113results about How to "Solve real-time" patented technology

The invention discloses a task integrity judgment method for multi-device cooperative work under complex constraint conditions. The method comprises the steps that a comprehensive data collection system is established, multi-dimensional data of cooperative devices are collected in real time and preprocessed, constraint modeling is combined, and the complex constraint conditions are quantified into a computable constraint condition model; a multi-agent collaborative evaluation model is constructed, each device participating in collaborative work is regarded as an agent by the multi-agent collaborative evaluation model, and evaluation of the integrity of the whole task is achieved by simulating the collaborative process between the devices; automatically extracting advanced features from the multi-dimensional data by using a deep learning technology; and a distributed real-time analysis system is designed, and large-scale data processing and continuous model optimization are realized in combination with an adaptive optimization mechanism. According to the method, the reliability and efficiency of the cooperative task can be improved, powerful support can be provided for equipment task decision making, and the method plays an important role in improving the intelligent level of application guarantee and the decision making accuracy.

The invention discloses a method for an AI model maternal and infant assistant from question answering to accurate recommendation, belongs to the technical field of artificial intelligence, and comprises the steps of user demand input and analysis, user portrait construction and dynamic updating, commodity recommendation and optimization, feedback optimization and strategy adjustment. According to the method and the system, the change of user demands and interests can be flexibly coped with, the recommendation accuracy and the user satisfaction degree can be improved, accurate understanding and processing of complex demands are ensured, the real-time performance and accuracy of user portraits are ensured, the change of the user demands and interests can be flexibly coped with, and more accurate and personalized recommendation is realized. The recommendation accuracy and the user satisfaction are improved, and the user can understand what a certain commodity is recommended, so that the trust of the user to the recommendation system is enhanced.

The invention relates to the technical field of American ginseng quality evaluation, and discloses an American ginseng quality evaluation method based on multi-task deep learning, a computer readable storage medium and a computer program product. The method comprises the following steps: collecting near infrared spectrum data of each American ginseng sample; determining the ginsenoside content of each American ginseng sample to obtain label data of each American ginseng sample; according to the collected near infrared spectrum data and the obtained label data, constructing and training an American ginseng quality detection model; according to the constructed American ginseng quality detection model, producing area tracing and ginsenoside content prediction are carried out on American ginseng to be evaluated. According to the method, the improved multi-task deep neural network is constructed, so that the feature extraction capability is improved, nondestructive origin traceability and total ginsenoside content prediction of American ginseng can be realized at the same time, the detection accuracy and efficiency are improved, and the defects of a traditional method in the aspects of real-time performance and nondestructive detection are overcome.

The invention provides an underground long-distance pipeline three-dimensional mapping method and system based on three-dimensional Gaussian sputtering. The method comprises the steps that a color map, a depth map and IMU data are collected; pixel coordinate conversion is carried out to obtain point cloud coordinates; inputting the depth map, the color map and the IMU data into a pose estimation algorithm, and processing to obtain a predicted pose of the IMU data, visual pose estimation and visual pose estimation information; initializing three-dimensional Gaussian, performing back propagation through a three-dimensional Gaussian loss function, and optimizing to obtain an applicable three-dimensional model; on the basis of the initialized three-dimensional Gaussian, using a GS-SLAM algorithm to update the applicable three-dimensional model and visual pose estimation information, and using a three-dimensional Gaussian loss function to process to obtain an optimized camera pose; the initial camera pose is updated through Kalman filtering, and pipeline mapping operation is completed. The technical problems that the equipment memory is large, the labor cost and the time cost are high, errors are easy to accumulate, pose deviation is easy to occur, and the model is difficult to maintain relatively high accuracy are solved.

The invention discloses a method for predicting and suppressing harmonic waves generated by photovoltaic grid connection, which relates to the technical field of harmonic wave prediction, and comprises the following steps: detecting a three-phase current signal of a photovoltaic grid connection circuit by using an ip-iq detection method, and decomposing the three-phase current into an active component and a reactive component through Clarke conversion and Park conversion; according to the method, high-precision detection and separation of harmonic waves in the photovoltaic grid-connected system are realized by combining an ip-iq method with Clarke and Park transformation, dynamic modeling and harmonic signal prediction are performed by using the LSTM model, the defect of poor adaptability of a traditional method to nonlinear loads is overcome, and the prediction error is as low as 0.1%. Through combination of LSTM prediction and APF dynamic compensation, the compensation current opposite to the harmonic current is generated in real time, the harmonic suppression efficiency and the power grid stability are significantly improved, the electric energy quality is optimized, and the dynamic change of a complex load is adapted.

