70 results about "Process measurement" patented technology

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.

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 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 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.

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.

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.

The application discloses an engineering project cost whole-process collaborative management method and system, relates to the technical field of engineering project cost management, and comprises a cost matter main line construction module, which is used for receiving project estimation, design budget estimate, bidding control price, contract price, process measurement, change visa, claim and item data of final account, performing unified matter code mapping and cross-stage integration processing, and generating cost matter unit data. In the application, the project estimation, the design budget estimate, the bidding control price, the contract price, the process measurement, the change visa, the claim and the item data of the final account are subjected to the unified matter code mapping and the cross-stage integration processing by the cost matter main line construction module, the cost matter unit data is generated, cross-stage item integration record data is formed by the different-stage item data, and thus a stable main line structure is established at the data level, and consistent organization and centralized management of the cross-stage item data are realized.

The invention relates to the technical field of drilling construction engineering, discloses a cast-in-situ bored pile pore-forming process measuring and monitoring system and a pore-forming method thereof, and solves the technical problem that the working efficiency of an existing percussion drill is uncontrollable. The stress sensor is mounted on a rotating shaft of the guide wheel; the sonic sensor is fixed at the guide wheel; the rotary encoder is arranged on one side of the percussion drill body wheel disc; the single-chip microcomputer receives electric signals of the stress sensor, the sonic sensor and the rotary encoder and preprocesses electric signal data; the data management platform is in communication connection with the single-chip microcomputer through a wireless communication unit, and the data management platform sorts and analyzes the electric signal data. According to the technical scheme, the underground working state of the percussion drill is monitored through the monitoring system, the working parameters of the percussion drill are adjusted, and therefore the working efficiency of the percussion drill is controlled.

The application provides a method and system for in-machine measurement based on a double-turntable machine tool and triggered by rotation of a turntable, which comprises the following steps: determining a generatrix height of a workpiece to be measured based on a workpiece size on a 2D drawing of the workpiece to be measured, and setting a measurement parameter according to the generatrix height; calculating positions of each theoretical measurement point according to the measurement parameter, and obtaining a distribution of each theoretical measurement point of the workpiece to be measured in a 3D space; generating an in-machine measurement path according to the distribution of each theoretical measurement point, processing the in-machine measurement path into an executable measurement program; and measuring the workpiece to be measured by using rotation of a fifth axis of the machine tool according to the measurement program, and obtaining measured data of each measurement point of the workpiece to be measured. The application does not need a 3D model of the workpiece to be measured, only needs some necessary parameter settings of a user according to a size on a 2D drawing, and can realize in-machine measurement by relying on rotation of the fifth axis of the machine tool, so that more applicable scenarios are added for in-machine measurement.

The invention discloses a transformer on-load tap-changer on-line sensing method, which comprises the following steps of: constructing an action time sequence based on action time sequence accurate identification of electromagnetic transient process measurement; based on the action time sequence, synchronously extracting electrical, mechanical and thermal multi-dimensional state features from multi-source sensor data, and constructing a multi-dimensional feature vector; based on the multi-dimensional feature vector, performing defect identification and state quantitative evaluation through a comprehensive health index and a mahalanobis distance to obtain a defect probability; and performing state evolution trend analysis and adaptive updating of a normal state model based on the state quantitative evaluation result. The method solves the problems that the traditional monitoring technology is difficult to accurately capture the action time sequence of the tap switch in a complex electromagnetic environment, the multi-source state information fusion is difficult and the dynamic self-adaptive capability is lacked, and realizes the high-precision, multi-dimensional and online comprehensive sensing and early defect sensitive identification of the operation state of the on-load tap switch of the transformer.

Housing (2), with a venting device (10) arranged on the housing (2), which allows the supply of ambient pressure to the interior of the housing (2), wherein the venting device (10) is formed from a sleeve-like protective cap (11) with lateral openings (12) and a liquid-repellent membrane, wherein inside the venting device (10) a cavity is formed to receive the membrane and the membrane is arranged such that an opening in the wall of the housing (2) provided for the pressure supply is covered, characterized by that the sleeve-like protective cap (11) has a narrow, circumferential widening (14) in the area of ​​its side surface, so that a defined drip edge is formed.

