247 results about "Adaptive environment" patented technology

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

The invention discloses an intelligent abnormity early warning system for an industrial Internet of Things edge node, which comprises an edge sensing layer, an edge intelligent layer and a cloud edge collaboration layer, and is characterized in that the edge sensing layer is used for data acquisition and security preprocessing; the edge intelligent layer is used for intelligently analyzing the data transmitted by the edge sensing layer and constructing a dynamic space-time diagram neural network model and a dynamic edge knowledge graph; a digital twinning threshold engine is set, when the digital twinning threshold engine detects abnormity, reverse reasoning is carried out along the knowledge graph, and self-adaptive early warning is carried out; and the cloud edge collaboration layer is used for carrying out collaboration processing on early warning triggered by the digital twin threshold engine. According to the invention, the spatial-temporal features of the equipment group are learned through the set dynamic spatial-temporal diagram neural network; the digital twin threshold engine generates a dynamic threshold based on physical simulation and adapts to environment and load changes; the edge layer realizes lightweight real-time analysis; and a full-link embedded chaotic encryption and national cipher SM4 algorithm is adopted.

The invention discloses an internal and external network security service passing method and system based on edge computing, and belongs to the technical field of network security, and the method comprises the steps: obtaining multi-dimensional attribute information of an edge node, generating a security policy basic data set, generating a traffic feature fingerprint and a self-adaptive environment adaptive fingerprint, and calculating a fusion matching degree; edge security entity nodes are generated in combination with service scene adaptive threshold clustering, and then a security policy meta-model with a security policy blueprint as a core is constructed; analyzing the security policy meta-model through a multi-modal semantic analysis engine, generating a dynamic security enhancement model, and mapping the dynamic security enhancement model to a hierarchical security control model; based on the hierarchical model, outputting edge node internal and external network safety passage configuration and a corresponding safety passage ledger through a scenarized configuration generation algorithm; according to the method, adaptive generation, dynamic optimization and accurate execution of the security policy are realized, and the security, the automation level and the operation and maintenance efficiency of internal and external network passing in the edge computing environment are effectively improved.

The invention relates to an intelligent power grid cooperative scheduling method for automobile access, and relates to the field of electric automobile charging scheduling and intelligent power grid optimization. The method comprises the following steps: acquiring multi-source real-time data of an electric vehicle, a power grid and a charging station, constructing a collaborative scheduling graph structure, and performing multi-target optimization to generate an initial charging guide strategy and a charging station resource allocation scheme; then, reinforcement learning iteratively optimizes the guide strategy to dynamically adapt to environmental changes; further optimizing charging time and power parameters through a dynamic adaptive optimization algorithm, and realizing charging station congestion early warning and selection suggestions in combination with a probability prediction algorithm; a charging station power distribution strategy is optimized based on early warning information, fine optimization is carried out by adopting a genetic algorithm, and a scheduling scheme is evaluated and adjusted in real time through a feedback control algorithm. According to the invention, dynamic, refined and global optimization of electric vehicle charging scheduling is realized, the stability of a power grid, the operation efficiency of a charging station and the charging experience of a user are improved, and new energy consumption is promoted.

The invention provides a multi-agent-based task cooperative execution method and device, and the method comprises the steps: carrying out the calculation based on the task type of a target task and the current system state of a multi-agent system, and obtaining an index weight; determining a task allocation scheme of each subtask based on the index weight and the agent bidding information of each subtask; and determining an execution strategy of each sub-task based on the task attribute of each sub-task, the task allocation scheme and the environment perception data of the multi-agent system. According to the method and device provided by the invention, the index weight is calculated according to the task type of the target task and / or the current system state of the multi-agent system; the task allocation scheme of each subtask is determined based on the index weight and the bidding information of each subtask corresponding to the plurality of agents, so that dynamic task allocation adapting to environment change is realized, the task allocation rationality is improved, and the task execution efficiency and task completion quality of the system are improved.

