2939 results about "Real time acquisition" patented technology

The present application refers to a process and a system for collecting, storing, analyzing and visualizing electrocardiographic (ECG) data collected by portable ECG device (100) with embedded wireless communication technology, additionally containing a portable ECG device (100), a hermetically sealed box (200) of medicines containing a lock system comprising a lock (220), a first device (310) to be accessed by the patient, a second device (320) to be accessed by the medical professional, a platform for graphical display of ECG data, and a remote data storage and processing central. The hardware board of the portable ECG device contains wireless communication technology, global positioning system (GPS), start button (141), rechargeable battery charging port, and an equipment configuration and programming port. The battery (120) is Li-Ion type, containing LEDs indicating device operation, and waiting for connection. The battery status is displayed on the app display. In one embodiment, an electrode (130B) is positioned on the bottom cover (110B) of the housing (110A, 110B, 110C) of the ECG device (100).

An adaptive air intake control system and method for cooling towers, coupled with airflow diversion for energy saving, belongs to the field of adaptive control system technology. The system includes an information acquisition unit, an execution unit, and an adaptive control unit. The information acquisition unit collects environmental wind parameters and key operating parameters in real time. The execution unit includes multiple independent fan blade groups with circumferential partitioning and vertical layering for precise execution of commands. The adaptive control unit executes operational decisions, selecting the dominant operating mode from airflow diversion energy saving, anti-freezing energy saving, and anti-drip energy saving modes; it then identifies and outputs wind condition characteristics and thermal anomaly indicators through key parameter status identification, and generates control commands based on the adaptive control strategy and command generation. The system forms a closed loop, capable of adaptively adjusting the identification model and control strategy based on feedback. A corresponding control method is also disclosed, enabling intelligent and precise control of air intake in cooling towers under complex environments, improving cooling efficiency and energy saving while ensuring anti-freezing safety.

The application discloses a kind of based on running state determination continuous material processing equipment energy efficiency regulation and control method, it is related to industrial automation and process control technical field, this method includes: real-time acquisition equipment operating parameter, material state parameter and environmental parameter, form multisource operation data set, obtain the load state, health state and efficiency state of equipment, form the safety operation constraint boundary of current time, optimization is carried out by heuristic optimization algorithm, obtain optimal energy efficiency operating parameter set.Energy efficiency regulation and control instruction sequence is generated, control response deviation is calculated and corrected, and the parameter value range in threshold parameter and energy efficiency evaluation function is adjusted by periodic statistical analysis.The application realizes continuous material processing equipment energy consumption optimization by the closed-loop regulation and control method of multisource data fusion, state determination rule chain and comprehensive energy efficiency evaluation, improves the stability of system operation, control precision and working condition adaptability, enhances the reliability of long-term operation.

This application relates to a three-phase imbalance monitoring method and apparatus for a reverse power control cabinet. The method includes: real-time acquisition of a first target parameter of the reverse power control cabinet; determining the current imbalance degree of the reverse power control cabinet based on the first target parameter, and determining an influencing factor based on the current imbalance degree; determining the load type of the reverse power control cabinet, and adjusting a threshold range according to the load type; inputting historical imbalance degrees into a preset long short-term memory network model to obtain a predicted imbalance degree, comparing the current imbalance degree and the predicted imbalance degree with the adjusted threshold range to obtain a first comparison result, and comparing the influencing factor with a safety margin to obtain a second comparison result; determining the imbalance state level based on the first comparison result and the second comparison result, and determining a monitoring strategy for the reverse power control cabinet based on the imbalance state level. The reverse power control cabinet in this application belongs to the smart grid industry within the new energy industry.

The invention relates to an electrolytic hydrogen safe operation boundary construction and application method and system based on dynamic characteristic identification, and electronic equipment. The method comprises the following steps: acquiring actually measured dynamic response data of an electrochemical device under a dynamic excitation working condition; identifying real dynamic characteristic parameters through a system identification algorithm based on actually measured dynamic response data to replace idealized dynamic setting to construct a high-fidelity security model with a coupling sub-model; a high-fidelity security boundary is obtained based on high-fidelity security model simulation; operating parameters are collected in real time and compared with the high-fidelity safety boundary, and an instantaneous risk component is calculated; when the instantaneous risk component exceeds a safety control threshold, a power limit instruction is generated to perform closed-loop limitation on the input power. According to the method, the limitation of a traditional steady-state model is broken through, the transient out-of-limit risk can be accurately predicted and intervened, and the intrinsic safety of the electrolytic hydrogen system under the fluctuation working condition is greatly improved.