The invention provides a parallel water pump intelligent algorithm optimization combination and control method, which comprises the following steps of: configuring a sensor and a calculation module on an edge node of each water pump, acquiring and preprocessing operation data of each water pump in real time, and generating a unified real-time feature vector and a time sequence feature; dynamically calculating the service life state of the water pump according to the real-time feature vector, and quantifying the residual service life; designing a dynamic start-stop scheme based on a fuzzy logic algorithm according to the service life state, and balancing load requirements and water pump service life distribution; and based on federal learning, the residual life of the water pump is optimized in a global perspective, and an operation strategy based on a start-stop scheme is dynamically optimized and adjusted. A complete solution is formed, and a brand new technical path is provided for efficient and reliable operation of the parallel water pumps.

The invention relates to the technical field of electric power operation and maintenance, and particularly discloses an unmanned aerial vehicle insulator defect identification method based on improved yov8n, and the method comprises the steps: firstly, improving a down-sampling module and feature map processing, enhancing the capability of a model for extracting insulator defect features, and improving the detection accuracy; and secondly, a Focaler-SIoU loss function is introduced, the target positioning performance is optimized, and particularly, the robustness of the model is improved when samples difficult to classify are processed. In addition, the model is lightened through the LAMP algorithm, and the parameters and the calculation amount of the model are reduced, so that the model is more suitable for running on mobile equipment such as an unmanned aerial vehicle and the like. And finally, through training verification and deployment into an unmanned aerial vehicle detection system, real-time and efficient defect identification is realized, so that the problems of low efficiency, poor real-time performance and the like in the prior art are solved, the insulator defect detection precision can be improved, and meanwhile, the complexity and the calculation amount of the model are reduced so as to adapt to the real-time detection requirement of an unmanned aerial vehicle platform.

The invention belongs to the technical field of ship and ocean engineering, and discloses a ship seakeeping intelligent forecasting method and system based on a physical information neural network. According to the method, a CFD-RAO method is combined with PINNs, and rapid and high-precision prediction of ship seakeeping is realized by constructing a coupling model of a physical equation and a neural network loss function. According to the invention, rapid and high-precision prediction of ship seakeeping is realized. The method not only can effectively cope with multi-degree-of-freedom coupling motion under complex sea conditions, but also has high real-time performance and adaptability, and provides more scientific and reliable technical support for ship design and navigation safety.

The invention discloses a coal mine hidden disaster-causing multi-source factor superposition disaster-causing risk prediction method, and relates to the technical field of coal mine construction risk prediction. The method comprises the following steps of S01, information acquisition and processing, S02, risk assessment model construction, S03, risk early warning result display, S04, accident risk assessment, S05, risk information three-dimensional visualization, S06, emergency rescue resource management and S07, monitoring and early warning technology application. According to the method, through utilization of a GIS information acquisition platform, multi-source data of a coal mine can be effectively integrated, and the risk of the coal mine can be effectively analyzed; comprising production monitoring, geographic information, safety management and control and the like; the method overcomes the defect of incomplete data acquisition in the prior art, and ensures that the risk assessment can be performed based on comprehensive and accurate data, thereby improving the monitoring capability of the hidden disaster-causing factors of the coal mine.

The invention relates to a pedestrian flow modeling and real-time simulation method, system and device and a medium, and the method comprises the following steps: carrying out the probability analysis of the moving direction of pedestrians in a railway platform environment based on a cellular automaton theory, and carrying out the modeling; based on the probability model of the pedestrian moving direction, according to the pedestrian decision-making process, modeling the pedestrian decision-making process by adopting a modeling method based on a multi-agent system; and simulating the pedestrian decision model by using an iterative vector quantization Q-Learning algorithm to obtain the overall distribution condition of the pedestrian flow for the next intelligent analysis of the digital twin system. The method can be widely applied to the computer and automation field and the railway video monitoring field.

