591 results about "Adaptive environment" patented technology

The invention discloses a building energy consumption dynamic optimization method and system based on BIM and reinforcement learning, and belongs to the technical field of building energy management and intelligent control, and the method comprises the steps: building a BIM containing building component physical attribute parameters, and generating a building digital twinborn body with dynamic thermal attribute evolution; extracting the spatial topological relation and the physical property parameters of the components, and constructing a multi-dimensional state space of a preset reinforcement learning model; embedding physical constraint conditions, and training the reinforcement learning model to generate a multi-objective optimization strategy of the energy equipment; and analyzing the multi-objective optimization strategy into an equipment control instruction set, and feeding back the equipment control instruction set to the building digital twin for real-time physical attribute simulation. According to the method, the physical accuracy of the BIM and the self-adaptive decision-making ability of reinforcement learning are combined, adversarial training under physical constraints is introduced, an energy consumption optimization strategy which conforms to actual operation limitation and dynamically adapts to environmental changes can be generated, and the energy utilization efficiency and the system response speed are remarkably improved.

The invention provides an electric power multi-modal data perception and digital twinborn simulation cooperative interaction method and system, and relates to the field of electric power equipment management and maintenance, and the method comprises the steps: carrying out the electric power multi-modal data perception of data collected by edge equipment, and obtaining the state characteristics of single equipment; constructing a digital twinborn model based on the state features of all devices in the to-be-monitored power system; constructing a power equipment graph knowledge base by using a digital twinborn model, and extracting global state features of the equipment from the power equipment graph knowledge base through a graph neural network so as to construct a global state feature time sequence of the equipment; inputting the global state feature time sequence into the trained time sequence prediction model, and predicting the equipment state at the future moment; according to the multi-modal data, parameters of the digital twin model are adjusted in real time, environmental changes are flexibly adapted, time sequence modeling and fault trend analysis functions are combined, the running state of the equipment is predicted, and the comprehensiveness and foresight of equipment monitoring are further improved.

The invention relates to the technical field of computers, and discloses a carbon emission calculation method based on deep learning, which comprises the following steps: S1, collecting multi-source data through a sensor, satellite remote sensing, an intelligent electric meter and a monitoring system, carrying out cleaning, denoising and missing value filling on the collected data, and in addition, carrying out data processing on the collected data; the structured data and the unstructured data are integrated through a multi-source data fusion technology; s2, performing feature extraction on the original data by adopting a deep feature learning model, and screening out key features contributing to carbon emission prediction; and S3, performing real-time decomposition and anomaly detection on the data by combining an autoregressive integral moving average model and an online learning algorithm. By combining an autoregressive integral moving average model and an online Bayesian learning method, carbon emission calculation can dynamically adjust parameters according to real-time data to adapt to environmental changes, seasonal fluctuations and emergencies, so that the flexibility and real-time performance of a prediction model are remarkably improved.

The invention relates to the technical field of economic resource management, and discloses an economic resource management optimization method based on intelligent decision making. According to the method, multi-source heterogeneous data, including resource stock, demand fluctuation and the like, of an economic system are collected firstly; a dynamic resource pool is divided based on multi-dimensional feature analysis, and a nonlinear optimization model is constructed to predict resource supply and demand changes so as to generate an allocation scheme; and then optimizing a distribution path by using a multi-stage decision tree, updating model parameters through an adaptive learning mechanism according to market feedback and constraint condition changes, and correcting a deployment scheme in real time. In addition, key technical details such as an elastic quota adjustment formula and a fuzzy clustering algorithm membership function are given. According to the method, complex data can be effectively integrated, resources are scientifically scheduled, supply and demand are accurately predicted, a distribution path is optimized, environmental changes are adapted, the efficiency and benefits of economic resource management are remarkably improved, and scientific and reasonable resource management decision support is provided for economic subjects.

