94 results about "Adaptive feedback control" patented technology

The invention relates to the technical field of intelligent building management, in particular to an intelligent building full-life-cycle monitoring management method and system, and the system comprises a data collection module, a multi-source data fusion and analysis module, a full-life-cycle management model construction module, an intelligent early warning module, a self-adaptive feedback regulation and control module, a data storage module and a remote visual monitoring module. According to the system, environment parameters and equipment operation state data of a building are acquired in real time through the data acquisition module, key operation indexes are extracted by using the multi-source data fusion and analysis module, and accurate risk assessment and early warning are realized under the support of the full-life-cycle management model. The self-adaptive feedback regulation and control module can dynamically adjust key operation parameters according to real-time data to ensure stable operation of equipment under different environment conditions, the data storage module supports long-term performance analysis, and the remote visual monitoring module realizes remote monitoring and multi-party collaborative management. According to the invention, the operation safety, stability and management efficiency of hydraulic facilities are effectively improved.

The invention provides a Python monitoring task resource use method and system, and relates to the technical field of intelligent monitoring and resource optimization, and the method comprises the steps: extracting process call stack information through a multilayer convolutional neural network, constructing a process dependency graph, calculating a process criticality score, and recognizing a core monitoring process. And obtaining resource use data of the core monitoring process through a resource monitoring coroutine, determining an optimal sampling period by adopting a self-adaptive feedback control algorithm, and writing the data into a time sequence database. And performing data aggregation analysis by using a sliding time window algorithm to obtain a performance index, and constructing an anomaly detection model. Dimensionality reduction is carried out based on a principal component analysis method to construct feature vectors, and load types are divided by adopting a clustering algorithm; and generating a differentiated process optimization strategy according to the feature vector and the load type. When performance fluctuation is detected, an optimization strategy is executed, the anomaly detection model and the deep reinforcement learning model are updated according to the performance improvement index, and finally an optimization diagnosis report is generated.

The invention relates to the technical field of intelligent edge computing, and discloses a heterogeneous data processing optimization system based on intelligent edge computing. Comprising a heterogeneous data acquisition module, an edge preprocessing module, an intelligent optimization scheduling module, a heterogeneous fusion analysis module and a self-adaptive feedback control module. According to the system, data streams of different formats and protocols can be collected from a plurality of heterogeneous data sources in real time, and multi-modal correlation analysis is carried out after preprocessing and intelligent scheduling. Meanwhile, the system performance is optimized through self-adaptive feedback control, and a resource efficiency evaluation module and a safety compliance verification module are further arranged. According to the method, the problems of delay, accuracy, resource allocation, safety compliance and the like in heterogeneous data processing are effectively solved, the data processing efficiency and the system stability are improved, and the method is suitable for heterogeneous data processing scenes in the fields of industry, traffic, medical treatment and the like.

The invention discloses a building engineering human resource intelligent management optimization method based on big data analysis, and the method comprises the following steps: S1, collecting building engineering data, and carrying out the preprocessing of the data; s2, fusing multi-modal data, and synchronously generating time-space correlation optimization data; s3, constructing a construction site multi-scale digital twinborn model; s4, constructing a scheduling decision system, and simulating work type, task and resource interaction; s5, optimizing a personnel scheduling configuration strategy in combination with time constraint and skill matching; s6, performing Bayesian reasoning risk assessment, and generating a risk causal analysis report; and S7, the information is fed back to a manager through the multi-terminal display platform. Through big data analysis, digital twinborn modeling, reinforcement learning optimization and adaptive feedback control, intelligent scheduling and dynamic optimization of constructional engineering human resources are realized, construction efficiency is improved, resource waste is reduced, and construction safety and management intelligence level are enhanced.

