11322 results about "Mathematical model" patented technology

The present invention relates to a digital twin processing method and system, and a cloud platform. The method comprises: acquiring production system elements, carrying out abstraction definition and parameterization description on the production system elements by means of digital-to-analog knowledge fusion of a mathematical model and a mechanistic model, so as to construct a digital twin ontology model; analyzing and reconstructing model data to obtain a mapping model of which object variables can be directly accessed and operated by a collective motion control method, so that the model is visualized at the cloud; and using an external data source to drive parameter update and operation matching of the model by means of a motion control method, so as to complete cooperative deployment and synchronous evolution of an actual physical device and the model in the production process on a cloud server. According to the present invention, a model is constructed by means of digital-to-analog knowledge fusion of a mathematical model and a mechanistic model, the model is mapped to achieve motion visualization, model parameter update and operation matching on the cloud are achieved, and then cooperative deployment and synchronous evolution of a physical device and the model are completed.

The present application provides a method for pipeline defect detection using the magnetostrictive characteristics of ultrasonic guided waves, which relates to the technical field of pipeline defect detection, including: obtaining the geometric and material parameters of multi-layer heterogeneous pipelines, and establishing a mathematical model of the pipeline; according to the mathematical model, calculating the dispersion curve and modal characteristics of ultrasonic guided waves in the pipeline; selecting a suitable excitation frequency and guided wave mode, and optimizing the detection parameters of ultrasonic guided waves; presetting defects of different types and sizes in the pipeline, and analyzing the influence of defects on guided wave propagation characteristics through numerical simulation; building a multi-layer heterogeneous pipeline ultrasonic guided wave detection experimental system, collecting guided wave signals under different defect conditions, and extracting time-frequency domain characteristic parameters. The present invention can realize the effective detection and evaluation of internal defects of multi-layer heterogeneous pipelines at different temperatures, and provides an important technical means for ensuring the safe operation of pipelines.

The invention relates to the technical field of multi-axis linkage machining, and discloses a multi-axis linkage machining path generation method, device and equipment and a storage medium, and the method comprises the steps that modeling is conducted on a multi-axis machine tool and a workpiece, and a machine tool kinematics model and a workpiece obstacle avoidance scene mathematical model are obtained; a state space and an action space are constructed, and a machining path double-delay depth deterministic strategy gradient model is obtained; performing time deviation calibration to obtain a time synchronization sensing data set; performing path generation to obtain an initial processing path; the initial machining path is optimized, and an optimized machining path is obtained; according to the method, a directed acyclic graph is constructed, and task division and parallel optimization are performed to obtain a multi-axis linkage machining path, so that multi-objective optimization of machining time, cutting force fluctuation and tool wear is realized, and the machining efficiency and the machining quality are improved.

The invention provides a servo hydraulic system optimization control method and system for a press machine test platform, and relates to the technical field of press machines, and the method comprises the steps: building a state space expression and building a mathematical model of a servo hydraulic system based on dynamic characteristics by deducing mathematical expressions of all components of the system; building a predictive control algorithm based on the mathematical model, solving a quadratic programming problem by adopting a rolling optimization strategy through building a state space predictive model and determining a multi-objective optimization function to obtain an optimal control sequence, and taking a first element of the optimal control sequence as control input at the current moment; feedforward compensation and pressure feedback compensation are introduced, the feedforward compensation constructs a compensation model based on an inverse system of a press transfer function and pre-corrects a control signal, and the pressure feedback compensation designs a digital filter based on a pressure error, fuses a feedforward compensation signal with a pressure feedback compensation signal and superposes the fused signal with the control signal; and obtaining a final control signal.

The invention discloses an energy management and safety protection cooperation method for a liquid cooling industrial and commercial energy storage system, and particularly relates to the technical field of energy storage system management. A battery electrochemical model, a heat distribution diagram, temperature gradient data and electrical parameters are used as input, and a battery temperature change trend curve is output; a liquid cooling control strategy is set according to the prediction result; fusing the temperature gradient abnormal parameters, the temperature trend risk and the multi-modal environment data abnormal parameters, starting a fire risk assessment model, predicting the fire probability and position, calculating a fire risk coefficient, generating a fire risk report and setting safety protection measures; a multi-objective optimization mathematical model is constructed based on the energy efficiency ratio, the full life cycle income and the battery health degree, energy storage operation data and power grid requirements are combined, a Pareto optimal solution set is generated by adopting a non-dominated sorting genetic algorithm, and a charging and discharging strategy and liquid cooling parameters are optimized; the liquid cooling pipeline layout is optimized through reinforcement learning, and the problem that the battery temperature cannot be effectively managed is solved.

