65 results about "Evolution strategy" patented technology

The invention discloses a linear guide rail optimization design method and device, a medium and a program product, and the method comprises the steps: (1) constructing a simulation model and a MaOP design model which can optimize the rigidity, modal and weight at the same time based on the linear guide rail structure and static load analysis; (2) generating an elite population based on a Latin hypercube and diversity criterion, obtaining target values of the elite population, establishing a database, and constructing a Gaussian process function model; (3) designing mixed mutation operation based on OCC to generate a filial generation guide rail set; (4) designing a DEP-driven DPM evolutionary strategy to generate a candidate guide rail set, and selecting an optimal candidate guide rail; and (5) obtaining each target value of the optimal candidate guide rail, updating the database and the Gaussian process function model, returning to the step (3) until all optimization targets meet requirements, and outputting an optimal parameter value. According to the method, the MaOP process aiming at the rigidity, the modal and the weight of the linear guide rail can be effectively balanced, and higher precision and better comprehensive performance are achieved.

The invention belongs to the technical field of machine learning models, particularly relates to a personnel performance evaluation method based on IWOA-SVM, and solves the problems that a traditional support vector machine (SVM) is low in precision, difficult in parameter selection and the like in performance intelligent evaluation. The method comprises the steps that Tent chaotic mapping and a pseudo-opposition learning strategy are utilized to increase the diversity and quality of an initial population, and the whale algorithm (WOA) is prevented from falling into local optimum; the global optimization capability of the WOA is improved by adopting a differential evolution mechanism; a penalty factor and kernel function parameters of the SVM are optimized through an improved whale algorithm (IWOA), and performance evaluation can be effectively carried out while optimal parameters are obtained. According to the method, the whale algorithm can be improved by using Tent chaotic mapping, pseudo-opposition learning and a differential evolution strategy, SVM parameters are searched in a global range, and better model performance is obtained.

The invention discloses an intelligent food storage tank internal environment adaptive control system and control method, and belongs to the technical field of artificial intelligence and Internet of Things control. The system specifically comprises a multi-mode sensing module, a feature extraction and preprocessing module, a reinforcement learning module, an instruction adaptive control module and a digital simulation module. The multi-modal sensing module deploys a sensor and an environment adjusting device in an array mode; the feature extraction and preprocessing module carries out filtering, standardization and feature extraction on the data; the reinforcement learning module generates an optimization control strategy by using an algorithm; the instruction self-adaptive control module converts the strategy into an instruction and accurately adjusts parameters of the environment adjusting device; the digital simulation module constructs a digital model for simulation learning and evolution of a strategy agent. Compared with a traditional monitoring system, the method has the technical advantage of generating an optimization strategy, solves the problem of insufficient adaptive control capability caused by a fixed strategy of the traditional monitoring system, and provides a more efficient monitoring service.

The invention discloses a development management system and method for an API interface, and relates to the technical field of software engineering. The system comprises: an intention understanding and design engine, which analyzes a multi-modal service demand into a structured API development task; the agents collaboratively develop a network, and the multiple AI agents are dispatched to generate API specifications, codes, test cases and deployment configurations in parallel; the API ecological dynamic map construction module is used for automatically constructing and updating a dynamic map of dependency and data flow direction between APIs based on a generated product; a continuous learning and evolution engine analyzes the atlas and operation data to identify optimization points and generate or execute evolution policies. All the modules form a closed loop, and output of the evolution engine is fed back to the intention understanding engine to optimize subsequent design. The corresponding method comprises the steps of demand analysis, intelligent collaboration, atlas construction and closed-loop optimization. According to the method, automatic development, intelligent collaboration and continuous architecture optimization of the API are realized, and the development efficiency and the system maintainability are improved.

The invention discloses a linear guide rail lightweight optimization design method and device, a medium and a program product, and the method comprises the steps: (1) constructing a lightweight optimization mathematical model on the basis of a linear guide rail structure and load characteristics under the condition of satisfying frictional resistance and first-order modal constraints; (2) quantizing a parameter effect based on a PCE model and constructing an effect space to generate a population; (3) designing an evolutionary strategy guided by a bidirectional information individual; (4) constructing a double-layer Stacking integration model based on DBSCAN clustering, and screening an optimal filial generation guide rail; and (5) if the current optimal filial generation guide rail meets the optimization requirement, outputting an optimal guide rail parameter value, otherwise, returning to the step (3) until the optimization requirement is met. According to the method, the weight of the guide rail can be minimized for the effect space of high-effect guide rail parameters, transition optimization for redundancy and low-effect parameters is avoided, and better lightweight performance is achieved.

