CN118430092A - Data general acquisition method based on MCC system - Google Patents
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Abstract
The invention discloses a data general acquisition method based on an MCC system, which relates to the technical field of data analysis and comprises the following steps: the method comprises the steps of defining targets and requirements of data acquisition, including data types, data sources and acquisition frequencies to be acquired, constructing an overall architecture of an MCC (multi-core logic controller) system, deploying hardware equipment for data acquisition, and configuring a corresponding software development environment and an MCC software library; the sensor is installed to the hardware device, and hardware device parameters including sampling rate and channel selection are configured in the software development environment. According to the invention, the MCC system is configured, key index data of the running state of equipment, the production condition of a production line and the quality condition of a product in the production process are captured in real time, a predefined threshold value is set for monitoring, and once the threshold value is exceeded, the system automatically triggers an early warning notice so as to set the threshold value according to a preset safety range, thereby timely preventing potential equipment failure or shutdown condition, and maximizing the production efficiency and the equipment reliability.
Description
Technical Field
The invention relates to the technical field of data analysis, in particular to a data general acquisition method based on an MCC system.
Background
The MCC system is also called a modularized control and communication system, is an advanced system architecture for the field of industrial automation and control, combines control and communication functions, aims to realize efficient collection, processing and transmission of data in industrial processes, is widely applied to industrial automation along with continuous development of communication technology, can be used for monitoring equipment states and running conditions in factories, can collect and analyze data in real time through connecting sensors to predict the health condition of the equipment, and is timely maintained and optimized, thereby being not only suitable for improving the efficiency and precision of industrial automation, but also supporting the requirements of modern factories on data intellectualization, sustainable development and flexible production, and enabling the industrial processes to be more intelligent and controllable.
In the prior art, as the data volume increases, the transparency of the data flow of the MCC system is reduced in the process of data acquisition and management, so that the source, the flow direction and the processing process of the data are difficult to track, and the supervision difficulty of the data quality and the accuracy is further increased.
Disclosure of Invention
The invention aims to provide a data general acquisition method based on an MCC system, which aims to solve the problems in the background technology.
In order to solve the technical problems, the invention adopts the following technical scheme:
a data general acquisition method based on MCC system includes the following steps:
Step 1, defining the targets and requirements of data acquisition, including the types of data to be acquired, data sources and acquisition frequencies, constructing an overall architecture of an MCC (multi-core logic controller) system, deploying hardware equipment for data acquisition, and configuring a corresponding software development environment and an MCC software library so as to be communicated with the hardware equipment;
Step 2, installing a sensor to a hardware device, configuring hardware device parameters in a software development environment, including sampling rate, channel selection and serial port communication parameters, ensuring normal communication with a PC end or other devices, and planning a data stream to acquire data from the hardware device to obtain a data set;
step 3, creating metadata for the data set, adding a unique metadata tag for each data item, recording the source, acquisition time and acquisition mode information of the data, performing data processing on the data in the data set, storing the processed data in a database, and creating a data backup mechanism for periodically backing up the data to prevent the data from being lost or damaged;
Step 4, monitoring the state of each link of data acquisition, processing and storage in real time, displaying a data flow diagram by using a visual interface, setting a data quality evaluation index, establishing a data quality checking mechanism, and analyzing the root cause of the data quality problem;
And 5, collecting historical data, constructing a prediction model, carrying out prediction analysis on equipment faults, production requirements and quality problems, integrating a real-time monitoring system, monitoring key indexes and prediction results in the production process, combining the monitoring data with the prediction model, testing the data acquisition performance of the MCC system, and carrying out optimization adjustment to improve the efficiency and accuracy of data acquisition.
The technical scheme of the invention is further improved as follows: in the step 1, the process of defining the data acquisition target and the demand is as follows:
Step 101, determining data type, data source and acquisition frequency of data acquisition, wherein the data type comprises temperature, humidity, pressure, current and voltage, the data source comprises MCC data acquisition equipment and a sensor, and the data acquisition frequency is set according to application requirements;
102, constructing an MCC system overall architecture, designing a system architecture comprising a data acquisition layer, a communication layer, a data processing layer and a data storage layer, and deploying hardware equipment on a production site, namely MCC data acquisition equipment and a sensor;
Step 103, configuring a software development environment according to the requirements of the hardware equipment and the application, installing an MCC software library, setting a protocol required for communication with the hardware, ensuring the normality of the communication with the hardware equipment, and being used for communication and data acquisition with the hardware equipment.
