163 results about "Adaptive tracking" patented technology

The invention relates to the technical field of target tracking, in particular to a photoelectric tracking algorithm and system for self-adaptive target tracking, and the system comprises an image collection module, a cross-spectrum self-adaptive sensing module, an image processing and target recognition module, a self-adaptive tracking algorithm module and a servo control module, and constructs a closed-loop feedback link to achieve the synchronous optimization of parameters in a whole link. The algorithm comprises the steps of image acquisition and adjustment, target detection, parameter optimization, servo driving and feedback. The system can accurately estimate a high-speed turning target, is adaptive to complex environments such as low illumination / haze and the like, completes sheltered target recapture within 0.5 s, has servo compensation precision of + / -0.1 degree, is suitable for airport bird monitoring, port unmanned aerial vehicle tracking and highway vehicle monitoring scenes, and significantly improves tracking stability and practical value.

The invention discloses a complex water body pollution detection method and system based on an adaptive cruise technology, and belongs to the technical field of environment monitoring. The method overcomes the inherent defect that an existing monitoring scheme based on a static preset path cannot adapt to a dynamic pollution environment by constructing a closed-loop feedback control architecture of real-time sensing, central decision making and dynamic execution. Generating initial global pollution distribution estimation and planning an initial path by the main aircraft; the subordinate vehicle sails along the path and estimates the local pollution concentration and the gradient direction in real time; and the main aircraft fuses all data to update global estimation, and dynamically generates an instruction for guiding the subordinate aircraft to move towards a pollution concentration gradient rising direction after identifying a pollution plume or a hot spot, thereby realizing active tracking and high-resolution surveying and mapping. According to the invention, the efficiency, precision and intelligent response capability of pollution monitoring are significantly improved, and accurate and adaptive tracking of a dynamic pollution field is realized.

The invention provides a vehicle working condition virtual debugging simulation method based on multi-modal perception fusion, and relates to the technical field of digital twinning, and the method comprises the steps: achieving the precise fusion of multi-modal perception data through the synchronous collection of visual, radar and acoustic signals and the unification of a time reference and a coordinate system; constructing a vehicle dynamics state model based on mutual information weighting and recursive filtering, and obtaining dynamically consistent state vectors; combining space-time modeling and robust coding to generate driving characteristics capable of being physically explained, and inputting the driving characteristics into a simulation environment; in the digital twinborn model, parameter adaptive tracking is realized through online correction and joint recursive updating; and closed-loop feedback debugging is formed by using the health index and the fault probability, so that virtual and real consistent and dynamically adjustable vehicle working condition simulation and intelligent debugging are realized. According to the method, fusion of multi-mode sensing data can be realized, so that the simulation model dynamically tracks the working condition of the vehicle.

The invention belongs to the technical field of ship course control, and discloses an unmanned sailboat sail-rudder cooperative active-disturbance-rejection course control method based on ESO, and the method comprises the following steps: building a nonlinear course motion mathematical model; designing a self-adaptive tracking differentiator to smooth the expected course; the course, the yawing angular velocity, the sail aerodynamic moment and the comprehensive disturbance are observed in real time through ESO; based on a nonlinear state error feedback control law, in combination with the output of the tracking differentiator and the observed value of the ESO, generating a rudder torque control quantity, performing constraint processing on the rudder torque control quantity, and outputting a rudder angle instruction; and a dynamic optimal sail angle is decided according to the lift-drag ratio parameter, the apparent wind speed and the relative wind angle of the sail aerodynamic model corrected in real time, and a sail angle instruction is generated after the dynamic optimal sail angle is compensated and constrained by combining the course deviation. The adaptive capacity to the time-varying wind field and the model uncertainty is enhanced, and the anti-interference performance and the energy efficiency of the system are improved through dynamic optimization and compensation of the sail angle.

The invention relates to the technical field of drilling, and provides a roofbolter feedforward-feedback model-free adaptive tracking control method and system based on rotation force. According to the method, a valve-controlled hydraulic propulsion control model for driving a hydraulic cylinder and a corresponding reversing valve in the working process of the hydraulic jumbolter is established, and a nonlinear autoregressive moving average model between the propulsion force in the working process of the hydraulic jumbolter and the output driving current of the reversing valve is obtained through the valve-controlled hydraulic propulsion control model; through a nonlinear autoregression moving average model of the hydraulic jumbolter, an output identification observer deployed with a data-driven adaptive control algorithm of the hydraulic jumbolter is established, and the output identification observer performs adaptive tracking control on the working state of the hydraulic jumbolter through the deployed data-driven adaptive control algorithm. The tracking control problem of the valve control electro-hydraulic servo unit under the complex working condition is effectively solved.

