2552 results about "Flow transducer" patented technology

The invention discloses an underground pipe network leakage detection method fusing big data analysis and machine learning. The underground pipe network leakage detection method comprises the step of deploying an acoustic sensor, a pressure sensor and a flow sensor at preset positions of an underground pipe network. Performing noise reduction and standardization processing on the acquired operation data, extracting frequency domain, energy and time sequence characteristics of acoustic signals to generate acoustic characteristic vectors, and calculating initial space coordinates of a leakage position based on time difference of arrival of the acoustic signals and sensor arrangement; and generating a pressure feature vector, and analyzing the spatial-temporal correlation of the flow data to generate a flow feature vector. And fusing acoustic, pressure and flow feature vectors to form a multi-dimensional feature vector, inputting the multi-dimensional feature vector and the initial space coordinates into a pre-trained cascade deep learning model, and outputting the leakage probability and the leakage position of underground pipe network leakage. And when the leakage probability exceeds a preset threshold value, triggering an alarm mechanism. According to the invention, efficient detection and accurate positioning of underground pipe network leakage are realized, and high real-time performance and positioning accuracy are achieved.

A system for AI-supported leak detection and localization in water distribution infrastructures, consisting of: a large number of distributed sensor nodes mounted along a water pipe, each sensor node comprising the following: a pressure sensor configured to measure local hydraulic pressure fluctuations within the pipeline with a resolution of at least 0.01 bar; a flow sensor configured to measure the volume flow within the pipeline with an accuracy of at least ±0.5% of the measured value; an acoustic sensor configured to detect vibration signatures caused by leaks in a frequency range between 50 Hz and 20 kHz; an embedded microcontroller with integrated analog-to-digital conversion circuitry for digitizing sensor outputs; a wireless communication module configured to transmit time-synchronized sensor data to a cloud-based processing platform; and a local energy subsystem with a rechargeable battery and an optional circuit for generating photovoltaic energy; the cloud-based processing platform includes an artificial intelligence engine that comprises the following: a data acquisition module configured to receive and decode the transmitted sensor data and to perform time alignment; a supervised learning module that is trained on historical data of flagged leaks and non-leaks to classify incoming sensor patterns; an unsupervised learning module configured to detect anomalies by modeling normal operating baselines of the pipeline; and a topology-aware localization module configured to determine leak coordinates using the topology of the pipeline network, modeling the propagation of hydraulic waves, and estimating the arrival time difference from multi-node acoustic detections; and wherein the system is configured to provide real-time leak alerts and georeferenced visualization via a remote monitoring interface.

The invention relates to the technical field of fluid control, and discloses a fluid control intelligent adjusting method and system for a micro-fluidic chip, and the method comprises the steps: carrying out the data collection of a self-adaptive fluid controller, a micro-fluidic channel pressure sensor array, a temperature sensor array and a flow sensor array based on the micro-fluidic chip; obtaining real-time fluid parameter monitoring data; performing feature extraction to obtain a multi-dimensional fluid feature matrix; establishing a three-dimensional fluid model of the microfluidic channel, and solving to obtain flow field distribution data; inputting the flow field distribution data into a Kriging agent model, establishing a mapping model of channel width, depth, length and fluid control performance, and calculating to obtain an optimal geometric parameter combination; and performing multi-level calculation on the fluid pressure uniformity, the temperature distribution uniformity and the flow stability to obtain a hierarchical fluid parameter control strategy. According to the invention, the problem of multi-physics field coupling is effectively solved, the calculation complexity of the control system is reduced, and the execution efficiency of the control strategy is improved.

According to the technical scheme, the automatic feeding system comprises a feeding bin, a pneumatic conveying pipeline, an air source device and a discharging bin, and further comprises a distributed sensor set used for monitoring the flux, pressure, temperature and vibration frequency spectrum of a preset point of the pneumatic conveying pipeline; comprising a flow sensor, a pressure sensor, a temperature sensor and a vibration acceleration sensor which are arranged at a plurality of preset points of a conveying pipeline, and a digital twin model building module which is used for building three-dimensional modeling of the pneumatic conveying pipeline and mapping state parameters of all positions of a pipeline distribution sensor group in real time; the blockage analysis module is used for marking a blockage position to three-dimensional modeling in real time based on data acquired by the distributed sensor group, extracting a multi-parameter change curve in a time period T before blockage, constructing a blockage feature library, and generating a blockage risk level through a weight change rate difference value of a feeding bin and a discharging bin; and the graded dredging execution module is configured with a differentiated dredging strategy, and the differentiated dredging strategy is started according to the blockage risk grade.

