WO2020172913A1 - 一种基于组态的生产指标可视化监控系统及方法 - Google Patents

一种基于组态的生产指标可视化监控系统及方法 Download PDF

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WO2020172913A1
WO2020172913A1 PCT/CN2019/077739 CN2019077739W WO2020172913A1 WO 2020172913 A1 WO2020172913 A1 WO 2020172913A1 CN 2019077739 W CN2019077739 W CN 2019077739W WO 2020172913 A1 WO2020172913 A1 WO 2020172913A1
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indicators
production
module
index
monitoring
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PCT/CN2019/077739
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French (fr)
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徐泉
秦莹
丁进良
初延刚
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东北大学
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/02—Agriculture; Fishing; Forestry; Mining

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  • the invention relates to the technical field of production index monitoring, in particular to a configuration-based visual monitoring system and method for production indexes.
  • the production index visualization monitoring system is the most frequent link in the industrial process, and it is also a window that directly reflects the quality and output of the final product of the industrial process. At present, there are few domestic research and applications on the production index visualization monitoring system and method and the function is single .
  • "201711283037.X (a visual analysis system and method for mineral processing production indicators)” realizes the integration and configuration of various process indicators of mineral processing production and analyzes and visualizes abnormal indicators.
  • “201811009657.9 (a visualization system and method for the correlation of mineral processing production indicators)” realizes the analysis of the correlation between mineral processing production indicators, and the analysis of the time series relationship between production indicators, and finally the relationship between the analyzed production indicators Visible.
  • 201811010246.1 (a visual monitoring system and method for mineral processing production indicators based on process flow) provides a visual configuration design of production indicators based on production process flow, and supports visualization schemes with statistical characteristics of real-time data, historical data, and historical data.
  • Production indicator monitoring Configure the production indicator monitoring algorithm to realize the monitoring of production indicators.
  • the above patents mainly include the monitoring of abnormal mineral processing production indicators and provide alarm processing, visual analysis of the relationship between production indicators, 201811010246.1 mainly provides production process configuration tools, and real-time and historical monitoring of indicator data; multiple views Visibility is limited to the classified monitoring of indicators.
  • the above-mentioned patent lacks the complete logical structure of each process, lacks the detection of production index data, the visualization scheme of production index is limited to displaying real-time data and historical data, and the multi-view monitoring is limited to the index classification level, and does not involve Analysis and feedback of monitoring results.
  • the technical problem to be solved by the present invention is to provide a configuration-based visual monitoring system and method for production indicators in order to achieve visual monitoring of production indicators in view of the above-mentioned shortcomings of the prior art.
  • the present invention provides a configuration-based visual monitoring system for production indicators, including a factory basic information module, a visual monitoring configuration design environment module, and data detection Module, visual and visual analysis module, production index monitoring module, and monitoring result analysis module;
  • the factory basic information module is used to model the basic information of the factory and realize the management of the basic information of the factory;
  • the visual monitoring group The state design environment module is used to construct each process flow based on the basic information module of the factory, use the logical relationship of the production process flow to construct the flow of each process and its sub-processes, and configure the equipment, production indicators and alarms of each process and its sub-processes Events, production process rule constraints, expert experience knowledge and algorithm constraints;
  • the visual and visual analysis module is used to provide production indicator visualization and visual analysis solutions.
  • This module not only includes visualization of real-time data, historical data and statistical characteristics, but also Supports comparative analysis of index data, visual analysis of production index association relationships, and visualization of production index multi-views;
  • the data detection module is used to detect the relationship between production index data;
  • the production index monitoring module is used to implement a visual monitoring group The visual monitoring of the process flow completed by the state design environment module, combined with the relationship between the production index data analyzed by the expert experience, knowledge and data detection module, and the visual analysis of the index with the help of the visual and visual analysis module, the actual demand for key indicators arranged monitored; monitoring result of said analysis module is not monitored for the failure analysis and new exception occurred during the production index monitor while recording the alarm monitor and collect production index monitoring module ,
  • the visual monitoring configuration design environment module includes a graphic element library submodule, a drawing panel submodule, a function bar submodule, a project process submodule, a configuration submodule, and a first process library submodule;
  • the production index monitoring module includes a second process library sub-module, a production index monitoring and configuration sub-module, a process monitoring sub-module, and an alarm recording sub-module.
  • the factory basic information module includes an organizational structure basic information management unit, a process flow basic information management unit, an equipment file basic information management unit, an index file basic information management unit, a measurement unit basic information management unit, and a material basic information management unit And basic information management unit of personnel files;
  • the basic information management unit of the organizational structure is used to manage the hierarchical and functional structure information among the various departments within the factory;
  • the process flow basic information management unit is used to manage each process flow and its sub-process flow
  • the equipment file basic information management unit is used to manage all equipment information involved in each process flow
  • the index file basic information management unit is used to manage all index information involved in each technological process
  • the measurement unit basic information management unit is used to manage equipment and the measurement unit of the index
  • the material basic information management unit is used to manage material information involved in the production process
  • the personnel file basic information management unit is used to manage personnel information involved in the production process.
  • the graphic element library sub-module includes the shapes of common graphic element nodes and connection lines, and the shapes of graphic elements and connection lines customized according to requirements;
  • the drawing panel sub-module drags the nodes in the graphic element library to the drawing panel by mouse dragging, and configures the endpoints, anchor points and process status;
  • the function bar sub-module includes the functions of saving, importing, backing, forwarding, clearing, zooming and refreshing;
  • the saving function is used to save the newly constructed technological process or sub-process to the database or locally in text format;
  • the import function is used to convert a local file into a text format and import it to the drawing panel;
  • the back function is used to go back to the interface state of the previous operation;
  • the forward function is used to restore the operation of the back function;
  • the clear function is used to Clear the current drawing panel;
  • the zoom function is used to zoom in or out the current drawing panel, which can not only overview the entire process flow, but also zoom in to view each sub-process in detail;
  • the refresh function is used to initialize the entire drawing panel;
  • the project process sub-module is used to display the processes and sub-processes of the currently configured project, and use a five-pointed star in different colors after the name of each process to indicate the current design status of each process;
  • the configuration sub-module includes a process configuration sub-module and an index configuration sub-module, which are used to configure the basic information, process events, process indicators, and constraint conditions of the selected process, and connect each process node through the mouse, while connecting Online configuration of input and output index types between each process;
  • the first process library sub-module is used to manage the general basic process units that have been built to quickly build a new process flow chart, thereby improving the reusability and reusability of the basic process components.
  • the data detection module is used to detect the relationship between the index data, specifically including the detection of the association relationship between the indicators, the detection of the time series relationship between the indicators, the detection of the principal variables between the indicators, and the relationship between the indicator data and the dimensions. Detection of two-way association relationship;
  • the correlation between the indicators is detected through the correlation between the Pearson correlation coefficient and the information entropy analysis indicator, including the relationship between input and input indicators, between input and output indicators, and between output and output indicators; Sun correlation coefficient is used to analyze the linear relationship between indicators; the information entropy is used to analyze the nonlinear relationship between indicators;
  • the detection of the time sequence change relationship between the indicators refers to the analysis of the delay correlation between the production indicators through the Pearson correlation coefficient, that is, whether the production indicators are related by a sampling time interval;
  • the detection of the principal component variables between the indicators uses principal component analysis and principal component analysis based on the kernel function to project the set of indicators that affect the indicators to be measured from high-dimensional to low-dimensional, so as to reduce the dimensionality of production indicators.
  • the real-time data visualization is used to reflect whether the current production operation status is normal and whether the production-related indicators achieve the expected purpose; visualization is performed by means of charts and real-time data curves;
  • the historical data and statistical characteristic visualization is used to reflect the historical trend of the indicator within a period of time and visualize the statistical characteristics of the historical data, and flexibly view the historical data of different time periods through the form of time sliding window; meanwhile, it provides mobile, Interactive operation of zooming, floating prompt box, and refreshing;
  • the index data comparative analysis visualization is represented by parallel coordinate graphs by simultaneously displaying multidimensional data of different dimensions; the parallel coordinate graph displays data of multiple dimensions, each coordinate axis represents a dimension, and each dimension represents A production index;
  • the visualization of the production index association relationship is used to display the association relationship between the production indexes.
  • the relationship between the input index and the input index, the output and the output index is represented by a scatter diagram;
  • the association relationship between the process index and the operation index is represented by a graph.
  • the bipartite graph representation in the theory; the bipartite graph refers to the process index and the running production index as two independent point sets, and the relationship between the points in the two sets is represented by the mapping relationship of the two point sets;
  • the production index multi-view visualization is classified according to the process and index type to which the index belongs, and the multi-view visualization scheme is designed to provide multi-view interactive technology; each process and index category corresponds to a view. Among them, the process is the main view and the index is classified It is a sub-view embedded in the process view; the operator clicks on a specific process to navigate to the process to view detailed information.
