29 results about "Real time algorithm" patented technology

The invention relates to the technical field of industrial automation control, in particular to a container liquid level measurement method, system and equipment of a pressure sensor and a medium, and provides a method for eliminating gas phase pressure influence based on a double-sensor differential measurement mechanism and dynamically correcting liquid density and sensor drift errors in combination with a temperature compensation model. The temperature data are linked in real time through a three-dimensional compensation function, density calculation and drift correction are synchronously adjusted, and multi-parameter dynamic decoupling and self-adaptive compensation are achieved; a collaborative measurement mechanism is adopted to integrate bottom and top sensor data, and a built-in temperature probe and a preset threshold are combined to complete multi-source signal fusion and compensation calculation; a dynamic calibration method is designed, and system errors are eliminated through reference pressure point calibration and structure coefficient correction; constructing a closed-loop signal processing flow, and integrating high-precision analog-to-digital conversion, digital filtering and real-time algorithm to output a compensated liquid level value; the method improves the measurement precision and stability, and is suitable for temperature change, gas phase pressure fluctuation and multi-medium scenes.

The invention discloses a webpage performance monitoring analysis method, system and device and a storage medium, relates to the technical field of webpage monitoring, and aims to overcome the limitation of an existing webpage performance monitoring technology. The method comprises the following steps: constructing a high-availability infrastructure to cope with large-scale concurrent access; front-end performance data are collected in real time, and a stream processing technology and a real-time algorithm are adopted for cleaning and aggregation; analyzing data by using a machine learning model, mining performance trends and abnormities, and establishing an intelligent reference; starting a scene intelligent adaptation module, accurately identifying a webpage application scene, and dynamically customizing a monitoring strategy; constructing a monitoring system covering data acquisition, analysis, reporting, alarming and optimization suggestion; and a storage medium is optimized, and data integrity and accessibility are ensured. The method has the advantages that diversified monitoring indexes, intelligent and accurate prediction performance, efficient real-time feedback and dynamic optimization of resource utilization are provided, website performance and user experience are remarkably improved, and a solid guarantee is provided for stable and efficient operation of the website.

The invention discloses an electronic switch assembly and a control method of a split-phase starting single-phase induction motor for a household clothes dryer. The assembly comprises an MCU, the MCU is loaded with a real-time algorithm software control program, and the starting speed state of the motor can be detected by analyzing and processing the change of steady-state positive sequence amplitude current in the starting process. Based on real-time positive sequence amplitude current analysis processing, the MCU calculates and detects an overspeed condition in real time, disconnects an auxiliary winding and connects a heater element so as to accurately control the transition of the motor to single-phase operation until a rated rotating speed is reached. Based on real-time positive sequence amplitude voltage and current analysis and processing, the overload condition of rated rotor speed reduction can be detected, and the auxiliary winding can be switched on again to maintain the operation performance of the motor. The assembly includes a housing having a heat sink for efficient thermal management and overheating protection of electronic devices. And an effective, reliable and low-cost solution is provided for the operation of the efficient clothes dryer by ensuring the accurate control on the starting and operating conditions of the motor and the overload protection.

The invention discloses a gas leakage monitoring system for an environment-friendly ring main unit, and belongs to the technical field of environment-friendly ring main unit monitoring. The system comprises a real-time acquisition looped network supervision platform, and a joint data real-time acquisition and storage module, an external environment gas dynamic analysis module, an internal environment gas risk analysis module and an emergency regulation and control decision processing module which are in communication connection with the real-time acquisition looped network supervision platform; multi-source data fusion monitoring of internal and external environments of the ring main unit is realized through the joint data real-time acquisition and storage module, the one-sidedness problem of single parameter monitoring is solved, and the leakage risk identification accuracy is improved; second-level data processing and grading risk early warning are realized by means of a real-time algorithm, and the fault response time is shortened; a grading emergency response mechanism is established based on the comprehensive risk index, different risks are precisely handled, and the operation and maintenance cost is reduced while the equipment safety is guaranteed.

