6830 results about "Dynamic models" patented technology

The present invention provides an energy storage synchronous coordinated management method and system based on virtual synchronization technology. The method comprises: selecting an appropriate energy storage device according to the requirements of a power grid and collecting related parameters; building a dynamic model of a virtual rotor, and setting rotational inertia of the rotor and torque parameters; building an adaptation layer; configuring a line monitoring apparatus, carrying out a safety operation when the fault occurs in the adaptation layer, or carrying out dynamic control and predictive planning in the absence of faults. The present invention utilizes intelligence to reduce reliance on experience, and enables dynamic optimization to improve the automation level of energy storage control.

The invention belongs to the technical field of electromechanical equipment operation and maintenance, and discloses an electromechanical equipment self-adaptive intelligent early warning system based on multi-source sensing data. The system is composed of a multi-source sensing module, an edge data acquisition and preprocessing module, a data cleaning and multi-dimensional feature extraction module, an equipment state dynamic modeling module, an intelligent fault prediction and trend analysis module, a self-adaptive early warning threshold generation and dynamic adjustment module, and an intelligent decision and remote cooperation module. The system is composed of a multi-source sensing module, an intelligent low-carbon operation and maintenance management and control module and a digital twin system integration and full-period mapping module, multiple sensors are deployed through the multi-source sensing module to acquire multi-dimensional data of equipment, cleaning and calibration are performed through the edge data acquisition and preprocessing module, deep processing is performed through the data cleaning and multi-dimensional feature extraction module, and multi-dimensional feature extraction is performed through the multi-source sensing module. The data integrity and accuracy are ensured; and data are quickly transmitted among the modules, so that the monitoring system can accurately present the running state of the equipment in real time.

The invention discloses a risk control credit monitoring method based on cloud computing, and belongs to the technical field of cloud computing, and the risk control credit monitoring method based on cloud computing comprises the following steps: S1, collecting user transaction data and behavior track data in real time; s2, cleaning and standardizing the data; s3, constructing a multi-dimensional risk assessment model based on machine learning; s4, dynamically generating a credit score according to the risk characteristics; s5, triggering an early warning mechanism for abnormal transactions in real time; and S6, generating a visual risk control report and updating a monitoring strategy. According to the method, multi-source heterogeneous data are integrated through federated learning, hierarchical privacy protection is realized in combination with homomorphic encryption and differential privacy, risk assessment real-time performance is improved by using a hybrid cloud resource scheduling and dynamic model updating technology, and a compliance audit closed loop is constructed based on a block chain and interpretability analysis.

The invention discloses a heterogeneous double-mechanical-arm tail end pose mapping method, system and device and a storage medium, and the method comprises the following steps: S1, obtaining the angular velocity, position and tail end pose information of each joint of a main mechanical arm, and calculating the spatial position and pose of the tail end of the main mechanical arm; s2, motion trail data in the teaching process of the main mechanical arm is collected, a long-short-term memory network model is established, the pose of the future time step length is predicted, and the motion trail of the auxiliary mechanical arm is planned; s3, data information is transmitted, and the problem of communication delay is solved; s4, the pose information of the tail end of the main mechanical arm is solved, the joint angle is adjusted, and the error is adjusted in combination with proportional-integral-differential control; s5, the position and speed signals are filtered, a mechanical arm dynamic model is established, an adaptive control law is designed, and a trajectory smoothing algorithm is adopted; and S6, establishing a virtual obstacle map, and limiting a safe working space. According to the method, comprehensive and accurate mapping and prediction of the tail end poses of the heterogeneous double mechanical arms are achieved.

The invention provides a surface crack detection method and system based on array surface waves, and relates to the technical field of crack detection, and the method comprises the following steps: carrying out noise reduction and enhancement processing on an original signal collected by a workpiece, establishing an environment compensation mechanism, extracting a key sonic path distance characteristic and a crack characteristic from the processed signal, the features are input into a neural network to predict the crack size, theoretical verification is carried out in combination with a microdynamics model, and a final detection result is obtained through fusion of a prediction result and a theoretical result.