  • the remaining process views are zoomed out by zooming technology and displayed as thumbnails; at the same time, click the specific sub-view under the process Process navigation enters the sub-process of the process to view the detailed information; under the selected process or sub-process, the operator clicks on the specific classification index to view the index detailed information in the classification index view of the process or sub-process, and the remaining index categories
  • the view is reduced by zoom technology and displayed as a thumbnail.
  • the second process library sub-module is used to display all current process flows, including sub-processes at all levels, to help users navigate to specific processes; the operator clicks on the corresponding process, the process monitoring sub-module will synchronize Navigate to the process to display the process flow of the process;
  • the production indicator monitoring and configuration sub-module is used to display and configure the indicators of each process and configure the visualization scheme for the indicators.
  • the operator can filter out the indicators based on the actual needs based on the configured indicators of the visual monitoring configuration design environment module.
  • the process monitoring sub-module is used to display the clicked process in the second process library sub-module, and trigger the visual monitoring configuration design environment module to define the constraints, process events, and process status of the process, the process click event, the operator passes Double-click to view the real-time status of the sub-process; at the same time, monitor the real-time data curve and historical data curve of the configured monitoring indicators of the process, and display the configured visualization scheme of each indicator under the process, and monitor production indicators more efficiently through visual analysis;
  • the alarm record sub-module is used to display the alarm information of the current period.
  • Each alarm record includes the alarm time, the name of the process where the alarm is located, the name of the faulty equipment and the name of the abnormal index; symbol prompts are provided before the equipment name and the index name.
  • the present invention also provides a configuration-based visual monitoring method for production indicators, including the following steps:
  • Step 1 Collect and enter the basic production information of the factory through the basic information module of the factory, construct the basic information unit and store it in the database, and realize the management of the basic information of the factory;
  • the management of the basic information includes the basic information management of the organizational structure and the process Process basic information management, equipment file basic information management, index file basic information management, measurement unit basic information management, material basic information management, personnel file basic information management.
  • Step 2 By using the basic information management unit in the factory basic information module, construct a visual monitoring configuration design environment for each production process.
  • the specific method is:
  • Step 2.1 Select the basic primitive to be constructed from the primitive library, click the selected primitive and drag it to the process drawing panel;
  • Step 2.2 Draw the configuration interface according to the actual production process, and configure the endpoints, anchor points, and process status information of each process;
  • Step 2.3 Configure the basic process information, process events, and constraint attributes in the process configuration submodule, and draw the directed connection between each process through the mouse to indicate the actual production process;
  • Step 2.4 Configure indicators for each process in the indicator configuration submodule, and configure indicator types, including input indicators, output indicators, input and output indicators, controlled quantities and controlled quantities;
  • Step 2.5 Add algorithms based on the existing indicator types, and select different algorithms for modeling in the form of a selection box;
  • Step 2.6 Save the configured process to the local database and display it to the project process submodule through the save function button of the function bar submodule.
  • the project process submodule displays the current project process flow, and different logo colors indicate the current design of the process Status, red means configuration is complete, green means configuration is not completed, yellow means not configured;
  • Step 2.7 save the configured process to the process library, and centrally manage the general basic process units that have been constructed, thereby improving the reusability and reusability of basic process components;
  • Step 2.8 Save the drawn and configured flowchart as text format data, and then save the data to the local database or export it as a text file and save it locally;
  • Step 3 On the basis of the visual monitoring configuration design environment module, use the data detection module to detect the relationship between the index data, the specific method is:
  • Step 3.1 Detect the relationship between indicators, including the relationship between input and input indicators, between input and output indicators, and between output and output indicators; analyze the linear relationship between indicators through Pearson correlation coefficient, and use mutual information Analyze the nonlinear correlation between indicators;
  • Step 3.1.1 Use Pearson correlation coefficient to analyze the linear relationship between the indicators. If the analysis result shows that the linear relationship between the indicators is strong, then there is a linear correlation between the indicators. If the analysis result shows that there is no linear relationship between the indicators, go to step 3.1 .2, whether there is a nonlinear relationship between the detection indicators;
  • Step 3.1.2 analyze the non-linear correlation between indicators through mutual information
  • Step 3.2 Detect the time series relationship between the indicators, and analyze the delay correlation between the production indicators through the Pearson correlation coefficient, that is, whether the production indicators are related by a sampling time interval;
  • Step 3.3 Detect the latent variables between the indicators, so as to realize the dimensionality reduction of the indicators by using the latent variables instead of the original indicator variables; use the principal component analysis and the principal component analysis methods based on the kernel function to detect the latent variables and non-linear variables respectively.
  • Latent variables between linear indicators using the conclusion of step 3.1, if the relationship between the indicator data is linear, then go to step 3.3.1, if the relationship between the indicator data is non-linear, then go to step 3.3.2;
  • Step 3.3.1 Use principal component analysis to detect the latent variables between the indicators, use principal component analysis to solve the eigenvalues of the indicator data, and arrange them according to their values from large to small, and select the largest k among them to make these k main components The percentage of components in all principal components exceeds the set threshold, and the principal components corresponding to these k eigenvalues, namely latent variables, are used to replace the original indicators to achieve dimensionality reduction of the original indicator data;
  • Step 3.3.2 extract nonlinear features based on the principal component analysis of the kernel function, map the index set to the high-dimensional linear feature space through the nonlinear function, and then use the principal component analysis method to calculate the principal component components in the high-dimensional space. Realize the dimensionality reduction of the original index data;
  • Step 3.4 Display the results obtained by the data detection module analysis through the visualization function in the visualization and visual analysis module in a more intuitive way;
  • Step 4 Based on the configured production indicators in the visual monitoring configuration design environment module, use the data detection module to analyze the various relationships between the indicators, and use the visual and visual analysis modules to visualize the production indicator data Analyze to help people understand the relationship between indicator data from a visual perspective.
  • the specific methods are:
  • Step 4.1 Use graphs or real-time data curves to represent real-time data to reflect whether the current production operation status is normal and whether the production-related indicators meet the expected goals;
  • Step 4.2 Use historical data curves to represent historical data, and flexibly view historical data trends in different time periods through time sliding windows, and provide interactive operations, including moving, zooming, floating prompt boxes, and refreshing;
  • Step 4.3 Use a parallel coordinate diagram to show the comparative analysis of index data, and display multidimensional data of different dimensions at the same time;
  • Step 4.3.1 display data of multiple dimensions through a parallel coordinate graph, and each coordinate axis represents a dimension
  • each dimension represents a production index, by setting different units for each dimension to describe data of different magnitudes
  • Step 4.3.3 Each dimension displays the current value of the indicator data. By setting the upper and lower limits, it reflects the current operating status of the indicator. If the limit value is exceeded, an alarm will appear, and the axis where the abnormal indicator data is located is displayed in red;
  • Step 4.3.4 Under normal working conditions, each production index is within the upper and lower limits, and the overall outline of the parallel coordinate graph is roughly the same. If the overall outline has an abnormal shape, it indicates that the working condition is abnormal; the operator observes the overall graph Contour to judge the production operation;
  • Step 4.4 With the aid of the bipartite graph method, the Sankey diagram is used to express the relationship of production indicators;
  • Step 4.4.1 In the bipartite graph, the process indicators and the operating indicators are regarded as two independent point sets, and the relationship between the points in the two sets is represented by the mapping relationship of the two point sets;
  • Step 4.4.2 translate the mapping relationship in the bipartite diagram to the Sankey diagram, the color bar on the left represents the process index, and the color bar on the right represents the operation index that affects the process index;
  • Step 4.4.3 distinguish different indicators by setting different colors on the left and right color bars
  • the color bars on the left flow to the right, indicating the factors affecting the indicators on the left;
  • Step 4.4.4 According to the key indicators selected in step 3 and the relationship between each process indicator and operation indicator, calculate the contribution rate of each process indicator to the process indicator, and determine the proportional relationship of each indicator in the figure;
  • Step 4.4.5 Provide interactive operations.
  • the mouse is hovering over the area of a process indicator on the left, the left indicator that affects the indicator is displayed separately, and the percentage is displayed to indicate the degree of influence on the indicator;
  • Step 4.5 Use multi-view interactive technology to realize the visualization of indicator multi-view monitoring
  • Step 4.5.1 each process corresponds to a main view, and each indicator category corresponds to a sub-view;
  • Step 4.5.2 embed the index classification view into the process view
  • Step 4.5.3 The operator clicks on the specific process navigation to enter the process.
  • the process view is enlarged by zooming technology and displays detailed information.
  • the remaining process views are reduced by zooming technology and displayed as thumbnails;
  • Step 4.5.4 click on a specific classification index.
