5909results about "Stochastic CAD" patented technology

The invention provides an equipment fault early warning method based on a neural network, a medium and equipment, and the method comprises the steps: collecting equipment state information at regular time, recognizing abnormal equipment, obtaining operation parameters and structure parameters of the abnormal equipment and a layout schematic diagram of a system where the abnormal equipment is located, constructing a dynamic coupling topology link, and extracting associated parameters; verifying and generating a first error weight in combination with a physical kinematics equation, generating a second error weight by matching historical fault data, and comprehensively generating fault state information; a detection mode is switched based on risk level evaluation, a system dynamic coupling topology link, associated parameters, abnormal equipment information and fault state information are input into a neural network model, fault link information is output, and finally first early warning information and second early warning information are generated according to a dynamic detection mode to realize two-stage early warning. According to the method, the fault conduction relation between equipment is modeled through the graph neural network, and fault path prediction and systematic risk early warning are realized.

The invention relates to the technical field of oil exploration, and discloses a geological digital twinborn model construction method and system in oil exploration. The method comprises the steps of receiving a multi-source heterogeneous geological data flow of a target exploration area, performing multi-scale alignment and feature fusion through a geological feature decoupling model, and generating a three-dimensional geological attribute field distribution map and the like. Constructing an adaptive grid division model to generate a dynamic flow field simulation instruction set; simulating dynamic response under reservoir exploitation disturbance based on a multi-physics field coupling model and optimizing simulation instruction set parameters; and iteratively updating parameters through a quantum computing optimization framework, and outputting a model construction sequence to an exploration decision platform. The system comprises a data receiving module, a feature processing module, a grid instruction generation module, a simulation optimization module and an output module. Multi-source data are integrated, the geological process is accurately simulated, the exploration decision is optimized, and the oil exploration efficiency and exploitation benefits are improved.

The invention discloses a municipal road state sensing method and system based on sensor fusion, and the method comprises the steps: collecting multi-modal road information data from a sensing network along a municipal road, and generating a standardized road feature vector; the method comprises the following steps: extracting frequency domain characteristics of pavement bearing capacity through time-frequency analysis, establishing a coupling model of vehicle load and pavement response, and calculating various road state indexes; outputting a structure health index and marking a thermodynamic diagram of a key damage position; real-time traffic flow data and meteorological environment parameters are fused, and an early warning level adaptive adjustment model based on fuzzy reasoning is established; and when a risk entropy value output by the early warning level adaptive adjustment model exceeds a dynamic threshold value, triggering a multi-level collaborative maintenance decision scheme and generating a visual road state digital twinborn body. According to the invention, the problem of insufficient road state sensing precision in a complex environment is solved, and dynamic evaluation and intelligent early warning of the full life cycle health state are realized.

The invention relates to a building design scheme multi-objective optimization comparison and selection method and device, equipment and a medium. The method comprises the steps of generating a multi-dimensional design parameter set by obtaining building information model data and parameterized design data; performing multi-dimensional target analysis and evaluation by using a multi-field joint simulation platform to generate a multi-dimensional evaluation index; a dynamic multi-objective optimization model is constructed through a dynamic weight adaptive algorithm in combination with project stage demands and user interaction data; carrying out iterative optimization by adopting an improved non-dominated sorting genetic algorithm to obtain an optimized design scheme gene sequence result, and introducing a spatial topology connectivity constraint to generate a Pareto optimal solution set; and according to the Pareto optimal solution set, generating an optimization scheme through user weight adjustment and scheme screening. According to the method, the optimal design scheme set meeting the project requirements can be quickly and efficiently generated and screened out, the project stage requirements and user preferences are met, and the design efficiency and the scheme quality are improved.

