20results about How to "Realize self-learning" patented technology

The invention provides a petrochemical production process anomaly diagnosis and optimization method and system integrated with a knowledge graph, and relates to the technical field of petrochemical production, and the method comprises the steps: collecting technological parameters and equipment state data, carrying out the preprocessing and feature extraction through adaptive wavelet threshold denoising and depth variational mode decomposition, and carrying out the feature extraction; a heterogeneous graph neural network is constructed, a causal relationship graph is constructed in combination with a neural causal discovery network, the causal relationship graph is optimized through knowledge distillation, missing relationships are complemented to obtain a knowledge graph, real-time features are compared with normal feature distribution of a pre-trained contrast diffusion model, and after anomaly detection is triggered, the real-time features are extracted. A causal inference engine is combined with a knowledge graph to analyze an anomaly propagation path, an anomaly root and a prediction trend are determined based on multi-agent reinforcement learning, a multi-objective optimization problem is constructed according to the knowledge graph, an optimization strategy is generated and dynamically adjusted, and an optimization result is evaluated by combining a knowledge distillation hybrid model. And finally, progressive optimization is realized and the knowledge graph is updated.

The invention belongs to the field of intelligent control systems, and particularly relates to a shield attitude real-time intelligent control method based on machine learning, according to the method, a data acquisition module can more comprehensively and accurately reflect the actual condition of shield construction, and in the control process, key parameters such as the working state of a jack and the pressure of a hydraulic system are monitored in real time, so that the control accuracy is improved. Abnormal conditions can be found in time, an emergency plan can be started, and the safety of shield construction is guaranteed. According to the real-time monitoring and feedback optimization mechanism, the safety and reliability of shield construction are effectively improved, and the construction risk is reduced.

The invention relates to an urban distributed energy resource scheduling method and system based on a world model, and the method comprises the steps: collecting the operation data of various distributed energy resources in an urban power system in real time, inputting the operation data into the world model for processing, and outputting a distributed energy resource scheduling scheme, wherein the world model comprises a virtual simulation sub-model, a situation awareness device and an intelligent decision-making device, and the step of outputting the distributed energy resource scheduling scheme comprises the following steps: establishing the virtual simulation sub-model of the urban power system based on real-time collected operation data in combination with historical operation data; based on the virtual simulation sub-model, performing situation awareness and analysis by adopting the situation awareness device; and based on the situation awareness and analysis result, an intelligent decision maker is adopted to automatically generate a distributed energy resource scheduling scheme. Compared with the prior art, the method has the advantages of realizing virtual scheduling simulation of energy resources, quickly and accurately identifying exceptions, timely adjusting strategies and the like.

The present application relates to a kind of facial expression recognition methods based on attention mechanism and self-distillation, comprising the following steps: step 1, constructing facial expression recognition dataset;Step 2, constructing facial expression recognition model, the model is composed of feature extraction module, adaptive channel attention module, remodeling module and self-distillation network;Step 3, the input facial expression image is preprocessed;Step 4, using the training set image in the above facial expression recognition dataset trains facial expression recognition model;Step 5, using the facial expression recognition model trained to the test set and validation set in dataset are inferred classification.The method designs a kind of reasonable and efficient facial expression recognition scheme, it is well guided model to pay attention to those important feature information by the mode of attention mechanism;By a new self-distillation form, refine compressed network knowledge, improve the robustness of shallow network output feature and reduce the complexity of model in inference stage.

The present application relates to the field of science and education display, and particularly relates to a display control method of a multi-axle three-dimensional dynamic pseudo-motion phenomenon display device, which comprises the following steps: S1, setting up a three-dimensional dynamic pseudo-motion phenomenon display device; S2, representing the pseudo-motion phenomenon effect of the display device by a control function G; S3, establishing a target function library, selecting the best function from the target function library, and obtaining the optimal solution of each coefficient in the control function G by using the least square method; S4, determining the rotation angular velocity range of the control function G; S5, combining the control function G to establish an input parameter database, constructing a machine learning control algorithm, and thus self-adjusting the associated parameters affecting the control function G. The present application can present the best display effect for different people in different environments.

