43results about How to "Solve real-time" patented technology

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.

This application relates to a nitrogen humidifier, comprising a first housing, a connecting member, an air inlet pipe, a liquid inlet pipe, a second housing, a flow restrictor, and a connector. The connecting member is disposed at a first opening of the first housing and has a first air passage, an air inlet passage, and a liquid inlet passage, which are respectively connected to an external controlled air source and a liquid source. The air inlet pipe and the liquid inlet pipe respectively introduce gas and liquid into a first accommodating space for premixing. The first air passage connects to a second accommodating space of the second housing, where the flow restrictor is disposed. The flow restrictor agitates the ejected mixed airflow to achieve secondary mixing. The connector is disposed at a third opening of the second housing to exhaust the mixed gas. This nitrogen humidifier, through independent gas and liquid supply and a two-stage mixing design, effectively improves humidification efficiency and humidity stability, solving the problems of low mixing efficiency and poor humidity control in existing technologies, and is suitable for high-precision process requirements.

This invention discloses a method for planning a spacetime flight path for launch vehicle debris avoidance, relating to the field of launch vehicle space debris avoidance technology. The method includes: establishing a directed graph model of the flight path based on the flight window at each altitude level; determining constraints based on the directed graph model, including graph search constraints and flight path constraints; combining the constraints with a debris avoidance trajectory planning problem model to establish a flight path planning model; the debris avoidance trajectory planning problem model is a model concerning rocket dynamics constraints, orbital insertion constraints, and debris avoidance constraints; and solving the flight path planning model to obtain the optimal flight trajectory. This invention significantly reduces launch vehicle fuel consumption through global optimization of the flight path; simultaneously, the method can be solved in real time, improving the timeliness and reliability of launch vehicle launches, and has certain engineering application potential.

This invention provides a vehicle dynamic stability control method based on reinforcement learning and variable pole configuration, relating to the field of vehicle dynamics control. This method constructs multi-dimensional criteria for the centroid sideslip angle phase plane and energy phase plane to identify the vehicle's stability state and energy evolution trend in real time. It utilizes a reinforcement learning agent to make online decisions about the expected closed-loop pole positions of the system based on the current state, enabling the configuration of aggressive poles to improve flexibility under stable conditions and conservative poles to ensure stability under unstable conditions. Furthermore, this invention designs a safety monitoring mechanism that includes pole physical domain constraints, enabling the reshaping of the vehicle's dynamic characteristics through feedback control without altering the driver's steering intentions, effectively solving the challenge of real-time trade-off between flexibility and safety in intelligent chassis control.

The application provides a fuel cell unmanned aerial vehicle energy management system, method and unmanned aerial vehicle. The management system comprises a multi-source data acquisition module, an information fusion and state construction module, an integrated prediction and uncertainty quantification module, a risk-sensitive reinforcement learning decision module, a power distribution execution module and an online learning update module. The method of the application constructs a complete technical closed loop of "multi-source information fusion-integrated prediction and uncertainty quantification-risk-sensitive reinforcement learning decision-online evolutionary learning", realizes intelligent balance and unification between real-time performance, global economy and operation safety of energy management in a complex flight environment by constructing an enhanced state space that fuses multi-source real-time information, and combining an integrated prediction model with uncertainty quantification capability and a risk-sensitive deep reinforcement learning decision maker.

The invention discloses a method and device for sending buried point data of terminal equipment, a storage medium and a computer program product, and relates to the field of computers.The method comprises the steps that under the condition that it is determined that the terminal equipment triggers a buried point code, buried point data corresponding to a target service event is generated through the buried point code, the burying point data is used for indicating service event information generated when the terminal equipment executes the target service event; storing the burying point data into a cache queue; and sending the buried point data in the cache queue to a back-end server according to a preset sending strategy so as to indicate the back-end server to analyze a service operation path of the target service event according to the buried point data.

