36results about How to "Improve perception accuracy" patented technology

The invention provides a phytoplankton chromatography sequence identification method and a phytoplankton chromatography sequence model building method, and belongs to the technical field of image enhancement identification. The method comprises the following steps: firstly, acquiring microscopic chromatography sequence data of phytoplankton, performing view field extraction and serialization recombination, and constructing a three-dimensional data set; then, constructing a three-dimensional recognition model containing physical perception and a sequence aggregation mechanism, extracting single-frame semantic features by the model by adopting a parameter-shared twin network, and introducing a physical definition prior module to calculate a space-frequency domain quality score of a slice; secondly, designing a deep perception sequence aggregation module, and adaptively aggregating key features of a high signal-to-noise ratio by taking definition scores as gating signals and combining spatial context information between slices; and finally, training and optimizing the model based on the image-level weak supervision label to obtain an optimal model. According to the method, the problems of information truncation and out-of-focus noise interference caused by extremely shallow depth of field of high-power microscopic imaging are solved, and full-depth-of-field stereoscopic perception can be realized under the condition that frame-by-frame fine labeling is not needed.

The present application relates to the field of wisdom agriculture and image recognition technology, specifically to a crop disease and pest monitoring system based on image recognition, comprising: an environment perception module, which obtains the environmental context parameters of the monitoring area through multi-modal sensor nodes; a multi-source context analysis module, which constructs a disease and pest outbreak risk model in combination with historical disease and pest distribution spatio-temporal data and calculates the disease and pest risk level; an adaptive strategy generation module, which dynamically reconstructs the image acquisition mode and feature extraction strategy according to the risk level and the edge side resource constraint state, and generates adaptive perception instructions; an image acquisition execution module, which adjusts the physical acquisition parameters and algorithm calculation density of the visual sensor in response to the instructions, and executes image acquisition and recognition; the present application breaks the traditional rigid acquisition mode and realizes the optimal balance between monitoring efficiency and device survival period.

The invention discloses an obstacle sensing method, a cleaning robot, a storage medium and a program product, and the obstacle sensing method comprises the steps: obtaining sensing data of a sensing sensor of the cleaning robot; in response to the situation that the cleaning robot is detected to be in a diving state according to the sensing data, a distance measuring sensor of the cleaning robot is controlled to emit first emitting light of a first wavelength, and the diving state is used for representing that the cleaning robot is immersed in water; the distance measuring sensor is controlled to receive first reflected light, the first reflected light is formed by reflecting the first emitted light through a first obstacle, and the first obstacle is immersed in water; and determining a first distance between the first obstacle and the cleaning robot according to the first emitted light and the first reflected light. According to the method, when it is detected that the cleaning robot dives into the water, the first distance of the underwater first obstacle is sensed based on the light of the first wavelength emitted by the distance measuring sensor, and the underwater driving risk of the cleaning robot can be reduced.

The invention relates to a sensing method and device, a cleaning robot, a computer readable storage medium and a computer program product. The cleaning robot comprises a TOF sensor and a binocular camera, and the method comprises the steps that a first depth image of a target scene is collected through the TOF sensor, and three-primary-color images of different visual angles in the target scene are collected through the binocular camera; performing view angle alignment and pixel point alignment on the first depth image and three-primary color images of different view angles to obtain second depth images corresponding to different view angles; on the basis of a pre-trained image processing model, performing feature extraction and feature fusion on the second depth image and the three-primary color image of different visual angles to generate a target depth image; performing synchronous positioning and map construction based on the target depth image and the three-primary color image of any view angle to obtain a three-dimensional map of the target scene; the three-dimensional map is used for navigation perception of the cleaning robot. By adopting the method, the sensing accuracy of the cleaning robot to the target scene is improved.

The invention provides vehicle glass, a vehicle glass assembly and a vehicle, the vehicle glass comprises laminated glass, a first bonding layer and a first glass plate, the first glass plate is connected with the laminated glass through the first bonding layer or arranged in the laminated glass through the first bonding layer, and the first bonding layer is arranged between the first glass plate and the laminated glass. The first glass plate comprises a first signal transmission area and a first shielding area, and the first shielding area is arranged around the first signal transmission area. Therefore, the problems of local optical deformation and distortion of the glass are reduced, and the diopter performance of the vehicle glass in a sensor area is remarkably improved.