  • the classification view is zoomed in by zooming technology and the category index is displayed.
  • the remaining process views are zoomed out by zooming technology and displayed as thumbnails;
  • Step 5 Configure monitoring operation parameters in the production index monitoring module, the specific method is:
  • Step 5.1 Refer to the relationship between the index data analyzed by the data detection module and the visual and visual analysis module, and use the information presented by each visualization scheme in the visual and visual analysis module to visually display the correlation and relationship between production indicators.
  • the degree of importance provides a basis for configuring monitoring indicators;
  • Step 5.2 Combined with expert knowledge and experience, supplement the missing indicators due to incomplete system analysis, and configure the parameters of the production indicator monitoring module;
  • Step 5.3 Build a production indicator monitoring operating environment in the production indicator monitoring module, and configure monitoring operating parameters
  • Step 5.3.1 display all current process flow through the second process library sub-module, including all levels of sub-processes;
  • Step 5.3.2 by clicking on the process in the second process library sub-module, the process monitoring sub-module synchronously navigates to the process and displays the process flow of the process;
  • Step 5.3.3 Configure the key indicators to be monitored for each process in the production indicator monitoring and configuration sub-module according to actual needs, and configure the visualization scheme for the configured indicators of the process;
  • Step 5.3.4 Double-click to view the real-time status of the sub-processes of the process; display all the indicators that have been configured in the process, the real-time data curve of the indicators, and the historical data curve; and display various visualization schemes at the same time to facilitate monitoring and analysis of the configured indicators;
  • Step 5.3.5 Display the alarm information of the current period through the alarm record submodule.
  • Each piece of information includes fault time, fault process name, fault equipment name and abnormal index name; and analyze the type of equipment failure and indicator abnormality, such as equipment shutdown , The index exceeds the upper limit, the index exceeds the lower limit;
  • Step 6 Analyze unhandled abnormalities and faults in the production index monitoring process through the monitoring result analysis module;
  • Step 6.1 Analyze unknown faults in the process flow being monitored through the monitoring result analysis module
  • Step 6.2 Simultaneously monitor and collect the alarm records in the production indicator monitoring module, and trace the causes of equipment, indicator abnormalities or failures by analyzing the alarm records one by one;
  • Step 6.3 Use the data detection module to detect and analyze historical data
  • Step 6.4 Reconfigure the monitoring indicators in the production indicator monitoring module based on the mechanism, expert knowledge and experience;
  • Step 6.5 finally realize effective feedback on abnormal production index monitoring, and realize dynamic adjustment of production index monitoring.
  • the configuration-based visual monitoring system and method of production indicators provided by the present invention realizes the management of the factory logical structure and basic information, and designs the visual monitoring configuration design environment Module, configure the production process, realize the complete configurability of production index monitoring.
  • it has realized the integration of the knowledge and experience of various production experts, as well as the various relationships between the detection data, and assisted users in monitoring indicators, making the monitoring of the production process more accurate and efficient.
  • It supports multiple visualization schemes through visual analysis technology to help users detect and understand data; through multi-view interaction technology, the system automatically generates indicator overview views and different levels of nested views to facilitate user interaction between different views.
  • it also realizes the analysis of unknown faults in the running process, reconfigures key production indicators and then monitors, which can realize effective feedback in time, so as to realize the dynamic adjustment of production indicator monitoring, making the production indicator visualization monitoring system dynamic Evolution ability.
  • Figure 1 is a structural block diagram of a configuration-based visual monitoring system for production indicators according to an embodiment of the present invention
  • FIG. 2 is a flowchart of a method for visual monitoring of production indicators based on configuration provided by an embodiment of the present invention.
  • This embodiment takes the third beneficiation plant as an example, and uses a configuration-based visual monitoring system and method for production indicators of the present invention to monitor the production indicators of the third beneficiation plant.
  • a configuration-based visual monitoring system for production indicators includes: factory basic information module, visual monitoring configuration design environment module, data detection module, visual and visual analysis module, production indicator monitoring module And monitoring result analysis module.
  • the factory basic information module is used to model the basic information of the factory, which helps the operator to efficiently create the process flow and realize the management of the basic information of the factory.
  • This module includes basic information management unit of organizational structure, basic information management unit of process flow, basic information management unit of equipment file, basic information management unit of index file, basic information management unit of measurement unit, basic information management unit of material, basic information management unit of personnel file .
  • the basic information management unit of the organizational structure is used to manage the hierarchical and functional structure information among the various departments within the factory;
  • the process flow basic information management unit is used to manage each process flow and its sub-process flow
  • the equipment file basic information management unit is used to manage all equipment information involved in each process flow
  • the index file basic information management unit is used to manage all index information involved in each technological process.
  • the measurement unit basic information management unit is used to manage equipment and the measurement unit of the index
  • the material basic information management unit is used to manage material information involved in the production process
  • the personnel file basic information management unit is used to manage personnel information involved in the production process.
  • the visual monitoring configuration design environment module is used to construct each process flow based on the basic information module of the factory, use the logical relationship of the production process flow to construct the flow of each process and its sub-processes, and configure the equipment and production of each process and sub-process Indicators, alarm events, production process rule constraints, expert experience knowledge and algorithm constraints. Including graphic element library sub-module, drawing panel sub-module, function bar sub-module, project process sub-module, configuration sub-module and first process library sub-module.
  • the graphic element library sub-module includes the shapes of common graphic element nodes and connecting lines, and the shapes of graphic elements and connecting lines can also be customized and added to the graphic element library according to requirements.
  • the nodes in the graphic element library are dragged to the drawing panel by mouse dragging, and the endpoints, anchor points, and process status are configured.
  • the function bar sub-modules include save, import, back, forward, clear, zoom, and refresh functions;
  • the save function is used to save the newly constructed process flow or sub-process to a database or locally in a text format;
  • the import function is used to convert a local file into a text format and import it into the drawing panel or into the database;
  • the back function is used to go back to the interface state of the previous operation;
  • the forward function is used to restore the operation of the back function;
  • the clear function is used to clear the current drawing panel;
  • the zoom function is used to zoom in or out the current drawing panel, It can not only overview the entire process flow, but also zoom in to view each sub-process in detail;
  • the refresh function is used to initialize the entire page.
  • the project process sub-module is used to display the processes and sub-processes of the currently configured project.
  • a five-pointed star in a different color is used after each process name to indicate the current design status of each process. Red indicates that the configuration is completed, and green indicates that the configuration is not completed. Yellow means not configured;
  • the configuration sub-module includes a process configuration sub-module and an index configuration sub-module, which are used to configure the basic information, process events, process indicators and constraint conditions of the selected process, and connect the connection between each process node through the mouse, and at the same time Configure the input and output index types between each process on the connection line.
  • the first process library sub-module is used to manage the general basic process units that have been built to quickly build a new process flow chart, thereby improving the reusability and reusability of the basic process components.
  • the data detection module is used to detect the relationship between the index data; including the detection of the correlation between the indicators, the detection of the time series relationship between the indicators, the detection of the principal variable between the indicators, and the detection of the two-way correlation between the indicator data and the dimensions.
  • the detection of the association relationship between indicators refers to analyzing the association relationship between indicators through Pearson correlation coefficient and information entropy, including the relationship between input and input indicators, between input and output indicators, and between output and output indicators;
  • the Sun correlation coefficient is used to analyze the linear relationship between the indicators;
  • the information entropy is used to analyze the nonlinear relationship between the indicators.
  • the detection of the time sequence change relationship between the indicators refers to analyzing the delay correlation between the production indicators through the Pearson correlation coefficient, that is, whether the production indicators are related by a sampling time interval.
  • the detection of principal component variables between the indicators refers to the use of Principal Component Analysis (PCA) and Kernel Principal Component Analysis (KPCA) to influence the set of indicators to be tested from high-dimensional Project to low-dimensional to achieve dimensionality reduction of production indicators.
  • PCA Principal Component Analysis
  • KPCA Kernel Principal Component Analysis
  • the principal component analysis refers to obtaining principal component variables through linear space transformation, and projecting high-dimensional space variables into low-dimensional principal component space; the principal component analysis based on the kernel function is used for non-linear feature extraction through non-linear function Map the index set to a high-dimensional space and then realize PCA.
  • the visual and visual analysis module is used to provide production indicator visualization and visual analysis solutions.
  • This module not only includes the visualization of real-time data and historical data, but also supports comparative analysis of indicator data, visual analysis of production indicator association relationships, and multi-view visualization of production indicators .
  • the real-time data is used to reflect whether the current production operation status is normal and whether the production-related indicators achieve the expected purpose. Use charts and real-time data curves for visualization.
  • the historical data and statistical characteristics are used to reflect the historical trend of the indicator in a period of time and to visualize the statistical characteristics of the historical data, and the historical data of different time periods can be flexibly viewed in the form of a time sliding window. It also provides interactive operations, including moving, zooming, floating prompt boxes, and refreshing.
