26949results about "Pump control" patented technology

A portable fan includes: an airflow portion and a display screen. The airflow portion includes an air outlet, the air outlet is disposed surrounding a periphery of the display screen. The display screen has an outer surface at least partially curved; and / or the display screen is configured to display at least one of: a remaining battery power level, a current air speed, a battery power level during charging, and an operating state.

The invention discloses a fan multi-mode fault diagnosis method based on voiceprint feature and vibration data fusion. The method comprises the steps that S1, voiceprint signals and vibration signals generated in the fan operation process are synchronously collected through a plurality of sensors; s2, preprocessing the collected voiceprint signal and the vibration signal; s3, fusing the preprocessed voiceprint signal and vibration signal through a feature level weighted fusion algorithm; s4, performing coarse-grained fault screening under a multi-level framework; s5, performing fine-grained fault diagnosis under a multi-level framework; s6, after the fine-grained fault diagnosis is completed, the system outputs a corresponding fault early warning signal according to the type, the specific part and the severity of the fault; and S7, the system dynamically adjusts the fault diagnosis model by monitoring the running state and the fault condition of the fan in real time. According to the method, the fault type of the fan can be accurately identified, the severity of the fan can be evaluated, and the overall intelligent level and the real-time response capability of a fault diagnosis system are improved.

The invention relates to the technical field of multi-sensor fusion, in particular to a high-reliability multi-sensor fusion pump station monitoring system and an intelligent early warning control method, and the method comprises the steps: 1, carrying out the synchronous fusion processing of data based on the sampling frequency difference of multi-source heterogeneous sensors, so as to guarantee the consistency of time axes; 2, calculating the consistency difference of the data of each sensor according to the synchronized data, and dynamically shielding an abnormal data source to avoid false alarm interference; 3, real-time operation state modeling is carried out based on the data change trend after dynamic shielding; and 4, accurate early warning and decision optimization are realized. According to the high-reliability multi-sensor fusion pump station monitoring system and the intelligent early warning control method, data synchronous fusion processing is performed based on the sampling frequency difference of the multi-source heterogeneous sensors, so that the consistency of a time axis is ensured, and the problem of influence of asynchronous data on the state judgment accuracy is effectively solved.

The invention discloses a water supply pipe network water hammer control method and system based on multifunctional module fusion, and relates to the technical field of data identification. A time sequence diagram neural network prediction and traceability module which realizes water hammer risk prediction and propagation path traceability based on a causal constraint graph neural network model; the reinforcement learning intervention decision module is used for generating an active intervention strategy through a reinforcement learning agent, forming closed-loop control, abstracting a water supply pipe network into a graph structure, and modeling in combination with a time sequence, so that a propagation path of pressure waves can be comprehensively reflected, a blind area of traditional local modeling is overcome, and the comprehensiveness and accuracy of water hammer event detection are improved; and a causal analysis result is input as an adjacent matrix, so that the interference of irrelevant edges on prediction in topology is effectively eliminated, and the learning efficiency and causal traceability of the model are improved.

The invention provides a main helium fan fault diagnosis system and method based on deep learning, and belongs to the technical field of main helium fan fault diagnosis, and the method comprises the steps: fusing vibration, current, temperature and other multi-source sensor data, and carrying out the fault mode recognition and root positioning through an attention mechanism convolutional neural network; meanwhile, a health index prediction model and a dynamic early warning threshold value are introduced, and multi-level real-time early warning is achieved; in addition, through online learning and transfer learning technologies, the diagnosis model is endowed with sustainable evolution and rapid deployment capabilities. According to the method, fault diagnosis, trend prediction, early warning decision and adaptive learning are integrated, a new thought is provided for intelligent operation and maintenance of the main helium fan, and the method has important value in the aspect of improving safety, reliability and economical efficiency of a nuclear power unit.

