65results about How to "Avoid local optima" patented technology

A photovoltaic array fault diagnosis method based on a Transform-BiLSTM model relates to a photovoltaic array fault diagnosis method, and comprises the following steps: S1, inputting photovoltaic array sample data under normal and different fault working conditions, carrying out standardized conversion on the sample data, and dividing a training set and a test set through VMD modal component decomposition; s2, initializing parameters of a Transform-BiLSTM model and parameters of an IIVYA algorithm, and setting the number of hedera helix populations, the upper and lower limits of independent variables, the maximum number of iterations and the dimension size; s3, initializing a hedera helix population; s4, calculating the fitness value of the new position of the hedera helix according to the position updating formula of each hedera helix population; s5, judging whether the number of iterations reaches the maximum or not, comparing the optimal fitness values stored in all iterations, and outputting optimal parameters; and S6, constructing an optimal Transform-BiLSTM model by using the optimal parameter value obtained by the IIVYA algorithm, and carrying out diagnosis test on the photovoltaic array fault.

The invention relates to a multi-target optimization method for pressure and flow characteristics of a CDC electromagnetic pilot overflow valve, and belongs to the technical field of automobile suspensions. The method comprises the following steps: (1) establishing a three-dimensional equivalent model of the CDC electromagnetic pilot overflow valve; (2) building a dynamic model according to the built three-dimensional equivalent model and a working principle; (3) building a hydraulic simulation model according to the kinetic model; (4) forming a training sample to train the neural network model; (5) initializing ant colony algorithm parameters; (6) starting ant colony algorithm iteration; (7) updating pheromones; (8) judging whether a termination condition is met or not; (9) outputting an optimal parameter combination and an optimized pressure-flow characteristic curve; and (10) determining the importance degree of the influence factors through a Pareto method. The ant colony algorithm and the annealing algorithm are combined, the local optimum problem is effectively avoided, and global convergence is accelerated.

The invention discloses an AGV obstacle avoidance path planning method in an unmanned warehousing system, and the method comprises the steps: employing nested Logistic mapping and Skew Tent mapping to generate a chaotic initialization population to replace a population generation mode of a conventional differential evolution algorithm, carrying out the Levy flight operation after the selection operation, and carrying out the global path search on an environment map, thereby achieving the optimal path planning. And obtaining an AGV global feasible path avoiding obstacles in the environment map, and when the AGV has an unknown obstacle on the feasible path, re-planning the local path by adopting a dynamic window method so as to enable the AGV to realize an obstacle avoidance process in an interval where the unknown obstacle is located. The improved differential evolution algorithm enhances the global search capability through chaos initialization and a Levy flight strategy, and the AGV can quickly respond to an unknown obstacle through a dynamic window rule. The adaptability and the accuracy of path planning are improved, and the obstacle avoidance capability and the safety of the AGV in a complex environment are enhanced at the same time.

The invention relates to the technical field of cargo transportation optimization, and discloses a combined fleet cargo transportation optimization method and system based on data processing, and the method comprises the following steps: S101, collecting cargo feature data and ship feature data, and respectively representing the data through first feature data and second feature data; s102, preprocessing the first feature data and the second feature data to obtain third feature data, fourth feature data and fifth feature data; s103, constructing a matching degree prediction model according to the improved graph neural network model, and outputting the matching degree of the cargo to the ship; the cargo-ship bipartite graph structure comprises cargo nodes, ship nodes, edges, initial features of the cargo nodes, initial features of the ship nodes and initial features of the edges; and S104, optimizing the allocation scheme according to the improved genetic algorithm to obtain an optimal allocation scheme. The efficiency, accuracy and flexibility of cargo and ship distribution are improved.

The invention discloses a thermal power plant circulating water pump operation optimization method using a multi-objective culture differential evolution algorithm, a storage medium, equipment and a computer program product. Comprising the steps of collecting historical frequency converter instructions in the thermal power plant circulating water system, circulating water pump parameters under the corresponding frequency converter instructions and condenser vacuum degrees in the thermal power plant circulating water system, determining the normal use range of the frequency converter instructions, and establishing a comprehensive objective function; and in the application range of the frequency converter instruction, with the purpose of minimizing the comprehensive objective function, optimizing the frequency converter instruction by using a multi-objective culture differential evolution algorithm, determining the optimal frequency converter instruction, issuing the optimal frequency converter instruction to obtain the optimal circulating water pump parameter and the optimal condenser vacuum degree, and operating the thermal power plant circulating water pump. The power generation capacity of the thermal power plant is improved on the basis of reducing the energy consumption of the thermal power plant circulating water system, so that the energy consumption is reduced, and the power generation cost is reduced.

