240 results about "Adaptive strategies" patented technology

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 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 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 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 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 discloses a task scheduling method, electronic equipment and a storage medium. The method is applied to a configuration center side and comprises the following steps: acquiring a configuration instruction subjected to permission verification; acquiring multi-dimensional service scene data based on the configuration instruction, and inputting the multi-dimensional service scene data into a preset adaptive strategy model to generate a corresponding target adaptive strategy; the preset adaptive strategy model is obtained based on rule learning and transfer learning training; and performing visual simulation verification on the target adaptive strategy, and issuing the target adaptive strategy passing the verification to a server side. According to the scheme, the target self-adaptive strategy adaptive to the current service scene is generated through the multi-dimensional service scene data collected in real time and the preset self-adaptive strategy model, and the target self-adaptive strategy passing the visual simulation verification is issued to the server side for scheduling execution, so that the optimal task scheduling in the dynamic service scene can be realized, and the task scheduling efficiency is improved. And the autonomy and maintainability of the system are improved.

The invention discloses a road disease real-time identification embedded method and system based on a lightweight CNN and an attention mechanism. According to the method, a lightweight feature extraction module integrating reconfigurable convolution and channel attention is constructed, a multi-scale feature fusion network of grouped convolution and cross-stage connection is adopted, and a three-level prediction head focusing on diseases of different sizes is configured, so that the feature sensing and positioning capability on tiny diseases is remarkably improved. A composite loss function fusing classification, regression and attention perception is adopted in model training, and optimization is carried out in combination with self-adaptive strategies such as course learning and difficult case mining. Finally, the model scale is compressed through network pruning, quantification and knowledge distillation technologies, the model is finally deployed on edge computing equipment, and high-precision and low-delay real-time detection and response of road diseases are achieved through a reasoning acceleration engine. According to the invention, the problems of low precision and slow speed of road small target disease detection on an embedded terminal in the prior art are effectively solved.

The invention relates to the technical field of tourism resource information processing, in particular to a tourism hotel dynamic scoring and recommending method and system based on multi-factor fusion, and the method comprises the steps: constructing a dynamic game portrait containing a user space-time behavior mark and hotel multi-modal attributes, and carrying out the dynamic scoring and recommending of a hotel based on the dynamic game portrait; the method comprises the following steps: analyzing a recessive preference vector of a user and an adaptive strategy vector of a hotel by simulating an interactive game process of the user and hotel attributes, performing dynamic equalization matching on the recessive preference vector and the adaptive strategy vector to generate a hotel situation score, generating an anti-consensus recommendation sequence according to the hotel situation score, and performing recommendation on the hotel situation according to the anti-consensus recommendation sequence. The anti-consensus recommendation sequence is subjected to interpretable packaging, and a final recommendation result with a decision guide clue is output to the user, so that the user can obtain a personalized hotel recommendation list and can also understand logic and diversity values behind recommendation, and the user experience is improved. Therefore, the transparency, the user trust degree and the exploration satisfaction degree of the recommendation system are improved.

The invention discloses a self-adaptive brushless motor control method and system, and relates to the field of intelligent control, and the method comprises the steps: collecting the original data of the operation state of a motor through a sensor group, and carrying out the preprocessing; time-varying parameter identification is completed through combination of an extended Kalman filtering algorithm and a radial basis function neural network, an evaluation index system is constructed based on an analytic hierarchy process to obtain a comprehensive evaluation value, and a related trend is predicted through a long and short-term memory neural network; constructing a multi-modal control strategy library, determining an adaptive strategy, optimizing core parameters by using an improved particle swarm optimization algorithm, generating a control instruction, and outputting a corresponding current through a power driving module; and monitoring motor parameters in real time, comparing with a control target value, calculating deviation, correcting an identification result, adjusting a strategy threshold value, and updating and optimizing an objective function. The method has the advantages that by accurately sensing the state of the motor, dynamically adapting the control strategy and optimizing parameters in real time, it is ensured that the motor stably and efficiently operates under the complex working condition, and the characteristics of energy conservation and long service life are achieved.