The invention belongs to the field of industrial automation and intelligent manufacturing, particularly provides a metallurgical fan multi-physics field coupling and dynamic performance optimization method based on digital twinning, and aims to solve the technical problems of performance optimization and fault prediction of a metallurgical fan under complex operation conditions. The method comprises the following steps: acquiring multi-physical field data such as fluid mechanics, thermodynamics and structural mechanics through a distributed sensing network and a real-time data acquisition technology, and performing cleaning, denoising and feature extraction on the data; the method comprises the following steps: constructing a high-precision multi-physics field coupling digital twin model based on a geometric structure and material characteristics of a metallurgical fan, and carrying out model calibration in combination with historical data and real-time sensing data; the dynamic performance of the draught fan under different operation conditions is simulated through the co-simulation technology, the operation parameters of the draught fan are dynamically adjusted through a data-driven optimization algorithm, real-time optimization of the performance is achieved, and the purposes of improving the operation efficiency, reducing energy consumption and prolonging the service life of equipment are achieved.

The invention discloses a digital twin enabling intelligent pump station preventive operation and maintenance system. Comprising a dynamic twin construction module, a multi-source heterogeneous multi-modal data acquisition module, an edge computing and cloud collaboration module, an equipment health degree evaluation module, a predictive maintenance decision module, a cross-system data fusion module, a self-evolution knowledge graph module, an intelligent diagnosis and early warning module, a self-adaptive maintenance decision module and a man-machine collaboration interaction module. And the dynamic twin construction module comprises a physical-virtual synchronous calibration mechanism and an equipment degradation parameter dynamic updating mechanism. According to the method, the limitation problem of a traditional static model is solved, the method can adapt to nonlinear changes under complex working conditions, the comprehensive judgment and prediction capability of the system on the equipment state can be enhanced, the energy utilization efficiency is improved, the energy consumption is reduced, the decision and verification mechanism is perfected, and the data acquisition and processing problem is improved; the problems of timeliness and flexibility of the model are solved, and the computing architecture and the response capability are optimized.

The invention provides a compressor control method and system. The method belongs to the technical field of compressor control, and comprises the following steps: collecting multi-dimensional data in the operation process of a compressor in real time through a multi-parameter sensor network; preprocessing the collected multi-dimensional data, and constructing a data set; learning and optimizing a compressor control strategy based on historical data and a current environment state through a reinforcement learning model based on a deep neural network; continuous iterative updating is carried out through analog simulation and online learning; and on the basis of the adaptive neural network model, the running state and performance parameters of the compressor in a future period of time are predicted in real time, and the network structure and parameters are dynamically adjusted according to the current working condition and historical data of the compressor. Multi-dimensional data in the operation process of the compressor are collected in real time through a multi-parameter sensor network. The data are preprocessed, including denoising, missing value filling, abnormal value detection and the like, so that the data quality is ensured.

The invention discloses a tunnel segmented exhaust method and system based on train working conditions, a medium and a product, and relates to the technical field of ventilation. The method comprises the steps that a target tunnel is divided into a plurality of control sections according to fan installation configuration of the target tunnel; acquiring train passing data and train configuration information of the control section, and calculating a flue gas growth value of the control section in a preset time period; collecting flue gas concentration data of the control section, and calculating a ventilation demand index of the control section in combination with the flue gas growth value; collecting wind direction parameters of the control sections, determining a ventilation control relationship between the adjacent control sections based on the ventilation demand indexes and the wind direction parameters, and generating a ventilation cooperation scheme; according to the ventilation cooperation scheme, operation parameters of the tunnel fans in all the control sections are calculated, and control instructions corresponding to the operation parameters are sent to the tunnel fans. By implementing the application, the fan operation can be adjusted according to the actual train operation condition, and the ventilation effect is optimized.

