109 results about "Process measurement" patented technology

The invention relates to the technical field of industrial process measurement, and discloses a high-precision liquid level measurement anti-interference calibration method, which comprises the following steps of: 1, synchronously acquiring a liquid level signal, a mechanical vibration signal, an electromagnetic field intensity signal and a temperature distribution signal through a multi-mode sensor array to form multi-physical field data; 2, constructing a hypergraph adjacency tensor based on the coupling relationship of the multi-physics field data, and generating a Laplacian matrix constraint; step 3, performing CP tensor decomposition with hypergraph constraint on an original signal tensor formed by the multi-physical field data, and separating a liquid level signal kernel from an interference kernel; and step 4, inputting the decomposed signal kernel characteristics and the sensor impedance parameters into a depth deterministic strategy gradient algorithm to realize hardware acceleration of a processing flow. A multi-physics field coupling relation is constructed through a hypergraph adjacency tensor and a Laplacian matrix, and the limitation of a traditional binary correlation model is broken through.

The invention discloses a Linux process code segment measurement enhancement method based on a dual-system architecture. The method comprises the following steps: S1, collecting a process reference value; and S2, adding a kernel key data monitoring mechanism. And S3, measuring the process in real time. Based on a trusted execution environment, a prototype system for measuring the process in real time when the Linux operating system runs is realized on the basis of trusted startup, and the prototype system provides a mechanism for immediately initiating real-time measurement when kernel key data operation is abnormal while periodically measuring a process code segment. Kernel key data comprises a system call table, an interrupt descriptor table and SELinux enabling and configuration information, and the scheme solves a certain TOCTTOU problem and enhances the security of the Linux system. According to the method, real-time measurement on the processes related to kernel key data operation during operation of the computing component operating system is completed, the safety of the system is enhanced in the operation process of the operating system, the credibility of a computing platform is guaranteed, and the method conforms to the active immune credible idea.

To process measurements acquired using measurement instrumentation (41, 42), a processing system (50) is operative to perform a physics informed data-driven processing technique to determine at least one parameter related to an asset (20) of an electric power system (10). The processing system (50) is operative to perform a preprocessing (70) of measurements to generate input data, with the preprocessing (70) comprising identifying a subset of measurement locations for which the measurements are reliable for inputting to the data-driven processing technique and using measurements obtained at the identified subset for generating the input data. The processing system (50) is operative to estimate local heat loss, using the data-driven processing technique.

To process measurements acquired using measurement instrumentation (41, 42), a processing system (50) is operative to perform a data-driven processing technique to determine at least one parameter related to an asset (20) of an electric power system (10) and at least one uncertainty measure.

The present invention discloses a casting process measurement and control system based on cloud computing, comprising a data acquisition module, a process control module and a mold detection module, characterized in that: the data acquisition module is used to collect equipment operation data and visual images and temperatures of castings, and enter comprehensive parameters of castings to be produced into the system; the process control module is used to analyze the visual images and temperatures of castings, further analyze the operation parameters of the equipment, and adjust the operation parameters of the equipment according to the analysis results; the mold detection module is used to analyze the comprehensive parameters of castings, select raw materials and make corresponding casting molds according to the analysis results; the data acquisition module, the process control module and the mold detection module are electrically connected to each other; the data collection module comprises a sensor module, a visual module and a casting comprehensive parameter entry module; the sensor module is used to collect equipment operation data; the present invention has the characteristics of high accuracy and high production efficiency.

Measurement system for process automation in industrial or private environments, comprising a first field device for acquiring initial process measurement data and a computing arrangement for evaluating the acquired initial process measurement data, wherein the computing arrangement is configured to determine a new installation position and / or a new parameterization of the first field device based on the evaluation of the acquired initial process measurement data in order to improve the measurement result.