The invention discloses a cross-platform content generation and distribution method based on a multi-modal AI, and belongs to the technical field of cross-platform content generation and distribution, and the method comprises the steps: carrying out the content analysis and feature extraction of an original material based on a multi-modal AI model, and generating a structured content label and a semantic vector. By combining a vector matching degree formula of target portrait features and platform features, an adaptation strategy is dynamically generated, it is ensured that content not only conforms to platform rules, but also can accurately reach a target group, the conversion rate and user viscosity are finally improved, visual, text and semantic vectors are aligned through a Transform multi-modal fusion model, a cross-modal joint representation vector is generated, and the user experience is improved. And in combination with an adversarial generative network, differentiated variants of the same theme are generated in batches, and a content diversity score mechanism ensures that generated contents are balanced between creativity and compliance.

A non-signalized intersection automatic driving vehicle safety decision-making method based on deep reinforcement learning comprises the steps that firstly, time sequence states of a vehicle and other adjacent vehicles are obtained through vehicle and environment interaction, the time sequence states are input into a time-space social attention network, and time social attention captures the time sequence dependency relation of the vehicles through a one-dimensional convolution and self-attention mechanism; the space social attention adopts a full-connection network and a self-attention mechanism to model a space interaction relationship between vehicles, after feature alignment and dimension fusion are performed on the two vehicles, space-time attention features are generated, and a driving strategy is output through a TD3 algorithm; a self-adaptive strategy correction mechanism is introduced, the risk margin is dynamically adjusted by taking real-time collision time as a constraint, and a danger strategy is corrected in combination with a constraint prediction model; meanwhile, the risk is evaluated through the position relation between each HVs at the intersection and the potential collision area, and the TD3 is guided to learn a safer behavior mode through the dynamic association of the risk and the reward, so that the safe and stable control of the automatic driving vehicle at the non-signalized intersection is realized.

The invention relates to the technical field of fluid machinery control, and discloses an intelligent vibration reduction control method and system for a centrifugal pump, and the method comprises the following steps: obtaining vibration signal data to obtain frequency spectrum characteristics; when the spectrum feature exceeds a threshold value, abrupt change parameters are extracted, pipeline system coupling data are fused, and a multi-pump cooperation state vector is generated; generating a distributed decision instruction sequence according to the state vector classification; fusing the feedback information according to the instruction sequence to obtain an initial adaptive strategy parameter; the operation parameters are adjusted according to the initial parameters, the efficiency is calculated, loop iteration optimization is carried out until the efficiency reaches the standard, and final self-adaptive strategy parameters are obtained; and the stability is verified after the final parameters are deployed, and loop optimization is returned if the verification fails. Through multi-pump cooperation and double closed-loop optimization, the stability, efficiency and reliability of cooperative operation of the pump set can be improved.

The invention belongs to the technical field of robot perception and decision making, and discloses a multi-modal fused intelligent target perception method and system, and the method comprises the steps: obtaining multi-modal data in a distribution network operation scene, carrying out the preprocessing, fusing the knowledge of the distribution network operation field, and generating knowledge-enhanced multi-modal features; carrying out cross-modal alignment processing on the knowledge-enhanced multi-modal features, carrying out graph structure-based joint semantic and space alignment on isomorphic modals, and carrying out feature projection-based binding alignment on heterogeneous modals to obtain consistent aligned multi-modal features in a shared semantic space; based on a dynamic adaptive strategy, screening and fusing the aligned multi-modal features to generate unified fusion features; and inputting the fusion features into a target perception model, and outputting an image segmentation result and a point cloud semantic segmentation result of the distribution network operation target. According to the method, high-precision segmentation and positioning of the target in a complex distribution network scene are realized, and safe and efficient operation of the robot is effectively supported.