The invention relates to the technical field of intelligent building data processing, and provides a BIM-based bridge collision detection method, which is used for solving the problem of collision early warning lag caused by space-time dislocation of a static model and real-time monitoring data in bridge construction. A comprehensive point cloud data set with a unified space-time reference is generated through multi-source data dynamic fusion and a quantum optimization algorithm, a self-evolutionary dynamic BIM model is constructed, and digital mapping of millimeter-level precision of a construction scene is achieved. A collision risk prediction model is constructed based on causal reasoning and multi-physics field coupling, a multi-dimensional relationship among construction parameters, environment variables and structural response is associated, and a linkage effect caused by mechanical motion is predicted. According to the method, the time dimension limitation of a traditional static model is broken through, full-process closed-loop control of perception-analysis-decision-verification is formed, the real-time performance of collision early warning and the controllability of construction safety are remarkably improved, and the method is suitable for high-precision construction scenes such as complex bridge rotation and cantilever casting.

Provided in the present invention are an energy-storage synchronous and coordinated management method and system based on a virtual synchronization technique. The method comprises: selecting a suitable energy storage device on the basis of grid requirements, and collecting parameters of the energy storage device; on the basis of the parameters of the energy storage device, establishing a dynamic model of a virtual rotator, and setting a rotator rotational inertia and a torque parameter; establishing an adaption layer, so as to realize the interaction between the virtual rotator and a physical energy storage device; providing a line monitoring apparatus in a grid so as to collect real-time data of the energy storage device and detect, in real time, whether a fault occurs, and the adaption layer executing a safety operation when a fault occurs, and performing dynamic control and prediction planning when no fault occurs, so as to solve the problem of the uneven distribution of resources; and the adaption layer determining whether grid nodes and the real-time data of the energy storage device are normal, and if the grid nodes and the real-time data of the energy storage device are abnormal, performing detection and setting again, and if the grid nodes and the real-time data of the energy storage device are normal, saving a log and ending the process. In the present invention, an intelligent means is used to replace a conventional experiential control method, such that the dependence on experience is reduced, and dynamic optimization can be performed to increase the level of automation of energy storage control.

The invention discloses a psychological health assessment system based on big data. The psychological health assessment system comprises a data acquisition module, a data processing module, a dynamic modeling module, a risk assessment module and an intervention generation module. The data acquisition module acquires a heterogeneous data source of a user in real time through a distributed interface and generates structured data; the data processing module receives the structured data to generate a multi-modal feature vector; the dynamic modeling module receives the multi-modal feature vector, constructs a user psychological state atlas through a graph neural network, and updates a topological structure of the atlas based on a time sliding window to generate a psychological state evolution signal; the risk assessment module receives the psychological state evolution signal and forms an early warning signal; and the intervention generation module receives the early warning signal, generates a personalized intervention instruction and transmits the personalized intervention instruction to the user terminal. The psychological health assessment system based on the big data can solve the problems of calculation redundancy, poor geometric adaptability and low knowledge distillation efficiency when a traditional attention mechanism processes a circular region.

The invention provides an intelligent sewage treatment process optimization method and system. The method comprises the following steps: continuously collecting the flux attenuation rate, transmembrane pressure difference and pollutant spectrum data of a ceramic membrane, carrying out time sequence analysis, constructing a pollution accumulation dynamic model, and predicting a critical threshold value; when a threshold value is reached, triggering an ultrasonic cavitation device, adjusting high-frequency vibration to generate a microbubble group, stripping pollutants by utilizing shock waves, and calculating membrane flux recovery data; fusing the spectral data and the recovery data to extract an actual pollution stripping efficiency value, and comparing a theoretical value to obtain a difference proportion; if the actual value is lower than the theoretical value, the cleaning period is shortened, the fluid pressure is increased to strengthen the stripping effect, and meanwhile, dynamic model parameters are reversely corrected based on the pressure adjustment amplitude to form closed-loop regulation and control. According to the invention, intelligent prediction, self-adaptive cleaning and closed-loop optimization of ceramic membrane pollution are realized, the sewage filtering efficiency is improved, and the energy consumption and cleaning cost are reduced.