An intelligent networked energy recovery ventilator includes a main body, a filtering component, an air guiding fan, a heat exchange core, a host driving controller and a gas detection module. The gas detection module is disposed in the main body and electrically connected to the host driving controller. The gas detection module detects temperature, humidity and air pollution in the air to generate detection data, the detection data is transmitted to a networked cloud computing service device through IoT communication, and the networked cloud computing service device collects, analyzes and monitors the detection data in real time and intelligently selects a control instruction for transmitting to the gas detection module to control the host driving controller to enable the air guiding fan and dynamically adjust operation frequency and output air volume of the air guiding fan, thereby achieving gas exchange and heat exchange between indoor field and outdoor field.

This invention relates to the field of thermal power generation cost accounting technology, and proposes a dynamic modeling method and system for the cost per kilowatt-hour based on the forward and reverse balance method. The method includes: real-time acquisition of data on instantaneous flow rate of the coal feeder, coal pile volume, density, coal analysis upon entering the furnace, unit operation, and fuel price; calculation of dynamic coal quality data using a multimodal fusion algorithm; calculation of forward fuel cost based on flow rate, coal quality, and price data; calculation of reverse balance theoretical cost by inferring the theoretical standard coal quantity based on unit operation data; comparison and verification of forward and reverse costs to determine the final fuel cost; and calculation of dynamic cost per kilowatt-hour by combining variable cost and power generation data. This invention achieves a nonlinear dynamic mapping from multi-dimensional real-time data to accurate cost per kilowatt-hour, enabling high-precision, real-time, and automated dynamic modeling of the cost per kilowatt-hour for thermal power generation enterprises.

The application discloses a multi-modal-based digital human interaction control method and device, and relates to the technical field of artificial intelligence digital human, and specifically comprises the following steps: collecting multi-source heterogeneous multi-modal data of user interaction with a digital human in real time; constructing a dynamic adaptive multi-modal space-time hypergraph Transformer network, deeply fusing the multi-source heterogeneous multi-modal data, and obtaining a fused interaction feature vector; generating a multi-dimensional interaction situation tensor based on the fused interaction feature vector; generating a digital human response action sequence, voice content, expression posture and interaction strategy according to the multi-dimensional interaction situation tensor, and driving the digital human to execute; acquiring feedback data of the digital human after executing the response in real time, and optimizing parameters of the dynamic adaptive multi-modal space-time hypergraph Transformer network based on the feedback data; and effectively improving the naturalness and response accuracy of digital human interaction, and reducing the interaction misunderstanding rate and user cognitive load.

The application discloses a kind of public dining room food safety supervision method and system in the technical field of food safety supervision.The supervision method includes: S1, for personnel, dishes, agency, event four kinds of data entities, respectively corresponding person code, dish code, agency code, event code Coding rules are formulated;S2, real-time data acquisition generates corresponding code, and establishes the association relationship of dish code and person code, agency code, event code with dish code as connection node;S3, four kinds of data entities and its associated data are stored, and key association data is stored in block chain;S4, receive query request, according to the code of any one kind carried by request, output all data entity information associated with the code.The supervision method breaks the problem of traditional dining room supervision data island in each link, realizes dish, personnel, agency, event data full-link penetration, and any dimension query can quickly obtain complete evidence chain, and key data is stored in block chain, and data credibility is high.

The present disclosure relates to the technical field of electronic commerce, in particular to an e-commerce marketing method and system based on time sequence promotion, the method comprising: collecting user behavior data, commodity attribute data, marketing history data and time sequence time node data of an e-commerce platform to obtain a standardized time sequence data set; extracting user behavior time sequence features, commodity heat time sequence features and marketing effect time sequence features based on the standardized time sequence data set to construct a time sequence feature matrix; determining core promotion content, target population and promotion channels of each promotion period through a preset time sequence prediction model to generate an initial time sequence promotion strategy; performing a promotion operation at a corresponding time sequence node according to the initial time sequence promotion strategy, collecting user feedback data and promotion effect data in real time and inputting the data into the time sequence prediction model to dynamically adjust the promotion content, channel delivery ratio and time sequence node arrangement, etc., to form a time sequence promotion strategy; and performing effect evaluation on the time sequence promotion strategy based on evaluation indexes to generate an evaluation report.