The invention relates to an Internet of Vehicles computing power scheduling method based on a graph neural network and deep reinforcement learning, and the method comprises the steps: S1, collecting the state information of network entities in the Internet of Vehicles and the association information between the entities in real time, and constructing a dynamic time-space attribute graph; s2, inputting the dynamic space-time attribute graph into a pre-trained graph neural network encoder, and outputting a node embedding feature set containing a high-order topological relation and global graph embedding features through multi-layer message passing and feature aggregation; s3, the node embedding feature set and / or the global graph embedding feature are / is used as the state of a deep reinforcement learning agent and input to a strategy network, the computing power scheduling action at the current moment is output, and the computing power scheduling action comprises the steps of assigning an unloading target node for a to-be-processed computing task and distributing corresponding computing and communication resources; s4, dispatching actions are distributed to the corresponding network entities to be executed, environment feedback is collected and used for model updating and next round of dispatching, and the method has the advantages of improving dispatching efficiency, adaptability and system energy efficiency and the like.

The invention discloses a simulated war chess system for demonstrating surface target air defense combat. The simulated war chess system comprises an attacker controller, a defender controller, a referee controller and a database, the database comprises a map storage device, an aggressive weapon storage device and an air defense firepower unit storage device; the defense party controller receives a target defense scheme given by the judgment controller, and extracts a terrain map of a defense target, an air defense firepower unit and various parameters of the air defense firepower unit from a database; deploying a configuration scheme and a moving plan of an air defense firepower unit so as to keep the air defense capability of the defense party above a first specified threshold value; the attacker controller receives a target attack plan given by the judgment controller, extracts a terrain map of a defense target from a database, and extracts an attack weapon and various parameters of the attack weapon; the land-based weapons are used for attacking the air defense firepower units of the defense party; and the air defense capability of the defender is reduced to be below a second specified threshold value within the preset time.

The invention discloses a power flow optimization method and system containing a new energy power distribution network, and relates to the technical field of power flow optimization of a power distribution system. The method comprises the following steps: firstly, establishing a model for representing uncertain wind power and photovoltaic output, establishing a steady-state power flow model containing a new energy distribution network system, and realizing power flow calculation of the new energy distribution network; then, equality constraints and inequality constraints are determined, a multi-target optimal power flow model is established with the minimum economic cost and the minimum network loss as targets, and a Pareto set evaluation model is established; and finally, adopting a fast non-dominated sorting genetic (NSGA-II) algorithm with an elitist strategy to carry out power flow optimization solution on the power distribution system containing the new energy. The result shows that the method can effectively balance a plurality of targets, the optimized power flow distribution remarkably improves the operation stability and economy of the system, and a feasible optimization scheme can be provided for a power distribution system containing high-proportion renewable energy sources in the future.

The invention discloses a large-scale satellite measurement and control scheduling method and system based on a self-organizing learning algorithm, and the method comprises the steps: building a large-scale satellite measurement and control scheduling model, and enabling the optimization target of the large-scale satellite measurement and control scheduling model to maximize the task income; generating a measurement and control scheduling scheme by using a regular algorithm; and when the termination condition is not satisfied, calculating the income, updating the state of the next moment, calculating the strategy probability, generating an action selection operator according to the strategy probability, generating a new measurement and control scheduling scheme by using the selection operator, repeating the process until the termination condition is satisfied, and outputting the final scheme. According to the method, a large-scale satellite measurement and control scheduling integer programming model is constructed, genetic algorithm parameters are trained through a deep reinforcement learning algorithm, and a satellite measurement and control scheduling scheme is generated by using the optimal genetic algorithm parameters, so that the diversity of populations can be kept, falling into a local optimal solution is avoided, and the robustness and toughness of search are improved.

The invention relates to the technical field of control, in particular to an intelligent control method and system for a photovoltaic adhesive production line, and the method comprises the steps: obtaining multi-source technological parameters of the photovoltaic adhesive production line, and predicting the production quality of photovoltaic adhesive through a preset prediction model; calculating the instantaneous fluctuation degree of the multi-source process parameters in the production process and the prediction error of the prediction model in the historical time; calculating a prediction deviation degree of the prediction model according to the instantaneous fluctuation degree and the prediction error; and taking a sum value of the predicted deviation degree and a prediction result of the prediction model as a correction value of the production quality of the photovoltaic adhesive, and when the correction value exceeds a preset range, adjusting the multi-source process parameters. The stability of the production quality and the production efficiency of the photovoltaic adhesive production line are improved.

