55 results about "Evolutionary learning" patented technology

The invention discloses an AIGC enabling auxiliary system for college second foreign language teaching and an implementation method. The system is cooperatively operated by five core modules; the intelligent voice recognition and user demand analysis module analyzes the learning demand of a learner by using a voice-to-text technology, and assists the customization of teaching content; the AIGC content generation module gathers multi-source data, generates personalized teaching resources by using a frontier model and continuously updates and evolves the personalized teaching resources; the learning progress management and evaluation module collects learning data, dynamically adjusts a personalized learning plan and provides evaluation and feedback; the real-time interaction and feedback module builds a multi-dimensional interaction channel, collects feedback of all parties, analyzes and processes the feedback and presents an application result; the multi-platform support module guarantees the stable operation of the system on a plurality of operating systems and devices, and efficiently completes data synchronization sharing and performance optimization adaptation. According to the invention, an all-around learning environment is established for second foreign language learners in universities, and the learning efficiency and experience are significantly improved.

The invention discloses a multi-agent evolution reinforcement learning method based on multi-strategy optimization and scene memory, and the method comprises the following steps: initializing a multi-agent reinforcement learning team and an evolution learning team, including observation characterization and strategy characterization of agents; updating a reinforcement learning team strategy according to the interaction experience and the scene memory; updating an evolutionary learning team strategy according to the population fitness by using an evolutionary algorithm; performing feature extraction and dimension reduction on the global state through a state encoder; updating the memory in the buffer pool based on a scene memory mechanism; and carrying out strategy interaction on the reinforcement learning team and the evolutionary learning team to obtain a current optimal strategy. Through a multi-strategy optimization mechanism, the problem of limited strategy interaction between a reinforcement learning team and an evolutionary learning team in a traditional evolutionary reinforcement learning method is solved, and meanwhile, the decision-making quality and learning efficiency of the intelligent agent are optimized by utilizing a scene memory mechanism. The method not only can improve the convergence speed of the algorithm, but also can enhance the adaptability of multiple agents to a dynamically changing non-stationary environment, thereby making a decision favorable for a team.

The invention provides a new drug molecule design method and device fusing convex optimization and evolutionary learning, and belongs to the field of biological information.The method comprises the steps that in a generative adversarial network, a generator is adopted to generate a small molecule sequence, and the generator is established by adding an attention mechanism model in a long-short-term memory network; evaluating the authenticity of the small molecule sequence by adopting a discriminator, wherein the discriminator is established by adopting a convolutional neural network improved based on convex optimization; updating the parameters of the generator by adopting a strategy gradient method, and updating the parameters of the discriminator by adopting a gradient descent method; and generating a final small molecule sequence by the generator corresponding to the final generator parameter. According to the method, the small molecules which are highly similar to original samples and have higher effectiveness and innovativeness can be generated, the diversity and quality of the generated molecules are improved, and particularly, the method has excellent performance in the aspect of generating molecules with specific functions.

The invention relates to the technical field of text data processing, and discloses a text data extraction method, system, equipment and medium based on multi-modal fusion and self-evolution learning, which comprises the following steps of: performing feature extraction and spatial alignment on a printed text, a handwritten annotation and a dynamic table of a mixed format document to obtain a semantic feature of an image-text table, and inputting the semantic feature into a dynamic analysis layer; analyzing metaphor expressions and synonymous heterogeneous fields through field extraction and a context semantic reasoning mechanism, and outputting structured data; performing grammar compliance verification by adopting a regularization engine, and performing comparison verification through a federal learning mechanism; and inputting the verified data into the reinforcement learning model, updating the analysis rule and the model parameters through strategy iteration, and feeding back the updated analysis rule and model parameters to the dynamic analysis layer to complete closed-loop optimization. According to the method, the processing precision and efficiency of the complex document are greatly improved, the manual intervention requirement is remarkably reduced, and meanwhile, the privacy protection and compliance requirements are met.