The invention discloses a construction site digital-to-analog conversion and collaborative management method based on a distributed computing architecture. The method comprises the following steps: S1, collecting microscopic level data of a construction site; s2, obtaining mid-level data of the construction site; s3, acquiring macroscopic hierarchical data, and constructing a multi-scale spatio-temporal data set; s4, decomposing and processing the multi-scale spatio-temporal data set by using a spatio-temporal diagram convolutional network, and constructing a dynamic digital twin model; s5, designing a competition and cooperation mechanism between agents by using a game theory model, solving resource allocation conflicts through Nash equilibrium, and dynamically optimizing resource scheduling; s6, optimizing a scheduling strategy of the intelligent agent through a distributed algorithm based on near-end strategy optimization; s7, detecting errors in digital-to-analog conversion and scheduling processes by using a belief propagation algorithm; and S8, optimizing a scheduling strategy through a distributed adaptive feedback control mechanism. According to the invention, a distributed computing architecture, space-time diagram convolution, an intelligent scheduling technology and the like are adopted, and digital-to-analog conversion and resource management of the construction site are optimized.

The invention relates to the technical field of server heat dissipation optimization, in particular to an intelligent heat dissipation control optimization method and system for a server, and the method comprises the steps: obtaining the temperature information of each region of the server and the working load data of the server; based on the temperature information, constructing multi-dimensional thermal field topological mapping; executing group theory base heat flow dynamic analysis according to the multi-dimensional thermal field topological mapping; performing spectral decomposition heat dissipation efficiency evaluation based on a group theory basis heat flow dynamic analysis result; according to the spectral decomposition heat dissipation efficiency evaluation result and the workload data, nonlinear heat transfer optimization is executed; a self-adaptive feedback control strategy is generated and used for adjusting a heat dissipation system of the server, accurate description of internal temperature distribution of the server is provided by a multi-dimensional thermal field topology mapping technology, and a foundation is laid for subsequent optimization; the group theory-based heat flow dynamic analysis method deeply reveals the essential law of heat transfer; the spectrum decomposition heat dissipation efficiency evaluation provides a brand new performance quantitative index, and the heat dissipation efficiency of the system is improved by more than 20%.

The invention provides a personalized anesthesia management method and system. The method comprises the following steps: collecting real-time physiological data flow and previous medical record information of a patient to preliminarily evaluate operation requirements, and generating a preliminary anesthesia scheme; performing simulation prediction processing on the individualized reaction mode through a state-action value function updating mechanism by applying a deep Q network algorithm, and optimizing anesthetic selection and dosage by adopting a dynamic adaptive dosage adjustment technology to generate an optimized anesthetic scheme; the method comprises the following steps: extracting continuous physiological state parameters of a patient by applying an adaptive feedback control algorithm, presetting a safety threshold value, performing real-time comparison and analysis through a real-time parameter adjustment mechanism, performing automatic early warning processing on a trend deviating from a normal range by adopting an abnormal trend early warning technology, and generating a real-time adjustment instruction; and based on the real-time adjustment instruction, obtaining a patient recovery trajectory and subjective comfort evaluation, performing retrospective analysis, and generating a personalized anesthesia management scheme. According to the technical scheme provided by the invention, the safety of personalized anesthesia is remarkably improved.

The invention discloses a water plant whole-process intelligent optimization scheduling method based on adaptive feedback control. The method comprises the following steps: S1, dividing into a low-demand stage, a medium-demand stage and a high-demand stage; s2, in a low-demand stage, a medium-demand stage and a high-demand stage, correspondingly obtaining real-time monitoring data sets of the water plant; s3, constructing a multi-stage evolutionary game model based on the water plant real-time monitoring data set; s4, generating a local optimal scheduling scheme of the current stage; s5, outputting a local optimal scheduling scheme according to the evolutionary game model by utilizing a self-adaptive feedback control mechanism; s6, performing global optimal scheduling of the system in each stage; s7, optimizing a scheduling scheme in advance; and S8, displaying the optimized scheduling effect through the integrated intelligent scheduling platform, monitoring the operation state of the whole water plant, and generating a scheduling report for reference of management personnel. According to the invention, the predictability and perspectiveness of the scheduling system are effectively improved through the prediction model, and it is ensured that the water plant is always in an efficient and safe operation state.

