Dynamic multi-parameter process monitoring method and system for glass production process

Abstract

The invention relates to the technical field of data monitoring, and discloses a dynamic multi-parameter process monitoring method and system for a glass production process, and the method comprises the steps: analyzing the technological parameters of the glass production process; monitoring parameters of the process parameters are extracted, a sensor network in the glass production process is constructed, and multi-sensor signals in the glass production process are collected in real time; defining a filtering algorithm of the multi-sensor signal, filtering the multi-sensor signal, performing data fusion on the multi-filtered signal, constructing an analog-to-digital conversion module of the fused signal, and converting the fused signal into a readable signal; real-time data of the readable signals are analyzed, a parameter-time curve of the glass production process is constructed, and parameter dynamic changes of the glass production process are analyzed; and constructing an early warning mechanism of the glass production process and determining an adjustment scheme of abnormal parameters so as to execute dynamic multi-parameter monitoring of the glass production process. According to the invention, the comprehensiveness and accuracy of dynamic multi-parameter monitoring in the glass production process can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data monitoring, and in particular to a dynamic multi-parameter process monitoring method and system for a glass production process. Background Art

[0002] Dynamic multi-parameter monitoring refers to the simultaneous real-time monitoring and recording of two or more different physical or chemical parameters that change over time and are analyzed and processed by the monitoring system to achieve comprehensive understanding and control of a process, environment or system.

[0003] At present, the main monitoring method for the glass production process is machine vision technology. This method uses image processing technology to detect defects on the glass surface. Since it can only detect the quality parameters of the finished glass products and cannot monitor other parameters in the glass production process, it is impossible to accurately monitor and control the production process parameters during the glass production process, resulting in low efficiency of the production process. Summary of the invention

[0004] The present invention provides a method and system for dynamic multi-parameter process monitoring of a glass production process, the main purpose of which is to improve the comprehensiveness and accuracy of dynamic multi-parameter monitoring of a glass production process.

[0005] To achieve the above object, the present invention provides a dynamic multi-parameter process monitoring method for a glass production process, comprising: Acquire production data of a glass production process, determine production requirements of the glass production process, and analyze process parameters of the glass production process based on the production data and the production requirements; Extracting monitoring parameters of the process parameters, constructing a sensor network of the glass production process according to the monitoring parameters, configuring a clock circuit of the sensor network, and using the sensor network to collect multi-sensor signals of the glass production process in real time based on the clock circuit; Define a filtering algorithm for the multi-sensor signal, construct a filtering circuit for the multi-sensor signal based on the filtering algorithm, filter the multi-sensor signal based on the filtering circuit to obtain a multi-filtered signal, perform data fusion on the multi-filtered signal using a preset signal fusion algorithm to obtain a fused signal, construct an analog-to-digital conversion module for the fused signal, and convert the fused signal into a readable signal based on the analog-to-digital conversion module; Constructing a data analysis module for the glass production process, analyzing the real-time data of the readable signal, constructing a parameter-time curve of the glass production process using the data analysis module according to the real-time data, and analyzing the dynamic changes of the parameters of the glass production process according to the parameter-time curve; According to the dynamic changes of the parameters, an early warning mechanism for the glass production process is constructed. According to the early warning mechanism, the abnormal parameters and normal parameters of the glass production process are divided, and the adjustment plan for the abnormal parameters is determined. According to the normal parameters, the abnormal parameters and the adjustment plan, dynamic multi-parameter monitoring of the glass production process is performed.

[0006] In order to solve the above problems, the present invention also provides a dynamic multi-parameter process monitoring system for a glass production process, the system comprising: A process parameter analysis module, used to obtain production data of a glass production process, determine production requirements of the glass production process, and analyze process parameters of the glass production process based on the production data and the production requirements; A sensor signal acquisition module, used to extract monitoring parameters of the process parameters, build a sensor network of the glass production process according to the monitoring parameters, configure a clock circuit of the sensor network, and based on the clock circuit, use the sensor network to collect multi-sensor signals of the glass production process in real time; A signal conversion module, used to define a filtering algorithm for the multi-sensor signal, construct a filtering circuit for the multi-sensor signal based on the filtering algorithm, filter the multi-sensor signal based on the filtering circuit to obtain a multi-filtered signal, perform data fusion on the multi-filtered signal using a preset signal fusion algorithm to obtain a fused signal, construct an analog-to-digital conversion module for the fused signal, and convert the fused signal into a readable signal based on the analog-to-digital conversion module; A parameter dynamic change module, used to construct a data analysis module for the glass production process, analyze the real-time data of the readable signal, construct a parameter-time curve of the glass production process based on the real-time data using the data analysis module, and analyze the dynamic change of the parameters of the glass production process based on the parameter-time curve; A dynamic multi-parameter monitoring module is used to construct an early warning mechanism for the glass production process according to the dynamic changes of the parameters, divide the abnormal parameters and normal parameters of the glass production process according to the early warning mechanism, and determine the adjustment plan for the abnormal parameters, and perform dynamic multi-parameter monitoring of the glass production process according to the normal parameters, the abnormal parameters and the adjustment plan.