The invention relates to the technical field of artificial intelligence, and discloses a method and system for assessing the propagation risk of bacteria in a chicken coop, and the method comprises the steps: collecting the multi-modal perception data of the bacteria in the chicken coop in real time through a distributed sensor array, carrying out the spatial-temporal feature extraction of the multi-modal perception data through a dual attention spatial-temporal diagram neural network, and obtaining the spatial-temporal feature of the bacteria in the chicken coop; the method comprises the following steps: generating a bacterial propagation risk prediction map with confidence evaluation, inputting the bacterial propagation risk prediction map into a digital twin platform, simulating a dynamic simulation environment of a chicken house by fusing a BIM model and CFD to obtain a CFD-BIM coupled dynamic simulation model, and constructing a deep reinforcement learning decision model based on the CFD-BIM coupled dynamic simulation model. And after the prevention and control execution instruction is fed back to the digital twin platform for virtual verification, a visual decision report is output. The method can improve the accuracy of risk assessment of bacterial propagation in the henhouse.

The invention provides a wafer-level test yield prediction and process optimization method and system based on big data, and relates to the technical field of wafer-level tests.The wafer-level test yield prediction and process optimization method comprises the steps that wafer manufacturing process data is obtained, a time-sequence-sensitive multi-modal data fusion network is constructed, a LSTM and GNN combined mixed architecture is adopted, and a multi-modal data fusion network is constructed; self-adaptive weight distribution is carried out through an attention mechanism to predict the yield; and when the predicted yield is lower than a threshold value, triggering a layered progressive process optimization system, and adjusting process parameters through an equipment parameter optimization layer and a process level optimization layer. According to the invention, the accurate prediction of the wafer test yield and the automatic optimization of the process parameters are realized, the production efficiency is improved, and the manufacturing cost is reduced.

The invention relates to the technical field of battery energy storage, and discloses an energy storage operation optimization method and system considering battery life loss, and the method comprises the steps: initializing battery energy storage electrical parameters, constructing a battery life loss model, and obtaining the health state change of a battery; constructing a battery operation cost model, defining a multi-objective optimization algorithm and constraint conditions, and optimizing the battery operation cost; and carrying out adaptive scheduling through a model prediction control and reinforcement learning algorithm, and determining an optimal charging and discharging strategy of the energy storage system. By constructing the battery life loss model and the multi-target optimization algorithm, economical operation of the energy storage system and prolonging of the battery life are achieved, the charging and discharging strategy of the energy storage system can be dynamically adjusted according to the power market cost fluctuation, the load requirement and the battery health state, and the operation cost of the energy storage system is reduced.

The invention discloses an intelligent cable digital comprehensive monitoring system, and relates to the technical field of electric power engineering, the intelligent cable digital comprehensive monitoring system comprises a digital monitoring center, the digital monitoring center is in communication connection with the following modules: a holographic sensing monitoring module used for deploying an intelligent sensing system and collecting monitoring data including cable operation parameters and environmental data, and the collected data is preprocessed. According to the invention, real-time and comprehensive monitoring of the cable body and the surrounding environment is realized through the holographic sensing monitoring module and the digital twinning technology, the virtual model of the cable is dynamically updated, and the physical state change of the cable is reflected in real time, so that operation and maintenance personnel can find potential problems in cable operation in time, and the working efficiency is improved. And the service life trend and the fault risk of the cable are predicted in advance through the simulation prediction module, and differential maintenance strategies are formulated, so that the possibility of sudden faults of the cable is effectively reduced, the operation reliability of the cable is remarkably improved, and stable operation of a power system is ensured.

The invention discloses a digital twinning-based wastewater and dirty salt treatment process optimization method and system, and relates to the technical field of intelligent wastewater and dirty salt treatment. The method comprises the steps that a digital twinborn model synchronously mapped with a processing system is established, and the digital twinborn model integrates the technological parameter incidence relation of an evaporation section, a crystallization section and a filtering section; collecting component data and flow data of the polluted salt wastewater corresponding to each process section in real time; correcting model characteristic parameters of each process section in the digital twin model based on the component data and the flow data collected in real time; according to the corrected digital twin model and the optimization target, generating a process optimization strategy corresponding to each process section; and monitoring the component data of the treated discharge water, reuse water, industrial salt and solid waste in real time, and if the data does not reach the standard, triggering model parameter correction, optimization strategy iteration or cross-section collaborative logic recalculation. According to the invention, intelligent cooperation of wastewater and dirty salt treatment is realized.