This invention provides a method and system for UAV path planning based on a fusion algorithm, relating to the field of UAV autonomous navigation and path planning technology. The method includes the following steps: acquiring the starting coordinates, ending coordinates, and obstacle information in the flight environment of a flight mission, and constructing a flight environment spatial model; defining path constraints and optimization objectives, and generating an initial set of feasible paths based on the flight environment spatial model, path constraints, and optimization objectives; performing weighted fusion and guided enhancement on the initial set of feasible paths to obtain a weighted fused path and a guided enhanced path; employing a particle swarm optimization algorithm to construct an initial particle swarm based on the initial feasible path set, the weighted fused path, and the guided enhanced path, and iteratively optimizing the initial particle swarm to obtain a globally optimal particle; and decoding the globally optimal particle to obtain the final flight path. This invention significantly improves the efficiency and quality of UAV path planning in complex static obstacle environments.

The invention provides a reinforcement learning text generation method and system based on BART, and belongs to the technical field of natural language processing. The method comprises the steps of obtaining a BART model for text generation; dialogue data of the intelligent outbound system is obtained, a text data set is formed, and a pre-trained BART model is obtained; constructing a question and answer pair data set based on dialogue data of the intelligent outbound system, and obtaining a fine tuning BART model based on the data set; further obtaining a trained BART model through reinforcement learning optimization based on the fine tuning BART model; and finally, performing text generation by utilizing the trained BART model to obtain a generated text. According to the method, language styles and subtle differences can be accurately captured, the stability of RLHF is improved, the training cost is reduced, the efficiency is improved, and a more stable and efficient solution is provided for wide application of text generation in commercial application.

The application discloses a new energy station frequency control parameter collaborative setting method and application thereof, and the method comprises the following steps: constructing an optimized parameter vector x containing a network type and a network type station heterogeneous control parameter; establishing a comprehensive objective function f(x) which fuses the system frequency deviation minimization and the parameter adjustment cost minimization, and generating an initial training sample database corresponding to the comprehensive objective function based on sampling; constructing an anisotropic Kriging surrogate model based on the initial training sample database; constructing a sparse degree weighted adaptive expected improvement SW-AEI adding point criterion based on the anisotropic Kriging surrogate model, and screening an optimal candidate vector with the maximum sparse degree weighted adaptive expected improvement value; updating a new sample to the training sample database to recalibrate the anisotropic sensitivity hyperparameters in the anisotropic Kriging surrogate model until a convergence condition is met, and outputting the optimal collaborative control parameter.

The invention discloses an interactive intelligent teaching system based on multiple modes, and relates to the technical field of intelligent teaching management systems. The system comprises a multi-modal perception layer, a semantic and feature fusion engine, a cognition-emotion two-dimensional analysis module, a strategy decision center and a multi-dimensional interaction execution terminal. Multi-source data of vision, hearing, physiology, behaviors and the like are synchronously collected in real time, and an improved MLP regression network is utilized to construct a cognition-emotion two-dimensional state model of a learner; when the state of the student is monitored to be abnormal, the strategy decision center dynamically generates an optimal teaching path in combination with a knowledge graph and a historical successful case, and drives a virtual digital teacher to execute immersive intervention; according to the method, the problems of emotion perception deficiency, feedback lagging and insufficient individuation in traditional online teaching are solved, and intelligent closed loop and adaptive optimization of the teaching process are realized.

The invention discloses a multi-material topological optimization method for aircraft structure design, and belongs to the technical field of aerospace. The method comprises the following steps: establishing an expanded design area; carrying out grid division; establishing a material interpolation model; finite element analysis is carried out, and the sensitivity of objective function quality constraint conditions is obtained; sequentially updating the design field variable, the filtering field variable and the physical field variable, and applying implicit floating projection constraint; judging whether a convergence condition is met; performing smooth design; re-dividing the structure subjected to smooth design by using an original grid, and performing sensitivity analysis; and calculating an error value, judging whether an accurate smooth design condition is met or not, and if so, ending optimization and outputting an optimal structure. According to the method, under the condition that good convergence performance is guaranteed, material selection can be carried out in the topological optimization process, meanwhile, the iteration step number needed by topology is reduced through the three-field technology, optimization efficiency is improved, and the final structure has a clear and smooth boundary and can be directly used for manufacturing.