The invention discloses a complex scene traffic perception method and system based on fuzzy logic and data enhancement, and relates to the technical field of automatic driving environment perception. The method comprises the steps of obtaining an RGB image and point cloud data, and performing preprocessing operation to obtain a sample set; expanding the sample set by using a forced enhancement mechanism to obtain an expanded sample set; performing exclusive feature enhancement operation on the expanded sample set according to the scene complexity to obtain enhanced features; performing dynamic weight calculation and feature fusion on the enhanced features based on factors influencing traffic perception by using a fuzzy logic algorithm to obtain fusion features; and performing perceptual prediction on the fused features by using a perceptual prediction model to obtain a perceptual prediction result. The method combines forced weather enhancement and fuzzy logic fusion technologies, adapts to complex scenes, and solves the problem that multi-modal sensing robustness and generalization are insufficient in severe weather.

This application relates to a method for cross-border digital certificate mutual recognition based on dynamic trust anchors, comprising: collecting and preprocessing multi-dimensional trust assessment data of cross-border certificate authorities; collaboratively training a global model using federated XGBoost combined with a scenario-based attention mechanism to output a real-time trust score; constructing a hierarchical dynamic trust chain based on the score and writing the evidence data into the blockchain; generating proofs based on the Groth16 algorithm to complete certificate verification under privacy protection; deploying smart contracts to execute trust warnings, dispute arbitration, and rule updates; monitoring score changes, adaptively adjusting, and triggering an abnormal circuit breaker mechanism. This invention achieves real-time updating of trust weights and dynamic reconstruction of the trust chain, improving the efficiency and security of cross-border mutual recognition while ensuring data privacy compliance.

The invention provides a dynamic multi-node atom magnetometer health management method and system. The dynamic multi-node atom magnetometer health management method comprises the following steps: acquiring original data of each atom magnetometer node in real time by using a data acquisition module; preprocessing the original data of each atom magnetometer node, and extracting health-related features; based on the extracted health-related features, identifying a single-node sudden fault based on an abnormal detection model of an isolated forest, and outputting a health state score of each node; predicting future health states of the nodes based on a performance degradation trend prediction model of the long-short-term memory network; analyzing health state propagation and coupling effects among nodes based on a multi-node collaborative health analysis model of a graph neural network; and executing a corresponding strategy according to the health assessment result of each atom magnetometer node to complete dynamic multi-node atom magnetometer health management. By applying the technical scheme of the invention, the technical problem that health status assessment in the prior art mostly depends on manual data analysis and lacks real-time performance and systematicness is solved.

This invention relates to the field of marine water sample detection and control technology, and discloses an automated control system for stratified marine water sample collection, including parameter acquisition, feature recognition, spatial retrieval, and sampling control modules. The feature recognition module uses pressure feedback to convert the medium property sequence into differential features and extracts dynamic control boundaries to offset the heave and oscillation of the controlled detection components. The spatial retrieval module constructs a three-dimensional parametric surface based on the dynamic control boundaries and discretizes it to generate an octree topology space. It establishes a hash map index table that corresponds to the trigger state and the memory addressing base address. By addressing the base address with a single instruction, the algebraic calculation process is eliminated, and the trigger delay jitter caused by the calculation is eliminated. This invention realizes the transformation from fixed depth triggering to active physical and chemical boundary tracking control mode, eliminates instruction lag and decouples spatial fluctuation interference, and ensures that the sampling action is accurately locked to the physical and chemical boundary.

The invention relates to the technical field of electric power engineering construction, in particular to a disassembly and assembly construction planning method and system for an angle steel iron tower, electronic equipment and a medium. The method comprises the following steps: acquiring multi-source heterogeneous data in an angle iron tower construction scene and carrying out standardized preprocessing to obtain structured construction data; inputting the structured construction data into a field fine-tuning large model to execute a construction demand analysis operation, and generating a task decomposition list; performing resource scheduling and progress planning processing on the task decomposition list, and constructing a dynamic construction plan; and the linkage construction knowledge graph performs risk identification processing on the dynamic construction planning plan, and performs deviation analysis processing on the dynamic construction planning plan based on the current construction data to generate an adjustment suggestion. Through the mode, the technical problem that the accuracy and the real-time performance of a planning scheme are difficult to consider in the practical application of angle steel tower construction in the prior art is solved, and the scientificity, the reliability and the overall engineering benefit of construction planning are improved.

