2467results about "Testing/calibration for volume flow" patented technology

The invention provides a flowmeter online calibration system and method, and the method comprises the steps: firstly collecting a real-time operation data set of a target flowmeter in a pipeline operation environment, and covering dynamic monitoring data and environment state data; preprocessing the data set to obtain an operation data sequence capable of describing the change trend of the operation state of the flowmeter at multiple continuous time nodes, inputting the operation data sequence into a pre-trained calibration parameter generation model, and generating a calibration parameter set comprising a flow correction coefficient, an environment compensation factor and an error adjustment threshold value; and adjusting and measuring output data in real time based on the calibration parameter set, generating a calibrated flow measurement result, comparing the calibrated flow measurement result with a standard flow reference value for verification, and if the deviation is greater than an error adjustment threshold value, regenerating the calibration parameter set and performing iterative adjustment until the deviation is within a threshold value range, thereby realizing high-precision and self-adaptive online calibration of the flowmeter.

The invention relates to the technical field of flow measurement calibration, in particular to a flow instrument intelligent calibration system based on multi-sensor fusion, which comprises a data acquisition unit, a deep coupling compensation unit and a closed loop verification unit, a data acquisition unit obtains a differential pressure value, an environment temperature, a pipeline pressure, a vibration frequency spectrum and sensor accumulated working time, a depth coupling compensation unit constructs an aging prediction model based on a Weibull distribution life model, and a temperature-pressure coupling equation and a vibration compensation mechanism are combined to obtain an aging real-time value and a time sequence deviation. Multi-parameter coupling characteristics are extracted through a neural network, an environment disturbance compensation coefficient matrix is constructed, a joint compensation amount is generated through dynamic weight distribution, a closed-loop verification unit optimizes model parameters, the problems that multi-source disturbance coupling analysis is insufficient and calibration precision is low in the prior art are solved, accurate calibration of a flow instrument under complex working conditions is achieved, and the calibration precision is improved. The metering stability is improved.

The invention relates to the technical field of electromagnetic flowmeter calibration, and discloses an electromagnetic flowmeter online calibration system and an acquisition and storage device. The system comprises a signal acquisition module, a data processing module, an error analysis module, a parameter calibration module and a real-time monitoring module. The signal acquisition module captures an original signal waveform through a multi-channel sensor array and acquires flow signal data to generate a state set; the data processing module uses a wavelet packet transformation and adaptive filtering combined algorithm to reduce noise and extract features; the error analysis module constructs a dynamic time warping error matching model to obtain an error correction coefficient; the parameter calibration module adopts a fuzzy control and proportional integral differential composite strategy, and adjusts excitation current and electrode sensitivity according to an error correction coefficient; the real-time monitoring module establishes a rolling optimization model with time-varying constraints, and generates a calibration instruction in combination with signal stability and power consumption limitation. According to the system, online accurate calibration of the electromagnetic flowmeter is realized, and the measurement accuracy and the equipment operation stability are improved.

The invention discloses a zero point correction method for an electromagnetic flowmeter based on pressure compensation, which relates to the technical field of industrial flow measurement, and is characterized in that abnormal data is corrected through multi-parameter data acquisition and preprocessing by using Kalman filtering denoising and an isolated forest algorithm, so that the accuracy of the data is ensured; a multi-parameter compensation model is further constructed, an LSTM network is combined with an attention mechanism to dynamically distribute weights, and the weights are lightly deployed to an embedded microcontroller through a TensorFlow Lite framework, so that rapid reasoning and real-time correction are realized; in the operation process, an embedded system reads preprocessed data in real time, a model is input to obtain a zero drift compensation value, compensation is carried out by comprehensively considering factors such as fluid density, pipeline vibration and electromagnetic interference, and a self-adaptive weight adjustment mechanism is introduced to optimize compensation precision; the measuring precision and the long-term operation reliability of the electromagnetic flowmeter are remarkably improved, and the electromagnetic flowmeter is suitable for flow measurement under complex working conditions.