The present application relates to the technical field of equipment maintenance, and more particularly to an intelligent predictive equipment maintenance system, comprising a data acquisition module, a data processing module, a maintenance decision module, a maintenance execution module, a user interaction module and a closed-loop optimization module. The data acquisition module collects equipment operating parameters through the deployment of a sensor array to generate standardized data streams; the data processing module extracts time sequence features and performs state deduction through a virtual model to output fault analysis results; the maintenance decision module calls enterprise resource information for multi-objective optimization to generate a dynamic maintenance plan; the maintenance execution module assists in guiding the execution of maintenance through augmented reality and records data; the user interaction module dynamically displays equipment status and risk information; and the closed-loop optimization module updates model parameters based on feedback data. The system realizes predictive maintenance under data driving through module collaboration, effectively reduces unplanned downtime and resource waste, and improves fault diagnosis accuracy and operation and maintenance efficiency.

The invention relates to an automobile seat posture self-adaption system and method based on TOF sensors. The automobile seat posture self-adaption system comprises the front-row seat TOF sensors installed above front-row seats, rear-row seat TOF sensors installed above rear-row seats and a seat posture position self-adaption adjusting system integrated in an automobile controller. The front-row seat TOF sensor and the rear-row seat TOF sensor scan seat three-dimensional position data in real time through infrared light reflection, and the seat posture and position self-adaptive adjusting system is used for recognizing and distinguishing passengers and recording the posture of the passengers according to infrared light scanning and storing the posture and position of the seats of the passengers. According to the passenger seat posture position memory, the corresponding position is automatically adjusted after the corresponding passenger sits on each seat again; the system has the advantages that by introducing a self-learning and self-adaptive mechanism, the self-learning and self-adaptive capabilities of the system are realized, and the requirements of different passengers on seats can be better met.

The invention discloses a power system oscillation mechanism identification method and a model construction method, and relates to the field of power system low-frequency oscillation, and the method comprises the steps: collecting the active power data of a generator and the active power data of a tie line; performing linear normalization and principal component analysis dimension reduction processing on the data, and constructing a multi-dimensional time sequence to-be-tested sample; a sample is input into a pre-trained deep belief network (DBN) model, the model is formed by stacking a plurality of restricted Boltzmann machines (RBM), data deep features are extracted through unsupervised layer-by-layer greedy pre-training, and parameter optimization is performed in combination with supervised fine tuning. According to the method, negative damping oscillation and forced power oscillation can be automatically identified, the uncertainty of manual feature selection is avoided, the influence of noise and amplitude difference on the identification precision is effectively suppressed, and the adaptability of the model to an actual power grid complex disturbance condition is improved.

The invention provides a visual online measurement method for complex vortex flow of a vortex tube, solves the problems of low measurement precision and poor stability and reliability of a non-intrusive flow test technology, and carries out accurate inversion, identification and continuous correction on characteristic parameters of the vortex tube based on multi-source heterogeneous experimental test data; according to the characteristics that numerous hot spots in the vortex tube are distributed in a scattered mode and the positions of the hot spots are random, a vortex tube wall sound wave transceiver array arrangement scheme and an effective sound wave propagation path are constructed, and the visual measurement method for vortex tube complex vortex flow in any form is constructed based on the acoustic tomography technology. According to the method, the temperature distribution of gas in the vortex tube can be accurately inverted, a double-helix cold and hot air flow structure is indirectly described through a temperature field, the complex flow online inversion method based on digital twinning can overcome the defects in the prior art, and the method has the advantages of real-time online non-intrusive measurement and wide temperature measurement range.

The invention discloses an official document automatic formatting method and system, which relates to the field of artificial intelligence technology, and comprises the following steps: inputting an official document for identification and typesetting; preprocessing the input official document data; identifying the elements of the input official document data; preprocessing the data in the training library and inputting it into the word segmentation model of HMM for word segmentation, and outputting the word segmentation label; converting the word segmentation label, character and word of each word segmentation in the training library into vectors and inputting them into a language big model, and iteratively training the vectors; inputting the BERT model output vectors into the language big model to obtain semantically complete word segmentation vectors, and segmenting official document paragraphs of different elements based on the semantically complete word segmentation vectors and appropriate segmentation rules; dividing the segmented official document data into lines according to the upper limit of the number of words in each line, and then obtaining the semantically complete official document typesetting, which is conducive to improving work efficiency and smooth government orders.