The invention relates to a traveling wave distance measurement method and module based on online speed measurement, and belongs to the field of automatic control application of a power system. Comprising two traveling wave distance measurement modules, and each traveling wave distance measurement module is composed of a distance measurement CPU, an FPGA, a dual-port RAM, an SFP single-mode optical module, an AD sampling module, a PT / CT conversion module, a PWM module and a COM communication module. The method has the advantages that the stability of timing time, sampling time and communication delay of traveling wave devices at the two ends is guaranteed by adopting a hard real-time design without participation of a distance measurement CPU, the communication delay time is calculated online through a hard real-time algorithm, and distance measurement errors caused by time synchronization in the double-end traveling wave distance measurement process are greatly reduced; on-line measurement of wave velocities of traveling waves with different frequencies is realized by adopting an on-line velocity measurement method, and distance measurement errors caused by inaccurate wave velocities are reduced; therefore, the reliability and the accuracy of fault traveling wave distance measurement are improved.

The invention discloses an intelligent inspection method for a wind driven generator, and the method comprises the following steps: S1, equipment installation: arranging an AI edge server in each fan, and carrying out the real-time algorithm analysis of all collected data in the fan; s2, fan cabin inspection: a first track robot and a first track are arranged in the fan cabin, the first track is provided with a U-shaped bend at the upper part of the cabin, so that the first track robot moves on the first track to inspect the cabin, and an inspection result is output; s3, inspecting a vertical tower drum; s4, wheel hub inspection; and S5, performing man-machine interaction. The invention further discloses an intelligent inspection system for the wind driven generator. According to the inspection mode adopted by the invention, remote, intelligent and regular inspection can be realized, the safe operation of machinery equipment can be ensured, the personnel can be kept away from danger, the inspection efficiency is improved, the personnel cost can be reduced, and the inspection system has the advantages of safety, reliability and low cost.

The invention provides a scheduling method and system for ship segmented lightering vehicles. The method comprises the following steps: establishing a basic database; adjusting algorithm parameters of the genetic algorithm, and configuring each algorithm parameter and a corresponding calculation result into a training database; training a generative adversarial network model based on the training database; acquiring a real-time lightering task, and generating real-time algorithm parameters through the generative adversarial network model; and obtaining an optimal lightering task sequence corresponding to the real-time lightering task based on the real-time algorithm parameters and a genetic algorithm, and outputting the optimal lightering task sequence. By adjusting genetic algorithm parameters, the problem that algorithm parameters are difficult to select can be solved. And when the individual fitness is calculated, the distance is converted into time for calculation, so that the calculation accuracy is improved. By adding the priority encouragement value for calculation, the requirement that part of tasks need to be scheduled preferentially can be met. And advantages and disadvantages of different schemes can be quantitatively judged to adapt to changes of actual conditions, and intelligent and efficient management and control of ship segmented lightering vehicles are realized.

The invention discloses a single-motor steering wheel force feedback simulation system and method, and relates to the technical field of automobile simulation driving. The defects in the prior art are overcome. The system comprises a simulation control module, a vehicle state acquisition module, a transmission module, a single-motor driving module and a control unit, wherein the simulation control module is used for acquiring steering wheel angle information; the vehicle state acquisition module is used for receiving real-time state data of a virtual vehicle in a simulation environment; the transmission module is used for converting one-way rotary motion of a motor into multi-dimensional force feedback acting on a steering wheel; the method comprises the following steps: acquiring a steering wheel angle theta and running state data of a virtual vehicle in real time; the mechanical response is delayed as lt; 10ms, the road sense is very real; the pure mechanical transmission path is high in efficiency, only one motor is used, and the peak power consumption is far lower than that of a multi-motor system; the performance of the system does not depend on an extremely complex real-time algorithm or an intelligent material sensitive to temperature, the anti-interference capability is high, and the service life is long.

The invention relates to a multivariate data dynamic fusion correction method for a high-throughput plant phenotype platform in a field environment. Compared with the prior art, the multivariate data dynamic fusion correction method solves the defect that multi-environment data fusion correction is difficult to realize. The method comprises the following steps: acquiring environment data and crop phenotype data; performing wind speed and wind direction correction; performing illumination correction; performing temperature correction; and fusing and correcting the data. According to the method, the multi-source data is corrected and fused through a real-time algorithm, so that multi-level dynamic fusion correction of high-precision phenotypic data is realized.