The invention relates to an intelligent building energy-saving control method and system self-adaptive to environment changes, and belongs to the technical field of intelligent buildings. The energy-saving control method comprises the following steps: acquiring data of sensors distributed inside and outside a building and external weather forecast data; performing space-time alignment processing on the sensor data, generating a time-synchronized multi-dimensional environment data stream, performing anomaly detection processing, and outputting a pre-processed structured environment data set; performing environmental parameter prediction according to the structured environmental data set and the external weather forecast data to obtain an environmental parameter prediction result in a future preset time period, and constructing a multi-objective optimization function; acquiring a real-time power grid carbon emission coefficient and equipment operation state parameters, and generating a dynamic constraint condition set; and solving the multi-objective optimization function based on a hierarchical optimization algorithm, taking the dynamic constraint condition set as an input parameter, and outputting an equipment control instruction and an optimization control decision of an execution priority. The energy consumption can be reduced while the comfort degree is ensured.

The invention provides a self-adaptive impedance matching method and system of a microwave frequency band. The method comprises the following steps: performing initial impedance measurement on a plurality of microwave component ports to obtain a complex impedance parameter as a matching reference; in the operation process, a current impedance state and a reflection coefficient are collected through a reflection monitoring mechanism, and a dynamic impedance state vector is constructed; inputting the state information into an adaptive algorithm module, generating a matching parameter vector and configuring an adjustable device to realize real-time adjustment of a matching network; calculating a matching error according to the current reflection state and a target reference value, and driving parameters to be iteratively updated; after the matching precision requirement is met, the final parameters are loaded to the controller to complete actual configuration; meanwhile, the system periodically collects environmental parameters and matching effects, and dynamically judges whether rematching is needed or not so as to maintain long-term stable operation of the system. The method has the capabilities of automatic sensing, closed-loop optimization and environmental change adaptation, and can improve the matching efficiency and signal transmission quality of a microwave system under multi-band and high-dynamic working conditions.

The invention provides a multi-AGV distributed path planning method based on graph reinforcement learning, which is used for solving the technical problem of low path planning efficiency of a multi-AGV system in an information limited dynamic environment. The method comprises the following steps: establishing a dynamic heterogeneous graph model in multi-AGV collaborative path planning; constructing a distributed partially observable Markov decision model based on a graph structure in combination with path planning and collaborative decision; on the basis of a multi-agent deep reinforcement learning framework of a graph neural network and a multi-head attention mechanism, a multi-AGV cooperative path planning algorithm for coping with an information-limited dynamic environment is designed, information aggregation is realized on the basis of local perception and neighborhood interaction by adjusting weights of nodes and edges in a dynamic heterogeneous graph, and a multi-AGV cooperative path planning algorithm for coping with an information-limited dynamic environment is realized. And the environment change is updated and adapted in real time. According to the method, the path planning efficiency can be improved through an information aggregation mechanism in an information-limited dynamic environment, multi-AGV cooperative path planning is optimized through the graph neural network, and efficient and stable operation of the system is ensured.

The invention relates to the technical field of environment perception, and discloses an optimization method and device for a multi-climate self-adaptive environment perception cooling fan system, and the method comprises the steps: collecting multi-point temperature, humidity and air pressure data, and carrying out the filtering processing, and obtaining an environment state vector; executing mathematical modeling of the multi-degree-of-freedom cooling system according to the environment state vector to obtain a linearized state space model; performing temperature gradient estimation and humidity compensation based on the linearized state space model to obtain an adaptive control law; inputting the adaptive control law into a model-free adaptive prediction controller to obtain a rolling optimization control sequence; weight self-adaptive adjustment and heat dissipation efficiency hierarchical prediction control are carried out on the rolling optimization control sequence based on climate conditions, fan rotating speed control and wind direction adjustment execution signals are generated, the problem that a traditional heat dissipation system is not sensitive to space temperature distribution and humidity changes is solved, and the heat dissipation control precision under different humidity conditions is improved.