This invention discloses an intelligent path planning method for heavy-duty robotic arms used in the dismantling of decommissioned photovoltaic (PV) modules. The method involves modeling and training the dismantling task of decommissioned PV modules using an actor-critic algorithm, followed by intelligent path planning for the heavy-duty robotic arm based on the constructed model, generating locally optimal work trajectories. Heavy-duty robotic arms face scenarios with few obstacles in the dismantling of decommissioned PV modules, and their path planning problem can be reduced to an optimization task for a single robotic arm. This solution employs an actor-critic algorithm to achieve efficient and stable path generation, supporting automated dismantling. Furthermore, it introduces the concept of entropy to enhance exploratory capabilities, avoid local optima, and ensure the robotic arm can adapt to the irregular shapes of the modules and dynamic environmental changes.

The invention relates to the technical field of unmanned aerial vehicle reconnaissance systems, in particular to an unmanned aerial vehicle multi-target cognitive electronic reconnaissance system which comprises an unmanned aerial vehicle platform, an unmanned aerial vehicle, a ScurterN task simulation module used for intercepting radar signals and a simulator used for modeling unmanned aerial vehicle multi-target cognitive electronic reconnaissance task scenes. The radar is arranged in the reconnaissance task area, a plurality of pseudo target modules are arranged in the reconnaissance task area, a plurality of pseudo targets are obtained from the position of the radar according to a nearest neighbor method, the arc centers of the plurality of pseudo targets are solved and are not updated and iterated continuously, and finally a pseudo target is formed; and the unmanned aerial vehicle multi-target cognitive electronic reconnaissance strategy learning module is used for convergence of a radar reconnaissance strategy and building an unmanned aerial vehicle multi-target cognitive electronic reconnaissance strategy network. According to the method, convergence of more than four radar reconnaissance strategies can be simplified, and the convergence speed of the more than four radar reconnaissance strategies is improved.

The invention relates to a dynamic environment autonomous navigation method and system based on sub-target generation hierarchical reinforcement learning. The method comprises the following steps: constructing a double-layer framework in which a top layer is responsible for target scheduling and a bottom layer is responsible for high-frequency execution; the top layer constructs a local occupation grid map according to the laser radar, constructs strip-shaped sub-target probability distribution based on a global reference path, and regulates and controls the entropy of the strip-shaped sub-target probability distribution and the commitment time limit between the top layer and the bottom layer through the environment self-adaptive exploration temperature; and the bottom layer follows the current sub-target within the committed time limit, so that the robot drives to the real-time sub-target. According to the method, strip-shaped probability sub-target distribution is constructed around a global reference path, the distribution entropy and the underlying committed time limit are regulated and controlled by utilizing environment feature-driven exploration temperature single knob linkage, and sub-target screening and issuing are completed in combination with safety expansion and reachability constraint; and a bottom-layer high-frequency closed-loop actuator follows within a commitment period and triggers advanced re-mining when the risk is increased, so that safe and smooth real-time arrival of a final target is realized.

The invention provides an economic management data analysis device and method, and the method comprises the steps: obtaining original data based on an original enterprise insurance management system and a crawler engine, carrying out the preprocessing of the original data, and carrying out the investment and asset data analysis based on the preprocessing of the original data; according to the method, the Boolean's rule and the multi-objective optimization algorithm are combined, maximization of benefits of enterprises is taken as an objective function, insurance premium related data, compensation and claim settlement data, investment and asset data, business scale and market share, customer satisfaction and employee satisfaction are adopted to optimize the NSGA-II algorithm, and decision making is carried out. According to the method, the existing data are fully utilized, the relevance between the data is mined, and the insurance premium strategy and the investment strategy are optimized, so that the maximization of enterprise revenue is realized. Meanwhile, through continuous monitoring and strategy adjustment, the enterprise can adapt to the continuously changing market environment, and the competitive advantages of the enterprise are kept.

The application discloses a kind of factory inspection robot path planning methods based on improved big cane mouse algorithm and dynamic window method fusion, it is related to the field of path planning, this method first constructs factory workshop three-dimensional environment model;Second, the triple improvement is carried out to basic big cane mouse algorithm:design adaptive weight linearly decreasing with iteration number, dynamically balance global exploration and local development capability;Introduce sine-cosine strategy, through specific parameter control and sine-cosine direction optimization, search dimension is enriched to avoid local optimum;Adaptive factor is embedded to core calculation formula, introduce nonlinear function, improve global path planning precision and convergence speed, simultaneously, fusion dynamic window method constructs "global-local" double-layer path planning framework.Experiments show that, in complex dynamic environment of factory, global path planning error is reduced by 15%-20%, dynamic obstacle avoidance response time is shortened to within 0.3s, can satisfy robot all-weather inspection demand, guarantee inspection efficiency and operation safety.