The invention relates to a TPA-LSTM-based shunting area wet and slippery road surface vehicle type-based dynamic early warning and speed guiding method. The method comprises the following steps: S1, collecting traffic flow operation time sequence data in a highway shunting area wet and slippery road surface scene; s2, predicting speed limit values of different vehicle types for safe passing on the wet and slippery road surface in the shunting area; s3, formulating early warning information release content; s4, determining an early warning information issuing mode; s5, determining an information issuing position of the highway shunting area, wherein the information issuing position is comprehensively determined according to an existing shunting area ETC portal layout principle in combination with a warning information issuing standard; and S6, early warning information is pushed to the vehicle end, and the ETC door frame receives the optimal passing speed information of the wet and slippery road surface pushed by the traffic management background and issues the information to vehicle-mounted OBU equipment with a voice broadcast function of the vehicle. According to the method, the real-time performance, the dynamic adaptability, the personalized early warning and the driver response effect are remarkably improved, so that the occurrence rate of accidents in the diversion area in a wet and slippery road scene in rainy days is greatly reduced.

The invention provides a track fastener state recognition method based on a three-section type parallel network architecture. The method comprises the steps that a track inspection image is acquired; constructing a convolutional neural network (CNN) backbone network, and performing multi-scale feature extraction on the image of the track fastener; performing convolution processing and up-sampling processing on the fastener features by using the local feature branches to obtain local fastener features; performing global average pooling and convolution processing on the fastener features by using the global feature branches; the method comprises the following steps of: processing the characteristics of the fastener by using a mask generation branch of a framework based on Vision Transform, and generating a prototype mask of the track fastener; local, global and mask fastener features are fused; performing non-maximum suppression and mask generation on the fastener feature map; and analyzing the health state of the track fastener. According to the method, the CNN backbone network and the Vision Transform are combined, a new three-section parallel network architecture is provided, accurate identification and state analysis of the track fastener are realized through instance segmentation, and the identification accuracy in a complex environment is improved.

The invention discloses an infrared small target detection model construction method and an infrared small target detection method, belongs to the technical field of computer vision, and particularly relates to infrared image small target detection. The problems that in an existing infrared image small target detection technology, detection is difficult, light weight is not enough, and real-time performance is insufficient are solved. The method comprises the following steps: the trained infrared small target detection model is used for obtaining a finally detected small target according to an input original infrared image. The construction method of the infrared small target detection model and the infrared small target detection method are used for detecting an infrared image small target.

The invention provides an index system construction method and system for electricity market operation, and the method comprises the steps: integrating data from different sources of an electricity market, and carrying out the preprocessing of the data; pre-processed data are obtained, and self-supervised data extraction is carried out on the data from the pre-processed data; constructing a power market multi-task meta-learning optimization model; a real-time operation index system of the electricity market is constructed and dynamically adjusted; a multi-target strategy optimization algorithm is constructed, and an optimal market strategy is designed according to the market indexes adjusted in real time; and carrying out system learning and system adaptive updating according to the optimal market strategy, and keeping the optimal performance. By combining meta-learning and self-supervised learning technologies, the system can quickly adapt to different market environments and scenes, automatically mine and generate key operation indexes from large-scale unmarked power market data, and realize real-time updating and optimization of the index system.

The invention discloses a boiler NOx emission estimation method based on a width learning system and integrated learning. The boiler NOx emission estimation method comprises the steps of 1, data processing; step 2, constructing an integrated learning prediction model; step 3, performing real-time estimation; according to the invention, the hybrid quantum dune cat swarm algorithm QSCSO is provided, a Levy flight strategy and a quantum strategy are added on the basis of the SCSO for improvement, the problems of low convergence precision, low optimization efficiency and the like caused by limitation of global search and easy falling into local optimum of the SCSO are solved, and a modeling method based on QSCSO-BLS is designed for individual learning device construction. According to the method, the training precision and generalization ability of the BLS model during modeling of the single-working-condition system are improved, meanwhile, the boiler NOx emission estimation method based on QSCSO-BLS and integrated learning is provided, and the problem that the model generalization ability is poor during modeling of the BLS full-working-condition system is solved.