The invention relates to the technical field of tourism big data, in particular to a multi-modal tourism route personalized generation method and system based on deep learning, and the method comprises the steps: constructing and incrementally updating a tourism knowledge hypergraph, extracting hidden time sequence preference and cross-entity cooperation signals of a user through a space-time perception graph neural network, and generating an interest drift model; performing deep fusion and intention analysis on the interest drift model and a context signal sensed in real time, and decoding to generate a candidate route concept skeleton by introducing an attention competition mechanism; and carrying out multi-dimensional simulation deduction and adaptability evaluation on the candidate route concept skeleton, carrying out iterative optimization through a reinforcement learning strategy based on deduction feedback, and finally outputting a personalized tourist route with optimal robustness, so that the robust tourist route which is highly personalized and adapts to environmental changes can be generated, and the robustness of the tourist route is improved. Therefore, recommendation accuracy and user experience are improved.

The invention discloses a power transformation and distribution station room operation method and device based on a force control dexterous hand, and belongs to the technical field of robots. According to the technical scheme provided by the embodiment of the invention, the distributed multi-source sensor array is utilized to obtain the consistent environment data, the operation situation description is dynamically generated to adapt to the environment change, the complete operation scheme is intelligently generated in combination with the cross-domain strategy knowledge graph, and real-time feedback is performed through the second multi-mode sensing data flow acquired during control, so that the control accuracy is improved. The problems of low efficiency and safety risk coexistence in the operation of the power transformation and distribution station room are solved.

The invention relates to the technical field of robot control, and discloses a robot cerebellum reinforcement learning method and system based on adaptive environment change, and the method comprises the steps: collecting multi-modal sensing data in the operation process of a robot; based on the multi-modal sensing data, determining an environment sudden change increment and an environment characteristic variable quantity, and obtaining a main strategy network according to the environment sudden change increment and the environment characteristic variable quantity; outputting a basic action vector of the robot based on the main strategy network, and performing value evaluation on the state of the robot to obtain a value evaluation quantity; performing updating calculation on the basic action vector based on the value evaluation quantity to obtain a compensation action vector; and performing vector superposition fusion on the basic action vector and the compensation action vector to obtain a final control vector, and taking the final control vector as a cerebellum learning action instruction of the robot, thereby realizing dynamic adaptation of a reinforcement learning strategy and cerebellum rhythm control. And the motion stability and the learning convergence efficiency of the robot in the unstructured environment are optimized.

A building-integrated artificial intelligence system forming a continuous ambient neural field is disclosed. The system includes a distributed multimodal sensor lattice, an on-premise symbolic cognition engine, and an adaptive environmental control kernel operating entirely at the building edge without reliance on external cloud services. Sensor data from optical, thermal, acoustic, airflow, pressure, structural, electrical, and chemical modalities are transformed into non-identifying occupancy vectors, behavioral glyphs, risk indicators, and environmental state descriptors. A privacy-governed policy graph determines sensor permissions, redaction thresholds, consent conditions, emergency overrides, and jurisdiction-specific compliance parameters. The neural field predicts occupancy loads, optimizes HVAC, ventilation, and lighting, detects accidents and structural anomalies, classifies emergent risks, and generates redacted event capsules for audit and emergency dispatch. A federated topology enables multiple buildings to exchange compressed symbolic templates to improve predictive accuracy without transmitting raw data. The system provides a universal, regulation-aligned AI nervous system for autonomous building operations.

The invention relates to the technical field of photovoltaic power generation detection, in particular to an outdoor photovoltaic module attenuation rate test method and test system, and the method comprises the steps: a multi-dimensional environment and operation data collection process; constructing and updating a digital twin reference model; a self-adaptive environment compensation and normalization process; and attenuation rate calculation and state evaluation. Nonlinear measurement errors caused by spectrum red shift / blue shift, large-angle incidence and a temperature hysteresis effect are effectively stripped, and real-time output data are mapped to equivalent performance data under a standard test condition, so that the measurement uncertainty is remarkably reduced, and sensitive capture and accurate calculation of a micro power attenuation trend are realized.

The invention discloses an agent intention understanding method based on multi-modal information fusion, relates to the technical field of natural language processing, and solves the technical problem that multi-agent intention understanding cannot be realized in a dynamic environment. Voice, text and visual features are mapped to the same hidden space through maximum mean value difference minimization, distribution difference between modes is eliminated, joint features in the hidden space can capture cross-mode complementary information at the same time, and the richness and anti-noise capability of feature expression are enhanced. CRF (Conditional Random Field) is combined with a special dictionary in the scheduling field to carry out candidate set expansion on fuzzy keywords, and specific semantic variants in the field are captured. The optimal grammar of the scheduling instruction is analyzed and decomposed into a feature vector causal relationship pair, and components of an intention and logic dependence of the intention are clarified. A dynamic knowledge graph is constructed based on the causal relationship, probability dependence among parameters is quantized by using a Bayesian network, the causal relationship strength is dynamically adjusted, and environmental changes are adapted.