The invention discloses a monitoring method, a monitoring device, equipment, a medium and a product of a transformer substation monitoring meter. The method comprises the following steps: acquiring initial measurement data; determining a target processing strategy corresponding to the initial measurement data, and processing the initial measurement data of the monitoring meter based on the target processing strategy to obtain processed measurement data; if the processed measurement data meets a correction condition, correcting the processed measurement data to obtain target measurement data; according to the technical scheme, the general monitoring module for different types of transformer substation monitoring meters is provided, and the monitoring stability and reliability of the transformer substation monitoring meters can be improved.

To process measurements acquired using measurement instrumentation (41, 42), a processing system (50) is operative to perform a physics informed data-driven processing technique to determine at least one parameter related to an asset (20) of an electric power system (10). The processing system (50) is operative to perform a preprocessing (70) of measurements to generate input data, with the preprocessing (70) comprising identifying a subset of measurement locations for which the measurements are reliable for inputting to the data-driven processing technique and using measurements obtained at the identified subset for generating the input data. The processing system (50) is operative to estimate local heat loss, using the data-driven processing technique.

The present invention relates to a method for testing a welded joint produced by resistance welding, in which, firstly, in a preparatory phase, the dependence of a welding quality parameter (8), for example the lens size, on the welding time in the form of a lens growth function (16) or an extended lens growth function (16') is determined from test data with specified nominal process data (3, 4) of the welding process, then a self-learning system (1), for example a neural network, is trained with training data which, on the one hand, contain nominal process data (3, 4) and measured process measurement data (5, 6, 7, 8) as input data and, on the other hand, welding quality parameters, for example lens sizes, determined by means of the lens growth function as target data (10).and then, during operation after a welding process, a welding quality parameter (9, 9'), for example the lens size, is determined by means of the trained self-learning system on the basis of the specified nominal process data and the recorded process measurement data.

The invention relates to the technical field of instrument and process measurement, in particular to a multi-range pressure gauge self-adaptive switching system and method based on time sequence feature perception, and the system comprises a data collection module which generates a smooth pressure time sequence signal in real time; the time sequence feature sensing module receives the smooth pressure time sequence signal and generates a pressure prediction trend signal of a future short-time window based on the pressure change trend feature; the self-adaptive decision-making module compares the pressure prediction trend signal with a preset current measuring range threshold value range; the switching execution module responds to the pre-switching trigger signal when the switching locking signal is in an unlocking state, and outputs the pre-switching trigger signal to the multi-range sensor group to complete switching of working channels; and the data fusion module performs weighted fusion processing on the two paths of output signals according to the switching process state signal provided by the switching execution module. According to the invention, the problem of switching lag of an existing pressure measurement system in the face of a pressure transient working condition can be solved.

The disclosure relates to a field device (10) for detecting a process measurement variable, which is designed in particular as a level measuring device for detecting a level of a medium, wherein the field device (10) comprises: - a sensor arrangement (11) which is arranged to detect a measurement signal correlating with the process measurement variable, and - a communication arrangement (12) which is set up for data communication (1) with an access device (20), wherein the field device (10) is set up to receive authentication data (2) based on at least two security factors, and wherein the field device (10) is set up to authenticate the data communication (1) between the field device (10) and the access device (20) for evaluating at least a part of the authentication data (2) which is based on at least one of the at least two security factors.

The invention relates to the technical field of semiconductor manufacturing, in particular to a semiconductor machine control method and device, a medium and a product, and the method comprises the steps: training an initial weight prediction model according to training time sequence data until a weight prediction error is not greater than a preset target value, and obtaining a target weight prediction model; inputting the test time sequence data into the target weight prediction model, and predicting a target weight; the test time sequence data comprises process parameter time sequence data, machine state parameter time sequence data and process measurement result time sequence data; according to the predicted target weight and the actual feedback data and historical feedback data of the current batch of wafers, obtaining process parameter prediction data of the current batch of wafers; and controlling the semiconductor machine to execute the target technological process on the current batch of wafers according to the technological parameter prediction data. And at least according to the dynamic change of the actual working condition of the machine, the process parameters of the machine can be dynamically tracked and predicted in real time.