The invention relates to the technical field of electric power material distribution management, particularly discloses an electric power material distribution management system and method based on big data, and aims to solve the problems of dispersed material data, inaccurate demand prediction, low distribution efficiency, improper inventory management and the like in an existing system. According to the specific technical scheme, the method comprises the steps of integrating material names, material description information and supplier information, and establishing a unified electric power material database; carrying out preliminary classification and refined classification on the materials by using a clustering algorithm, and adding a label for each type of materials; historical demand data are analyzed, a demand prediction model is constructed, and the prediction precision is improved; dynamic optimal distribution path planning is realized by utilizing a geographic information system technology and a linear programming algorithm, and the distribution efficiency is improved; the stock is dynamically adjusted by calculating the safe stock and the reorder cycle, and stock overstock or shortage is avoided; the distribution plan is dynamically adjusted according to the inventory level and the demand frequency, the distribution process is monitored in real time, and on-time distribution is ensured.

The invention relates to the field of intelligent automobile data processing, and discloses an automobile user behavior prediction method based on a data model, and the method comprises the following steps: 1, collecting user multi-modal data which comprises a user driving track, a purchase record, a vehicle-mounted video and a voice instruction; 2, carrying out standardization processing on the multi-modal data, and constructing a data matrix; 3, calculating a covariance matrix of the data matrix, and improving the stability of the covariance matrix through an optimization algorithm; step 4, eliminating noise features in the covariance matrix based on a random matrix theory, and extracting features with relatively high signal intensity; and 5, optimizing the cooperative relationship between the signal features by adopting a feature interaction modeling method. According to the method, the technical scheme of combining multi-modal data acquisition and standardized processing is adopted, and the fusion of high-dimensional and diversified data under the same analysis framework is realized by unifying the scale and distribution of different modal data.

The invention discloses a belt sand ship safety dynamic management system, and the system comprises a ship information collection module which is used for collecting ship information, including position, speed and course information; the tide change trend prediction module is used for collecting tide information of the navigation area and obtaining the tide height and the tide change trend of the area where the ship is located in combination with the ship information; and the shallow water area depth change prediction module is used for collecting the sea chart data of the navigation area, and judging the depth change of the shallow water area during low tide by combining overlay analysis of the tide height data and the current sea chart water depth data. By integrating the tide change trend prediction module, the shallow water depth analysis module and the channel safety planning module, the system can monitor tide fluctuation and shallow water depth dynamic change in real time, and provide real-time data-based safety channel planning in combination with the ship draft.

The invention discloses a real-time road edge fusion method for driving of an automatic driving vehicle. The method comprises the following steps: step 1, initializing a data source; the data source comprises a visual perception data source S, a millimeter wave radar data source L and a vehicle driving information data source Z; 2, updating the road edge line based on the visual perception data source S to obtain a visual road edge line yS (x); updating the road edge line based on the millimeter wave radar data source L to obtain a radar road edge line yL (x); the driving state and the conversion matrix RT are updated based on the vehicle driving information data source Z, and a turning road edge line yZ (x) under the vehicle turning state is obtained; 3, performing fusion processing on the visual road edge line yS (x), the radar road edge line yL (x) and the turning road edge line yZ (x) to obtain a fused road edge line yR (x) after fusion; and 4, taking the fused road edge line yR (x) after fusion as one input value of the road edge line at the next moment, and updating the road edge line y (x) at the next moment. The parking lot autonomous valet parking system and the parking lot autonomous valet parking method have the advantages of obtaining a more robust road edge through fusion of multi-source data, improving safety and reliability of automatic driving and the like.

The invention discloses a method for monitoring tool status in a high-speed milling process, which relates to the technical field of intelligent manufacturing and mechanical processing. A plurality of groups of sensors are deployed to monitor the operating status of a tool head and a tool body of a tool in real time, and after processing, fatigue parameters of the tool head operating status and stability parameters of the tool body operating status are obtained, and then a tool head status fatigue index Zph and a tool body operating status imbalance index Zwd are respectively obtained, and then compared with a set corresponding matching table, and the surface flatness and dimensional error reference values ​​of a workpiece formed after the comparison are compared with the parameters of a target workpiece, so as to judge whether the current tool operating status meets the accuracy requirements of the target workpiece. The method judges the qualified status of the tool through multi-source data fusion and dynamic analysis, so as to improve the processing accuracy and efficiency of high-speed milling.