The technical scheme of the invention is further improved as follows: in the step 2, the process of collecting data from the hardware device by planning the data flow is as follows:
step 201, confirming compatibility of an interface type of a sensor and MCC data acquisition equipment, connecting an output port of the sensor to an input port of the MCC data acquisition equipment, ensuring correct connection of a power supply and a signal line of the sensor, and avoiding short circuit or wrong wiring;
step 202, configuring parameters of MCC data acquisition equipment and sensors in a software development environment, setting a sampling rate of data acquisition, configuring data acquisition channels according to the types and the number of the sensors, and configuring gains and ranges according to the output range of the sensors;
step 203, using the serial port to communicate, and configuring the baud rate, data bit, stop bit and check bit parameters of the serial port communication;
Step 204, after parameter configuration is completed, communication test is performed to ensure that communication between hardware equipment and a PC end or other equipment is normal, a test tool is used for sending and receiving data, whether the data can be transmitted correctly or not is checked, if the communication is found to have problems, debugging and repairing are performed according to error prompt or log information, problems in aspects of connection lines, interface setting, drivers and the like are checked, and corresponding adjustment or updating is performed;
Step 205, after the hardware device is installed and configured, planning a path of data flow for collecting data from the hardware device, and determining a path and a mode of data transmission from the hardware device to a software development environment and then to data storage and analysis;
And 206, starting data acquisition through configured hardware equipment and software environment, transmitting acquired data to a designated position through a serial port according to a preset sampling rate and channel selection, classifying, marking and converting the data to obtain a data set, wherein the data set is divided into a historical data set and a real-time data set, and storing the data set in a database.
The technical scheme of the invention is further improved as follows: in the step 3, the process of creating metadata by the data set is as follows:
Step 301, defining metadata data items according to the characteristics and requirements of data of a dataset, defining metadata items to be recorded, including data sources, acquisition time, acquisition mode, data format, sensor identification and data size, and creating metadata templates to provide consistent metadata structures for each metadata item to be added;
Step 302, assigning a unique identifier to each metadata item, and associating metadata with the corresponding metadata item;
step 303, performing data cleaning, conversion and standardization processing on the metadata, wherein the data cleaning is used for removing noise, errors, repeated items or missing values in the data, the data conversion is used for converting the data into a proper format or unit, the data standardization is used for performing standardization processing on the data so as to facilitate subsequent analysis or comparison, and the processed metadata is imported into a database for storage, so that the integrity and accuracy of the data are ensured;
step 304, a data backup mechanism is established, and backup operation is automatically executed periodically according to a backup strategy.
The technical scheme of the invention is further improved as follows: in the step 4, a data quality evaluation index is set, and the process of establishing a data quality inspection mechanism is as follows:
Step 401, identifying key monitoring points in a data stream, and respectively monitoring data acquisition, data processing and data storage links, wherein the data acquisition links monitor the connection state of a data source, the data acquisition frequency and the state of a sensor, the data processing links monitor the running state, the processing speed and the abnormal processing condition of a data processing pipeline, and the data storage links monitor the data storage capacity, the storage speed and the storage safety of a data storage system;
Step 402, according to the actual flow condition of the data, displaying the whole flow of the data from acquisition to storage by using a data flow diagram, and displaying the data flow diagram on a visual interface;
Step 403, defining a data quality evaluation index, comprehensively evaluating the accuracy, the completeness, the consistency, the timeliness and the availability of the data, wherein the accuracy is used for evaluating the accuracy and the precision of the data, the matching degree with the actual situation is achieved, the completeness is used for evaluating whether the data is complete or not, whether the data has missing values or not, the consistency is used for evaluating whether the data in the data set are consistent in different storage or processing stages, the timeliness is used for evaluating whether the update speed and the delay degree of the data meet real-time requirements or not, and the availability is used for evaluating the availability of data storage and access;
Step 404, formulating a data quality inspection rule according to the data quality evaluation index and combining the historical data set to score the data quality, and integrating the data quality scoring result to obtain the data quality evaluation index so as to realize real-time monitoring of the data quality;
step 405, setting a data quality grade in combination with a data quality evaluation index, setting a corresponding evaluation threshold, checking the data stream in real time to evaluate the quality of the data stream, and determining a quality evaluation result of the data stream, wherein the data quality grade is classified into a data quality grade, a B data quality grade, a C data quality grade and a D data quality grade, and the data quality grade is gradually decreased from the a grade to the D grade;
Step 406, based on the quality evaluation result of the data stream, analyzing and determining the root cause of the problem occurrence, and displaying the data quality state in the data flow graph through different color marks.
The technical scheme of the invention is further improved as follows: the data quality evaluation index is obtained by the following steps:
Extracting data samples from the data set for evaluation, scoring the accuracy, integrity, consistency, timeliness and availability evaluation indexes of each data sample, converting the scores into quantized values, and obtaining quantized scores of each evaluation index;
determining a target mean value of each evaluation index based on the historical data dataset;
Collecting a scoring data set of each evaluation index, combining the target mean value of each evaluation index to obtain the scoring data variance of each evaluation index, and squaring the scoring data variance of each evaluation index to obtain the standard deviation of each evaluation index;
and carrying out weighted summation on the normalized scores, and opening square root on the weighted summation result to obtain the data quality assessment index.