The application discloses a blood sugar management applet with home support function. A data acquisition interaction module is connected with an external blood glucose meter device through a Bluetooth communication protocol to collect blood sugar data of a user in real time; a home collaborative management module realizes safe sharing of blood sugar data of the user among family members, and the family members can check relevant blood sugar data, diet and exercise records through the applet end; and an intelligent analysis and reminding module is based on an LSTM neural network model, performs trend analysis on historical blood sugar data after a risk early warning trigger, generates personalized health reminding information automatically in combination with preset rules, and sends the information to the user and associated family members in a multi-modal form. The application effectively improves the blood sugar management efficiency of patients, reduces the risk of data leakage, and can significantly reduce the fasting blood sugar value and the 2-hour postprandial blood sugar value of patients compared with the prior art, reduces the cost of manual intervention, and has significant clinical application value and social and economic benefits.

This invention relates to the field of cervical and lumbar spine rehabilitation technology, and more particularly to a cervical and lumbar spine rehabilitation training system integrating posture monitoring and motion guidance. The system includes the following modules: a data acquisition and preprocessing module, used to collect real-time three-dimensional posture data of the user's cervical, thoracic, and lumbar spine and related joints, and generate motion data after standardization processing; and a micro-deviation analysis module, which generates a personalized micro-deviation accumulation model based on the motion data, predicts micro-deviations in real time, and generates personalized training adjustment strategies based on the user's physiological characteristics, historical training data, and current movements. In this invention, the micro-deviation analysis module extracts joint position, angle, velocity, and acceleration features from the standardized motion data, and combines this with nonlinear system modeling, user physiological characteristics, and historical training data to realize a personalized micro-deviation accumulation model, generating real-time micro-deviation prediction and training adjustment strategies.

The application discloses a kind of tunnel construction dynamic measurement methods based on inertial navigation auxiliary, comprising: obtaining tunnel construction priori information and stage division parameter;With the measuring equipment of loaded inertial measurement unit, laser radar and odometer, it is installed on construction machinery, travels along tunnel axis, and real-time collection multi-source data;Based on point cloud feature density, the stage where it is located is automatically identified, and corresponding multi-level adaptive constraint model is dynamically switched, including adaptive nonholonomic kinematics constraint, environmental feature constraint based on point cloud data and design section, and absolute correction constraint based on template matching automatically identifying natural existing reserved reference point in tunnel;Extended Kalman filter is constructed to fuse multi-level constraint and dynamically adjust weight;Through loop detection and graph optimization, cumulative error is eliminated and control network is updated.The application realizes adaptive high-precision dynamic measurement of whole process of tunnel construction, does not need artificial layout beacon, effectively solves the problem of unstable positioning accuracy in complex environment.

The present application belongs to the technical field of intelligent processing of electric shovels, and discloses an electric shovel intelligent excavation prompting system based on a hybrid encoder and a Transformer decoder, comprising: a data acquisition module, a sensor is arranged on the electric shovel to acquire real-time operating parameters of the electric shovel and real-time environmental information parameters of the working environment of the electric shovel; a hybrid encoder module, which dynamically constructs the relationship between nodes and edges through a graph neural network for the real-time operating parameters and the environmental information parameters, and converts the relationship into graph structure data; and removes noise information from the graph structure data through a cross-modal variational autoencoder to obtain encoded structure data; and a Transformer decoder module, which captures the correlation of the encoded structure data based on a self-attention mechanism, acquires working condition information of the electric shovel, and outputs excavation prompting information. The present application realizes intelligent processing of real-time operating parameters and environmental information parameters of the electric shovel, outputs excavation prompting information to assist the electric shovel driver in operation, and improves the efficiency and safety of electric shovel operation.

The present application belongs to the technical field of glass product production management and data processing, and relates to a glass product production management method and system. The method comprises: collecting glass surface ripple signals and process parameters of a production line in real time; calculating a local curvature sequence and a local curvature entropy reflecting the sharpness of the micro-waveform of the signals, and adaptively determining a dynamic bandwidth adjustment factor of a variational mode decomposition algorithm based on the local curvature entropy; using the dynamic bandwidth adjustment factor to perform mode decomposition on the glass surface ripple signals, and extracting a characteristic component matching the frequency of a roller fault; calculating a physical real wear index after eliminating the influence of the speed according to the characteristic component and the glass drawing speed, and making a device maintenance decision based on the index. The present application can accurately identify device defects under complex working conditions by adaptively adjusting algorithm parameters and decoupling the influence of speed, thereby improving the accuracy of maintenance decisions.