The invention discloses an online calibration method for a mass flow meter, and belongs to the technical field of industrial process measurement and metering. Comprising the following steps: constructing a dual-channel data acquisition system; instantaneous mass flow data and accumulated mass flow data of the calibrated mass flow meter and the standard reference flow meter are collected in real time, and a standardized data sequence is obtained; extracting flow stability characteristic parameters by adopting multi-time scale analysis, and identifying a flow change event; the current operation state of the calibrated mass flow meter is subjected to grading judgment, and the corresponding working condition grade is determined; error comparison data used for representing the measurement deviation of the calibrated mass flow meter is generated; carrying out integrity, stability and consistency evaluation on the error comparison data, and judging whether the error comparison data meets a preset data quality criterion or not; and when a data quality criterion is met, obtaining a corresponding basic weight parameter according to the current working condition grade, and carrying out correction and normalization processing on the basic weight parameter to obtain a corrected and normalized weight parameter.

The invention relates to the technical field of industrial process measurement, and particularly discloses an online maintenance method and system based on a pressure transmitter, and the method comprises the steps: synchronously starting a pressure signal sampling window and a background noise monitoring window, and collecting pressure data and auxiliary diagnosis parameters; carrying out physical validity test based on signal continuity on the pressure data, removing abnormal data segments, generating a credible data set, and further calculating a robust center estimation value of the credible data set; dynamically adjusting a data quality acceptance threshold according to the auxiliary diagnosis parameters, and performing composite verification on a robust center estimation value and data distribution based on the threshold; executing zero point writing with verification and generating a complete traceability record only when the verification is passed, and otherwise, interrupting and outputting a diagnostic warning; according to the method, through multi-dimensional data fusion and intelligent decision making, instantaneous interference is effectively filtered out, absolute reliability of an online calibration reference is ensured, and traceability of a maintenance process is improved.

The utility model discloses an anti-seismic whole-process measurement station-changing-free guiding device, which belongs to the technical field of accurate attitude measurement and guiding of tunneling equipment, and comprises a shield tunneling machine, a shield tunneling trolley, a total station, a laser emitting device, a multi-path double-screen laser target and a rearview prism, the shield tunneling trolley is arranged on one side of the shield tunneling machine, and the total station is arranged on the other side of the shield tunneling machine. The laser emitting device is mounted at the bottom of the total station; a relation is established through the total station, the adjustable laser transmitter, the multi-path double-screen laser target and the rearview prism, when the system works, the adjustable laser transmitter keeps a fixed transmitting angle to irradiate the multi-path double-screen laser target, the multi-path double-screen laser target collects a laser azimuth angle at a high frame frequency, and the total station measures the rearview prism in real time in a tracking mode. The method has the advantages that the system has shock resistance, and when the trolley vibrates, the optical path of the adjustable laser transmitter and the total station measure the rear-view orientation relationship synchronously change during vibration, so that the influence of vibration on the measurement result is solved.

The present invention discloses a data security processing and remote attestation model protection system and method for machine learning, belonging to the fields of computer program analysis technology, information security, and data services. By combining the Pearson correlation coefficient, FP-Growth algorithm, and cosine similarity to fill missing values ​​in discrete data, SMOTE-based minority group oversampling, and constructing a measurement engine, the system inserts stubs into the machine learning model program code to complete the integrity measurement of the modeling process and modeling results. The present invention adopts the above-mentioned data security processing and remote attestation model protection system and method for machine learning, traversing the control flow graph of the static model program code through a breadth-first search method to calculate the trusted execution process measurement value of each sub-process, and comparing each measurement value with the trusted measurement value to verify the measurement result, thereby ensuring that the code and results during the modeling process have not been tampered with.