The invention relates to an RIS auxiliary enhanced communication method based on an evolutionary guidance strategy gradient in the field of wireless communication, and provides an evolutionary guidance strategy gradient (EGPG) algorithm for joint optimization of transmitting end precoding and an RIS reflection phase shift matrix in an RIS auxiliary communication system. In combination with a strong fitting characteristic of deep learning, a gradient characteristic of reinforcement learning and a strong exploratory property of evolutionary learning, an idea of evolutionary learning is adopted to carry out iterative optimization on intelligent agent actions, so that sample diversity is increased for deep reinforcement learning, and strategy network optimization of deep reinforcement learning is guided. Compared with a traditional optimization algorithm, the method has a simple and clear optimization framework and low calculation complexity, and meanwhile, compared with a gradient-based deep reinforcement learning algorithm and an evolutionary reinforcement learning algorithm, the method has faster convergence and better system performance.

The invention relates to the technical field of multi-objective optimization, in particular to a multi-mechanism deep integrated multi-objective optimization framework and an optimization method and application thereof, comprising a reinforcement learning module, a genetic algorithm module, a bidirectional evolutionary learning coupling module, a Pareto experience pool module, a preference management module and a graph neural network module, the bidirectional evolutionary learning coupling module is used for realizing bidirectional information interaction between the reinforcement learning module and the genetic algorithm module, and the reinforcement learning module generates candidate solutions and feeds the candidate solutions back to the genetic algorithm module; the genetic algorithm module screens the elite solution with the crowding distance larger than a set threshold value in the first Pareto layer and updates the strategy of the feedback reinforcement learning module. According to the method, the strategy learning ability of reinforcement learning and the global population search ability of the genetic algorithm are fused, and the overall optimization efficiency and the approximation ability to the complex Pareto frontier are remarkably improved.

The invention provides a database relationship intelligent discovery and ER graph construction method and a self-evolution learning method, and belongs to the technical field of ER graph construction, and the database relationship intelligent discovery and ER graph construction method comprises the steps of S1, metadata collection and preprocessing; s2, vectorizing domain sensing metadata; s3, recalling a multi-strategy candidate relationship; s4, performing LLM structured reasoning and verification; s5, carrying out data sampling inspection and cross validation; s6, hierarchical negative sample self-evolution learning; and S7, generating and visualizing an ER graph. According to the invention, a layered negative sample self-evolution learning mechanism and the structured reasoning ability of LLM are organically combined, and LoRA rapid fine tuning and a multi-stage verification process are supplemented. The defects of accuracy, generalization ability, cold start efficiency, reasoning transparency, automation integration and the like in the aspect of discovery of the primary and foreign key relationship of the relational database in the prior art are effectively overcome, and a more advanced and practical solution is provided.

The invention provides a real-time three-dimensional reconstruction method based on online evolutionary learning, and relates to the technical field of three-dimensional reconstruction methods. The real-time three-dimensional reconstruction method based on online evolutionary learning specifically comprises the following steps: S1, online evolutionary learning, S101, pseudo-label generation: adopting a pseudo-label generation algorithm to generate reliable labels for unlabeled data; s102, updating a self-adaptive model, and dynamically adjusting model parameters according to real-time feedback; s103, sensing and optimizing the environmental resources; s104, carrying out continuous optimization and evolution; s105, terminal output and real-time three-dimensional reconstruction output are carried out; s2, self-adaptive three-dimensional reconstruction; and S3, dynamic and static cooperation and heterogeneous model fusion. According to the method, self-adaptive reconstruction can be carried out under an unsupervised learning and reinforcement learning framework, so that a reconstruction model can be automatically optimized in a dynamic environment, dependence on labeled data is reduced, and complexity and precision of a reconstruction algorithm are adjusted in real time according to computing power and network conditions so as to ensure efficient operation of a system under different resource conditions.

The invention discloses a virtual simulation-based intelligent evolutionary learning method for a running trajectory of an unmanned aerial vehicle, and the method comprises the steps: reading historical trajectories from a trajectory library of the unmanned aerial vehicle, and carrying out the sorting according to a set evaluation standard; setting a threshold to distinguish high-quality samples and low-quality samples, adding the high-quality samples and part of the low-quality samples extracted according to a set rule into a sample set, and dividing the sample set into a training set and a test set according to a proportion; defining a loss function considering the comprehensive cost, and determining a comprehensive evaluation index through system simulation; and training the evolutionary learning model, controlling the aircraft in a simulation environment, and outputting a new aircraft trajectory sample. And if a system simulation evaluation index requirement is met, ending the process, otherwise, combining a new track generated by simulation with the previous track to form a new track library, and returning to carry out iterative optimization again. The method can effectively improve the efficiency and precision of aircraft trajectory optimization, and reduces the training cost at the same time.