The invention discloses a cascade reservoir collaborative flood control scheduling decision-making method of a coupling meteorological-hydrological-hydraulic model. The method comprises the steps of multi-source meteorological data fusion and probability forecast generation, dynamic coupling hydrological-hydraulic simulation, risk entropy driven collaborative optimization decision-making, digital twin platform verification and correction, instruction execution and closed loop feedback. Through multi-technology fusion and an intelligent optimization mechanism, the scientificity, timeliness and safety of flood control scheduling are remarkably improved. In the weather forecast stage, multi-source data are integrated to output ensemble rainfall forecast scene data, rainfall input uncertainty is reduced from the source, it is ensured that initial driving precision of flood simulation is improved, and a reliable input basis is provided for subsequent model coupling; in the hydrological-hydraulic dynamic coupling stage, a coarse-fine grid dynamic division strategy is adopted to reduce redundancy calculation, the asynchronous pipeline technology enables the flood routing calculation efficiency to meet the real-time scheduling requirement, and the simulation speed is greatly accelerated.

A method and apparatus for evaluating vulnerability of monopile foundations of offshore wind turbines are provided. The method includes collecting offshore wind farm location data and wind-wave characteristic data, and simulating a wind-wave time course according to the offshore wind farm location data and the wind-wave characteristic data; determining a wind-wave dynamic load based on the wind-wave course; obtaining lateral soil resistance data of monopile foundations with a plurality of rock-soil strength parameters, and inputting the wind-wave dynamic load into a 3D finite element model; using the lateral soil resistance data of monopile foundations with a plurality of rock-soil strength parameters as boundary conditions of the 3D finite element model; and giving a limit state of monopile foundations, and determining vulnerability of monopile foundations on basis of the dynamic response result of the monopile foundations and the limit state of monopile foundations.

The present invention relates to the technical field of proton exchange membrane fuel cells. Disclosed are a fuel cell parameter identification method and apparatus, and a device and a storage medium. The method comprises the following steps: constructing a semi-empirical model for a proton exchange membrane fuel cell; calculating a theoretical value of an output voltage of the semi-empirical model, and constructing an objective function by means of a mean square error between the theoretical value and an actual value of the output voltage of the semi-empirical model; on the basis of the semi-empirical model, determining a plurality of parameters to be identified, and using said plurality of parameters as decision variables to construct a plurality of constraint conditions for the objective function; on the basis of the plurality of constraint conditions, constructing an optimization model for the proton exchange membrane fuel cell by using the minimization of the objective function as an optimization objective and using said plurality of parameters as variables to be solved; and solving the optimization model by means of a multi-policy sparrow search optimization algorithm, so as to obtain a plurality of optimal parameters to be identified. In the multi-policy sparrow search optimization algorithm in the present invention, Tent chaotic mapping is introduced to initialize a population, the number of populations is increased, and then the two populations are merged. An adaptive feedback mechanism is added in a follower position update stage and a vigilant position update stage, thereby reducing the convergence accuracy under a limited number of iterations. A DE / best / 1 mutation policy and a dynamic scaling factor sf are used to update the position of a sparrow. The global optimization capability and the local optimization capability of an algorithm are improved, thereby also improving the accuracy of fuel cell parameter identification.

The invention provides a method for diagnosing a structural service behavior based on a high dam accident mode and a failure path, and the method comprises the steps: constructing a high dam accident case database, and extracting accident feature parameters; by applying multi-scale layered adaptive corresponding analysis, through feature weight calculation, risk-out mode layered weighting, physical similarity index establishment and structural difference compensation, the problem of unbalanced feature weight is solved, and a weighted similar project sorting list is generated; constructing a multi-failure mode coupled time sequence adaptive network model, solving the problem that a failure path time sequence evolution mechanism is imperfect, and outputting a time sequence failure path prediction model; and in combination with the similar project sorting list and the time sequence failure path prediction model, performing project analogy analysis, and generating a high dam safety state evaluation report. The technical problems that the operation time of the ultra-high dam is short, the monitoring information is insufficient, the numerical analysis result is difficult to completely reflect the engineering practice and the like are solved, and a new technical path and a new solution are provided for high dam safety assessment and risk management.