The invention discloses a vortex shedding flowmeter monitoring method and device based on deep learning, a medium and a product, and relates to the field of electrical digital data processing.The method comprises the steps that historical time sequence data of a vortex shedding flowmeter are obtained and preprocessed to obtain standardized data; dividing the standardized data into a training set and a verification set in a preset proportion; according to the flow characteristics of the vortex shedding flowmeter, segmenting the training set according to a preset flow threshold interval, and respectively constructing deep learning models to obtain a flow model set; verifying a plurality of deep learning models in the traffic model set by using the verification set, and optimizing the traffic model set; acquiring real-time data of the vortex shedding flowmeter, and performing model calculation according to the flow threshold interval to which the real-time data belongs to obtain a flow correction value; when the flow meter performance obtained through calculation based on the flow correction value is lower than a preset performance threshold value, abnormal early warning information is generated. According to the vortex shedding flowmeter, the monitoring timeliness of the vortex shedding flowmeter can be improved, and the maintenance cost of the vortex shedding flowmeter is reduced.

The invention provides an online calibration method and system for a flowmeter of a natural gas well, and belongs to the technical field of natural gas exploitation and transportation, firstly, a multiphase flow state sensing signal in a transportation pipeline of the natural gas well and a structural deformation monitoring signal of the flowmeter to be calibrated are obtained, and then interactive interference characteristic analysis is conducted on the two kinds of signals; the method comprises the following steps: obtaining a flow state-deformation interaction interference characteristic set, generating a multiphase flow adaptive calibration factor based on the flow state-deformation interaction interference characteristic set, inputting the multiphase flow adaptive calibration factor into a metering signal processing unit to dynamically correct an original metering signal, and obtaining a corrected metering output signal; and finally, collecting matching degree data of the correction signal and the real-time multiphase flow state sensing signal to form a calibration adaptive feedback signal, thereby comprehensively considering the influence of the multiphase flow state and structural deformation, and improving the metering precision and reliability of the flowmeter.

The invention relates to the technical field of intelligent sensor application, and discloses a water meter flow real-time correction method and system based on an intelligent sensor, and the system comprises a flow signal collection module, a signal feature decomposition module, a frequency domain feature fusion module, an error compensation decision module and a correction instruction execution module. The method comprises the following steps: acquiring multi-dimensional vibration waveform data of a water meter pipeline through a multi-band sensor, performing time-frequency domain decomposition and energy spectrum feature extraction to obtain a local frequency domain feature tensor set, and performing dynamic noise suppression and band correlation optimization to obtain a local optimization feature tensor set; and performing global feature reconstruction based on frequency domain energy distribution to generate an error compensation decision feature map, and finally generating a real-time correction control signal. Through multi-dimensional signal acquisition and intelligent feature processing, the precision and dynamic adaptability of flow metering in a complex pipe network environment are improved, and the method is suitable for high-precision flow correction of an intelligent water meter and has the advantages of being high in anti-interference capacity, accurate in compensation and the like.

The invention provides a gas ultrasonic transducer fast matching method, device and equipment and a storage medium, original measurement data such as flight time are obtained by deploying an ultrasonic transducer in a gas ultrasonic flowmeter in a gas conveying pipeline, and multi-dimensional data acquisition is carried out in combination with other sensor parameters. Wavelet noise reduction and dynamic time warping processing are carried out on collected signals, signal quality and time sequence consistency are improved, time domain, frequency domain, environment and statistical features are extracted, and multi-dimensional feature vectors are formed. Modeling is carried out through a time sequence feature branch and an environment feature branch, weighted fusion is carried out by adopting an attention mechanism, and the recognition capability of the model on key features is enhanced. A machine learning model is trained based on fusion features, a multi-objective loss function and a data enhancement strategy are adopted, the prediction precision and generalization ability of the model are improved, and a compensation value is output to calibrate the original traffic in real time. According to the method, the accuracy and stability of gas flow measurement in a complex environment are effectively improved, and the method has a good engineering application prospect.

The invention relates to the technical field of flow measurement and control, and particularly discloses a cheese filling flow real-time calibration control method and system.The cheese filling flow real-time calibration control method comprises the following steps that working condition information of cheese in a filling pipeline is collected in real time, and the working condition information comprises the temperature, the pressure, an original flow signal, the pump rotating speed and the proportional valve opening degree; dynamically selecting a filtering order according to the signal-to-noise ratio of the original flow signal, and outputting the filtered flow; and based on the acquired working condition information, predicting the dynamic viscosity of the cheese in real time through a neural network, fitting a flow-working condition relation function and calculating a target flow value. The signal-to-noise ratio of an original flow signal is dynamically adapted to optimize filtering processing, signal distortion caused by interference such as temperature drift and mechanical vibration is effectively eliminated, and the flow detection precision is improved; by means of the neural network, the dynamic viscosity of cheese is predicted in real time, the model is dynamically adjusted, the problems that traditional viscosity estimation lags behind, and the batch adaptability is poor are solved, and an accurate basis is provided for flow target value calculation.