The invention provides a self-adaptive operation strategy optimization method and system for a refrigeration system, and relates to the technical field of automatic control, and the method comprises the steps: obtaining temperature and humidity data and fluid boundary parameters, reconstructing a temperature and humidity distribution field through a physical constraint neural network, extracting enthalpy difference features, constructing a topological coupling network, and calculating and correcting a refrigeration load. And on the basis of energy consumption and enthalpy value uniformity, refrigeration resource distribution parameters are optimized, accurate regulation and control of the dynamic load area are achieved, the energy efficiency of the system is improved, the enthalpy value uniformity requirement of each area is met, and the environment change adapting capacity of the system is enhanced.

The invention relates to an intelligent ship network optimization method, system and device based on multi-link convergence and a medium. The method comprises the following steps: firstly, acquiring multi-network link connection parameters of the intelligent ship, constructing an adaptive environment, modifying a protocol stack, and integrating a physical link into a virtual link to realize resource convergence; and based on the real-time parameters of the virtual link and the physical link, dynamically distributing traffic by adopting an improved aggregation protocol, and generating a load balancing scheme. And then, regularly sending a detection packet quality index, and predicting a link quality trend in combination with historical data and navigation longitude and latitude. And finally, dynamically adjusting flow distribution according to a prediction result, an application real-time demand, a bandwidth demand and a current position tariff characteristic, and generating a ship-shore communication scheme considering both efficiency and cost. By adopting the method, the stability, reliability and economy of ship-shore communication of the intelligent ship can be effectively enhanced, and the method is suitable for multi-scene network monitoring optimization.

The invention relates to the field of biological fermentation engineering and discloses an environment-adaptive fermentation process temperature adjusting method which comprises the following steps: acquiring temperature field data in a fermentation tank through a sensor array to obtain initial temperature distribution data; performing microbial activity associated thermodynamic feature extraction on the initial temperature distribution data to obtain fermentation thermodynamic feature data; according to fermentation thermodynamic characteristic data, spatial-temporal distribution density analysis of temperature gradient difference is carried out, and temperature gradient spatial-temporal distribution density data is obtained.According to the method, through a depth deterministic strategy gradient algorithm, a self-adaptive temperature adjusting model is constructed, closed-loop temperature control of the fermentation process is achieved, the model can automatically adjust the temperature according to real-time data, and the temperature control accuracy is improved. The dynamic change in the fermentation process is responded, the adjustment precision is improved, the provided self-adaptive fermentation temperature adjustment method can achieve refined and intelligent temperature adjustment and control in the fermentation process, the fermentation efficiency is improved, and the method has wide application prospects.

The invention is suitable for the technical field of electrical equipment detection, and provides a ceramic insulator intelligent evaluation method based on spectral feature analysis, and the method comprises the steps: obtaining spectral data through 120 GHz high-frequency laser, synchronously collecting environment and electrical parameters, extracting 10-dimensional spectral features, and fusing the 10-dimensional spectral features into a multi-modal feature vector; training is carried out by using an integrated learning model fused by a random forest and XGBoost and an LSTM time sequence model, and a dynamic weight updating mechanism is combined to adapt to environmental changes; outputting aging grade probability distribution, aging trend prediction in the future 6 months and a risk value gt; and 3.0, early warning is triggered. According to the scheme, evaluation precision, dynamic adaptability and prediction capability are improved, and reliable support is provided for operation and maintenance of a power system.

The invention discloses an interference prediction method and device fusing an incremental KD tree and a graph convolutional neural network. The method comprises the following steps: S1, extracting time sequence features of sparse historical interference samples; s2, constructing a dynamic graph structure according to an incremental KD tree model to capture a spatial relationship among sparse historical interference samples; s3, extracting a local sub-graph based on the spatial relationship between sparse historical interference samples, and inputting the local sub-graph into the graph convolutional neural network to obtain a prediction result; s4, obtaining a new sparse historical interference sample, and carrying out incremental parameter updating on the local subgraph based on a prediction result to obtain an incremental parameter updating result; and S5, judging whether a concept drift problem exists in the confrontation process, if the concept drift problem exists, adopting an online updating mechanism to adapt to environmental changes, then returning to S1, and if confrontation is finished, exiting. According to the method, the problem that interference behavior time change and space correlation are difficult to describe at the same time in a traditional method is solved, and the problem of calculation efficiency of a traditional graph model in a dynamic scene is solved.