The invention relates to the technical field of anomaly detection, and discloses a context-aware-driven multi-dimensional anomaly detection early warning method. The method comprises the following steps: collecting real-time context data in a target monitoring scene, and generating an initial feature set containing an environment parameter sequence and a behavior pattern map; a first detection model and a second detection model matched with the scene type are constructed according to the scene types, the first model comprises a dynamic correlation function of environment indexes and abnormal probabilities, and the second model comprises a nonlinear mapping rule of behavior characteristics and risk levels; and based on the real-time context deviation degree and the characteristic fluctuation coefficient, a target model is triggered to generate a dynamic early warning instruction, and the dynamic early warning instruction is pushed to an execution module to adjust a trigger threshold of an abnormal response strategy or a priority of a risk disposal process. According to the method, multi-dimensional data is combined, the adaptability and accuracy of anomaly detection are improved through dynamic model triggering and response strategy adjustment, and the method is suitable for various monitoring scenes.

The invention relates to the technical field of operation and maintenance management, in particular to a digital twin hydraulic engineering operation and maintenance monitoring system and method. The method comprises the following steps: acquiring water conservancy project related data; performing three-dimensional geometric feature extraction on the related data of the water conservancy project to obtain three-dimensional geometric data of the water conservancy project; hydrological structure data association is carried out according to the three-dimensional geometric data of the water conservancy project, and a digital twinborn basic model is generated; global optimization is carried out on the digital twinborn basic model, and a dynamic digital twinborn hydraulic engineering model is generated; performing hydraulic engineering remote monitoring based on the dynamic digital twin hydraulic engineering model to generate hydraulic engineering monitoring data; and performing hydraulic engineering composite state sensing on the hydraulic engineering monitoring data to generate a hydraulic engineering dynamic monitoring map. According to the method, the dynamic model is constructed through the digital twinning technology, precision, intelligence and collaboration of operation and maintenance monitoring of the water conservancy project are achieved, and the precision and intelligent level of operation and maintenance monitoring of the water conservancy project are improved.

The invention discloses a construction progress dynamic optimization method and system based on BIM and computer vision, and particularly relates to the technical field of building construction management, and the method comprises the steps: carrying out the automatic registration of a BIM model and a construction site image; processing the construction site image by adopting a visual identification algorithm to generate a visual identification result; constructing a four-dimensional dynamic BIM model, and mapping a visual identification result to a corresponding component in real time through multi-feature similarity calculation; the progress deviation is monitored by using key path dynamic identification and a deviation propagation matrix, and the risk is predicted by combining a Bayesian network and Monte Carlo simulation. The BIM and computer vision technologies are fused, a construction progress optimization system integrating automatic registration, dynamic monitoring, risk prediction and intelligent decision making is constructed, and the problems that traditional manual inspection data collection is low in efficiency, progress monitoring is lagged, risk prejudgment is fuzzy and resource allocation is extensive are solved; accurate monitoring, risk early warning and resource optimization configuration of the construction progress are realized.

The invention relates to the technical field of engineering control, in particular to a hoisting control method of a safe and portable hoisting cage, which comprises a hoisting pre-stage, data pre-acquisition and analysis before hoisting, a hoisting control stage and hoisting landing control. In the prior art, position closed-loop control mainly depends on a stroke encoder and a position sensor, dynamic swing deviation caused by wind disturbance is difficult to accurately pre-judge and counteract in real time, and the defects of control lag and insufficient response exist. According to the scheme, the motion trail and posture of the cage are predicted in advance based on simple pendulum dynamic model numerical solution and the Runge-Kutta method, high-precision deviation detection is carried out in combination with a virtual-real vision calibration technology and AR enhanced display, a cage motion state control model based on feedforward prediction is established, trail prediction errors are greatly compressed, and the accuracy of the motion state of the cage is improved. The forward-looking inhibition of the movement of the cage, especially wind-induced swinging, is realized, and the accuracy and the response speed of an active swinging inhibition measure are obviously improved.

The invention belongs to the field of artificial intelligence, particularly relates to an intelligent regulation and control system for an injection molding process of an industrial control system, and aims to solve the problem that high-precision cooperative regulation and control are difficult under material batch fluctuation, mold state change and environmental disturbance. The system comprises a multi-source sensing module, a dynamic modeling module, a self-adaptive decision-making module, an execution feedback module and a knowledge evolution module, and high-stability and high-adaptability intelligent regulation and control of the injection molding process are achieved through a mixed digital twin model integrating a physical mechanism and data driving, confidence-guided multi-objective optimization and continuous evolution of a process knowledge graph.