The invention provides a reflux valve supporting remote monitoring of the Internet of Things. The reflux valve comprises a valve body provided with a fluid inlet, a fluid outlet and an internal flow channel, and an electric actuating mechanism is configured for controlling the opening degree of the valve; a microprocessor and a memory are arranged in the control unit, and the control unit processes data of the sensor, calculates a fluid density correction coefficient and a pressure correction coefficient, corrects original flow data collected by the flow sensor and generates an actual flow value; the control unit further identifies the current fluid type and adaptively adjusts the countercurrent detection threshold value of the upstream and downstream pressure difference based on the relationship between the flow and the pressure data; when the pressure difference exceeds the adjusted threshold value, the electric executing mechanism executes emergency turn-off operation; meanwhile, a wireless communication unit is arranged, real-time data transmission is supported, control parameters issued by external monitoring equipment are received, and intelligent control and remote management are achieved; the intelligence and the operation efficiency of the reflux valve can be effectively improved, and the reflux valve is suitable for safety protection and operation optimization under the complex fluid working condition.

The invention relates to the technical field of pipeline safety monitoring, in particular to a pipeline water leakage detection platform and method.The pipeline water leakage detection platform comprises a sensor layer, an edge calculation module and a pipeline water leakage detection system of a cloud analysis platform, the sensor layer comprises a pressure sensor, a sonic sensor, a flow sensor and an environment sensor, and a distributed installation structure is adopted; the edge calculation module is used for collecting pipeline state data in real time, the edge calculation module comprises an equipment box, a built-in power supply module, a data processor (ARM Cortex-M series + FPGA acceleration unit) and a communication module (NB-IoT or LoRa), and sensor data is subjected to localization preprocessing and then uploaded to a cloud analysis platform. A cascade neural network model (CNN + LSTM) and a leakage positioning engine are deployed on the cloud platform, leakage judgment and accurate positioning are achieved through multi-source data fusion and a deep learning algorithm, and efficient and accurate pipeline leakage detection and positioning are achieved through data collection of a sensor layer, preprocessing of an edge calculation module and intelligent analysis of the cloud platform.

The invention provides an intelligent optimization control method and device for a mold cooling system, and relates to the field of intelligent optimization control devices. The intelligent optimization control device based on the mold cooling system comprises a hardware part, a software control system, a model calculation module and an optimization algorithm, the hardware part comprises a temperature sensor, a flow sensor, a controller, a cooling liquid pump, a cooling loop and an actuator, the software control system is used for receiving and processing real-time data from the sensors, and the model calculation module is used for calculating the model calculation module according to the real-time data. The model calculation module is used for analyzing temperature and flow changes in the cooling process. High-precision and low-delay temperature and flow sensors are utilized, and sensor data are corrected in real time through an error correction module, so that the measurement precision and the response speed are greatly improved, and the problem that the precision and the response speed of the sensors are limited is solved.

The invention discloses an intelligent double-liquid grouting system and a grouting method. The system comprises a liquid A stirring barrel, a liquid B stirring barrel, a double-liquid grouting pump, a slurry mixer, a PID (Proportion Integration Differentiation) regulating valve, a control system, a flow meter, a pressure meter, a flow sensor and a pressure sensor. The liquid A and the liquid B are respectively prepared and then sent out by a double-liquid grouting pump, enter a slurry mixer to be compounded and mixed, and are injected into a target stratum through a directional grouting pipe. The system collects and analyzes flow and pressure data through a control system, and closed-loop regulation and control of grouting flow and proportion are achieved through PID regulation logic. The PID regulating valve has the functions of over-voltage current limiting and overflow prevention at the same time. The method is suitable for reinforcement and leakage treatment under a complex stratum in geotechnical engineering, and has the advantages of being high in matching precision, high in mixing efficiency, good in safety, high in adaptability and the like.