The application discloses a kind of weakly supervised image semantic understanding methods based on multi-task learning, comprising the following steps: obtaining task missing image, constructing multi-level task sharing encoder, extracting high-level semantic information layer by layer, input corresponding decoder branch;Construct public space-task space feature mapping module, through the unaligned task fusion module and task interaction mapping module, update each subtask feature by mapping;Task adaptive feature update module is constructed, and multi-level iterative update unaligned task feature;Task adaptive weakly supervised image semantic understanding framework is constructed, model loss function is established, image data with task missing is input into model, and obtains multi-task prediction result such as semantic segmentation, depth estimation, surface normal estimation.The application is according to the data information of task label unaligned, through the mapping interaction of public space and task space, fully fuses unaligned task feature, iteratively generates high-quality multi-task prediction result, can effectively handle weakly supervised problem with task missing, and simultaneously improves each task prediction accuracy.

The application discloses an optimization method of a rolling wing blade connecting rod mechanism, and belongs to the field of rolling wing aircrafts.The blade swing angle is represented by structural parameters of the blade connecting rod mechanism, and the structural parameters of the blade connecting rod mechanism, including the lengths of the connecting rods, the coordinates of key hinge points and configuration bias, are defined as a variable vector; multiple target functions are constructed for multiple key working conditions such as hovering, forward flight and transition, the multiple target functions not only quantify the performance of the connecting rod mechanism itself, such as kinematic fitting error with a target motion law, dynamic stability index and force transmission efficiency index, but also cover aerodynamic efficiency index and aerodynamic acoustic index which are directly related to the performance of the whole machine; and the optimal structural parameters of the blade connecting rod mechanism are obtained by solving the multiple target functions under constraints, so that the global optimization of the structural parameters of the blade connecting rod mechanism under multiple working conditions, multiple targets and multiple constraints can be realized.

This invention discloses a method for assessing the health status of wind turbine blades based on LNN and an improved LightGBM model, relating to the field of wind turbine health status assessment technology, particularly for assessing the health status of wind turbine blades. The method involves acquiring acoustic signature signals, vibration signals, and SCADA data from the wind turbine, and preprocessing the data. An attention mechanism is used to fuse acoustic signature and vibration features, and a multi-dimensional feature matrix is ​​constructed using SCADA data. Dynamic feature depth extraction is performed using a liquid neural network (LNN) to obtain a high-dimensional feature vector characterizing the blade's health status. This vector is then input into the improved LightGBM model to assess the blade's health status level. This invention achieves accurate assessment of blade health status by correlating and reconstructing acoustic signature, vibration signals, and SCADA data, combining the advantages of LNN in capturing dynamic features with the efficient classification capabilities of the improved LightGBM model, thus providing a reliable decision-making basis for wind turbine blade maintenance.

This invention discloses a field-circuit collaborative analysis method and system based on reinforcement learning and Bayesian optimization. The invention utilizes a Bayesian optimization algorithm to optimize the parameter values ​​of each electronic component in a selected topology. Based on the optimized parameter values, a reinforcement learning-based parameter search environment for electronic components in the RF device circuit topology is constructed, modeled as a Markov decision process. An equivalent circuit model of the RF device is designed according to the optimal action. The equivalent circuit model of the RF device is connected to other circuit systems for circuit-level simulation analysis. By integrating reinforcement learning and Bayesian optimization, two intelligent algorithms, and fully leveraging their respective advantages in discrete structure search and continuous parameter optimization, a high degree of automation and intelligence is achieved throughout the entire field-circuit collaborative analysis process. While ensuring the physical rationality of the model, the efficiency and accuracy of the collaborative analysis are significantly improved.

This invention discloses a method for constructing a road cavity detection model based on 3D ground-penetrating radar (GPR). The method includes constructing a hybrid spatiotemporal detection model, which comprises a feature preprocessing module, a soft clipping module, a spatiotemporal feature extraction module, and an output module. The feature preprocessing module performs feature pre-extraction on preprocessed and labeled data, outputting a pre-extracted feature map. The soft clipping module performs spatiotemporal perception on the pre-extracted feature map, outputting a clipped feature map. The spatiotemporal feature extraction module analyzes the clipped feature map, outputting feature information with global spatiotemporal information. The output module integrates the feature information and predicts the existence of cavities and the range of their occurrence channels. By combining the correlations between data in the spatiotemporal dimension, it accurately detects underground cavities, solving the problems of high computational load and difficulty in adjusting model parameters in existing 3D GPR high-resolution data interpretation. This provides an efficient and reliable technical solution for intelligent road cavity detection and infrastructure safety maintenance.

