53 results about "Soft sensor" patented technology

Disclosed in the present invention is a lightweight active online modeling method fusing mechanism and dynamic memory, which comprises the following steps: S1, data preprocessing and model initialization; S2, online data representativeness evaluation based on dynamic memory of process data; S3, online data informativeness evaluation based on dynamic memory of label data; S4, online monitoring of a mechanism state; S5, parallel hybrid active sampling policy; and S6, online modeling of a soft sensor model. The present invention separately designs memory policies for process data and label data and only reserves high-value data, thereby satisfying a lightweight hardware requirement; in addition, the present invention designs a time delay-based mechanism state online monitoring method to detect a real concept drift of the process, and further provides the parallel hybrid active sampling policy for detecting both a real concept drift and a false concept drift; in combination with dynamic memory and a kernel incremental regression method, the computation amount for model updating and the hardware overhead for edge deployment are reduced.

A method to perform oil in produced water analysis allows measuring the large volume of oil in produced water reliably. In the method, a time-series and physics based machine learning model of a gas oil separation plant is generated, advisory actionable items for maintaining a crude oil quality within a pre-determined threshold are generated based on machine learning model coefficients and outputs of soft sensors, and then the advisory actionable items are presented to a user.

The invention relates to the technical field of production line control, and discloses a drinking water production line water quality real-time monitoring and intelligent adjusting system based on the Internet of Things. Registration equipment is unified with units, and NTP time synchronization is performed to eliminate deviation; the shift / CIP calibrates the zero range, and the EWMA monitors the drift weight drop; constructing state vectors containing IDs / batches, and performing auditing and warehousing; median / bilateral filtering denoising is carried out, and grid interpolation is unified; sliding window multi-scale aggregation is carried out, and self-adaptive sampling is carried out according to curvature and events; normalizing and interpolating a near-time training window; estimating indexes difficult to measure by a soft sensor, and depicting residual chlorine by a mechanism model; and carrying out uncertainty weighted fusion, and outputting estimation and intervals and auditing. According to the method, the whole process of real-time monitoring, quick response and traceable compliance is realized, the risk caused by chemical shortage or ratio error and environmental protection constraint is remarkably reduced, the capacity loss caused by cleaning and plan conflict is reduced, and the factory water quality stability and the treatment transparency are improved.

The invention provides an intelligent control method for natural gas continuous heat treatment equipment based on digital twinning, and relates to the technical field of heat treatment equipment control. The method comprises the steps that a digital twinning model of the natural gas continuous heat treatment equipment is established, and production monitoring data collected on line are utilized; the method comprises the following steps: predicting combustion process characteristics and unmeasurable states through a digital twin model, performing multi-layer strategy search by adopting a hierarchical control strategy according to a state prediction result, performing safety constraint simulation verification on the multi-layer strategy in the digital twin model, and predicting unmeasurable state parameters through a soft sensor. And introducing the quality index as a soft constraint condition into optimization control, and generating a control execution parameter meeting the constraint condition. The technical problems that in the prior art, natural gas continuous thermal treatment equipment is poor in control effect and unstable in workpiece quality are solved. The technical effects that the natural gas continuous heat treatment equipment is intelligently, accurately and reliably controlled, and the product quality is improved are achieved.

The invention discloses a semiconductor packaging process dynamic optimization system and method, and relates to the technical field of semiconductor packaging process optimization. A dynamic optimization system for a semiconductor packaging process comprises a data acquisition module, a fingerprint generation module, an index evaluation module, an optimization model construction module, a parameter solving module, a parameter bias module, a feedback detection module and a test analysis module. According to the method, a soft sensor model taking batch fingerprints as a core is introduced into a semiconductor packaging process, and dynamic process data and equipment state information are combined, so that high-precision prediction of a key process quality index and an energy consumption index is realized; through fusion of an extreme value theoretical model and a tail risk quantile evaluation method, deviation correction is carried out on a preliminary prediction result of the soft sensor, and robustness and safety of process quality prediction are improved.

The invention relates to the technical field of fluid machinery intelligent control, and discloses a centrifugal circulating pump non-differential pressure sensor variable pressure control method based on edge intelligence. Water pump rotating speed and power signals are collected, subjected to normalization processing and then input to a lightweight recurrent neural network (RNN) model deployed at an edge device end; the RNN model serves as a soft sensor, and the flow and the lift value of the most unfavorable point of the pipe network are deduced in real time; and the system dynamically calculates the target lift based on a pipe network characteristic curve according to the reasoned flow, the target lift serves as a set value and forms a closed-loop control loop with the actual lift fed back by the RNN, the rotating speed adjustment amount is output to the frequency converter through a PID algorithm, and finally accurate variable pressure control without the physical differential pressure sensor is achieved. According to the method, the dependence on a tail end physical sensor is eliminated, the cost and the complexity are reduced, meanwhile, the real-time control and the data security of ultra-low delay are realized by utilizing edge calculation, and the response speed, the control precision and the energy-saving effect of the system are effectively improved.