The application provides a sewer network monitoring method and system, the method comprising: collecting multi-source monitoring data of the sewer network in real time; constructing a directed weighted graph topology model based on basic geographic information data, basic structure data and a graph theory algorithm; identifying a topology structure problem of the sewer network according to the directed weighted graph topology model and correcting the topology structure problem; diagnosing a functional defect state of the sewer network and an internal physical defect state of the sewer network; extracting a liquid level sequence in the multi-source monitoring data and predicting a water level change trend of a key node of the sewer network; mapping the corrected standard topology structure file, the functional defect state, the internal physical defect state and the water level change trend to a hydraulic model to construct a digital twin body that is real-time synchronized with the sewer network. The application effectively monitors the sewer network by fusing multi-source monitoring data, identifying a pipe network background structure problem, diagnosing a functional defect and predicting a water level change trend.

This application provides an energy-saving method and system for stamping processes based on fluctuating load hedging, relating to the field of cycle control technology. The method includes: real-time acquisition of load signals of each stamping press in each stamping cycle, identifying the peak load mutation point corresponding to the main blanking or drawing process in that cycle, and using the peak load mutation point as the current cycle phase reference point of the stamping press; obtaining the timestamp of the current cycle phase reference point of each stamping press relative to a unified time reference, and calculating the phase difference between any two stamping presses; when the phase difference between two stamping presses is less than a preset safety threshold, selecting at least one of the two stamping presses as the target stamping press; determining a compensation time amount according to a preset compensation rule, and sending a cycle adjustment command to the target stamping press to advance or delay the start time of its next stamping cycle relative to the original start time by the compensation time amount, thereby increasing the phase difference between the two stamping presses.

This invention relates to the fields of wildfire monitoring, computer vision, and digital twin technology. It proposes a method and system for real-time wildfire spread simulation using digital twins and dynamic parameter inversion. The method dynamically inverts the surface combustible moisture content through white balance adaptive correction and HSV color feature extraction; maps physical camera parameters to a virtual camera; and uses a spatial ray casting algorithm and a digital elevation model to calculate the three-dimensional geographic coordinates of the fire point; it determines the wildfire spread direction by weighting the wind field vector and the maximum upslope vector in real time; it calculates the true slope of the dynamic path by taking points forward from the fire point as the starting point and using the grid accuracy of the digital elevation model as the step size; and it inputs the dynamic parameters into the wildfire spread model for simulation to generate the fire spread boundary. This invention significantly improves the physical realism and practical accuracy of wildfire spread simulation in complex environments through virtual-real fusion and physical correction mechanisms.

The application discloses a plant essential oil extraction equipment control method based on artificial intelligence, which comprises the following steps: constructing a dynamic space-time graph of a distillation process; collecting historical data and associating quality labels, training a space-time graph neural network model for state inference and prediction; generating an offline reinforcement learning experience dataset based on the model processing historical data; training an offline reinforcement learning strategy model in a virtual environment with the model; in online operation, collecting data in real time, generating control instructions by the strategy model after state inference by the model, and executing after verification; and continuously collecting new data to update the model. The application realizes self-adaptive closed-loop control guided by quality without relying on an accurate mechanism model, and improves production quality stability and process adaptability.

This application discloses an emotion assessment method and apparatus based on deep temporal modeling. The method includes: real-time acquisition of speech data and EEG signals of a target object; conversion of the acquired speech data into an audio sequence and the EEG signals into an EEG sequence according to a preset time interval; inputting the audio sequence into a first sub-network of an emotion extractor to obtain speech emotion results; inputting the EEG sequence into a second sub-network of the emotion extractor to obtain brain activity emotion results; evaluating the emotional state of the target object based on the speech emotion results and brain activity emotion results at different times to obtain multiple emotion scores; fusing the multiple emotion scores of the target object within a target time period to obtain a comprehensive emotion score evaluation; and determining the emotional state type of the target object according to a preset emotion score scale.

The application discloses an elevator safety detection and measurement switch, which integrates a multi-dimensional sensing module, an intelligent fault prediction module, a multi-source data fusion module and a self-adaptive control module, can collect related monitoring data such as electrical, mechanical and environmental parameters, elevator guide rail inclination and traction machine current in real time, realizes accurate prediction of the fault risk level and type of the elevator safety detection and measurement switch through an improved CNN+attention mechanism+LSTM fault prediction model, establishes an associated diagnosis model of the elevator guide rail plumbness in combination with a multi-source data fusion algorithm, adopts a multi-scene self-adaptive threshold adjustment mechanism to formulate a hierarchical control strategy, realizes early warning, accurate processing of the fault of the elevator safety detection and measurement switch and linkage response with other safety monitoring systems of the elevator, improves the fault prediction accuracy of the elevator safety detection and measurement switch to more than 98%, shortens the fault processing time by 60%, and quantifies the influence of the guide rail deviation on the elevator energy consumption and switch operation, and significantly improves the integrity, intelligence and economy of the elevator safety monitoring system.