The invention discloses an intelligent prediction method for fatigue damage evolution of an aero-elastic sheet based on digital twinning, and the method comprises the following steps: S1, constructing a finite element model, carrying out the modal calibration, and building a digital twinning model; s2, acquiring real-time monitoring data; s3, constructing a crack-induced potential energy tensor field to obtain a crack development potential energy probability distribution diagram; s4, generating a probability prediction map by adopting a potential energy driven map evolution coupling algorithm; s5, constructing a topological damage mapping function, and predicting a dynamic evolution process of topological damage along with load time; s6, applying a time folding algorithm in the dynamic evolution process to obtain a fatigue state submerged space representation; and S7, inverting the topological damage state of the current structure based on the contrast twinborn variational self-encoding network, updating the digital twinborn model, and finally outputting the structural damage evolution trend. According to the method, digital twinning and graph evolution algorithms are fused, and intelligent prediction of the fatigue damage of the aero-elastic sheet is realized.

The invention provides a simulation method and system for intelligent fusion and dynamic prediction of electronic warfare intelligence. According to the simulation method, electromagnetic signals are collected in real time through a multi-domain sensor, a dynamic adversarial network (DAN) is adopted for frequency spectrum matching and abnormal positioning, and interference data are eliminated in combination with space-time verification; constructing a semantic association map of radar, communication and geographic information, fusing time domain / space domain features through space-time attention, and dynamically allocating confidence to realize multi-source data fusion; predicting a threat evolution trend based on a space-time Transform, outputting a threat level, a threat type and a threat intention, and generating a dynamic situation map in combination with a mixed density network; an online incremental learning framework is adopted to adapt to a new signal mode, a prediction module is updated preferentially, and a multi-path distribution mechanism driven by edge-cloud collaborative optimization reinforcement learning is adopted to dynamically adjust the pushing priority, so that low-delay transmission of key threat intelligence is realized.

The invention discloses a multi-modal wind turbine generator electromechanical transient modeling method based on artificial intelligence, and relates to the technical field of new energy power generation modeling, and the method comprises the steps: carrying out the causal association mining and dependence path recognition of a standardized spatio-temporal data cube through a causal discovery algorithm, constructing a causal topological graph, and carrying out the modeling of the new energy power generation. Performing cross-modal feature fusion through a space-time diagram attention network to generate a high-order feature tensor; dividing the high-order feature tensor into meta-learning task pools according to different models and environmental conditions, and training a cross-model general parameterization framework by adopting a double-layer optimization strategy; constructing a composite working condition generator based on the trained cross-model general parameterization framework, and performing constraint through a causal regularization loss function to form an extended test working condition set; and performing control parameter optimization on the extended test working condition set by adopting a multi-modal deep reinforcement learning algorithm to obtain optimized control parameters. According to the method, a foundation is laid for realizing electromechanical transient modeling with high robustness and high generalization ability through accurate causal modeling and cross-modal fusion.

The invention provides a river water quality parameter supervision method and system based on deep learning. The method comprises the steps of self-calibration multi-source data acquisition, diffusive water quality prediction, extreme water quality parameter simulation, reverse diffusion pollution positioning and water quality parameter intelligent supervision. The invention relates to the technical field of river water quality supervision, in particular to a river water quality parameter supervision method and system based on deep learning. By introducing a graph convolutional neural network and a physical diffusion constraint model, space-time diffusion trend modeling of pollutants in a river channel is realized; constructing an extreme pollution event simulation and attribution mechanism by combining a generative adversarial network and physical verification; further adopting a multi-modal Bayesian inversion model and a graph deconvolution structure to realize accurate source tracing of the pollution source; the system can dynamically sense hydrological changes, construct an adaptive threshold judgment mechanism, realize prediction, tracking and response to pollution risks, and provide efficient and intelligent technical support for river ecological safety management.