The invention discloses an unmanned ship event triggering adaptive tracking control method based on an improved optimization backstepping technology. The method comprises the following steps: S1, establishing a motion mathematical model of a three-degree-of-freedom unmanned ship considering external interference; s2, designing a fixed time disturbance observer based on the motion mathematical model, and estimating external marine environment disturbance without depending on velocity derivative information; s3, designing an event triggering mechanism based on the position error of the unmanned ship so as to measure a control input signal without depending on an additional sensor; and S4, adopting an improved optimal backstepping control strategy based on actor-commentator reinforcement learning, and directly estimating a cost function gradient by using a neural network so as to generate a final control law, thereby eliminating complex matrix differential operation required in a traditional method. The invention also discloses a system for realizing the method. According to the invention, the control precision, robustness and real-time performance of trajectory tracking of the unmanned ship in a complex marine environment are improved.

The invention discloses a photovoltaic support adaptive tracking control method and system, and relates to the technical field of adaptive control, and the method comprises the steps: collecting a sky image, illumination intensity, photovoltaic panel temperature and support attitude data through employing a plurality of sensing units; according to the method, real sunlight judgment is realized through multi-dimensional feature fusion, and pseudo peak values formed by adjacent-row reflected light, ground object reflected light and cloud shadow edge local strong light can be effectively identified by combining irradiation field illumination gradient analysis, photovoltaic panel temperature synchronism verification and cloud shadow edge position matching; when a suspicious pseudo peak value is detected, the accuracy of sunlight position judgment is further ensured through a dual mechanism of historical irradiation curve comparison and sun geometric position verification, the tracking deviation caused by false peak value misjudgment in a traditional single-point measurement or current feedback strategy is avoided, the photovoltaic support is enabled to be always aligned with the real optimal illumination direction, and the accuracy of sunlight position judgment is ensured. And local heating and aging risks of the assembly are reduced.

The invention relates to the technical field of photoelectric tube detection, in particular to a photoelectric tube high-voltage excitation type solar blind ultraviolet discharge detection method and system, and the method comprises the steps: obtaining original voltage sequence data collected by a high-speed transimpedance sampling circuit in real time; positioning feature points of each suspected pulse according to the original voltage sequence data, and extracting pulse waveform features; obtaining a physical confidence factor of each suspected pulse according to the falling edge attenuation characteristics; calculating effective discharge energy flow in unit time based on the physical confidence factor; calculating a discharge stability coefficient according to a coupling relationship between the time correlation entropy and the effective discharge energy flow; according to the method, a scheme from microscopic physical judgment, waveform form deconstruction and time law analysis to multi-dimensional trend prediction is constructed, so that the contradiction between the detection sensitivity and the quantification precision is solved; and adaptive tracking monitoring of the partial discharge process is realized.

The invention provides an adaptive tracking method for a complex scene. The adaptive tracking method comprises the following steps: respectively extracting template features and search features; similarity matching features are obtained through calculation, and feature fusion is carried out on the similarity matching features and the search features to obtain self-adaptive fusion features; respectively inputting the adaptive fusion features and the search features into a preset candidate region generation network to select a plurality of candidate regions; performing refined semantic matching calculation on the template features and each candidate region to obtain similarity features; respectively obtaining a matching semantic score and a coordinate frame of the similarity between each candidate region and the template in the matching semantic branch, and obtaining a foreground probability score and a coordinate frame of each candidate region as a foreground target in the foreground probability branch; calculating to obtain a punishment score of each candidate region; and performing weighted calculation on the penalty score, the matching semantic score and the foreground probability score of the same candidate region to obtain a final score of each candidate region, and selecting the candidate region with the highest score as a tracking target.