[0007] The embodiment of the present invention analyzes the process parameters of the glass production process based on the production data and the production demand, and can reduce the production of unqualified products and improve the overall product quality by accurately controlling the process parameters; optionally, the embodiment of the present invention constructs a sensor network for the glass production process according to the monitoring parameters, and can monitor the key parameters of the production process in real time, quickly obtain data, and realize instant control and adjustment of the production process; the embodiment of the present invention can significantly improve the quality and efficiency of signal processing by defining the filtering algorithm of the multi-sensor signal, enhance the stability and reliability of the system, and provide a more accurate basis for data fusion and decision support; the embodiment of the present invention can be based on the analog-to-digital conversion By changing the module and converting the fusion signal into a readable signal, the data management and utilization efficiency in the glass production process can be significantly improved, and strong technical support can be provided for the monitoring, optimization and automation of the production process; the embodiment of the present invention can timely discover abnormal conditions in the production process by analyzing the dynamic changes of the parameters in the glass production process according to the parameter-time curve, ensure that the product meets the quality standards, and reduce the defect rate. Finally, the embodiment of the present invention can maintain the fluctuation of parameters within the normal range by performing dynamic multi-parameter monitoring of the glass production process according to the normal parameters, the abnormal parameters and the adjustment scheme, reduce the unstable factors in the production process, and improve the stability and reliability of the production line. Therefore, the present invention can improve the comprehensiveness and accuracy of dynamic multi-parameter monitoring of the glass production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A schematic diagram of a process flow of a dynamic multi-parameter process monitoring method for a glass production process provided by an embodiment of the present invention; Figure 2 A functional module diagram of a dynamic multi-parameter process monitoring system for a glass production process provided by an embodiment of the present invention; The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0009] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0010] The embodiment of the present application provides a dynamic multi-parameter process monitoring method for a glass production process. The execution subject of the dynamic multi-parameter process monitoring method for a glass production process includes but is not limited to at least one of the electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided in the embodiment of the present application. In other words, the dynamic multi-parameter process monitoring method for a glass production process can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and big data and artificial intelligence platforms.

[0011] Reference Figure 1 FIG. 1 is a flow chart of a method for dynamic multi-parameter process monitoring of a glass production process provided by an embodiment of the present invention. In this embodiment, the method for dynamic multi-parameter process monitoring of a glass production process includes: S1. Acquire production data of a glass production process, determine production requirements of the glass production process, and analyze process parameters of the glass production process based on the production data and the production requirements.

[0012] The embodiments of the present invention can better understand the impact of different process parameters on product quality by acquiring production data of the glass production process, thereby optimizing these parameters and improving product performance and consistency. The production data refers to various information and values ​​directly related to production activities during the glass production process.

[0013] The embodiment of the present invention can help formulate a more effective production plan, improve the operating efficiency of the production line, reduce downtime, and increase production by determining the production demand of the glass production process. The production demand refers to the specific conditions and requirements that must be met in order to achieve the production target during the glass production process.

[0014] Optionally, as an embodiment of the present invention, the production demand of the glass production process may be determined by process flow analysis.

[0015] The embodiment of the present invention can reduce the generation of unqualified products and improve the overall product quality by analyzing the process parameters of the glass production process based on the production data and the production demand, and accurately controlling the process parameters. The process parameters refer to a series of key variables that affect the production process and the quality of the final product during the glass production process.

[0016] As an embodiment of the present invention, analyzing the process parameters of the glass production process based on the production data and the production demand includes: Based on the production requirements, identifying parameters to be analyzed in the glass production process; According to the parameters to be analyzed, mining parameter variable data and quality variable data of the production data; Calculating the correlation coefficient between the parameter to be analyzed and the production demand according to the parameter variable data and the quality variable data; Based on the parameter variable data and the quality variable data, construct a linear regression model between the parameter to be analyzed and the production demand; Analyze the regression coefficient and significance level of the linear regression model; Analyze the influence coefficient of the parameter to be analyzed and the production demand based on the correlation coefficient, the regression coefficient and the significance level; When the influence coefficient meets a preset influence coefficient threshold, the parameter to be analyzed is used as a process parameter of the glass production process.

[0017] The parameter to be analyzed refers to a key variable that may affect the quality and production efficiency of the final product during the glass production process. The parameter variable data refers to the actual measured or recorded data related to each process parameter during the glass production process. The quality variable data refers to the data related to the final quality of the product during the glass production process. The correlation coefficient refers to a statistic used to measure the strength and direction of the linear relationship between two variables. The linear regression model refers to a statistical analysis method used to describe the linear relationship between two or more variables. The regression coefficient refers to a parameter that describes the strength and direction of the relationship between the independent variable and the dependent variable in the regression model. The significance level refers to a probability threshold value pre-set in the hypothesis test to determine whether the result of the statistical test is significant enough to reject the null hypothesis. The influence coefficient refers to a parameter used to measure the degree of direct influence of a change in an independent variable on a change in a dependent variable. The preset influence coefficient threshold refers to a numerical standard pre-set in data analysis or model prediction to determine whether the influence of an independent variable on a dependent variable is significant or important.