The application relates to the technical field of industrial process measurement, and particularly discloses a pressure transmitter online maintenance method and system, a pressure signal sampling window and a background noise monitoring window are synchronously started, pressure data and auxiliary diagnosis parameters are respectively collected; physical validity inspection is carried out on the pressure data based on signal continuity, abnormal data segments are removed, a reliable data set is generated, and then a robust center estimation value is calculated; a data quality acceptance threshold is dynamically adjusted according to the auxiliary diagnosis parameters, and the robust center estimation value and data distribution are compoundly checked based on the threshold; only when the checking is passed, zero point writing with verification is executed and complete traceability records are generated, otherwise, the process is interrupted and a diagnostic warning is output; through multi-dimensional data fusion and intelligent decision, the application effectively filters out transient interference, ensures the absolute reliability of the online calibration benchmark, and improves the traceability of the maintenance process.

The invention proposes a method for the computer-assisted prediction of values of a first process variable, comprising: automated training of a computer-implemented model, which comprises relationships between the captured process measurement values of the second process variable and the values to be predicted of the first process variable, using the previously captured process measurement values, the following steps being carried out in an automated manner in the course of the training: i) scaling the process measurement values, in particular standardizing the process measurement values, ii) carrying out a data imputation in order to supplement missing process measurement values, iii) detecting and removing outliers in the process measurement values, iv) performing a feature selection in the process measurement values, v) increasing a quantity of the process measurement values, vi) using a specific window size for the process measurement values when training the computer-implemented model, vii) selecting a specific type for the computer-implemented model and hyperparameters for training the computer-implemented model, predicting the values of the first process variable by applying the previously trained computer-implemented model.

To provide a sensor system that can appropriately process measurement data obtained at an appropriate measurement timing on a plurality of sensor units.SOLUTION: A sensor system 1 includes a plurality of sensor units 30 and a communication unit 20. The plurality of sensor units 30 store measurement values in a memory, obtained by performing measurements based on a synchronization signal having a sensor unit-specific second cycle, which is phase-adjusted based on a synchronization signal having a first cycle common to the plurality of sensor units 30. At least one of the plurality of sensor units 30, the communication unit 20, an information processing device 10, and a control device generates a calculated value by reading and calculating measurement values stored in the memory by at least two or more sensor units 30 of the plurality of sensor units 30.SELECTED DRAWING: Figure 12

The invention belongs to the technical field of rock-soil thermophysical property testing, and particularly relates to a wall-attached in-situ rock-soil layer heat conductivity coefficient measuring device and method.The wall-attached in-situ rock-soil layer heat conductivity coefficient measuring device comprises an expansion module used for enabling the measuring device to be attached to the wall face of a rock-soil layer, and a heating module used for providing heat for the wall face of the rock-soil layer; the expansion module is used for expanding the wall surface of the rock-soil layer, the heating module is used for heating the wall surface of the rock-soil layer, the measuring module is used for measuring the temperature change of the wall surface of the rock-soil layer, and the central controller is electrically connected with the expansion module, the heating module and the measuring module and used for controlling the working state of each module and summarizing and processing measured data so as to calculate the heat conductivity coefficient of the rock-soil layer. The measurement result is closer to the actual in-situ state of the rock-soil layer, and the data reliability is improved.

An apparatus to process configuration information for monitoring of artificial intelligence (AI) or machine learning (ML) (AI / ML) based beam management, process measurements of a first set of beams using at least part of the configuration information to obtain a first set of beam measurements, compare the first set of beam measurements to predicted beam results obtained by inputting a second set of beam measurements into one or more AI / ML models used for the AI / ML based beam management, determine an event has occurred based on the configuration information and the comparing of the first set of beam measurements to the predicted beam results, wherein the event is indicative of one or more errors of the one or more AI / ML models and generate an event driven report based on the event, the event driven report comprising information relating to the performance of the one or more AI / ML models.

The present disclosure provides a software development whole-process measurement method and system, relating to the technical field of computers, wherein the method comprises: obtaining development process data; wherein the development process data comprises at least one of code repository data, merge request information, project management data, task information and unstructured document data related to the project; based on a preset association rule and the merge request information, performing cross-source association processing on the code repository data, the project management data, the task information and the unstructured document data related to the project to generate fusion data; and calculating a composite measurement index based on the fusion data. Through automatic association and unified analysis, all-round measurement covering process efficiency, collaboration quality and knowledge inheritance is realized, and natural language processing technology is used to intelligently identify risks and knowledge gaps from unstructured documents, provide early warning and accurate problem tracing, and improve development transparency and decision-making efficiency.