The invention relates to the technical field of image processing, in particular to a numerical control milling machine workpiece three-dimensional modeling method and system. The method comprises the following steps: clamping a workpiece on a numerical control milling machine, controlling the workpiece to rotate at a constant speed, synchronously performing axial feeding, and collecting a correction spiral image sequence and a groove bottom image sequence; performing space-time registration on the corrected spiral image sequence and the groove bottom image sequence to extract workpiece modeling feature data; performing conversion from polar coordinates to rectangular coordinates based on the workpiece modeling feature data, resolving three-dimensional space coordinates and corresponding surface attribute values, and generating workpiece three-dimensional point cloud data; and inputting the three-dimensional point cloud data of the workpiece into three-dimensional modeling software, and carrying out gridding processing and texture rendering to obtain a three-dimensional digital model of the workpiece. According to the method, the workpiece is driven to execute self-adaptive tracking spiral motion, so that visual shielding caused by dense threads is avoided, and high-precision three-dimensional modeling of the small-helix-angle deep-groove workpiece is achieved.

The invention relates to the technical field of radiotherapy, and discloses a multi-modal image fusion guided chest tumor adaptive radiotherapy system. The system comprises an asynchronous bimodal sensing module, a spectrum preprocessing module, a parameter inversion module, a boundary construction module and a biomechanical calculation module. According to the method, the feedback voxel-level velocity field is utilized to execute Lagrange reverse warping on the high-energy projection with physical time delay, and motion artifacts are eliminated; and dynamically updating the Young modulus of the lung tissue mechanical model based on the inverted real-time electron density, and solving a full-field three-dimensional displacement field in combination with double boundary conditions of body surface flexibility and internal skeleton rigidity so as to reconstruct a tumor target region and generate a beam control instruction. According to the method, through closed-loop interaction of image physics and biomechanics, the problems of dual-energy imaging motion artifact interference and insufficient lung tissue fixed parameter modeling precision are solved, and accurate self-adaptive tracking of a tumor target region is realized.

The invention discloses an intelligent real-time monitoring system and an early warning method for a steel structure construction process, and particularly relates to the technical field of intelligent manufacturing equipment industry process monitoring and early warning. The method is used for solving the problem that an existing early warning method based on a static digital twinborn model is high in false alarm rate or reduced in sensitivity due to long-term evolution mismatch of a model and an entity structure. According to the method, sensor monitoring data and model theoretical data are compared in real time, an abnormal fluctuation mode is recognized, and whether the abnormal fluctuation mode is derived from construction stage conversion or not is judged; when the systematic deviation is confirmed, extracting and analyzing construction activity data associated with the systematic deviation to calculate causal association strength; when the association strength exceeds a threshold value, analyzing association characteristics of the characteristic parameters and the deviation at the current construction stage; and finally, dynamically adjusting the rigidity parameter distribution of the digital twin model according to the correlation characteristic, and performing accurate safety state evaluation and early warning by using the adjusted model, thereby realizing the self-adaptive tracking of the model on the real state of the structure and improving the early warning accuracy.

The application provides a photovoltaic air conditioner and a control method and system thereof, and belongs to the technical field of photovoltaic air conditioners, and comprises the following steps: monitoring the thermal comfort state of a building interior, the real-time operation power of the photovoltaic air conditioner and outdoor state parameters; calculating predicted power generation and predicted photovoltaic air conditioner power; calculating the absolute value of the difference between the predicted power generation and the predicted photovoltaic air conditioner power, and judging whether the difference exceeds a preset source-load power error threshold value; if the difference exceeds the source-load power error threshold value, hierarchical adjustment is performed according to the size relationship between the predicted power generation and the predicted photovoltaic air conditioner power; and when an energy storage device is configured, the energy storage device is controlled to store or release energy. The application solves the source-load mismatch problem caused by photovoltaic output fluctuation at a low cost by real-time monitoring of the thermal comfort state, photovoltaic power generation and air conditioner operation power, and hierarchical adjustment or energy storage control based on a predicted difference, so that adaptive tracking of photovoltaic fluctuation and efficient local consumption are realized.

This application provides an adaptive muffler and its control method for a variable frequency oil-free screw air compressor. The muffler includes a muffler body with an air inlet at one end and an exhaust port on the side wall. A linear drive mechanism is installed at the other end of the muffler body. A piston plate is installed inside the cavity of the muffler body, and the piston plate is rigidly connected to the output shaft of the linear drive mechanism. The linear drive mechanism is connected to a controller. When the controller detects a change in the operating frequency of the air compressor, it calculates the target position of the piston plate under the current operating condition based on the real-time collected air compressor operating frequency and exhaust temperature, combined with the transmission loss calculation formula. The controller then drives the piston plate to move to the target position through the linear drive mechanism, thereby automatically adjusting the muffler's silencing frequency band and achieving adaptive tracking and noise reduction of the air compressor's fundamental frequency. This adapts to the full operating conditions of the variable frequency air compressor.