The invention provides a fire early warning system based on an intelligent platform, and relates to the field of fire early warning, and the system comprises a plurality of fire monitoring devices which are configured at different monitoring points and are used for collecting the temperature, smoke and video data of a monitoring area in real time; the integrated monitoring platform is used for receiving, displaying and managing the real-time fire information of each monitoring point location, and the integrated monitoring platform presents a monitoring picture through a Web interface of a B / S architecture and has the functions of real-time alarm, video monitoring, video playback and historical alarm query; and the industrial-grade ring network switch is used for establishing a stable communication channel between the fire monitoring equipment and the integrated monitoring platform so as to realize high-bandwidth and low-delay data transmission. Based on the system provided by the invention, the fire condition can be monitored in real time, the fire detection accuracy is improved, the false alarm rate is reduced, a visual interface is provided, and management and traceability analysis are facilitated.

The invention relates to a visual identification system and method for goods sorting and transportation, and relates to the technical field of logistics transportation control, and the visual identification system comprises an image collection module which collects original goods images of goods in transportation according to different visual angles; the image processing module is used for cleaning and standardizing the original article image to obtain an article multi-view linked image and extracting a logistics distribution position; the model construction module is used for aggregating historical article images into a training set and generating a multi-view sorting and distinguishing model by combining with a target detection algorithm for iterative training; the screening identification module is used for judging an article multi-view linked graph according to a multi-view sorting judgment model, and determining an article sorting conclusion in combination with a logistics distribution position; the updating optimization module is used for filling an article multi-view linked graph into an article database according to an article sorting conclusion and optimizing a multi-view sorting judgment model; a sorting discrimination model is constructed through a multi-view image fusion algorithm and a deep convolutional neural network, and the precision and efficiency of intelligent logistics sorting are improved.

The invention relates to the technical field of major activity safety guarantee, and particularly discloses a major activity safety guarantee risk management system and method based on risk portray.The system comprises a data footstone layer, an analysis, study and judgment layer, a decision action layer and a verification optimization layer, and the data footstone layer constructs a dynamic hidden danger library following the four-dimensional principle; multi-source data are gathered through a standardized model, risks are quantified based on a weighted RPN formula, and a data injection and dynamic updating mechanism is matched. And the analysis, research and judgment layer generates a risk portrait report containing a structured list through scene modeling and multi-dimensional aggregation analysis. The decision action layer converts the portraits into tactical actions such as special plans, resource presetting and special drilling. And the verification optimization layer realizes risk monitoring regulation and knowledge precipitation through closed-loop management. According to the invention, the conversion from passive response to data-driven active defense is realized, the risk identification and pre-judgment precision is improved, the resource configuration and emergency efficiency are optimized, the system self-learning evolution is driven, and the risk recurrence rate of the same kind is reduced.

The invention relates to the technical field of ocean, and provides a method and system for optimizing wave parameters based on inner and outer double-layer iteration. The method for optimizing the wave parameters based on inner and outer double-layer iteration comprises the following steps: constructing a wave model based on a wave mode taking a spectral energy balance equation as a core; the wave model comprises a plurality of empirical parameters and adjustment coefficients, namely to-be-optimized parameters, and then to-be-optimized parameter vectors are formed; searching an optimal parameter vector by using an inner and outer double-layer iterative optimization method, so that the error between the output wave data and the observation wave data of the wave model is minimum; the error between the output wave data and the observation wave data is represented by adopting a regularization error function, and the regularization error function is a normalized weighted mean square error introducing a priori constraint regularization term. According to the method, a wave model parameter optimization system with intelligent adjustment, closed-loop feedback and continuous evolution characteristics can be constructed, and the method can be widely applied to high-precision parameter optimization and automatic calibration of a wave mode.

This invention discloses an intelligent control method and system for motorcycle calipers. The method involves real-time acquisition and preprocessing of multi-modal data, including vehicle speed, wheel speed, piston displacement, brake disc temperature, tilt angle, and lateral acceleration, using multiple sensors. Based on the data, braking demand and adhesion coefficient are calculated, and tilt angle and lateral acceleration are used to identify straight-line, curved, or emergency braking scenarios. The data is input into a pre-trained braking control machine learning model to generate a target braking force variation curve. Based on this curve, the drive motor and caliper are controlled for braking, and the model is periodically optimized based on braking feedback and historical data. A component degradation model is used to predict the braking efficiency decay trend, and the braking force is compensated and the model is updated. Closed-loop control is used to adjust the motor current in real-time to follow the target braking force. The brake disc temperature is continuously monitored, and thermal fade compensation is triggered when the temperature exceeds a threshold. This invention achieves adaptive, precise, and reliable intelligent braking for motorcycles.