Methods of creating and using digital twins of vegetation / crops in relation to their growing environments. Some embodiments include creating a digital twin of the growing environment using sensor data from a subsurface sensor suite, creating a digital twin of the vegetation crop using data from an overhead sensor suite, and using at least one mathematical model to relate the digital twin of the crop to the digital twin of the growing environment. In some embodiments, the mathematical model(s) implement a root cause analysis that uses a limiting factor approach. In some embodiments, the subsurface sensor suite is on a multi-sensor soil probe. In some embodiments, the multi-sensor soil probe comprises a video microscope, a tip stress sensor, a sleeve stress sensor, and a volumetric- water-content sensor. Other related systems, devices, methods, and software are also disclosed.

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

The invention relates to the technical field of unmanned aerial vehicle inspection, and discloses a power transmission line unmanned aerial vehicle inspection task intelligent allocation collaborative planning method based on a genetic algorithm. The method comprises the following steps: firstly, collecting and analyzing basic information of a power transmission line, an unmanned aerial vehicle, a surrounding environment and weather, and establishing a space model of unmanned aerial vehicle inspection by using a grid method; based on the collected information, establishing a mathematical model to allocate inspection tasks of the unmanned aerial vehicle; a genetic algorithm is used for coding and optimizing a routing inspection path, meanwhile, a fitness function is designed to comprehensively consider multiple factors of a search effect, and quantization, weight distribution and optimization selection are carried out; and an optimal solution is found through iterative optimization, and intelligent distribution and collaborative planning of unmanned aerial vehicle inspection tasks are realized, so that the requirements of regional inspection are met. According to the technical scheme, the flight threat degree under different meteorological conditions can be quantitatively evaluated by considering the wind speed, rainfall and visibility, and the flight plan is adjusted according to the flight threat degree.

The invention provides a reinforcement learning-based desilting strategy optimization method and system, and relates to the technical field of intelligent water area desilting, and the method comprises the steps: carrying out the multi-scale decomposition, adaptive histogram equalization, contrast optimization and edge sharpening processing of a collected image, and obtaining an optimized desilting environment image; then constructing a three-dimensional voxel grid model based on the optimized image, and establishing an environment interference source mathematical model and a dynamic evolution rule of sludge distribution; and finally, inputting the image features into a dredging strategy optimization network, through interaction with a simulation environment, updating network parameters based on a dual time sequence difference algorithm, generating a motion track planning scheme and an operation parameter configuration scheme of the dredging robot, and optimizing a value function on line according to an actual dredging effect. The desilting efficiency can be effectively improved, and the desilting cost is reduced.

The invention relates to the technical field of heat exchange modules, and discloses a multi-cavity cooperative control method and device for a micro-channel heat exchange module and electronic equipment, and the method comprises the steps: carrying out the numerical simulation calculation of a plurality of heat exchange cavities of the micro-channel heat exchange module, and obtaining a training data matrix; coefficient calculation and heat flow coupling relation analysis between cavities are carried out, and a heat flow distribution mathematical model is generated; substituting the heat flow distribution data, the temperature distribution data and the flow distribution data into a non-homogeneous heat conduction equation, and constructing a multi-objective optimization function; executing chaotic mapping processing and multi-target collaborative differential evolution to obtain an optimal control scheme set; carrying out Q-learning state value function solving, and generating a cooperative control instruction; and inputting the cooperative control instruction into an execution unit, and performing real-time adjustment and online updating on inlet temperature parameters and flow parameters of each heat exchange cavity to obtain a target heat flow distribution model. Therefore, information interaction and optimized cooperation among the heat exchange cavities are achieved, and the heat exchange efficiency is remarkably improved.

