388 results about "Adaptive strategies" patented technology

The invention relates to the technical field of data security management, in particular to a self-adaptive data security management and risk early warning system based on intelligent analysis under a cloud platform. Comprising a multi-dimensional data acquisition module; an intelligent analysis module; a self-adaptive strategy generation module; a risk early warning module; and a user behavior portrait construction module. In the design, the security policy can be dynamically adjusted along with the risk situation of the cloud platform, the problem that a static policy cannot adapt to real-time change is solved, and dynamic mapping of risk characteristics-policy parameters is realized; according to the design, the one-sidedness of single-dimension analysis is broken through, multi-modal feature association modeling of user behaviors is achieved, an abnormal behavior triggering threshold value is accurately recognized, and the integrity and accuracy of risk feature analysis are improved; the security policy can be continuously optimized through historical event data, so that protection efficiency attenuation caused by long-term static operation is avoided, and an autonomous lifting link of data driving, algorithm optimization and policy evolution is realized.

The invention discloses an underwater detector cluster adaptive detection method and system based on distributed reinforcement learning, and the method comprises the steps: obtaining detector state data and environment parameter data, obtaining a standardized state matrix through adaptive standardization processing, building a group topological feature matrix based on spatial relation quantification, and obtaining a group topological feature matrix; generating a final feature representation; constructing an action prototype matrix and an action coupling tensor, optimizing an action space based on constraint conditions, and obtaining a feasible action set through multilayer evaluation and screening; a comprehensive reward function is constructed based on a multi-dimensional reward mechanism, strategy optimization and experience optimization are carried out, and a final optimization strategy is obtained; a multi-layer decision evaluation system and a real-time monitoring mechanism are established, and an experience knowledge base is constructed and continuously optimized. According to the invention, through multilevel feature extraction, dynamic action optimization, adaptive strategy learning and knowledge accumulation, the cooperative performance and environment adaptability of the underwater detector cluster are improved.

The invention is suitable for the technical field of video acquisition, and particularly relates to a video stream acquisition method based on deep learning, and the method comprises the steps: carrying out the video acquisition through a self-adaptive strategy, and obtaining real-time collected video data; performing parameter adjustment through edge calculation to obtain preprocessed video data; performing key frame selection on the preprocessed video data to obtain key frame data; performing inter-frame difference compression on the key frame data through the optimized variational auto-encoder model, and outputting video frame compression data; and when the original video needs to be recovered, analyzing the video compression data, reconstructing through the pre-trained neural network model to obtain a reconstructed video sequence, and generating a complete video according to the reconstructed video sequence. According to the method, the non-key frame is directly generated in the decoding process by using the deep learning model, so that the compression ratio can be remarkably improved, and the video playing continuity and the image quality, especially the performance in high-dynamic and complex scenes, can be improved.

The invention discloses a driving situation prediction and adaptive strategy generation system based on a cloud multi-mode large model. Comprising a multi-modal data acquisition and situation prediction module, a self-adaptive strategy generation and remote optimization module, a collaborative driving and multi-vehicle strategy coordination module, a cloud driving intelligent monitoring and feedback module and a dynamic risk assessment and path optimization module. Various input data from the vehicle and the environment are processed through the cloud, risk factors such as sudden obstacles, weather deterioration and complex traffic conditions in the automatic driving situation are predicted in advance, cloud computing provides powerful data processing capacity and storage resources, a large model deployed at the cloud can train data collected by the vehicle, and the risk factors can be predicted in advance. Therefore, the perception and prediction capabilities of the automatic driving system are improved, various coping strategies aiming at the risks are generated, and the vehicle is helped to make a safer and more intelligent driving decision in a complex scene through dynamic generation and remote optimization of an adaptive strategy.

A system for UAV collaborative coverage path planning based on a Q-learning adaptive ant colony algorithm including a memory, an image collection device, and a plurality of UAVs loaded with a path planning module configured to: construct a 3D model in a collaborative coverage environment, by performing a cell division on the 3D model based on a scanning range of an airborne radar of each UAV, obtain one or more sub-regions; by establishing constraints of the UAV and the environment based on the determined 3D model of the region to be searched, establish a problem total cost model; perform a plurality of rounds of iterations, calculate a reward value of each ant colony and determine whether a maximum iteration count is reached, if the maximum iteration count is reached, enter a new round of iteration, otherwise, output a path corresponding to a current round of iteration as a final path.