The invention provides an energy interconnection framework parameter matching design method and system and a medium, and belongs to the technical field of power systems, and the method comprises the steps: determining energy interconnection framework parameters, including a framework type, a port type and a port number; determining constraint conditions of the energy interconnection framework parameters, constructing full life cycle use efficiency as a target function, calculating a fitness value according to the target function, optimizing the energy interconnection framework parameters through an improved litsea rotundifolia optimization algorithm, and obtaining the energy interconnection framework parameters with the optimal fitness value; and determining an energy interconnection framework parameter matching design scheme according to the optimal energy interconnection framework parameter. According to the method, the optimal energy interconnection framework parameter matching design scheme can be quickly generated, and the energy interconnection framework formed by the design scheme can realize the optimal use efficiency.

This invention discloses a USV path planning method based on an enhanced Star-Raven Optimization algorithm, comprising the following steps: Step 1, establishing a USV path planning modeling framework, including: a path mathematical model, a path quality evaluation function model, and an environment model; Step 2, designing the enhanced Star-Raven Optimization algorithm flow, including a periodic elite regrouping mechanism, a dynamic individual elimination and rebirth mechanism, and a distance-guided initialization sorting strategy; Step 3, applying the enhanced Star-Raven Optimization algorithm to solve for the Bezier curve control points, generating the optimal path, and conducting experimental research and comparative analysis. This invention can synergistically optimize multiple objectives such as path length, energy consumption, and navigation feasibility. Simulation results show that the proposed algorithm outperforms the standard Star-Raven Optimization algorithm and particle swarm optimization variants in terms of convergence performance and path generation, and can generate smooth and collision-free USV executable trajectories, possessing good engineering application value.

The invention provides a transportation line combination method, electronic equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring a plurality of transportation lines in a logistics network; according to a line combination constraint, combining the plurality of transportation lines to obtain an initial line set; wherein the route combination constraint comprises a first deadhead constraint, and the first deadhead constraint is used for constraining the deadhead distance and / or deadhead time between different routes in the same initial route combination; and taking the initial line set as a line combination result and outputting the line combination result. In the transportation line combination process, the empty driving condition is considered, so that the initial line set obtained after combination has good performance in the empty driving dimension, and the vehicle empty driving condition is relieved.

The application provides a medical image segmentation method and system based on a CNN-Transformer parallel encoder, and relates to the technical field of medical image segmentation, and the specific steps comprise: inputting a standardized medical image into a parallel encoder, extracting local texture and global semantic feature maps, and aligning the resolution and spatial position through a spatial correlation matrix; screening lesion features based on an anatomical structure prior feature set, dynamically allocating weights to obtain fusion features; taking a lesion gold standard mask as a label, obtaining an initial lesion probability feature map through a decoder, and calculating a bias value; iteratively optimizing a parameter combination to output an optimal configuration parameter combination, and obtaining a final lesion probability feature map, which is classified and activated, thresholded, and outputted as a segmentation mask. The application effectively avoids the local optimal solution of parameter optimization, combines a bias value threshold-driven rapid screening mechanism, fine-tunes parameters for different modal images without full retraining, and effectively breaks through the bottleneck of poor adaptability of the prior art.

The present invention discloses a method and system for predicting the effect of sewage treatment based on deep learning, and the method includes data collection, data preprocessing, construction of a treatment effect prediction model, hyperparameter optimization and sewage treatment effect prediction. The present invention relates to the technical field of sewage treatment data prediction, and specifically refers to a method and system for predicting the effect of sewage treatment based on deep learning. The present invention obtains the original data of sewage treatment through data collection; adopts a data preprocessing method of data cleaning, data labeling, data encoding, data normalization and data set segmentation; adopts an improved spatiotemporal attention graph convolution model to predict the effect of sewage treatment, which can effectively extract key time series features in time series data and complex spatial relationships involved in the sewage treatment process; adopts a hunting optimization algorithm to optimize the model hyperparameters, which can conduct targeted exploration of the parameter space when processing sewage treatment data with multiple variables and complex spatiotemporal characteristics.