An online measurement gear precision evaluation method considering geometric errors of a large-specification gear making machine tool is characterized in that a measurement path is planned based on the large-specification machine tool which is provided with an online measurement system and contains X, Y, Z and C axes, an online measurement system geometric error model is established, the influence of the geometric errors on a measurement result is analyzed, and the precision of the large-specification gear making machine tool is evaluated. And obtaining a theoretical comparison position corresponding to the actual measurement point, and evaluating the processed measurement result. A measurement path is planned according to an involute helical gear measurement principle and shaft configuration characteristics of a large-specification gear making machine tool; establishing a geometric error model in combination with the measurement path; and analyzing the influence of the geometric error on the measurement result, correcting the measurement result, and evaluating the corrected measurement result. According to the invention, the online measurement evaluation accuracy of the large-specification gear making machine tool is improved.

The invention relates to a field device (100) comprising a measuring arrangement (10) which is configured to detect a process measurement variable, a screen (20) which has a first screen area (21) and a second screen area (25), wherein the first screen area (21) is a touch-sensitive area for detecting an operator input from a user, and wherein the second screen area (25) is a touch-insensitive area, and a control arrangement (30) which is configured to process the operator input for operating the field device (100).

The invention relates to an electrochemical process measuring device (10) having a process sensor unit (30), the sensor unit (30) comprising a reference electrolyte tank (50) having a reference electrolyte and a reference electrode (54), and a measurement electrolyte tank (60) having a measurement electrolyte liquid (62), a measurement electrode (64), a gas selective membrane (90) separating the reference electrolyte (52) from the sample (13 '), and a pressure equalization channel (84) fluidly connecting the tank interior (56) to the atmosphere (11). The pressure equalization channel (84) is defined by an equalization channel body (80) made of a hydrophobic material having a contact angle Theta higher than 90 DEG. This allows a very narrow pressure balance channel to be achieved without the risk of clogging by electrolyte droplets, thereby significantly reducing the evaporation loss of measuring the electrolyte.

A measuring device (1) for process measurement technology comprising a measuring sensor (2), a measuring transducer (3) and a sensor neck (4) for connecting the measuring sensor (2) to the measuring transducer (3), wherein the sensor neck (2) is formed from a plurality of sensor neck tube segments (7); and method for assembling a measuring device.

The present invention provides a batch process modeling and final product quality prediction method based on big data, which belongs to the technical field of fine chemical and pharmaceutical batch process modeling and final product quality prediction, and specifically includes using an encoder to nonlinearly map process measurement variables and control input variables to higher-dimensional state variables, using a recurrent neural network to build a state variable dynamic model; mapping the state variables back to the measurement variables through a decoder to form nonlinear mapping parameters; using batch process historical operation big data to learn nonlinear mapping parameters and dynamic models; and combining an attention mechanism to establish a batch process final product quality prediction model. The present invention avoids staged modeling and learning, and the final product quality prediction model explicitly considers the impact of historical state variables on the final product quality, overcoming the problem that the recurrent neural network predicts the final product quality performance as the input length increases.

The present application relates to the field of instruments and process measurement technology, in particular to a multi-range pressure gauge adaptive switching system and method based on time sequence feature perception, the system comprising a data acquisition module that generates a smooth pressure time sequence signal in real time; a time sequence feature perception module that receives the smooth pressure time sequence signal and generates a pressure prediction trend signal for a future short time window based on pressure change trend characteristics; an adaptive decision module that compares the pressure prediction trend signal with a preset current range threshold range; a switching execution module that responds to a pre-switching trigger signal when a switching lock signal is in an unlocked state and outputs to a multi-range sensor group to complete switching of a working channel; and a data fusion module that performs weighted fusion processing on two output signals according to a switching process state signal provided by the switching execution module. The present application can solve the problem of switching lag in existing pressure measurement systems when facing pressure transient working conditions.