[0018] Optionally, the correlation coefficient between the parameter to be analyzed and the production requirement calculated based on the parameter variable data and the quality variable data can be determined by calculating the Pearson correlation coefficient.

[0019] Optionally, the regression coefficient and significance level of the linear regression model may be analyzed by least square method and calculation of t statistics, respectively.

[0020] S2. Extract monitoring parameters of the process parameters, construct a sensor network for the glass production process according to the monitoring parameters, configure a clock circuit for the sensor network, and based on the clock circuit, use the sensor network to collect multi-sensor signals for the glass production process in real time.

[0021] The embodiment of the present invention can timely discover potential safety hazards by extracting the monitoring parameters of the process parameters, so as to take measures to prevent accidents and ensure the safety of workers. The monitoring parameters refer to key variables that need to be monitored and controlled in the glass production process, such as temperature parameters, pressure parameters, gas composition parameters, etc.

[0022] The embodiment of the present invention can monitor the key parameters of the production process in real time by constructing the sensor network of the glass production process according to the monitoring parameters, quickly obtain data, and realize instant control and adjustment of the production process. The sensor network refers to a system composed of multiple sensor nodes, which are interconnected by wired or wireless means and work together in the glass production process to monitor and control various process parameters.

[0023] As an embodiment of the present invention, the sensor network of the glass production process is constructed according to the monitoring parameters, including: Determining multiple sensors for the glass production process according to the monitoring parameters; Analyze application scenarios of the multi-sensor, and determine sensor materials and communication protocols of the multi-sensor according to the application scenarios; According to the sensor material, the multi-sensor is optimized to obtain an optimized multi-sensor; Determining the network topology of the optimized multi-sensor according to the production requirements corresponding to the glass production process; A sensor network for the glass production process is constructed according to the network topology, the optimized multi-sensor and the communication protocol.

[0024] Among them, the multi-sensor refers to a collection of various sensors used to monitor different process parameters in the glass production process. The application scenario refers to the specific environment and working conditions in which the multi-sensor will be deployed and function in the glass production process. The sensor material refers to various physical and chemical materials used to manufacture sensors, which can respond to specific process parameters. The communication protocol refers to a set of rules and standards for data transmission in a sensor network. The optimized multi-sensor refers to the configuration, adjustment and improvement of multiple sensors in a sensor network to improve the performance, reliability and efficiency of the entire sensor system. The network topology refers to the various sensor nodes in a sensor network and the connection method and layout between them.

[0025] Optionally, the sensor material and communication protocol of the multi-sensor determined according to the application scenario can be determined by application scenario analysis.

[0026] Optionally, the determining of the optimized multi-sensor network topology structure according to the production demand corresponding to the glass production process may be determined by a distributed topology method.

[0027] The embodiment of the present invention can collect data at preset time intervals by configuring the clock circuit of the sensor network, thereby ensuring the continuity and real-time performance of the data. The clock circuit refers to an electronic circuit that provides an accurate time reference for each sensor node in the sensor network, ensuring that the entire network can operate synchronously according to a predetermined time standard.

[0028] As an embodiment of the present invention, the clock circuit for configuring the sensor network includes: Analyzing a clock requirement of the sensor network, and determining a time synchronization protocol for the sensor network based on the clock requirement; According to the accuracy requirement corresponding to the clock requirement, construct a real-time clock module of the sensor network; Constructing a clock circuit topology of the sensor network and configuring circuit elements of the clock circuit topology; According to the time synchronization protocol, construct a clock management unit of the sensor network; A clock circuit of the sensor network is constructed according to the clock circuit topology, the real-time clock module, the circuit elements and the clock management unit.

[0029] Among them, the clock requirement refers to the specific requirements of the sensor network for time synchronization and clock accuracy during operation. The time synchronization protocol refers to a set of rules and algorithms used to achieve time synchronization between various sensor nodes in the network. The accuracy requirement refers to the accuracy level required for clock synchronization and timing in the sensor network. The real-time clock module refers to a specially designed circuit or chip that can operate independently without power failure and provide accurate current time information. The clock circuit topology refers to the structural layout of the distribution and transmission of clock signals in the sensor network. The circuit elements refer to the basic components that make up the electronic circuit, such as crystal oscillators, resistors, capacitors, etc. The clock management unit refers to an electronic circuit or module specifically used to manage and control clock signals.

[0030] Optionally, the real-time clock module of the sensor network is constructed using a phase-locked loop technology according to the accuracy requirement corresponding to the clock requirement.

[0031] The embodiment of the present invention can ensure that all collected data are highly consistent in time by using the sensor network to collect multi-sensor signals of the glass production process in real time based on the clock circuit. This synchronization allows the data captured by different sensors to correspond accurately, providing a solid foundation for subsequent data analysis and processing. The multi-sensor signal refers to the signals of various physical or chemical parameters collected by multiple sensor units during the production process.