The invention relates to the technical field of artificial intelligence question answering systems, and discloses an interactive question answering system based on multi-model parallel reasoning. The system comprises an interactive interface module, a query cognition construction module, a hierarchical index module, a multi-model parallel reasoning module, an interactive answer synthesis module and a tool calling adaptation module. The system constructs query cognition mapping by deeply analyzing a time sequence query stream containing texts and media of a user, and drives dynamic evolution of hierarchical indexes according to the query cognition mapping. The multi-model parallel reasoning is based on evolution strategy coordination processing, and knowledge slices with state vectors are generated. And finally, synthesizing a natural language answer attached with the interaction intention unit, and adapting the natural language answer to an external tool calling instruction. According to the system, the cognition and retrieval precision under complex query is improved through intention-driven dynamic indexing, and automatic closed loop from information question answering to business operation is realized through executable answers.

According to the optimization method for data acquisition of the unmanned aerial vehicle in the wireless power supply Internet of Things, a nonlinear energy collection model is adopted, an optimization target is modeled as a mixed integer nonlinear programming problem, and the optimization problem is decomposed into an equipment service sequence generation module (a main module) and an unmanned aerial vehicle trajectory optimization module (a sub module). In the main module, the service priority of equipment is dynamically adjusted on the basis of equipment data volume and historical service times, meanwhile, in view of mutual conflict of optimization targets, a multi-objective evolutionary strategy is adopted to generate a Pareto frontier solution set, and an optimal equipment access sequence of each round is screened from the Pareto frontier solution set, and in the sub-module, for the continuous action parameter adjustment problem of the unmanned aerial vehicle, the optimal equipment access sequence of the unmanned aerial vehicle is obtained. The input dimension of the neural network is reduced by constructing a lightweight state space, and a multi-target reward function including data collection amount and energy consumption is designed, so that collaborative optimization and long-term balance of equipment service fairness, the data collection amount and the energy consumption of the unmanned aerial vehicle are realized.

The application relates to the technical field of battery life prediction, and discloses a lithium ion battery capacity prediction method, which comprises the following steps: obtaining lithium ion battery data, performing normalization processing and variational mode decomposition preprocessing on a battery capacity sequence to obtain a data set serving as model input; performing multi-scale decomposition on the battery capacity sequence obtained by S01 by using a self-adaptive mode selection mechanism based on an MAPE criterion, to obtain intrinsic mode functions (IMFs) of the best decomposition mode number; the lithium battery data is preprocessed by using variational mode decomposition (VMD), the battery capacity sequence is multi-scale decomposed by using a self-adaptive mode selection mechanism based on an MAPE criterion, and intrinsic mode functions (IMFs) of the best decomposition mode number are obtained; a self-adaptive step Gaussian random walk strategy, an auxiliary correction strategy and a differential evolution strategy are introduced to improve a white whale optimization algorithm (WOA), so that the robustness of the algorithm and the precision of the final solution are improved.

The embodiment of the invention discloses an attack event processing method and device, electronic equipment, a storage medium and a program, and the method comprises the steps: obtaining event triggering operation associated data of a target triggering event in a target system in real time; performing broad-spectrum detection on the event triggering operation associated data through an attack event broad-spectrum mapping model to generate broad-spectrum attack detection associated data; performing intention reasoning on the broad-spectrum attack detection associated data through an attack event dynamic evolution engine to obtain a to-be-verified attack intention hypothesis of the target trigger event; and determining an attack event dynamic evolution strategy matched with the to-be-verified attack intention hypothesis, and performing attack detection on current real-time operation associated data of the target trigger event through the attack event dynamic evolution strategy. According to the technical scheme of the embodiment of the invention, the monitoring efficiency and detection precision of the attack event can be improved, and the response efficiency of the attack event is improved.

The application discloses a kind of building energy-saving scheme intelligent generation method based on two-stage agent-assisted evolution, belong to building energy-saving field, according to resident building model decision variable and range, generate uniform feasible solution with rejection sampling method, and the real target value obtained by EnergyPlus simulation is stored in sample set DB;Uniformly construct reference vector in target space and divide space, DB sample is divided into corresponding vector file according to the nearest distance, and the initial RBF global agent model is constructed.The first stage selects the crowding density minimum non-dominated solution from each vector file as the initial population, evaluates the individual using the global agent model, fills the solution according to the rules, updates the local agent model, and enters the second stage when the hyper volume increment value is greater than the threshold value, otherwise directly enters S5, and the second stage dynamically updates the reference vector according to the solution quality.The application uses the above method, by combining agent model and phased evolution strategy, significantly improves the efficiency and accuracy of building energy-saving optimization.

