CN116777114A - Visual production management method for discrete workshops - Google Patents
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Abstract
The invention discloses a visual production management method for a discrete workshop, which relates to the technical field of visual production management.
Description
Technical Field
The invention relates to the technical field of visual production management, in particular to a visual production management method for a discrete workshop.
Background
A discrete shop (Discrete Event Simulation) is a method for simulating and analyzing a discrete event system. A discrete event system refers to a system consisting of a series of discrete, discontinuous events, where each event occurs at a particular time and may have an impact on the state of the system.
Discrete shop simulations are commonly used to study and optimize production and operational processes, particularly systems involving discrete events, such as manufacturing shops, logistics systems, service centers, and the like. The system can help analysts to know bottlenecks in the system, optimize resource utilization, evaluate the influence of different decision strategies and the like, and in discrete workshop simulation, events in the system are processed according to a certain sequence, and each event can possibly cause other events to occur. The simulation may simulate randomness between events based on a random number generator to more closely approximate the behavior of an actual system. By running multiple simulation experiments, a large amount of data can be collected and analyzed, performance indexes of the system can be evaluated, and decision support is provided.
However, the traditional workshop production management method generally relies on manual recording and manual input, is prone to error and inaccurate data, lacks real-time performance and accuracy, meanwhile, production states and key indexes in the traditional workshop production management method are generally displayed in a paper report or simple chart form, real-time, visual and comprehensive production monitoring cannot be provided, an operator often needs to spend a great deal of time and effort to sort and analyze the data, abnormal conditions and adjust production strategies are difficult to find in time, potential modes and rules in the data cannot be fully mined even if the data are sorted and analyzed, and deep analysis and decision support capability of a production process are limited, so that a discrete workshop production management method with visual analysis and decision support is needed to solve the problems.
Disclosure of Invention
The invention aims to solve the problems that the data acquisition in the prior art lacks real-time performance and accuracy, comprehensive production monitoring cannot be provided, abnormal conditions are difficult to discover in time, and a production strategy is difficult to adjust.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the invention provides data acquisition and processing, wherein a sensor and monitoring equipment are used for acquiring discrete workshop data, the monitoring data comprise equipment states, production progress and quality indexes, and the acquired data are preprocessed and cleaned;
visual display and monitoring, designing a visual interface based on the acquired data, and feeding the data back to the visual interface;
and the data analysis and decision support are carried out by utilizing the data provided by the visual interface, and the specific steps are as follows:
step 1, data analysis and mining
Collecting data of a discrete workshop, and storing the data in a data structure, wherein the data structure adopts a database and a data frame;
the statistical analysis method of descriptive statistics, correlation analysis and regression analysis is selected to carry out exploratory analysis on the data,
a data mining technology of cluster analysis, a classification algorithm and association rule mining is selected to find hidden modes and rules from the data;
generating a visual chart, a graph and an interactive visual interface according to the data analysis result;
step 2, decision support function
Generating a decision support suggestion by combining an optimization algorithm with the input and data analysis result of the visual interface;
based on the optimization target and constraint conditions, making a decision and optimizing by using a genetic algorithm;
the decision support advice is visually presented to an operator, and a chart and a dashboard are selected according to the need to display the optimization result and the prediction effect;
fault alarming and exception handling, setting fault and exception thresholds, and triggering corresponding fault alarming when the data exceeds the thresholds;
visual analysis and optimization are carried out, and the analysis and optimization of the production process are carried out by utilizing data provided by a visual interface.
