model, the reward function weight in reinforcement learning is adjusted in a self-adaptive mode on the basis, and accurate response and intelligent scheduling of different scenes are achieved. The system forms a perception-prediction-scheduling closed loop, significantly reduces the waiting time of passengers through multi-objective optimization, reduces the energy consumption, and improves the safety and emergency processing capability. The method is suitable for various high-rise building elevator group control systems, and has high intelligence, flexibility and wide application value.

The invention relates to the technical field of computer storage, and discloses a computer memory data processing system, which comprises the following modules: an intelligent cache prefetching module, which is used for predicting a future memory access address based on deep learning and reinforcement learning technologies and loading data into a cache in advance; the heterogeneous storage management module is used for dynamically classifying the data based on the data popularity value and realizing migration of the data among different storage media; and the storage consistency guarantee module is used for managing the data consistency among different storage layers through the improved MES I protocol. Cache prefetching is optimized by introducing deep learning and reinforcement learning technologies, and by predicting a future memory access address in real time and loading data in advance, the system effectively reduces the cache missing rate, improves the cache hit rate, remarkably reduces the memory access delay, and improves the memory access efficiency. And the system can keep more efficient data access performance in a large-scale data processing scene.

The invention discloses a hybrid energy storage system charging prediction and energy regulation and control method, and relates to the technical field of power systems and energy management, and the method comprises the following steps: obtaining the real-time operation data of an optical storage charging system; by adopting the dynamic regulation and control algorithm and the self-adaptive learning mechanism, the light storage charging system can predict and correct the power change of photovoltaic power generation in real time, the photovoltaic power is utilized to the maximum extent, the power consumption of a power grid is reduced, and the energy utilization efficiency and the economic benefit are improved. Meanwhile, the dynamic charging and discharging strategy based on error correction effectively avoids the problems of overcharging and deep discharging of the battery, prolongs the service life of the battery, and improves the long-term stability and safety of the system. The system optimizes the charging and discharging plan according to the power grid electricity price fluctuation, and combines an early warning mechanism to monitor the battery state in real time, thereby reducing the operation cost, reducing the potential safety hazard, and guaranteeing the efficient and reliable operation in the whole life cycle.

The invention relates to the technical field of optical fiber communication, and discloses an optical fiber connector optical loss automatic detection method, device and equipment, and the device comprises a modulation signal dynamic adjustment module, an environment parameter adaptive calibration module and a deep learning prediction model. The modulation signal dynamic adjustment module enhances the separability of optical fiber signals through dynamic phase modulation, and the detection precision is improved; the environmental parameter self-adaptive calibration module compensates the influence of temperature, humidity and vibration on optical signals in real time, and the stability of the system is improved; the deep learning prediction model is based on multi-dimensional data fusion and weight dynamic optimization, high-precision prediction of the performance degradation trend of the optical fiber connector is achieved, closed-loop logic from data collection to real-time prediction is formed, experiments show that the detection precision of the method can reach 99.2%, the prediction accuracy is improved to 93% or above, the optical loss calculation error is reduced to 1%, and the method can be widely applied to the field of optical fiber connectors. And meanwhile, the response speed is increased to millisecond level. The method is suitable for monitoring and predicting the optical fiber performance in a complex environment, and the precision and the real-time processing capability are remarkably improved.

The invention discloses a collaborative unmanned aerial vehicle cluster path planning and scheduling system, and particularly relates to the technical field of unmanned aerial vehicle intelligent control, and the system comprises a multi-mode sensing unit which is composed of a heterogeneous sensor array composed of LiDAR, binocular vision and millimeter wave radar, and an output dynamically updated three-dimensional Gaussian mixture map; the decision control unit is used for implementing double-layer optimization of mixed integer programming task allocation and artificial potential field path planning; the dynamic communication network adopts a hybrid networking protocol of TDMA backbone nodes and 802.11 ax terminal nodes; an energy management module; aiming at the insufficient environment perception and dynamic modeling capability in the prior art, the method achieves the effects that the centimeter-level positioning precision and the dynamic obstacle recognition rate are greater than 92%, the environment model is delayed and compressed to be within 200ms, the response speed is increased by 5 times by setting multi-modal sensor fusion, constructing a dynamically updated 3D Gaussian mixture map and combining an LSTM network to predict the obstacle trajectory in real time, and the dynamic obstacle recognition rate is greater than 92%. And the obstacle avoidance reliability in a complex scene is obviously enhanced.