The invention discloses a motion-induced heart rate variability prediction method based on a random forest algorithm. The motion-induced heart rate variability prediction method comprises the following steps: S1, collecting multi-modal physiological data of a user during a motion period; s2, synchronously processing the data through a multi-channel signal fusion technology, and generating a time-aligned multi-dimensional data stream; s3, based on genome, epigenetic and physiological data, extracting heart rate variability related high-dimensional features by using holographic quantum calculation, and generating a biological feature library; s4, integrating the data sequence and the biological feature library, and performing time sequence feature extraction by using self-supervised learning; s5, in model training, optimizing a decision tree structure by utilizing reinforcement learning and distributed calculation; s6, adjusting the weight of the training sample in real time through an adaptive weighted resampling technology; s7, optimizing the random forest model by applying evolutionary learning; and S8, in combination with the biological characteristics of the user, a prediction result is visualized by using a quantum enhanced holographic projection technology. According to the invention, accurate prediction and dynamic management of motion-induced heart rate variability are realized.

The invention relates to the technical field of computers, and discloses a test case generation method and device, equipment and a medium, and the method comprises the steps: receiving a software test demand through a demand analysis agent, extracting a test point of the software test demand, and constructing a target knowledge graph according to the test point; generating an initial test case based on the test target knowledge graph through the case generation agent; executing the initial test case through the execution verification agent to obtain a test result; the test result comprises validity judgment and judgment basis of the initial test case; and in response to the test result, judging that the initial test case is an invalid test case, optimizing strategy parameters of each agent in the agent system based on the judgment basis through the evolutionary learning agent, and generating a new test case. The application can respond to the change of the test demand in real time, automatically adjust the test strategy, and improve the test quality and efficiency.

The present invention discloses a self-evolutionary evaluation system for the teaching effectiveness of an educational agent, which relates to the field of educational artificial intelligence. The system includes a feature fusion module, a model construction module, a trigger judgment module, a self-evolution module, and a mapping graph update module. The present invention utilizes a multimodal learning algorithm for unified coding and feature extraction, thereby constructing a multidimensional feature vector that comprehensively reflects the cognitive and emotional states of students, thereby improving the accuracy and scientific nature of teaching effectiveness evaluation. By introducing an evolutionary learning mechanism, through mutation, crossover, and screening operations on inefficient or fluctuating strategies, combined with a simulation environment for strategy pre-evaluation, the teaching strategy is continuously optimized, thereby improving the strategy adaptability and continuous teaching optimization capabilities of the educational agent.

The invention discloses a thermoelectric decoupling comprehensive evaluation method based on self-evolution learning, and the method comprises the steps: building a thermocouple transformation evaluation index system and an index calculation model, and calculating the comprehensive weight of a secondary index in the evaluation index system; constructing historical environment state vectors based on historical multi-source information, and clustering the historical environment state vectors to obtain a plurality of scene clusters; at a new evaluation moment, acquiring a real-time environment state vector at the moment, determining corresponding scene clusters, and for each scene cluster, dynamically correcting the comprehensive weight through a self-evolution mechanism constrained by a rule base and driven by deviation; and obtaining secondary index quantitative data of each thermoelectric decoupling transformation scheme through the index calculation model, and calculating a comprehensive score of each thermocouple transformation scheme in combination with the corrected comprehensive weight of each secondary index.

This invention discloses a method, system, apparatus, and storage medium for patent citation network analysis. The method includes: constructing a patent citation network in graph form based on a patent dataset; dividing the patent citation network into a predetermined number of subgraphs, and establishing two coupled systems for each subgraph; obtaining node features of each node through the first system; determining a first degree of influence based on the node features and citation relationships; updating the second degree of influence of the second system based on the first degree of influence; obtaining a steady-state network structure by alternately optimizing the first and second systems through subgraphs; and obtaining a target network structure based on the steady-state network structure through evolutionary learning. This invention, based on the idea of ​​evolutionary dynamics, trains the patent citation network graph model through citation relationships. In network applications, it can more effectively extract information from patents and, to a certain extent, explain the citation relationships between patents. It can be widely applied in the field of network analysis and processing technology.