The invention is further arranged to: the data acquisition and processing specifically comprises the following steps:
step 1, acquiring discrete workshop data by using a sensor and monitoring equipment
Selecting corresponding sensors and monitoring equipment according to the requirements and monitoring targets of the discrete workshops, and installing and configuring the sensors and the monitoring equipment;
determining the type of data to be acquired, including equipment state data, production progress data and quality index data;
setting sampling frequency and acquisition time interval of the sensor and the monitoring equipment on the premise of meeting the real-time and precision requirements of data acquisition;
step 2, preprocessing and cleaning the acquired data
Preprocessing the collected original data, including data cleaning, denoising and missing value supplementing;
the invention is further arranged to: the visual display and monitoring method comprises the following specific steps:
step 1, according to the requirements and key indexes of a discrete workshop, determining the data content and a visual form to be displayed, wherein the production progress, the equipment utilization rate and the quality indexes are displayed by using charts, instrument panels and progress bars;
step 2, constructing a Web interface by using JavaScript and HTML, and constructing a visual chart by using Python and a related library by using (Matplotlib, plotly, dash);
step 3, monitoring the state and the production progress of the production equipment in real time, and feeding data back to a visual interface, wherein the steps are as follows:
establishing connection with a data acquisition system to acquire real-time data update;
the acquired data are transmitted to a visual interface by using a WebSocket to update in real time;
according to the change of the data, a state chart and a real-time refreshing mode are selected to update corresponding data display elements in the visual interface;
providing interaction functions of operators and interfaces, including zooming in and out, screening and view switching;
the invention is further arranged to: the specific steps of fault alarming and exception handling are as follows:
step 1, setting fault and abnormal threshold values and monitoring real-time data
Setting fault and abnormal thresholds according to the equipment and process characteristics of the discrete workshops;
monitoring key data of a discrete workshop in real time, including temperature, pressure and vibration, and triggering corresponding fault alarm when the monitored data exceeds a preset threshold value;
step 2, marking faults and abnormal conditions in a visual interface and providing treatment suggestions
On the visual interface, selecting a chart, a graph and a color code to mark faults and abnormal conditions;
providing corresponding processing suggestions according to the types and the severity of faults and anomalies, wherein the processing suggestions adopt text prompts, warning frames and pop-up windows, and providing solutions and guidance for operators;
the invention is further arranged to: the visual analysis and optimization specifically comprises the following steps: analyzing and optimizing the production process by utilizing data provided by a visual interface, and formulating and implementing an improvement scheme based on an analysis result;
the invention is further arranged to: the specific steps of analyzing and optimizing the production process by utilizing the data provided by the visual interface are as follows:
the key data of the production process, including the yield of the production line, the equipment utilization rate and the process parameters, are loaded and displayed by using the visualization tool;
performing data exploration and analysis by using a visualization tool, including drawing a chart, making an instrument panel and constructing a thermodynamic diagram;
the invention is further arranged to: based on the analysis result, an improvement scheme is formulated and specific steps are implemented as follows:
according to the analysis result, a specific improvement scheme and a specific target are formulated;
displaying the expected effect of the improvement in the visual interface;
the improvement scheme is implemented, and the implementation effect of the improvement measures is continuously monitored and evaluated through a visual interface.
Compared with the known public technology, the technical scheme provided by the invention has the following beneficial effects:
the method provides support for decision making, generates decision making suggestions through an optimization algorithm, sets fault and abnormal thresholds at the same time, timely monitors real-time data, can timely find and process faults and abnormalities, reduces production interruption, improves production stability and reliability, utilizes data provided by a visual interface to analyze and optimize a production process, helps find improvement potential, and shows improvement effects in the interface, continuously monitors and evaluates implementation effects of improvement measures.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely, and it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. 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.
The invention provides a technical scheme that: the visual production management method for the discrete workshops is characterized by comprising the following steps of:
s1, data acquisition and processing
Step 1, collecting various data of a discrete workshop by using a sensor and monitoring equipment;
selecting corresponding sensors and monitoring equipment according to the requirements and monitoring targets of the discrete workshops, and installing and configuring the sensors and the monitoring equipment;
determining the type of data to be acquired, including equipment state data, production progress data and quality index data;
setting sampling frequency and acquisition time interval of the sensor and the monitoring equipment on the premise of meeting the real-time and precision requirements of data acquisition
Step 2, preprocessing and cleaning the acquired data;
preprocessing the collected original data, including data cleaning, denoising and missing value supplementing;
s2, visual display and monitoring
Step 1, designing a visual interface to display the production state and key indexes of a discrete workshop based on acquired data, and determining the data content and the visual form to be displayed according to the requirements and the key indexes of the discrete workshop, wherein the production progress, the equipment utilization rate and the quality indexes are displayed by using charts, instrument panels and progress bars;
step 2, constructing a Web interface by using JavaScript and HTML, and constructing a visual chart by using Python and a related library by using (Matplotlib, plotly, dash);
step 3, monitoring the state and the production progress of the production equipment in real time, and feeding data back to a visual interface, wherein the steps are as follows:
establishing connection with a data acquisition system to acquire real-time data update;
the acquired data are transmitted to a visual interface by using a WebSocket to update in real time;
according to the change of the data, a state chart and a real-time refreshing mode are selected to update corresponding data display elements in the visual interface;
and providing interaction functions of operators and interfaces, including zooming in and out, screening and view switching, so that the operators can adjust monitoring and display modes according to requirements.