The invention discloses a reinforcement learning modeling method and device fusing multi-source heterogeneous data, and the method comprises the steps: obtaining the multi-source heterogeneous data from different data sources and having different data formats through an intelligent agent, carrying out the feature extraction through employing a corresponding feature extraction technology, obtaining different modal features, and carrying out the feature extraction through employing a corresponding feature extraction technology; effectively fusing different modal features to obtain environment state representation, and constructing an intelligent model of the intelligent agent by using a deep reinforcement learning algorithm based on the obtained environment state representation; different modal features extracted from different data sources are effectively fused, the fused features can comprehensively and accurately represent the current environment state, the intelligent model of the intelligent agent is constructed by using a deep reinforcement learning algorithm based on the fused environment state representation, and the intelligent model can obtain more comprehensive and accurate environment information, so that the intelligent model can be applied to the intelligent system of the intelligent agent. And the strategy is adjusted in real time to adapt to the change of the environment, so that the generalization ability and the accuracy of the intelligent model are effectively improved.

The invention discloses a multi-agent dynamic task allocation and collaborative path-finding system based on label-free distributed deep reinforcement learning. The system comprises the following steps: step 1, receiving state information and environment perception data of each agent in a multi-agent system based on distributed deep reinforcement learning; step 2, extracting feature representations of the environmental perception data and the intelligent agent state information, and performing multi-source heterogeneous information fusion through an attention mechanism to obtain state-task matching features; 3, transmitting the state-task matching characteristics to a multi-agent network in real time, realizing task allocation negotiation among agents by adopting a hierarchical scheduling and state exchange mechanism based on task priorities, dynamically detecting newly added task types, and performing incremental learning; the online updating iteration of the model is realized to assist the multi-agent optimization task allocation strategy and the path planning action; compared with the prior art, the method has the advantages that by applying the distributed deep reinforcement learning technology and a state exchange mechanism between intelligent agents, the system can quickly adapt to environment changes and task dynamics, and the resource utilization rate and the task completion efficiency are improved.

The invention discloses an industrial machine vision recognition and analysis system, relates to the technical field of machine vision recognition, and is used to solve the problem of poor industrial machine vision recognition. The invention combines total variation denoising, contrast-limited adaptive histogram equalization, random geometric transformation and other technologies to enhance image diversity, improve the system's generalization ability for images in different environments, extract image texture and edge features, provide high-quality data for subsequent analysis, use unsupervised learning to extract image potential features and patterns, and locate abnormal areas through template matching, so as to adaptively discover defects in the production process, dynamically update the model based on a small amount of labeled data, optimize feature representation in real time, and adapt to environmental changes. Finally, semi-supervised learning and pseudo-label generation technology are used to expand the training set, improve the generalization ability of the model, maintain efficient learning when labeled data is limited, reduce manual labeling costs, and improve the accuracy of automated quality inspection.

The embodiment of the invention provides a laser-based high-low hoisting box girder safety angle calculation method. Accurate monitoring and risk early warning in a box girder hoisting process are realized through multi-source data fusion and dynamic safety evaluation. According to the method, a mapping relation between environment parameters and distance measurement errors is established, and a self-adaptive environment compensation mechanism is adopted to improve measurement precision; performing optimization fusion on laser ranging and crown block attitude data by using Kalman filtering and confidence coefficient weight; time sequence features are extracted based on a long-short-term memory network, and a sliding time window and an autoregression model are combined to predict a box girder inclination angle change trend, so that risk grading early warning is realized. According to the method, the self-adaptive updating of the model is realized by continuously optimizing the database, and the safety monitoring level and the early warning capability in the construction process of the high-low hanging box girder are effectively improved.

The invention discloses an intelligent monitoring method and a monitoring system for an IDC (Internet Data Center) machine room environment, particularly relates to the field of IDC machine room environment monitoring, is used for solving the problems of multi-parameter relevance and real-time early warning, and aims at solving the problems of multi-parameter relevance and real-time early warning by constructing a high-dimensional topological structure, a Bayesian network model and a dynamic threshold adjustment mechanism of the IDC machine room environment. The relevance and the causal relationship among the multi-dimensional parameters are effectively captured, and the analysis and prediction capability on the complex environment data is remarkably improved. And by utilizing a persistent coherence algorithm and random forest regression, nonlinear correlation of parameters is accurately identified, and the limitation of a traditional method in multi-parameter analysis is overcome. Through Granger causal relationship test, a parameter causal sequence is determined, and a basis is provided for accurate prediction of environmental changes. A dynamic threshold adjustment mechanism flexibly adapts to environment changes, false alarm and missing alarm are reduced, it is ensured that the system rapidly identifies abnormities and triggers alarms in a complex environment, and the intelligence and stability of the system are remarkably enhanced.