The application provides a multi-modal sensor time synchronization method for road monitoring, comprising: acquiring sensor data for target road detection, and determining a communication time delay; according to actual network conditions and environmental changes, optimizing a compensation value in real time to obtain an optimized data set; based on the complete optimized data set, determining a network topology structure; according to the optimized network topology structure, determining a time synchronization signal sequence of relative time delay information of each node in the network; and optimizing time synchronization of all sensors for target road detection. The application introduces a dynamic environment modeling and adaptive network topology optimization strategy, effectively overcomes the shortcomings of traditional time synchronization technology, and can realize high-precision multi-modal data time synchronization in a complex dynamic environment.

This invention discloses a multi-dimensional privacy information perception and collection method. It constructs a formal description model of multi-dimensional privacy information, characterizing privacy information from multiple perspectives, including its composition, attributes, permissions, and control. Based on active learning and transfer learning algorithms, it builds a metadata intelligent perception, identification, and labeling method adaptable to various application scenarios and privacy protection strategies. This patent's advantages in adapting to multiple scenarios and information types allow it to adapt to the impact of changing application scenarios on privacy properties, reduce the concealment of privacy information caused by multiple information types, and improve the accuracy of privacy information perception. It focuses on perceiving multiple types of privacy information, comprehensively perceiving different privacy types such as text, images, and sound. Compared to existing single-type privacy perception methods, it is more beneficial for subsequent privacy strategy formulation and privacy control implementation, improving user privacy security. It has strong privacy perception capabilities, can process different types of data in parallel, and offers accurate privacy positioning and fast data processing.

This invention relates to a non-line-of-sight target detection method based on a smart metasurface on curved roads, comprising: setting up a smart metasurface device for relaying the detection of non-line-of-sight targets within a non-line-of-sight range; illuminating the smart metasurface device with a detection radar; and modulating the beam emitted by the detection radar based on the smart metasurface device to change the beam's propagation path and illuminate the non-line-of-sight target; the detection radar receiving the echo signal returned by the smart metasurface device and performing non-line-of-sight target detection and early warning based on the echo signal. This invention can accurately obtain the distance, speed, and angle information of non-line-of-sight (weak) targets, and has advantages such as all-weather and 24-hour operation, high sensing accuracy, wide sensing range, and low construction cost.

This invention provides a triaxial force sensor, comprising an elastic body and a strain gauge assembly. The elastic body includes connecting cylinders at both ends and a strain force detection area in the middle. The strain force detection area in the middle of the elastic body is configured as a weak connection structure, which includes at least four strain beams. The area enclosed by the strain beams is arranged in a square shape on the projection plane. The strain gauge assembly is attached to each strain beam. This invention can accurately measure forces in three mutually perpendicular directions and has the advantages of simple structure and fast response speed.

The invention relates to the technical field of learning methods, in particular to an AI-based broadcast television network fault prediction and self-healing method, which comprises the following steps of: extracting a channel quality difference value through edge detection, recombining to form a change trend set, performing three-point windowing to execute differential analysis, identifying a confidence interval compression trend, and extracting a prediction and detection symbol relationship. And identifying an error reversal direction, and finally generating a repair trigger signal label set. According to the method, a dynamic trend structure of a prediction difference value is constructed, a label index is extracted and triggered in combination with symbol consistency and direction reversal features, aggregation and label binding of time sequence features are completed, a training sample set capable of being used for learning is generated, the association identification capability between the features is enhanced, the sensing precision of weak change signals is improved, and the early warning range is expanded. Abnormal misjudgment is reduced, a judgment basis based on trend evolution is formed, and reliable data support is provided for fault prediction and self-healing response.

The invention relates to the technical field of image processing and vehicle safety protection, and discloses a complex industrial scene-oriented vehicle intelligent auxiliary safety protection system and method. The method comprises the steps of collecting video stream data, carrying out obstacle identification and generating space distribution information, dynamically constructing a three-dimensional warning space, analyzing an intrusive obstacle, tracking a motion track, generating a graded brake control signal and sending the graded brake control signal to a brake execution mechanism. The system comprises a video acquisition unit, an obstacle identification unit, a space mapping unit, a warning space construction unit, a space analysis unit, a target tracking unit, a decision unit and an instruction output unit. According to the method, obstacles are accurately recognized through image recognition and space mapping, a self-adaptive three-dimensional warning space is dynamically constructed according to vehicle driving parameters, graded braking signals are generated in combination with obstacle categories and movement tracks, and the accuracy and reliability of vehicle intelligent auxiliary safety protection in a complex industrial scene are achieved.