The invention discloses an MEMS thermal flow sensor based on a glass film structure and a preparation method of the MEMS thermal flow sensor. The sensor comprises a glass inner silicon substrate, a back cavity, a silicon through column, a heating resistor, a temperature measurement resistor, an environment temperature measurement resistor, a chromium adhesion layer, a PAD port and a silicon nitride protection layer. Wherein the heating resistor is located in the center, and the temperature measuring resistors are symmetrically distributed on two sides of the heating resistor; the back cavity is located on the back of the glass inner silicon substrate, and the heating resistor, the temperature measuring resistor and the environment temperature measuring resistor lead a PAD port to the back of the glass inner silicon substrate through a silicon through column. A thermal temperature difference principle is adopted, and upstream and downstream temperature measuring resistors form a Wheatstone bridge. When the flow velocity is not zero, the Wheatstone bridge outputs, and the larger the flow velocity is, the larger the output is. Based on the glass and silicon composite substrate, the suspension film structure is formed through the silicon etching process, transmission of heat to the substrate is greatly reduced, and the heat utilization rate is increased.

The invention discloses a detection and positioning method, device and system for coping with multi-pipeline leakage of a water supply pipe network. The method comprises the following steps: constructing a pipe network model, deploying sensors, creating a complex hydraulic pipe network model, screening an optimal monitoring point, and generating a pressure and flow sensor arrangement scheme; outputting a pipe network topological graph and a sensor deployment result; performing scene approximation, constructing an optimization model to enable the hydraulic data under the original normal working condition to fit the hydraulic data under the leakage scene, finally introducing hydraulic data correction, and outputting possible leakage nodes and corresponding hydraulic parameters; reducing a leakage pipe network range, and continuously updating and screening a pipeline range possibly having leakage based on preliminary inference of leakage nodes; after iterative optimization, outputting a local pipe network area where leakage is most likely to occur; and establishing an observer, positioning a specific leakage point, dynamically estimating leakage parameters in a suspected leakage pipe section, comprehensively analyzing a filtering result, and outputting a specific leakage pipe section and a leakage position coordinate.

The invention relates to the technical field of urban drainage management, and discloses a municipal sewage pipe network dynamic drainage system and a control method, and the system comprises a sewage pipe network, a central controller, a pump station, a valve and an edge control terminal; the control method comprises the following steps that real-time data are obtained from a plurality of sensors arranged in a sewage pipe network, the plurality of sensors comprise a flow sensor, a water level sensor, a pressure sensor and a rainfall sensor, and the obtained real-time data comprise the sewage flow, the water level, the pipeline pressure and the external rainfall inflow in the pipe network; based on the collected real-time data, dynamic hydraulic modeling is carried out on the sewage pipe network through a central controller, a mathematical model for describing the hydraulic state change of the pipe network is established, and the mathematical model comprises spatio-temporal evolution of flow, water level and pressure. According to the invention, by constructing a drainage scheduling mechanism based on real-time data modeling and edge control linkage, dynamic adjustment and stable control of the running state of the sewage pipe network are realized.

The invention provides an automatic judgment device for a safety valve and a verification method, and relates to the technical field of safety valve verification. The automatic judgment device comprises clamping seats which are provided with an air inlet valve and a pressure release valve, are arranged at intervals along a first horizontal direction and are used for fixing a safety valve to be tested, the translation assembly comprises a connecting piece, and the end part of the connecting piece is provided with a sealing plate matched with the safety valve to be tested for sealing; the reversing valve is arranged to be communicated with an outlet of the safety valve to be tested and alternatively communicated with the flow sensor or the bubble meter, and the adjusting piece of the adjusting assembly is arranged to drive the sealing plate to adjust the preset angle until the sealing plate is sealed with the safety valve to be tested, and the limiting piece is controlled to move to abut against the sealing plate. The first driving mechanism is arranged to drive the sealing plate to move to selectively seal the safety valve to be tested so as to verify the setting pressure or the sealing performance of the safety valve to be tested. According to the automatic judgment device, the angle of the sealing plate can be adjusted, so that the sealing performance is improved, and the stability of the sealing plate is guaranteed.