The invention discloses an aircraft flight process soft measurement algorithm based on an improved flow model. According to the method, a conditional cycle real value non-volume preserving model (CP-RNVP) and a conditional streaming soft sensor (CFSS) framework are provided for solving the problems that the data distribution difference is obvious and samples are unbalanced under the multi-working-condition operation condition of an aero-engine. The CP-RNVP model comprises three key components: (1) an adaptive mask mechanism based on feature correlation, wherein the range and granularity of feature selection are dynamically adjusted according to feature importance and correlation; (2) the multi-period affine coupling layer is used for modeling a complex period mode in data by introducing learnable multi-frequency parameters; and (3) a non-explicit condition injection method is adopted, condition information features are extracted through the sub-network and are adopted as modulation items in affine coupling transformation, and joint learning of multi-working-condition distribution is realized. Based on a CFSS soft measurement framework developed by a CP-RNVP model, multi-level training verification of soft measurement is realized by utilizing reversibility of a flow model, a soft measurement head is designed in the process of data mapping and inverse transformation, and multi-level soft measurement prediction and verification are carried out. Experiments show that the method has a remarkable effect on solving the multi-working-condition imbalance problem, compared with an existing method, the soft measurement precision is remarkably improved, and meanwhile the data generation quality and the distribution fitting capacity are better. The method is suitable for the fields of aero-engine state monitoring, health management and the like.

The invention discloses an automatic control method and system for fumaric acid production. The method comprises the following steps: collecting process variables in a fermentation process; constructing a soft measurement model fusing physical modeling and a residual neural network to obtain an acid production rate prediction value and prediction confidence; calculating a feed-forward control quantity, and adjusting the weight according to the prediction confidence to obtain a feed-forward control output; constructing a state vector, and establishing a Markov decision process; training a neural network control strategy by adopting a near-end strategy optimization algorithm, and outputting a proportionality coefficient, an integral coefficient and a differential coefficient; calculating feedback control quantity, fusing the feedback control quantity with feed-forward control output, and applying change rate and amplitude limitation to obtain final control output; and switching to a feedback priority control mode under a specific condition, and regularly updating a soft measurement model and a control strategy parameter. According to the invention, high-precision stable control of pH in the fumaric acid fermentation process is realized, the production efficiency and the process robustness are improved, and the method is suitable for automatic control of the large-scale fermentation process.

The invention relates to a method for training a soft sensor for determining a target measured value of a fuel cell system (100), the fuel cell system (100) comprising a first sensor (10) for detecting a first measured value, a second sensor (20) for detecting a second measured value, and a control device (50), the control device (50) is designed to determine a target measured value from at least the first measured value and the second measured value on the basis of a model stored on the control device (50). Furthermore, the fuel cell system (100) comprises a target value sensor (40) for determining a target measurement value. The method comprises, as a step, operating (320) at least one system unit (110) of the fuel cell system (100) that affects a target value in at least one first operating point. Furthermore, the method comprises, as a step, adjusting (340) at least one model parameter of the model stored on the control device (50) in such a way that a deviation between the target value determined by the soft sensor and the target value determined by the target value sensor (40) at the first operating point of the system unit (110) is at least reduced.

A system and method for estimating a parameter for a reactive absorbance unit are provided. An exemplary method includes creating a kinetic model of an absorbance process, setting a range for each of a plurality of input parameters, based, at least in part, on operational data measured from the reactive absorbance unit. A sampling technique is used to generate a plurality of input vectors in the range of each of the plurality of input parameters. A plurality of output vectors is generated from the plurality of input vectors. A predictive model is trained with the plurality of output vectors and the plurality of input vectors. The parameter is estimated from the predictive model. The parameter is used in a control model for the reactive absorbance unit.

Provided is an industrial process soft sensor method based on a federated stochastic configuration network, including: acquiring historical industrial process auxiliary data and corresponding product quality data; finding out optimal hidden layer parameters; processing, by a central server, to obtain global parameters, and downloading same to each factory as hidden layer parameters for a local model; obtaining output weights of a current network through an optimization algorithm, and uploading same to the server for weighted aggregation; and when the number of hidden layer nodes in a current network exceeds a maximum given value or a residual in current iteration meets an expected tolerance, completing modeling to obtain the global federated stochastic configuration network without adding new nodes. The present disclosure effectively improves prediction performance of models and protects data privacy, thus meeting industrial process soft sensor requirements.