The application discloses a construction behavior anomaly detection method and system based on a lightweight large model, and relates to the technical field of artificial intelligence; the method comprises the following steps: collecting multi-source heterogeneous data of a construction site, including video streams, sensor data and building information model (BIM) data, and performing spatiotemporal alignment preprocessing; constructing a construction behavior analysis architecture based on a lightweight large model, utilizing a lightweight visual feature extraction network and a spatiotemporal graph convolution behavior recognition network to extract entity features, position information and action spatiotemporal features of personnel, equipment and materials; generating a fusion feature vector through a cross-modal feature fusion encoder combined with a self-attention mechanism; finally, utilizing a multi-modal large language model reasoning engine combined with a preset knowledge graph to perform logical reasoning, identifying the interaction relationship between personnel and the environment, thereby determining construction behavior anomalies and generating early warning information. The method effectively improves the accuracy and efficiency of construction site anomaly behavior detection, reduces the consumption of computing resources, and enhances the intelligent level of safety monitoring.

The invention discloses an intelligent fault monitoring and control method and device for an overflow ball mill, and belongs to the technical field of intelligent monitoring of mining machinery. According to the device, a multi-source sensing signal of the overflow ball mill is obtained in real time through an integrated signal conditioning and self-adaptive acquisition hardware subsystem, a physical prior guided edge reasoning calculation core and an industrial control execution interface unit, and an equal-angle vibration signal flow is generated through a hardware closed-loop control loop; calling a fault mechanism mapping knowledge base, and initializing operation parameters of a feature extraction circuit in each processing channel to obtain a decoupled component exclusive feature vector; and finally, calculating a physical state index in real time, carrying out adaptive weighted fusion on the exclusive feature vectors of the components, and outputting a health state evaluation instruction and a control instruction of the key components in parallel through a fault discriminator. The problems that existing general monitoring equipment cannot adapt to rotating speed drift, lacks edge intelligence and is delayed in control response can be effectively solved from the hardware level.

The invention discloses a traffic signal control method based on machine visual perception and space-time sequence prediction, and relates to the technical field of intelligent traffic signal control. The traffic signal control method based on machine vision perception and time-space sequence prediction comprises the following steps: S1, machine vision perception: acquiring real-time image data of a traffic intersection through image acquisition equipment, preprocessing the image data, and extracting traffic flow key parameters; and S2, space-time sequence prediction: constructing a PCA-LSTM traffic flow prediction model based on principal component analysis PCA and a long short-term memory neural network LSTM, inputting the preprocessed traffic flow historical data, and outputting a short-term traffic flow prediction result. The traffic signal control method based on machine visual perception and time-space sequence prediction is comprehensive in data coverage and low in acquisition cost; and the prediction performance is better: through abnormal data processing and dynamic principal component selection of the PCA-LSTM model, the prediction precision is improved by 2%-5% compared with the traditional LSTM, and the calculation efficiency is improved by 15.01%.

This invention discloses a soft measurement method based on PDA-GTCN-mBiGRU, belonging to the field of industrial process control and data processing technology. The method includes: constructing an input matrix containing the current time and historical time; constructing a PDA-GTCN-mBiGRU model, in which the upper branch first uses a gated temporal convolution module to extract temporal features, and then processes them through a parallel dual attention mechanism module; the lower branch uses a multi-time-step bidirectional gated loop module to extract global bidirectional dependency features; the features output from the upper and lower branches are concatenated and fused, and then weighted twice through a self-attention mechanism layer, finally outputting the predicted value through a fully connected layer. This invention, through the collaborative driving of the upper and lower branches, achieves a complementary fusion of local fine-grained feature capture and global temporal deep modeling, ultimately significantly improving the prediction accuracy and robustness of the soft measurement model when facing highly complex and strongly coupled industrial processes.