The invention relates to the technical field of water supply pipeline detection, and discloses an underground water supply pipeline health grade evaluation and risk prediction method, which comprises the following steps: sensor arrangement: arranging a flow sensor, a pressure sensor and a sonic sensor at key positions of an underground water supply pipeline; and data acquisition: acquiring signals of the sensor in real time through a data acquisition module, wherein the signals comprise flow, pressure and sound wave signals. Preprocessing the data: carrying out preprocessing such as denoising and normalization on the collected signals; and multi-source data fusion: inputting flow, pressure and sound wave signals into a deep neural network model, and performing feature extraction and fusion analysis. And health level assessment: assessing the health level of the pipeline based on the output result of the deep neural network model. And risk prediction: predicting abnormal working conditions possibly occurring in the future and risk levels of the abnormal working conditions by analyzing the current pipeline state and historical data. And abnormal positioning: accurately positioning an abnormal position in combination with the propagation time of the sensor signal and a positioning model of the deep neural network.

The invention discloses a cattle and sheep farm feeding safety detection method and system based on real-time monitoring, and the method comprises the steps: installing a flow velocity sensor, a flow sensor and a pressure sensor at key positions of a feed conveying pipeline, obtaining the time sequence data of feed conveying in the pipeline, carrying out the preprocessing and feature extraction of the data, and carrying out the feature extraction of the data; and constructing a feed conveying state prediction model, performing online prediction on the conveying state in real time, and triggering early warning when the deviation between a predicted value and an actual value exceeds a threshold value. And specific abnormal types such as pipeline blockage, leakage or equipment failure can be judged by analyzing the change trend of the abnormal data. And abnormal information is pushed to operation and maintenance personnel through the mobile App to assist in quickly positioning and processing problems. According to the method, the model can be continuously optimized by using new abnormal data, the identification capability of different types of anomalies is improved, a decision basis is provided for pipeline maintenance, and the operation reliability and safety of the feed conveying pipeline are effectively improved.

A fluid control device includes a fluid pipe section including a fluid inlet and outlet connectable in series to a fluid pipe. A fluid valve is coupled in series within the fluid pipe section separating a fluid inlet and outlet side and controlling a fluid flow. An electric motor is mechanically connected to the fluid valve. A temperature sensor is connected to the fluid pipe section monitoring a temperature of the fluid flow. A pressure sensor is connected to the fluid pipe section monitoring a pressure of the fluid flow. A flow rate sensor is connected to the fluid pipe section monitoring a flow rate of the fluid flow. A control device processor is electrically connected to the electric motor and electrically connected to the sensors. A communication device coupled to the control device processor is for wirelessly connecting to a remotely disposed fluid monitoring and control system.

The invention discloses a gas flow sensor combination device based on temperature and pressure compensation and a flow correction method. The gas flow sensor combination device comprises a flow sensor combination, a pipeline mechanism, a data processing terminal, an environment pressure sensor and an environment temperature sensor. The pipeline mechanism comprises an air inlet pipeline and an air outlet pipeline, and a plurality of branches arranged in parallel are connected between the two pipelines; the flow sensor combination comprises laminar flow sensors arranged on the branches respectively and electromagnetic valves used for controlling the branches to be opened and closed, and the laminar flow sensors comprise laminar flow elements arranged on the corresponding branches. The laminar flow element is provided with a pressure difference sensor device used for measuring the pressure difference of an inlet and an outlet, a tail pressure sensor used for measuring the pressure and the temperature of fluid flowing through the laminar flow element, and a tail temperature sensor. The laminar flow sensor is used for measuring parameters such as temperature, pressure and pressure difference of fluid in real time, the parameters are calculated and corrected to obtain the flow in a standard state, and measured flow data are more scientific and accurate through temperature and pressure compensation.

A flow rate assembly can include a housing having a measurement channel extending along the housing axis and through a portion of the housing between the first and second ends of the housing, an outer cup portion positioned at least partly within the housing, and a transducer positioned within the outer cup portion and sealed from fluid flow past the outer cup portion, the transducer having a width perpendicular to the housing axis and greater than the width of the measurement channel, the transducer configured to generate an ultrasonic signal and to direct the ultrasonic signal through the measurement channel.