The technical scheme of the invention is further improved as follows: the calculation formula of the data quality evaluation index is as follows:
；
；
；
wherein, Represents an index of the quality assessment of the data,Representing the total number of data quality assessment indicators,Represent the firstThe weight of each data quality assessment index reflects the importance of the assessment index,Represent the firstA quantitative score for each data quality assessment indicator,Represent the firstThe target average of the individual data quality assessment indicators,Represent the firstStandard deviation of the individual data quality assessment indicators, representing the fluctuation range of the score,The range of the values is as follows，Representing the number of scoring samples,Represent the firstThe first sample is atA quantitative score for each data quality assessment indicator.
The technical scheme of the invention is further improved as follows: the plurality of data quality levels correspond to a plurality of evaluation thresholds, wherein the evaluation thresholds include an upper threshold and a lower threshold;
the plurality of data quality levels and the plurality of evaluation thresholds satisfy the following relationship:
class a data quality level ; The data quality of the A-level data quality grade is high, and green is adopted for marking;
B-stage data quality level ; The data quality of the B-level data quality grade is good, and blue is adopted for marking;
Class C data quality rating ; The data quality of the data quality grade of the C-level data is to be improved, and the data is marked by yellow;
D stage data quality level ; The data quality problem of the data quality grade of the D-level data is more, and red is adopted for marking;
wherein, The index is evaluated for the quality of the data,For a lower threshold value corresponding to a data quality level of data a and an upper threshold value corresponding to a data quality level of data B,For a lower threshold value corresponding to a B-level data quality level and an upper threshold value corresponding to a C-level data quality level,For a lower threshold value corresponding to a C-level data quality level and an upper threshold value corresponding to a D-level data quality level,，，。
The technical scheme of the invention is further improved as follows: in the step 5, the process of testing the data acquisition performance of the MCC system is as follows:
step 501, in combination with a historical data set of a class a data quality level or a class B data quality level, extracting historical data related to equipment faults, production requirements and quality problems collected by an MCC data acquisition equipment, and dividing the historical data set into a training set and a test set;
Step 502, model training is performed based on data of a machine learning algorithm and a training set, a test set is used for testing and evaluating the trained model, a trained prediction model is combined with a real-time monitoring system, key performance indexes in the production process, including the running state of equipment, the production condition of a production line and the quality condition index of a product, are determined and monitored, and the accuracy and the effectiveness of the prediction model are evaluated by comparing a prediction result with an actual result;
Step 503, configuring a real-time data stream access system, capturing key index data in the production process, setting an upper limit threshold and a lower limit threshold of a key index, and triggering an early warning mechanism when actual data exceeds the threshold;
Step 504, monitoring the running state of the equipment, the production condition of the production line and the key indexes of the quality condition of the product in real time, and adjusting and optimizing the MCC data acquisition equipment according to the test result to improve the efficiency and accuracy of data acquisition.
The technical scheme of the invention is further improved as follows: set pre-warning logic, when continuouslyThe data of each time point exceeds the control limit to trigger early warning, and the expression mode is as follows:
；
wherein, Representing the early warning signal when the value is larger thanWhen the method is used, the triggering early warning is indicated,The observed value of the time point is represented,From 1 toRepresenting a succession ofThe time point at which the time point is the same,The upper threshold value is represented as such,Represents a lower threshold value of the limit,Is a conditional expression, which indicates whenGreater thanTime takingIf the value of (1)Less than or equal toThen takeFor ensuring that the early warning logic is only entered when the observed value exceeds a threshold;
Performance of the predictive model was evaluated using poor validation:
；
wherein, Represents a cross-validation score for evaluating the average performance of the model over a plurality of data subsets,For the number of folds, the number of subsets into which the data is divided, typically 10 folds or 5 folds,Represent the firstA folded sample index set is provided that,Represent the firstThe actual value of the individual samples is calculated,Represent the firstA predicted value of each sample, the predicted value being obtained by dividingThe model obtained by training the data outside the fold is predicted,Represent the firstAverage actual value of folded sample, moleculeRepresent the firstThe sum of squares of prediction errors of all samples is compromised, and denominatorRepresent the firstThe sum of squares of the deviations of all samples from the mean value is traded for normalized prediction error,The value of (2) is in the range of 0 to 1.
By adopting the technical scheme, compared with the prior art, the invention has the following technical progress:
1. The invention provides a data general acquisition method based on an MCC system, which is characterized in that the MCC system is configured, key index data of the running state of equipment, the production condition of a production line and the quality condition of products in the production process are captured in real time, a predefined threshold value is set for monitoring, and once the threshold value is exceeded, the system automatically triggers an early warning notice so as to set the threshold value according to a preset safety range, thereby timely preventing potential equipment failure or shutdown condition and maximizing the production efficiency and the equipment reliability.