The invention provides a software supply chain risk detection protection method and system, and the method comprises the steps: obtaining a third-party component from a software supply chain, and analyzing the multi-level dynamic behaviors of the third-party component; predicting a risk behavior pattern by using a Bayesian network algorithm, and constructing a behavior pattern library; monitoring a behavior of the third-party component during operation, and recording a behavior data stream; identifying an abnormal behavior deviating from a normal behavior mode by applying a time sequence anomaly detection algorithm of deep learning; evaluating risk levels of the abnormal behaviors by using a graph neural network, and generating an abnormal behavior list in combination with relevance and propagation paths among the behaviors; according to the list, a response strategy in the safety control measure library is dynamically adjusted through an adaptive strategy selection algorithm, and safety control measures are formulated for the third-party component. According to the invention, the identification and prediction capabilities of potential risk behaviors are improved.

The invention discloses a data center machine room AI energy-saving control method and system, a digital twin model of a machine room operation state is constructed through a holographic perception and heterogeneous data fusion technology, centimeter-level monitoring of an equipment state and environmental parameters is realized, and the system integrates a laser radar array, an acoustic sensor and a gas sensor network. The time-space alignment of multi-modal data is completed by combining edge computing nodes, holographic mapping including thermodynamic characteristics, vibration characteristics and gas leakage risks is formed, historical temperature control strategy characteristics are extracted by adopting a variational auto-encoder based on a dynamic strategy generation mechanism of generative artificial intelligence, and a load trend is predicted by combining a long-short-term memory network. Constructing a self-adaptive strategy pool; the multi-agent reinforcement learning framework enables temperature control, equipment scheduling and power grid response to form game optimization, the strategy robustness in a complex scene is improved, and the system innovatively fuses power grid real-time electricity price and carbon transaction data so as to establish a multi-target decision system.

The invention relates to the technical field of elevator equipment state monitoring and fault diagnosis, and discloses an elevator traction machine running state real-time diagnosis method and system based on high-frequency sampling. According to the method, vibration (larger than or equal to 20 kHz), current (larger than or equal to 10 kHz), sound / sound emission, temperature and rotating speed signals of a traction machine are synchronously collected through a high-frequency multi-mode sensor array; capturing early weak fault transient characteristics; the edge computing unit completes data preprocessing, time synchronization, feature extraction and anomaly detection, and uploads key data to a cloud end through cloud-edge collaboration; the cloud end adopts a working condition self-adaptive strategy and a multi-modal fusion model to carry out deep diagnosis, and outputs fault types, positions and grades; and combining incremental learning and a degradation model to realize health quantification and residual life prediction. Through fusion of high-frequency data capture and an intelligent algorithm, the early fault detection capability, variable working condition adaptability and diagnosis real-time performance of the traction machine are improved, and a solution is provided for predictive maintenance of an elevator.

The invention discloses a reinforcement learning-based virtual power plant response optimization scheduling system and method, and relates to the technical field of virtual power plant intelligent scheduling. The system comprises an environment modeling module, an intelligent agent module, a multi-agent coordination module and a self-adaptive optimization module which are respectively used for constructing a multi-dimensional state space and a layered action space, generating and optimizing an action strategy based on an Actor-Critic network, executing a scheduling instruction through a layered multi-agent structure and realizing conflict consensus, and dynamically adapting to state space change in combination with incremental learning and meta-learning mechanisms. The system and the method have the advantages of fine state modeling, efficient action response, adaptive strategy updating, stable agent coordination and the like, and can keep the continuity, the stability and the optimality of a scheduling strategy in an operation environment in which multi-source heterogeneous power resources participate in scheduling cooperatively, market rules change frequently and load fluctuation is violent.

The invention discloses a unified multi-modal alignment method and system based on mixed experts and low-rank adaptation, and belongs to the field of artificial intelligence and multi-modal learning. According to the method, firstly, a CLIP model serves as a basic model, and a unified modal encoder is constructed in combination with a modal perception hybrid expert strategy and a low-rank adaptive strategy; the unified modal encoder comprises an anchor modal marker corresponding to each anchor modal, an extended modal marker corresponding to each extended modal and a plurality of stacked multi-modal hybrid expert modules; then, on the basis of universal modal alignment measurement and knowledge distillation and cross-modal optimization strategies, multi-modal data samples are sampled in batches, and fine tuning training is conducted on the unified modal encoder. Through a single encoder and single training, alignment of any number of anchor modes and extension modes is realized, a special model does not need to be trained for each mode independently, the transferability of cross-domain and downstream tasks can be improved, and the method is suitable for tasks such as multi-mode understanding, generation and reasoning.