S3, data analysis and decision support
Step 1, data analysis and mining
Collecting data of a discrete workshop, and storing the data in a data structure, wherein the data structure adopts a database and a data frame;
carrying out exploratory analysis on the data by adopting a statistical analysis method of descriptive statistics, correlation analysis and regression analysis to know the characteristics, the relationship and the distribution of the data;
a data mining technology of cluster analysis, a classification algorithm and association rule mining is selected to find hidden modes and rules from the data;
the modes and the rules can help understand the relation between the workshop production process and key factors, and a visual chart, a graph and an interactive visual interface are generated according to the data analysis result as required, so that the insight and the discovery of the data are intuitively displayed, the interactive function is provided based on the visual interface, and operators can adjust production parameters, optimize production plans and the like
Step 2, decision support function
Generating a decision support suggestion by combining an optimization algorithm with the input and data analysis result of the visual interface;
based on the optimization target and constraint conditions, making a decision and optimizing by using a genetic algorithm;
and visually presenting the decision support advice to an operator, and selecting a chart and displaying the optimization result and the prediction effect in a dashboard form according to the requirement.
Realizing decision support and plan output aiming at minimizing total production time in production plan, and specific Python codes:
s4, fault alarming and exception handling
Step 1, setting fault and abnormal threshold values and monitoring real-time data
Setting fault and abnormality thresholds according to the equipment and process characteristics of a discrete workshop, and selecting trigger conditions for the faults and the abnormalities, wherein the trigger conditions comprise that the temperature of the equipment exceeds a certain upper limit or lower limit, the yield is lower than an expected value and the like according to the actual situation of the workshop;
monitoring key data of a discrete workshop in real time, including temperature, pressure and vibration, and triggering corresponding fault alarm when the monitored data exceeds a preset threshold value;
step 2, marking faults and abnormal conditions in a visual interface and providing treatment suggestions
On the visual interface, a chart, a graph and a color code are selected to mark faults and abnormal conditions so that an operator can quickly identify problems;
according to the type and severity of faults and anomalies, corresponding processing suggestions are provided, the processing suggestions are selected from text prompts, warning boxes and pop-up windows, solutions and guidance are provided for operators, and the operators can take appropriate measures to solve the faults and anomalies according to the prompts and the suggestions on the visual interface so as to reduce production interruption.
Monitoring temperature sensor data, triggering fault alarm and providing processing advice when the temperature exceeds a preset threshold, and specifically providing Python codes:
the code uses a matplotlib library to map temperature and trigger fault alarms and provide process advice based on temperature values.
S5, visual analysis and optimization
Step 1, analyzing and optimizing the production process by utilizing data provided by a visual interface
The key data of the production process, including the yield of the production line, the equipment utilization rate and the process parameters, are loaded and displayed by using the visualization tool;
visualization tools are used for data exploration and analysis, including drawing charts, making dashboards, building thermodynamic diagrams to find potential problems and opportunities for improvement in the production process, analyzing correlations and trends between data, identifying bottlenecks, inefficiencies, and adverse problems, and determining potential and goals for improvement.
Step 2, based on the analysis result, making an improvement scheme and implementing
Based on the analysis result, specific improvement scheme and target are formulated
The expected effect of the improvement scheme is displayed in the visual interface, the expected increase and decrease of the output can be displayed through simulation, emulation and predictive analysis, the improvement scheme is implemented, and the implementation effect of the improvement measures is continuously monitored and evaluated through the visual interface so as to be adjusted and optimized in time.
Drawing a trend chart of yield and equipment utilization rate, and formulating an improvement scheme based on an analysis result, wherein the improvement scheme comprises specific Python codes:
in summary, the visual production management method for the discrete workshops provided by the invention provides support for decision making through data analysis and mining, potential rules and trends are mined, decision making suggestions are generated through an optimization algorithm, meanwhile, fault and abnormal thresholds are set, real-time data are monitored in time, faults and anomalies can be found and processed in time, production interruption is reduced, production stability and reliability are improved, analysis and optimization of a production process are carried out by utilizing the data provided by a visual interface, the development potential is found, the improvement effect is displayed in the interface, and the implementation effect of improvement measures is continuously monitored and evaluated.
Although the present invention has been described with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described, or equivalents may be substituted for elements thereof, and any modifications, equivalents, improvements and changes may be made without departing from the spirit and principles of the present invention.