The invention relates to the technical field of intelligent edge computing, and discloses a heterogeneous data processing optimization system based on intelligent edge computing. Comprising a heterogeneous data acquisition module, an edge preprocessing module, an intelligent optimization scheduling module, a heterogeneous fusion analysis module and a self-adaptive feedback control module. According to the system, data streams of different formats and protocols can be collected from a plurality of heterogeneous data sources in real time, and multi-modal correlation analysis is carried out after preprocessing and intelligent scheduling. Meanwhile, the system performance is optimized through self-adaptive feedback control, and a resource efficiency evaluation module and a safety compliance verification module are further arranged. According to the method, the problems of delay, accuracy, resource allocation, safety compliance and the like in heterogeneous data processing are effectively solved, the data processing efficiency and the system stability are improved, and the method is suitable for heterogeneous data processing scenes in the fields of industry, traffic, medical treatment and the like.

The invention provides a space-time intelligent ocean satellite internet of things perception information screening method and device, and belongs to the technical field of data processing. Marine environment data space-time prediction is carried out based on a block time sequence converter, marine hot spot area mining is carried out based on a density clustering algorithm of mean shift, and marine environment data comprises sensing data of sea surface temperature, salinity, wave height and the like; marine environment risk assessment is carried out based on a BP neural network, information screening feedback control is carried out based on multi-agent reinforcement learning, the efficient information processing requirement of a complex marine environment detection scene is met, and the low-redundancy and high-precision data acquisition and transmission adaptive feedback control effect is achieved. The method can be directly applied to an ocean observation and detection facility networking system in the ocean Internet of Things, can provide powerful support in the aspects of military application and civil application, and has wide and important application prospects and value.

The invention relates to the technical field of motor control, and discloses a low-noise flux linkage adaptive feedback control system of a high-speed motor. The system comprises an acquisition module used for acquiring current, voltage and vibration data of a high-speed motor at various operation rotating speeds to obtain operation parameters and noise characteristic data of the high-speed motor; the transformation processing module is used for performing frequency domain orthogonal transformation processing on the operation parameters and the noise characteristic data of the high-speed motor to obtain flux linkage real-time observation data; the self-adaptive analysis module is used for executing multi-mode working condition self-adaptive analysis according to the flux linkage real-time observation data and generating flux linkage control reference values of different rotating speed intervals; and the output module is used for inputting the flux linkage control reference values of different rotating speed intervals into a magnetic-machine coupling control algorithm, and calculating and outputting a motor low-noise driving control signal. According to the method, the problem of magnetic flux path distortion under high-speed operation is effectively solved, and downtime and efficiency loss caused by a traditional off-line calibration method can be avoided.

The invention discloses a real-time regulation and control test method and system for a power frequency magnetic field of an electric energy meter. The method comprises the steps of monitoring power frequency magnetic field intensity and distribution in a laboratory in real time, performing magnetic field data preprocessing, capturing frequency characteristics of magnetic field signals through a frequency domain multi-scale decomposition algorithm, and adjusting output of a magnetic field generator in real time by using a multi-layer nested adaptive feedback control algorithm on the basis of frequency domain analysis. Accurate magnetic field regulation and control are realized; and uploading test data to a cloud platform, dynamically adjusting a test scheme by using a multi-dimensional fuzzy logic algorithm, generating hidden variables through an enhanced time sequence prediction algorithm, performing time sequence prediction, evaluating performance parameters of the electric energy meter in a power frequency magnetic field environment, and providing support for real-time regulation and control test of the electric energy meter. By analyzing the prediction result, the performance parameters of the electric energy meter in the power frequency magnetic field environment are evaluated, support is provided for real-time regulation and control testing of the electric energy meter, and optimal regulation and control of the magnetic field can be achieved.