The application discloses a two-wheeled vehicle self-balancing trajectory tracking control parameter updating method and a two-wheeled vehicle, and the method comprises the following steps: constructing an ESO-ADRC-MPC layered anti-disturbance controller comprising an upper-layer trajectory controller and a lower-layer roll controller to obtain a target roll angle and a front wheel steering angle control instruction; and constructing a perception-decision-evaluation network comprising a perception module, a decision module and an evaluation module to perform online self-learning updating on a prediction control parameter of the upper-layer trajectory controller and a parameter of the lower-layer roll controller; the ESO-ADRC-MPC layered anti-disturbance controller is designed in a layered manner according to a heading and roll, a target roll angle is used as an intermediate quantity, trajectory tracking and self-balancing are simultaneously realized, and the challenge of under-actuation in a low-speed working condition can be effectively coped with.

The invention belongs to the technical field of automatic control, and particularly relates to a self-adaptive control method for the temperature of an all-electric melting glass kiln, which comprises the following steps: acquiring operation parameters of a plurality of heating sections; for any heating section, determining the electric heat conduction volatility according to the resistance difference at the current moment and the liquid level height deviation rate; according to the ratio of the discharging amount to the maximum yield of the kiln at the current moment, the ratio of the power to the rated power of the transformer and the charge conduction ratio, the thermal load response sensitivity is determined; and correcting the basic gain by using the electric conduction volatility and the thermal load response sensitivity, determining an adaptive power regulation value, and further controlling the voltage regulation of the transformer. According to the invention, through multi-dimensional physical parameter fusion and adaptive gain compensation, temperature control lag and oscillation caused by thermal inertia are effectively suppressed.

This invention provides a real-time visual trajectory correction system and control method for a robotic arm applied to electromagnetic induction heating forming of ship curved plates. The system includes the following steps: synchronously acquiring joint angles, images, and heating power and rate; real-time reconstruction of local point clouds in the heating area; using observed values ​​and process parameters as input, recursively estimating and predicting the height and normal vector of the curved plate surface; generating pre-correction commands based on online correction of the end-effector pose; simultaneously generating joint fine-tuning amounts based on visual ranging deviations; and finally, superimposing these to obtain the final joint control commands to drive the robotic arm. The system includes a data acquisition and point cloud reconstruction module, a deformation prediction module, a trajectory pre-correction module, a distance closed-loop fine-tuning module, and a command synthesis and execution module. This invention integrates process parameters into deformation prediction and coordinates with visual closed-loop control, achieving real-time and accurate trajectory correction even at extremely narrow operating distances.

The invention discloses a data processing method and device and a program product. The method comprises the steps that inquiry information associated with the power system is acquired, and the inquiry information is used for inquiring the operation state of the power system; in the electric power system, operation data associated with the inquiry information is determined, and the operation data is used for representing the operation state of the electric power system; a question and answer model is called to analyze the operation data and the inquiry information to obtain a reply result matched with the inquiry information, and the question and answer model is obtained by training historical feedback information obtained in at least one feedback information channel; the historical feedback information is used for representing a feedback result for feeding back the accuracy degree of the historical reply result, and the historical reply result is matched with the historical inquiry information. The technical problem of low answering accuracy of the question and answer model is solved.

This invention discloses a device operation process optimization system and device based on interactive behavior data, belonging to the field of human-computer interaction technology. The invention performs facial recognition on a designated device for the user to be operated on, recording all identified users as potential users. If several potential users exist, user filtering is performed to determine the primary user to complete the device operation; otherwise, the operation is directly performed based on the potential user. If user filtering is performed, multiple interaction judgments are conducted using camera interaction behavior data to determine whether to execute the human-computer interaction operation, thereby identifying the primary user. During the execution of the human-computer interaction operation, a simultaneous human-computer interaction operation judgment is performed to select the issued interaction behavior command. Based on the response of the potential users to the interaction behavior command, the corresponding primary user is determined to complete the device operation. This solves the problem in existing technologies where systems struggle to accurately identify target users, leading to incorrect identity matching and potential payment errors.