2. The invention provides a data general acquisition method based on an MCC system, which can effectively improve the accuracy and efficiency of data collection while automatically collecting and arranging a large amount of data by the MCC system, supports real-time data monitoring and analysis, can timely discover data abnormality and take corresponding processing measures, further improves the accuracy and reliability of data acquisition, further supports deep data analysis and production process optimization, is beneficial to identifying possible production problems or potential problems in the production process, and can quickly discover potential problems in the production process so as to adjust production strategies and improve product quality.
3. The invention provides a data general acquisition method based on an MCC system, which supports data analysis and optimization through real-time monitoring and early warning so as to achieve the multiple effects of improving resource utilization efficiency and decision support to improve product quality.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments described in the present application, and other drawings may be obtained according to these drawings for a person having ordinary skill in the art.
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a flow chart of setting data quality assessment index and establishing data quality inspection mechanism according to the present invention;
FIG. 3 is a flow chart of the data quality assessment index acquisition of the present invention;
fig. 4 is a flow chart of the invention for testing the data acquisition performance of the MCC system.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Embodiment 1, as shown in fig. 1-3, the present invention provides a data general acquisition method based on an MCC system, comprising the following steps:
The method comprises the steps of 1, defining data acquisition targets and requirements, including data types, data sources and acquisition frequencies to be acquired, constructing an overall architecture of an MCC system, deploying hardware equipment for data acquisition, configuring a corresponding software development environment and an MCC software library so as to communicate with the hardware equipment, defining the data types, the data sources and the acquisition frequencies, wherein the data types comprise temperature, humidity, pressure, current and voltage, the data sources comprise MCC data acquisition equipment and sensors, setting the data acquisition frequencies according to application requirements, constructing the overall architecture of the MCC system, designing a system architecture comprising a data acquisition layer, a communication layer, a data processing layer and a data storage layer, deploying hardware equipment on a production site, namely MCC data acquisition equipment and sensors, configuring a software development environment according to the hardware equipment and the application requirements, installing the MCC software library, setting protocols required by communication with the hardware equipment, ensuring the communication normality with the hardware equipment, and performing communication and data acquisition with the hardware equipment;
Step 2, installing a sensor to a hardware device, configuring hardware device parameters in a software development environment, including sampling rate, channel selection, and serial port communication parameters, ensuring normal communication with a PC end or other devices, planning data flow to collect data from the hardware device to obtain a data set, confirming the compatibility of the interface type of the sensor and the MCC data collection device, connecting an output port of the sensor to an input port of the MCC data collection device, ensuring correct connection of a power supply and a signal line of the sensor, avoiding short circuit or error wiring, configuring parameters of the MCC data collection device and the sensor in the software development environment, setting the sampling rate of data collection, configuring the data collection channel according to the type and the number of the sensor, ensuring that the device can collect data according to expectations, configuring gain and measuring range according to the output range of the sensor, using the serial port for communication, configuring parameters of baud rate, data bit, stop bit and check bit of serial port communication, performing communication test after completing parameter configuration, ensuring normal communication between hardware equipment and PC end or other equipment, using test tool to send and receive data, checking whether the data can be transmitted correctly, if the communication is found to have problems, debugging and repairing according to error prompt or log information, checking problems in connection line, interface setting, driving program and the like, performing corresponding adjustment or update, planning data flow path from hardware equipment after completing hardware equipment installation and configuration, determining data transmission path and mode from hardware equipment to software development environment, then to data storage and analysis, starting data acquisition through configured hardware equipment and software environment, selecting according to preset sampling rate and channel, transmitting the acquired data to a designated position through a serial port, classifying, marking and converting the data to obtain a data set, dividing the data set into a historical data set and a real-time data set, and storing the historical data set and the real-time data set into a database;
Creating metadata for a data set, adding a unique metadata tag for each data item, recording source, acquisition time and acquisition mode information of the data, carrying out data processing on the data in the data set, storing the processed data in a database, establishing a data backup mechanism, periodically backing up the data to prevent data loss or damage, defining the data items of the metadata according to the characteristics and requirements of the data in the data set, definitely defining the metadata items needing to be recorded, including the data source, the acquisition time, the acquisition mode, the data format, a sensor identifier and the data size, creating a metadata template, providing a consistent metadata structure for each metadata item to be added, distributing a unique identifier for each metadata item, associating metadata with the corresponding metadata item, carrying out data cleaning, conversion and standardization processing on the metadata, carrying out data cleaning for removing noise, errors, repeated items or missing values in the data, carrying out data conversion into a proper backup format or unit, carrying out standardization processing on the data so as to facilitate subsequent analysis or comparison, guiding the metadata items to the metadata item to be processed into the database, and carrying out the data backup mechanism to ensure the accuracy and the integrity of the metadata to be stored according to the established policy;