The invention discloses a servo motor dynamic overload detection and protection method based on a heat accumulation model, which relates to the technical field of servo system fault protection application, and comprises the steps of constructing a heat accumulation equivalent model, dynamically detecting an overload state, executing a heat dissipation recovery strategy, implementing a multi-stage protection mechanism, self-adaptive environment compensation and a historical data learning function. Through discretization processing of overload curve data and a nonlinear acceleration calculation strategy, the problems of protection lag and false triggering caused by traditional single-parameter detection are avoided, it is ensured that the motor can respond in time in light load, heavy load and load fluctuation scenes, the burn-out risk of the motor is reduced, and through integration of an environment temperature sensor and a heat dissipation condition evaluation unit, the reliability of the motor is improved. The heat accumulation coefficient and the heat dissipation attenuation parameter are dynamically adjusted, the problem of model deviation caused by environment interference is solved, when heat dissipation deterioration and temperature feedback abnormity are detected, the heat dissipation recovery period is automatically prolonged, calculation logic is corrected, and the continuous operation reliability of the system is improved.

The invention provides an AI agent generation method, a platform system, electronic equipment and storage equipment. The AI agent generation method comprises the following steps: acquiring a normalized task data set; performing feature extraction on the standardized task data set to construct task feature data; a hierarchical distributed architecture is adopted, initial parameters of the intelligent agent are optimized in combination with a genetic algorithm, the basic structure and the initial state of the intelligent agent are determined, and an initialized intelligent agent architecture is obtained; performing reinforcement learning training and optimization on the initialized agent architecture based on the task feature data to generate an AI agent; wherein the agent architecture is repeatedly executed in a simulation environment by using a near-end strategy optimization algorithm and task feature data, and strategy network parameters of the agent architecture are continuously adjusted according to reward feedback of task completion to obtain a trained and optimized AI agent. According to the invention, the trained intelligent agent can quickly adapt to the change of the environment, and the strategy of the intelligent agent can be adjusted in time.

The invention discloses a building environment dynamic regulation and control method and system based on Internet of Things driving, particularly relates to the field of building environment intelligent regulation and control, and is used for solving the problems of multi-region environment dynamic regulation and control and fine management. The environment data of each sub-region of the building is accurately acquired and synchronized, real-time region division is realized in combination with an adaptive clustering algorithm, and the accurate capture of environment features and the reasonability of space division are ensured. On the basis, the independence and interaction strength of the sub-regions are comprehensively evaluated by calculating an environmental independence index, and a scientific basis is provided for refined and coordinated regulation and control. Based on a rule engine and a decision tree reasoning algorithm, an optimal regulation and control strategy is automatically generated, and continuous optimization is performed in combination with a Q learning algorithm, so that the system can adapt to environmental changes, dynamically adjust regulation and control actions, realize intelligent optimization and self-learning of the regulation and control strategy, and remarkably improve the regulation and control efficiency and accuracy of the building environment.

The invention provides a cross-modal adaptive matching online external parameter calibration method and system for a laser radar and a camera, and belongs to the technical field of online external parameter calibration of the laser radar and the camera, initial external parameters are obtained by using a static calibration result, a laser radar point cloud is projected to a visual angle of a virtual camera, an LIP image aligned with a camera image is generated, and a calibration result is obtained. Performing cross-modal segmentation on the LIP image and the RGB image, extracting a semantic mask, generating a cross-modal feature point pair through a strategy, and performing preliminary external parameter calculation in combination with a PnP algorithm; global optimization is carried out through a multi-modal loss function, and an inter-frame smoothing and increment optimization strategy is introduced to ensure accurate estimation of external parameters. Finally, the optimized external parameter matrix is applied to projection and matching of subsequent frames, and can adapt to environment change and data dynamic update in real time. The method has high precision and real-time performance, overcomes the defects that off-line calibration depends on a specific calibration target and cannot cope with dynamic environment changes, and adapts to application requirements in high-speed motion and complex environments.