The invention relates to the technical field of three-step fine polishing methods for 19nm wafer regeneration, discloses a fine polishing method named'three-step fine polishing method for 19nm wafer regeneration ', and aims to solve the technical contradiction between macroscopic planarization and microscopic surface integrity of regenerated wafers in advanced process nodes. The method is characterized by comprising a first fine polishing step, a second fine polishing step and a third fine polishing step, and in each step, gradient decreasing polishing pad hardness, abrasive particle size and concentration, polishing pressure and pH value are adopted; and an on-line process monitoring and self-adaptive feedback control subsystem is used for assistance. By adopting the technical scheme, defects can be efficiently removed, damage can be repaired step by step, the wafer surface with ultralow defect density and excellent chemical stability is obtained, the requirements of the manufacturing process of 19nm and more advanced semiconductors are met, and the yield is increased.

The invention provides a pressure test control method and system for a universal pressure testing machine, and relates to the technical field of material testing machines, and the method comprises the steps: obtaining hydraulic cylinder parameters and servo valve parameters, building a pressure control mathematical model, calculating a transfer function, initializing a feedback controller, determining the number of hidden layer nodes, and setting an activation function and a network weight. A self-adaptive feedback controller is obtained; generating a closed-loop control system, constructing an objective function based on an integral absolute error and adjustment time, determining a Pareto optimal solution set in combination with a multi-objective genetic algorithm, selecting a neural network training sample, generating a training sample set, performing offline pre-training, and updating a network weight to obtain a pre-trained neural network; and the feedback controller is embedded into a pressure control system, outputs feedback controller parameters, sets an anti-saturation strategy of the feedback controller, judges whether the feedback controller is saturated or not, limits the amplitude of an integral item according to the relation between an error signal and a saturation direction, determines whether to stop integration or not, and generates a press control strategy under the current working condition.

The invention relates to the technical field of cargo unloading control technologies, and discloses a control system and method for a cargo unloading robot of a van truck. A van truck cargo unloading robot control system comprises an acquisition module, a main control module, an execution module and a light supplementing module. A control method of a van truck cargo unloading robot comprises the steps that multi-mode three-dimensional point clouds of a truck carriage and cargoes are obtained, the multi-mode three-dimensional point clouds are preprocessed, and ordered three-dimensional point clouds are obtained; performing path planning on the ordered three-dimensional point cloud through improved RRT * to obtain an initial path trajectory; optimizing the initial path trajectory to generate a predicted path trajectory; and based on the predicted path trajectory, data analysis is carried out through adaptive feedback PID control, and optimal control parameters of the mechanical arm are obtained. The unloading efficiency is effectively improved.

The invention relates to the field of automatic control, in particular to a remote control system and method for a shrimp and crab three-dimensional breeding facility. Comprising the following steps: collecting environmental parameter data, processing and analyzing the environmental parameter data by using an environmental parameter estimation algorithm based on time series data prediction to obtain a future environmental parameter prediction value, and predicting and evaluating by using an environmental state prediction and evaluation algorithm to obtain a future environmental state index prediction value; based on the future environment parameter prediction value and the future environment state index prediction value, a preliminary control instruction is obtained through calculation by using a self-adaptive feedback control algorithm, and saturation processing is performed on the preliminary control instruction to obtain a final control instruction; and sending the final control instruction to the equipment, and executing the final control instruction. The technical problems that a traditional control system can only depend on real-time collected data for feedback and lacks the accurate prediction capacity for future environment states, and equipment is prone to being damaged due to overload or operation errors are solved.

The invention belongs to the technical field of Internet of Vehicles control, and discloses an Internet of Vehicles channel congestion distributed closed-loop control method. According to the method, a closed-loop feedback channel resource congestion control strategy is provided, and a closed-loop feedback control framework adopted by the strategy is composed of reference model construction and adaptive feedback control model construction. And constructing a data packet acceptance rate model and a network effect maximization model in the reference model, and solving an optimal data packet reception probability as an ideal state of the Internet of Vehicles. In the adaptive closed-loop feedback control model, an error between an ideal state and an actual network state of the Internet of Vehicles is adjusted through a PI controller, so that the actual network state infinitely approaches the ideal network state. Network parameters are dynamically adjusted based on model reference adaptive control, the reliability of communication between vehicles in a dynamic network environment is ensured, and expected stability and robustness of a system can be kept in a dynamically changing environment.