The conceptual model of service provision system that incorporates context recognition and operational management of cargo transportation is presented. The system tackles issues such as interoperability among wireless communication networks, sensors, embedded systems (GPS, GPRS, GIS), IoT, and distributed data warehouses, all while facilitating multi-dimensional decision-making. The objective is to establish a conceptual model of the context-aware architecture for a smart service provision system, aiding in e-management decisions during unforeseen circumstances. This invention introduces an innovative conceptual model to monitor geographical positions of transport vehicles, ensuring appropriate services and rapid decision-making for risky situations. The proposed conceptual model of the Smart-CASPS architecture stands out for its novel component-based system using the computer- based cargo transportation ontology. Through real-time algorithms, it offers on-the-fly service provision and route re-planning to address safety concerns. This system, equipped with big data ontology-driven hubs, efficiently manages data from various sources, fostering adaptability, sustainability, and safety compliance in cargo transportation.

The application relates to a kind of online speed measurement based on travelling wave distance measurement method and module, belong to the application field of electric power system automatic control. Including two travelling wave distance measurement modules, each travelling wave distance measurement module is composed of distance measurement CPU, FPGA, double-port RAM, SFP single-mode optical module, AD sampling module, PT / CT conversion module, PWM module, COM communication module. The application has the advantages that: by adopting the hard real-time design without distance measurement CPU participation, the stability of timing time, sampling time and communication delay of two-end travelling wave device is ensured, and the communication delay time is calculated online through the hard real-time algorithm, the distance measurement error caused by time synchronization in the process of double-end travelling wave distance measurement is greatly reduced; by adopting online speed measurement method, the online measurement of different frequency travelling wave velocity is realized, and the distance measurement error caused by inaccurate velocity is reduced; thereby the reliability and accuracy of fault travelling wave distance measurement are improved.

The invention belongs to the technical field of image processing, and particularly relates to a stereoscopic vision parallax prediction method based on an energy function and an attention fusion network. The objective of the invention is to solve the problem that a real-time algorithm is difficult to obtain high precision in stereoscopic vision parallax prediction. The method comprises the following specific steps: acquiring a data set: training a network model by adopting two public virtual and real stereo matching data sets; the method comprises the following steps of: constructing a network model: constructing an Entry Function and Attention Interference network, and optimizing cost body construction and cost aggregation by utilizing an attention mechanism; designing a minimum loss function; training a network model: inputting the acquired data set into the network model for training; finely adjusting model parameters; and storing model parameters. According to the method, an end-to-end supervised method is adopted, a UNet-like network architecture is integrally used, cost body construction and cost body aggregation are improved, and the efficiency of the model is improved by using an attention mechanism, so that the real-time stereoscopic vision parallax prediction method based on deep learning is realized.

This invention discloses an automatic identification method for the polarity of high-frequency pulse current waveforms based on deep learning, belonging to the field of power equipment insulation fault detection technology. This method deeply utilizes the characteristics of the pulse signal waveform to identify the first wave and its polarity. Firstly, under laboratory conditions, the propagation characteristics of the first wave at different typical discharge locations and types are obtained by injecting steep pulses. The response signal waveforms at each outgoing line coupling end are measured. Then, using the waveform sequence as input vectors, a deep learning network is constructed. Considering convolutional neural networks, a sample library of typical response waveforms for each injection method and location is established using a digital image matrix as input. The sample library is continuously expanded through adversarial learning. An artificial neural network is used to train the first wave waveform and polarity on the input waveform sequence, and then uses the artificial neural network to identify waveform details. This achieves fully automatic first wave polarity identification, suitable for real-time algorithm applications in online monitoring.

The invention discloses a display support dynamic trajectory compensation adjusting system and method based on real-time feedback, in the technical scheme of the invention, the system comprises a sensing module adopting a six-axis MEMS inertial measurement unit and a joint encoder, a calculation module for data processing and an execution module, the sensing module collects six-dimensional disturbance data and joint state data in real time through a six-axis MEMS inertial measurement unit and a joint encoder; the calculation module adopts a composite control calculation strategy of feedforward adjustment calculation based on a coefficient matrix and feedback adjustment calculation based on set parameters to calculate an optimal compensation adjustment control instruction through a real-time algorithm; according to the invention, a complete sensing, calculating, decision-making and execution closed-loop control system is established, the display can be effectively prevented from being disturbed and shaking in an unsteady state environment, and the system has the advantages of fast response, high precision and strong adaptability.