The invention discloses a cognitive impairment risk intelligent matching intervention system based on multi-modal data, and belongs to the technical field of cognitive impairment, and the cognitive impairment risk intelligent matching intervention system specifically comprises the following steps: collecting a tactile interaction sequence and a face video data stream when a user executes a cognitive intervention task, and constructing an original multi-modal data set; extracting tactile operation track features and reconstructing a heart rate variability sequence, and generating a synchronous multi-mode feature set; identifying a difference interval of state fluctuation and extracting behavior and physiological features in the interval to form an instant physical and mental load parameter set; in combination with an interaction efficiency index in the task execution record, generating a joint state feature vector, and outputting a personalized adaptation instruction through a pre-trained sub-state-parameter association model; and constructing an adaptive strategy model by using the multi-modal features and an adaptive instruction to realize millisecond-level dynamic adjustment from the real-time multi-modal features to task parameters. According to the method, real-time perception and accurate matching of the cognitive emotion load of the user are realized, and the individuation degree and instantaneity of intervention are improved.

The invention relates to the technical field of heterogeneous data fusion processing, in particular to a heterogeneous data fusion processing platform for multi-industry information system integration, which comprises an industry knowledge graph module for acquiring business data of each industry, extracting a core entity relationship of each industry and constructing a field knowledge graph of at least two industries; when the system is used, the limitation of fixed weighted average is broken through by constructing a lightweight domain knowledge graph for each industry, extracting a core entity relationship and dynamically adjusting the fusion weight of each data feature, so that the purpose of dynamically adapting to the semantic feature difference of different industries is achieved; the method can be applied to cross-department data fusion of smart cities conveniently, semantic conflict intensity is accurately quantified by using a dual-channel neural network, adaptive strategy grading processing is combined, efficient resolution is performed through unit conversion during low conflicts, semantic integrity is guaranteed by using field isolation fusion during high conflicts, and conflict resolution efficiency and accuracy can be improved.

The invention relates to a data synchronization method and system for intelligent dirty data detection and restoration based on DataX. According to the method, an intelligent dirty data processing engine is embedded between a Reader plug-in and a Writer plug-in of DataX, type matching, format verification, constraint conflict pre-detection and abnormal value recognition are completed online, and type conversion, format standardization, abnormal value replacement or flexible writing are carried out on dirty data according to a preset or self-adaptive strategy. And finally, the Writer executes insertion, updating, skipping or log recording only according to an instruction, and a traceable governance log is generated in the whole process. According to the method, the synchronization-cleaning capability is embedded into the DataX framework, so that the synchronization success rate and the data quality are remarkably improved, the external ETL dependence and the artificial script cost are reduced, and the method has the advantages of high intelligence, high throughput and flexible configuration.

The invention relates to the technical field of industrial process prediction, in particular to a tundish erosion prediction method based on a hierarchical hybrid expert framework. The method comprises the following steps: collecting time sequence physical field data of the tundish, and screening features to construct a unified feature space; shunting the feature space to obtain a time sequence feature and a statistical aggregation feature; a statistical aggregation feature training classifier is utilized to generate a calibration posterior probability, and a gating network is constructed; generating an initial mode subset based on a posterior probability and training a corresponding expert model; the confidence of data to be measured is obtained by the gating network, and a single expert model is selected for prediction or multiple expert models are fused through a self-adaptive strategy for weighted prediction according to whether the confidence exceeds a threshold value or not; and finally, reconstructing the predicted value into an erosion thickness absolute value through inverse transformation. According to the method, the accuracy and adaptability of tundish erosion prediction are effectively improved.

The invention discloses a C + + Mock class automatic generation method based on an inverse memory agent and a contrast knowledge graph, and aims to solve the problems that a virtual function coverage gap in a large C / C + + code library is difficult to find, the Mock code maintenance cost is high, and the automatic repair efficiency is low. According to the method, a difference context matrix is constructed by analyzing and merging request patch metadata, and an incremental abstract syntax tree is generated; generating a backtracking queue based on the event intensity and the semantic overlapping degree, and extracting a virtual function change list; the multi-scale difference extraction is completed in combination with an external expansion operator according to the rule that the path is not disassembled and the node is movable; a self-adaptive strategy table and a Mock specification are generated, through code synthesis and lightweight compilation verification, automatic self-healing is achieved through root cause positioning and minimum cost repair, finally, a result is written into a knowledge graph, and parameters are dynamically adjusted. According to the invention, Mock generation accuracy and version adaptability are improved, manual intervention is reduced, and test maintenance efficiency is improved.