The invention relates to an anti-electromagnetic interference intelligent cooling fan system based on dynamic filter circuit design. The system comprises a dual analysis module used for performing time domain and frequency domain dual analysis on a rotating speed feedback signal of a double-ball fan to obtain a multi-dimensional electromagnetic interference characteristic model, and configuring filter network parameters to obtain a target three-stage cascade dynamic filter network; the signal purification module is used for collecting first fan operation parameters and inputting the first fan operation parameters into a target three-stage cascade dynamic filter network for signal purification to obtain filtered Hall sensor signals and filtered power supply signals; the closed-loop control calculation module is used for performing closed-loop control calculation to obtain a non-linear PWM duty ratio control quantity and a phase distribution parameter; and the phase difference control module is used for carrying out phase difference control to obtain a multi-fan cooperative control strategy. According to the invention, high-precision control and anti-interference capability in a wide rotating speed range are realized, and the problem of mutual influence of electromagnetic interference when multiple fans are connected in parallel is solved.

Apparatus and methods are described including placing, into a subject's body, a blood-pump tube, with an impeller disposed within the blood-pump tube. At least one blood-pressure-measurement element extends to at least an outer surface of the blood-pump tube, such that the distal end of the blood-pressure-measurement element is in direct fluid communication with a bloodstream of the subject outside the blood-pump tube at a location that is proximal to blood-inlet opening(s) defined by the blood-pump tube. Blood is pumped through the blood-pump tube, using the impeller. Pressure of the bloodstream of the subject outside the blood-pump tube is measured by measuring blood pressure at the distal end of the left-ventricular blood-pressure-measurement element. Other applications are also described.

The invention provides a tunnel fan group cooperative control dynamic optimization method and system. According to the method, dynamic data of vehicles in a tunnel are tracked through the Leiyu fusion technology, and a multi-dimensional game parameter set is constructed in combination with edge computing node analysis data and environment monitoring indexes. Based on a dynamic game theory model, fan cooperative response intensity is dynamically adjusted, smoke exhaust efficiency and evacuation channel wind speed constraint are balanced, fan control priority is calculated, differential rotating speed and deflection angle control is driven, and game strategy weight is iteratively optimized after environment feedback data is generated. And realizing rolling time domain control until the vehicle movement and the environment index reach a stable interval. According to the technical scheme provided by the invention, dynamic collaborative optimization of tunnel smoke exhaust and evacuation safety is realized, and the ventilation response efficiency and the evacuation channel safety in an emergency scene are improved.

The invention comprises a pumping apparatus, system and method for increasing the flow of the in a first direction to boost liquid flow and in a reverse second direction to remove blockages and / or self-clearing, with operation having an rotor / impeller that can use a shredder and / or shearing action utilizing blades for processing to pass solids, debris and other things to prevent clogging and / or self-cleaning of the unit.

A portable fan with predictable battery life and a method for predicting battery life thereof. The portable fan includes a fan body, where a circuit board assembly and a storage battery are installed in a shell of the fan body, a microprocessor and a current collector are encapsulated in the circuit board assembly, a load and a display device are provided on the fan body, and the display device includes a function lighting display area and a battery life display area. According to the portable fan, the display device can be used to indicate a working mode that the fan body is currently turned on and the working battery life that the storage battery can provide under the corresponding working mode. An intuitive and standard time unit is used for indication, so that a clear visual perception effect is achieved.

A handheld fan includes an air supply portion and a handheld portion. The handheld portion includes a first outer shell and a battery module arranged in the first outer shell. The battery module includes a battery housing, and a first storage battery and a second storage battery which are arranged in the battery housing. The first storage battery and the second storage battery are connected in series through an electrical connector. The first storage battery and the second storage battery are arranged in a battery housing. The first storage battery and the second storage battery are connected in series or in parallel through an electric connector, which increases the battery capacity of the handheld fan, so that when a user uses the handheld fan, the battery life can be longer, bringing a better user experience.

The invention discloses a tunnel fan energy-saving control method and system, a storage medium and a program product, and relates to the field of general control or regulation systems.The method comprises the steps that historical train flow data are obtained, and the predicted train flow of the next time period is obtained through calculation according to the historical train flow data; collecting real-time gas concentration data and wind speed and direction data; calculating a theoretical gas concentration increment based on an aerodynamic model, the predicted train flow, the real-time gas concentration data and the wind speed and direction data; calculating according to the theoretical gas concentration increment and a pollutant diffusion model to obtain a pollutant diffusion trend; determining a target section according to the pollutant diffusion trend and a preset gas concentration threshold value; according to the position information of the target section and the pollutant diffusion trend, the minimum starting number of tunnel fans and the corresponding minimum rotating speed are obtained through calculation; and generating a fan control instruction according to the minimum number of started fans and the minimum rotating speed. According to the method, the effect of reducing the energy consumption of the tunnel fan while the ventilation effect of the tunnel is maintained is achieved.