The invention relates to a train brake pad thickness non-contact real-time monitoring method and system, the system comprises an image acquisition module, a trigger sensor and a data processing module, the image acquisition module comprises a camera and a light source arranged at one side of the camera; the data processing module comprises a target area coarse positioning module, a search area fine definition module, a candidate edge feature search point module, an edge score screening module, an edge fitting module and a thickness calculation output module. Through the image acquisition module installed beside the rail, the brake pad image is automatically shot and the abrasion thickness is accurately calculated when a train runs normally, non-contact detection is achieved, the problems of missing detection risks, low efficiency, measurement errors and the like existing in traditional manual regular detection are solved, and therefore it is ensured that a brake system is in a safe state all the time; meanwhile, the operation and maintenance cost is greatly reduced, and intelligent guarantee is provided for safe operation of the train; the practicability is high, and the popularization significance is high.

The application provides a construction worker unsafe behavior intelligent identification method and early warning system, the method comprises the following steps: obtaining a to-be-detected image of a construction site; inputting the to-be-detected image into a construction worker unsafe behavior identification model to obtain an unsafe behavior identification result of a construction worker; wherein the unsafe behavior at least includes an unworn state, a wrong wearing state or a blocked state of personal protective equipment; and the construction worker unsafe behavior identification model is a neural network model for target detection. The detection accuracy is significantly improved, and the fine-grained identification capability is enhanced. Since a learnable position coding component is introduced into the backbone network, the model can explicitly model the spatial position relationship of the feature map, enhance the spatial position perception capability of the personnel (especially small targets and blocked targets) in the construction scene under complex background interference, solve the problem that the traditional convolutional network is not sensitive to absolute position information, and improve the feature extraction accuracy in a complex background.

The invention discloses a high-altitude operation safety state monitoring device, and relates to the technical field of high-altitude operation, the inner side of a fixed support frame is rotatably connected with a hollow rotating rod, one end of the hollow rotating rod is provided with a clamping gear, one end of the fixed support frame is rotatably connected with a reciprocating screw rod in a penetrating manner, and the side end of the reciprocating screw rod is provided with a multi-tooth inserting and clamping block through a screw rod seat; according to the safety rope pressing device, pressing adjustment is carried out through the dead weight of a safety rope and the dead weight of a worker, the tightness of the safety rope is monitored and processed in real time in cooperation with reciprocating pushing limitation of a spring, and the safety rope pressing device has the advantages that the safety rope pressing device is simple in structure, convenient to operate and high in practicability. By means of the safety rope control device, a worker can conveniently know the state of the safety rope in real time in the using process, real-time control over the safety rope by the worker is improved, the safety rope control convenience is improved, and the safety coefficient of the worker in the working process is guaranteed.

The application belongs to the technical field of locomotive driver ergonomics. The application provides a virtual reality-based evaluation system for the fatigue of a locomotive driver. The disclosed embodiment builds a highly simulated driving scene, simulates long-time and high-intensity driving tasks, reproduces various situations that may cause driver fatigue in a risk-free manner, and monitors and analyzes the physiological response, behavioral change and psychological state of the driver in the simulation process in real time through high-precision equipment, effectively solving the problems of lack of accuracy and real-time of the existing evaluation methods, and timely and accurately evaluating and finding the fatigue degree of the locomotive driver in the task execution process, thereby providing safety guarantee for locomotive driving.

The application discloses a kind of multi-unmanned aerial vehicle navigation method, system, equipment and medium based on distributed model predictive control and virtual pipeline, and the method comprises the following steps: constructing virtual pipeline, forming the flyable area of unmanned aerial vehicle group;In each sampling period, each unmanned aerial vehicle generates the assumed trajectory in prediction time domain based on its current state using its dynamic model, and broadcasts to neighbor unmanned aerial vehicle;Obstacle intrusion judgment and virtual pipeline dynamic processing, construct linear separation hyperplane, generate local convex safety sub-pipeline constraint;Inter-machine collision risk judgment is carried out and inter-machine collision avoidance constraint is dynamically activated;Set target cost function, and construct optimization problem in combination with the above constraint;The optimal prediction control input sequence of current time is obtained by solving, and its first control instruction is executed;The above process is repeatedly executed until all unmanned aerial vehicles pass through virtual pipeline.The application can simultaneously solve multiple challenges such as dynamic environment adaptability, calculation real-time and trajectory smoothness.