The invention discloses an electrocardiosignal feature extraction and classification method and a sensor system, and particularly relates to the technical field of electrocardiosignal processing and mode recognition. The core of the method is that an original electrocardiosignal is obtained through a sensor, after preprocessing, a self-adaptive strategy based on real-time signal quality evaluation is adopted to dynamically select and fuse time domain, frequency domain and nonlinear features, and then a machine learning model integrated with an incremental learning function is utilized to classify the features; the corresponding system comprises a sensor module, a preprocessing module, a feature extraction module, a classification module and a system control module which are connected with one another. According to the core structure, the dynamic optimization of the feature extraction strategy along with the signal quality is realized, and the classification model can be continuously self-updated according to new data, so that the adaptability of electrocardio analysis and the accuracy of long-term monitoring are improved.

The invention discloses a network security evaluation system and method based on generative artificial intelligence, and belongs to the technical field of network and information security, and the system comprises a dynamic attack scene generation module, an automatic evaluation module, a user feedback collection module, an adaptive strategy optimization module, a multi-modal data fusion evaluation module, and a visual report generation module. The method comprises the steps of collecting desensitization threat intelligence and constructing an attack template, generating simulation attack data in an isolation environment through a generative AI, scheduling a test task and calculating indexes such as a detection rate and a false alarm rate, combining a user feedback optimization strategy and fusing multi-source data to construct a risk map, and finally generating an interactive evaluation report. According to the network security evaluation system and method based on generative artificial intelligence, comprehensive, continuous and intelligent evaluation of the security protection capability of an information system is realized through dynamic attack scene generation, multi-modal data fusion and a self-evolution evaluation strategy.

The invention provides a test case generation and test method and device, equipment and a storage medium, relates to the technical field of software testing, and aims to understand logics and requirements of different software systems and automatically generate adaptive test cases. According to the specific technical scheme, a test task request is received; determining a target agent from the plurality of agents through a priority strategy, a load strategy, a capability matching strategy and an adaptive strategy between the test task and the plurality of agents; obtaining a source code, a business requirement document and a historical test case of the software system associated with the test task through the target agent; reasoning and planning by using a large language model through the target agent based on the source code of the software system associated with the test task, the business demand document and the historical test case, determining a test plan, and generating a test case; and running the generated test case through the target agent according to the test plan, and generating a test result and defect information.

The invention provides a method, a system, equipment and a medium for analyzing running states of multiple automatic warp tying machines, and belongs to the technical field of automatic warp tying machines. Equipment health, warp tying efficiency, warp process, joint quality, fault early warning and associated loom data are acquired from multiple pieces of equipment in real time; a standard data set is formed through intelligent routing transmission, data cleaning, format conversion and aggregation processing; and adopting a self-adaptive strategy to match an optimal analysis model for various types of data for analysis processing, comparing a result with a multi-machine cooperative operation reference value, marking abnormity, and summarizing state results according to device numbers. Multi-device data full-link closed-loop management is realized, and the monitoring efficiency and accuracy are improved; through cross-device data association verification and multi-dimensional feature fusion, the fault positioning and prediction capability is enhanced; operation state results are fed back to an equipment end in real time, stable operation of equipment is effectively guaranteed, fault risks are reduced, and production efficiency and warp tying quality are improved.