The present application discloses a method, device, and equipment for automatically generating dangerous scenarios in vehicle function testing. By controlling the vehicle to run in a random traffic environment, a collision event is generated between the vehicle and a random collision target in the random traffic environment; collision state data of the vehicle and the corresponding collision target in each collision event is obtained; when the current state data of the vehicle matches the collision state data of the vehicle in the collision event during vehicle function testing, the collision target in the collision event is controlled to move according to the corresponding collision state data to generate a dangerous scenario. It is realized that dangerous scenarios can be automatically generated on the existing continuous test road, with high adaptability to the road structure. The behavior of the vehicle to be tested has been considered during the scenario generation process, and the probability of generating high-risk dangerous scenarios is high. There is no need for a relatively complex real-time algorithm, which basically does not affect the system real-time performance and is easy to implement.

The invention provides a household radiotherapy rehabilitation management system and method based on multi-mode radiotherapy state pre-judgment, and the system employs a sensor technology, such as an accelerometer, a pressure sensor and the like, carries out the multi-mode motion monitoring of a household patient, monitors the thoracic fluctuation and tidal volume in real time, carries out the quantitative evaluation, and reports to a background. The background adjusts the home training parameters (target thoracic fluctuation and target tidal volume) of the patient in combination with personalized information, historical data and real-time monitoring results of the patient. The personalized tidal volume and thoracic height target values are recommended based on factors such as the body type and the illness state of a patient, and breath-holding training is allowed after the conditions reach the standard. Instant feedback can be provided through wireless transmission and a real-time algorithm, multi-modal data fusion is used for improving quantification accuracy, parameters are dynamically adjusted according to patient data to achieve individuation, collaborative management of hospitals and families is promoted through cloud sharing data, and the problems of lack of real-time feedback, insufficient quantification, individualized adaptation and data intercommunication are solved.

This invention proposes a high-performance collaborative processing method for ultra-high precision pointing measuring instruments. It consists of a master-slave processor. The master processor ensures periodic update rates through external communication, while the slave processor handles complex algorithms and transmits results to the master to improve its real-time algorithm performance. The master software is designed with a multi-task architecture to ensure real-time performance. A "master-slave interaction task" is periodically initiated, triggering a data reception interrupt on the slave and completing master-slave data interaction. Other tasks handle real-time algorithms and communication. The master-slave interaction task has a timeout exit mechanism, and its execution is determined by the timeout status of other tasks at the timed start. To ensure data transmission reliability, the master-slave communication bus uses unidirectional transmission and reception to avoid transmission conflicts caused by duplex mode. Data transmission uses checksums, and data to be sent in multiple periods is packetized and assigned sequence numbers to avoid duplicate reception or data loss. This invention can be widely applied to embedded platforms using a master-slave dual-processor architecture.

The application discloses a motion route adaptability planning system of a wheel series robot, and relates to the technical field of route planning of the wheel series robot; in the application, a route planning module obtains working record data from a database, the type, position and occurrence time of obstacles in a target region are counted based on a plurality of working record data, an obstacle hotspot map is drawn, an initial route is planned in combination with task parameters, and finally, the initial route is adaptively and backup-optimized through the obstacle hotspot map to obtain a motion route; according to the technical scheme, the obstacle hotspot map is drawn through the working record data, high-risk obstacle regions can be identified in advance, the calculation frequency of real-time algorithms is reduced, and motion fluctuation caused by frequent path updating is avoided; and the backup route planned in advance can enable the robot to directly switch the route without real-time calculation when encountering obstacles, shorten the obstacle-avoiding response time, and solve the path lag problem.

The invention discloses a vehicle global optimization energy management method based on an energy map real-time algorithm for a hybrid electric vehicle. The method comprises the following steps: acquiring working condition information data and vehicle system data; according to the working condition information data and the vehicle system data, an SOC optimal track and an SOC optimal domain under the corresponding working condition and the vehicle power system are obtained; constructing an energy map drawing model according to the SOC optimal domain, and obtaining an energy map and an SOC track by training and testing the energy map drawing model; and constructing an optimal SOC trajectory generation model according to the energy map and the SOC trajectory, correcting the SOC trajectory by using the optimal SOC trajectory generation model, and obtaining an SOC reference trajectory. According to the method provided by the invention, the obtained SOC reference trajectory is similar to the optimal trajectory obtained by a dynamic planning algorithm, the oil-saving capability is remarkable, the calculation load of the algorithm is greatly reduced, and a foundation is laid for real-time application of energy management.