  • the index data comparative analysis refers to simultaneously displaying multidimensional data of different dimensions, in the form of a parallel coordinate graph.
  • the parallel coordinate graph displays data of multiple dimensions, each coordinate axis represents a dimension, and each dimension represents a production index.
  • the visualization of the production index association relationship is used to display the association relationship between the production indexes.
  • the relationship between the input index and the input index, the output and the output index is represented by a scatter diagram;
  • the association relationship between the process index and the operation index is represented by a graph
  • the bipartite graph refers to the process index and the running production index as two independent point sets, and the relationship between the points in the two sets is represented by the mapping relationship of the two point sets.
  • the multi-view visualization of production indicators refers to the classification of indicators according to their respective processes and indicator types, the design of a multi-view visualization scheme, and the provision of multi-view interactive technology.
  • Each process and index category corresponds to a view.
  • the process is the main view, and the index is classified into sub-views, which are embedded in the process view. The operator can click on a specific process to navigate to the process to view detailed information.
  • the remaining process views are reduced by zooming technology and displayed as thumbnails; at the same time, under the process, they can click on the specific subprocess to navigate to enter the subprocess view of the process Detailed information; in the selected process or sub-process, the operator can also click on a specific classification index to view the detailed information of the index in the classification index view under the process or sub-process.
  • the remaining index category views are reduced by zooming technology. Display as a thumbnail.
  • the production index monitoring module is used to realize the visual monitoring of the process flow completed by the visual monitoring configuration design environment module, combining expert experience, knowledge and the relationship between the index data analyzed by the data detection module, and with the help of visual and visual analysis
  • the module visually analyzes the indicators and configures key indicators to monitor according to actual needs, including the second process library sub-module, production indicator monitoring and configuration sub-module, process monitoring sub-module, and alarm recording sub-module.
  • the second process library sub-module mainly displays all current process processes, including all levels of sub-processes. Its purpose is mainly to help users navigate to specific processes. The operator can click on the corresponding process, and the process monitoring panel will synchronize navigation Go to the process to show the process flow of the process;
  • the production indicator monitoring and configuration sub-module is used to display and configure the indicators of each process and configure the visualization scheme for the indicators.
  • the operator can filter out the indicators based on the actual needs based on the configured indicators of the visual monitoring configuration design environment module.
  • the process monitoring sub-module is used to display the clicked process in the process library, and trigger the visual monitoring configuration design environment module to define the constraints, process events, and process status of the process.
  • the process click event the operator can double-click to view the sub-process
  • the real-time status of the process at the same time, it can monitor the real-time data curve and historical data curve of the configured monitoring indicators of the process, and display the configured visualization scheme of each indicator under the process, and monitor production indicators more efficiently through visual analysis.
  • the alarm record submodule is used to display the alarm information of the current period.
  • Each alarm record includes the alarm time, the name of the process where the alarm is located, the name of the faulty device and the name of the abnormal index; a symbol prompt is provided before the device name and the index name, and the red circle It means that the equipment is stopped, the yellow circle means that the equipment temperature is too high or the consumption of raw materials is excessive, the red up arrow indicates that the indicator exceeds the upper limit, and the green down arrow indicates that the indicator exceeds the lower limit.
  • the monitoring result analysis module is used to analyze new abnormalities and failures that have not been monitored during the production index monitoring process, such as equipment failures, unqualified quality index inspections, etc.; at the same time, monitor and collect alarm records in the production index monitoring module, By analyzing the alarm record information one by one, tracing the causes of equipment and indicator abnormalities/faults, using the data detection module to detect and analyze historical data, and combine the mechanism, expert knowledge and experience to reconfigure the monitoring indicators in the production indicator monitoring module to realize the monitoring of production indicators Abnormal effective feedback realizes the dynamic adjustment of production index monitoring, so that the production index visualization monitoring system has dynamic evolution capability.
  • a configuration-based visual monitoring method for production indicators includes the following steps:
  • Step 1 Collect and enter the basic information of the third beneficiation plant, construct the plant's basic information unit and store it in the database, and realize the plant's management of the basic information.
  • it includes basic information management of organizational structure, basic information management of process flow, basic information management of equipment files, basic information management of index files, basic information management of measurement units, basic information management of materials, and basic information management of personnel files.
  • Organizational structure basic information management is the management of the levels and functions between the energy, equipment, materials, cost and other departments of the third mineral processing plant.
  • the basic information management of the process flow is the management of all processes and sub-processes of each process in the third beneficiation plant, including suspension filter press, air compression station, suspension roaster, etc.;
  • the basic information management of equipment archives is the management of all equipment information of the third beneficiation plant, including high-pressure roller mills, filter presses, centrifugal air compressors, etc.;
  • the basic information management of index files is the management of all index information of the third beneficiation plant, including the outlet flow of the underflow pump, the frequency of the underflow pump, the water pressure in front of the furnace, the operating rate of the magnetic separator, etc.;
  • the basic information management of measurement units is the management of all equipment and index measurement units in the third mineral processing plant, including %, ton, times, GJ, kg/t, h, etc.;
  • Material basic information management is the management of material information involved in the production process of the third beneficiation plant
  • the basic information management of personnel files is the management of personnel information in the production process of the third beneficiation plant
  • Step 2 Use the basic information management unit of the third beneficiation plant in the basic information module of the plant to construct a visual monitoring configuration design environment for the production process of the plant.
  • the specific method is:
  • Step 2.1 Select the basic graphic element of the suspension baking furnace process from the graphic element library, click the selected graphic element and drag it to the process drawing panel;
  • Step 2.2 Draw the flow chart of the configuration interface according to the actual production process of the third beneficiation plant, and configure the end point, anchor point, process status and other information of the suspension roaster process;
  • Step 2.3 Configure the basic information of the suspension roasting furnace process in the process configuration sub-module (the font color is set to black, the font size is set to small four, and the text font is set to Song Ti), process events (the mouse click means entering the sub-process of the floating roasting furnace , Double-click the mouse to select the suspension roasting furnace process, configure the indicators for this process, and display the relevant remarks of the suspension roasting furnace process with the mouse floating), restriction conditions (rules: supply 65 ⁇ 10t/h, supply 12 ⁇ 5m3/ h. Share concentration 50 ⁇ 5%, discharge water 48 ⁇ 8m3/h, grinding concentration 80 ⁇ 2%, spinning concentration 50 ⁇ 10%, spinning pressure 125 ⁇ 35Kpa) and other attributes;
  • Step 2.4 Configure the indicators of the suspension baking furnace process in the indicator configuration submodule, and configure the indicator types, including input indicators, output indicators, input and output indicators, controlled quantities and controlled quantities;
  • the indicators for the process configuration of the suspension roaster include: strong magnetic concentrate output, beneficiation comprehensive concentrate SiO2, three-magnetic high-quality goods position, strong magnetic ore warehouse, first-class recovery rate and weak high-quality goods position;
  • Step 2.5 Based on the existing index types of the suspension roaster process, add an algorithm through the selection box;
  • the added algorithm is an autoregressive moving average model (Autoregressive moving average model, namely ARMA);
  • Step 2.6 Click the save button of the submodule in the function bar to save the configured suspension roaster process to the database, and its name will be displayed in the directory of the project process submodule.
  • the project process submodule displays the process flow of the suspension roaster, with different identifications. The color indicates the current design status of the process, and the logo of the suspension roaster process is red to indicate that the configuration is complete;
  • Step 2.7 save the configured suspension roaster process to the process library, and centrally manage the general basic process units that have been built, so as to improve the reusability and reusability of the basic beneficiation process components;
  • Step 2.8 According to step 2.1 to step 2.7, configure the various processes of the third beneficiation, including suspension filter, air compression station, and desalinated water station, and draw the directed connection between each process through the mouse, indicating that the actual third beneficiation is in production
  • the process of saving it as JSON format data can be saved to the database or exported as a JSON file and saved locally.
  • Step 3 On the basis of the visual monitoring configuration design environment module, the data detection module is used to detect the relationship between the index data.
  • the selected production index data is shown in Table 1:
  • Step 3.1 Detect the relationship between indicators, including the relationship between input and input indicators, between input and output indicators, and between output and output indicators. Pearson's correlation coefficient is used to analyze the linear relationship between indicators, and the non-linear relationship between indicators is analyzed through mutual information.
  • some of the production indicators in Table 1 are selected, including the production of beneficiation comprehensive concentrate (wet weight), the production of weak magnetic concentrate, the comprehensive lump ore rate, the weak quality flavor, the ore volume of block 1# and waste stone 1#.