The invention designs a complex industrial process soft measurement modeling method based on double-layer dynamic random configuration width learning, and aims to better extract dynamic characteristics from industrial data and improve the performance of a soft sensor. According to the method, a double-layer dynamic feature analysis model is established in a mapping feature layer, a slow feature analysis method is used in the first layer to extract intrinsic essential features in industrial data, and the influence of noise in the data on the model is reduced; in the second layer, dynamic features in data are further extracted from features extracted by a slow feature analysis method by adopting a deterministic jump cyclic reserve pool, and meanwhile, nodes of an enhanced feature layer are obtained by adopting a random configuration method in the enhanced feature layer, so that the quality of the enhanced nodes is improved. The method designed by the invention not only has good dynamic feature extraction capability, but also improves the prediction performance of the soft sensor.

The invention belongs to the crossing field of biomedical engineering, robots and acoustic metamaterials, and discloses a miniature magnetic control robot with ultrasonic sensing feedback, a magnetic soft clamp robot comprises a clamp main body and two embedded ultrasonic soft sensors, and the clamping or loosening state of a U-shaped opening of the clamp main body can be adjusted under the action of an external magnetic field; and the clamping force of the clamp main body can be wirelessly monitored in real time by ultrasonically detecting the deviation of the natural resonant frequency of the embedded ultrasonic soft sensor. According to the invention, the embedded ultrasonic soft sensor EUSS and the magnetic driving module are coupled and integrated, so that the flexible control of the magnetic driving module and the real-time feedback function of the photonic crystal sensor are integrated; the problems that a traditional magnetic control robot lacks wireless and real-time feedback capacity, and an existing capsule robot lacks a monitoring feedback function in the medicine release process can be effectively solved.

The invention belongs to the technical field of biological process monitoring and optimization, and discloses a fermentation-hydrolysis process parameter automatic control system and method, and the method comprises the steps: collecting online process parameter time sequence data for pre-selected key process parameters; taking the online process parameter time sequence data as real-time input, and utilizing a pre-trained soft measurement model to obtain a key metabolite estimation concentration value sequence; calculating to obtain a key metabolite estimation generation rate value sequence based on the key metabolite estimation concentration value sequence; forming a real-time two-dimensional state vector based on the current estimated concentration value of the key metabolite and the current estimated generation rate value of the key metabolite; comparing and evaluating the real-time two-dimensional state vector with a predefined decision logic; when the decision logic judges that a preset triggering condition is met, a decision signal for starting hydrolysis is obtained; and the optimal opportunity for starting hydrolysis is accurately captured.

Proposed is a multi-channel soft-sensor-based interface device and an operating method therefor. The glove-type multi-channel soft-sensor-based interface device can recognize the degree of flexibility of finger joints. The device may include a sensing module which includes a plurality of strain sensors and which collects strain sensing signals of the plurality of strain sensors. The device may also include a switching unit configured to select any one of the strain sensing signals of the plurality of strain sensors. The device may further include an amplification module configured to amplify the strain sensing signal selected by the switching unit. The device may further include an analog-to-digital (A / D) converter configured to convert the amplified sensing signal into a digital signal.

The application discloses a kind of microbial manufacturing process bacteria concentration perception method and system based on experience transmission, method includes: according to the historical data of microbial manufacturing process, construct bacteria concentration prediction general model, obtains its model parameters based on source system historical data training and establishes target system model structure;By introducing latent variable quantification model difference between source system and target system, a unified experience transmission probability framework is constructed;Based on the framework, the iterative algorithm combining expectation and gradient descent is used to estimate the target system parameters until convergence;The converged parameters are substituted into the general model to construct an online soft sensor, and process auxiliary variable data is collected in real time and the bacteria concentration perception value is output.The application effectively suppresses negative transfer by explicitly modeling the differences between systems, and in the industrial scenario where data is scarce and distribution is inconsistent, it realizes robust, accurate and real-time perception of bacteria concentration.

A plant process interface method according to one embodiment of the present invention comprises the steps of: loading a process design drawing; assigning attribute data to an entity shown in the process design drawing; loading a widget for displaying digital twin data via the attribute data; and displaying the process design drawing, the entity, and the widget on one canvas. The digital twin data may include sensor data, soft sensor data calculated from the sensors, or external environment data, and the widget may be a modal window expressing the digital twin data as text, an image, a table, a graph, or a 3D model.