Step 4, monitoring the state of each link of data acquisition, processing and storage in real time, displaying a data flow graph by using a visual interface, improving the transparency of data management so as to trace the data flow later, setting a data quality evaluation index, establishing a data quality inspection mechanism, analyzing the root cause of a data quality problem, identifying key monitoring points in the data flow, respectively monitoring data acquisition, data processing and data storage links, wherein the data acquisition links monitor the connection state of a data source, the data acquisition frequency and the state of a sensor, the data processing links monitor the running state, the processing speed and the abnormal processing condition of a data processing pipeline, the data storage links monitor the data storage capacity, the storage speed and the storage safety of a data storage system, displaying the whole flow of data from acquisition to storage by using the data flow graph according to the actual flow condition of the data, the data flow graph is displayed on a visual interface, data quality assessment indexes are defined, comprehensive assessment is carried out on the accuracy, the completeness, the consistency, the timeliness and the usability of the data, the accuracy is used for assessing the accuracy and the accuracy of the data, the matching degree with the actual situation is achieved, the completeness is used for assessing whether the data is complete or not, whether the missing value exists or not is achieved, the consistency is used for assessing whether the data in the data set are consistent in different storage or processing stages or not, the timeliness is used for assessing whether the update speed and the delay degree of the data meet real-time requirements or not, the usability is used for assessing the usability of the data storage and access, a data quality check rule is formulated according to the data quality assessment indexes and the historical data set is combined, the data quality scoring is carried out, the comprehensive data quality scoring result is obtained, the data quality assessment index is obtained, and real-time monitoring on the data quality is achieved, setting a data quality grade in combination with a data quality evaluation index, setting a corresponding evaluation threshold value, checking the data stream in real time to evaluate the quality of the data stream, and determining a quality evaluation result of the data stream, wherein the data quality grade is divided into an A-level data quality grade, a B-level data quality grade, a C-level data quality grade and a D-level data quality grade, the data quality grade is gradually decreased from the A-level to the D-level, and based on the quality evaluation result of the data stream, the root cause of the occurrence of the problem is analyzed and determined, and the data quality state is displayed in the data flow diagram through different color marks;
Further, the data quality evaluation index is obtained by the following steps:
Extracting data samples from the data set for evaluation, scoring the accuracy, integrity, consistency, timeliness and availability evaluation indexes of each data sample, converting the scores into quantized values, and obtaining quantized scores of each evaluation index;
determining a target mean value of each evaluation index based on the historical data dataset;
Collecting a scoring data set of each evaluation index, combining the target mean value of each evaluation index to obtain the scoring data variance of each evaluation index, and squaring the scoring data variance of each evaluation index to obtain the standard deviation of each evaluation index;
Carrying out weighted summation on the normalized scores, and opening square root on the weighted summation result to obtain a data quality evaluation index;
further, the calculation formula of the data quality evaluation index is:
；
；
；
wherein, Represents an index of the quality assessment of the data,Representing the total number of data quality assessment indicators,Represent the firstThe weight of each data quality assessment index reflects the importance of the assessment index,Represent the firstA quantitative score for each data quality assessment indicator,Represent the firstThe target average of the individual data quality assessment indicators,Represent the firstStandard deviation of the individual data quality assessment indicators, representing the fluctuation range of the score,The range of the values is as follows，Representing the number of scoring samples,Represent the firstThe first sample is atThe quantitative scores of the individual data quality assessment indicators, it should be noted,Indicating that the quality of the data is very poor,Representing excellent data quality, highThe value is close toIndicating that the data quality is near the target level on all evaluation indexes, lowThe value is close toIndicating that the data quality has problems on a plurality of evaluation indexes, and needs to be improved;
further, the plurality of data quality levels corresponds to a plurality of evaluation thresholds, wherein the evaluation thresholds include an upper threshold and a lower threshold;
The plurality of data quality levels and the plurality of evaluation thresholds satisfy the following relationship:
class a data quality level ; The data quality of the A-level data quality grade is high, and green is adopted for marking;
B-stage data quality level ; The data quality of the B-level data quality grade is good, and blue is adopted for marking;
Class C data quality rating ; The data quality of the data quality grade of the C-level data is to be improved, and the data is marked by yellow;
D stage data quality level ; The data quality problem of the data quality grade of the D-level data is more, and red is adopted for marking;
wherein, The index is evaluated for the quality of the data,For a lower threshold value corresponding to a data quality level of data a and an upper threshold value corresponding to a data quality level of data B,For a lower threshold value corresponding to a B-level data quality level and an upper threshold value corresponding to a C-level data quality level,For a lower threshold value corresponding to a C-level data quality level and an upper threshold value corresponding to a D-level data quality level,，，；
And 5, collecting historical data, constructing a prediction model, carrying out prediction analysis on equipment faults, production requirements and quality problems, integrating a real-time monitoring system, monitoring key indexes and prediction results in the production process, combining the monitoring data with the prediction model, testing the data acquisition performance of the MCC system, and carrying out optimization adjustment to improve the efficiency and accuracy of data acquisition.