The invention provides a time frequency remote measurement and transmission terminal and method for an electric energy metering device, and the terminal comprises a provincial time frequency reference standard module which provides a provincial time frequency reference standard; the prefecture-level common-view device traces the provincial-level time frequency reference standard to the local by adopting a satellite common-view method through a network; transmitting the local time frequency reference standard quantity to a time frequency remote quantity transmission terminal through an optical fiber; and the time frequency remote quantity transmission terminal generates a standard time frequency reference signal required by the electric energy metering device, and transmits the standard time frequency reference signal to the electric energy metering device through a cable. The technical problems that prefecture-level time service equipment does not have the time service capacity, calibration consumes a large amount of manpower and material resources, the clock source loss rate is high, field calibration is interfered by the environment, and consequently the detection precision of an electric energy metering device is poor are solved.

The invention relates to the technical field of specialized robot control, and provides an intelligent specialized robot portable image and data integrated remote control system. The system comprises a data acquisition module, a data preprocessing module, a control module, a robot and a battery management module, the data acquisition module acquires data in real time, and the data preprocessing module carries out filtering, denoising and normalization processing; the control module adopts an SRO-control strategy algorithm, combines MDPD-k-means to perform clustering enhancement on a classification scene, optimizes a control strategy to generate a self-adaptive control signal, and ensures high-precision operation of the robot in a complex environment; the battery management module introduces a Sin-CosHM-FDO algorithm to carry out optimization management on a charging and discharging strategy, so that the use efficiency and the service life of the battery are effectively improved; the adaptive capacity and the operation precision of the robot in complex tasks are improved, and the stability and the energy efficiency management of the system are enhanced.

The invention relates to an explosion risk integrated intelligent decision-making method driven by multi-source and multi-modal data fusion, and belongs to the technical field of dust monitoring. The method comprises the following steps: S1, unifying multi-source and multi-modal data to the same spatial-temporal scale by adopting a multi-source and multi-modal data spatial-temporal fusion method; s2, constructing a dust explosion risk classification early warning model based on deep learning based on historical explosion risk data and dust concentration and spark detection data monitored in real time; and S3, according to different explosion risk levels, constructing a multi-source and multi-mode data fusion driving and cloud-edge-end hierarchical collaboration-based dust explosion risk intelligent decision model. The efficiency of aluminum magnesium dust explosion risk early warning and disposal decision making can be improved, and timely early warning and decision making are achieved.

This invention discloses a valve control device and system for extraction pipelines and its usage method, including a valve drive motor, a valve drive linkage, a trolley, and a track. The valve drive linkage is fixed to the rotational power output shaft of the valve drive motor, and the valve drive motor is fixed to the trolley via a motor frame. The trolley can slide along the track. The trolley drives the valve drive motor to move, and the valve drive linkage controls the valve status of each borehole pipeline. This invention controls the valve drive motor to rotate the valve drive linkage, thereby driving the valve wrench to rotate, achieving valve control of the extraction pipeline. Furthermore, by moving the trolley, the valve drive motor and valve drive linkage are moved and positioned, enabling valve control of multiple extraction pipelines. Compared to previous methods using explosion-proof electric three-way valves and explosion-proof electric check valves, this invention offers advantages such as low cost, small size, light weight, easy installation, no need for significant modifications to the extraction pipelines and valves, and a simple control system.

Embodiments of the present application relate to the field of automatic programming, and disclose a data synchronization method and device, electronic equipment and a storage medium. The method comprises: receiving a synchronization instruction input by a user for data synchronization, and generating a data replication message; sending the data replication message to a data synchronization service, and determining whether the data synchronization service receives the data replication message to obtain a first determination result; when the first determination result is that the data synchronization service receives the data replication message, the data synchronization service calls a data replication service; the data replication message is parsed through the data replication service, and each change data in the data replication message is synchronized to each copy database. The technical scheme of the embodiments of the present application solves the technical problems of poor real-time performance and data inconsistency in the data synchronization method in the prior art, and realizes the technical problems of improving the real-time performance of data synchronization and avoiding data inconsistency.