An engine for aeromechanical instability abatement includes a sensor configured to capture data from rotating blades of the engine system, a airflow effector device, and an engine controller. The engine controller is configured to control the airflow effector device according to a nominal schedule, detect, based on a signal from the sensor indicating a vibration amplitude of the rotating blades within a frequency band, an incipient instability condition, in response to the incipient instability condition being present, determine a modified control parameter for at least one of the airflow effector device, and control the airflow effector device according to the modified control parameter, deviating from the nominal schedule.

The invention discloses a dredge pump control system and method for river regulation, and relates to the technical field of dredge pump control. The dredge pump control system comprises a sensing module, a centralized control module, an energy-saving execution module and a control parameter recording module; the sensing module is arranged on a multi-element sensor at a key part of a dredge pump for acquiring data, and the data is pre-processed by a microprocessor and then transmitted to the centralized control module at a high speed by a wireless transmission module, so that an accurate basis is provided for system control; according to the invention, by integrating multiple sensors and an advanced control algorithm, the dredging efficiency and accuracy of the dredge pump of the dredger are obviously improved; the sensing module can monitor key parameters of the dredge pump in real time, such as suction pressure, discharge flow, pump shaft temperature and sediment concentration, and it is ensured that the running state of the dredge pump is comprehensively known; and the centralized control module dynamically optimizes a control sequence by using a model predictive control and fuzzy logic control algorithm so as to adapt to variable working conditions.

A device, having: a base having an outer boundary and a plurality of base voids, formed from a first material having a first coefficient of thermal expansion (CTE); beads that line ones of the base voids, formed from a second material having a second CTE that differs from the first CTE, wherein each of the beads has a bead void; and a thermoelectric junction around the outer boundary, or within one or more of the bead voids.

The invention discloses a primary helium fan health state evaluation method based on multi-sensor data fusion. According to the method, a vibration sensor, a temperature sensor, a current sensor and the like are used for comprehensively monitoring the primary helium fan, a deep learning model is used for carrying out feature extraction and fusion on multi-source heterogeneous data, a fault diagnosis knowledge base based on a knowledge graph is constructed, and real-time and accurate evaluation of the health state of the primary helium fan and intelligent diagnosis of a potential fault mode are achieved. Meanwhile, a reinforcement learning algorithm is introduced in the method, the maintenance strategy of the main helium fan is automatically optimized, the equipment reliability is improved to the maximum extent, and the whole life cycle cost is reduced. The method provided by the invention has good adaptability and expandability, can be popularized and applied to nuclear power and other industrial fields, and provides important technical support for improving the intelligent level and operation safety of key equipment.

The invention relates to the technical field of environment perception, and discloses an optimization method and device for a multi-climate self-adaptive environment perception cooling fan system, and the method comprises the steps: collecting multi-point temperature, humidity and air pressure data, and carrying out the filtering processing, and obtaining an environment state vector; executing mathematical modeling of the multi-degree-of-freedom cooling system according to the environment state vector to obtain a linearized state space model; performing temperature gradient estimation and humidity compensation based on the linearized state space model to obtain an adaptive control law; inputting the adaptive control law into a model-free adaptive prediction controller to obtain a rolling optimization control sequence; weight self-adaptive adjustment and heat dissipation efficiency hierarchical prediction control are carried out on the rolling optimization control sequence based on climate conditions, fan rotating speed control and wind direction adjustment execution signals are generated, the problem that a traditional heat dissipation system is not sensitive to space temperature distribution and humidity changes is solved, and the heat dissipation control precision under different humidity conditions is improved.