This application provides a recommendation method, apparatus, and computer-readable storage medium based on multi-objective optimization. The method includes: acquiring user rating information, including ratings from multiple users for multiple items; clustering users into different clusters using K-means clustering based on the user rating information; predicting the predicted ratings of users for items in each cluster based on a probability propagation algorithm with improved resource allocation; generating an initial population in each cluster based on the predicted ratings of users for items; and solving a multi-objective optimization problem based on an accuracy objective function and a diversity objective function using an evolutionary strategy guided by accuracy-preference users, thereby obtaining the target recommendation result for each cluster. By using the above method to guide the evolutionary direction of the algorithm through user preferences, the target recommendation result can be made more biased towards the accuracy objective function while taking into account diversity.

The application relates to the technical field of data encryption and discloses an AI driving-based adaptive optimization method of a post-quantum cryptographic algorithm. The method collects multi-source cryptographic parameter data, generates a standardized cryptographic parameter set through adaptive noise injection and parameter normalization preprocessing; a dynamic optimization network model based on multi-head attention and reinforcement learning is constructed, an adversarial evolution strategy is adopted for training, a quantum attack scene is simulated to screen an anti-quantum parameter combination; a differential parameter space mapping algorithm is used to convert into an optimal encryption protocol configuration and correct conflicts; through iterative optimization of a meta-learning framework, local parameter sensitivity and global anti-attack capability are fused, and an adaptive post-quantum cryptographic algorithm is output. The application effectively improves the performance and security of the post-quantum cryptographic algorithm, can optimize the algorithm according to different scene requirements, and provides a reliable technical scheme for coping with quantum computing threats.

The invention provides a sub-array optimal distribution method and system based on a quantum antelope optimizer, and belongs to the field of array signal processing. The invention aims to solve the problem of inaccurate estimation of distributed array performance attenuation and direction of arrival due to reduction of the number of snapshots and change of interference noise under the conditions of small sampling snapshots and impact noise. According to the method, the array structure is optimized under the impact noise condition, received signals are processed by constructing an infinite norm weighted fraction low-order covariance matrix, and the method is more suitable for the environment where the number of snapshots is reduced and the impact noise is severe; a quantum optimization mechanism is introduced, and the performance and search efficiency of an original algorithm in the distributed array research direction are improved based on an evolution strategy of a quantum rotation angle and a quantum rotation door; according to the method, the direction finding accuracy of the distributed array system can be improved, and the situation that the array performance influence factors in the actual direction finding application are not considered sufficiently and the performance improvement is not large due to the fact that the directional diagram of the array serves as the optimization target can be effectively avoided.

The invention provides a photovoltaic array wind load prediction method and system based on depth prediction network optimization, and belongs to the technical field of civil engineering structure wind resistance and artificial intelligence application crossing. According to the method, firstly, a deep prediction network is constructed to replace traditional cross validation, and self-adaption and efficient tuning of XGBoost model hyper-parameters are achieved; secondly, a multi-target composite loss function is obtained based on a self-adaptive hyper-parameter optimization model of a deep prediction network, and the prediction precision of the model is remarkably improved through a gradient guidance and evolutionary strategy hybrid search algorithm; finally, in combination with SHAP analysis, it can be revealed that the wind direction angle and the dip angle are key factors influencing the wind load, a direct theoretical basis is provided for wind resistance optimization design of the photovoltaic array, and meanwhile, a photovoltaic array wind load prediction model has the advantages of being high in optimization efficiency, high in generalization ability and the like.

The invention relates to a product subversive innovation opportunity generation method based on scene innovation, and the method comprises the steps: firstly determining the maturity of a product through a Gompertz curve, and judging the opportunity of a subversive innovation window; secondly, performing mainstream product scene construction, performing formalized expression on a scene based on an extension principle, and forming a modification rule and an evolution strategy of scene elements in combination with characteristics of subversive innovation so as to perform scene evolution to mine a subversive opportunity scene; secondly, positioning a functional difference region in the opportunity scene based on the sub-scene and the compatibility function, and deeply digging user requirements in the subversive opportunity scene by using a fishbone diagram method to find a product subversive innovation opportunity; and finally, forming a judgment criterion of the product subversive innovation opportunity according to the characteristics of various types of subversive products so as to judge the type of the innovation opportunity. According to the method, enterprises can be helped to systematically identify high-value subversive innovation opportunities, and the limitations that a traditional method is single in dimension and insensitive to market changes are overcome by integrating multi-dimensional scene elements.