The invention belongs to the field of road vehicle control, and relates to an automatic driving vehicle collision risk prediction method for a long-tail phenomenon, which comprises the following steps of: screening high-risk track samples, and realizing high-fidelity modeling and synthesis of rare high-risk driving behaviors by adopting a track generation method fusing a diffusion probability model and a Transformer structure. Therefore, the learning ability of the model to the long-tail sample is enhanced; besides, in consideration of the fact that in a long-tail scene, the kinematics characteristics and the traffic flow density characteristics of the vehicles generally have significant fluctuations, the kinematics characteristics and the traffic flow density characteristics of the surrounding traffic participants are constructed and extracted, and the future trajectories of the surrounding vehicles are predicted through multi-modal characteristic fusion; on the basis, a probability risk assessment mechanism for different risk scenes is constructed, the potential collision risk or behavior anomaly probability is dynamically assessed, and finally refined prediction of potential high-risk events in the complex traffic environment is achieved.

The invention discloses an aircraft surface flow field intelligent reconstruction method and system based on multi-source data fusion, and belongs to the technical field of flow field intelligent prediction. Acquiring multi-source sensor data on the surface of the aircraft, and constructing a multi-source data set; a flow field reconstruction model based on Transform is constructed; a multi-source data set is adopted to train a flow field reconstruction model; and outputting an aircraft surface flow field reconstruction result based on the trained flow field reconstruction model, and visually displaying the aircraft surface flow field reconstruction result. Based on the strong nonlinear mapping capability of the deep learning model, through training of a large amount of sensor data, high-order correlation characteristics among pressure, heat flow and friction resistance stress can be automatically extracted, through fusion of sparse discrete data, a continuous physical field of the whole surface of the aircraft can be effectively reconstructed, and the method is suitable for the aircraft. The limitation of a traditional analytical model in dealing with a strong nonlinear problem is broken through, and the precision and reliability of aircraft flow field prediction are improved.

The invention relates to the technical field of collaborative optimization, in particular to a geometric parameter collaborative optimization method of a taper hole machining cutter, which comprises the following steps: by constructing a high-fidelity digital twin model, integrating multi-physics field coupling and machine tool dynamic characteristics based on a finite element method, generating a geometric parameter-performance data mapping set and training and calculating an agent model; outputting a Pareto solution set through multi-target global optimization; and constructing a constraint range based on the solution set, and calling a digital twin model to carry out local optimization to obtain an optimal geometric parameter combination. The method comprises a self-correction mechanism: correcting a material constitutive relation and a friction coefficient through experimental data; staged adaptive learning, NSGA-II and DBSCAN clustering are adopted, and the efficiency is optimized along with the method; and a Pareto stability index and transfer learning are introduced, so that the result robustness and the cross-task reusability are improved. According to the method, high-precision and high-efficiency geometric parameter collaborative optimization of the taper hole machining tool can be realized.

The invention belongs to the field of digital delivery, and particularly relates to a digital delivery topology mapping method and system for multi-source real-time data fusion, and the method comprises the steps: obtaining factory building distribution, equipment distribution and operation control logic and preset function block operation logic, and constructing a hierarchical clustering function mapping space through combining an association analysis and clustering algorithm; in response to a target function demand, obtaining a layered response mapping path in combination with a deep search algorithm; layered synchronous response and distributed node anomaly monitoring are realized based on the path, the three-dimensional simulation model and the display system equipment performance and the network state. Tracing abnormities based on a monitoring result in combination with a hidden Markov algorithm and a forward reasoning model, performing iterative verification after conflict resolution until the function is free of abnormities, and updating a mapping space; and adjusting the demand repeating steps to obtain a complete and updated mapping space, and realizing accurate function and picture collaboration under multi-source data fusion.