[0032] S3. Define a filtering algorithm for the multi-sensor signal, construct a filtering circuit for the multi-sensor signal based on the filtering algorithm, filter the multi-sensor signal based on the filtering circuit to obtain a multi-filtered signal, perform data fusion on the multi-filtered signal using a preset signal fusion algorithm to obtain a fused signal, construct an analog-to-digital conversion module for the fused signal, and convert the fused signal into a readable signal based on the analog-to-digital conversion module.

[0033] The embodiment of the present invention can significantly improve the quality and efficiency of signal processing, enhance the stability and reliability of the system, and provide a more accurate basis for data fusion and decision support by defining the filtering algorithm of the multi-sensor signal. The filtering algorithm refers to a series of mathematical methods and calculation processes for processing multi-sensor signals, which aims to improve the quality of the signal through specific operations to make it more suitable for subsequent processing and analysis.

[0034] As an embodiment of the present invention, the filtering algorithm for defining the multi-sensor signal includes: determining a filter for the multi-sensor signal and analyzing a filter type of the filter; Determining a feedforward coefficient and a feedback coefficient of the filter according to the filter type; According to the feedforward coefficient and the feedback coefficient, the initial filtering algorithm of the filter is defined by the following formula: ; in, Represents the filter at time point The output signal, Represents the filter at time point The input signal, From the current time point The number of input signals to trace forward, Represents the filter at time point The output signal, From the current time point The number of output signals to trace back, represents the feedforward coefficient, represents the feed-back coefficient, represents the number of feedforward coefficients, Indicates the number of feedback coefficients; Using preset experimental data to test the algorithm performance of the initial filtering algorithm; When the algorithm performance meets the preset algorithm performance standard, the initial filtering algorithm is used as the filtering algorithm for the multi-sensor signal.

[0035] Among them, the filter refers to a digital or analog device used to process multi-sensor signals. The filter type refers to the category of filters used to achieve specific filtering purposes in signal processing. The feedforward coefficient refers to the coefficient used to describe the relationship between the current and previous input signals and the filter output. The feedback coefficient refers to the coefficient used to describe the internal feedback path of the filter. The initial filtering algorithm refers to the mathematical model of the filter and its corresponding calculation steps preliminarily determined based on the filter type and expected performance. The algorithm performance refers to the performance and effect of the initial filtering algorithm in achieving its design goals. The preset algorithm performance standard refers to the performance requirements or indicators pre-set according to specific application scenarios and requirements.

[0036] Optionally, the feedforward coefficient and the feedback coefficient of the filter determined according to the filter type may be determined by an adaptive filter technology.

[0037] The embodiment of the present invention can allow signals within a specific frequency range to pass through and filter out other frequency components by constructing the filtering circuit of the multi-sensor signal based on the filtering algorithm, thereby achieving bandwidth selection. The filtering circuit refers to an electronic circuit designed to process the input signal to achieve the above-mentioned technical effect.

[0038] Optionally, as an embodiment of the present invention, the filtering circuit for constructing the multi-sensor signal based on the filtering algorithm may be constructed by intelligent system integration technology.

[0039] The embodiment of the present invention can improve the quality of the multi-sensor signals and enhance the performance of the entire signal processing system by filtering the multi-sensor signals based on the filtering circuit to obtain multi-filtered signals. The multi-filtered signals refer to multiple sensor signals after filtering.

[0040] The embodiment of the present invention uses a preset signal fusion algorithm to fuse the multi-filtered signals to obtain a fused signal, which can make up for the lack of coverage or the limitation of perception ability of a single sensor, and reduce the error or uncertainty that may exist in a single sensor. The fused signal refers to a single signal obtained after processing by the signal fusion algorithm.

[0041] As an embodiment of the present invention, the method of using a preset signal fusion algorithm to perform data fusion on the multiple filter signals to obtain a fused signal includes: Performing normalization processing on the multiple filtered signals to obtain multiple normalized signals; Determining fusion algorithm parameters of the preset signal fusion algorithm, wherein the fusion algorithm parameters include: an observation matrix, a signal weight, and a noise covariance; According to the preset signal fusion algorithm, the multi-normalized signals are calculated to obtain an initial fusion signal, and the signal covariance of the initial fusion signal is calculated;

[0042] According to the fusion algorithm parameters and the signal covariance, the initial fusion signal is updated using the following formula to obtain a fusion signal: ; in, represents the fusion signal, represents the observation matrix corresponding to the algorithm parameters, Indicates Sensors, Indicates The signal weight of each sensor, Indicates that the multi-normalized signal corresponds to Sensors at time The normalized signal, represents the transposed matrix of the observation matrix, represents the noise covariance, represents the previous level fusion signal of the previous time step, represents the covariance of the previous fusion signal, Indicates the total number of sensors.