  • Step 3.1.1 Use Pearson correlation coefficient to analyze the linear relationship between the indicators. If the analysis result shows that the linear relationship between the indicators is strong, then it is considered that there is a linear relationship between the indicators. If the analysis result shows that there is no linear relationship between the indicators, go to step 3.1.2, whether there is a nonlinear relationship between the detection indicators;
  • Step 3.1.2 analyze the non-linear correlation between indicators through mutual information.
  • the indicators selected in Table 1 are detected by linear correlation, and the results are shown in Table 2.
  • the Pearson correlation coefficient between the production of comprehensive concentrate (wet weight) and the production of weak magnetic concentrate is 0.926, indicating that the two indicators have a very strong positive correlation;
  • the Pearson correlation coefficient between the comprehensive lump rate and the production of weak magnetic concentrate is -0.023 , Indicating that the two indicators have a weaker negative correlation.
  • Step 3.2 Detect the time series relationship between the indicators, and analyze the delay correlation between the production indicators through the Pearson correlation coefficient, that is, whether the production indicators are related by a sampling time interval.
  • some production indicators in Table 1 are selected, including the output of the beneficiation comprehensive concentrate and the beneficiation comprehensive concentrate SiO 2 .
  • the correlation coefficient at time t of the SiO 2 time-delayed ore beneficiation comprehensive concentrate output is shown in Table 3.
  • the Pearson correlation coefficient is 0.54786
  • the beneficiation comprehensive concentrate output has the greatest correlation with the beneficiation comprehensive concentrate SiO 2 .
  • Step 3.3 Detect the latent variables between the indicators, and replace the original indicator variables with latent variables to achieve dimensionality reduction of the indicators.
  • Principal component analysis (PCA) and principal component analysis (KPCA) methods based on kernel function are used to detect latent variables between linear indicators and latent variables between nonlinear indicators.
  • the selected production indicators include 3-1 ball mill current, 3-1 cyclone feed pressure, 3-1 cyclone feed concentration, 3-1 cyclone feed flow, 3-1# Pump operating frequency, 3-1 ball mill discharge valve opening, 3-1 ball mill feed mineral water valve opening and 3-2 cyclone feed pressure.
  • the production index data is shown in Table 4. Through step 3.1, it is concluded that there is a linear relationship between the index data, the correlation coefficient matrix is shown in Table 5, and the following latent variables among the linear indexes are detected.
  • Principal component PCA analysis is performed to obtain the eigenvalues, from largest to smallest: 145.56, 21.53, 5.44, 3.66, 1.03, 0.67, 0.28, -5.41, the first two eigenvalues (145.56+21.53/(145.56+21.53+5.44) +3.66+1.03+0.67+0.28-5.41)>0.9), indicating that the linear correlation between the set of variables is very strong.
  • the principal elements corresponding to the first two eigenvalues (145.56, 21.53) can be used to replace the original 8 indicators , To achieve dimensionality reduction of indicators.
  • Step 3.4 Display the results obtained by the data detection module analysis through the visualization function in the visualization and visual analysis module in a more intuitive way.
  • Step 4 Based on the configured production indicators of the visual monitoring configuration design environment module, with the help of the data detection module to analyze the various relationships between the indicators, use the visual and visual analysis modules to visually analyze the indicator data.
  • the specific methods are:
  • Step 4.1 Use graphs or real-time data curves to represent real-time data to reflect whether the current production operation status is normal and whether the production-related indicators meet the expected goals;
  • the real-time data selects the production (wet weight) of the beneficiation comprehensive concentrate in the comprehensive production index
  • Step 4.2 Use historical data curves to represent historical data, and flexibly view historical data trends in different time periods through time sliding windows, and provide interactive operations, including moving, zooming, floating prompt boxes, and refreshing;
  • the historical data selects the beneficiation comprehensive concentrate output (wet weight) in the comprehensive production index, and the data time selects 2018-5-1 to 2018-5-7.
  • Step 4.3 Use the parallel coordinate diagram to show the comparative analysis of index data, which can display multidimensional data of different dimensions at the same time;
  • the indicators are selected for comparison and analysis of the index data of beneficiation comprehensive concentrate output (wet weight), comprehensive lump rate, beneficiation comprehensive concentrate moisture, beneficiation comprehensive concentrate SiO 2 , measured sinter taste and beneficiation comprehensive concentrate CaO.
  • Step 4.3.1 display data of multiple dimensions through a parallel coordinate graph, and each coordinate axis represents a dimension
  • Step 4.3.2 the output of the beneficiation comprehensive concentrate (wet weight), the comprehensive lump ore ratio, the beneficiation comprehensive concentrate moisture, the beneficiation comprehensive concentrate SiO2, the measurement of the sinter taste, and the beneficiation comprehensive concentrate CaO each represent a dimension, and the indicators can be set by different units. Describe data of different magnitudes;
  • Step 4.3.3 Each dimension displays the current value of the indicator data, and sets the upper and lower limits to reflect the current operating status of the indicator;
  • the index range of the production (wet weight) of the beneficiation concentrate is 260t to 350t.
  • the index data value is 355t, and there is an alarm that exceeds the upper limit.
  • the coordinate axis of is displayed in red;
  • Step 4.3.4 Under normal working conditions, the output of the beneficiation comprehensive concentrate (wet weight), the comprehensive lump rate, the beneficiation comprehensive concentrate moisture, the beneficiation comprehensive concentrate SiO 2 , the estimated sinter taste, and the beneficiation comprehensive concentrate CaO should all be within the upper and lower limits within the scope, the overall outline of the parallel coordinate diagram should be roughly the same. If the overall outline has an abnormal shape, it means the working condition is abnormal. The operator can judge the production operation by observing the overall outline of the graph;
  • Step 4.4 With the aid of the bipartite graph method, the Sankey diagram is used to express the relationship of production indicators;
  • Step 4.4.1 In the bipartite graph, the process indicators and the operating indicators are regarded as two independent point sets, and the relationship between the points in the two sets is represented by the mapping relationship of the two point sets;
  • Step 4.4.2 translate the mapping relationship in the bipartite diagram to the Sankey diagram, the color bar on the left represents the process index, and the color bar on the right represents the operation index that affects the process index;
  • the indicators are selected: CaO of beneficiation comprehensive concentrate, SiO 2 of beneficiation comprehensive concentrate, sinter taste, comprehensive lump rate, and moisture of beneficiation comprehensive concentrate as the process indicators; primary overflow recovery rate of selected indicators, flat ring tailings grade , Weak tail grade, strong fine SiO 2 , and weak magnetic fine grade are operating indicators.
  • Step 4.4.3 Distinguish different indicators by setting different colors on the color bars on the left and right sides; each color bar on the left flows to the right, indicating that the right indicators affect the left indicators;
  • Step 4.4.4 According to the key indicators selected in step 3 and the relationship between each process indicator and the operating indicator, calculate the contribution rate of each operating indicator to the process indicator, and determine the proportional relationship of each operating indicator in the figure;
  • Step 4.4.5 Provide interactive operation.
  • the operating indicators that affect this indicator will be displayed separately;
  • the contribution rate of the weak product is 57%
  • the contribution rate of the strong SiO 2 is 8%
  • the contribution rate of the Sanmagnet product is 7%
  • the contribution rate of the primary overflow recovery rate is 7%.
  • Step 4.5 Use multi-view interactive technology to realize multi-view visualization of mineral processing production indicators
  • the processes involved include suspension filter press, air compression station, suspension roaster, brewing room, suspension grinding workshop, etc.; index classification includes equipment index, quality index, process index, cost index, measurement index, energy index ;
  • Step 4.5.1 each process corresponds to a main view, and each indicator category corresponds to a sub-view;
  • Step 4.5.2 embed the index classification view into the process view
  • Step 4.5.3 When the operator clicks on the floating roaster, he can navigate to the process.
  • the process view is enlarged by zooming technology and displays the real-time data and historical data of the configured indicators in the process. At this time, the remaining process views are performed by zooming technology. Zoom out and move to the far right of the interface as a thumbnail;
  • Step 4.5.4 In the suspension roasting furnace process, while selecting the equipment index classification, the equipment indexes in the configured indexes under the process are selected at the same time.