The invention discloses an AI-driven robot working environment safety sensing method, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting the visual, laser radar, force sense and environment parameter data of a working environment in real time through a multi-mode sensor; performing feature extraction and semantic understanding on the acquired data by using a deep learning algorithm to generate multi-dimensional perception information of the environment; based on a preset security policy model, in combination with the multi-dimensional perception information, predicting and classifying potential risks; according to a risk classification result, a behavior strategy of the robot is dynamically adjusted, and active safety decision is realized; and through the continuous learning module, the deep learning model and the security policy model are optimized online by using newly collected data so as to adapt to environmental changes.

The invention discloses a multi-source environment monitoring data fusion method based on adaptive Bayesian reasoning. The method comprises the following steps: S1, collecting an environment monitoring data set; s2, obtaining a preprocessed environment monitoring data set; s3, carrying out uncertainty evaluation on the preprocessed environment monitoring data set; s4, forming an initial prior probability; s5, calculating the posterior probability distribution of each environment monitoring data source through the Bayesian theorem, and dynamically updating the weight of each data source in the environment monitoring data fusion process according to the posterior probability distribution; s6, performing weighted fusion on each environment monitoring data source according to the dynamically updated weight to form fused environment monitoring data; and S7, carrying out post-processing on the fused environment monitoring data. According to the invention, the data fusion process can adapt to environmental changes in real time, so that the accuracy of data fusion is improved.

The invention discloses an adaptive environment weighted data fusion navigation and positioning method for a manned submersible in a polar region, which belongs to the technical field of ocean navigation and positioning, is used for navigation and positioning of the submersible in the polar region, and comprises the following steps: acquiring data of IMU (Inertial Measurement Unit), acoustics, optics, environment sensors and Doppler sensors, and carrying out de-noising, time synchronization and coordinate conversion; modeling the polar region environment variable, calculating the adaptive weight, and dynamically adjusting the contribution degree of the sensor by adopting an exponential decay function; introducing an environment factor to correct the weight, and performing nonlinear state estimation by adopting an EKF (Extended Kalman Filter); performing error modeling by using historical data, and optimizing a weight adjustment strategy in combination with machine learning; and filtering optimization is performed on the fusion result, and multi-scale correction is performed in combination with prior environment information. Compared with the prior art, the influence of a single sensor error on a final positioning result is reduced, the positioning precision of the submersible in a polar region complex environment is improved, and the stable navigation capability is kept under the condition that sensor data suddenly changes or is lost.

The invention belongs to the technical field of optical fiber sensing analysis, and particularly relates to an intelligent self-adaptive environment multi-parameter passive optical fiber sensing analysis system which comprises an optical fiber sensor array module, an intelligent signal processing module, a self-adaptive adjustment control module, a comprehensive monitoring alarm module and a remote control center. By fusing an optical fiber sensing technology and an intelligent algorithm, real-time monitoring and accurate analysis of environmental changes are realized, and the system has the advantages of high transmission rate and electromagnetic interference resistance, has wide application prospects in the fields of environmental monitoring, industrial production control and the like, and is suitable for popularization and application. The environment management and control difficulty in the detection period is analyzed through the environment management and control analysis module, and the inspection performance of the optical fiber sensor array module in the detection period is analyzed through the inspection influence analysis module when the environment control high-difficulty signal is generated, so that whether the influence caused by the inspection performance factor is eliminated or not is judged; management personnel can reasonably carry out follow-up management scheme planning, and the intelligent degree is high.