In embodiment 2, as shown in fig. 4, on the basis of embodiment 1, the present invention provides a technical scheme: preferably, in step 5, the process of testing the data acquisition performance of the MCC system is as follows:
Combining a historical data set of a class A data quality grade or a class B data quality grade, extracting historical data related to equipment faults, production requirements and quality problems collected by MCC data acquisition equipment, dividing the historical data set into a training set and a testing set, performing model training based on data of a machine learning algorithm and the training set, performing test evaluation on the trained model by using the testing set, combining a trained prediction model with a real-time monitoring system, determining and monitoring key performance indexes in the production process, including the running state of equipment, the production condition of a production line and the quality condition index of a product, comparing the prediction result with an actual result, evaluating the accuracy and the effectiveness of the prediction model, configuring a real-time data stream access system, capturing key index data in the production process, setting an upper limit threshold and a lower limit threshold of key indexes, triggering a pre-warning mechanism when the actual data exceeds the threshold, monitoring the running state of the equipment, the production condition of the production line and the key index of the quality condition of the product in real time, and adjusting and optimizing the MCC data acquisition equipment according to the test result, and improving the efficiency and accuracy of data acquisition;
Further, the pre-warning logic is set when continuously The data of each time point exceeds the control limit to trigger early warning, and the expression mode is as follows:
；
wherein, Representing the early warning signal when the value is larger thanWhen the method is used, the triggering early warning is indicated,The observed value of the time point is represented,From 1 toRepresenting a succession ofThe time point at which the time point is the same,The upper threshold value is represented as such,Represents a lower threshold value of the limit,Is a conditional expression, which indicates whenGreater thanTime takingIf the value of (1)Less than or equal toThen takeFor ensuring that the pre-alarm logic is only entered when the observed value exceeds a threshold,The greater the value of (2) is, the more severe or longer the duration the data exceeds the threshold;
Performance of the predictive model was evaluated using poor validation:
；
wherein, Represents a cross-validation score for evaluating the average performance of the model over a plurality of data subsets,For the number of folds, the number of subsets into which the data is divided, typically 10 folds or 5 folds,Represent the firstA folded sample index set is provided that,Represent the firstThe actual value of the individual samples is calculated,Represent the firstA predicted value of each sample, the predicted value being obtained by dividingThe model obtained by training the data outside the fold is predicted,Represent the firstAverage actual value of folded sample, moleculeRepresent the firstThe sum of squares of prediction errors of all samples is compromised, and denominatorRepresent the firstThe sum of squares of the deviations of all samples from the mean value is traded for normalized prediction error,The value of (2) ranges from 0 to 1, it should be noted that whenWhen the predicted value and the actual value of the representation model are completely consistent, namely the model performance is optimal, whenWhen the model is close to 0, the deviation between the predicted value and the actual value of the model is larger, namely the model performance is poorer,The closer to 1, the better the ability to generalize and the predictive performance of the representation model.
The foregoing is merely illustrative embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about variations or substitutions within the technical scope of the present application, and the application should be covered. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (9)
1. The utility model provides a data general acquisition method based on MCC system which is characterized in that: the method comprises the following steps:
Step 1, defining the targets and requirements of data acquisition, including the types of data to be acquired, data sources and acquisition frequencies, constructing an overall architecture of an MCC (multi-core logic controller) system, deploying hardware equipment for data acquisition, and configuring a corresponding software development environment and an MCC software library;
Step 2, installing a sensor to the hardware equipment, configuring hardware equipment parameters in a software development environment, including sampling rate, channel selection, and serial port communication parameters, and planning a data stream to acquire data from the hardware equipment to obtain a data set;
Step 3, metadata is created for the data set, unique metadata tags are added for each data item, the source, the acquisition time and the acquisition mode information of the data are recorded, data processing is carried out on the data in the data set, the processed data are stored in a database, and a data backup mechanism is established;
Step 4, monitoring the state of each link of data acquisition, processing and storage in real time, displaying a data flow diagram by using a visual interface, setting a data quality evaluation index, establishing a data quality checking mechanism, and analyzing the root cause of the data quality problem;
And 5, collecting historical data to construct a prediction model, carrying out prediction analysis on equipment faults, production requirements and quality problems, integrating a real-time monitoring system, monitoring key indexes and prediction results in the production process, combining the monitoring data with the prediction model, testing the data acquisition performance of the MCC system, and carrying out optimization adjustment.