The invention discloses a three-dimensional garage vehicle entering risk assessment method based on multi-source sensor data fusion. The method comprises the following steps: step 1, synchronously acquiring multi-source data; step 2, processing the acquired two-dimensional image data; step 3, processing the obtained 2D pixel-level bounding box and the comprehensive confidence of the vehicle; 4, extracting a vehicle state characteristic quantity for risk assessment; step 5, determining a vehicle warehousing risk assessment index and performing risk assessment; the implementation system of the method comprises a sensor network module, a data processing and fusion module, a dynamic weight calculation module and a comprehensive risk assessment module. Potential collision risks such as vehicle body inclination, overspeed, superelevation and too close distance between the rear wall of the garage and the vehicle can be accurately recognized, and the probability of collision accidents can be remarkably reduced under complex conditions.

The invention relates to the technical field of environmental sanitation vehicle intelligent control, and discloses a residual operation time prediction control method for an environmental sanitation sprinkler. The method comprises the steps that the rotating speed of a water pump motor is collected in real time through a rotating speed sensor, after time alignment and Kalman filtering preprocessing, a rotating speed-flow mapping table is inquired, and a real-time flow parameter is obtained through linear interpolation; in combination with the state of a spraying branch valve, the instantaneous total flow of the system is calculated through weighted fusion, and stability verification and smooth compensation are carried out; during the effective operation period when the water pump runs and the valve is opened, the accumulated water consumption is calculated according to the instantaneous total flow and the effective operation time, and the remaining water amount is updated in real time; and the sustainable operation time is predicted based on the remaining water amount and the instantaneous total flow, and when the predicted time is lower than a threshold value, low-water-level early warning is triggered, and a shutdown instruction is output. According to the method, the water quantity management precision and the operation safety of the environmental sanitation watering cart are improved, accurate prediction of the remaining operation time is realized, and the utilization efficiency of water resources is improved.

The present application relates to the technical field of computer vision, in particular to a secret room abnormal behavior detection method and system based on multi-channel feature fusion. The method comprises: acquiring secret room video frame data, locking target personnel through target detection and tracking algorithm; extracting multi-channel features, including global appearance features, key point features, time sequence dynamic features and local area abnormal features; based on a metric learning framework, training a feature mapping network using a triplet loss function to construct a discrimination boundary between normal behavior and abnormal behavior; and performing consistency measurement on the behavior features of the same person at different time points to realize abnormality determination by calculating the Euclidean distance in the feature space. The present application models behavior features in all directions through a four-channel parallel feature extraction architecture, effectively detects unknown types of abnormalities by introducing an open set recognition mechanism, and breaks through the limitations of single-frame detection by combining the consistency modeling of the same person, thereby significantly improving the accuracy and robustness of abnormal behavior detection in the secret room scene, reducing the false positive rate to below 1%, and having the generalization detection ability for new types of abnormal behavior.

The invention discloses an electric arc additive and subtractive manufacturing multi-source information online sensing device and method. The device comprises a substrate, double mechanical arms, an additive and subtractive system, a multi-source signal collecting system and a signal processing system. The base plate supports the workbench, the double mechanical arms execute material increasing and decreasing operation, the material increasing system achieves preliminary forming of metal parts, and the material decreasing system improves precision. The multi-source signal acquisition system acquires whole-flow information through a high-speed camera, a high-frequency microphone, a thermal imager and a magneto-optical imager, the whole-flow information is preprocessed by a signal acquisition card and then transmitted to the signal processing system, and online monitoring is realized through data fusion, feature extraction and machine learning modeling; the method comprises the steps of model establishment, information collection, additive and subtractive machining and signal judgment. The problems that in the prior art, signal collection is single, and the whole additive and subtractive process cannot be adapted are solved, efficient and high-precision integrated forming is achieved, the sensing precision is high, and the method is suitable for the high-precision requirement field.

The invention discloses a vehicle automatic driving ice-snow pavement anti-skid control method and device, and the method comprises the steps: recognizing an ice-snow pavement according to the pavement state data, and judging whether the ice-snow pavement has a skid risk or not according to the vehicle dynamic data; the road surface state data comprises road surface accumulated snow thickness, road surface ice and snow pixel proportion, road surface texture features and road surface flatness, and the vehicle dynamic data comprises yaw velocity, acceleration and wheel speed; if the road surface has the slipping risk, the preset risk level of the slipping risk is determined based on the maximum wheel speed difference of the vehicle, the offset of the yaw velocity and the transverse acceleration of the vehicle, corresponding control operation is executed, and the control operation comprises motor torque reduction and regenerative braking strength adjustment. By fusing the road surface state data and the vehicle dynamic data, the perception hysteresis of the road surface state is reduced, and the perception precision is improved, so that the control stability is improved; and through cooperative control of the torque and the regenerative braking strength, the anti-skid efficiency is improved.