The invention provides an energy-saving cloud manufacturing multi-target scheduling method and system for improving rate-driven heterogeneous aggregation, and the method comprises the steps: A, setting algorithm parameters, job attributes and machine constraints, generating a weight vector and a neighborhood, and randomly binding an initial aggregation method; b, generating an initial population, performing heuristic decoding, and initializing an ideal point and an external archive set; c, calculating a dynamic switching threshold value based on the current iteration progress; d, executing sequential crossover and swap mutation operators to generate offspring individuals, and performing heuristic decoding based on consistency increment evaluation; e, updating an ideal point and maintaining an external archive set; f, executing self-adaptive environment selection according to the dominating relation and the relative improvement rate, and updating a neighborhood solution and a bound aggregation method; and G, if the termination condition is not met, returning to the step D, otherwise, outputting a non-dominated scheduling scheme set. The method has the advantages that the convergence problem under the multi-target conflict is effectively solved through self-adaptive cooperation of heterogeneous strategies. According to the method, heuristic batch decoding and time sequence linkage are adopted, a heuristic decoding algorithm with cluster constraints is designed, through real-time calculation of idle increments and switching losses, deep fusion of cross-process and cross-region resources is achieved, the cooperation efficiency of the whole cloud manufacturing process is guaranteed, and the maximum completion time and the total manufacturing cost can be balanced on the premise that production constraints are guaranteed; and thus, a high-quality collaborative scheduling solution set is stably obtained.

The invention relates to the technical field of audio processing, discloses an AI-based cloud terminal dynamic audio processing method, and aims to solve the problem that an existing scheme cannot dynamically adapt to scene changes. The scheme mainly comprises the steps of collecting environmental noise data, and extracting environmental perception features after preprocessing, framing and frequency domain transformation; performing framing processing on the played audio data, extracting an MFCC feature, a logarithmic short-time energy feature and a zero-crossing rate feature, and obtaining a scene recognition result through a CNN + LSTM scene classification model; splicing the environment perception features and the scene recognition result into a joint feature vector, and inputting the joint feature vector into an AI model to obtain an adaptive gain curve parameter, a scene adaptive noise suppression parameter and an anti-distortion dynamic range control parameter; processing the played audio to obtain optimized audio data; and updating AI model parameters through a PPO algorithm based on the reward value of the user feedback data. According to the invention, intelligent audio processing dynamically adapting to environments and scenes can be realized, and the audio experience of the cloud terminal is effectively improved.

The invention discloses a LiDAR-4D radar 3D target detection method based on dynamic denoising and dual-domain gating fusion, and the method achieves the robust 3D target detection in severe weather through the three steps: dynamic denoising, adaptive fusion, and precise detection. According to the method, a semantic guidance mechanism is introduced into a feature extraction layer, the perception ability of the network to foreground target points and the inhibition ability of the network to background noise points are enhanced, and meanwhile, the denoising threshold value can be dynamically adjusted by integrating the real-time point cloud density and weather category information through a denoising strategy of the denoising threshold value self-adaptive to environment change, so that the real-time point cloud density and the weather category information can be dynamically adjusted. Therefore, the balance between noise suppression and foreground point reservation is realized under different complex weather conditions, the fusion weight of the laser radar and 4D radar modality can be adaptively adjusted explicitly according to the real-time weather condition through a double-domain gating mechanism fusing external weather perception information, the perception ability of feature fusion to the external environment is enhanced, and the real-time weather perception information is obtained. And the rationality of the fusion strategy and the overall robustness are improved.

The invention provides a body-equipped robot control system and a body-equipped robot, and relates to the technical field of robots and computers. The system comprises a sensing module, a self-adaptive environment learning module, a man-machine interaction center module and an execution module, the sensing module is used for acquiring multi-modal data, and the multi-modal data comprises multi-modal environment sensing data and multi-modal man-machine interaction data; the self-adaptive environment learning module is used for carrying out environment perception and dynamic modeling based on the multi-modal environment perception data and generating a behavior strategy instruction; the man-machine interaction center module is used for performing intention and emotion analysis based on the multi-modal man-machine interaction data and generating an interaction feedback instruction; and the execution module is used for executing the behavior strategy instruction and the interaction feedback instruction and carrying out physical interaction with the environment and the user. The environment adaptability, the task execution efficiency and the man-machine interaction naturalness of the robot are improved.