This application discloses a method for optimizing the hub profile of an axial flow fan blade. The method includes: real-time acquisition of fan operating conditions to obtain the current fan speed and outlet back pressure, forming an operating condition vector; inputting the operating condition vector into a pre-trained adaptive prediction model, which outputs the optimal hub profile parameter vector corresponding to the current operating condition; the optimal hub profile parameter vector is set as a dynamic variable related to the operating condition vector, and the optimal hub profile parameter is pre-obtained based on a multi-objective comprehensive performance evaluation function with blade passing frequency sound pressure level and fan aerodynamic efficiency as optimization objectives, through adaptive optimization under different operating conditions; and dynamically adjusting the hub profile of the physical axial flow fan according to the optimal hub profile parameter. Thus, real-time adaptive adjustment of the axial flow fan hub profile to operating conditions is achieved.

The application relates to the technical field of hybrid solid state disks, and discloses a hybrid solid state disk dynamic optimization method and device based on a large language model, which systematically classifies the parameter space of a hybrid solid state disk, constructs a parameter sensitivity matrix based on the classified parameters, determines the influence difference parameters of the parameters on performance under different workloads, collects system state information of the hybrid solid state disk in real time, generates an optimized parameter configuration scheme based on a large language model according to the system state information, the influence difference parameters and historical configuration information. The application can realize real-time perception of work load changes and dynamic focusing on the most sensitive parameter combination under the current load, realize collaborative reasoning and adaptive adjustment among multiple parameters, significantly reduce the write amplification rate and operation delay, and solve the problem that the response efficiency, system stability and energy consumption performance of the hybrid SSD are poor under a dynamic work load in the prior art.

This invention provides a mechanical equipment management system based on QR code recognition. The beam yard is divided into several grids, with at least one camera installed in each grid to collect video streams of the area in real time. A unique QR code label is affixed to each piece of construction equipment, and an embedded terminal collects dynamic data from the equipment, transmitting it to the cloud via a wireless network. The cloud server accesses the video stream, identifies and decodes the QR code labels, and performs 3D modeling of the beam yard, displaying the location and power of the construction equipment in real time. Through a collaborative perception architecture using gridded cameras as the primary means and automatic cruise equipment as a secondary means, combined with equipment data collectors and cloud computing, real-time positioning, status monitoring, and optimized scheduling of mechanical equipment within the beam yard are achieved.

This invention relates to the field of laser cutting control technology, specifically a BIM model-based laser cutting control method for H-beams. The invention constructs a BIM model based on target design drawings, determines the cutting path, bevel type, and cutting angle at the ends of each H-beam component, categorizes the H-beam components, and determines the processing priority of each category group based on the overall assembly sequence of the BIM model. A cutting task queue is generated according to the processing priority from high to low, and the spatial position and attitude angle of the laser cutting head are collected in real time. The time-series dataset of the cutting process is registered with the cutting path of the current cutting task in spatial coordinates. The processing deviation between the actual cutting path and the path to be cut is calculated, and the remaining cutting path and attitude angle corresponding to the current cutting task are corrected. This improves the utilization efficiency of the cutting equipment and the timeliness of component delivery, ensuring stable and compliant H-beam component cutting quality.

The application discloses an automatic asphalt pavement crack pouring machine and a use method thereof, and belongs to the technical field of road maintenance equipment, and comprises a sensor module and a computer system, wherein the computer system comprises a high-definition camera, a laser radar sensor and a computer with an AI algorithm model built-in, is used for collecting road surface images and three-dimensional topographic data in real time, realizes automatic crack identification, positioning and form analysis, and generates a digital crack map, and an optimal path planning module adopts a permutation and combination algorithm based on a tree structure to list all possible crack access sequences, and constructs a weighted path graph in combination with start point and crack end point coordinates. The application can realize automatic and continuous operation, so that the single-day crack pouring capacity is improved to several thousand meters, which is several times of that of traditional manual operation, construction efficiency is greatly improved, the AI recognition accuracy reaches 0.1mm level, the crack width error is extremely small, crack filling fullness is greatly improved, and operation accuracy is significantly improved.