The invention relates to the technical field of pressure detection, in particular to an oil well pressure anomaly detection method and system based on deep learning, and the method comprises the following steps: collecting a control signal and pressure data, calculating a slope difference and fluctuation to mark a sudden change point, extracting a multi-dimensional feature group to generate a training sample, and constructing a deep learning model to recognize pressure difference anomaly. The oil well pressure abnormal time period is predicted in real time. According to the method, accurate attribution of abnormal fluctuations in the oil well operation process is achieved by constructing the time sequence corresponding relation between control signals and pressure difference sudden change, the target pertinence of pressure change recognition is improved, a sliding structure extracts multi-dimensional dynamic characteristics, the capacity of capturing wellhead or underground pressure micro-amplitude abnormity is enhanced, and the training sample is combined with a control behavior label, so that the accuracy of the abnormal fluctuations in the oil well operation process is improved. The distinguishing capability of the model to different abnormal forms is optimized, the adaptability under complex working conditions is improved, a real-time prediction mechanism ensures that oil well pressure state recognition has continuity, rapidness and high confidence, and risk early warning and operation guarantee in the oil extraction process are effectively supported.

The invention discloses a cooling cooperative control method, and the method comprises the steps: obtaining a temperature parameter, a load state parameter and a cooling system parameter of a transformer, and forming a parameter set; constructing a gating circulation unit model, and predicting thermal load distribution of each region of the transformer by adopting the parameter set through the gating circulation unit model; and performing real-time distribution on cooling resources by adopting a dynamic region distribution algorithm. According to the cooling cooperative control method, temperature and load changes of all areas of the transformer are predicted in real time through a gating circulation unit model; intelligent distribution of cooling resources is achieved through a dynamic area distribution algorithm, the cooling cooperative control method can dynamically adjust the flow speed of a cooling medium and the rotating speed of a fan according to real-time temperature and load changes, it is ensured that all areas of a transformer obtain appropriate cooling resources, and extra cooling is preferentially provided for areas with the temperature abnormally increased; and a cooling strategy is optimized through multiple iterations in combination with historical operation data, so that the response speed and accuracy of the cooling system are remarkably improved.

The invention discloses a real-time prediction method for muck improvement parameters of a shield under-crossing existing tunnel, and the method comprises the steps: carrying out the refined preprocessing of non-under-crossing section shield tunneling data, enabling an obtained data set to be used for the training of a machine learning integrated prediction model, and obtaining a muck improver dosage integrated prediction model; meanwhile, slump experiments are carried out for different types of stratums, the optimal water content and foam injection ratio parameter range is determined, and a reliable decision basis is provided for a shield driver. The quality of a data set and the prediction precision of a machine learning model are remarkably improved through a finer data preprocessing means; the machine learning integrated prediction model fuses learning capabilities of different machine learning models, so that the prediction precision of the model is further improved; and machine learning and experimental means are combined to realize accurate and rapid prediction of the use amount of the modifier, so that safe and smooth tunneling of the shield under the existing tunnel is ensured, and the method has certain timeliness and economical efficiency.

The invention relates to a data-driven smelting control optimization method, and belongs to the technical field of smelting control. Comprising the steps that working condition classification is carried out through historical operation data of a current smelting furnace by adopting clustering analysis, association rule mining is carried out in each cluster, and a parameter rule base is established; obtaining the working condition category of the current smelting furnace temperature through the parameter rule base according to the smelting furnace operation production data; according to the current furnace temperature characteristic data and the working condition category, the change of the furnace temperature is predicted in real time; according to the current working condition type and the predicted furnace temperature data, a control parameter combination with the optimal energy consumption is selected from the parameter data set; and monitoring the state of the smelting furnace in real time, predicting the furnace temperature, adjusting the control parameter combination of smelting operation, and performing early warning through the furnace temperature fluctuation degree. Furnace temperature prediction is carried out through working condition recognition, then control parameter combinations are screened according to prediction results and working condition categories, and therefore the smelting process is regulated and controlled to achieve energy-saving optimization.