The present invention relates to a multi-source data dynamic fusion correction method for a high-throughput plant phenotyping platform in a field environment, which solves the defect that it is difficult to realize multi-environment data fusion correction compared with the prior art. The present invention comprises the following steps: acquiring environmental data and crop phenotypic data; performing wind speed and wind direction correction; performing light correction; performing temperature correction; and data fusion correction. The present invention corrects and fuses multi-source data through a real-time algorithm, thereby realizing high-precision multi-level dynamic fusion correction of phenotypic data.

The present invention discloses a real-time dam deformation monitoring method based on GNSS satellites. The method mainly comprises: receiving real-time satellite data uploaded by GNSS reference stations and GNSS monitoring stations, parsing to obtain satellite observation data of each monitoring station; processing the satellite observation data using a static baseline solution mode, and obtaining the static three-dimensional coordinates of each monitoring station through adjustment calculation; calculating the real-time three-dimensional coordinates of each monitoring station using an RTK real-time algorithm, and then subtracting them from the static three-dimensional coordinates to obtain the real-time deformation of each monitoring station; establishing a station-centered coordinate system, and then converting the real-time deformation into a three-dimensional deformation in the station-centered coordinate system; constructing a dam coordinate system, calculating the rotation angle between the dam coordinate system and the station-centered coordinate system; and converting the three-dimensional deformation of each monitoring station in the station-centered coordinate system into a three-dimensional deformation in the dam body coordinate system. The method can intuitively display the deformation magnitude of the dam body in each direction in the dam body coordinate system.

The invention discloses a real-time RRT-DWO algorithm under a dynamic and shape time-varying obstacle path planning environment. The traditional path planning technology generally only considers the speed change of the obstacle, and the problem of introducing the new dynamic dimension of the shape change of the obstacle is solved. In order to cope with the complexity caused by shape change, the invention provides an RRT-DWA (Rapid-Extended Random Tree) algorithm based on an RRT (Rapid-Extended Random Tree) and a DWA (Dynamic Window Application) method, and develops a dynamic real-time RRT-DWA algorithm on the basis of the RRT-DWA algorithm so as to improve the real-time performance. In environments with different obstacle numbers and deformation rates, the dynamic real-time RRT-DWA algorithm has excellent performance in indexes such as success rate and average time, and has more obvious advantages especially under the condition that the deformation rate is accelerated. Figure 1 is an RRT algorithm sampling and searching process, and the algorithm is used as a core module for dynamic real-time RRT-DWA to obtain a global path.

The invention discloses a modular design method for a bidirectional driving device, which comprises the following steps: defining a transmission system as three functional modules, respectively configuring a one-way transmission unit for a first power input module and a second power input module, and configuring a two-way transmission unit for the first power input module and the second power input module on the basis of the physical characteristics of the one-way transmission units; defining a transmission logic for realizing automatic selection and isolation of a power path; defining a power switching logic based on working condition prediction: designing a control unit which is configured to receive a vehicle state signal, and pre-judging and triggering preparation or switching of a power source based on a pre-stored working condition map and a real-time algorithm; defining a mechanical interface and an electrical interface among the first power input module, the second power input module and the power output module; the three function modules and the control unit are integrated through defined interfaces, and the transmission system with the predictive power switching function is constructed. According to the method, the overall operation efficiency in a multi-power-source driving scene is optimized.

The invention relates to a humidity calibration device, calibration system and calibration method, and belongs to the technical field of humidity sensor calibration detection. The temperature of liquid and the temperature of gas in a closed cavity are monitored synchronously, and real-time algorithm compensation is carried out by using an original humidity correction table, so that measurement errors caused by gas-liquid temperature difference are effectively overcome; the system integrates a drying agent, various saturated salt solutions and water, realizes 0-100% full-range humidity coverage, drives fan blades to accelerate balance through an external motor, simulates a real ventilation environment, and finally improves the humidity calibration precision under the condition that external temperature control is not needed.