The utility model discloses a kind of heat pipe control inclined sky light, it is related to sky light technical field, including shell, face frame, mist blue diffusion plate, light source and radiator, the shell bottom is equipped with window, the radiator is embedded in window and outer surface is flush with shell outer contour, constitute the part of complete outer surface of shell;The shell has oblique outer convex side wall part, and the side wall part forms non-perpendicular angle with mist blue diffusion plate;The heat pipe control inclined sky light further includes warning component, it is located in the inside of the oblique outer convex side wall part, and includes: warning light source passage, the color change card movably arranged in warning light source passage, the drive part of driving color change card movement.This heat pipe control inclined sky light can improve heat dissipation efficiency, realize high temperature automatic warning and reduce the advantages of security risk.

The application is suitable for the technical field of data processing, and provides a negative feedback monitoring method applied to media data, which comprises the following steps: step S1: media data collection and fusion: collecting media data and user interaction behavior data from different channels, and constructing a unified user session sequence model to time-correlate multi-modal data within a single user session; step S2: multi-modal negative feedback feature fusion: processing the media data, extracting negative feedback features of text, vision, audio and user behavior modes, and analyzing the correlation between different modal negative feedback features by using a cross-modal correlator. The application effectively avoids the problem of cross-session data fragmentation by limiting the operation range of the user session sequence model to a single continuous access period.

The invention discloses an obstacle early warning system and an obstacle early warning method, and relates to the technical field of Internet, the system comprises a vehicle terminal and a cloud platform; when the vehicle terminal detects that a road obstacle exists on the current driving road, an obstacle rich media early warning packet of the road obstacle is generated, and the obstacle rich media early warning packet comprises target image data of the road obstacle; transmitting the barrier rich media early warning packet to a cloud platform through a vehicle-road communication channel; the cloud platform performs risk assessment on the road obstacle based on the target image data, and determines a risk level corresponding to the road obstacle; and performing early warning on the road obstacle based on the risk level. According to the invention, the cloud platform can determine the risk level of the obstacle based on the image data of the obstacle sent by the vehicle terminal, and carries out obstacle early warning based on the risk level, thereby solving a technical problem that the real-time performance and precision of an existing obstacle early warning scheme which depends on manual reporting are not high.

This invention relates to a low-carbon park control method and system based on digital twins and dynamic carbon footprint. It constructs a digital twin simulation model that maps to the physical entity of the low-carbon park. In the day-ahead optimization phase, a day-ahead operation plan for the park's energy system is formulated with the objective of minimizing the total daily operating cost within the scheduling cycle. This plan serves as the baseline and operational boundary for real-time control rolling optimization. In the real-time control phase, model predictive control is used to perform rolling optimization of the day-ahead operation plan based on the predicted curve of the system state in each control cycle. The first control command in the optimized sequence obtained in the current control cycle is applied to the actual system, and a reinforcement learning agent is used to dynamically adjust the dynamic carbon cost weight in the model predictive control objective function. Compared with existing technologies, this invention achieves accurate perception, forward-looking simulation, and intelligent decision-making for the entire process of the park's energy system, improving the overall optimization performance and operational robustness of the system under uncertain environments.

The invention relates to the technical field of power system safety monitoring, in particular to a power transmission line foreign matter intrusion distance measurement algorithm and related products, and the method comprises the steps: generating three-dimensional model data of a target area; generating a target recognition model for recognizing an invading object; a visible light image of a target area is collected in real time, intrusion object recognition is carried out through a target recognition model and a multi-frame difference method, and if an intrusion object is detected through any method, a distance measurement mechanism is triggered; calculating the initial distance between the central point of the invading object and the lead; calculating the final distance between the central point of the invading object and the lead, and if the final distance is smaller than a set threshold, triggering an alarm; according to the invention, through fusion of the laser point cloud and the visible light image, efficient detection and accurate distance measurement of foreign matter invasion of the power transmission line are realized, and the problem of insufficient distance measurement precision and real-time performance of a traditional method in a complex environment is solved; and with the help of the target identification capability of the deep learning model and the assistance of the multi-frame difference method, higher identification precision is realized.