The invention discloses a pathological image segmentation method and system based on coevolution generation type difficult sample mining, and the method comprises the steps: constructing a mask synthesis engine guided by biological information, and generating a cell nucleus mask through introducing a cell affinity matrix and structure prior based on a graph theory; establishing a segmentation-oriented adversarial renderer, and aligning the generated image with a real image in a feature space by using a multi-layer feature discriminator; implementing a closed-loop co-evolution strategy, dynamically identifying vulnerability categories by using performance feedback of the segmentation model, and guiding a generator to carry out adaptive difficult sample mining; and obtaining a cell nucleus segmentation result through alternate mutual promotion of the generator and the segmentation model and regression fine tuning of real data. According to the method, the problems that in existing small sample learning, generated data lacks biological rationality and visual fidelity cannot be converted into segmentation performance are solved, and the segmentation precision and generalization ability of the model are remarkably improved under the condition of extremely few labeled data.

The invention provides a large-area flight recovery energy efficiency optimization method fusing deep learning and a genetic algorithm, belongs to the technical field of intelligent scheduling and optimization, and solves the problem of multi-target efficient recovery after large-scale flight delay. According to the technical scheme, the method comprises the following steps: firstly, constructing standardized scheduling input characteristics and economic loss factors by using real flight data; secondly, designing a multi-objective function, and comprehensively considering total delay, economic loss, the number of serious delay times and scheduling fairness; then, generating a scheduling sample based on an evolutionary strategy and constructing a training set; thirdly, training a deep neural network agent model to replace a high-cost evaluation function; and finally, combining an agent model and an elite screening mechanism, and quickly searching an optimal scheduling scheme in evolutionary optimization. The method has the advantages that large-scale flight recovery scheduling tasks are efficiently completed, global efficiency and local fairness are both considered, a command department is helped to rapidly formulate a recovery strategy, and the method has high practical application value.

This invention discloses a knowledge graph-based intelligent auxiliary decision-making generation method for railway stations, comprising the following steps: S1, collecting multi-source operational data of the railway station and preprocessing it; S2, establishing a node set according to preset entity mapping rules and establishing an edge set based on semantic decision relationships to generate a knowledge graph; S3, generating vector representations of each node in the knowledge graph, selecting a starting node, and performing structural traversal based on multi-hop adjacency relationships to establish a state subgraph; S4, performing strategy path evolution in the state subgraph and modeling the conflict relationships between path sequences; S5, parsing the task dependencies in the paths, assembling node content and associated attributes, generating and issuing auxiliary decision-making instructions; S6, collecting feedback data and incrementally updating node attributes and path evolution strategies. This invention enables the structured generation of auxiliary decision-making instructions for railway stations, improving the stability of the decision-making process.

The invention discloses a bird identification method and system based on dynamic multi-modal fusion and a self-evolution lightweight model, and belongs to the technical field of bird identification. The method comprises the steps of collecting multi-modal data, preprocessing the multi-modal data to obtain a voiceprint feature map and an image tensor, extracting a voiceprint high-level semantic feature vector and an image high-level semantic feature vector from the voiceprint feature map and the image tensor respectively based on a lightweight convolutional neural network, and fusing the voiceprint high-level semantic feature vector and the image high-level semantic feature vector through a dynamic fusion module to obtain a voiceprint high-level semantic feature vector and an image high-level semantic feature vector; the method comprises the steps of obtaining a fusion feature vector, constructing a lightweight recognition model, training the lightweight recognition model through a self-evolution module by adopting a hybrid evolution strategy, deploying the trained lightweight recognition model to an application terminal, and carrying out bird recognition and result output through the trained lightweight recognition model at the application terminal. According to the invention, by dynamically fusing the multi-modal information and the self-evolution lightweight model, the accuracy, robustness and adaptive ability of bird recognition in a complex environment are improved.