The invention discloses a diabetes health management system and method based on an autonomous evolution large model and a program product, and the system comprises a multi-modal data collection and preprocessing module which is used for collecting and preprocessing multi-modal data, related to diabetes, of a patient; the multi-modal embedding and feature fusion module is used for uniformly embedding and coding the multi-modal data and aligning and fusing the multi-modal data into a fusion feature; the health management suggestion generation module is used for generating personalized health management suggestions based on the fusion features; the evolutionary learning module is used for evoluting the health management suggestion generation module based on a reinforcement learning model; and the credible explanation and visualization module is used for providing basis traceability and trend display of the health management suggestions. According to the method, multi-modal modeling and self-adaptive learning are realized, and the interpretability is high.

The invention relates to the technical field of foundation pit engineering monitoring, and discloses a deep and large foundation pit deformation monitoring method and system based on close-range photogrammetry, and the method comprises the steps: receiving risk region data, and generating a standardized image set; self-adaptive three-dimensional reconstruction is carried out in combination with domain knowledge, and a three-dimensional grid model with semantic annotation is generated; extracting a model deformation mode and performing semantic annotation to form a structured set; based on this, a geometric domain and knowledge domain bidirectional information channel is constructed, and a knowledge-geometry collaborative metric graph is created; a multi-scale space-time memory structure is used for achieving self-evolution learning and resource optimization allocation, a resource allocation strategy is output, and a monitoring knowledge base is updated; according to the deformation mode, semantic interpretation and the updated knowledge base, risk assessment is executed, intervention suggestions are generated, graded early warning information and a multi-level decision support scheme are output, and automatic processing and optimization from data to decision are achieved.

The application provides a spectrum sensing method and system based on multiple agents, a storage medium and a terminal. The method comprises the following steps: constructing a state vector of uplink transmission of multiple primary users; inputting the state vector into M agents for deep reinforcement learning, acquiring M basic spectrum sensing strategies, and M is a natural number greater than 1; performing evolutionary learning on the M basic spectrum sensing strategies to acquire a population of offspring spectrum sensing strategies; performing integrated learning on excellent strategies in the population of offspring spectrum sensing strategies to acquire an optimal spectrum sensing action. The spectrum sensing method and system based on multiple agents, the storage medium and the terminal based on the collaborative design and adaptive optimization of multiple agents can realize high-precision and strong-robust spectrum intelligent sensing.

The application discloses an implementation method and system for self-evolution learning of an intelligent agent, and belongs to the technical field of intelligent agent reinforcement learning, large language models, memory and cognitive intelligence, and transfer learning; the method comprises the following steps: acquiring an environment state; inputting the environment state into a pre-trained strategy to output an optimal action and executing the action; after executing the action, a feedback signal is acquired; the feedback signal comprises an environment response and a task completion degree; and a new strategy is obtained by optimizing the strategy according to the feedback signal. The task performance of the application is continuously upgraded: the intelligent agent can be continuously optimized when facing long-range or repetitive tasks, and the intelligent agent will become more and more skilled in processing cross-platform complex tasks. The application reduces the research and application cost: the intelligent agent can reduce the dependence on manual work, can autonomously discover reinforcement learning rules, does not need to continuously manually annotate data, and relies on environment feedback for iterative optimization.

The invention discloses a self-evolution intelligent agent system based on a double-layer ReAct mechanism and a working method, and relates to the technical field of intelligent agent systems. The system comprises a global planning subsystem, a local execution subsystem and an evolutionary learning subsystem which are connected with one another, the agent module is used for receiving an external task and generating an ordered workflow corresponding to the external task in combination with historical knowledge, and the ordered workflow comprises a plurality of atomic task units; the execution module is used for executing the local task of each atomic task unit to obtain a final short-term memory flow, a final execution report and a target business result corresponding to the external task, and a target tool is selected to execute the current task in combination with a previous process short-term memory flow cycle during execution; and the knowledge updating module is used for analyzing and processing the final short-term memory flow and the final execution report, obtaining a target knowledge fragment and updating historical knowledge. By adopting the system provided by the invention, the processing efficiency and accuracy can be improved, and real self-evolution is realized.