The invention belongs to the technical field of robot control, and particularly relates to a mechanical arm RBF network dynamic self-adaptive control method under the constraint of a time-varying mechanism, which comprises the following steps of: constructing a mechanical arm dynamic model, determining a system specified time convergence standard, combining a joint motion reference trajectory of a mechanical arm, defining a trajectory tracking error, and determining a mechanical arm dynamic model based on a dynamic nominal model. Constructing a stable robust control law of the nominal dynamical model under the specified time; and selecting a radial basis function, and designing an RBF network adaptive control law in a specified time to dynamically fit a comprehensive nonlinear disturbance term in the system. The problem that the convergence time of a traditional method is uncontrollable is solved, the RBF neural network is adopted to dynamically approach comprehensive disturbance, the network weight is updated online through the adaptive law of the time-varying gain, the anti-jamming capability is remarkably improved, a model driving method and a data driving method are combined, and the convergence time of the model driving method and the convergence time of the data driving method are greatly improved through a dynamic model decoupling and feedforward compensation strategy. And more accurate and low-delay trajectory tracking control is realized.

The invention relates to the technical field of communication management, and provides a communication base station traffic prediction management system based on deep learning, which comprises a multi-source data acquisition module used for acquiring base station space-time traffic data, user behavior data, network state data, external influence factors and data set slice service parameters; the spatio-temporal feature processing module is used for performing spatio-temporal alignment, noise filtering and slice feature coding on the multi-source data; and the dynamic model prediction module is used for constructing a multi-modal prediction network of a space-time Transform, a graph neural network and a slice exclusive sub-model. A base station association graph based on geographical distance and service correlation is constructed, spatial features are extracted through a multilayer graph convolutional network, slice features and spatial-temporal features are fused by using a gating mechanism, a Pareto optimal strategy is generated by using a multi-objective optimization algorithm, and a strategy library is updated in combination with a forgetting factor, so that the probability of forgetting is reduced. The response time of the system in an abnormal scene is shortened, and the strategy optimization efficiency is improved.

A human-guided vision-force fused impedance iterative learning control method for a robotic arm, comprising: analyzing a robot-environment interaction dynamics equation, solving a visual servo acceleration model, and making use of the equation to establish a human-robotic arm-environment interaction dynamics model in an image feature space; acquiring an image feature position and speed curve of a human-guided robot completing an assembly task, and using dynamic movement primitives for coding and generalization; and designing an impedance iterative learning controller which uses image feature tracking errors as control input, learning impedance characteristics when the human-guided robot performs a contact operation, identifying unknown contact dynamics under the interaction between the robot and the environment, and counteracting identified contact interference in the feature space, so as to implement a flexible assembly operation. The control method solves the problems in existing assembly operations that human-robotic arm-environment coupling nonlinear dynamics, unknown contact dynamics of intensive contact assembly tasks and poor generalization of assembly scenarios require relearning for different scenarios, etc.

The invention relates to the technical field of precise injection molding control, and discloses an injection molding process parameter self-adaptive compensation system which comprises a multi-source sensing module used for collecting mold cavity pressure, melt temperature and screw displacement information in the injection molding process in real time; the data fusion module is used for performing time sequence synchronization, space registration and noise suppression on the multi-source sensing data; the dynamic modeling module is used for constructing a dynamic model of an injection molding process state based on the fused process data and outputting a state estimation value; and the stability analysis module is used for carrying out system stability analysis based on the dynamic model and generating a control compensation strategy. Cooperative control of precise positioning and high-pressure response is realized through a composite driving framework and a dynamic disturbance compensation technology; an incremental learning algorithm and a multi-dimensional quality evaluation system are combined, a process parameter self-evolution mechanism is constructed, material characteristic drift and complex working condition disturbance are effectively dealt with, and closed-loop optimization from process control to quality feedback is formed.

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 provides a power plant heat tracing system intelligent optimization method based on environment temperature fluctuation, and relates to the technical field of temperature control of a power plant heat tracing system, and the method comprises the following steps: S1, data collection and preprocessing: deploying a sensor array, collecting an operation data signal # imgabs0 # of a power plant heat tracing pipeline, carrying out the preprocessing of the operation data signal # imgabs1 #, and storing the preprocessed operation data signal # imgabs0 #; outputting a standardized feature vector; s2, model establishment and parameter optimization: constructing a physical-data dual-drive heat transfer model, and inputting standardized feature vector optimization initial parameters; compared with the prior art, the method has the following beneficial effects: firstly, control parameters can be automatically adjusted according to factors such as thermal load change, external environment temperature fluctuation and equipment aging; and secondly, the temperature trend in the future short time can be predicted by constructing a heat transfer dynamic model, the control strategy is adjusted in advance, the temperature is prevented from being out of control, heat dissipation can be increased in advance or heat source input can be adjusted in advance, and safe and efficient operation of the system is effectively guaranteed.