The invention discloses a ship collision avoidance optimization method under the condition of uncertain obstacle ship motion information, and the method comprises the steps: constructing a membership function for measuring the ship collision risk degree, so as to construct a ship collision risk degree mathematical model, and obtaining the current ship collision risk degree; based on international maritime collision avoidance rules, according to a preset ship collision risk degree range, ship collision avoidance danger stages and ship encounter situations and way-giving relations corresponding to the ship collision avoidance danger stages are divided and obtained, and support is provided for formulating an autonomous collision avoidance strategy for the intelligent ship under different collision avoidance situations by combining a DWA collision avoidance algorithm and a VO collision avoidance algorithm; aiming at the condition that the ship navigation state has discontinuous output, a CNN-GRU ship navigation state prediction network is provided, the ship navigation state and the uncertainty of the state are predicted based on AIS historical data, and the uncertain distribution of the position of the ship navigation state is obtained, so that under the condition that a moving obstacle cannot be continuously perceived, the ship navigation state can be accurately predicted. It can still be ensured that the intelligent decision-making system provides effective collision avoidance decision support for the intelligent ship, the uncertainty of the ship position is fully considered, and the ship field is optimized.

The invention belongs to the technical field of power prediction, and discloses a new energy power prediction method and system based on multi-source meteorological data fusion, and the method comprises the steps: obtaining meteorological data from different meteorological data sources in real time or at regular time through a data obtaining module; carrying out preliminary screening and preprocessing on the obtained data by utilizing a data processing module; the data fusion module is used for integrating meteorological data from different sources by adopting an advanced data fusion algorithm to eliminate data redundancy; a mathematical model or a machine learning model suitable for new energy power prediction is constructed by using the prediction model module based on meteorological data of multi-source fusion; and the result output and evaluation module is used for outputting the prediction result to a user in a visual form, and carrying out real-time or regular evaluation on the prediction result. The method has the characteristics of diversified data sources, advanced data fusion technology, flexible prediction model construction, high real-time performance and accuracy, wide application scenarios and the like.

The invention discloses an interference suppression method for a random phase modulation electromagnetic metasurface. The interference suppression method comprises the following steps: constructing a mathematical model for a modulated radar echo signal; wherein a random phase modulation electromagnetic metasurface is installed on the surface of a target, a radar transmitting signal enters the random phase modulation electromagnetic metasurface, and after random time domain phase modulation, a radar echo signal is generated; discrete sampling processing is carried out after digital down-conversion and dechirping are carried out on radar echo signals, and then additional phase information is estimated after self-mixing processing is carried out. Phase compensation is carried out on the radar echo signal after discrete sampling processing under the condition that noise is considered, and then time domain differential transformation and discrete Fourier transformation are carried out; estimating a phase mutation moment by using a subspace algorithm, and reconstructing an interference modulation sequence based on an estimation result; and demodulating random phase interference by using the reconstructed interference modulation sequence, and performing matched filtering processing to obtain an output signal.

The invention belongs to the technical field of ship path tracking, and discloses a ship path tracking method based on adaptive line-of-sight guidance and fuzzy adaptive PID, and the method comprises the steps: presetting a path point, and obtaining a real-time coordinate; obtaining an actual expected heading angle # imgabs0 # and a ship observation heading angle # imgabs1 #, subtracting the actual expected heading angle # imgabs0 # and the ship observation heading angle # imgabs1 # to obtain a heading deviation # imgabs2 #, and calculating a heading deviation rate # imgabs3 #; the course deviation and the course deviation rate are input into a fuzzy self-adaptive PID motion control module, and gain parameters are output through a fuzzy control algorithm; the gain parameter and # imgabs4 # are input into a PID controller to calculate a ship rudder angle # imgabs5 #; inputting # imgabs6 # into the motion mathematical model to update the motion state, and forming a closed-loop updated motion state to enable the ship to approach a preset path point; and judging a ship path after the state information is updated. The expected course of the ship is calculated in real time and the sideslip angle of the ship and the control parameters of the ship are adjusted in real time through self-adaptive sight guidance, and the control parameters are automatically adjusted in real time by means of the fuzzy self-adaptive PID motion control module to achieve accurate motion control, so that the ship travels according to a preset planned path.

The invention belongs to the technical field of stirring control, and particularly relates to an intelligent speed regulation stirring control method and system and computer equipment, and the method comprises the steps: determining a target control value of stirring control according to the requirements of a stirring technology; various sensors are installed on the stirring equipment, it is ensured that key parameters in the stirring process are accurately collected in real time, the data collection frequency is set, and it is ensured that dynamic changes in the stirring process are captured; filtering processing is carried out on the collected sensor data, noise interference is removed, the sensor data is corrected, and the accuracy of the data is ensured; key features in the stirring process of the combustion furnace are extracted through a data analysis method, and a mathematical model of the stirring process is established for prediction and control; a closed-loop control system is established, the stirring speed of the combustion furnace is adjusted according to data monitored in real time, the stability and accuracy of the stirring process are ensured through an intelligent algorithm and real-time parameter monitoring, insufficient stirring or excessive stirring is avoided, and therefore the product quality and the production efficiency are improved.