The embodiment of the invention provides an intelligent agent memory indexing method and system based on intention recognition. The method is applied to the technical field of artificial intelligence and comprises the steps of obtaining real-time question-answer data, and performing preliminary intention classification on the real-time question-answer data by utilizing a domain knowledge rule library; extracting a structured description from the real-time question and answer data after the preliminary intention classification, performing deep intention analysis in stages, and outputting a standardized intention description text; according to the standardized intention description text, acquiring an Agent operation context, performing multi-dimensional retrieval to obtain an adaptive strategy, executing the adaptive strategy, and returning a strategy evaluation result; according to a strategy evaluation result, carrying out microscopic feedback and macroscopic feedback to update a strategy library; the Agent operation context is obtained through the following steps that semantic features of a standardized intention description text are captured, and the Agent operation context corresponding to the deep semantic features is recorded based on a fine-grained metadata labeling system. According to the invention, a complete closed loop from intention identification to strategy multiplexing to strategy optimization is realized.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a generation strategy optimization method, device and equipment based on a dynamic environment and a medium. Correcting the generated action vector by combining a domain constraint strategy to obtain a compliant action vector, constructing a multi-dimensional reward vector according to feedback after execution, scaling the reward vector into a reward signal, and finally updating the pre-training generative model by adopting a self-adaptive strategy optimization module based on the reward signal and an interaction track to obtain a reward result. And collaborative evolution of strategy generation and environmental response is realized. According to the method, by introducing dynamic environment information and domain constraints, compliance correction and optimization updating of the generative strategy are realized, and the stability and practicability of the model in a complex environment are improved.

The invention discloses a data fusion method and system for a multi-source micro-grid, and belongs to the technical field of micro-grids, and the method specifically comprises the steps: carrying out the intelligent aggregation of multi-source operation and maintenance data through hierarchical data collection and preprocessing, employing a deep learning enhanced spatio-temporal data fusion model and a dynamic adaptive strategy; formulating an intelligent data distribution strategy, and dynamically optimizing a data transmission path and bandwidth resource allocation; the real-time monitoring system integrated with the AI monitors the state of the micro-grid, and once an abnormity or a potential risk is found, automatic early warning is carried out and is fed back to a scheduling system and an operator through a visual interface, so that the operation and maintenance efficiency and safety of the micro-grid are improved.

The invention discloses a multi-satellite task scheduling method based on a genetic algorithm, and belongs to the technical field of satellite task scheduling. Establishing a scheduling model based on satellite and ground station visible window data, and integrating a scheduling period constraint, a task uniqueness constraint, an equipment protection time constraint and a frequency band and orbit type matching constraint by taking task income maximization as a target; a simulated annealing local search mechanism is introduced into the genetic algorithm, local optimum is jumped out through a probability acceptance inferior solution, and a self-adaptive strategy of mutation probability is dynamically adjusted according to population fitness change; setting an early stop mechanism to terminate iteration in advance when the fitness is continuously not improved; and finally generating a scheduling scheme. According to the method, the problems that a traditional genetic algorithm is prone to falling into local optimum and constraint processing is rigid are solved.

The invention discloses an enterprise cloud asset global data integration management method based on a data center station, and aims to solve the problems of data source dispersion, complex management, data islanding and the like, and the method comprises the following steps: automatically identifying and classifying various data sources of an enterprise through an evolutionary algorithm, and generating a dynamic data source directory; based on the directory, a self-adaptive data access strategy is generated, unified access of structured, semi-structured and unstructured data and data synchronization of a cross-cloud platform are realized, a unified data model is constructed in a data platform, and unified mapping of data of different sources is realized through a semantic mapping technology. In the data transmission process, the optimal path set is adopted to ensure high efficiency and stability of data transmission. According to the method, the received data is dynamically treated through a self-adaptive strategy, a cross-cloud data virtualization layer is constructed based on the treated data, transparent management of data resources is realized, and data analysis is performed by adopting real-time processing and a differential privacy enhancement algorithm.