This invention discloses a soil volumetric water content (VWC) measurement system based on LoRa-RSSI and a drone. The system comprises several underground LoRa nodes for collecting environmental monitoring data; a host computer embedded in the soil; and an aerial node carried by a small drone. Combining IoUT technology, this invention designs an innovative system for soil VWC measurement based on LoRa received signal strength and a drone. The system utilizes the changes in LoRa-RSSI between the soil's internal transmitter and the drone's aerial receiver during the drone's angular rotation. Combined with a Long Short-Term Memory (LSTM) network, it collects differential LoRa-RSSI values ​​and uses a deep learning (DL) algorithm to calculate soil VWC, achieving relatively accurate soil VWC data. This invention eliminates the need for depth measurement of VWC data, utilizes soft sensors to measure soil VWC, and offers low cost, high efficiency, and small measurement error, providing a novel approach for soil VWC measurement design.

The present invention discloses a method for predicting the number of steel plate defects based on a Gaussian-Poisson hybrid variational autoencoder. Aiming at the fact that the mapping relationship between production process data and the number of steel plate defects is different under different steel plate rolling conditions, this method proposes a Gaussian-Poisson hybrid variational autoencoder as a data-driven soft sensor model. This model uses multiple parallel Gaussian-Poisson variational autoencoders to fit the mapping relationships under different conditions, fuses the parallel outputs with the help of a weight network, and constructs a loss function with the likelihood function values of all data. The proposed method realizes the online prediction of the number of steel plate defects in actual industry through data-driven soft sensor technology and can provide an interval estimate of the number of steel plate defects.

To provide enhanced search capabilities in a process control system, a knowledge repository is generated that includes both contextual data and time series data. The contextual data organizes process plant-related data according to semantic relations between the process plant-related data and the process plant entities. When a user submits a process plant search query related to process plant entities within a process plant, search results are obtained by identifying a data set from the knowledge repository. The contextual data categorizes process parameters so that users can search for a particular process parameter category. Users can tag previous searches to execute them once again at a later time. Users can also execute queries for predicted or future states of process plant entities, batch queries regarding batch processes, soft sensor analytics and monitoring applications, parameter lifecycle applications, perturbation applications, step testing applications, or batch provisioning and scheduling applications using the knowledge repository.

Methods and systems for controlling vertical glass distribution are provided. A traversing pyrometer periodically measures a parison actual temperature after the parisons exit a blank mold. The thermal camera takes a thermal image of each glass container after the glass container exits the blow mold. A vertical glass signature extraction module extracts a vertical glass distribution signature. A parison temperature estimator determines a parison estimated temperature for each vertical glass distribution signature obtained based on the vertical glass distribution signature, a most recently measured parison actual temperature and a parison stretch time. A parison temperature summer compares the parison estimated temperature to a parison set point temperature to determine a parison temperature error. A parison temperature control controls a blank mold contact time based on the parison temperature error.

The application discloses a NOx redundancy detection method based on soft and hard sensors in an SCR denitration system, and the method comprises the following steps: constructing a NOx detection model by using a NOx analyzer and a plurality of soft sensors, selecting a most credible data from input data according to a preset switching decision, and calculating the NOx concentration at the outlet of the SCR denitration system; the switching decision in the NOx detection model comprises the following steps: calculating the quality indexes of the NOx analyzer and all the soft sensors; analyzing the process constraint condition and the mechanical constraint condition of the SCR denitration system, and eliminating uncredible input data; for each remaining input data, the NOx concentration at the outlet of the SCR denitration system is calculated, and a tolerance model is analyzed; and finally, the NOx concentration at the outlet of the SCR denitration system is output, the fault detection of the NOx analyzer is realized, and the redundant switching of the soft sensor is realized without relying on additional hardware.

A method for providing a widget interface according to an embodiment of the present invention comprises the steps of: loading a process design drawing; assigning attribute data to an entity shown in the process design drawing; loading a widget displaying digital twin data by means of the attribute data; and displaying the process design drawing, the entity, and the widget on one canvas. The digital twin data may include sensor data, soft sensor data calculated from a sensor, or external environment data, and the widget may be a modal window representing the digital twin data as text, an image, a table, a graph, or a 3D model.

The invention provides a D-pantothenic acid fermentation multi-target dynamic optimization method based on biological-digital twinning, which comprises the following steps: constructing a biological soft sensor through multiple machine learning model algorithms, and combining the constructed biological soft sensor with a neural network. Meanwhile, fermentation process conditions are optimized by applying a particle swarm algorithm and a genetic algorithm, so that the whole fermentation process simulation monitoring under data driving is realized, the problems of insufficient accuracy and poor robustness when a traditional prediction technology is used for processing complex fermentation data are effectively solved, and the predictability and controllability of the fermentation process are improved.