The invention provides an unmanned aerial vehicle infrared target positioning system fusing multi-model Kalman filtering and deep learning, and relates to the technical field of unmanned aerial vehicle navigation and positioning. The main body system comprises a data acquisition module, a multi-source data fusion module, a feature extraction enhancement module, a dynamic model optimization module, a target state estimation module, a positioning calculation module and a flight decision planning module, and the data acquisition module is provided with a high-resolution infrared sensor and accurately captures target infrared radiation information; a high-definition vision camera and a high-precision laser radar are integrated at the same time, environment data are collected in an omnibearing mode, and the data collection module can collect the environment data according to the environment complexity and the real-time change of the target motion state by adopting a self-adaptive sampling strategy based on fuzzy logic, particle swarm optimization and a self-adaptive threshold value. And the sampling frequency and the resolution of the sensor are dynamically and intelligently adjusted.

The invention belongs to the technical field of OLED ink-jet printing, and discloses an OLED nozzle driving waveform regulation and control method, device and equipment and a storage medium, and the method comprises the steps: obtaining the structure parameters of an OLED nozzle and the physical and chemical parameters of ink, constructing a theoretical dynamic model about the driving voltage and the ink droplet jet volume based on the electroacoustic analogy theory, and calculating the driving waveform of the OLED nozzle according to the theoretical dynamic model; and constructing a voltage duration influence function, an ink droplet volume correction coefficient and an ink droplet steady-state speed function to optimize the theoretical dynamic model to obtain an ink droplet ejection theory-data hybrid model, and calculating to obtain a predicted ink droplet ejection volume according to a real-time driving waveform. Establishing a corresponding model prediction algorithm cost function and a driving waveform constraint condition so as to regulate and control the real-time driving waveform in real time; the driving waveform of the OLED nozzle is regulated and controlled through the ink droplet jetting theory-data mixing model, the model prediction algorithm cost function and the driving waveform constraint condition, and the printing precision and efficiency are improved.

The invention relates to the technical field of industrial automatic detection and intelligent operation and maintenance, in particular to a machine vision and big data fused linear slide rail defect identification and prediction system, which comprises a machine vision acquisition module, a time sequence feature extraction module, a big data analysis platform, a defect prediction and decision module and the like. The machine vision acquisition module acquires a slide rail image through an annular camera array and a multi-angle light source, and the acquisition quality is ensured through motion compensation; the time sequence feature extraction module utilizes a space-time attention model to mine defect features; the big data analysis platform is associated with the multi-source data to construct a defect evolution graph; the defect prediction and decision-making module outputs residual life prediction and maintenance instructions based on double-target reinforcement learning, and the dynamic model updating unit optimizes the model according to errors. According to the method, high-precision identification, prospective prediction and intelligent decision making of the defects of the linear sliding rail are realized, the equipment reliability can be effectively improved, the operation and maintenance cost is reduced, and the method is suitable for a complex industrial environment and has wide application value.

The present invention relates to the technical field of oil and natural gas exploitation, and discloses a real-time monitoring and intelligent control system for hydraulic fracturing cracks in oil reservoirs, including: a real-time monitoring module, which is used to collect crack geometric morphology parameters and construction operation parameters in real time during crack expansion; a nonlinear modeling module, which is connected to the real-time monitoring module, and is used to construct a nonlinear dynamic model of crack expansion according to the collected crack expansion parameters and construction operation parameters, and output a predicted value of crack morphology; an optimization control module, which is connected to the nonlinear modeling module, and is used to calculate the optimized construction parameters according to the nonlinear dynamic model, including injection pressure, injection flow rate and viscosity of fracturing fluid. The present invention realizes precise control of crack expansion, intelligent optimization of construction parameters and efficiency improvement through the closed-loop linkage of real-time monitoring, nonlinear modeling, optimization control and feedback execution.