The present invention belongs to the technical field of fracture network optimization in oilfield exploitation. Specifically disclosed are a method and system for optimizing complex fracture networks in shale reservoirs. The method comprises: S1, on the basis of geological-engineering bimodal data fusion, constructing a three-dimensional digital twin of heterogeneous reservoirs; S2, synchronously calculating the interactions among fluid flow, rock fracturing and proppant transport during a hydraulic fracturing process; and S3, automatically and iteratively optimizing a combination of fracturing operation parameters. By means of constructing a closed-loop system consisting of geological modeling, multi-field coupling simulation, intelligent optimization and dynamic correction, efficient design and real-time optimization of complex fracture networks in shale reservoirs are realized.

The invention relates to the technical field of data processing and identification, in particular to an intelligent fire fighting equipment fault identification method and system, and the method comprises the steps: carrying out the execution according to a set first period: generating simulation data through a constructed digital twinborn model; acquiring operation data of real equipment through a sensor network; comparing a deviation value between the simulation data and the real equipment data; when the deviation value exceeds a preset threshold value, triggering a causal inference engine; calibrating digital twin model parameters and adjusting equipment operation parameters; the prior art mainly depends on fixed threshold alarm, early progressive faults of equipment are difficult to capture, and response lags behind; according to the scheme, digital twin simulation and active flaw detection are combined, and deep insight of the health state of the equipment is formed by periodically injecting micro-amplitude disturbance signals into the equipment and analyzing the dynamic response characteristics of the equipment; according to the invention, tiny degradation of equipment performance can be captured in a fault incubation period, so that maintenance intervention is triggered in advance, and the advancement and accuracy of fault early warning are remarkably improved.

The invention discloses a method for constructing a complex fracture network rock mass permeation grouting model based on COMSOL, relates to the technical field of rock mass mechanics, and solves the problem that an established model is greatly different from actual engineering due to collapse of an obtained fracture sample caused by the defects of low sampling efficiency and large sample capacity. The rock mass permeation grouting model construction method comprises the steps that geological exploration is conducted on a rock mass, and fracture geometric parameters of the rock mass are extracted and processed; fitting correction is conducted on the geometric parameters of each fracture, and an optimal distribution probability function is obtained; according to the optimal distribution probability function, utilizing a Latin hypercube sampling algorithm to generate a discrete fracture network model; performing format conversion on the discrete fracture network model so as to import COMSOL simulation software; performing geometric repair and multi-physical field coupling on the discrete fracture network model through COMSOL simulation software to obtain a virtual discrete fracture model, and performing entity verification, inversion analysis and grouting optimization to obtain an optimal discrete fracture model.

The invention relates to the technical field of electric drive transmission system service life prediction, and discloses an AI-based electric drive transmission system comprehensive service life prediction method, which comprises the following steps: acquiring electric drive system operation parameters and mechanical part degradation data; arranging the data into a system operation full-period input tensor and a component degradation full-period input tensor, and respectively extracting multi-order feature tensors through a bidirectional recurrent neural network and a gated time convolutional network; performing cross-modal fusion by using a cross attention fusion network in combination with physical constraints to obtain a system-component degradation fusion feature vector; and after dimension reduction, inputting a multi-layer perceptron network output life prediction value, and generating a residual service life interval with confidence in combination with historical cases. The method integrates multi-source data, realizes cross-domain feature dynamic association and physical constraint fusion, improves the precision and reliability of life prediction, is suitable for intelligent operation and maintenance of the electric drive system, and provides a scientific basis for equipment maintenance.

The invention provides a tunnel construction structure health on-line monitoring method and system, and the method comprises the steps: obtaining stress data, crack images and seismic wave signals of tunnel construction, and carrying out the preprocessing; obtaining and constructing stress-crack joint representation mapping, generating a first joint feature vector, and predicting a lining health state of a future # imgabs0 # time step; generating a second joint feature vector, and determining a stratum stress field of the future # imgabs1 # time step according to the second joint feature vector; constructing a tunnel lining damage probability model according to the stress data and the crack image, and determining an overall construction risk score; and dividing the construction risks into a plurality of grades. According to the method, data multi-modal fusion, structure-stratum joint modeling and AI enhanced prediction and early warning are achieved, the defects in the prior art are effectively overcome, the method can be widely applied to engineering scenes such as tunnel construction, subway construction and underground space development, and the construction safety and the structure health monitoring capacity are remarkably improved.