A sensor array for measuring a force is provided. The sensor array includes a plurality of soft sensor elements, each of the plurality of soft sensor elements including a sensor, a deformable element configured to be deformable by the force, and at least one element that reacts to the sensor to measure the force by deformation of the deformable element, and at least a part of the deformable element extends between the sensor and the at least one sensor-reacting element. Further, a method for measuring a force using the sensor array is also provided.

The present invention relates to the technical field of monitoring key indicators in industrial processes, and specifically discloses a soft measurement method for key process indicators based on the evolution of parameters of a dynamic mechanism model. The present invention establishes an embedded hybrid model based on a mechanism model and a GraphTrans model, estimates the parameters of the mechanism model through the GraphTrans model, ensuring the accuracy of the embedded hybrid model. At the same time, the mechanism model provides specific physical meanings for the dynamic mechanism model parameters output by the GraphTrans model, and completes the prediction of key indicators in industrial processes through the embedded hybrid model, solving the problem that in the traditional soft measurement method introducing a comprehensive state space, the conflict between static parameters and dynamic characteristics in the calculation process of weighted sum will restrict the application effect of the integrated mechanism model.

The present invention relates to a method for determining a volume flow (V_dot) which is specific for a volume flow of a fuel cell system (100), in particular a solid oxide fuel cell system, comprising - controlling (110), by a control unit (FCCU), a volume flow valve (30) through which the volume flow flows, in order to adjust the volume flow valve (30) in dependence on a control signal (S30), - controlling (120) a first sensor (10) by the control unit (FCCU), whereby the first sensor (10) determines a first measured value (p1, t1) which is specific for a volume flow, - controlling (130) the first sensor (10) by the control unit (FCCU), whereby the first sensor (10) transmits the first measured value (p1, t1) to the control unit (FCCU), in particular via a first data connection (D1), - controlling (140) a second sensor (20) by the control unit (FCCU), whereby the second sensor (20) determines a second measured value (p2) which is specific for the volume flow, - controlling (150) the second sensor (20) by the control unit (FCCU), whereby the second sensor (20) transmits the second measured value (p1, t1) to the control unit (FCCU), in particular via a second data connection (D2), - Determining (160), by the control unit (FCCU), a volume flow (V_dot) as a function of the control signal (S30), the first measured value (p1, t1), and the second measured value (p2).

A sensor arrangement for measuring a force is provided. The sensor arrangement comprises two or more soft sensor elements and a body, wherein the two or more soft sensor elements are at least partially located within the body and wherein the body comprises one or more openings. A contact portion of each of the two or more soft sensor elements extends through the one or more openings in the body, and the body is at least partially made of a material limiting the deformation of the contact portion of each of the two or more soft sensor elements when a force is applied to the sensor arrangement. Further, a method for manufacturing a corresponding sensor arrangement is provided.

The invention discloses a gasification furnace soft measurement method and system based on a mechanism model and electronic equipment, and relates to the technical field of industrial automation. According to the method, after a first target parameter including the specific enthalpy of cooling water before entering the water cooling wall of the gasification furnace, the specific enthalpy of water or a water-vapor mixture after cooling and total reaction heat obtained through a mechanism model of the gasification furnace is obtained, the first target parameter is input into a wall surface heat exchange model established based on the energy conservation relation of the water cooling wall; and the wall surface heat loss coefficient of the gasification furnace is obtained, and then the obtained wall surface heat loss coefficient is input into the mechanism model so as to enable the mechanism model to determine the working state of the gasification furnace. Therefore, the problems that when the working state of the gasification furnace is determined by adopting a mechanism model, the wall surface heat loss coefficient is estimated through experimental data or an empirical model, so that an estimation result is easy to distort, and production is difficult to effectively guide are solved.

Assessing sustainability performance of an enterprise is a challenging task. Embodiments of present disclosure provide a method and system for SDG performance assessment of an enterprise with a conceptually simpler data model and processing pipeline. Enterprise data collected from hard and soft sensors is mapped to appropriate indicators of the SDGs. Further, a semantic network is constructed with nodes corresponding to each indicator and edges connecting nodes belonging to same SDG. Each node of the semantic network is further linked to a first layer of a neuro fuzzy network which calculates degree of impact of the indicator on Social, Economic and Environment values. Output of the first layer activates second layer of the neuro fuzzy network which determines BBV scores indicating whether the indicator is a burden, benefit, or vulnerability. The BBV scores are transformed to a colour space to generate a colour that indicates SDG performance of the enterprise.