The invention discloses an end-to-end learning vehicle-mounted communication sensing integrated system based on a PON (Passive Optical Network) channel and a training method thereof. The system comprises a sensor unit, a transmitting end coding unit, an actual PON channel unit, a noise adaptive network unit, a receiving end decoding unit and a sensing task evaluation unit. The sensor unit is used for collecting data; the transmitting end coding unit is used for coding the input data into a transmitting signal; the actual PON channel unit is used for actual data transmission; the noise self-adaptive network unit is used for establishing a micro agent model of a PON channel, realizing gradient back propagation and simulating actual channel characteristics; the receiving end decoding unit is used for decoding the received signal; and the perception task evaluation unit is used for evaluating the target recognition performance and feeding back to the training process. The training method comprises the following steps: constructing an end-to-end joint learning framework, taking target identification accuracy, mAP50 and mAP50-95 as optimization targets, jointly optimizing encoder and decoder parameters through back propagation, and dynamically adapting to channel change by using a noise adaptive network. The mean value / variance network is adopted to form the noise self-adaptive network, and the noise self-adaptive network and the codec form an end-to-end joint learning system, so that the error rate of the system is effectively reduced, and the transmission quality and the sensing performance of vehicle-mounted communication are improved.

The invention discloses a selenium nutrition prediction and process optimization method for selenium-rich pork processing, which is applied to a cloud server and comprises the following steps: receiving a first process parameter sent by a terminal; the method comprises the following steps of: preprocessing a first process parameter to obtain a second process parameter by utilizing actual measurement values at different time points in a multi-time processing process of a preprocessed selenium-enriched pork sample in an optimal processing mode, classifying the second process parameter in time and space, and performing decomposition, feature screening and fusion on the classified data to obtain a selenium-enriched pork sample; and obtaining fused features, inputting the fused features into a plurality of initial prediction models to obtain an initial prediction result under a second process parameter, and inputting the initial prediction result into a fusion model to obtain a target prediction result comprising a target total selenium retention rate, a target selenomethionine proportion and a target bioavailability. And then substituting the optimal process parameters into a target optimization function to obtain a total optimization value, and after obtaining target optimal process parameters through the total optimization value, sending the target optimal process parameters and a corresponding target prediction result to a terminal.

The application discloses a positioning method and device, a first node, a network function, a storage medium and a computer program product. The method comprises the following steps: a first node measures a first signal and a second signal to obtain a measurement result, the first signal comprises a reference signal transmitted through a first path, and the second signal comprises a reference signal transmitted through a second path; the first path comprises a transmission path directly from a second node to the first node or a transmission path from the second node to the first node after being reflected by a fixed object, and the fixed object is located at a preset position; the second path comprises a transmission path from the second node to the first node after being reflected by a target; the position information of the target is determined by using the measurement result, and the position information of the target is transmitted to a first function through a first communication interface; or the measurement result is transmitted to the first function through the first communication interface, so that the first function determines the position information of the target, and the first function is associated with a perception service.

The invention discloses a sensing method and device, communication equipment, a storage medium and a computer program product, and the method comprises the steps: first communication equipment transmits a first reference signal set and first information; wherein the first information indicates that all reference signals in the first reference signal set are used for sensing the same target and / or indicates that the reference signals in the first reference signal set are used for sensing a plurality of points of the same target, and the large-scale channel information of the plurality of points of the target has an association relationship.

The application discloses a kind of communication perception channel construction method, device, equipment, storage medium and program, applied to wireless communication technical field, wherein, the method comprises: according to the scattering signal of acquisition, constructs quasi-static multipath channel model, and determines the prior distribution of the frequency domain channel gain vector of quasi-static multipath channel model;Perception channel model and communication channel model are established based on the quasi-static multipath channel model;The posterior probability density function of the channel gain of the perception channel model is determined based on the prior distribution;Based on the posterior probability density function and the communication channel model are soft fused, obtain the communication perception channel.The embodiment of the application can realize the fusion of communication stage and perception stage, reduce signal loss, can improve communication quality, enhance perception accuracy, can improve the satisfaction degree of user to system.