The application relates to the technical field of talent evaluation, and discloses a multi-dimensional adaptive examination generation method and system based on a dynamic skill map. The method comprises the following steps: real-time analysis of recruitment website and enterprise job description data, extraction of atomized skill phrases containing skill names, levels and correlation degrees, and generation of unified semantic representation; obtaining a standardized skill map through a large language model and transmitting the standardized skill map to a central database through a blockchain; generating an evolution strategy based on skill node weights and user historical answering characteristics, and differentiating and executing the evolution strategy by edge nodes; selecting a target edge node and configuring a skill knowledge cache; analyzing a candidate natural language answering request to generate ability matching characteristics, determining a candidate test question set, and outputting a test paper; and updating an ability evaluation model and an evolution strategy in combination with an answering result and a response time. The application can improve the authority and reliability of the evaluation result to meet the scientific and accurate needs of enterprises for talent evaluation.

The invention discloses an urban rail transit vehicle bottom connection scheme compiling method based on a VAO-GWO bionic intelligent algorithm. The method comprises the steps that 1, task modeling and preprocessing are conducted on the basis of original data of a train working diagram, so that a vehicle connectable directed acyclic graph is constructed, and a target function for vehicle bottom application optimization is defined; 2, setting fusion parameters of the VAO and the GWO, and generating an initial population; 3, calculating an objective function value of a vehicle bottom continuing scheme corresponding to each individual in the initial population, and sorting the population according to the objective function value; 4, calculating a grey wolf convergence coefficient and a dynamic inertia weight; 5, executing population iteration updating operation based on the fusion evolution strategy; step 6, performing population elite selection and parameter adaptive adjustment; step 7, outputting historical information of current iteration; 8, judging whether an early termination condition is met or not; and step 9, if an early termination condition is met or the maximum number of iterations is reached, outputting a final urban rail transit underbody connection scheme.

A recursive symbolic intelligence system is disclosed that employs continuously evolving symbolic nodes represented as multi-dimensional vectors with physical, cultural, and optionally functional sub-components. The system implements a mathematically defined recursive update function s(i)(t+1)=α·s(i)(t)+β·f(adj)({s(j)(t)})+γ·f(input)(v(i)), wherein α, β, and γ are tunable weighting factors; f(adj), aggregates contributions from semantically and topologically adjacent nodes; and f(input), processes incoming multi-modal input including text, audio, video, and sensor data. A tamper-evident ledger configured with a cryptographic hashing function such as SHA-256 records each symbolic update, and a scheduling module employing a multi-armed bandit algorithm together with a meta-learning engine utilizing covariance matrix adaptation evolution strategy dynamically optimizes processing resources and hyper-parameters. This system provides a continuous, adaptive, and auditable framework for dynamic knowledge representation applicable to domains such as autonomous systems, adaptive content generation, and symbolic legacy encoding.

A financial bond field-oriented instruction data set generation and enhancement method comprises the following steps: a basic extension stage: performing semantic retrieval and diversified extension on a seed instruction based on a retrieval enhancement generation technology to generate an initial instruction set; in the evolution iteration stage, the complexity of the initial instruction set is improved through a multi-path evolution strategy, and a high-order instruction set is generated; in the knowledge alignment and confrontation verification stage, the professional consistency of instructions is verified through a bond domain knowledge graph, and wrong instructions are filtered through a confrontation mechanism; and a quality screening and data set merging stage: calculating an instruction and answer consistency score, screening standard data, merging the standard data with the seed instruction set, and outputting an enhanced data set. In addition, a corpus preprocessing mechanism is optimized. According to the method, a two-stage data enhancement assembly line is constructed, the retrieval enhancement generation RAG, the multi-strategy evolutionary algorithm, the knowledge graph constraint and the confrontation game mechanism are combined, automatic generation of high-quality instruction data in the financial field is achieved, and the method is suitable for bond analysis and investment decision making.

The application provides a flexible job shop scheduling method and system based on an evolutionary strategy and meta-reinforcement learning, comprising the following steps: a certain number of flexible job shop scheduling problem instances are randomly generated to form a training data set, and the training data set is replaced every fixed update round; a meta-reinforcement learning framework based on an evolutionary strategy is constructed to train a meta-model, the optimal parameters of the meta-model are determined by minimizing the total average completion time of a verification set, and the meta-model is used as an initialization model to adapt to new tasks in an inference process; the completion time of test data is obtained by using the trained meta-model, and the optimal result for each instance in the test data is obtained by fine-tuning each instance a limited number of times.

A method for optimizing an application of an evolutionary-strategy-based application of Artificial Intelligence (AI) is provided. The application may be performed on a pre-determined surface. The method may include selecting a first group of candidates, determining a mean and standard deviation associated with the first group of candidates, using a static scaling factor to formulate a size of a population of candidates for generation, using the mean and the standard deviation to generate, according to the size, the population of candidates, and selecting a second group of candidates from among the population of candidates. Each member of the second group of candidates is closer to a minimum value of the surface than a remainder of the population of candidates. The remainder of the population of candidates may be formed from a group of non-selected candidates among the population of candidates.