The invention discloses a data quality treatment method and system based on an AI Agent, and belongs to the technical field of artificial intelligence and data treatment. An initial data semantic distribution map is constructed, cross-dimension correlation feature factors are extracted, a multi-scale quality anomaly sensitive factor matrix is constructed, time sequence evolution weights are embedded, and a dynamic feature evolution trajectory is formed; performing perception modeling on the evolution trajectory by using an AI Agent, generating a multi-level quality risk thermodynamic diagram, extracting a deviation dense region and constructing an anomaly propagation path set; in combination with upstream and downstream data links and task flow information, calculating a potential impact factor weight, constructing a causal traceability map, and injecting a correction strategy label for a key field node; the AI Agent autonomously selects an adaptive strategy combination according to the target data segment, and performs online intervention on the target data segment; according to the method, closed-loop treatment of the data quality problem from perception and judgment to intervention and feedback is realized, and the method has the advantages of self-adaption, high interpretability and the like.

The invention relates to the technical field of agent path planning, in particular to a multi-agent collision-free path planning method based on a fusion DQN algorithm. The multi-agent collision-free path planning method comprises the following steps: firstly, constructing a two-dimensional grid map as an environment, and carrying out feature extraction by utilizing a CNN (Convolutional Neural Network); then, behavior clone learning is carried out through the expert model to obtain a BC model; the core innovation lies in that a BC model and a CNNDQN model are fused, an adaptive strategy learning framework is constructed, and intelligent dynamic combination of expert experience and reinforcement learning exploration is realized by adopting uncertainty estimation, antagonistic knowledge distillation and performance perception sampling technologies; and finally, further processing an initial path output by the fusion model by a CBS algorithm, and completing multi-agent collision-free path planning. According to the method, the accuracy and efficiency of path planning are optimized through a mixed learning strategy.

The invention relates to artificial intelligence generation content AIGC, in particular to an AIGC test case self-adaptive generation system based on risk feedback, which comprises a semantic variation generation module for selecting a variation operator from a variation operator library according to a self-adaptive strategy, performing variation processing on a seed case provided by a basic test case library by using the variation operator, and generating a semantic variation result; generating a new test case; the AIGC model takes the test case as model input and performs model output to the risk assessment and quantification module through an AIGC model interface; the risk assessment and quantification module is used for performing multi-dimensional risk assessment on a model output result by utilizing the risk detection model library, calculating a risk level and generating a test report; the risk feedback processing module is used for storing the high-risk test case, performing feature extraction and converting a risk assessment result into an operable risk feedback signal according to extracted features; according to the technical scheme provided by the invention, the defects of low efficiency and difficulty in automatically adapting to environment change can be overcome.

The invention discloses a logistics management system based on artificial intelligence, and the system is used for improving the efficiency and intelligent level of logistics scheduling and resource configuration, and comprises the following steps: building a unified data set through collecting and positioning, sensor and operation log and other types of logistics data; a modeling method is used for fusion to form a data structure containing time-space and transportation state information, the system adopts a causal analysis model to identify key influence relations between logistics events, a cross-modal feature extraction technology is combined to generate representation vectors such as transportation tracks, loads and states, and the logistics events are subjected to self-adaptive analysis through a self-adaptive strategy. And the causal information and the multi-modal features are fused to generate a decision basis, and the fusion proportion is dynamically adjusted according to actual operation feedback, so that transportation path optimization, distribution task planning and reasonable resource allocation are realized. The system has a continuous learning capability, and can keep the accuracy and flexibility of scheduling decisions in a continuously updated data environment.

The invention belongs to the technical field of image processing. According to the target detection method and system in the dark dusty environment, adaptive improvement processing is carried out on an original model for a dark dusty scene, a target in the dark dusty environment can be rapidly identified and detected through an environment adaptive strategy and an attention mechanism, and the target detection efficiency is improved. The accuracy of target detection is greatly improved; according to the method, a multi-layer detection network is constructed, a target detection network is divided into a plurality of adaptation layers, namely a low-illumination adaptation layer, a dust adaptation layer, a comprehensive adaptation layer and a standard processing layer according to different environment brightness and dust concentration, adaptive processing can be performed according to different environment conditions, and the image processing efficiency and precision are greatly improved.

The invention provides a traffic control protection method, device and equipment based on congestion prediction, and aims to accurately identify a congestion rule and predict a propagation path by constructing a feature vector set based on historical data and a congestion propagation path prediction mechanism so as to realize timely release of congestion early warning information. Risk assessment grading protection and optimal intervention opportunity dynamic optimization are adopted, the protection opportunity is accurately mastered, and corresponding protection intensity is matched; a regional risk redistribution strategy and a protection effect gradient analysis technology are introduced, risk transfer control among multiple regions is coordinated, and disordered diffusion of congestion risks is avoided; in combination with cross-space-time effect tracing analysis and a strategy conflict detection mechanism, the actual contribution degree of each protection element is evaluated, and a weight adaptive strategy set and a protection capability matrix are constructed; finally, multiple links such as prediction, evaluation and coordination are fused to form a self-adaptive comprehensive protection system, and active prevention and intelligent management and control of urban traffic congestion are realized.