The invention belongs to the technical field of measurement and evaluation in the machining process, and relates to a measurement characteristic error evaluation method for complex parts of a helicopter. The method comprises the steps that a measurement characteristic error evaluation model is constructed, measurement characteristic error evaluation is a process of processing and calculating measurement data, measurement points of part characteristics serve as input of an evaluation process, and an evaluation result can serve as output to be compared with tolerance requirements through a certain evaluation algorithm; if the evaluation calculation result is within the tolerance range, the evaluation item is qualified; if the calculation result is out of the tolerance range, the evaluation item is unqualified; and according to the measurement characteristic error evaluation model, evaluating whether the pose and the size error of the part before machining meet the requirements or not.

Measuring device (1) for process measurement technology, comprising: - a number n of sensors (2_1 to 2_n), wherein each sensor (2_i) of the number n of sensors (2_1 to 2_n) is configured to provide associated sensor data (x i ) to generate a total of n sensor data (x1, ..., x n ) is generated using the number n of sensors (2_1 to 2_n), - a learning unit, wherein the learning unit is trained to calculate values ​​of a number d of parameters of a hypothesis function based on training data, and - a measuring instrument diagnostic unit (4) designed to measure a number m of diagnostic values ​​(ŷ1, ..., ŷ m ) depending on the number n of sensor data (x1, ..., x n ) based on the hypothesis function and the calculated values ​​of the number d of parameters of the hypothesis function.

The invention relates to a field device (100) comprising a measuring arrangement (10) for recording a process measurement variable and a display arrangement (20) with a display (21) for displaying a display page (1) and a display module (22), wherein the display module (22) is configured to generate the display page (1) based on at least one template (2) and to transfer the display page (1) to the display (21).

The in-process measurement system (H) of the present application includes an in-process measurement device (20) and an output device (30). The output device (30) acquires measurement data measured by the in-process measurement device (20) by relatively moving a measurement position on the surface of a workpiece (W) at least in the circumferential direction relative to the workpiece (W), and performs a plurality of analyses related to the machining quality of the workpiece (W) using any one of a low-frequency component, a high-frequency component, and the three of the low-frequency component and the high-frequency component in the frequency component of the measurement data, and outputs a plurality of analysis results.

The invention relates to the field of mine safety, and provides an in-situ dynamic monitoring method and system for the damage depth of a bottom plate. The base plate damage depth in-situ dynamic monitoring method comprises the following steps: constructing in a working face base plate to form a three-dimensional monitoring network consisting of a plurality of drill holes; a fixed-point water injection section is isolated at the bottom of each drill hole through a packing device; along with advancing of the working face, multiple water injection tests are conducted on each fixed-point water injection section, and the water injection flow and the water injection pressure are recorded; and calculating a permeability coefficient according to the water injection flow and the water injection pressure, and taking sudden increase of the permeability coefficient as a criterion for the rock stratum to enter a damaged state so as to determine the damage depth of the floor. According to the in-situ dynamic monitoring method for the damage depth of the floor, the whole dynamic evolution process of the damage depth of the floor rock stratum in the mining process can be continuously and accurately obtained in real time, and the crossing from final-state actual measurement to process actual measurement is realized.

The present invention discloses a model predictive control method based on a dynamic output feedback strategy, which is applied to polyhedral uncertain systems. When physical devices in the polyhedral uncertain system are operating, data is transmitted through sensors. The model predictive control process is as follows: a dynamic output feedback strategy including a controller and an observer is established. Based on the model predictive control problem, the polyhedral uncertain system, the controller, and the observer are described using state equations. The observer is used to constrain the data transmitted by the sensor and process measurement wild values in the data. The controller is used to perform model predictive control on the processed output data to obtain the control input at the next moment, and the control input is applied to the physical device through an actuator. The present invention is targeted at polyhedral uncertain systems with measurement wild values. The study uses a saturation function to study an observer-based dynamic output feedback strategy, mitigate the impact of measurement wild values, and ensure the robustness and asymptotic stability of the system.