A compressor case having a composition gradient defining a first coefficient of thermal expansion and a second coefficient of thermal expansion that differs from the first coefficient of thermal expansion; and a thermoelectric junction operationally coupled to the composition gradient, wherein the composition gradient is formed of either of a plurality of dissimilar metals or of plastic with fillings or fibers.

The invention relates to the technical field of air compressors, and discloses a centrifugal air compressor surge suppression method and device.The method comprises the steps that pressure pulsation feature extraction is conducted on a pressure signal of a centrifugal air compressor, and pressure pulsation features are obtained; extracting a flow statistical feature and a frequency band energy feature of the flow signal to obtain a surge early warning feature space; carrying out characteristic decomposition on the bearing vibration signal, and extracting a characteristic frequency to obtain a surge early warning characteristic set; inputting the surge early warning feature set into a three-channel neural network for surge early warning prediction to obtain a surge early warning level; according to the surge early warning grade, control parameters of a guide vane angle controller, an anti-surge valve opening controller and a return valve opening controller are generated, the guide vane angle, the anti-surge valve opening and the return valve opening are synchronously adjusted, and a surge margin value is calculated. According to the method, accurate control over the guide vane angle, the anti-surge valve opening and the return valve opening is achieved, the surge margin value is calculated, and the control accuracy of the guide vane angle, the anti-surge valve opening and the return valve opening is improved. And real-time optimization and adjustment of the control strategy are ensured.

The invention discloses an electrical automatic control method and system. The method comprises the following steps: acquiring temperature data and load current data in an electrical cabinet; denoising the temperature data by adopting a wavelet transform algorithm; filtering the load current data by adopting a Kalman filtering algorithm; establishing a temperature rise compensation model based on a long short-term memory (LSTM) neural network, wherein the output of the temperature rise compensation model is a temperature rise predicted value in the electrical cabinet; setting a multi-objective function of temperature rise control in the electrical cabinet; solving by adopting a multi-target ant colony algorithm to obtain an optimal control strategy of the cooling fan; and starting, stopping and speed adjustment of the cooling fan are controlled according to the optimal control strategy of the cooling fan. Aiming at frequent starting and stopping of a cooling fan of an electrical cabinet in the prior art, a temperature rise compensation model is established based on a long short-term memory (LSTM) neural network, a multi-objective optimization function including temperature rise deviation and starting and stopping times of the cooling fan is set, an improved multi-objective ant colony algorithm is adopted for solving, and starting and stopping of the cooling fan are reduced.

The invention relates to the technical field of water pump energy-saving control, and discloses a water pump energy-saving control system based on an intelligent data model, which comprises a data sensing module, a modal analysis module and the like. The data sensing module collects flow, lift and power data and is provided with a dynamic monitoring window; the modal analysis module divides operation modes and generates an energy efficiency characteristic matrix; the rule construction module extracts a core energy efficiency factor and constructs an energy-saving regulation and control strategy; the priority scheduling module analyzes the control priority matrix and evaluates an energy efficiency deviation index; a mapping calibration module fits the optimal energy efficiency reference curve to generate an energy efficiency deviation gradient map; the control execution module generates a regulation and control instruction set according to the map. The system realizes fine management and energy efficiency optimization of water pump operation through multi-mode energy efficiency analysis and dynamic regulation, solves the problems of poor adaptability of traditional control working conditions, low energy-saving efficiency and the like, is suitable for water pump energy-saving control in the fields of industry, municipal administration and the like, and is accurate and efficient.

The invention relates to the technical field of high-pressure dredge pump simulation calculation, and discloses a construction and application method of a high-pressure efficient dredge pump simulation calculation model. A model is established based on dynamic fluid-solid coupling parameters (mud pulsating pressure, impeller dynamic stress and the like), soil multiphase rheological parameters (particle phase volume fraction, viscous resistance coefficient and the like) and pipeline transient resistance parameters (transient pressure drop gradient and the like), and factors such as rotating speed-power matching and the like are incorporated to calculate an operation envelope and a critical failure threshold. A multi-physics field coupling analysis system comprising parameter configuration, dynamic simulation (integrating a flow field and a structural vibration solver) and a safety evaluation module is developed, after actually measured data and self-defined parameters are input, dynamic flow velocity distribution, pressure pulsation characteristics and the like are obtained through fluid-solid bidirectional coupling calculation, and an optimal configuration scheme is generated. The system supports database management, multi-rheological model matching and three-dimensional visualization, the simulation precision and the engineering efficiency are improved, and support is provided for performance optimization and safety evaluation of the high-pressure working condition.