The invention provides a flow control method and a mass flow controller, and the method comprises the steps: obtaining the current environment temperature of the mass flow controller, and determining a temperature sensitivity coefficient according to the current environment temperature; a control parameter corresponding to the current environment temperature is obtained according to the temperature sensitivity coefficient and a preset control parameter, and the opening degree of a control valve is adjusted according to the fluid flow value detected by a flow sensor based on the control parameter, so that the fluid flow value approaches the flow set value. The temperature sensitivity coefficient is determined according to the temperature of the current environment where the mass flow controller is located, so that the control parameters in the process of feeding back and adjusting the fluid flow according to the flow set value are changed in real time, and the control valve adjusting quantity change caused by temperature change can be compensated; therefore, the problems of overshoot and delay caused by too large or too small adjusting amount are avoided, the adaptive capacity of the mass flow controller to different environment temperatures is improved, and the precision and stability of the mass flow controller for controlling fluid flow are guaranteed.

The invention provides a fluid pipe network intelligent management and control system and method based on multi-order spatial-temporal feature fusion, and the system comprises an embedded Internet of Things sensing layer which comprises an electric valve control mainboard and / or a pneumatic valve control mainboard embedded with a remote information transmission module, the flow sensor, the heat sensor and the water invasion sensor are connected with the control mainboard; the multi-order spatio-temporal feature processing layer comprises a spatio-temporal feature extraction module configured to perform sliding window slicing processing on time sequence data of pressure, flow velocity and temperature parameters; and the feature fusion module is configured to perform feature alignment on the vibration atlas, the voiceprint signals and the infrared thermal imaging data through a multi-head attention mechanism. According to the fluid pipe network intelligent management and control system and method based on multi-order spatio-temporal feature fusion, the management and control efficiency and safety of the fluid pipe network are remarkably improved through embedded multi-source sensing, multi-order spatio-temporal feature fusion and an intelligent decision framework.

The invention discloses an intelligent flow sensor test analysis method and system, and relates to the technical field of sensor testing, and the method comprises the steps: applying a stepped flow excitation signal to a tested sensor through a standard flow generation device, and synchronously collecting the dynamic response data of the sensor; extracting a feature parameter set from the response data; performing similarity matching calculation to obtain a matching degree evaluation value of each error type; quantifying the induction relation strength among different error types; generating a composite weight coefficient in combination with the matching degree evaluation value and the evoked relationship strength; and making a multi-stage compensation strategy based on the composite weight coefficient. The method has the advantages that by combining error correlation network modeling driven by historical data, induction mechanisms and interaction relations of different error types can be analyzed, the pertinence of a compensation strategy is greatly improved, a multi-stage error suppression mechanism is formed, stable control over comprehensive errors is achieved under complex working conditions, and meanwhile the system maintenance requirement is lowered.

A mass flow controller and methods of providing for mass flow control are provided. A mass flow controller includes a control valve configured to control flow of a fluid in a flow path. A first flow sensor detects a first set of fluid flow parameters, and a second flow sensor detects a second set of fluid flow parameters. The first and second flow sensors are of distinct flow measurement types. The mass flow controller further includes a controller configured to determine a set of first mass flow rates based on the first set of fluid flow parameters detected by the first flow sensor and calibrate the second flow sensor based on the determined set of mass flow rates of the first mass flow sensor and the second set of fluid flow parameters.

The invention relates to the technical field of high-pressure hydrogenation reactor control, and discloses a multi-stage pressure optimization control method and system for a high-pressure hydrogenation reactor, and the method comprises the steps: obtaining temperature, pressure and flow monitoring data, generating original parameter data, and deploying a distributed control network; collecting and analyzing multi-stage pressure sensor data, and generating a pressure change influence factor; sensing a valve state, and generating a valve change influence factor; collecting data of a flow sensor, and dividing a control area after calibration; and calculating a pressure optimization index based on the control area data, integrating multiple factors to generate comprehensive control data, and further generating a pressure control signal and outputting an instruction. The system comprises a control network deployment module, a pressure analysis module, a valve analysis module, a control area analysis module and a pressure control signal generation module. Through multi-dimensional data fusion, a distributed control network and dynamic optimization calculation, the accuracy and stability of pressure control of the high-pressure hydrogenation reactor are improved, and the method is suitable for efficient and safe production in the field of petrochemical engineering.