In some implementations, a memory apparatus may receive a command to write data to volatile memory of the memory system. The memory apparatus may compare the data to one or more data patterns to identify whether the data matches a data pattern of the one or more data patterns. The memory apparatus may increment a reference counter associated with the data pattern based on identifying that the data matches the data pattern. The memory apparatus may write, based on identifying that a measure flag associated with the data pattern is set, the data to the volatile memory.

The invention discloses a control method and system for suppressing broadband oscillation of an energy storage system. According to the method, a self-adaptive control branch based on the phase locking principle is introduced into a current control loop of the energy storage converter, the oscillation frequency in a power grid is identified in real time, and a corresponding additional internal electromotive force signal is generated and superposed into an original control signal, so that the equivalent impedance characteristic of the energy storage converter is dynamically optimized, and the energy storage converter is optimized. And effective damping of broadband oscillation is realized. The method can adaptively track the changing oscillation frequency, does not need to modify the structure of a main circuit, can significantly enhance the system damping, effectively inhibits the broadband oscillation in a power electronic power grid, improves the stability of the power grid through the upgrade of a control algorithm, and has the advantages of low cost, easy integration and high adaptability.

The application provides a kind of adaptive tracking type nondestructive testing method and system for special equipment inspection, belongs to detection technical field, method includes gathering equipment historical operation and detection data to construct multidimensional time series dataset, trains intelligent model combined with deep reinforcement learning and convolutional neural network;The model is deployed to edge computing system, and the initial reference baseline of multidimension is established on site, and the current state is compared with baseline in continuous detection, through the two-way decision mechanism of intelligent model and preset logic threshold, dynamically drive mechanical arm to adjust probe pose and synchronously optimize ultrasonic excitation parameters, to actively compensate the change of coupling state, maintain the optimal detection condition, and accurately identify and evaluate defects in stable state;The system includes defect accurate identification and evaluation module, etc.;The application realizes the dynamic self-optimization of detection condition under non-stop complex working condition, improves the defect detection rate, quantitative accuracy and repeatability of detection results.

The application discloses an EMC interference source rapid positioning method, system and equipment, and relates to the electrical measurement technical field.The method comprises the following steps: based on the electromagnetic interference signal conduction path and the radiation path of the measured equipment, a monitoring sensor network is laid out; the monitoring sensor network is connected, and interference signal data of the measured equipment is collected in real time; the collected interference signal data is subjected to frequency spectrum analysis and interference type determination; the data collection mark bit is used as a positioning engine, adaptive interference tracking analysis is carried out according to the determined interference type, and interference source positioning results are obtained.The application solves the technical problems that the existing electromagnetic interference source positioning method cannot position the interference source in real time when the equipment is normally operated, and the positioning efficiency is low and the accuracy is poor, and achieves the technical effect that adaptive tracking analysis is carried out through real-time collection of interference signal data, and efficient and accurate interference source positioning is realized under the normal operation state of the equipment.

This invention discloses an adaptive maximum power point tracking method, system, and storage medium for solar cells, belonging to the field of photovoltaic power generation technology. The method includes: extracting mechanical correction data and temperature regulation data of the solar cell for the current day according to a preset time slot, and constructing a multi-dimensional feature vector; combining the multi-dimensional feature vector and the corresponding recorded electrical operation data to form the daily operation record; based on the daily operation record, clustering and grouping the multi-dimensional feature vector by fusing the feature distance and time-series distance to obtain multiple control intervals; calculating corresponding control parameters based on the multi-dimensional feature vector and electrical operation data respectively; and upon arrival of the next day, reading the corresponding control parameters according to the control interval to which the current time belongs to complete the tracking of the solar cell's maximum power point. This invention can improve the accuracy of maximum power point tracking.

The application discloses an animal cell adaptive tracking method and system based on deep learning, and belongs to the technical field of cell image analysis; a YOLOv10 deep learning model is combined with a Btrack adaptive tracking algorithm, animal cell samples are first dyed and microimage collection is carried out, a cell detection model is trained after image preprocessing, data labeling and augmentation, the model is converted into an ONNX format to realize efficient inference; then, Kalman filtering prediction and three-round Hungarian algorithm matching are carried out to complete cell adaptive tracking, the problems of cell occlusion, division, overlap and loss of tracking can be effectively handled, and stable and continuous cell motion trajectories are generated; the application can be deployed without GPU, has strong generalization ability, high detection and tracking precision, is suitable for scenes such as microecological research, clinical sample testing, drug effect evaluation and cell behavior analysis, and can significantly improve the automation level and stability of animal cell detection, counting and dynamic tracking.