The invention relates to the technical field of pipeline stress fatigue detection, and discloses a natural gas station elbow tee joint stress fatigue digital intelligent analysis method and system and a product. The method comprises the following steps: establishing a multi-physics field coupling digital twinborn model of the elbow tee joint, integrating a geometric structure, material attributes and a fluid-solid coupling mechanism, and simulating flow-induced vibration stress and corrosion fatigue interaction; collecting real-time multi-source data of the elbow tee joint; real-time multi-source data is utilized to update boundary conditions and parameters of the digital twin model, and material constants are dynamically corrected through machine learning, so that self-calibration of the model is realized; performing stress distribution analysis based on the updated model to obtain stress field data, and performing fatigue crack propagation prediction in combination with real-time multi-source data to generate a prediction result; and based on the prediction result, evaluating fatigue failure risks, including crack growth rate, residual life evaluation and failure probability, and outputting alarm information or optimization decision information.

A high-altitude electrode discharge voltage prediction method and system, a device and a medium, relating to the technical field of electrode discharge. The method comprises: acquiring a measured dataset of a high-altitude true electrode, and performing normalization processing and data partitioning on the measured dataset in a preset selection ratio to generate an initial training set and a test set; on the basis of the degree of influence between a feature variable and a target variable in the initial training set, constructing a target training set; carrying out model construction on the basis of a support vector regression method and an ant lion optimization algorithm in combination with the target training set, to generate an intermediate intelligent model; using the test set to test the intermediate intelligent model, and determining a target intelligent model; and inputting the measured dataset into the target intelligent model for discharge voltage calculation, to generate discharge voltage prediction data corresponding to the high-altitude true electrode. By means of the ant lion optimization intelligent algorithm and the target intelligent model, the accuracy of prediction results can be improved.

The invention relates to the technical field of photovoltaic power generation, and discloses a BAPV-based roof photovoltaic intelligent monitoring system and a dynamic tuning method and device. The method comprises the following steps: scanning a preset BAPV assembly through three-dimensional laser, constructing a surface point cloud model, and establishing a three-dimensional coordinate system comprising BAPV assembly installation base points; acquiring a real-time environment parameter set corresponding to the BAPV component; calculating an optimal working angle of the BAPV component according to the real-time environment parameter set based on an improved particle swarm algorithm; simulating an all-weather shadow movement track according to the optimal working angle based on a Monte Carlo method, and generating a component shielding influence weight matrix; establishing a heat balance equation according to the component shielding influence weight matrix so as to calculate a spacing optimization parameter of the BAPV component; a control instruction is generated according to the spacing optimization parameters, and dynamic tuning of the BAPV assembly is completed according to the control instruction; the control instruction comprises a support rotation angle compensation amount, an inverter parameter adjustment amount and an abnormal component isolation instruction. And the pain points of modeling errors, static optimization limitation, thermal runaway risks and the like are solved.

The invention relates to the field of hydrology and water resource management and karst hydrodynamics, and discloses a karst basin coupling hydrology model prediction method, which comprises the following steps: S1, collecting and standardizing multi-source heterogeneous monitoring data of a karst basin; s2, constructing a hydrological model for dynamic coupling of surface runoff, fracture seepage and a pipeline network; s3, performing global optimization on the multi-dimensional hydrological parameters by adopting a hybrid intelligent optimization method; s4, performing iterative correction on the model based on a multi-level verification system; and S5, outputting a hydrological prediction result and generating disaster early warning information. By adopting a dynamic adjusting technology of karst area underground river and surface water flow network joint modeling, multi-karst water-containing medium characteristic analysis and integration of underground river and surface water system response characteristics, the problems that a traditional method cannot accurately predict underground water flow and flow velocity, and is difficult to deal with complexity of a karst water system, lack of data and the like are solved.