The application provides a model evolution learning method for vertical fields, comprising: obtaining high-quality field data; training a model into a first model with preliminary field knowledge through an instruction fine-tuning method; guiding the evolution of the first model using a large model, including: (1) first model answer generation, (2) the large model gives a systematic evaluation, (3) when the large model determines that the solution of the first model does not meet the expectation, a demonstrative answer is generated, the demonstrative answer, the answer of the last round model and the score of the large model are input into the first model, and S31-S32 are repeatedly executed until the large model finally approves the solution of the first model or the iteration number reaches a preset upper limit; fine-tuning the performance of the first model to generate a second model. Through the application, GPT4 distills high-quality field data, guides the evolution of the model, and uses the method of model self-game to get rid of the dependence on GPT4, and realizes the evolution of the self-capability of the model.

This invention discloses a hardware autonomous control method and system based on spatial intelligence and self-evolutionary learning, an electronic device, and a computer-readable storage medium. The system includes: a multi-source device discovery module for automatically scanning intelligent hardware devices and establishing a unified device model through multiple communication protocols; a capability reflection module for automatically extracting device control capabilities and parameter constraints from protocol metadata; a skill management module for storing and loading skill packages and establishing a mapping index from device type to skill package; a rule engine module for performing millisecond-level deterministic evaluation of sensor data and generating control commands; an intelligent decision engine module for orchestrating planning nodes and execution nodes based on state diagrams and making context-aware control decisions through a large language model; a command security module; an execution verification module; a multi-layer memory module; a preference learning module; and a multi-layer self-evolutionary engine module.

The invention discloses a glacier melt water resource cooperative regulation and ecological restoration system and method, and belongs to the technical field of water conservancy projects and ecological restoration. The system comprises a distributed dynamic sensing network used for acquiring multi-source environment data in a drainage basin; the multi-agent collaborative decision-making module is used for carrying out collaborative decision-making by utilizing a deep reinforcement learning agent, carrying out global multi-objective optimization through a central coordinator and generating a collaborative regulation and control instruction; the self-adaptive execution and feedback adjustment module is used for executing the instruction and collecting feedback data; and the system self-evolution learning module continuously optimizes the decision strategy based on experience playback and offline training. According to the method, intelligent coordinated regulation and dynamic optimization of glacier melt water diversion, groundwater recharge and vegetation irrigation are realized; the water resource utilization efficiency, the recharge precision and the vegetation recovery effect are effectively improved, and the system has the adaptation and evolution capacity for coping with environmental changes.

The present invention discloses a human-eye-like focusing metasurface system driven based on a teacher-involved evolutionary learning algorithm, which is applicable to the technical field of intelligent electromagnetic metasurfaces. The system includes a transmissive metasurface, an array probe, a focusing guide module, and an evolutionary learning module. When an external electromagnetic wave signal passes through the transmissive metasurface, the array probe installed behind the transmissive metasurface detects the external electromagnetic wave data, and the focusing guide module and the evolutionary learning module analyze it and output an adjustment strategy for the transmissive metasurface. The state of the transmissive metasurface changes, the array probe collects new data, the focusing guide module and the evolutionary learning module further analyze the intensity and characteristics of the external electromagnetic wave data, output the next adjustment command, and repeat the above process until focusing at the specified position. The present invention can achieve intelligent focusing at any position under multiple electromagnetic environments, does not require artificial adjustment, and can be used flexibly.

The present invention relates to the field of machine tool processing technology, and discloses a method and system for recovering machine tool processing files from interruption, wherein a method for recovering machine tool processing files from interruption comprises: performing G-code semantic analysis and graph structure conversion to convert a G-code processing program into a structured knowledge graph with semantic association; performing processing intent reasoning based on neural symbolic reasoning to understand the deep processing intent behind the program; realizing self-evolutionary learning of processing experience, and continuously learning and optimizing from historical experience; locating interruption recovery points based on intent graphs to accurately find the optimal recovery position; generating and executing interruption recovery strategies to achieve smooth recovery of the processing process; the present invention improves the success rate and accuracy of interruption recovery by deeply understanding the G-code semantics and processing intent, and is suitable for high-value parts processing scenarios.