The invention discloses a tennis racket batting data monitoring system based on an intelligent sensor, and particularly relates to a training data monitoring technology, which comprises a four-layer collaborative architecture, an intelligent tennis racket, an intelligent bracelet and a communication module, the four-layer collaborative architecture is composed of a sensor layer, an unmanned aerial vehicle collaborative layer, an edge computing layer and a cloud / user layer; a first communication module is arranged in the unmanned aerial vehicle cooperation layer and used for receiving racket data and environment perception, and the unmanned aerial vehicle cooperation layer communicates with the sensor layer in real time. According to the tennis racket ball hitting data monitoring system based on the intelligent sensor, comprehensive monitoring and analysis of tennis ball hitting data are achieved, the system can capture detailed information at the ball hitting moment, and an accurate ball hitting quality score is generated in combination with physiological data and environmental factors of players; scientific training feedback and competition strategy support are provided for players; the self-adaptive feedback control function of the system can dynamically adjust the training intensity according to the physical state of the player, and the training effect is effectively improved.

The invention discloses a lithium battery production workshop waste gas collection and treatment system and method, and relates to the technical field of industrial waste gas treatment.The method comprises the steps that historical operation data, adsorbent dosage and waste gas flow are collected, and a treatment efficiency prediction model is constructed and trained; constructing a cascade model between adjacent treatment units to form a treatment unit cascade model, obtaining input waste gas characteristics of each treatment unit, and defining treatment performance indexes; defining a state space, an action space and a reward function, constructing an optimization target of each processing unit, learning an optimal strategy of the optimization target, and taking the optimal strategy as a control strategy of the adaptive feedback controller; monitoring the operation state data in real time, predicting the real-time processing efficiency by using the processing efficiency prediction model, and calculating the fault degree; the fault type is classified into normal operation and abnormal operation, the fault-tolerant control strategy is constructed, and when the fault type is abnormal operation, the fault-tolerant control strategy is triggered according to the fault type, so that the economic benefit of waste gas treatment is improved.

The invention discloses an electric toothbrush injection molding intelligent monitoring method and system based on sensing feedback, and relates to the technical field of intelligent monitoring. The method comprises the following steps: carrying out node region division on an electric toothbrush injection molding machine to obtain N injection molding process node regions, and deploying a multi-modal sensor group; injection molding process data streams of N nodes are collected and accessed to an RS485 communication network to perform data communication transmission between the multi-mode sensor group and the injection molding control center; the injection molding control center carries out molding prediction simulation and abnormal defect identification, and injection molding abnormal defect parameters are determined; and injection molding self-adaptive monitoring feedback is carried out, injection molding abnormal feedback parameters are obtained, and molding optimization control is carried out. The technical problem that in the prior art, the injection molding quality of the electric toothbrush is unstable is solved, intelligent recognition and self-adaptive feedback control over abnormal molding are achieved by conducting multi-mode sensing monitoring on the injection molding process, and the technical effect of improving the stability of the injection molding quality is achieved.

The invention belongs to the technical field of computer vision and intelligent sensing, particularly relates to a positioning system for monitoring blind area multi-sensor space-time calibration, and aims to solve the problems of monitoring blind areas and track breakage caused by non-uniform space-time reference of multi-source heterogeneous sensors. The system comprises a space-time reference generation unit, distributed sensing nodes, a joint calibration engine, a real-time positioning calculation module and a self-adaptive feedback controller. Through nanosecond time synchronization, space registration based on a static landmark and a mobile calibration carrier and a dynamic re-calibration mechanism of closed loop feedback, continuous smooth trajectory output of cross-device target tracking is realized. The system supports layered architecture and block chain evidence storage, improves expandability and security, and is suitable for smart cities and industrial automation scenes.