The invention discloses a power distribution network overhead line risk refined early warning method and system. The method comprises the following steps: collecting meteorological time sequence data and power distribution network topology and line parameter data; and preprocessing the data, and constructing a prediction data set. And establishing a fault probability prediction model based on the meteorological data and the line parameters. Calculating the fault probability by using the mathematical model, correcting the model, training in combination with static topology and dynamic meteorological data, and constructing a fault probability network model. And analyzing the importance of the nodes and lines of the power distribution network, and evaluating the load loss proportion of the nodes. And carrying out normalization processing on the importance indexes. And comprehensively calculating the predicted fault probability, the node loss load and the importance index to obtain a branch risk parameter. Fine early warning is carried out according to a set early warning threshold value, and dynamic adjustment is carried out to adapt to the actual operation condition. The method has the advantages that comprehensive risk assessment is provided, and the accuracy of fault prediction and the timeliness of risk early warning are improved.

A system-side harmonic impedance estimation method and apparatus, and a storage medium and an electronic device. The method comprises: acquiring an h-th harmonic parameter equivalent circuit simplified on the basis of power grid characteristics, h being a positive integer greater than or equal to 2 (S1); on the basis of the h-th harmonic parameter equivalent circuit, establishing a system-side harmonic impedance estimation mathematical model (S2); acquiring amplitude and phase data of a harmonic voltage and a harmonic current at a PCC (S3); dividing the amplitude data of the harmonic voltage at the PCC into several stable intervals (S4); on the basis of the amplitude and phase data of the harmonic voltage and the harmonic current in the several stable intervals, establishing a variable coefficient regression model between a harmonic voltage phasor and a harmonic current phasor (S5); selecting an optimal window width of a kernel function of the variable coefficient regression model (S6); and calculating a system harmonic impedance on the basis of the variable coefficient regression model and the optimal window width (S7).

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

The invention relates to the technical field of path planning, in particular to a multi-unmanned aerial vehicle oil depot storage tank inspection intelligent optimization method based on a layering strategy. The method comprises the following steps: constructing an equivalent terrain mathematical model according to oil depot storage tank characteristics, and establishing a multi-objective optimization objective function; performing global path planning inspection of multiple unmanned aerial vehicles by using an improved wolf pack optimization algorithm; abnormities are monitored through a carried sensor, and abnormal information is uploaded to a terminal; the terminal issues the target abnormal point to the single unmanned aerial vehicle; performing single-machine local path optimization by adopting an improved particle swarm optimization algorithm; and updating the state of the inspection task. According to the method, global path planning and local path optimization are effectively combined through a layered inspection strategy, the defects of a traditional method in inspection full coverage and local emergency response are overcome, the intelligent and efficient level of an unmanned aerial vehicle inspection system is improved, and the method is suitable for complex oil depot storage tank inspection tasks and emergency disposal scenes.

The invention relates to the technical field of seepage monitoring, and discloses a seepage monitoring system based on a hydraulic engineering project. The system collects water level, flow velocity, seepage flow, soil humidity, soil temperature and soil pressure data through a data collection module, a data analysis module calculates standard soil humidity, standard water flow velocity, standard soil pressure, standard soil temperature and standard seepage flow according to the data, establishes a mathematical model, calculates seepage abnormal indexes, and calculates the seepage abnormal indexes according to the standard soil humidity, the standard water flow velocity, the standard soil pressure, the standard soil temperature and the standard seepage flow. The risk assessment and early warning module performs threshold judgment on the seepage abnormal indexes, sends out an overrun signal when an overrun condition occurs, predicts a future seepage trend by using a moving average method at the moment, generates a future seepage trend broken line graph and a seepage risk assessment report, and uniformly transmits the future seepage trend broken line graph and the seepage risk assessment report to the emergency response module; and when the emergency response module receives the overrun signal, an emergency plan is started, and related managers are informed of the emergency plan, the future seepage trend broken line graph and the seepage risk assessment report in a short message mode.