2. The universal data collection method based on the MCC system as set forth in claim 1, wherein: in the step 1, the process of defining the data acquisition target and the demand is as follows:
Step 101, determining data type, data source and acquisition frequency of data acquisition, wherein the data type comprises temperature, humidity, pressure, current and voltage, the data source comprises MCC data acquisition equipment and a sensor, and the data acquisition frequency is set according to application requirements;
102, constructing an MCC system overall architecture, designing a system architecture comprising a data acquisition layer, a communication layer, a data processing layer and a data storage layer, and deploying hardware equipment on a production site, namely MCC data acquisition equipment and a sensor;
and 103, configuring a software development environment according to the requirements of the hardware equipment and the application, installing an MCC software library, and setting a protocol required for communication with the hardware equipment for communication and data acquisition.
3. The universal data collection method based on the MCC system as set forth in claim 2, wherein: in the step 2, the process of collecting data from the hardware device by planning the data flow is as follows:
step 201, confirming compatibility of an interface type of a sensor and MCC data acquisition equipment, and connecting an output port of the sensor to an input port of the MCC data acquisition equipment;
step 202, configuring parameters of MCC data acquisition equipment and sensors in a software development environment, setting a sampling rate of data acquisition, configuring data acquisition channels according to the types and the number of the sensors, and configuring gains and ranges according to the output range of the sensors;
step 203, using the serial port to communicate, and configuring the baud rate, data bit, stop bit and check bit parameters of the serial port communication;
Step 204, after parameter configuration is completed, communication test is carried out, a test tool is used for sending and receiving data, whether the data can be correctly transmitted or not is checked, if the communication is found to have a problem, debugging and repairing are carried out according to error prompt or log information;
Step 205, after the hardware device is installed and configured, planning a path of data flow for collecting data from the hardware device, and determining a path and a mode of data transmission from the hardware device to a software development environment and then to data storage and analysis;
And 206, starting data acquisition through configured hardware equipment and software environment, transmitting acquired data to a designated position through a serial port according to a preset sampling rate and channel selection, classifying, marking and converting the data to obtain a data set, wherein the data set is divided into a historical data set and a real-time data set, and storing the data set in a database.
4. A universal data collection method based on MCC system according to claim 3, wherein: in the step 3, the process of creating metadata by the data set is as follows:
Step 301, defining metadata data items according to the characteristics and requirements of data of a dataset, defining metadata items to be recorded, including data sources, acquisition time, acquisition mode, data format, sensor identification and data size, and creating metadata templates;
Step 302, assigning a unique identifier to each metadata item, and associating metadata with the corresponding metadata item;
Step 303, performing data cleaning, conversion and standardization processing on the metadata, and importing the processed metadata into a database for storage;
step 304, a data backup mechanism is established, and backup operation is automatically executed periodically according to a backup strategy.
5. The method for universal data collection based on MCC system according to claim 4, wherein: in the step 4, a data quality evaluation index is set, and the process of establishing a data quality inspection mechanism is as follows:
Step 401, identifying key monitoring points in a data stream, and respectively monitoring data acquisition, data processing and data storage links, wherein the data acquisition links monitor the connection state of a data source, the data acquisition frequency and the state of a sensor, the data processing links monitor the running state, the processing speed and the abnormal processing condition of a data processing pipeline, and the data storage links monitor the data storage capacity, the storage speed and the storage safety of a data storage system;
Step 402, according to the actual flow condition of the data, displaying the whole flow of the data from acquisition to storage by using a data flow diagram, and displaying the data flow diagram on a visual interface;
Step 403, defining a data quality evaluation index, and comprehensively evaluating the accuracy, the integrity, the consistency, the timeliness and the usability of the data;
Step 404, formulating a data quality inspection rule according to the data quality evaluation index and combining the historical data set to score the data quality, and integrating the data quality scoring result to obtain the data quality evaluation index so as to realize real-time monitoring of the data quality;
step 405, setting a data quality grade in combination with a data quality evaluation index, setting a corresponding evaluation threshold, checking the data stream in real time to evaluate the quality of the data stream, and determining a quality evaluation result of the data stream, wherein the data quality grade is classified into a data quality grade, a B data quality grade, a C data quality grade and a D data quality grade, and the data quality grade is gradually decreased from the a grade to the D grade;
Step 406, based on the quality evaluation result of the data stream, analyzing and determining the root cause of the problem occurrence, and displaying the data quality state in the data flow graph through different color marks.
6. The method for universal data collection based on MCC system according to claim 5, wherein: the data quality evaluation index is obtained by the following steps:
Extracting data samples from the data set for evaluation, scoring the accuracy, integrity, consistency, timeliness and availability evaluation indexes of each data sample, converting the scores into quantized values, and obtaining quantized scores of each evaluation index;
determining a target mean value of each evaluation index based on the historical data dataset;
Collecting a scoring data set of each evaluation index, combining the target mean value of each evaluation index to obtain the scoring data variance of each evaluation index, and squaring the scoring data variance of each evaluation index to obtain the standard deviation of each evaluation index;
and carrying out weighted summation on the normalized scores, and opening square root on the weighted summation result to obtain the data quality assessment index.