The invention relates to the technical field of power system automation, discloses a remote operation and maintenance method, system and equipment for wide-area substation automation equipment, and aims to solve the problems of data islanding, inaccurate diagnosis, strategy stiffness, low safety cooperation efficiency and the like in remote operation and maintenance of the wide-area substation automation equipment. According to the method and the system, a comprehensive platform is constructed, and multi-source heterogeneous data acquisition, intelligent fusion analysis, fault diagnosis and prediction, adaptive strategy generation, security remote control and block chain evidence storage and collaborative operation and maintenance are integrated. The method can solve the problem of data islands, improve the diagnosis and prediction precision and timeliness, optimize the operation and maintenance strategy, prolong the service life of equipment, and enhance the remote operation and maintenance safety and cooperation efficiency.

The invention discloses a multi-scene visual algorithm evaluation method based on an electric power artificial intelligence platform, and the method comprises the following steps: S1, integrating data in an electric power scene, and generating a multi-modal data set; s2, storing and preprocessing the data set, and extracting key features; s3, based on a multi-stage active learning mechanism, selectively sampling data and optimizing a visual algorithm; s4, the optimized algorithm is migrated to different environments, parameters are adjusted through a self-adaptive strategy, and models adapting to different conditions are generated; s5, in combination with deep learning and expert knowledge, optimizing an evaluation process; s6, performing real-time fault injection and elasticity evaluation by using a fault mode library and a simulation technology; s7, constructing a double-layer digital twinning virtual evaluation environment, and verifying the validity of the algorithm under different conditions; and S8, recording and tracking the evaluation data and generating an evaluation report by using a distributed data management system. According to the invention, through an intelligent tuning and evaluation mechanism, the efficiency and reliability of power equipment monitoring and fault prediction are improved.

The invention belongs to the field of robot and computer vision, and particularly relates to a visual SLAM (Simultaneous Localization and Mapping) back-end optimization technology based on a data self-adaptive iterative reweighted robust kernel. Comprising the following steps: S1, a front-end data reading and processing module: a robot carries a camera to collect an image, performs feature extraction and feature matching on the image, and generates an initial camera pose and map points; s2, a rear-end optimization module: according to the initial camera pose and map point generated at the front end, calculating a median absolute deviation in an adaptive strategy, calculating a robustness parameter, then substituting the median absolute deviation and the robustness parameter into a target function, simplifying the target function by using an iterative reweighting strategy, and finally solving the optimal camera pose and map point by using an L-M algorithm; s3, the loopback detection module detects loopback according to whether the robot reaches a previous position or not, and transmits information to a rear end for processing; and S4, constructing a map module: constructing an environment map according to the optimized camera pose and map points.

A man-machine collaborative decision-making planning system and method based on environment adaptive trajectory optimization comprises a man-machine interaction module, a rule-based motion planning module and a learning-based parameter generation module, the man-machine interaction module performs data processing such as environment perception and state estimation according to data collected by an onboard sensor and a remote controller, and the parameter generation module generates parameter parameters according to the data. Input information required by the motion planning and parameter generation module is obtained; the motion planning module performs topological path search, visibility detection and trajectory optimization processing according to the local map and the user instruction information to obtain a planned trajectory of unmanned aerial vehicle tracking control; and the parameter generation module performs environment adaptive strategy processing according to the distribution information of the trajectory in the environment to obtain a speed constraint parameter for adjusting motion planning. According to the method, semantic information and depth information in environmental perception are considered, the speed parameters of the planner are automatically and dynamically adjusted, and the adaptability of the planned trajectory to different scenes is effectively improved, so that the cognitive and operation burden of a pilot in a complex environment is reduced, and the navigation efficiency and the system safety are improved.

The invention discloses a method and system for predicting juvenile depression based on intestinal flora, and relates to the technical field of bioinformatics and artificial intelligence, and the method comprises the steps: firstly, obtaining an original sequence of a microbiome, carrying out the preprocessing of the original sequence of the microbiome, and obtaining a feature matrix; and screening core flora characteristics with stable trans-folding by adopting characteristic importance evaluation and interpretability analysis based on a gradient boosting decision tree. A mixed weighted graph is constructed based on Spearman correlation and a proximity relationship, and an absolute value of a correlation coefficient is taken as an edge weight and an edge density is adjusted through a threshold adaptive strategy. And finally, through an improved graph attention neural network, based on edge weight attention, layer normalization and random inactivation, enhancing robustness, and adopting adaptive optimization to complete parameter learning. And determining a dynamic classification threshold according to the AUC of the target patient, and outputting a sample discrimination result and confidence. According to the method, the accuracy, stability and biological interpretability of juvenile depression recognition are remarkably improved.