  • the classification view is enlarged by zooming technology and displays the real-time data and the filtered equipment indexes. Historical data, at this time, the remaining process views are reduced by zooming technology, and displayed as thumbnails and moved to the far right of the sub-interface;
  • Step 5 Configure the monitoring operating parameters of the third beneficiation process in the production index monitoring module, the specific method is:
  • Step 5.1 Refer to the relationship between the beneficiation production index data analyzed in steps 3 and 4, and use the information presented in each visualization scheme in step 4 to visually display the relationship between the beneficiation production indicators and the importance of each index, which is the configuration Provide basis for monitoring indicators;
  • Step 5.2 Combined with expert knowledge and experience, supplement the missing indicators due to incomplete system analysis, and configure the parameters of the production indicator monitoring module;
  • step 3 the contribution rate of highly refined SiO 2 to the beneficiation comprehensive concentrate SiO 2 is 8%, but this index is not configured in step 2.6, and the highly refined SiO 2 is configured to the suspension roasting furnace process. in;
  • Step 5.3 construct a monitoring operation environment for mineral processing production indicators
  • Step 5.3.1 through the second process library sub-module to display the process flow of the third beneficiation plant, including all levels of sub-processes;
  • Step 5.3.2 click the suspension roaster process in the second process library sub-module, the process monitoring sub-module will navigate to the process synchronously, and display the process flow of the process;
  • Step 5.3.3 Configure the key indicators to be monitored for the suspension roaster process in the production indicator monitoring and configuration sub-module according to actual needs, and configure the visualization scheme for the configured indicators of the process;
  • the configured indicators are strong refined SiO 2 , strong magnetic concentrate output, beneficiation comprehensive concentrate SiO 2 , three-magnetic high-quality goods, strong magnetic ore warehouse, first-rate recovery rate, weak high-quality goods, and the visualization scheme of the configuration is Real-time data and historical data visualization solution;
  • Step 5.3.4 View the real-time status of the sub-process by double-clicking the suspension roaster process
  • Step 5.3.5 Display the alarm information of the current period through the alarm record submodule.
  • Each piece of information includes fault time, fault process name, fault device name, and abnormal indicator name; and analyze the type of equipment failure and indicator abnormality, such as equipment shutdown , The index exceeds the upper limit, the index exceeds the lower limit;
  • the alarm record is shown on 2018-09-22, and the current data of #6 of the 1-3-1 magnetic separator device in the suspension filter press process exceeded the upper limit of the index at 11:38.
  • Step 6 Analyze unhandled abnormalities and faults in the production indicator monitoring process through the monitoring result analysis module.
  • Step 6.1 Analyze unknown faults in the process being monitored through the monitoring result analysis module, such as equipment shutdown, unqualified index quality inspection, etc.;
  • Step 6.2 Monitor and collect the alarm records in the production indicator monitoring module, and trace the causes of equipment and indicator abnormalities/faults by analyzing the alarm records one by one;
  • Step 6.3 Use the data detection module to detect and analyze historical data
  • Step 6.4 Reconfigure the monitoring indicators in the production indicator monitoring module based on the mechanism, expert knowledge and experience;
  • Step 6.5 finally realize effective feedback on abnormal production index monitoring, and realize dynamic adjustment of production index monitoring.

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Abstract

本发明提供一种基于组态的生产指标可视化监控系统及方法，涉及生产指标监控技术领域。该系统包括用于对工厂基础信息进行建模的工厂基础信息模块，用于构建各工序的流程及其子流程的可视监控组态设计环境模块，用于提供生产指标可视化和可视化分析方案的可视与可视分析模块；用于探测生产指标数据之间的关系的数据探测模块，用于对工艺流程进行可视化监控的生产指标监控模块，用于分析生产指标监控过程中出现的未被监控到的新的异常和故障的监控结果分析模块；该方法基于该系统的各模块实现对生产指标的可视化监控。本发明提供的生产指标可视化监控系统及方法，实现了生产指标监控的动态调整，使生产指标可视化监控系统具备动态演化能力。

Description

一种基于组态的生产指标可视化监控系统及方法 技术领域
本发明涉及生产指标监控技术领域，尤其涉及一种基于组态的生产指标可视化监控系统及方法。