The invention discloses an adaptive planning system for a motion route of a wheel-train robot, and relates to the technical field of route planning of wheel-train robots. A route planning module obtains work record data from a database, counts the type, position and occurrence time of an obstacle in a target area based on the work record data, draws an obstacle hotspot map, and plans an initial route in combination with task parameters. And finally, carrying out adaptive standby optimization on the initial route through the obstacle hotspot map to obtain a motion route. According to the technical scheme, the obstacle hotspot map is drawn through the working record data, a high-risk obstacle area can be recognized in advance, the calculation frequency of a real-time algorithm is reduced, and motion fluctuation caused by frequent path updating is avoided; by means of a standby route planned in advance, when the robot encounters an obstacle, real-time calculation is not needed, the route is directly switched, the obstacle avoidance response time is shortened, and the problem of path lag is solved.

A system and method for algorithmically modifying, in real-time, one or more graphical user elements of an end user application for a SaMD or DHI product / platform. The method may enable real-time modification of a graphical user interface in response to determining that one or more user-generated responses in response to one or more CSIs are reflective of a targeted stimulus-response pattern. The targeted stimulus-response pattern may reflect a threshold of active therapeutic delivery for the SaMD or DHI to the end user. The method of algorithmically modifying the one or more graphical user elements may include rendering new elements or modifying existing elements, including changing the color of existing elements, for only the time period when the user-generated responses are reflective of the targeted stimulus-response pattern and / or for a pre-determined period of time before and / or after the user-generated responses are reflective of the targeted stimulus-response pattern.

An advanced multi-agent pathfinding and optimization system for logistic networks, revolutionizing fourth-party logistics (4PL) management through Multi-Agent Reinforcement Learning (MARL). It aims to optimize package transportation across multi-hub networks, shifting from a volume-driven approach to a rate-driven model. This system adapts dynamically to business fluctuations, redefining risk dynamics in logistics. It prioritizes shipments based on rate tiers, altering risk distribution and incentivizing efficiency over volume. The system addresses current logistical challenges by integrating multi-modal perspectives and anticipating real-time disruptions, overcoming scalability, adaptability, and dynamic change limitations. Important features include an advanced “GeoEngine” for accurate geocoding, a first / last mile engine with VRP solutions, real-time A* algorithm implementation for hub assignment, and a comprehensive RL model utilizing Q-learning with lightGBM. This system provides a more balanced, market-driven approach to logistics, offering a paradigm shift in efficiency, adaptability, and strategic rate management.

This invention relates to an adaptive time-frequency joint filtering method for electrostatic data from solid rocket motor exhaust. The method includes the following steps: constructing a main channel filtering algorithm to suppress the fundamental combustion oscillation, employing an adaptive IIR notch filter, with the core frequency detected by a real-time FFT algorithm and the bandwidth dynamically adjusted according to the combustion state; constructing an auxiliary channel to suppress harmonics, consisting of multiple parallel notch filters, each corresponding to a harmonic frequency, with the harmonic order adaptively determined based on the fundamental attenuation; addressing the phase distortion problem of traditional IIR filters by using forward-backward filtering to offset phase shift; particle pulse detection is the core of solid rocket motor exhaust electrostatic signal processing, aiming to accurately identify transient pulses (1-5 μs) generated by Al2O3 particle collisions; this invention uses a wavelet domain adaptive algorithm based on multi-scale noise separation, a time-varying threshold mechanism, and the SURE optimization criterion to suppress combustion oscillations and noise in the exhaust electrostatic signal while preserving particle pulse information.

This invention discloses a gas leak monitoring system for environmentally friendly ring main units (RNBs), belonging to the field of environmentally friendly RNB monitoring technology. The invention includes a real-time data acquisition and monitoring platform, which is communicatively connected to a joint real-time data acquisition and storage module, an external environmental gas dynamic analysis module, an internal environmental gas risk analysis module, and an emergency control decision processing module. The invention achieves multi-source data fusion monitoring of the internal and external environments of the RNB through the joint real-time data acquisition and storage module, solving the problem of the one-sidedness of single-parameter monitoring and improving the accuracy of leak risk identification. It utilizes real-time algorithms to achieve second-level data processing and graded risk warning, shortening fault response time. Based on a comprehensive risk index, a graded emergency response mechanism is established to accurately handle different risks, ensuring equipment safety while reducing operation and maintenance costs.