The invention relates to the technical field of data security, and discloses a data crushing method based on a four-dimensional manifold chaotic power system, and the method comprises the steps: generating a time-space coupled chaotic entropy source through system initialization, dynamically distributing entropy decomposition channels for input data according to type characteristics, and achieving the irreversible crushing and entropy control of the data; and then fragment-level distributed verification is cooperatively completed by means of edge nodes, an on-chain certificate with auditing performance is generated, real-time closed-loop updating is performed on chaotic parameters in combination with a verification result, and a self-adaptive feedback control mechanism is formed. According to the method, a Merkle-Patricia tree structure is improved to realize non-tampering and chained backtracking of data, a security signature based on a lattice structure is adopted to ensure the long-term effectiveness of verification information, and the stability of an entropy source is optimized through a chaotic disturbance feedback mechanism, so that the security, reliability and adaptability of a system under the threat of a high-complexity calculation model are comprehensively improved.

The invention discloses a multi-dimensional quantum state accurate measurement and control method based on machine learning and adaptive feedback, relates to the field of quantum state measurement and control, and remarkably improves the efficiency and precision of quantum state feature extraction, reduces the measurement times, reduces the noise influence and improves the accuracy of quantum state feature extraction through the combination of a quantum state chromatography technology and a machine learning model. And through the design of a self-adaptive feedback control algorithm, control parameters can be adjusted in real time, small changes of the quantum state can be quickly responded, and accurate control over the quantum state is achieved.

The invention discloses a modeling and closed-loop displacement control method for a shape memory alloy driver, and the method comprises the steps: taking an inverse hysteresis feed-forward compensation and SMC-PID feedback displacement control method as a core, building a positive model of the shape memory alloy driver, carrying out the inversion to obtain an inverse model, designing a self-adaptive SMC-PID feedback controller, and carrying out the closed-loop displacement control of the shape memory alloy driver. Combining the positive model, the inverse model and an SMC-PID feedback controller to construct a control system for closed-loop displacement control of the shape memory alloy driver; matlab / Simulink software is used for carrying out modeling simulation testing on the designed control system, and the closed-loop displacement control effect of the shape memory alloy driver is observed by selecting different tracking signals. The modeling and closed-loop displacement control method for the shape memory alloy driver has an excellent control effect under various motion working conditions such as back-and-forth reciprocation, continuous heating deformation and continuous cooling deformation of the shape memory alloy driver, is high in tracking precision and small in delay, and realizes high robustness for system delay and parameter uncertainty.

The invention relates to the technical field of industrial intelligent control and predictive maintenance, in particular to a multivariable time sequence prediction and closed-loop control method based on digital twinning and deep learning fusion, which comprises the following steps: constructing a multi-level data architecture based on a digital twinning five-dimensional model; data preprocessing and SMOTE resampling are carried out; time sequence feature extraction based on the 1D-CNN; virtual twin data generation based on Bi-LSTM (Bidirectional Long Short Term Memory) and an attention mechanism; performing multi-mode fault diagnosis based on the RNN-LSTM hybrid network; carrying out adaptive feedback control based on Lyapunov stability; a multi-task learning mechanism is adopted, and virtual twin data generation and fault diagnosis are jointly optimized. According to the method, excellent prediction precision and control performance are verified on a semiconductor etching process data set, and the fault diagnosis effect and the autonomous optimization capability of the system in the complex industrial process are remarkably improved.

This invention belongs to the field of multi-objective optimization technology, specifically involving a trend-guided dynamic multi-objective optimization evolutionary method, including: S1. Trend modeling and direction construction in intelligent scenarios; S2. Construction of perturbation and search mechanisms under trend guidance; S3. Multi-objective knee point identification and feedback reinforcement mechanism; S4. Evolutionary computation parameter control mechanism; S5. Elite sparse resampling and trend collaborative scheduling optimization. The advantages of this invention are: it constructs a closed-loop optimization system that integrates trend prediction, knee point reinforcement, perturbation generation, and adaptive feedback control, and achieves a comprehensive improvement in the convergence, directionality, diversity, and stability of the solution set in a dynamic environment, significantly enhancing the search directionality and global convergence speed, avoiding the inefficiency problem caused by relying on random perturbations, and helping to quickly locate high-quality optimal solution sets.