背景技术
生产指标可视化监控系统是工业过程中数据联系最为频繁的环节，也是直接体现工业过程最终产品的质量与产量的窗口；目前国内关于生产指标可视化监控系统及方法的研究和应用为数不多且功能单一。“201711283037.X(一种选矿生产指标可视化分析系统与方法)”实现对选矿生产各工序指标的整合和配置并对指标异常进行分析可视。“201811009657.9(一种选矿生产指标的关联关系可视化系统及方法)”实现了对选矿生产指标间关联关系的分析，以及对生产指标间时序变化关系的分析，最终将所分析的生产指标间的关系可视。“201811010246.1(一种基于工艺流程的选矿生产指标可视化监控系统及方法)”提供了基于生产工艺流程的生产指标可视化组态设计，支持实时数据、历史数据、历史数据统计特性的可视化方案，能够为生产指标监控配置生产指标监控算法，实现对生产指标的监控。以上专利主要包括对选矿生产指标异常的监控并提供报警处理，对生产指标间的关系进行可视分析，201811010246.1主要提供了生产工艺流程组态工具，并对指标数据进行实时、历史监控；多视图可视仅限于对指标的分类监控。但上述专利缺少对各工艺流程的完整逻辑结构，缺少对生产指标数据的探测，对生产指标的可视化方案局限于显示实时数据和历史数据，多视图监控局限于指标分类层面，同时也没有涉及对监控结果的分析以及反馈。
发明内容
本发明要解决的技术问题是针对上述现有技术的不足，提供一种基于组态的生产指标可视化监控系统及方法，实现对生产指标的可视化监控。
为解决上述技术问题，本发明所采取的技术方案是：一方面，本发明提供一种基于组态的生产指标可视化监控系统，包括工厂基础信息模块、可视监控组态设计环境模块、数据探测模块、可视与可视分析模块、生产指标监控模块及监控结果分析模块；所述工厂基础信息模块用于对工厂基础信息进行建模，实现工厂对基础信息的管理；所述可视监控组态设计环境模块用于构建基于工厂基础信息模块的各个工艺流程，利用生产工艺流程的逻辑关系，构建各工序的流程及其子流程，并配置各工序及其子工序的设备、生产指标、报警事件、生产工艺规则约束、专家经验知识和算法约束；所述可视与可视分析模块用于提供生产指标可视化和可视化分析方案，此模块不仅包括实时数据、历史数据及统计特性的可视化，还支持指标数据对比分析、生产指标关联关系的可视化分析及生产指标多视图的可视化；所述数据探测模块用于探测生产指标数据之间的关系；所述生产指标监控模块用于实现可视监控组态设计环境模块构建完成的工艺流程的可视化监控，结合专家经验、知识和数据探测模块分析得出的生产指标数据之间的关系、借助于可视与可视分析模块对指标的可视分析，根据实际需求对配置关键指标进行监 控；所述监控结果分析模块用于分析生产指标监控过程中出现的未被监控到的新的异常和故障，同时监控和收集生产指标监控模块中的报警记录，通过逐一分析报警记录信息，追溯设备、指标异常或故障的原因，利用数据探测模块对历史数据进行探 测分析，并结合机理、专家知识经验重新配置生产指标监控模块中的监控指标，从而实现对生产指标监控异常的有效反馈，实现对生产指标监控的动态调整，使得生产指标可视化监控系统具备动态演化能力；
所述可视监控组态设计环境模块包括图元库子模块、绘制面板子模块、功能栏子模块、项目工序子模块、配置子模块和第一工序库子模块；
所述生产指标监控模块包括第二工序库子模块、生产指标监控与配置子模块、工序监控子模块、报警记录子模块。
优选地，所述工厂基础信息模块包括组织结构基础信息管理单元、工序流程基础信息管理单元、设备档案基础信息管理单元、指标档案基础信息管理单元、计量单位基础信息管理单元、物料基础信息管理单元和人员档案基础信息管理单元；
所述组织结构基础信息管理单元用于管理工厂内部各个部门之间层次和职能结构信息；
所述工序流程基础信息管理单元用于管理各个工艺流程以及其子工序流程；
所述设备档案基础信息管理单元用于管理各个工艺流程中所涉及的所有设备信息；
所述指标档案基础信息管理单元用于管理各个工艺流程中所涉及的所有指标信息；
所述计量单位基础信息管理单元用于管理设备、指标的度量单位；
所述物料基础信息管理单元用于管理生产过程中涉及的物料信息；
所述人员档案基础信息管理单元用于管理生产过程中涉及的人员信息。
优选地，所述图元库子模块包括常见图元节点和连接线的形状，及根据需求自定义的图元和连接线形状；
所述绘制面板子模块通过鼠标拖拽方式将图元库中节点拖至绘制面板中，并配置端点、锚点及工序状态；
所述功能栏子模块包括保存、导入、后退、前进、清除、缩放和刷新功能；所述保存功能用于将新构建的工艺流程或子流程保存到数据库或以文本格式保存到本地；所述导入功能用于将本地文件转换为文本格式导入到绘制面板；所述后退功能用于后退到上一个操作时的界面状态；所述前进功能用于恢复后退功能的操作；所述清除功能用于清空当前绘制面板；所述缩放功能用于放大或缩小当前绘制面板，既能概览整个的工艺流程，也能放大局部具体查看各个子流程；所述刷新功能用于初始化整个绘制面板；
所述项目工序子模块用于显示当前所配置的项目的各工序及子工序，并在各工序名称后面用不同颜色的五角星表示目前各工序的设计状态；
所述配置子模块包括工序配置子模块和指标配置子模块，用于配置选定工序的基础信息、工序事件、工序指标、约束条件，并通过鼠标进行各个工序节点间的连线，同时在连线上配置各工序之间输入输出指标类型；
所述第一工序库子模块用来管理已经构建好的通用基础工序单元，以用于快速构建新的工艺流程图，从而提高基础工序组件的复用性和重用性。
优选地，所述数据探测模块用于探测指标数据之间的关系，具体包括指标间关联关系的探测、指标间时序变化关系的探测、指标间主元变量的探测及指标数据与维度之间的双向关联关系的探测；
所述指标间关联关系的探测通过皮尔逊相关系数和信息熵分析指标间的关联关系，包 括输入与输入指标之间、输入与输出指标之间、输出与输出指标之间的关系；所述皮尔逊相关系数用于分析指标间线性关系；所述信息熵用于分析指标间非线性关系；
所述指标间时序变化关系的探测是指通过皮尔逊相关系数分析生产指标之间的延迟相关性，即生产指标之间是否间隔一段采样时间而具有相关性；
所述指标间主元变量的探测通过主成分分析及基于核函数的主成分分析将影响待测指标的指标集从高维投影到低维，实现对生产指标的降维。
优选地，所述实时数据可视化用于反应当前生产运行状态是否正常，生产相关指标是否达到预期目的；采用图表、实时数据曲线的方式进行可视化；
所述历史数据及统计特性可视化用于反应指标在一段时间内的历史趋势并对历史数据的统计特性进行可视化，并通过时间滑窗的形式灵活的查看不同时间段的历史数据；同时提供移动、放缩、悬浮提示框、刷新的交互操作；
所述指标数据对比分析可视化通过同时展示不同量纲的多维数据，采用平行坐标图的形式进行表示；所述平行坐标图显示多个维度的数据，每个坐标轴表示一个维度，每个维度表示一个生产指标；
所述生产指标关联关系可视化用于显示生产指标之间的关联关系，输入指标和输入指标、输出和输出指标之间关系使用散点图表示；过程指标与运行指标之间的关联关系，采用图论中的二分图表示；所述二分图是指将过程指标和运行生产指标分别看成两个独立的点集，通过两个点集的映射关系表征两个集合中点的关联关系；
所述生产指标多视图可视化根据指标的所属工序和指标类型进行分类，设计多视图可视化方案，提供多视图交互技术；每一个工序和指标类别都对应一个视图，其中，工序为主视图，指标分类为子视图，嵌入到工序视图中；操作员点击具体工序以导航进入该工序查看详细信息，此时，其余工序视图通过缩放技术进行缩小，显示为缩略图；同时在该工序下点击具体的子工序导航进入该工序的子工序查看详细信息；在已选择工序或者子工序下，操作员点击具体分类指标，查看该工序或者子工序下该分类指标视图里面的指标详细信息，此时其余指标类别视图通过缩放技术进行缩小，显示为缩略图。
优选地，所述第二工序库子模块用于显示当前所有的工序流程，包括各级子工序，用来帮助用户导航到具体的工序；操作员通过点击相应的工序，工序监控子模块会同步导航到该工序，以显示该工序的工艺流程；
所述生产指标监控与配置子模块用于显示与配置各工序的指标以及对指标配置可视化方案，操作员能够在可视监控组态设计环境模块已配置指标的基础上，根据实际需求过滤出各工序关键指标，并通过配置功能配置各工序关键指标，实现对各工序关键指标的监控；同时对各个指标配置可视化方案以便进行可视化监控；
所述工序监控子模块用于显示第二工序库子模块中所点击的工序，触发可视监控组态设计环境模块定义该工序的约束条件、工序事件、工序状态，工序点击事件，操作员通过双击查看子过程的实时状态；同时监控该工序已配置监控指标的实时数据曲线和历史数据曲线，并显示该工序下各指标已配置的可视化方案，通过可视分析更高效的监控生产指标；
所述报警记录子模块用于显示当前时段的报警信息，每条报警记录包括报警时间，报警所在的工序名称，故障设备名称和异常指标名称；在设备名称、指标名称前提供符号提示。
另一方面，本发明还提供一种基于组态的生产指标可视化监控方法，包括以下步骤：
步骤1、通过工厂基础信息模块收集、录入工厂的生产基础信息，构建基础信息单元并将其存储至数据库，实现工厂对基础信息的管理；所述基础信息的管理包括组织结构基础信息管理、工序流程基础信息管理、设备档案基础信息管理、指标档案基础信息管理、计量单位基础信息管理、物料基础信息管理、人员档案基础信息管理。
步骤2、通过使用工厂基础信息模块中的基础信息管理单元，构建各生产流程可视监控组态设计环境，具体方法为：
步骤2.1、从图元库中选择需要构建的基础图元，点击所选图元将其拖到工艺流程绘制面板；
步骤2.2、根据实际生产工艺流程绘制组态界面，并配置各个工序的端点、锚点、工序状态信息；
步骤2.3、在工序配置子模块配置工序基础信息、工序事件、约束条件属性，并通过鼠标绘制各个工序间的有向连线，表示实际生产中的流程；
步骤2.4、在指标配置子模块为各工序配置指标、并配置指标类型，包括输入指标、输出指标、输入输出指标、被控量和控制量；
步骤2.5、在已有指标类型的基础上添加算法，并通过选择框的形式选择不同的算法进行建模；
步骤2.6、通过功能栏子模块的保存功能钮，将配置完成的工序保存至本地数据库并显示到项目工序子模块，项目工序子模块显示当前项目工序流程，不同的标识颜色表示目前该工序的设计状态，红色表示配置完成，绿色表示配置未完成，黄色表示未配置；
步骤2.7、同时将配置完成的工序保存至工序库，集中管理已经构建完成的通用基础工序单元，从而提高基础工序组件的复用性和重用性；