The invention relates to the technical field of flow detection, in particular to a fluid-based online calibration method, device and equipment, a storage medium and a computer program product. The method comprises the following steps: determining a calibration fluid according to physical properties of a current fluid; fluid parameters of calibration fluid in the flowmeter to be processed are obtained, wherein the fluid parameters comprise pressure, temperature and flow velocity; according to the calibration model and the fluid parameters, the reading error of the flowmeter to be processed is determined; based on the reading error, the flowmeter to be processed is calibrated, the fluid parameters are collected in real time, the error is calculated in combination with the calibration model, rapid and efficient calibration is achieved, and time waste is reduced; calibration fluid is dynamically selected, the flowmeter is adjusted in real time, and the adaptability of the method is improved; through automatic data acquisition, error calculation and calibration adjustment, the calibration time and cost are reduced, and the calibration efficiency is improved.

The invention belongs to the technical field of flowmeter verification, and discloses a two-phase flow analysis system and method based on a differential pressure flowmeter and an electromagnetic flowmeter. The method comprises the steps that a differential pressure flow meter and an electromagnetic flow meter are arranged on adjacent sections of a pipeline, the electromagnetic flow meter collects voltage signals and analyzes the voltage signals to obtain liquid phase volume flow, and liquid phase mass flow is obtained by combining temperature correction; the differential pressure flowmeter calculates the gas density by using an ideal gas law according to the temperature and the pressure; inputting multi-sensor time sequence data into the weight optimization model, and optimizing the volume gas content and the total mass flow rate; baseline parameters are calibrated during full-pipe single-phase flow to obtain zero drift, and real-time temperature and pressure dynamic correction are combined, so that the metering precision and the working condition adaptability are improved; the problem of verification deviation of a traditional flowmeter is solved, high precision and high reliability of complex gas-liquid two-phase flow metering are achieved, and efficient and reliable technical support is provided for two-phase flow precise metering and process control in the fields of chemical engineering, energy and the like.

An optimization method based on measurement data of an ultrasonic gas meter, includes: if a relative strength index obtained by analysis does not meet expectation, calculating the degree of influence of a measurement condition on the relative strength index and determining whether to compensate the measurement data of the gas meter according to magnitude of the degree of influence; constructing an initial compensation model for the measurement data of the gas meter; performing sensitivity analysis on the optimized compensation model after multi-variable optimization; completing the correction of display data of the gas meter by a compensation factor combined with real-time temperature and pressure data and obtaining fluid features in a pipeline; matching the gas meter with the corresponding optimization plan from the pre-constructed gas meter optimization knowledge graph based on the correspondence between the fluid features and an optimization plan; and executing the optimization plan to optimize the gas meter.

The invention discloses a flowmeter self-calibration method and system based on big data analysis. The method comprises the steps that multi-source data collection is conducted, and multiple kinds of data are collected and stored in a big data platform with a block chain evidence storage function; data preprocessing: adopting multiple algorithms to improve data quality; feature extraction and analysis: identifying key factors and monitoring fluid states by means of multiple technologies; calculating calibration parameters, and considering various coefficients and uncertainty analysis; performing automatic calibration, encrypting and writing parameters after digital twin simulation, and reserving a copy; and verifying the calibration effect by adopting a multi-mode verification mechanism. The system comprises a plurality of functional modules which work cooperatively. According to the invention, the data acquisition comprehensiveness is improved, the calibration period is shortened, the production efficiency is improved, the data integrity and reliability are ensured, key factors are accurately identified, the fluid state is monitored in real time, the calibration parameter adjustment is simulated, and the calibration effect is verified in a multi-mode manner, so that the reliability and accuracy of a flowmeter measurement system are improved.

A system performs a method for characterizing passage of a patient fluid through a conduit. The method includes quantifying flow of fluidic content through a conduit, where the fluidic content includes a patient fluid, estimating a concentration of a fluid component of the patient fluid in the fluidic content, and characterizing passage of the patient fluid loss through the conduit based on the quantified flow and the concentration of the fluid component. At least one of the quantified flow or the concentration of the fluid component is based on sensor data from a sensor arrangement coupled to the conduit. Other apparatus and methods are also described.