[0043] Among them, the multi-normalized signal refers to a signal set obtained by normalizing multiple sensor signals. The fusion algorithm parameters refer to a series of configuration parameters used in the signal fusion process. The observation matrix refers to a matrix that maps the state vector of the system to the observation space. The signal weight refers to a matrix used to determine the importance or confidence of each sensor signal in the fusion process. The noise covariance refers to a matrix used to describe the uncertainty of sensor observations. The initial fusion signal refers to the first fusion result obtained by preliminary processing and integration of multiple sensor data at the initial stage of the signal fusion process. The signal covariance refers to a statistic that describes the relationship between the components of the signal vector.

[0044] Optionally, the calculating of the signal covariance of the initial fusion signal may be performed by maximum likelihood estimation.

[0045] The embodiment of the present invention can provide more refined signal quantization by constructing the analog-to-digital conversion module of the fusion signal, thereby improving the measurement accuracy of the overall system. The analog-to-digital conversion module refers to an electronic device or circuit whose main function is to convert a continuous analog signal into a discrete digital signal.

[0046] As an embodiment of the present invention, the analog-to-digital conversion module for constructing the fusion signal includes: Analyzing signal characteristics of the fused signal, and constructing a signal conditioning port of the fused signal based on the signal characteristics; Based on the signal conditioning port, signal conditioning is performed on the fused signal to obtain a conditioned signal; Determining a conversion accuracy and a conversion resolution of the conditioned signal, and determining an analog-to-digital converter of the conditioned signal according to the conversion accuracy and the conversion resolution; Converting the conditioned signal using the analog-to-digital converter to obtain a converted output signal; Constructing a digital processing port for the converted output signal; An analog-to-digital conversion module for the fused signal is constructed according to the signal conditioning port, the analog-to-digital converter and the digital processing port.

[0047] Among them, the signal characteristics refer to a series of signal properties that need to be analyzed and understood before building an analog-to-digital conversion module for fusion signals. The signal conditioning port refers to the part of the analog-to-digital conversion module that receives the original analog signal and performs necessary preprocessing. The conditioned signal refers to the signal processed by the signal conditioning port. The conversion accuracy refers to the accuracy that can be achieved in the process of converting an analog signal to a digital signal. The conversion resolution refers to the minimum input voltage change that can be distinguished when converting an analog signal to a digital signal. The analog-to-digital converter refers to an electronic device whose function is to convert a continuous analog signal into a discrete digital signal. The conversion output signal refers to the digital signal generated after completing the conversion process from an analog signal to a digital signal. The digital processing port refers to an interface for connecting the output of the analog-to-digital converter with a digital signal processing system (such as a microprocessor, a digital signal processor, a field programmable gate array (FPGA) or other digital circuit).

[0048] Optionally, the analyzing of the signal characteristics of the fused signal may be performed by a spectrum analyzer.

[0049] Optionally, the conversion accuracy and conversion resolution of the conditioned signal may be determined by static testing and dynamic testing methods.

[0050] The embodiment of the present invention can significantly improve the data management and utilization efficiency in the glass production process by converting the fused signal into a readable signal based on the analog-to-digital conversion module, and provide strong technical support for the monitoring, optimization and automation of the production process. The readable signal refers to a signal that can be recognized and interpreted by a system, device or human.

[0051] S4. Construct a data analysis module for the glass production process, analyze the real-time data of the readable signal, and construct a parameter-time curve of the glass production process based on the real-time data using the data analysis module. According to the parameter-time curve, analyze the dynamic changes of the parameters of the glass production process.

[0052] The embodiment of the present invention can improve the automation and intelligence level of the glass production process by constructing the data analysis module of the glass production process, and realize refined management and efficient production, which has important technical significance and economic benefits. The data analysis module refers to a system for extracting valuable information from the glass production process and analyzing the information to better understand, control and optimize the production process.

[0053] Optionally, as an embodiment of the present invention, the data analysis module for constructing the glass production process can be constructed by big data processing technology.

[0054] The embodiment of the present invention can quickly find the bottleneck in the production process by analyzing the real-time data of the readable signal, and adjust the production line in time, thereby improving production efficiency. The real-time data refers to data directly related to production activities that is continuously collected by sensors, monitoring equipment or other data collection means during the glass production process.

[0055] Optionally, as an embodiment of the present invention, the real-time data of analyzing the readable signal may be analyzed by a data analysis method.

[0056] The embodiment of the present invention can monitor the changes of key parameters in the production process in real time by constructing the parameter-time curve of the glass production process using the data analysis module according to the real-time data, so as to ensure that the production is carried out according to the predetermined target. The parameter-time curve refers to a curve showing the trend of the key production parameters changing over time in the glass production process in the form of a chart.