The invention relates to the technical field of medical diagnosis, in particular to a dysphagia detection and automatic scoring system based on a high-resolution acoustic sensor. Comprising a three-dimensional curved surface type acoustic sensing array module, a self-adaptive environment noise compensation module, a swallowing stage dynamic segmentation module, a multi-dimensional feature extraction engine, a swallowing abnormal mode classifier and a dynamic weight scoring model. A self-adaptive environment noise compensation module, a time-frequency domain feature extraction module and a dynamic weight scoring model are combined, the swallowing process is monitored and scored in real time, the system automatically identifies and analyzes acoustic features of an oral cavity preparation period, a pharyngeal period and an esophageal period, and through multi-dimensional feature fusion, dynamic weighting and neural network optimization, the swallowing process is monitored and scored in real time. The method has the advantages of being non-invasive, easy and convenient to operate, high in real-time performance and the like, and can be widely applied to early screening, evaluation and follow-up visit of clinical dysphagia.

The invention relates to the technical field of power distribution cabinets, and discloses a power distribution cabinet with a self-adaptive environment adjusting mechanism, and the power distribution cabinet comprises a cabinet body which is used for fixing and supporting internal electrical elements; the automatic dehumidification assembly is mounted on the outer side of the cabinet body and is used for dehumidifying the cabinet body when detecting that the moisture content in the cabinet body is too high; the cleaning assembly is installed on one side of the cabinet body and used for conducting anti-blocking treatment on a filter screen arranged outside the cabinet body, and the driving assembly is installed outside the cleaning assembly and used for driving the cleaning assembly. By adding the automatic dehumidification assembly, the humidity in the power distribution cabinet can be effectively reduced, and the risk of electrical short circuit caused by a humid environment is reduced. By discharging moisture, safe operation of electrical equipment is guaranteed, and fire and electric shock accidents caused by moisture are avoided. And meanwhile, corrosion and aging processes can be slowed down, the service lives of the power distribution cabinet and internal components are remarkably prolonged, and the maintenance and replacement cost is reduced.

The invention discloses a transformer current rising test data analysis method based on deep learning, and relates to the technical field of power transformation equipment, and the method comprises the following steps: S1, data collection and preprocessing; s2, performing feature engineering; s3, dividing a data set; s4, model selection and architecture design; s5, model training; s6, performing model verification and hyper-parameter tuning; s7, testing and evaluating the model; s8, performing anomaly detection and adaptive learning; s9, result interpretation and model interpretability; and S10, continuously monitoring and updating the model. According to the transformer current rising test data analysis method based on deep learning, based on deep learning, in combination with technologies of feature engineering, model selection and architecture design, anomaly detection and adaptive learning and the like, the accuracy and robustness of a transformer current rising test are improved through comprehensive data analysis; the generalization ability of the model for unseen data can be improved, and the model can adapt to environmental changes in real time.

The invention discloses a building energy consumption control system based on deep learning. The building energy consumption control system comprises a data acquisition module, a data processing module, a deep learning module, an optimization control module and a real-time control module, the invention further discloses a control method of the building energy consumption control system based on deep learning. The energy consumption prediction model is constructed through the long-short-term memory network, and the user behavior model is constructed through the convolutional neural network, so that the energy consumption optimization control strategy is generated through the reinforcement learning algorithm based on the output results of the energy consumption prediction model and the user behavior model, and the related data of the building energy consumption system is effectively utilized. Intelligent control over the building energy consumption system is achieved, changes of environmental parameters and user requirements are dynamically adapted by optimizing an energy consumption control strategy, and the comfort level of a user is improved while energy saving and consumption reduction are achieved.

The invention discloses an FPSO stand pipe supporting structure stability online identification method and system, and belongs to the field of ocean engineering.The identification method comprises the following specific steps that firstly, the state of an FPSO stand pipe supporting structure is monitored in real time through a sensor system, and collected original sensor data are preprocessed; iI, training a stable recognition model by using historical sensor data, and globally optimizing the trained stable recognition model; the method can adapt to the rapid change of the environment in real time, improves the response capability of the whole system, effectively reduces the possibility of instability, remarkably reduces the probability of instability accidents, improves the safety of platform operation, achieves the dual optimization of safety and economy, and improves the reliability of the system. The comprehensive operation benefit of the system and the overall efficiency and accuracy of the stability online identification method are improved; the security risk of centralized storage of data can be avoided, the calculation efficiency of the system is improved, the network bandwidth pressure is reduced, the robustness and accuracy of the model are enhanced, and the fault-tolerant capability and stability of the system are improved.