This invention discloses a method for generating smooth trajectories for robotic arms that combines visual keypoint cues with a hierarchical diffusion model, applicable to complex robotic arm operations based on visual and verbal commands. The method constructs a hierarchical robotic arm operation framework. The high-level layer utilizes a sub-target generator guided by a visual-language model to predict the six-DOF pose sequence of the end effector as operation sub-targets from visual observations and verbal commands. The low-level layer proposes a dual diffusion model trained based on a random sampling strategy to generate smooth, low-curvature joint position trajectories between sub-targets. A keypoint extraction module that requires no training is introduced to provide visual cues. This invention improves the success rate of operations, reduces trajectory jitter and redundant motion, and enhances the smoothness and execution stability of robotic arm operations through the synergy of hierarchical planning and diffusion trajectory optimization.

The application discloses a kind of intelligent monitoring and control method and device for overflow type ball mill, belong to intelligent monitoring technical field of mine machinery.The device is through integration signal conditioning and adaptive acquisition hardware subsystem, edge inference calculation core of physical priori guidance and industrial control execution interface unit, real-time acquisition overflow type ball mill Multi-source perception signal, and through hardware closed loop control loop generation isometric vibration signal flow;Subsequently call fault mechanism mapping knowledge base, initialize the operation parameter of feature extraction circuit in each processing channel, obtain the decoupled component-specific feature vector;Finally, real-time calculation of physical state index, adaptive weighted fusion is carried out to component-specific feature vector, and the health state evaluation instruction and control instruction of key component are output in parallel in fault discriminator.The application can effectively overcome the problem that existing general monitoring equipment cannot adapt to speed drift, lack of edge intelligence and control response lag from hardware level.

This invention provides a traffic situation perception method, device, electronic device, and storage medium, belonging to the field of intelligent transportation technology. The method includes: acquiring multi-dimensional heterogeneous data, including human-dimensional data, vehicle-dimensional data, road-dimensional data, and cloud-dimensional data; acquiring the target scene categories corresponding to the multi-dimensional heterogeneous data and determining the scene priorities corresponding to the target scene categories; dynamically configuring perception resources according to the scene priorities to obtain configured perception resources, which are used to characterize sensor sampling frequency, sensor cooperation mode, and redundant device on / off status; and performing traffic situation perception based on the configured perception resources to obtain traffic situation perception results. The above solution can achieve real-time and accurate assessment of traffic situations, providing lane-level refined information for traffic management departments, supporting accurate decision-making, and effectively alleviating road network congestion and accident risks.

The application discloses a wheeled quadruped robot dynamic obstacle avoidance method, system and device, relates to the technical field of robot autonomous navigation and motion control, and comprises the following steps: collecting environment original point cloud data and pose information of a wheeled quadruped robot, pre-processing the point cloud data to obtain an obstacle cluster; calculating the centroid position and speed information of each obstacle cluster; predicting the position of the obstacle in a future time period; obtaining an initial predicted trajectory based on the pose information of the wheeled quadruped robot; combining the predicted position of the obstacle to construct a relative motion point stream of the obstacle; performing feature extraction to obtain a dual variable feature; constructing a model predictive control optimization problem; solving the optimization problem by using an alternating solution algorithm to obtain an optimal control sequence at the current moment; and finally generating an optimal control instruction to drive the wheeled quadruped robot to perform a dynamic obstacle avoidance action. The application can improve the motion real-time performance, stability and environmental adaptability of the wheeled quadruped robot in a complex environment.

The present application relates to the technical field of temperature control, in particular to a gradient temperature control method based on artificial intelligence, comprising: collecting multi-dimensional working condition parameters through a sensor array, constructing a dynamic characteristic matrix through cleaning and normalization; inputting the matrix into a long short-term memory network with attention mechanism, identifying time sequence correlation characteristics and time constant to construct a prediction model; mapping the characteristics to a virtual thermal dynamics simulation model for parallel simulation and rehearsal, evaluating energy distribution law and screening the optimal control increment sequence; converting the sequence into control signals to issue instructions to the actuator through the bus, executing active feedforward compensation; comparing the actual output with the simulation output in real time to calculate the residual error and dynamically correct the model parameters. The present application improves the forward-looking nature of temperature control decision-making through multi-dimensional feature extraction and virtual dimension rehearsal, improves the regulation stability of the controlled object under large-lag working conditions, and realizes precise active control of temperature gradient.