[0057] As an embodiment of the present invention, constructing a parameter-time curve of the glass production process using the data analysis module according to the real-time data includes: Based on the data analysis module, analyzing data characteristics of the real-time data; Calculating the moving average of the real-time data according to the data characteristics; Analyze the autocorrelation function of the real-time data according to the time series analysis technology corresponding to the data analysis module; Analyzing the hysteresis effect and data stability of the real-time data according to the autocorrelation function and the moving average; According to the hysteresis effect and the data stability, constructing a multi-parameter-time curve of the glass production process respectively; According to the time coordinate axis corresponding to the multi-parameter-time curve, the multi-parameter-time curve is integrated into a parameter-time curve; Among them, the data characteristics refer to the statistical and characteristic properties of real-time data. The moving average refers to a commonly used statistical tool for analyzing time series data, which reveals the basic trends and patterns of data by calculating the average value of data within a certain period of time. The autocorrelation function refers to a concept in time series analysis, which is used to measure the correlation between consecutive time points in a sequence. The lag effect refers to the phenomenon that the influence of one variable on another variable does not occur immediately, but is delayed in time. The data stationarity refers to the characteristic that the statistical properties of time series data do not change over time. The multi-parameter-time curve refers to the time series curve drawn for each key parameter.

[0058] Optionally, the moving average of the real-time data calculated according to the data characteristics may be calculated by weighted moving average and exponential moving average.

[0059] The embodiment of the present invention can timely discover abnormal conditions in the production process by analyzing the dynamic changes of parameters in the glass production process according to the parameter-time curve, ensure that the product meets the quality standards, and reduce the defect rate. The dynamic changes of parameters refer to the changes of various key parameters over time in the glass production process.

[0060] As an embodiment of the present invention, analyzing the dynamic change of parameters in the glass production process according to the parameter-time curve includes: Analyzing the change trend and change periodicity of the parameter-time curve, and calculating the curve slope of the parameter-time curve; Analyzing the parameter correlation of the glass production process based on the parameter-time curve; Analyzing the parameter prediction trend of the glass production process according to the change trend, the change periodicity, the curve slope and the parameter correlation; Identifying a sudden change point of the parameter-time curve; Identifying abnormal events in the glass production process according to the moving average corresponding to the parameter-time curve and the mutation point; According to the parameter prediction trend and the abnormal event, the dynamic change of the parameters of the glass production process is determined.

[0061] Among them, the change trend refers to the overall directionality or pattern of key parameters over time in the glass production process. The change periodicity refers to the regular repetitive pattern of parameter values ​​in time series data. The parameter correlation refers to the relationship or dependency between different parameters in the glass production process. The slope of the curve usually refers to the slope of the tangent of the curve at a certain point, which is a value that measures the rate of change of the curve at that point. The parameter prediction trend refers to the prediction of the direction and degree of change of a specific parameter in the future. The mutation point refers to a specific time point in the data sequence in data analysis, before and after which the statistical characteristics of the data (such as mean, variance, distribution, etc.) have changed significantly. The abnormal event refers to an event that does not conform to the normal pattern or expected behavior in the data set or actual application scenario.

[0062] Optionally, the analysis of the change trend and change periodicity of the parameter-time curve may be analyzed by an autoregressive integrated moving average model.

[0063] Optionally, the analysis of the parameter correlation of the glass production process based on the parameters of the parameter-time curve can be performed by calculating the correlation coefficient between the parameters.

[0064] S5. According to the dynamic changes of the parameters, an early warning mechanism for the glass production process is constructed. According to the early warning mechanism, the abnormal parameters and normal parameters of the glass production process are divided, and an adjustment plan for the abnormal parameters is determined. According to the normal parameters, the abnormal parameters and the adjustment plan, dynamic multi-parameter monitoring of the glass production process is performed.

[0065] The embodiment of the present invention can timely discover abnormal situations in the production process by constructing an early warning mechanism for the glass production process according to the dynamic changes of the parameters, so as to take measures to adjust and ensure the stable operation of the production line. The early warning mechanism refers to a systematic method for monitoring and controlling potential risks and abnormalities in the glass production process.

[0066] Optionally, as an embodiment of the present invention, the early warning mechanism for the glass production process according to the dynamic change of the parameter can be constructed by setting early warning thresholds and levels.

[0067] The embodiment of the present invention can quickly take measures to correct problems and reduce production interruptions and equipment failures by dividing the abnormal parameters and normal parameters of the glass production process according to the early warning mechanism, thereby improving the stability of the entire production process. The abnormal parameters refer to parameter values ​​that exceed the normal working range or standard during the glass production process. The normal parameters refer to parameter values ​​that meet the production process requirements, equipment and material standards during the glass production process, and can ensure smooth production and product quality to achieve the expected goals.

[0068] The embodiment of the present invention can quickly take adjustment measures for the abnormal parameters by determining the adjustment plan of the abnormal parameters, reduce delays in the production process, and quickly restore to a normal production state. The adjustment plan refers to a series of specific operational measures and plans formulated to correct the abnormal parameters that occur in the glass production process.

[0069] The embodiment of the present invention can maintain parameter fluctuations within a normal range by performing dynamic multi-parameter monitoring of the glass production process based on the normal parameters, the abnormal parameters and the adjustment scheme, reduce unstable factors in the production process, and improve the stability and reliability of the production line.