The invention discloses a virtual power plant scheduling method based on a large language model and deep reinforcement learning, and belongs to the technical field of virtual power plant scheduling. Comprising the following steps: constructing a virtual power plant multi-agent cloud edge collaborative scheduling framework based on large language model driving; predicting wind power, photovoltaic power and load power based on a large language model; constructing a mathematical model of virtual power plant optimization scheduling; converting the virtual power plant optimization scheduling model into a Markov game process in combination with a large language model; performing initialization training on the strategy network of the edge layer intelligent agent by adopting imitation learning to obtain a pre-trained edge layer intelligent agent strategy network; and based on the pre-training strategy network of the boundary layer intelligent agent, combining with a large language model and adopting an improved multi-agent near-end strategy optimization algorithm to solve a scheduling strategy.

The invention discloses an intelligent job shop scheduling method. The method comprises the following steps: S1, establishing a personalized improved NSGA-II algorithm module and a DQN-based adaptive decision algorithm module; s2, establishing a mathematical model taking the maximum completion time, the total load of equipment and the total energy consumption of a workshop as optimization targets; s3, in a personalized improved NSGA-II algorithm module, considering a workshop optimization target, adopting multiple strategies for initialization, and adding a user personalized demand to screen a target population; s4, in a DQN-based adaptive decision algorithm module, population features are extracted to construct an agent state space, and a deep Q network experience playback mechanism is utilized to train the relationship between agent capture scheduling information and a scheduling target to perform adaptive action decision; and S5, returning the optimal action predicted by the DQN model to the NSGA-II, adjusting the optimal parameter proportion according to the real-time state, and improving the multi-target solution quality.

The invention provides an urban water runoff control method and system, and relates to the technical field of urban water runoff control, and the method comprises the steps: obtaining historical rainfall data, real-time data, modeling parameters and urban drainage system parameters; carrying out rainfall frequency analysis processing on the historical rainfall data based on a preset Bayesian inference mathematical model to obtain feature information; carrying out regional division processing according to the feature information and the rainwater pipe network layout map to obtain a segmentation result; a rainfall prediction model is constructed according to the segmentation result, and a prediction change result is obtained through calculation; performing simulation processing according to the prediction result and the rainwater pipe network layout map to obtain a simulation result; and performing optimization processing on the initial control scheme based on a simulation result to obtain an urban water runoff dynamic control scheme. The urban drainage system is comprehensively analyzed and optimized by comprehensively considering various factors such as terrain, buildings and soil permeability characteristics and adopting a graph embedding algorithm and a multi-objective optimization method, and the overall efficiency of the system is improved.

The invention provides a material simulation test method and platform for mold flow analysis, and relates to the technical field of material simulation testing, and the method comprises the steps: carrying out the product demand analysis of a target test material, and obtaining the product demand information; performing injection molding analysis to obtain an injection molding control parameter threshold; obtaining an injection molding simulation test model; carrying out an injection molding simulation test, and carrying out image pre-acquisition to obtain a first pre-acquired image; defect feature analysis is carried out, attention area distribution is carried out, and a target attention area is obtained; an injection molding defect area image is obtained, and defect feature information is obtained; and feedback testing and parameter optimization are carried out, optimized injection molding control parameters are obtained, and injection molding control is carried out. The technical problems that in the prior art, injection molding analysis generally depends on a fixed mathematical model and limited technological parameters, and problems in the molding process cannot be recognized and adjusted in real time, so that the production efficiency and the product quality are low are solved.

The invention discloses a power distribution network distributed photovoltaic bearing capacity real-time evaluation method, which comprises the steps of determining a mathematical model for power distribution network photovoltaic bearing capacity evaluation, and constructing a Markov decision model for power distribution network photovoltaic bearing capacity dynamic evaluation based on the mathematical model; a soft actor-commentator SAC algorithm is used as a basic framework, graph sampling and aggregation GraphSAGE are used for improving the SAC algorithm, a graph deep reinforcement learning algorithm is constructed, the graph deep reinforcement learning algorithm is used for solving a Markov decision model, and a trained graph strategy network is obtained; carrying out the subsequent real-time evaluation of the distributed photovoltaic bearing capacity of the power distribution network through employing the trained graph strategy network; according to the method, the limitation that precise modeling is needed in a traditional method is effectively overcome, and a more accurate real-time evaluation result is provided.