7. The MCC system based data general acquisition method according to claim 6, wherein: the calculation formula of the data quality evaluation index is as follows:
；
；
；
wherein, Represents an index of the quality assessment of the data,Representing the total number of data quality assessment indicators,Represent the firstThe weight of the individual data quality assessment indicators,Represent the firstA quantitative score for each data quality assessment indicator,Represent the firstThe target average of the individual data quality assessment indicators,Represent the firstStandard deviation of the individual data quality assessment indicators,The range of the values is as follows，Representing the number of scoring samples,Represent the firstThe first sample is atA quantitative score for each data quality assessment indicator.
8. The universal data collection method based on the MCC system as set forth in claim 7, wherein: the plurality of data quality levels correspond to a plurality of evaluation thresholds, wherein the evaluation thresholds include an upper threshold and a lower threshold;
the plurality of data quality levels and the plurality of evaluation thresholds satisfy the following relationship:
class a data quality level ；
B-stage data quality level；
Class C data quality rating；
D stage data quality level；
Wherein,The index is evaluated for the quality of the data,For a lower threshold value corresponding to a data quality level of data a and an upper threshold value corresponding to a data quality level of data B,For a lower threshold value corresponding to a B-level data quality level and an upper threshold value corresponding to a C-level data quality level,For a lower threshold value corresponding to a C-level data quality level and an upper threshold value corresponding to a D-level data quality level,，，。
9. The MCC system based data general acquisition method according to claim 8, wherein: in the step 5, the process of testing the data acquisition performance of the MCC system is as follows:
step 501, in combination with a historical data set of a class a data quality level or a class B data quality level, extracting historical data related to equipment faults, production requirements and quality problems collected by an MCC data acquisition equipment, and dividing the historical data set into a training set and a test set;
Step 502, model training is performed based on data of a machine learning algorithm and a training set, a test set is used for testing and evaluating the trained model, a trained prediction model is combined with a real-time monitoring system, key performance indexes in the production process, including the running state of equipment, the production condition of a production line and the quality condition index of a product, are determined and monitored, and the accuracy and the effectiveness of the prediction model are evaluated by comparing a prediction result with an actual result;
Step 503, configuring a real-time data stream access system, capturing key index data in the production process, setting an upper limit threshold and a lower limit threshold of a key index, and triggering an early warning mechanism when actual data exceeds the threshold;
Step 504, monitoring the running state of the equipment, the production condition of the production line and the key indexes of the quality condition of the product in real time, and adjusting and optimizing the MCC data acquisition equipment according to the test result.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118655856A (en) * | 2024-08-15 | 2024-09-17 | 贵州开阳川东化工有限公司 | Intelligent production process data control method and system |
| CN118760954A (en) * | 2024-09-06 | 2024-10-11 | 南通宝佳精密机械有限公司 | Quality detection method and device for electromechanical equipment |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101017373A (en) * | 2006-12-28 | 2007-08-15 | 浙江大学 | Industrial process multiresolution softsensoring instrument and method thereof |
| CN108446380A (en) * | 2018-03-17 | 2018-08-24 | 上海锐垚科技有限公司 | The system for promoting industry quality level based on modularization information storage organization |
| US20210240680A1 (en) * | 2020-01-31 | 2021-08-05 | Element Ai Inc. | Method and system for improving quality of a dataset |
| CN118092404A (en) * | 2024-04-29 | 2024-05-28 | 哈尔滨宇龙自动化有限公司 | Method and system for preventive maintenance of PLC (programmable logic controller) network based on artificial intelligence |
-
2024
- 2024-07-01 CN CN202410871960.9A patent/CN118430092B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101017373A (en) * | 2006-12-28 | 2007-08-15 | 浙江大学 | Industrial process multiresolution softsensoring instrument and method thereof |
| CN108446380A (en) * | 2018-03-17 | 2018-08-24 | 上海锐垚科技有限公司 | The system for promoting industry quality level based on modularization information storage organization |
| US20210240680A1 (en) * | 2020-01-31 | 2021-08-05 | Element Ai Inc. | Method and system for improving quality of a dataset |
| CN118092404A (en) * | 2024-04-29 | 2024-05-28 | 哈尔滨宇龙自动化有限公司 | Method and system for preventive maintenance of PLC (programmable logic controller) network based on artificial intelligence |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118655856A (en) * | 2024-08-15 | 2024-09-17 | 贵州开阳川东化工有限公司 | Intelligent production process data control method and system |
| CN118760954A (en) * | 2024-09-06 | 2024-10-11 | 南通宝佳精密机械有限公司 | Quality detection method and device for electromechanical equipment |
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