The invention discloses a high-precision electromagnetic water meter system based on an adaptive algorithm and a measuring method thereof, and relates to the technical field of water meters, the high-precision electromagnetic water meter system comprises a signal acquisition unit, a signal processing and adaptive unit, a data processing and control unit, an abnormity early warning unit, a communication and data management unit and a system operation, maintenance and optimization unit. According to the system, electric signals are subjected to amplification, filtering and analog-to-digital conversion processing, strong signal overload can be avoided, various noise interferences can be removed, electromagnetic interferences in data transmission are reduced, stability during data transmission is guaranteed, fluctuation and distortion of the signals are reduced, an algorithm is automatically matched through an algorithm matching module, and the accuracy of data transmission is improved. The dynamic adjustment and continuous processing module is used for dynamically adjusting parameters and continuously processing signals, so that the parameter adjustment and the signal processing are interlaced to form a cyclic optimization process, the signal processing effect is continuously improved, errors of measured data are reduced, and high-precision measurement of micro flow is achieved.

The invention discloses an anti-seismic and anti-power-frequency-interference system and method for a vortex shedding flowmeter, and relates to the technical field of flow measurement, the system comprises a signal receiving module, a vibration monitoring module, a frequency identification module, a temperature and pressure measurement module and an interference signal processing module, interference signals in a flow-free state can be accurately processed, whether actual flow exists or not can be accurately judged by measuring the temperature and pressure of fluid in a pipeline and combining the temperature and pressure with vibration and frequency characteristic information, wrong measurement data generated due to the interference signals in the flow-free state are avoided, and the measurement accuracy is improved. According to the method and the system, the measurement problem caused by misjudgment can be reduced, meanwhile, information about the equipment operation state and the pipeline environment is provided for different types of interference signals through comprehensive analysis and judgment of the system, potential equipment faults and abnormal working conditions can be found in time, and a powerful data basis is provided for maintenance and management of the equipment.

The invention relates to the technical field of flow measurement, and discloses a multiphase flow flowmeter dynamic compensation system, which comprises a multi-modal data acquisition module, a dynamic compensation module and a dynamic compensation module, the feature extraction and standardization module performs frequency domain conversion and feature extraction on the data to generate a fusion feature matrix; the graph structure modeling module constructs a graph model based on the fusion features and extracts graph feature vectors; the compensation optimization module obtains an optimal compensation parameter through Bayesian optimization; the flow estimation module generates a compensated flow value according to the optimal compensation parameter; and the adaptive feedback module calculates a flow residual error and updates the graph structure modeling module and the compensation optimization module. By introducing a dynamic compensation and self-adaptive feedback mechanism, the method can adapt to the change of the flowing state of the multiphase flow in real time, the precision and stability of flow estimation are effectively improved, the problem that a traditional method is difficult to quickly respond to flow pattern change and working condition fluctuation is solved, and the real-time performance and reliability of flow measurement are remarkably improved.

The invention belongs to the technical field of float flowmeter detection, and particularly relates to a float flowmeter detection device for a visual imaging small oxygenerator, which comprises a detection bin, a test board is fixed on the left side of the interior of the detection bin, a float flowmeter is arranged above the test board, a pressing cylinder is fixed on one side of the test board, and the pressing cylinder is fixed on the other side of the test board. The output end of the pressing air cylinder makes contact with the upper end of the float flowmeter and presses the float flowmeter. An air pressure valve is fixed to the left side of the upper portion of the detection bin, the lower portion of the float flowmeter is connected with an air inlet pipe, the air inlet pipe is connected with an external air source pipeline, the external air source pipeline is connected with the air pressure valve, the upper portion of the float flowmeter is connected with an exhaust pipe, and the air pressure valve is manually adjusted. In the prior art, the position of the floater is manually identified by naked eyes, the precision is poor, and the pressure on the upper side and the lower side of the floater cannot be kept constant, so that the test quality is greatly reduced.

The invention relates to the field of metering calibration, and discloses a remote ultrasonic water meter metering calibration method and system based on the Internet of Things. The system comprises a plurality of modules, wherein a data acquisition module acquires flow, temperature, pressure and state information of the ultrasonic water meter in real time through the Internet of Things; the data preprocessing module cleans, screens and formats the acquired data to ensure the accuracy and consistency of the acquired data; the calibration parameter setting module sets calibration parameters such as a flow calibration point and a temperature compensation coefficient according to the technical specification and the calibration standard of the water meter; the calibration calculation module utilizes the parameters to calibrate data; the calibration result verification module compares the calibrated flow data with standard data and verifies whether the flow data are qualified or not; the result storage and transmission module stores the calibration result into a remote database and transmits the calibration result to related personnel or systems; the fault detection and alarm module monitors the state of the water meter in real time, and alarms are triggered once abnormity is found to inform personnel to deal with the abnormity.