[0070] The embodiment of the present invention analyzes the process parameters of the glass production process based on the production data and the production demand, and can reduce the production of unqualified products and improve the overall product quality by accurately controlling the process parameters; optionally, the embodiment of the present invention constructs a sensor network for the glass production process according to the monitoring parameters, and can monitor the key parameters of the production process in real time, quickly obtain data, and realize instant control and adjustment of the production process; the embodiment of the present invention can significantly improve the quality and efficiency of signal processing by defining the filtering algorithm of the multi-sensor signal, enhance the stability and reliability of the system, and provide a more accurate basis for data fusion and decision support; the embodiment of the present invention can be based on the analog-to-digital conversion By changing the module and converting the fusion signal into a readable signal, the data management and utilization efficiency in the glass production process can be significantly improved, and strong technical support can be provided for the monitoring, optimization and automation of the production process; the embodiment of the present invention can timely discover abnormal conditions in the production process by analyzing the dynamic changes of the parameters in the glass production process according to the parameter-time curve, ensure that the product meets the quality standards, and reduce the defect rate. Finally, the embodiment of the present invention can maintain the fluctuation of parameters within the normal range by performing dynamic multi-parameter monitoring of the glass production process according to the normal parameters, the abnormal parameters and the adjustment scheme, reduce the unstable factors in the production process, and improve the stability and reliability of the production line. Therefore, the present invention can improve the comprehensiveness and accuracy of dynamic multi-parameter monitoring of the glass production process.

[0071] like Figure 2, which is a functional module diagram of a dynamic multi-parameter process monitoring system for a glass production process provided by an embodiment of the present invention.

[0072] The dynamic multi-parameter process monitoring system 200 for glass production process of the present invention can be installed in an electronic device. According to the functions to be implemented, the dynamic multi-parameter process monitoring system 200 for glass production process can include a process parameter analysis module 201, a sensor signal acquisition module 202, a signal conversion module 203, a parameter dynamic change module 204 and a dynamic multi-parameter monitoring module 205. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by a processor of an electronic device and can complete fixed functions, which are stored in the memory of the electronic device.

[0073] In this embodiment, the functions of each module / unit are as follows: The process parameter analysis module 201 is used to obtain production data of a glass production process, determine production requirements of the glass production process, and analyze process parameters of the glass production process based on the production data and the production requirements; The sensor signal acquisition module 202 is used to extract the monitoring parameters of the process parameters, build a sensor network of the glass production process according to the monitoring parameters, configure the clock circuit of the sensor network, and use the sensor network to collect multi-sensor signals of the glass production process in real time based on the clock circuit; The signal conversion module 203 is used to define a filtering algorithm for the multi-sensor signal, construct a filtering circuit for the multi-sensor signal based on the filtering algorithm, filter the multi-sensor signal based on the filtering circuit to obtain a multi-filtered signal, perform data fusion on the multi-filtered signal using a preset signal fusion algorithm to obtain a fused signal, construct an analog-to-digital conversion module for the fused signal, and convert the fused signal into a readable signal based on the analog-to-digital conversion module; The parameter dynamic change module 204 is used to construct a data analysis module for the glass production process, analyze the real-time data of the readable signal, and construct a parameter-time curve of the glass production process based on the real-time data using the data analysis module, and analyze the dynamic change of the parameters of the glass production process based on the parameter-time curve; The dynamic multi-parameter monitoring module 205 is used to construct an early warning mechanism for the glass production process according to the dynamic changes of the parameters, divide the abnormal parameters and normal parameters of the glass production process according to the early warning mechanism, and determine the adjustment plan for the abnormal parameters, and perform dynamic multi-parameter monitoring of the glass production process according to the normal parameters, the abnormal parameters and the adjustment plan.

[0074] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A dynamic multi-parameter process monitoring method for a glass production process, characterized in that: The method comprises: Acquire production data of a glass production process, determine production requirements of the glass production process, and analyze process parameters of the glass production process based on the production data and the production requirements; Extracting monitoring parameters of the process parameters, constructing a sensor network of the glass production process according to the monitoring parameters, configuring a clock circuit of the sensor network, and using the sensor network to collect multi-sensor signals of the glass production process in real time based on the clock circuit; Constructing a data analysis module for the glass production process, analyzing the real-time data of the readable signal, constructing a parameter-time curve of the glass production process using the data analysis module according to the real-time data, and analyzing the dynamic changes of the parameters of the glass production process according to the parameter-time curve; According to the dynamic changes of the parameters, an early warning mechanism for the glass production process is constructed. According to the early warning mechanism, the abnormal parameters and normal parameters of the glass production process are divided, and the adjustment plan for the abnormal parameters is determined. According to the normal parameters, the abnormal parameters and the adjustment plan, dynamic multi-parameter monitoring of the glass production process is performed.