The invention relates to the technical field of electric power data analysis, in particular to an electric power data analysis method and device based on graph calculation, and the method comprises the steps: constructing an electric power network graph, carrying out the deep learning processing of multi-dimensional and heterogeneous data, and analyzing a dynamic change prediction trend; power dispatching is optimized in combination with reinforcement learning, an optimal power dispatching decision is matched with minimization of energy loss as a target, the technical problems that high-dimensional data and heterogeneous data are complex to process, the change trend of the power system cannot be accurately predicted and an effective dispatching decision is lacked in the operation process of the power system are solved, and the power system can be effectively dispatched according to the real-time power grid operation state and the prediction trend. According to the method, the aim of self-adaptive strategy optimization learning is achieved, power dispatching flexibility is greatly improved, energy loss minimization is taken as the aim, an optimal power dispatching decision is accurately matched through continuous iterative optimization, power system data analysis efficiency is improved, and strategies are adjusted to cope with continuously changing supply and demand conditions and power grid operation conditions; and the overall stability and reliability of the power grid are improved to the greatest extent.

The invention relates to the technical field of thermal management optimization, and discloses an active cooling and power optimization system of a plateau turbine generator, which comprises a multi-physics field modeling module, a reinforcement learning decision module, a strategy optimization training module and an edge execution control module, and is used for resolving a temperature field / flow field / stress field coupling state in real time, providing environment dynamic characterization, and optimizing the power of the plateau turbine generator. According to the method, multi-source sensing data is processed, a cooling liquid flow rate and cooling fin angle control instruction is generated, strategy iteration optimization is carried out in a digital twin environment, the physical rationality and energy efficiency optimality of a control algorithm are ensured, a lightweight strategy network is deployed, and sub-second-level real-time response and fault safety protection are achieved. Through intelligent control and multi-physical field cooperation, the temperature adjusting precision and the system response speed are improved, efficient power output of the turbine is kept under different altitudes and climate conditions through a self-adaptive strategy, and a reliable clean energy solution is provided for high-altitude areas in combination with multiple safety protection mechanisms.

The invention discloses a thermal power generating unit decommissioning path planning method and device based on meteorological and hydrological risks, equipment, a medium and a product, and relates to the field of power system decision optimization. The method comprises the steps of obtaining meteorological and hydrological risk data and a decommissioning index of a thermal power generating unit; according to the decommissioning index and the meteorological and hydrological risk data under the climate change, a progressive hierarchical screening logic method is adopted for screening and constraint progressive correction processing; performing mapping and nonlinear correlation interaction analysis by adopting Monte Carlo simulation and a gradient boosting decision tree and combining multiple groups of parameter combination data, such as water temperature and runoff volume in meteorological and hydrological variables; and performing collaborative optimization processing based on the adaptive strategy model and a set analysis function to obtain an optimal strategy, and then performing mapping and analysis processing in combination with an analysis result to obtain targeted strategy list information for planning the decommissioning path of the thermal power generating unit. The invention aims to realize the planning of the decommissioning path of the thermal power generating unit.

The invention belongs to the related technical field of structure topological optimization, and discloses a shell structure-oriented adaptive isogeometric topological optimization design method and equipment, and the method comprises the following steps: S11, constructing a parameterized model based on the material parameters of a to-be-optimized shell structure and a truncation level B-spline basis function, and constructing a parameterized model; discretizing the displacement field of the shell structure based on the parameterized model to obtain the displacement of each point of the shell structure; s12, constructing a topological optimization model of a to-be-optimized shell structure based on an isotropic material penalty method of a complete adaptive strategy, and performing topological optimization design on the shell structure to obtain a density design variable and active unit distribution of an iterative step; and S13, according to the maximum variable quantity of the density design variables of two continuous iteration steps and the density difference of adjacent active units, performing local refining and coarsening on the hierarchical grids of the to-be-optimized shell structure by using a complete self-adaptive strategy so as to complete the self-adaptive isogeometric topology optimization design of the shell structure. The precision and the efficiency are improved.