The invention belongs to the technical field of flow metering, and discloses a flow metering method, system and device based on the Internet of Things, and the method comprises the steps of monitoring data collection and transmission, monitoring data preprocessing, initial volume flow analysis, mass flow analysis and flow monitoring data display. The system comprises a monitoring data collection and transmission module, a monitoring data preprocessing module, an initial volume flow analysis module, a mass flow analysis module and a flow monitoring data display module. Multi-dimensional data acquisition is carried out by comprehensively using ultrasonic waves and a differential pressure type flow sensor, working condition identification is carried out by combining data acquisition nodes of the internet of things and a convolutional neural network, the volume flow is calculated according to a fusion weight matrix, and the mass flow is converted according to a mass flow calculation model. The mass flow rate of the natural gas can be measured at high precision under different working conditions, and the problem that the measurement precision is limited in a full working condition range in a traditional mode is solved.

The invention discloses an intelligent gas self-closing valve, and belongs to the technical field of gas valves, the intelligent gas self-closing valve comprises a valve body, a pressure sensor, a flow sensor and a sound-light alarm module are connected in the valve body, and the bottom of the valve body is connected with a shell; when micro leakage occurs and flow data sensed by the flow sensor are analyzed and processed by the circuit module, the circuit module gives an electrification instruction to control the electromagnet to work, the electromagnet generates magnetic force after being electrified to adsorb the iron sheet below the stand column, and the iron sheet drives the stand column to move downwards; the stand column drives the cut-off rod to move rightwards through the rocker arm, the cut-off head is attached to the air inlet, and air inlet is cut off, so that the self-closing valve is not prone to danger when micro leakage occurs, and when data collected by the flow sensor and the pressure sensor are abnormal, a control unit in the circuit module sends signals and feeds the signals back to the outside through the sound-light alarm module. Therefore, a user can know the abnormal condition of the self-closing valve in time.

The invention belongs to the technical field of industrial cooling, and particularly relates to a microflow cold liquid temperature control cooling system, method and device, and the system comprises a main control board which is internally provided with a main control module; the micro liquid pump is in signal connection with the main control module; and the cold liquid pipelines are arranged at two ends of the micro liquid pump. According to the system, method and device, liquid is driven to flow through the micro pump, at the moment, the liquid can absorb and dissipate heat more efficiently due to the characteristics of high heat capacity and heat conductivity, heat can be conducted and dissipated from a heating source quickly and effectively through effective cooperation of the metal heat conduction block, the heat conduction copper pipe, the heat dissipation fins and other components, and the heat dissipation efficiency is improved. Intelligent control over the system is achieved through the main control module, the flow speed of cooling liquid can be automatically adjusted according to actual temperature changes so that the optimal heat dissipation effect can be achieved, and the system state can be monitored in real time and stable operation can be ensured by adding assemblies such as a miniature temperature sensor, a pressure sensor and a flow sensor.

The invention relates to an automatic pole piece waste collecting method for an automatic lithium battery winding machine in the technical field of information, which comprises the following steps of: arranging a vacuum suction nozzle array below a cutting device, connecting the vacuum suction nozzle array to a central vacuum pump, setting proper vacuum degree and suction time according to material characteristics and sizes of cut waste, and collecting the cut waste by a vacuum pump; the cut waste is automatically sucked and conveyed by controlling starting and stopping of the vacuum pump and the suction force. A flow sensor is installed in a vacuum suction pipeline, the speed and pressure of sucked airflow are monitored in real time, whether the suction process is normal or not is judged according to a preset flow curve, and if the abnormal condition of insufficient suction or airflow turbulence occurs, parameters of a vacuum pump are adjusted in time; the method comprises the steps of collecting and analyzing images of sucked waste materials, recognizing and classifying material types and shape features of the waste materials by adopting a convolutional neural network algorithm, and estimating the quantity and weight of the waste materials in combination with suction time and vacuum degree data so as to provide a basis for subsequent waste material recovery and treatment.