The application discloses an adaptive tracking control method for a random nonlinear system based on output feedback, comprising the following steps of S1, constructing a mathematical model of a random nonlinear system with hysteresis input, unknown control direction, arbitrary switching and output constraint; S2, designing a linear state observer, and reconstructing unmeasurable states of the random nonlinear system by using output and control input of the random nonlinear system; S3, introducing a Nussbaum type function to process the unknown control direction, and constructing a barrier Lyapunov function to ensure that the output constraint is not violated; and S4, designing a framework based on a backstepping method, combining a radial basis neural network to approximate unknown nonlinear functions, designing a virtual control law, an adaptive law and an actual control law, and realizing adaptive tracking control of the output of the random nonlinear system on a reference signal.

This invention discloses an adaptive monitoring and early warning system for ship main engines, comprising a multi-source heterogeneous data acquisition and cleaning module, a dynamic operating condition self-identification module, an adaptive condition monitoring model library, an operating condition matching and model scheduling unit, a real-time monitoring and anomaly triggering unit, a multi-dimensional contribution tracing and decision support module, and a model adaptive update engine. The system performs anomaly detection by calling PCA-based sub-monitoring models to calculate and statistically analyze data through data cleaning, online operating condition identification and matching scheduling. When an anomaly is triggered, the system locates the suspected root cause by calculating the contribution of variables and generates maintenance suggestions based on a fault case library. If no anomaly is triggered, the system uses health data to continuously update model parameters and control limits through a sliding window mechanism. This invention achieves dynamic adaptive tracking of main engine operating conditions, self-learning evolution of monitoring models, and a closed loop from anomaly detection to intelligent decision support, improving the accuracy and reliability of ship main engine condition monitoring.

The invention discloses a distributed adaptive tracking control method for a heterogeneous unmanned cluster system under multiple information constraints. The method comprises the following steps: establishing a dynamic model of a leader and a follower for the heterogeneous unmanned cluster system; determining a first event triggering condition of a follower communication side and a second event triggering condition of a control side; constructing a self-adaptive event triggering controller of the follower; the adaptive event trigger controller samples, transmits and uses the output of the follower when the state measurement error satisfies a first event trigger condition; otherwise, using the output of the follower sampled last time; when the consistency estimation error meets a second event triggering condition, the adaptive event triggering controller samples and uses the local output error of the follower; otherwise, using the local output error of the last sampling to realize cooperative tracking control of the leader; and updating the adaptive event trigger controller based on the calculated controller gain vector. According to the invention, reliable and resource-efficient cooperative output tracking control is realized.

The application discloses a soil drought monitoring method based on dynamic optimization of multi-source data, relates to the technical field of soil monitoring, and comprises the following steps: delimiting a target region according to a preset rule, and constructing a multi-dimensional drought feature vector by using multi-source observation data. The probability of each cause type is obtained by calculating the Mahalanobis distance between the vector and the preset vector set centroid corresponding to different stress cause types. The same cause probability difference between the target region and the adjacent region is compared. If all the difference values are less than a preset mutation threshold, the soil drought operation path graph is generated by traversing and connecting. If there is a difference value greater than or equal to the threshold, mutation detection and resegmentation are performed on the adjacent region, and the new region corresponding to the minimum probability difference is taken as a new target for iterative calculation, so that the monitoring path is dynamically optimized, the monitoring range and path can be dynamically adjusted according to the regional heterogeneity of drought characteristics, and adaptive tracking and visual operation guidance of the soil humidity condition under complex drought causes are realized.

This invention discloses an adaptive tracking method and device for anti-dropped decoys based on a multi-model approach to measurement errors, belonging to the field of radar target tracking technology. The method includes constructing motion and centroid interference models for passive radar dropped decoys to determine the impact of interference on radar measurements; constructing a set of passive radar measurement models characterizing the time-varying characteristics of measurement noise, and establishing a hybrid system with uncertain measurement noise by combining the target motion model; employing an interactive multi-model architecture combined with a capacitive Kalman filter algorithm to perform interactive and parallel filtering on each sub-model of the hybrid system; updating the posterior probability of the measurement noise in each sub-model at the current moment in real time based on the measurement residuals and information covariance; and performing weighted fusion of the state estimates of each sub-model to obtain the adaptive tracking result. This invention effectively solves the tracking divergence problem caused by weak observability and time-varying measurement errors in passive radar under dropped decoy interference, significantly improving the tracking accuracy and robustness of the system.