The invention provides a large model evolution mechanism for resource-constrained end-side equipment, which comprises an evolution lexical element quantity calculation mechanism: evaluating the computing power load, the memory pressure and the I / O delay of mobile equipment in real time by utilizing a linear system load evaluator, mapping the evaluation result into the calculation workload of a large model through a segmentation mapping function, and calculating the evolution lexical element quantity; and calculating the optimal reasoning scale under the current system load. When the system load is relatively high, the large model abandons part of lexical elements; when the system load is low, more lexical elements are reserved; an evolution target lexical element selection mechanism: selecting an intermediate generation lexical element with a relatively low score in all the layers as an evolution target according to the distribution characteristics of attention scores of all the layers; and an evolution strategy execution module. According to the method, performance and energy consumption can be dynamically balanced in a resource-limited mobile terminal environment, adaptive adjustment of large model calculation overhead is realized, reasoning efficiency and system stability are improved, and an efficient evolutionary optimization scheme is provided for end-side large model reasoning.

The application discloses a structural damage identification method based on a proxy model and an improved covariance matrix adaptive evolution strategy, and relates to the technical field of structural damage identification.The application comprises the following steps: obtaining a structure to be identified, constructing a finite element model of the structure to be identified, and respectively applying excitation to the structure to be identified and the finite element model to obtain a data set; a hybrid network model based on a convolutional neural network and a long short-term memory network is constructed, and the data set is used for training to obtain a proxy model; the proxy model is improved by using a covariance matrix adaptive evolution strategy based on a model gradient to obtain an optimal solution; a variational Bayesian model correction method is used to correct the optimal solution to obtain the most possible value of a structural damage geometric size parameter, so that damage geometric size and boundary information of the structure to be identified are determined.The application can efficiently and accurately identify structural damage geometric size information.

The invention belongs to the technical field of computer vision, and particularly relates to an end-to-end shape recognition method and system based on a complex network. The method comprises the following steps: carrying out dense discretization on the edge of a to-be-identified image to obtain an edge description point set; selecting a series of key points on the obtained edge description point set at equal intervals; extracting local features of a key point surrounding edge description point set by using a shape context; taking each key point as a node, defining edges in the network according to spatial correlation among the key points, and constructing a multi-layer complex network by adopting a dynamic evolution strategy; local features and topological features of the complex network are processed through a graph convolutional network, and robust expression of the shape classification task is obtained; and performing classification through a full connection layer network to obtain a classification result, and realizing end-to-end shape recognition. According to the method, the adaptability and robustness of the method to different shape classification tasks are enhanced.

The invention discloses a rock section three-dimensional model construction method based on enhanced diffusion and terminal equipment, belongs to the technical field of model construction, and can solve the problems that an existing rock section three-dimensional model construction method is low in reconstruction precision and poor in stability. The method comprises the following steps: S1, constructing a section initial state according to rock section image data, wherein the section initial state comprises a plurality of section structure units generated according to the rock section image data, an initial connection relation of the section structure units, and uncertainty parameters and existence probability parameters of each section structure unit; s2, performing evolution prediction on the initial state of the section to obtain a section evolution diffusion space; s3, performing multiple rounds of enhanced diffusion evolution on the section state in the section evolution diffusion space to generate a section evolution strategy set; and S4, stable structure features are extracted from the section evolution strategy set, and a rock section three-dimensional model is generated according to the stable structure features. The method is used for constructing the rock section three-dimensional model.

The invention discloses a garment trend prediction and analysis method based on reinforcement learning, and relates to the technical field of garment trend prediction based on reinforcement learning, and the method comprises the steps: collecting multi-modal data, carrying out the preprocessing, and generating multi-modal garment data; performing trend scene evolution on the multi-modal clothing data through a trend evolution learning model to obtain a trend evolution strategy, and generating a trend development path; optimizing a production plan, inventory distribution and logistics scheduling according to the innovation style data set in combination with historical sales data and a market demand prediction result, and generating an inventory production plan; and collecting market response feedback data, performing inventory and production adjustment in combination with the inventory production plan, obtaining an inventory adjustment suggestion, identifying the difference between the inventory and the market demand, and generating a trend analysis report. According to the overall scheme, through dynamic prediction and real-time adjustment, efficient response of market demands is ensured, and the production efficiency and market adaptability of the clothing industry are improved.