The fuel oil flow sensor comprises a mechanical unit and an electrical unit, the mechanical unit comprises a driving assembly, a rectifying assembly and a rotating assembly which are sequentially arranged on a central shaft, the rotating assembly comprises an impeller, a volute spiral spring and a drum wheel which are sequentially arranged on the central shaft, and the impeller is connected with the drum wheel through the volute spiral spring. The impeller is connected with the central shaft through a bearing, magnets are respectively mounted on the impeller and the drum wheel, the electrical unit comprises an impeller sensing assembly and a drum wheel sensing assembly which respectively correspond to the magnets on the impeller and the magnets on the drum wheel, and an auxiliary turbine is arranged on one side, close to the medium outlet, of the drum wheel on the central shaft. The auxiliary turbine is arranged on one side, close to the medium outlet, of the drum wheel on the central shaft, and the rotating speed of the sensor is adjusted by controlling the rotating resistance of the auxiliary turbine in the medium, so that the rotating speed of the sensor is controlled within a certain range; the problem that the output signal stability, the measurement accuracy of the sensor and the service life of the bearing are affected by too fast rotating speed of the sensor is solved.

The invention discloses a flexible heat supply network heat supply method and system considering regional load balance, and relates to the field of energy system optimization scheduling, and the method comprises the steps: uniformly deploying flow sensors in a heat supply network, installing an intelligent heat meter on a heat supply pipeline of each user, and directly measuring the heat actually used by the user; collecting heat consumption demand data of each user in real time; historical load data and data collected in real time are utilized, weather forecast and a time period are combined, the load capacity of each area is calculated by setting an area load fluctuation trend algorithm, and the heat demand of each area user is predicted; and a heat supply pipe network regulation and control algorithm is set based on the heat requirements of the users in all the areas and the load capacity of the load data, and heat supply balance between the different areas is adjusted. The problem of excessive or insufficient heat supply of some areas is avoided, reasonable distribution of heat supply requirements of all the areas is ensured, and the stability and reliability of overall heat supply are improved.

The invention discloses a heat pipe heat exchanger fault real-time monitoring system and method based on multi-source data fusion, and the system comprises a multi-source data collection module which is used for collecting temperature data, pressure data, flow data, vibration data and image data in the operation process of a heat pipe heat exchanger; the invention relates to the technical field of heat pipe heat exchanger fault monitoring. According to the heat pipe heat exchanger fault real-time monitoring system and method based on multi-source data fusion, multi-source data such as temperature, pressure, flow, vibration and images are creatively fused, and compared with traditional single sensor monitoring, operation state information of the heat pipe heat exchanger can be captured in an all-around mode. Various sensors are arranged at key positions, such as temperature sensors at the inlet, the outlet, the pipe wall and the like, so that the internal temperature distribution of the heat exchanger can be comprehensively mastered; the pressure sensor and the flow sensor can reflect the flowing state of fluid; visual basis is provided for diagnosis of mechanical faults and external states by vibration and image data.

The invention provides an ultrasonic blood flow sensor based on temperature compensation, and relates to the field of medical instruments. The ultrasonic blood flow sensor based on temperature compensation comprises an ultrasonic probe assembly, a temperature sensor and a detection circuit, and the ultrasonic probe assembly is used for emitting ultrasonic waves to an extracorporeal circulation pipeline and receiving the ultrasonic waves penetrating through the extracorporeal circulation pipeline. According to the ultrasonic blood flow sensor based on temperature compensation, the problem that the measurement precision is reduced due to blood temperature changes can be effectively solved, and the ultrasonic blood flow sensor based on temperature compensation can be applied to application scenes such as extracorporeal circulation operations, such as the ECMOs process. Accurate blood flow monitoring is crucial to adjustment of ECMO system parameters, optimization of membrane lung oxygenation effect and guarantee of patient treatment effect. The sensor can provide high-precision and reliable blood flow data and help a doctor to more accurately evaluate the extracorporeal circulation state of a patient, so that a more accurate medical decision is made, and the treatment success rate is increased.