The invention belongs to the technical field of robot intelligent control, and particularly relates to a mechanical arm tracking control method based on transferable depth incremental reinforcement learning, which effectively solves the nonlinear optimal tracking control problem of a mechanical arm in a model-free mode by constructing a transferable incremental reinforcement learning framework. The method comprises the following steps: firstly, constructing a 1-degree-of-freedom depth increment model by utilizing one-step forward data offline learning, and providing universal dynamic representation for a mechanical arm which is difficult to accurately model; furthermore, an asynchronous depth value network is designed for the one-degree-of-freedom mechanical arm, stable and rapid convergence value function approximation is achieved through a separation base layer and an adaptive layer, and a cross-mechanical-arm migration mechanism is established, so that a pre-training model and a network base layer on the one-degree-of-freedom mechanical arm can be directly migrated to a high-degree-of-freedom mechanical arm subsystem; and the system difference is compensated only by updating the adaptive layer online, so that the repeated training overhead is remarkably reduced, and meanwhile, rapid, robust and adaptive tracking control on the mechanical arms with different degrees of freedom is realized.

The invention relates to a synergistic photovoltaic system based on double-sided power generation and intelligent tracking, and the system comprises a double-sided photovoltaic assembly module which converts direct sunlight and ambient reflected light into electric energy at the same time, obtains the total output voltage and total output current of a power generation layer, and collects environment state data in real time; the self-adaptive tracking bracket module is used for adjusting the spatial orientation of the double-sided photovoltaic module according to the bracket control instruction and acquiring the current horizontal angle and the current pitch angle of the tracking bracket in real time; and the intelligent MPPT control module is used for generating a converter control signal for maximum power point tracking and a bracket control instruction for adjusting a space orientation through an improved quantum particle swarm algorithm based on the total output voltage, the total output current, the environment state data, the current horizontal angle and the current pitch angle. Therefore, the problems of low maximum power point tracking precision, insufficient power generation efficiency and the like of the photovoltaic system in local shading and illumination dynamic change scenes are solved, and the comprehensive output efficiency of the photovoltaic system is improved.

The invention provides a moving ship self-adaptive tracking method based on space-time semantic association, which comprises the following steps of: firstly, adaptively determining an area range in which a target possibly appears in a current frame by combining collaborative prediction of ship movement track coherence and appearance feature consistency, greatly reducing a matching search space and reducing calculation overhead, and improving the tracking accuracy of the target in the current frame; then, the detection targets meeting the physical motion law and the target size change range are screened in the associated area, so that the matching accuracy is remarkably improved, and for missed detection targets caused by cloud layer shielding or signal weakening, the prediction positions of the missed detection targets are directly adopted for position covering to fill detection blind areas and avoid transient loss, so that the detection accuracy is improved. And finally, by utilizing the characteristics of irregularity, short life cycle and the like of the broken cloud motion, setting motion variable thresholds of speed, acceleration, course and the like and a continuous leak detection frame number threshold, and automatically filtering transient pseudo tracks which do not meet stable motion characteristics, thereby greatly reducing the false alarm rate and improving the accuracy of the false alarm. Therefore, accurate tracking of a real ship target is still kept in a multi-broken-cloud scene.

This invention relates to the field of refrigeration equipment control technology, specifically to an adaptive control method for condenser operation in silicone oil production lines. The method includes: First, based on historical production data, determining a reference range for the condenser outlet vapor phase temperature to produce silicone oil with preset quality indicators, and determining a target temperature within this range based on historical operating efficiency. Second, during production, using a model predictive control algorithm to adjust the condenser's operating parameters in real time, bringing the predicted future outlet temperature closer to the target temperature. Finally, based on the recent changes in actual operating efficiency with temperature and the difference between historical and predicted trends in outlet temperature, the target temperature is dynamically readjusted. This invention achieves adaptive tracking and coordination of the condenser with the de-boiling vessel conditions, ensuring key indicators such as product viscosity and volatile matter content while further achieving energy saving and efficient recovery of low-boiling-point substances in the production process.