The invention relates to the technical field of artificial intelligence and man-machine interaction, and discloses an AI multi-mode everything identification child interaction star key system, which comprises a multi-mode perception module, a data storage module, a self-adaptive strategy decision module, a situation arrangement and retrieval module, a content generation and safety control module, a multi-mode expression execution module and an asynchronous capability evaluation module. The system maps visual features to a semantic space through an attention mechanism to analyze a multi-modal anaphora intention, determines a cognitive age stage based on a user dynamic portrait matrix and matches an interaction strategy, generates reply content by using a large language model, and dynamically adjusts voice and action output parameters according to a real-time emotion vector. And finally updating the portrait by using a moving average algorithm. The problems that in the prior art, visual semantic alignment is lacked, and the content difficulty cannot follow dynamic evolution of the user ability are solved, and accurate intention understanding and personalized growth interaction are achieved.

The invention relates to the technical field of building safety, and discloses a safety transformation monitoring system for building construction and a method thereof. The system comprises a data acquisition and standardization module, a dynamic security entity relationship map construction module, an organization capability and cognitive load modeling module, a hybrid risk identification and conduction calculation engine, a self-adaptive security transformation strategy generation engine and a closed-loop self-calibration module. According to the method, a dynamic security entity relation graph is constructed and updated in real time, the cognitive load of management personnel is quantified, and the cognitive load is used as a dynamic adjustment factor of the conduction probability of risks on the graph; when a safety transformation strategy is generated, comprehensively evaluating a risk reduction effect, resource cost and additional cognitive load cost to output an optimal adaptive strategy; and finally, performing closed-loop self-calibration on the system model by utilizing execution feedback. According to the method, dynamic prospective risk prediction, self-adaptive strategy generation and continuous self-optimization are realized.

The invention discloses a honeypot automatic coping strategy generation method based on a large model. The method comprises the steps of S1, performing semantic analysis on dynamic attack behaviors; s2, performing context-aware threat reasoning; s3, adaptive strategy generation and semantic verification are carried out; s4, strategy executable compiling is carried out; s5, enhancing the efficiency of the closed-loop strategy; according to the method, the authority / service logic contradiction is thoroughly eliminated through a semantic consistency verification mechanism, so that the false alarm rate of the honeypot in the APT attack is reduced; an anti-recognition perturbation code injected by the low-entropy strategy compiling technology breaks through a traditional honeypot periodic response mode, and the fingerprint recognition success rate of an attacker is reduced; a resource penalty function of the Pareto optimal strategy sequence enables a trapping intensity mean value under limited resources to be improved; a double-channel updating mechanism promotes coevolution of a knowledge base and a constraint set, and the response generation speed for an unknown attack mode is shortened.

The invention discloses a safety door intelligent integrated control method and system based on the Internet of Things, and the method comprises the steps: deploying distributed miniature edge computing nodes, and carrying out the collection and preprocessing of multi-source Internet of Things data; constructing a hierarchical multi-modal deep learning model, and performing high-order feature extraction and fusion on multi-source data; constructing a digital twin model of the safety door intelligent integrated control system, and establishing virtual mapping of a physical system; an adaptive strategy based on reinforcement learning is adopted, fusion features and digital twinborn prediction serve as state input, and an optimal control action dynamically adjusted according to real-time data is output; a distributed constraint optimization and utility transfer cooperative control model is built, each system in the safety door integrated control system is used as an intelligent agent, cooperative control is carried out, accurate simulation of a physical state and prediction of a future state are achieved, and the safety door integrated control system has the advantages of being simple in structure and convenient to use. And efficient cooperative control is realized based on a cooperative control model of distributed constraint optimization and utility transfer.

The invention relates to the technical field of network security, in particular to an adaptive penetration test method and system based on reinforcement learning, and the method comprises the steps: constructing a state space model, defining an action space, designing a reward function, optimizing a strategy model, and analyzing a test result. According to the method, a reinforcement learning mechanism is combined with target environment feedback, so that multi-step attack path planning and adaptive strategy adjustment are realized, and the intelligence and coverage depth of penetration testing are remarkably improved. And meanwhile, through backtracking analysis of the state transition sequence, the potential threat path is comprehensively identified, and accurate guidance is provided for security protection.