This invention discloses a method and apparatus for flavor-oriented fermentation regulation of wampee vinegar based on reinforcement learning, relating to the field of artificial intelligence learning. The method includes: constructing a fermentation state-space model containing terpene concentration; setting flavor target encoding and flux-oriented reward function; using a physical information deep Q-network for continuous action decision-making; implementing multi-level intervention execution; and optimizing strategies through self-evolutionary learning and flux feedback. This invention achieves specialized monitoring and regulation of the characteristic aroma of wampee vinegar, solves the problem of non-monotonic coupling control of two microbial communities, reduces training samples by embedding prior knowledge of strains, ensures action safety by embedding physical and biological constraints, and improves batch-to-batch flavor consistency through delay compensation and self-evolutionary mechanisms.

The invention discloses an education agent teaching efficiency self-evolution evaluation system, which relates to the field of education artificial intelligence and comprises a feature fusion module, a model construction module, a trigger judgment module, a self-evolution module and a mapping graph updating module. According to the invention, unified coding and feature extraction are carried out by using a multi-modal learning algorithm, so that the multi-dimensional feature vector comprehensively reflecting the cognitive and emotional states of students is constructed, and the accuracy and scientificity of teaching efficiency evaluation are improved. An evolutionary learning mechanism is introduced, strategy pre-evaluation is performed by performing variation, intersection and screening operation on a low-efficiency or fluctuation strategy in combination with a simulation environment, a teaching strategy is continuously optimized, and the strategy adaptability and the teaching continuous optimization capability of an education intelligent agent are improved.

The invention discloses a pollutant prediction method, device and equipment based on an evolutionary learning strategy, and a storage medium, and relates to the technical field of pollutant prediction, and the method comprises the steps: carrying out the data exception processing of pollutant data, obtaining cleaned data, carrying out the feature conversion and time series data noise reduction of the cleaned data, obtaining feature information, and carrying out the data exception processing of the feature information; and identifying the feature information through a target pollutant prediction model to obtain a pollutant prediction result. Through multi-model fusion optimization, parameters are dynamically adjusted by using an evolutionary learning algorithm, a target pollutant prediction model is constructed, and the prediction precision of pollutants is improved.

The application discloses an evolution learning strategy-based pollutant prediction method and device, equipment and a storage medium, and relates to the technical field of pollutant prediction. The method comprises the following steps: performing data anomaly processing on pollutant data to obtain cleaned data, performing feature conversion and time series data noise reduction on the cleaned data to obtain feature information, identifying the feature information through a target pollutant prediction model to obtain a pollutant prediction result. The evolution learning algorithm is used to dynamically adjust parameters through multi-model fusion optimization, and the target pollutant prediction model is constructed, thereby improving the prediction accuracy of the pollutant.

The invention relates to the technical field of artificial intelligence, in particular to a dynamic optimization method and device of a model, equipment and a medium. According to the embodiment of the invention, the electronic equipment carries out the dynamic optimization of the weight of each analysis agent through an evolutionary learning algorithm based on the comparison between the transaction decision of the analysis agent agents on the target product and the current market trend of the target product, enables the analysis agent agents to evolve continuously along with the change of the market environment, and improves the efficiency. And a strategy structure is continuously optimized, and dynamic self-evolution and continuous adaptive capacity improvement of a system level are realized.

The present invention belongs to the field of network security technology, and specifically relates to a method, system, device, and medium for intelligently determining security events. The method comprises: collecting and parsing security logs of product objects; classifying and aggregating the parsed security logs to generate security events with multi-dimensional information; and for each security event, calculating a true and effective determination value for the security event based on the historical determination success rate of similar security events to determine whether the security event is a real event. The present invention improves the efficiency and accuracy of event determination by using manual feedback during initial deployment, then enters a self-evolutionary learning state with manual participation and assistance. It can be applied to different scenarios to assist security management personnel in their work, greatly reducing the workload and correspondingly improving work efficiency.