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 discloses an unmanned aerial vehicle-mechanical arm system cooperative control method for precise spraying. According to the method, through a depth camera and a point cloud reconstruction algorithm, three-dimensional modeling of a target surface and semantic recognition of a spraying area are achieved, and a spraying path and spraying posture data are generated. Based on spraying task requirements, a spraying film thickness experience estimation model is established, and path point-level spraying parameters are obtained. Load mass change, mechanical arm mass center change and spraying reaction force of each path point are calculated, and a coupling dynamic model is constructed in combination with the system state. And dynamic compensation control input is generated by using an adaptive sliding mode control algorithm. After spraying is completed, the actual spraying effect is collected through image and point cloud detection, color, film thickness and texture features are extracted and compared with task requirements, error feedback is generated, and control parameters are adjusted online. According to the method, the problems of dynamic change and error compensation in the spraying process are effectively solved, and the method is suitable for a high-precision spraying task in a complex environment.

The invention discloses a knowledge graph optimization method based on large model and multi-modal data fusion. The method comprises the following steps: S1, constructing a multi-modal data set; s2, forming a preprocessed multi-modal data set; s3, generating a unified semantic vector representation; s4, inputting the unified semantic vector representation into an adaptive Poisson distribution model, performing dynamic modeling on the arrival rate and distribution characteristics of the multi-modal data, and determining adaptive parameters of data sampling and updating; s5, generating a fused semantic representation set; s6, performing entity extraction and relation identification by using the fused data representation, and constructing a preliminary knowledge graph; and S7, carrying out automatic verification, redundant information elimination and structure adaptive adjustment on nodes, edges and attributes of the knowledge graph to form an optimized knowledge graph. According to the method, the data acquisition and atlas updating frequency can be adjusted according to the real-time data semantic change, and it is ensured that the atlas construction process has semantic driving performance and time sensitivity.

The invention relates to the technical field of cloud computing application, and discloses a cloud computing-based shale fracture modeling platform, which comprises a multi-source data acquisition module, a heterogeneous data fusion module, a fracture dynamic modeling module, a parameter optimization module and a cloud verification module. The multi-source data acquisition module acquires various data and uploads the data to the cloud; the heterogeneous data fusion module processes data isomerism to generate a fusion feature matrix; the crack dynamic modeling module constructs a three-dimensional dynamic model; the parameter optimization module optimizes physical property parameters; and the cloud verification module verifies the model in multiple dimensions and manages the version. The platform solves the problems of traditional modeling in the aspects of data acquisition, fusion, modeling algorithm, parameter optimization and model verification by means of the advantages of cloud computing, can efficiently process multi-source data, accurately construct a crack model, improve the precision and reliability of the model, realize comprehensive verification and effective management of the model, and provide powerful support for efficient exploitation of shale gas.

The invention relates to the technical field of motor control, and discloses a brushless motor adaptive control method and system based on artificial intelligence, and the method comprises the steps: collecting the multi-point temperature data of a motor, building a thermal dynamic model, and reconstructing the complete temperature field distribution; identifying a relation function between motor parameters and temperature, and establishing a temperature sensitive parameter model; in combination with temperature field distribution and a temperature sensitive parameter model, learning long-term influence of control actions on temperature distribution, and predicting temperature evolution trends under different control strategies; constructing a multi-objective optimization control strategy, and generating a motor control instruction and a heat dissipation control instruction; dynamically selecting an optimal heat dissipation strategy combination according to the generated control instruction, the current working condition and the predicted heat influence; according to the invention, high-precision control and thermal management optimization of the brushless motor in a temperature change environment are realized.

The invention relates to the field of supply chain management and optimization, in particular to a crude oil supply chain management method and system, and the method comprises the steps: constructing a supply chain dynamic model through multi-modal data fusion and causal analysis; real-time data and prediction data are generated based on a digital twinning technology, and cross-node association scenes and extreme climate conditions are simulated; a transportation path, an inventory allocation strategy and a delivery sequence are dynamically optimized through a multi-objective optimization algorithm and a flexible emergency adjustment module; an optimization scheme is automatically executed by using an intelligent contract system, feedback adjustment data and improved scheme data are generated based on real-time feedback, and closed-loop optimization is realized. The dynamic adaptive capacity of the crude oil supply chain in a complex environment is effectively improved, the cost, the inventory turnover rate and the delivery efficiency are optimized, and meanwhile the robustness and the emergency response capacity of the system are enhanced.