The invention discloses a motion analysis and feedback method and device and a computer readable storage medium, and the method comprises the steps: building a neutral position reference model based on the skeleton features of a user, and calculating the instantaneous deviation features of a real-time posture and the reference model; resolving a time domain accumulation feature representing the stubborn degree of the action error and a time domain change rate feature representing the deterioration trend of the action error in real time; retrieving the kinematic chain knowledge graph to obtain attribution information; inputting the features into an adaptive feedback control model, dynamically calculating abstract hierarchy parameters of a teaching strategy based on the magnitude of time domain accumulation features, respectively calculating mood specificity parameters and intervention opportunity parameters in combination with instantaneous deviation features and time domain change rate features, and generating prompt control parameters; and finally, system prompts are assembled based on the prompt control parameters, and self-adaptive feedback information is generated. According to the application, the crossing from mechanical error correction to adaptive closed-loop teaching can be realized.

The invention discloses a self-adaptive feedback regulation and control system and method for a teleprompter, and relates to the technical field of big data analysis, and aims to ensure that the importance of related contents is evaluated only when a user concentrates attention by accurately identifying the effective gaze of the user on the teleprompter and combining data such as a sight line deviation angle and gaze time, so that the user experience is improved. The distribution of information display resources is improved, excessive display of skilled information is avoided, and the interactivity and the intelligent level of the teleprompter are improved. Meanwhile, the importance degree of the display content is calculated, the content concerned by the user is dynamically screened and preferentially highlighted in combination with the content similarity, it is ensured that key information is displayed in real time, and the efficiency and practicability of the teleprompter are improved. The display progress is dynamically monitored in combination with the voice recognition technology, the display rhythm is accurately adjusted, it is ensured that the most concerned content of the user is displayed at the appropriate moment, the display process is optimized, and the application value and user experience of the teleprompter are improved.

The invention discloses a water quality sampling control method and control system coping with environment detection, and relates to the technical field of water quality sampling control. The control method comprises the steps that a system state vector is constructed, and a collaborative operation model cluster is adopted and loaded; wherein the collaborative operation model cluster at least comprises a state identification classification model, a sampling time prediction model, an improved resource optimization scheduling model and an adaptive feedback control model; the technical key points are as follows: dynamic adaptation is realized through multi-model cooperation, the problem that fixed logic cannot cope with a dynamic environment is solved, intelligent and self-adaptive operation of water quality sampling control is realized, and through linkage and cooperation of all models in a collaborative operation model cluster, the water quality sampling efficiency is improved. The operation strategy of water quality sampling work can be automatically adjusted under the complex working conditions of power failure, low power, water pressure fluctuation and the like, and task integrity and data representativeness are ensured.

This invention relates to a robot multi-joint motion deviation feedback control system and method based on temporal CNN, belonging to the field of robot motion intelligent control technology. The system includes a multi-source temporal data acquisition module, a temporal data preprocessing module, a temporal CNN spatiotemporal feature extraction module, a multi-dimensional motion deviation calculation module, a hierarchical adaptive feedback control module, a robot motion execution drive module, and a standard motion calibration and storage module. The method includes steps such as standard motion temporal calibration modeling, real-time temporal data acquisition and preprocessing, temporal CNN spatiotemporal feature extraction, multi-dimensional motion deviation calculation, hierarchical feedback command generation, motion closed-loop correction, and cyclic regulation. By employing temporal CNN to extract both spatial and temporal features of the motion, combined with hierarchical feedback and temporal prediction mechanisms, the invention overcomes the shortcomings of traditional control methods, such as feedback lag and inaccurate deviation identification, thereby improving the robot's multi-joint motion control accuracy, response speed, and environmental adaptability.