The present application belongs to the field of reusable launch vehicle trajectory planning and intelligent control, and relates to a reusable launch vehicle cruise phase intelligent trajectory planning method. By constructing a three-degree-of-freedom mass center motion dynamics model of the cruise phase launch vehicle, an offline optimal trajectory sample library covering a wide range of working conditions is generated based on the Gauss pseudospectral method, a deep neural network is designed to learn the mapping relationship between task parameters and trajectory features, and high-quality trajectory planning initial values are output online. In combination with the adaptive collocation method, the collocation distribution is dynamically adjusted, and the nonlinear programming problem is iteratively solved with the high-quality initial values as the starting point, thereby realizing efficient and accurate optimization of the trajectory. Simulation results show that the method significantly reduces the sensitivity of online optimization to initial guesses, still has fast response capability and high robustness under complex constraints and uncertain conditions, effectively improves the autonomous trajectory planning capability of the reusable launch vehicle cruise phase, and guarantees flight reliability and control accuracy.

The invention discloses an unmanned aerial vehicle group space structure regularity evaluation method based on brain-like calculation, and belongs to the technical field of unmanned aerial vehicle group space structure evaluation, and the method comprises the steps: calculating the optimal pose estimation of an unmanned aerial vehicle group under a global coordinate system, and extracting a global position set at a selected moment; constructing three variant matrixes of various distance deviations; calculating a correlation coefficient matrix and an autocorrelation value of each variant matrix; mapping the self-correlation value into structural regularity, and calculating confidence in combination with a consistency index; constructing a heterogeneous integrated learning framework based on a spiking neural network and a gradient boosting decision tree model, inputting the self-correlation value into the spiking neural network, inputting the self-correlation value and the structure regularity into the gradient boosting decision tree model, and executing a heterogeneous model fusion strategy to obtain an integrated evaluation result; through system performance verification and evaluation, a core output parameter set of the integrated system is generated, and high-precision global unified positioning in a denial environment is realized.

The present application relates to the technical field of data visualization, in particular to a heating system operation data visualization processing system, the system comprises a heat flux feature extraction module, a direction gradient difference calculation module, a fluctuation amplitude analysis module, a connection attribute regulation module and a dynamic trajectory optimization module.In the present application, through real-time filtering of heat flux density and direction vector calculation, the heat transfer path between multiple nodes can be dynamically associated and mapped, ensuring the continuity of heat flow changes and avoiding the delay and distortion of traditional static graphics when updating data. Using time series smoothing processing, the heat flow trend and periodic changes are accurately captured, and the fluctuation amplitude and direction can be intuitively presented through color and line width changes, showing the heat flow intensity and direction changes. This method enhances the time sequence perception of thermal environment data, improves trend identification and dynamic response capability, and clearly presents the changes in energy transfer process, effectively solving the shortcomings of traditional methods in real-time and spatial relationship expression.

The invention discloses a material price data analysis method based on artificial intelligence, and the method comprises the steps: S1, obtaining an original file from a diversified data source, analyzing text semantics, achieving the template-free adaptive analysis of a key field, and obtaining an initial data set; s2, for the initial data set, performing semantic normalization and logic association verification on material and specification expressions, and determining a unified material identifier set; s3, according to the unified material identifier set, extracting price values and associating market dynamic information to obtain a preliminary price data sequence; and S4, performing three-level intelligent verification of format, logic and market rationality on the preliminary price data sequence in sequence, and performing automatic correction on extraction deviation to obtain a corrected price sequence.

The application discloses a multi-source heterogeneous fusion-based AIoT global perception sensor module of a UAV, which comprises a perception layer composed of image, ranging and environmental physical quantity sensors; a data fusion processing unit based on an FPGA+ARM heterogeneous architecture, which is used for time-space synchronization, feature-level fusion and semantic analysis of multi-source data; a communication and interface unit; an isolated power management unit and a protective shell.The module completes pixel-level registration, RGB-D generation and lightweight AI inference at an edge end, outputs a global semantic map containing an obstacle category, depth information and environmental parameters, and transmits the global semantic map to a flight control and a ground station in real time through a CAN bus and wireless data transmission.The design realizes millisecond-level close coupling fusion of multi-source data, solves the problems of poor real-time performance, asynchronous conflict and bandwidth bottleneck in a traditional scheme, and significantly improves all-weather autonomous perception and obstacle avoidance capability of the UAV.