The invention discloses a blockage detection method, system and equipment for a flow measurement device and a medium, and relates to the field of blockage detection. The method comprises the following steps: acquiring an original pressure signal and an original flow signal of a to-be-measured flow measuring device, and generating an intrinsic mode function according to the original pressure signal and the original flow signal; extracting a time vector, a space vector and a frequency domain vector of the intrinsic mode function, obtaining an attention weight through a double-path cross attention mechanism, and performing weighted fusion on the time vector, the space vector, the frequency domain vector and the attention weight to obtain a multi-dimensional feature vector; inputting the multi-dimensional feature vector into a pre-trained blockage probability prediction model based on deep learning to obtain a blockage probability value; and constructing a state space according to the blockage probability value, the historical maintenance record and the equipment operation duration, balancing false alarm and leak detection punishment in combination with a reward function, and determining execution operation. By implementing the technical scheme provided by the invention, the accuracy and reliability of blockage detection are improved.

The invention relates to the technical field of error verification, and particularly discloses an error verification method and system for an intelligent water meter, which adopts a neural network model based on deep learning to carry out fine-grained time sequence feature extraction on time sequence data of temperature and pressure of a water body flowing through a multifunctional intelligent water meter in a target time period; according to the method, the time sequence change mode of the water body temperature and the pressure in each local time domain is captured, time sequence integral coding is carried out on the time sequence change characteristics of the water body temperature and the pressure in each local time domain to understand the time sequence accumulation effect of the water body temperature and the pressure on the water consumption in the global time domain, and intelligent prediction of the water consumption is achieved. And error verification is carried out on the intelligent water meter according to the difference between the prediction result and the measured value of the water meter. In this way, the measuring error of the intelligent water meter can be reflected more accurately, and then the error checking precision and the intelligent level of water affair management are improved.

The present invention provides a method for detecting abnormalities of a gas flow meter based on machine learning, which belongs to the technical field of gas metering equipment diagnosis, and includes: step S1, obtaining time series data of the gas flow meter within a preset time period, and using a prediction model to predict the predicted flow value corresponding to each observed flow value; step S2, calculating the flow deviation between the observed flow value and the corresponding predicted flow value; step S3, using a hypothesis test method to test the flow deviation, and according to the test result, obtaining the cumulative loss time when the flow deviation is greater than a preset flow deviation threshold within the preset time period; step S4, according to the cumulative loss time, obtaining the detection result of whether the gas flow meter has an abnormality. Beneficial effect: By using a pre-trained model to predict the flow, and calculating the flow deviation between the observed flow and the predicted flow, and combining the hypothesis test method to statistically calculate the cumulative loss time when the deviation exceeds the threshold, the abnormality of the gas flow meter can be automatically detected efficiently and accurately.

The embodiment of the invention relates to the technical field of semiconductor manufacturing, in particular to a volume calibration method, a controller and a gas mass flow verification system. Obtaining an accurate second volume verification parameter of the calibration tank when the second pipeline space is in a gas stable state, and calculating an accurate and high-precision second volume of the second pipeline space by combining a first volume verification parameter and a first volume of the calibration tank when the fourth valve is in a closed state; a target mole of gas in the calibration tank is diffused to a target pipeline space, an accurate third volume verification parameter of the calibration tank is obtained when the target pipeline space is in a gas stable state, and a third volume verification parameter of the calibration tank is obtained by combining a first volume verification parameter and a first volume of the calibration tank when a fourth valve is in a closed state; the third volume of the third pipeline space is accurate and high in precision, and the requirement of the semiconductor technology for volume precision is met.