The invention relates to the field of robot path planning, in particular to a multi-robot path planning method based on group control, which comprises the following steps: on the basis of a visibility graph, generating a communication undirected weighted graph, introducing a Laplacian matrix, and reflecting the connectivity of the graph according to a second small feature value of the Laplacian matrix; the arrival time of the robots is controlled by adjusting the side weights, space-time conflicts are avoided, the robot path planning sequence is determined according to the contribution value of the second small feature value, and the overall performance of multi-robot cooperation is optimized; according to the method, data in robot path planning are integrated through the Transform model, the comprehensiveness of environmental understanding is improved, spatial constraints of the path are optimized through a message passing mechanism of the graph neural network, the robot dynamically adapts to environmental changes, the path is generated through the generative adversarial network, diversity and high efficiency are achieved, local optimum is avoided, and the method is suitable for being popularized and applied. And dynamic obstacle avoidance and real-time path optimization are realized through cooperation of multiple models.

The invention discloses a natural gas pipeline station security and protection monitoring system and method, and relates to the technical field of natural gas pipe networks, the system comprises a gas sensor, at least one environmental sensor, a data acquisition unit and a data processing unit, the system also comprises a storage unit, the storage unit stores a dynamic compensation model in advance, and the dynamic compensation model is a dynamic compensation model; the dynamic compensation model is used for representing a nonlinear mapping relationship between the response deviation of the gas sensor and the environmental parameters monitored by the at least one environmental sensor. The storage unit storing the dynamic compensation model is arranged, the data acquisition unit is combined with original gas concentration data and environmental parameter data synchronously acquired in real time, and the data processing unit calls the model to calculate the dynamic compensation value and correct the original data, so that environmental change can be dynamically adapted in real time; the problem that fixed compensation cannot deal with nonlinear drift in a complex environment is solved.

The invention discloses a strategy updating method and system based on deep reinforcement learning, relates to the technical field of artificial intelligence, and solves the problem that behavior evaluation of a DRL agent is not accurate enough in the prior art. According to the embodiment of the invention, through the parallel evaluation of the value function network, the dominant function network and the Q value network, a triple verification mechanism of the state value, the action advantage and the action value is formed, and the possible deviation of a single evaluation function is effectively overcome, so that the evaluation accuracy is improved; the strategy gradient is calculated through comprehensive evaluation values, strategy parameters are updated, abundant signals provided by a multi-evaluation network can be effectively utilized, stable and efficient policy optimization can be achieved in a complex multi-task environment, and it is ensured that an intelligent agent can rapidly adapt to environment changes and make an optimal decision.

The invention provides a self-adaptive environment sensing method for an intelligent robot. The self-adaptive environment sensing method comprises the following steps: acquiring an initial image sequence, detecting a displacement track of a moving object in an environment, separating a sheltered region from a non-sheltered region, and determining a boundary fuzzy region for dynamically expanding a sheltered boundary; from a high-precision depth estimation result, key feature points are extracted, a preliminary three-dimensional scene model is reconstructed, interference of illumination interference on key feature point extraction is detected, and a corrected key feature point set is obtained by eliminating unstable key feature points in a region with severe illumination variation; according to the corrected key feature point set, establishing a corresponding relationship between the key feature points and the matching points, reducing ambiguity of the corresponding points through matching point epipolar limitation constrained on an epipolar line by corresponding point search, updating the three-dimensional scene model, and obtaining a high-integrity scene reconstruction result; and extracting navigation path information from the high-integrity scene reconstruction result, and generating a control instruction for autonomous navigation of the robot.

The invention relates to a zero-carbon park source network load storage coordination control method based on a deep reinforcement learning algorithm, and the method comprises the following steps: S1, collecting data in a park in real time, carrying out the standardization, denoising and feature extraction of the collected data, and forming a data set; s2, modeling the environment state space to comprehensively reflect the current state of the system; s3, defining an action space for adjusting the operation state of the system, realizing real-time regulation and control and forming the action space; s4, designing a reward function to realize dynamic balance among different targets; s5, training a strategy network and a value network by adopting an entropy regularization SAC algorithm, and optimizing a control strategy; and S6, in the system operation process, continuously collecting new operation data, updating the strategy network and the value network in real time by using the online learning capability of the SAC algorithm, and dynamically adjusting the control strategy to adapt to environmental changes. According to the invention, efficient utilization of new energy, low-carbon power purchase optimization and dynamic load regulation and control are realized, and the overall operation efficiency of the park is improved.