This invention discloses a spectral detection system and method based on an optical frequency conversion array, belonging to the field of computational spectroscopy. The system includes a spectral encoding array, a photoelectric detection unit, and a computational reconstruction unit. The spectral encoding array is composed of various heterogeneous luminescent materials with different ultraviolet excitation spectral characteristics, responsible for converting the ultraviolet light to be measured into visible fluorescence. The photoelectric detection unit uses a common silicon-based image sensor to acquire fluorescence intensity signals. The computational reconstruction unit, based on a pre-stored system response matrix, reconstructs the ultraviolet spectral distribution inversely using an algorithm. This invention improves the condition number of the response matrix through a heterogeneous material combination encoding strategy and utilizes the R / G / B channels of a color sensor to achieve information dimensionality enhancement, effectively eliminating the ultraviolet blind zone of silicon-based sensors. It has advantages such as low cost, miniaturization, solid-state design, and high reconstruction accuracy, and is suitable for portable environmental monitoring and biomedical diagnostics.

The embodiment of the invention provides a vehicle configuration interaction method, system and device, a readable medium and a program product. The method comprises the steps that vehicle operation parameters and historical behavior data of a user are obtained, configuration suggestions are generated, then the configuration suggestions are output to the user, feedback of the user for the configuration suggestions is received, and corresponding operation is executed. Through data driving and active interaction design, the problems of operation complexity and real-time performance of vehicle configuration management in the prior art can be solved, through combination design of voice broadcast and physical keys, traditional passive operation is improved into an active recommendation mode, the situation that a user needs to park a vehicle to operate a touch screen or memorize complex key combinations is avoided, and user experience is improved. The operation complexity is remarkably reduced, it is ensured that a user can complete configuration adjustment without operation interruption in the driving process, and the user experience and the operation efficiency are remarkably improved.

The invention discloses a ponding monitoring and early warning system based on edge intelligent processing. The ponding monitoring and early warning system comprises a data acquisition module, an edge intelligent processing unit and a decision module. The data acquisition module is configured at an accumulated water monitoring point and is used for acquiring accumulated water depth, water flow velocity and rainfall intensity data; the edge intelligent processing unit is arranged in the monitoring equipment and is used for locally extracting accumulated water depth change rate and change acceleration characteristics, evaluating data priority, identifying accumulated water state abnormity and dynamically deciding a data transmission strategy based on the priority and an abnormal mark; and the decision-making module generates multi-level early warning information according to the received data and can issue an instruction to adjust the transmission priority of the edge equipment. According to the system, real-time analysis and intelligent early warning of ponding characteristics are realized, the early warning timeliness is effectively improved through edge side data processing and dynamic transmission strategies, the data transmission quantity is reduced, and the stability and adaptability of the system in a complex environment are enhanced.

The invention discloses a power transmission tower state evaluation method and system based on inhaul cable tension, which is applied to the field of wind vibration control of a towering steel structure, and comprises the following steps: obtaining inhaul cable tension data of a to-be-evaluated power transmission tower in real time, and synchronously performing quick matching through a pre-constructed state evaluation sample database; and performing accurate prediction through a pre-constructed state evaluation model so as to comprehensively output an evaluation result. Wherein the database and the model are constructed in advance in the following mode: on the basis of a finite element model of a sample tower, wind action and inhaul cable tension time sequence data, state labels are generated through Kalman filtering data assimilation, then a state evaluation model is obtained through training, and a sample library is constructed. According to the method provided by the invention, the problem of quantitatively evaluating the power stability of the power transmission tower in real time in the wind-induced power process is solved, the evaluation mode transition from static post verification to dynamic real-time early warning is realized, and a core technical support is provided for precise and safe operation and maintenance of the power transmission tower.