Apparatus and methods are described including a ventricular assist device that includes a tube configured to traverse a subject's aortic valve. The tube defines a generally-cylindrical central portion, and a proximal conical portion that narrows from a proximal end of the central portion to a proximal end of the tube. The tube defines one or more blood inlet openings in a vicinity of a distal end of the tube, and one or more blood outlet openings that extend axially from the central portion of the tube at least partially into the proximal conical portion of the tube. A blood pump pumps blood into the tube from the subject's left ventricle through the one or more blood inlet openings, and from the tube into the aorta through the one or more blood outlet openings. Other applications are also described.

The invention relates to the technical field of tunnel ventilation control, in particular to a tunnel fan energy-saving control system which comprises a multi-parameter sensing module, an intelligent evaluation module, a dynamic prediction module, an energy-saving optimization module, a frequency conversion control module and an alarm emergency module. Wherein the multi-parameter sensing module is used for monitoring various environmental parameters in a tunnel in real time; the intelligent evaluation module is used for calculating the current ventilation demand; the dynamic prediction module is used for predicting the ventilation demand in a period of time in the future; the energy-saving optimization module is used for generating an optimal energy-saving control instruction; the frequency conversion control module is used for adjusting the operation frequency and speed of the fan according to the instruction; and the alarm emergency module is used for sending an alarm signal when an abnormal condition is detected. According to the invention, environmental parameters in the tunnel are monitored and intelligently managed in real time, a fan operation strategy is dynamically optimized, and the system has emergency response capability, so that the ventilation efficiency is remarkably improved, the energy consumption is reduced, and the construction safety is guaranteed.

The invention relates to the technical field of fault data processing, and provides a reciprocating natural gas compressor fault diagnosis system which comprises a data acquisition module, a data preprocessing module, a feature extraction module, a knowledge graph construction module, a fault analysis module, a maintenance decision module, an early warning module and a man-machine interaction module. The data acquisition module is used for acquiring operation parameters and historical data of the compressor in real time and transmitting the operation parameters and the historical data to the data preprocessing module; the preprocessing module is used for filtering, denoising and normalizing the data; the feature extraction module extracts fault features; the knowledge graph construction module establishes a relation graph of compressor faults, symptoms, reasons and maintenance measures according to historical data. The fault analysis module performs fault diagnosis, prediction and severity evaluation through a deep learning model; the maintenance decision module matches maintenance measures according to the analysis result and generates a plan; the early warning module generates early warning information when the condition is triggered; and the man-machine interaction module supports graph editing and maintenance.

The invention relates to the technical field of water pump control, and discloses a water pump energy-saving optimization control method and system. The method comprises the steps of obtaining water pump outlet pressure, pipe network tail end flow and water pump motor power monitoring signals; calculating a flow-pressure propagation time compensation coefficient and a pressure fluctuation compensation value to eliminate the influence of signal propagation lag and amplitude fluctuation; extracting an operation condition feature vector by combining the motor power frequency domain energy distribution feature and the pressure fluctuation compensation value, and matching the operation condition feature vector with an energy-saving parameter mapping rule base to generate an energy-saving parameter combination; correcting a water pump efficiency reference curve in real time based on the combination, outputting an efficiency attenuation factor, and generating a control deviation value in combination with a target water supply demand; according to the control deviation value, the water pump rotating speed adjusting quantity and a start-stop priority sequence are calculated, and the priority sequence is optimized in combination with historical operation data to generate a machine number control instruction; and finally synthesizing a control signal to drive an execution mechanism.