The invention discloses an unmanned aerial vehicle full-spectrum autonomous inspection system based on visual perception, and belongs to the technical field of unmanned aerial vehicle inspection, and in the unmanned aerial vehicle full-spectrum autonomous inspection system, a self-adaptive environment module can sense changes of the surrounding environment, weather conditions, obstacles, topographic relief and the like in real time and quickly adapt to the changes. And the unmanned aerial vehicle can keep stable flight and accurate data acquisition in a complex and changeable inspection environment. When strong wind or sudden weather change occurs, the self-adaptive environment module can automatically adjust the flight attitude and speed of the unmanned aerial vehicle, safety is ensured, and task continuity is maintained. Besides, by continuously learning environment characteristics, the module can predict potential risks and plan an obstacle avoidance path in advance, so that the autonomy and reliability of the system are improved. The self-adaptive capability remarkably improves the inspection efficiency and safety of the unmanned aerial vehicle, and reduces the risk of task failure or equipment damage caused by environmental factors.

The invention discloses an environment adaptability self-optimization method and system of an intelligent sensing robot, and relates to the technical field of robot control, and the method comprises the steps: obtaining a historical environment data set; acquiring a historical performance data set of the target robot; obtaining an optimal matching relationship; constructing a feature data set; constructing an environment-performance prediction model based on the feature data set; generating predicted performance data of the target robot; and optimizing and adjusting a strategy according to the predicted performance data. The technical problems that in the prior art, an intelligent sensing robot cannot dynamically and accurately predict self performance according to different complex and changeable environments, so that the environment adaptability of the intelligent sensing robot is insufficient, and diversified scene requirements are difficult to meet are solved, the performance of the intelligent sensing robot in the different environments is predicted, and the intelligent sensing robot is more intelligent. And optimization and adjustment can be carried out as required based on a prediction result, so that the method can better adapt to the environment, and the performance is improved.

The invention relates to the technical field of antenna calibration, and provides an RFID tag antenna adaptive calibration method based on complex environment identification. The method comprises the steps that warehouse attribute features are collected, a three-dimensional space is constructed based on the features, RFID signal interference simulation is carried out, and an interference area is obtained through clustering; in order to meet signal requirements, RFID antenna control parameter calibration is carried out on a plurality of areas, a calibration strategy is determined, and a warehouse position calibration table is constructed; querying a path by a reader, and matching a target interference area with a calibration strategy; external interference factors are acquired and read through a monitoring array, an optimization calibration strategy is analyzed and output, and an optimization parameter calibration scheme is generated; according to the method, the real-time position information of the RFID reader-writer is obtained, a scheme is input, a real-time calibration strategy is output, and the RFID antenna is calibrated in a self-adaptive mode, so that the technical problems that the RFID antenna calibration efficiency is low and the accuracy is poor due to the fact that a traditional RFID antenna calibration method cannot rapidly adapt to environment changes are solved.

The invention discloses a space error compensation and precision improvement method for a combined machine tool. The method comprises the steps of 1, system preparation and sensor arrangement; 2, real-time data monitoring and processing; 3, training a dynamic geometric error compensation model; 4, multi-modal data fusion is carried out; 5, generating and executing an intelligent numerical control instruction; and step 6, a post-processing and feedback mechanism. Through real-time dynamic error compensation and multi-modal data fusion, real-time errors of the machine tool in the machining process can be effectively recognized and corrected. The high-precision compensation mechanism ensures that the geometrical shape and the size of a workpiece can strictly meet the design requirements in a complex machining environment, the overall quality of a product is greatly improved, a real-time monitoring system and a dynamic updating mechanism enable a machine tool to rapidly adapt to environment changes, and the machining efficiency is improved. In combination with deep learning and data fusion technologies, the system can optimize a compensation strategy in real time along with continuous changes of processing conditions.