The invention relates to the technical field of remote water meter abnormity early warning, and discloses a remote water meter abnormity early warning method and system based on historical water volume, and the method comprises the steps: dividing a user water meter into three classes A, B and C according to the installation time of the user water meter, and employing a differentiated abnormity detection strategy. For the A-type users, establishing a water consumption prediction model based on historical consumption data of the A-type users; for the B-type users, finding similar reference water meters from the A-type users by using a K-means clustering algorithm; and for the C-type users, monitoring whether the accumulated water consumption for continuous N days is completely equal or not after the C-type users are installed. After real-time usage data is obtained, usage deviations of various users are calculated respectively, if the deviations exceed a set threshold value, abnormal early warning is triggered, and the early warning level is dynamically adjusted according to the number of abnormal duration days. According to the method, technologies such as machine learning and clustering analysis are comprehensively utilized, a set of comprehensive, flexible and efficient water consumption abnormity early warning system is constructed, and the refinement and intelligence level of urban water supply management can be remarkably improved.

The invention relates to the technical field of water meter flow, in particular to an ultrasonic water meter flow curve fitting system, which is characterized in that an edge computing device is used for primarily processing original data at a data acquisition end, a mean filtering algorithm is used for removing noise data, and the data after acquisition is subjected to filtering, denoising, abnormal value elimination and normalization processing; a dynamic evaluation model is constructed by adopting a time sequence prediction algorithm, the model calculates and predicts various data of future ultrasonic water meter flow by continuously inputting real-time data, and the model is optimized according to a prediction result, so that errors of the model are continuously reduced in the training process, and the prediction accuracy of the model is improved; modeling and fitting are conducted on the flow curves, calculated by the neural network model, of the ultrasonic water meter in different working states through the fitting model, the fitted curves can serve as a basic data source of a user water consumption behavior and system load characteristic advanced analysis model, and energy-saving analysis and fault positioning are facilitated.

The invention discloses a water meter quality defect detection method and device based on machine vision, and relates to the field of product detection.The water meter quality defect detection device comprises a multi-source collection module used for collecting water meter image data in the assembling process and storing the water meter image data; the marking module is used for manually marking the water meter image data as qualified or unqualified; the extraction and inspection module is used for receiving the water meter image data newly acquired by the multi-source acquisition module and the water meter image data marked as qualified in the marking module in real time, performing foreground extraction on the water meter image data, and comparing the newly acquired water meter image data with the water meter image data marked as qualified; checking whether the newly collected water meter image data source water meter is qualified or not; according to the method, the water meter image data are collected, the water meter image is cut and optimized, the qualified water meter image is selected in cooperation with manual detection, the qualified water meter image is compared with the subsequently collected water meter image data, and the quality of the water meter produced in the water meter assembly production process is inspected.

The invention discloses a water meter equipment fault self-checking maintenance management system, particularly relates to the field of water affair metering management, is used for solving the problem of fault missing report risk hidden danger pain points caused by self-checking failure, and is characterized in that self-diagnosis drift monitoring and heartbeat frame extremely simple coding are triggered through pseudo-flow rate pulses, and the energy consumption increment of an uplink link is reduced to a milliwatt level; the health score drives the rhythm to be rechecked, so that the deep sampling only occurs at a necessary moment; a differential patch is combined with a digital twinborn model to accurately position a failure branch, pushing and loading are only loaded with micro bytes, and downlink consumption and refreshing time are greatly shortened; shape-frequency complementary indexes are fused with unsupervised boundary learning, so that deviation judgment is sensitive and stable; the dual-redundancy monitoring timer verifies that the patch takes effect and then uploads an abstract, and the maintenance center can carry out remote self-repair under the condition of not interrupting metering and communication; and therefore, instant capture and directional repair of silent faults are realized, the autonomous reliability of the water meter is remarkably improved, and the operation and maintenance cost and the field intervention frequency are greatly reduced.

The invention relates to a method (100) for correcting measured values ​​of a mean flow velocity of a medium flowing through a pipeline, determined by means of an ultrasonic flow meter (1), wherein several ultrasonic transducers (10) are arranged on or in the pipeline (50) or a measuring channel (60) integrated into the pipeline and span an arrangement of at least three signal paths, wherein an electronic unit (20) operates the ultrasonic transducers, evaluates measurement signals from the ultrasonic transducers and outputs corrected measured values ​​of the mean flow velocity, where the electronic unit is used at each time period - using the measurement signals from ultrasonic transducers for each signal path, a local flow velocity of the medium is calculated, - calculated at least one, in particular dimensionless, functional relationship between local flow velocities from calculated local flow velocities, - calculates a baseline measurement for the average flow velocity from at least one specific local flow velocity, the base measurement value is corrected and output using a correction factor determined by comparison.

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.