Claims (7)
1. A method for visual production management in a discrete shop, the method comprising:
data acquisition and processing, wherein a sensor and monitoring equipment are used for acquiring discrete workshop data, the monitoring data comprise equipment states, production progress and quality indexes, and the acquired data are preprocessed and cleaned;
visual display and monitoring, designing a visual interface based on the acquired data, and feeding the data back to the visual interface;
and the data analysis and decision support are carried out by utilizing the data provided by the visual interface, and the specific steps are as follows:
step 1, data analysis and mining
Collecting data of a discrete workshop, and storing the data in a data structure, wherein the data structure adopts a database and a data frame;
the statistical analysis method of descriptive statistics, correlation analysis and regression analysis is selected to carry out exploratory analysis on the data,
a data mining technology of cluster analysis, a classification algorithm and association rule mining is selected to find hidden modes and rules from the data;
generating a visual chart, a graph and an interactive visual interface according to the data analysis result;
step 2, decision support function
Generating a decision support suggestion by combining an optimization algorithm with the input and data analysis result of the visual interface;
based on the optimization target and constraint conditions, making a decision and optimizing by using a genetic algorithm;
the decision support advice is visually presented to an operator, and a chart and a dashboard are selected according to the need to display the optimization result and the prediction effect;
fault alarming and exception handling, setting fault and exception thresholds, and triggering corresponding fault alarming when the data exceeds the thresholds;
visual analysis and optimization are carried out, and the analysis and optimization of the production process are carried out by utilizing data provided by a visual interface.
2. The visual production management method for a discrete workshop according to claim 1, wherein the specific steps of data acquisition and processing are as follows:
step 1, acquiring discrete workshop data by using a sensor and monitoring equipment
Selecting corresponding sensors and monitoring equipment according to the requirements and monitoring targets of the discrete workshops, and installing and configuring the sensors and the monitoring equipment;
determining the type of data to be acquired, including equipment state data, production progress data and quality index data;
setting sampling frequency and acquisition time interval of the sensor and the monitoring equipment on the premise of meeting the real-time and precision requirements of data acquisition;
step 2, preprocessing and cleaning the acquired data
Preprocessing the collected original data, including data cleaning, denoising and missing value supplementing.
3. The visual production management method for a discrete workshop according to claim 1, wherein the visual display and monitoring comprises the following specific steps:
step 1, according to the requirements and key indexes of a discrete workshop, determining the data content and a visual form to be displayed, wherein the production progress, the equipment utilization rate and the quality indexes are displayed by using charts, instrument panels and progress bars;
step 2, constructing a Web interface by using JavaScript and HTML, and constructing a visual chart by using Python and a related library by using (Matplotlib, plotly, dash);
step 3, monitoring the state and the production progress of the production equipment in real time, and feeding data back to a visual interface, wherein the steps are as follows:
establishing connection with a data acquisition system to acquire real-time data update;
the acquired data are transmitted to a visual interface by using a WebSocket to update in real time;
according to the change of the data, a state chart and a real-time refreshing mode are selected to update corresponding data display elements in the visual interface;
providing interactive functions of operators and interfaces, including zooming in and out, screening and switching views.
4. The visual production management method for a discrete workshop according to claim 1, wherein the specific steps of fault alarming and abnormality processing are as follows:
step 1, setting fault and abnormal threshold values and monitoring real-time data
Setting fault and abnormal thresholds according to the equipment and process characteristics of the discrete workshops;
monitoring key data of a discrete workshop in real time, including temperature, pressure and vibration, and triggering corresponding fault alarm when the monitored data exceeds a preset threshold value;
step 2, marking faults and abnormal conditions in a visual interface and providing treatment suggestions
On the visual interface, selecting a chart, a graph and a color code to mark faults and abnormal conditions;
according to the types and severity of faults and anomalies, corresponding processing suggestions are provided, and the processing suggestions adopt text prompts, warning boxes and popup windows to provide solutions and guidance for operators.
5. The method for visual production management of a discrete shop according to claim 1, wherein the visual analysis and optimization specifically comprises: and (3) utilizing the data provided by the visual interface to analyze and optimize the production process, and formulating and implementing an improvement scheme based on the analysis result.
6. The visual production management method for a discrete workshop according to claim 5, wherein the specific steps of analyzing and optimizing the production process by using the data provided by the visual interface are as follows:
the key data of the production process, including the yield of the production line, the equipment utilization rate and the process parameters, are loaded and displayed by using the visualization tool;
the visualization tool is used for data exploration and analysis, including drawing charts, making dashboards and constructing thermodynamic diagrams.
7. The visual production management method for a discrete workshop according to claim 5, wherein the improvement scheme is formulated and implemented based on the analysis result as follows:
according to the analysis result, a specific improvement scheme and a specific target are formulated;
displaying the expected effect of the improvement in the visual interface;
the improvement scheme is implemented, and the implementation effect of the improvement measures is continuously monitored and evaluated through a visual interface.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117389236A (en) * | 2023-12-11 | 2024-01-12 | 山东三岳化工有限公司 | Propylene oxide production process optimization method and system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117389236A (en) * | 2023-12-11 | 2024-01-12 | 山东三岳化工有限公司 | Propylene oxide production process optimization method and system |
| CN117389236B (en) * | 2023-12-11 | 2024-02-13 | 山东三岳化工有限公司 | Propylene oxide production process optimization method and system |
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