The invention discloses a single-epoch GNSS-oriented PAR ambiguity precision factor determination method, and relates to the technical field of satellite navigation and positioning. Comprising the following steps: dividing pseudo-range and carrier wave observed quantities in a GNSS double-difference mathematical model into two groups, and constructing a function relationship between a floating point solution # imgabs1 # and a floating point solution # imgabs2 # variance-covariance matrix of an ambiguity vector # imgabs0 #; constructing a single-frequency single-epoch single-system P-ADOP expression and establishing a shrinkage factor k de-inversion transformation analytic expression, and simultaneously obtaining a single-frequency single-epoch single-system GNSS non-inverse transformation P-ADOP approximation formula; and performing analogy derivation on the single-frequency single-epoch single-system non-inverse transformation P-ADOP approximate formula to obtain a single-frequency multi-system or multi-frequency single-epoch GNSS non-inverse transformation P-ADOP general approximate formula. The method has important theoretical value and practical guiding significance for GNSS optimal ambiguity subset rapid selection and positioning and partial ambiguity fixing success rate influence mechanism analysis.

The invention discloses a dynamic control method for a complex curved surface five-axis numerical control machining tool vector, which comprises the following steps of: scanning the surface of a target workpiece by using a high-precision three-dimensional scanner to obtain point cloud data; based on the point cloud data, establishing a mathematical model S (u, v) of the target complex curved surface; a mathematical model based on the target complex curved surface; establishing a comprehensive dynamic model of the five-axis numerical control system; optimizing the initial tool posture in combination with the kinetic model; collecting dynamic operation data of the cutter and the workpiece; on the basis of the dynamic operation data, a model predictive control (MPC) algorithm is used for adjusting the machining path and the cutter direction in real time; according to the method, a comprehensive dynamic model of a five-axis numerical control system is established, dynamic coupling effects of a machine tool kinematic chain, cutter flexibility, cutting force, a temperature field and a stress field are brought into a unified framework, and complex dynamic behaviors are accurately described and controlled through multi-body dynamic modeling and an optimization algorithm.

The invention relates to the technical field of process parameter design, and discloses a silver electrolysis process parameter design method and system based on multi-objective optimization. The method comprises the following steps: performing differential time sequence parameter decomposition on operation data of the silver electrolysis process to obtain a sensitive parameter set and a reference parameter set; performing reweighted periodic feature extraction on the sensitive parameter set and the reference parameter set, and establishing a dynamic mathematical model of the silver electrolysis process parameters; based on the dynamic mathematical model of the silver electrolysis process parameters, physical and chemical process numerical simulation is conducted through COMSOLMultiphysics software, and a response curved surface model is obtained; and according to the response curved surface model, a process parameter combination evaluation value sequence is generated, silver electrolysis process parameter multi-objective optimization is carried out based on the process parameter combination evaluation value sequence, and a silver electrolysis process parameter optimization scheme is output. According to the application, multi-physical-field collaborative simulation of electrode reaction, ion transmission, heat conduction and the like is realized, and performance index responses under different process parameters are accurately predicted.

A real-time quantitative characterization method, equipment and a medium for rock mass evolution are disclosed. The method includes: the multiphase field detection and monitoring data is fused; a deep learning model driven by physical principles is established based on the physical principles of fluid density; the deep learning model is trained using real-time fused data as input and corresponding evolution distribution images as output; a trained deep learning model is used to obtain an evolution distribution image based on multi-phase field detection and monitoring data of different time periods and types; a mathematical model is used to quantitatively characterize of physical and mechanical parameters in the whole process of progressive failure of the dynamic evolution of the rock mass based on the macroscopic mechanical parameters and evolution distribution images synchronized with multiphase field detection and monitoring data.

The invention discloses a fishery culture pond temperature control optimization method, and belongs to the technical field of PID control optimization, and the method comprises the specific steps: S1, constructing a fishery culture pond temperature control system; s2, improving a dandelion optimization algorithm; s21, using a multi-elite opposite learning strategy to improve a mathematical model of a decline stage of the dandelion optimization algorithm; s22, improving a mathematical model of a landing stage of the dandelion optimization algorithm by using a self-adaptive segmented search strategy; s3, optimizing PID controller parameters in the temperature control system of the fishery culture pond by using an improved dandelion optimization algorithm; s4, applying the optimal three parameters Kp, Ki and Kd obtained in the S3 to a PID controller of a temperature control system of the fishery culture pond, and optimizing the temperature control of the culture pond; the fishery culture pond temperature control system is optimized through an improved dandelion optimization algorithm, and the temperature control performance and the control precision of the system are integrally improved.