The invention provides a robot inspection control method and device, equipment and a storage medium, and belongs to the technical field of inspection control, and the method comprises the steps: obtaining a to-be-inspected area, and dividing the to-be-inspected area into a plurality of target inspection areas; for each target inspection area, target inspection operation is executed, and the target inspection operation comprises the steps that environment data of the inspection process of the target robot in the target inspection area is acquired; potential energy values corresponding to the environment data are determined according to the environment data, and a dynamic environment potential energy matrix of the target inspection area is determined according to the potential energy values; based on the dynamic environment potential energy matrix and through a biological foraging algorithm, an inspection path in the target inspection area is determined, and then the target robot is controlled to conduct inspection according to the inspection path. According to the robot inspection control method and device, the equipment and the storage medium provided by the invention, adaptive environment inspection can be realized, and the capability of coping with a complex environment in the robot inspection process is improved.

The invention provides a dynamic mapping method in a closed environment and an automatic driving vehicle, and the method comprises the steps: obtaining an initial map of the closed environment, and dividing the initial map into a plurality of first cells; determining the occupancy probability of each first cell according to the initial map to obtain a first probability; acquiring environment sensing data through a sensor; updating the first probability according to the environment perception data to obtain a second probability, and determining a first information entropy of each first cell according to the second probability; and taking a first cell where the autonomous vehicle is located as a root node, determining an increment of the first information entropy as a reward function value of a Monte Carlo tree, searching and planning a driving path through the Monte Carlo tree, and updating the initial map according to the environmental perception data in the driving process. The method solves the problem that in the prior art, due to the existence of static and dynamic obstacles in a closed environment, automatic driving measurement is difficult to adapt to environment changes to achieve self-adaptive map information updating.

A device control method and an apparatus, relating to the technical field of device control. The method comprises: acquiring the current operating environment state of a device; when it is determined that the reliability of an environment surrogate model fails to meet a standard, using a default control policy to control the operation of the device; acquiring interaction data between the default control policy and an environment; on the basis of the interaction data, updating a training data set; and, on the basis of the updated training data set, optimizing and adjusting weighting parameters of the environment surrogate model, so as to obtain an environment surrogate model of which the reliability meets the standard. The device control method enables the environment surrogate model to quickly adapt to environmental changes, and ensures that the control policy optimized on the basis of the environment surrogate model will not degrade, thus improving device performance and effects of energy conservation.

The application relates to the field of encoder control methods, in particular to a control method of a pull rope displacement sensor, which comprises the following steps: before control starts, an encoder automatically identifies an environment and requirements through a built-in intelligent algorithm, and optimal parameters are configured; in operation, signals are dynamically enhanced and intelligently filtered and optimized to eliminate noise; internal and external data are fused in real time, and a motion trend is predicted by using machine learning; a self-learning algorithm continuously optimizes encoding correction and adapts to environmental changes; based on data prediction, encoding instructions are intelligently generated and verified; according to real-time feedback, a control strategy is dynamically adjusted to realize adaptive optimization and adapt to changing working environments and task requirements.

The invention discloses an intelligent management system for a geological specimen storehouse, and relates to the field of storehouse environment management, and the system comprises a filing module which is used for collecting the type, number, storage position and adaptive environment parameter information of geological specimens, and building a specimen-environment-position association database; the comparison module is used for collecting temperature and humidity, gas components and illumination intensity in the storehouse in real time, and performing targeted comparison with specimen adaptive environment parameters; according to the invention, geological specimen related information and storehouse environment parameters are collected, multi-dimensional association is established, storage environment suitability is judged in real time through targeted comparison, key information is pushed in time through graded early warning, a regulation and control assembly is linked to directionally correct unmatched parameters, and an inspection path is intelligently planned and high-risk specimens are preferentially covered. Stable adaptation of a specimen storage environment is guaranteed, weathering damage is reduced, the storage life is prolonged, blindness of manual inspection and regulation is reduced, and management efficiency and accuracy are improved.