2. The dynamic multi-parameter process monitoring method for glass production process according to claim 1, characterized in that: Define a filtering algorithm for the multi-sensor signal, construct a filtering circuit for the multi-sensor signal based on the filtering algorithm, filter the multi-sensor signal based on the filtering circuit to obtain a multi-filtered signal, perform data fusion on the multi-filtered signal using a preset signal fusion algorithm to obtain a fused signal, construct an analog-to-digital conversion module for the fused signal, and convert the fused signal into a readable signal based on the analog-to-digital conversion module.

3. The dynamic multi-parameter process monitoring method for glass production process according to claim 2, characterized in that: The analyzing the process parameters of the glass production process based on the production data and the production demand includes: Based on the production requirements, identifying parameters to be analyzed in the glass production process; According to the parameters to be analyzed, mining parameter variable data and quality variable data of the production data; The correlation coefficient between the parameter to be analyzed and the production requirement is calculated according to the parameter variable data and the quality variable data.

4. The dynamic multi-parameter process monitoring method for glass production process according to claim 3, characterized in that: The step of analyzing the process parameters of the glass production process based on the production data and the production demand further includes: Based on the parameter variable data and the quality variable data, construct a linear regression model between the parameter to be analyzed and the production demand; The regression coefficient and significance level of the linear regression model were analyzed.

5. The dynamic multi-parameter process monitoring method for glass production process according to claim 4, characterized in that: The analyzing the process parameters of the glass production process based on the production data and the production demand further includes: analyzing the influence coefficient of the parameter to be analyzed and the production demand based on the correlation coefficient, the regression coefficient and the significance level; When the influence coefficient meets a preset influence coefficient threshold, the parameter to be analyzed is used as a process parameter of the glass production process.

6. The method for dynamic multi-parameter process monitoring of glass production process according to claim 5, characterized in that: The step of constructing a sensor network for the glass production process according to the monitoring parameters includes: Determining multiple sensors for the glass production process according to the monitoring parameters; Analyze application scenarios of the multi-sensor, and determine sensor materials and communication protocols of the multi-sensor according to the application scenarios; The multi-sensor is optimized according to the sensor material to obtain an optimized multi-sensor.

7. The dynamic multi-parameter process monitoring method for glass production process according to claim 6, characterized in that: The step of constructing a sensor network for the glass production process according to the monitoring parameters includes: According to the production requirements corresponding to the glass production process, the network topology structure of the optimized multi-sensor is determined, A sensor network for the glass production process is constructed according to the network topology, the optimized multi-sensor and the communication protocol.

8. The dynamic multi-parameter process monitoring method for glass production process according to claim 2, characterized in that: The clock circuit configured for the sensor network includes: Analyzing a clock requirement of the sensor network, and determining a time synchronization protocol for the sensor network based on the clock requirement; According to the accuracy requirement corresponding to the clock requirement, a real-time clock module of the sensor network is constructed.

9. The dynamic multi-parameter process monitoring method for glass production process according to claim 8, characterized in that: The clock circuit configuring the sensor network further includes: Constructing a clock circuit topology of the sensor network and configuring circuit elements of the clock circuit topology; According to the time synchronization protocol, construct a clock management unit of the sensor network; A clock circuit of the sensor network is constructed according to the clock circuit topology, the real-time clock module, the circuit elements and the clock management unit.

10. A dynamic multi-parameter process monitoring system for glass production process, characterized in that: The system is used to implement the dynamic multi-parameter process monitoring method for a glass production process as claimed in any one of claims 1 to 9, comprising: A process parameter analysis module, used to obtain production data of a glass production process, determine production requirements of the glass production process, and analyze process parameters of the glass production process based on the production data and the production requirements; A sensor signal acquisition module, used to extract monitoring parameters of the process parameters, build a sensor network of the glass production process according to the monitoring parameters, configure a clock circuit of the sensor network, and based on the clock circuit, use the sensor network to collect multi-sensor signals of the glass production process in real time; A signal conversion module, used to define a filtering algorithm for the multi-sensor signal, construct a filtering circuit for the multi-sensor signal based on the filtering algorithm, filter the multi-sensor signal based on the filtering circuit to obtain a multi-filtered signal, perform data fusion on the multi-filtered signal using a preset signal fusion algorithm to obtain a fused signal, construct an analog-to-digital conversion module for the fused signal, and convert the fused signal into a readable signal based on the analog-to-digital conversion module; A parameter dynamic change module, used to construct a data analysis module for the glass production process, analyze the real-time data of the readable signal, construct a parameter-time curve of the glass production process based on the real-time data using the data analysis module, and analyze the dynamic change of the parameters of the glass production process based on the parameter-time curve; A dynamic multi-parameter monitoring module is used to construct an early warning mechanism for the glass production process according to the dynamic changes of the parameters, divide the abnormal parameters and normal parameters of the glass production process according to the early warning mechanism, and determine the adjustment plan for the abnormal parameters, and perform dynamic multi-parameter monitoring of the glass production process according to the normal parameters, the abnormal parameters and the adjustment plan.

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