步骤2.8、将绘制并配置完成的流程图保存为文本格式数据，然后将数据保存至本地数据库或导出为文本文件保存至本地；
步骤3、在可视监控组态设计环境模块的基础上，应用数据探测模块探测指标数据之间的关系，具体方法为：
步骤3.1、探测指标间的关联关系，包括输入与输入指标之间、输入与输出指标之间、输出与输出指标之间的关系；通过皮尔逊相关系数分析指标间的线性关联关系，通过互信息分析指标间的非线性相关关系；
步骤3.1.1、使用皮尔逊相关系数分析指标间线性关联关系，如果分析结果表明指标间线性关系强，则指标间存在线性相关性，如果分析结果表明指标间不存在线性关系，转到步骤3.1.2，探测指标间是否存在非线性关系；
步骤3.1.2、通过互信息分析指标间非线性相关关系；
步骤3.2、探测指标间时序变化关系，通过皮尔逊相关系数分析生产指标之间的延迟相关性，即生产指标之间是否间隔一段采样时间而具有相关性；
步骤3.3、探测指标间的潜变量，从而通过使用潜变量代替原始的指标变量，实现对指标的降维；使用主成分分析和基于核函数的主成分分析方法分别探测线性指标间潜变量和非线性指标间的潜变量；利用步骤3.1的结论，如果指标数据间是线性关系，则转到步骤3.3.1， 如果指标数据间是非线性关系，则转到步骤3.3.2；
步骤3.3.1、利用主成分分析探测指标间的潜变量，利用主成分分析求解出指标数据的特征值并按其值从大到小进行排列，选择其中最大的k个，使得这k个主成分占全部主成分的百分比超过设定的阈值，使用这k个特征值对应的主元即潜变量代替原始指标，实现对原始指标数据的降维；
步骤3.3.2、利基于核函数的主成分分析提取非线性特征，通过非线性函数将指标集映射到高维线性特征空间，然后在高维空间中使用主成分分析方法计算其主元成分，实现对原始指标数据的降维；
步骤3.4、将经过数据探测模块分析得到的结果通过可视与可视分析模块中可视化功能以更直观的方式展示出来；
步骤4、以可视监控组态设计环境模块中已配置的生产指标为基础，借助数据探测模块分析得出的指标间各类关系，利用可视与可视分析模块对生产指标数据进行可视分析，以辅助人们从可视的角度去理解指标数据间的关系，具体方法为：
步骤4.1、采用图表或实时数据曲线的方式表示实时数据，反应当前生产运行状态是否正常，生产相关指标是否达到预期目的；
步骤4.2、采用历史数据曲线的方式表示历史数据，并通过时间滑窗的形式灵活的查看不同时间段的历史数据趋势，同时提供交互操作，包括移动、放缩、悬浮提示框、刷新；
步骤4.3、采用平行坐标图表示指标数据对比分析，同时展示不同量纲的多维数据；
步骤4.3.1、通过平行坐标图显示多个维度的数据，每个坐标轴表示一个维度；
步骤4.3.2、每个维度表示一个生产指标，通过对每个维度设置不同单位来描述不同数量级的数据；
步骤4.3.3、每个维度显示该指标数据的当前值，通过设置上下限，反应该指标当前的运行状态，若超出限制值有报警提示，异常指标数据所在的坐标轴显示为红色；
步骤4.3.4、在正常工况下，每个生产指标均在上下限范围内，平行坐标图的整体轮廓大致相同，若整体轮廓出现异常形状，表示工况异常；操作员通过观察图形的整体轮廓，判断生产运行情况；
步骤4.4、借助二分图的方法，采用桑基图的方式表示生产指标关联关系；
步骤4.4.1、在二分图中，将过程指标和运行指标分别看成两个独立的点集，通过两个点集的映射关系表征两个集合中点的关联关系；
步骤4.4.2、将二分图中的映射关系平移到桑基图中，左侧颜色条代表过程指标，右侧颜色条代表影响过程指标的运行指标；
步骤4.4.3、通过对左右两侧的颜色条设置不同的颜色来区分不同的指标；
左侧各个颜色条流向右侧，表示右侧指标影响左侧指标的因素；
步骤4.4.4、根据步骤3筛选出的关键指标以及各个过程指标与运行指标间关系，计算各工序指标对过程指标影响的贡献率，确定图中各个指标的比例关系；
步骤4.4.5、提供交互操作，当鼠标悬浮于左侧某个过程指标所在区域时，单独显示影响该指标的左侧指标，并显示百分比以表示对该指标的影响程度；
步骤4.5、采用多视图交互技术实现指标多视图监控的可视；
步骤4.5.1、每一个工序对应一个主视图，每一个指标分类对应一个子视图；
步骤4.5.2、将指标分类视图嵌入到工序视图中；
步骤4.5.3、操作员点击具体工序导航进入该工序，该工序视图通过缩放技术进行放大并显示详细信息，此时其余工序视图通过缩放技术进行缩小，显示为缩略图；
步骤4.5.4、在该工序中，点击具体分类指标，该分类视图通过缩放技术进行放大并显示该类别指标，此时其余工序视图通过缩放技术进行缩小，显示为缩略图；
步骤5、在生产指标监控模块中配置监控运行参数，具体方法为：
步骤5.1、参考数据探测模块和可视与可视分析模块分析得出的指标数据间的关系，借助可视与可视分析模块中各可视化方案所呈现的信息，直观显示生产指标间关联关系和重要程度，为配置监控指标提供依据；
步骤5.2、结合专家知识经验，补充因系统分析不全面所遗漏的指标，配置生产指标监控模块的参数；
步骤5.3、在生产指标监控模块中构建生产指标监控运行环境，配置监控运行参数；
步骤5.3.1、通过第二工序库子模块显示当前所有的工序流程，包括各级子工序；
步骤5.3.2、通过点击第二工序库子模块中的工序，工序监控子模块同步导航到该工序，显示该工序的工艺流程；
选中相应工序时，触发可视监控组态设计环境模块定义的该工序的约束条件、工序事件、工序状态，工序点击事件；
步骤5.3.3、根据实际需求在生产指标监控与配置子模块中对各工序配置所要监控的关键指标，以及对该工序已配置的指标配置可视化方案；
步骤5.3.4、通过双击查看该工序的子过程的实时状态；显示该工序已配置的所有指标，指标的实时数据曲线、历史数据曲线；同时显示各个可视化方案，便于监控分析已配置指标；
步骤5.3.5、通过报警记录子模块显示当前时段的报警信息，每条信息包括故障时间、故障工序名称、故障设备名称及异常指标名称；并分析出设备故障类型和指标异常类型，如设备停歇、指标超上限、指标超下限；
步骤6、通过监控结果分析模块分析生产指标监控过程的中出现的未处理的异常和故障；
步骤6.1、通过监控结果分析模块分析正在监控的工艺流程中未知故障；
步骤6.2、同时监控和收集生产指标监控模块中的报警记录，通过逐一分析报警记录信息，追溯设备、指标异常或故障的原因；
步骤6.3、利用数据探测模块对历史数据进行探测分析；
步骤6.4、结合机理、专家知识经验重新配置生产指标监控模块中监控指标；
步骤6.5、最终实现对生产指标监控异常的有效反馈，实现对生产指标监控的动态调整。
采用上述技术方案所产生的有益效果在于：本发明提供的一种基于组态的生产指标可视化监控系统及方法，实现了对工厂逻辑结构、基础信息的管理，设计了可视监控组态设计环境模块，组态生产工艺流程，实现了对生产指标监控的完全可配置性。同时，实现了对各类生产专家的知识和经验的集成，以及探测数据之间的各类关系，辅助用户进行指标监控，使得对生产过程的监控更加精准和高效。通过可视分析技术支持多种可视化方案，帮助用户探测和了解数据；通过多视图交互技术实现了系统自动生成指标概览视图和不同等级的嵌套 视图，方便用户在不同视图之间进行交互。此外，还实现了对正在运行的工艺流程中未知故障的分析，重新配置关键生产指标再监控，能够及时实现有效的反馈，从而实现对生产指标监控的动态调整，使得生产指标可视化监控系统具备动态演化能力。
附图说明
图1为本发明实施例提供的一种基于组态的生产指标可视化监控系统的结构框图；
图2为本发明实施例提供的一种基于组态的生产指标可视化监控方法的流程图。
具体实施方式
下面结合附图和实施例，对本发明的具体实施方式作进一步详细描述。以下实施例用于说明本发明，但不用来限制本发明的范围。
本实施例以选矿三选厂为例，使用本发明的一种基于组态的生产指标可视化监控系统及方法实现对该选矿三选厂的生产指标进行监控。
一种基于组态的生产指标可视化监控系统，如图1所示，包括：工厂基础信息模块、可视监控组态设计环境模块、数据探测模块、可视与可视分析模块、生产指标监控模块和监控结果分析模块。
其中，工厂基础信息模块用于对工厂基础信息的建模，有助于操作员高效的创建工艺流程，实现工厂对基础信息的管理。该模块包括组织结构基础信息管理单元、工序流程基础信息管理单元、设备档案基础信息管理单元、指标档案基础信息管理单元、计量单位基础信息管理单元、物料基础信息管理单元、人员档案基础信息管理单元。
所述组织结构基础信息管理单元用于管理工厂内部各个部门之间层次和职能结构信息；
所述工序流程基础信息管理单元用于管理各个工艺流程以及其子工序流程；
所述设备档案基础信息管理单元用于管理各个工艺流程中所涉及的所有设备信息；
所述指标档案基础信息管理单元用于管理各个工艺流程中所涉及的所有指标信息。
所述计量单位基础信息管理单元用于管理设备、指标的度量单位；
所述物料基础信息管理单元用于管理生产过程中涉及的物料信息；
所述人员档案基础信息管理单元用于管理生产过程中涉及的人员信息。
可视监控组态设计环境模块用于构建基于工厂基础信息模块的各个工艺流程，利用生产工艺流程的逻辑关系，构建各工序的流程及其子流程，并配置各工序及子工序的设备、生产指标、报警事件、生产工艺规则约束、专家经验知识和算法约束。包括图元库子模块、绘制面板子模块、功能栏子模块、项目工序子模块、配置子模块及第一工序库子模块。
所述图元库子模块，包括常见图元节点和连接线的形状，也可根据需求自定义图元和连接线形状并添加至图元库中。
所述绘制面板子模块，通过鼠标拖拽方式将图元库中节点拖至绘制面板中，并配置端点、锚点、工序状态。
所述功能栏子模块包括保存、导入、后退、前进、清除、缩放、刷新功能；
所述保存功能用于将新构建的工艺流程或子流程保存到数据库或以文本格式保存到本地；所述导入功能用于将本地文件转换为文本格式导入到绘制面板或者导入到数据库；所述后退功能用于后退到上一个操作时的界面状态；所述前进功能用于恢复后退功能的操作；所述清除功能用于清空当前绘制面板；所述缩放功能用于放大或缩小当前绘制面板，既能概览 整个的工艺流程，也能放大局部具体查看各个子流程；所述刷新功能用于初始化整个页面。
所述项目工序子模块用于显示当前所配置的项目的各工序及子工序，各工序名称后面用不同颜色的五角星表示目前各工序的设计状态，红色表示配置完成，绿色表示配置未完成，黄色表示未配置；
所述配置子模块包括工序配置子模块和指标配置子模块，用于配置选定工序的基础信息、工序事件、工序指标和约束条件，并通过鼠标指进行各个工序节点间的连线，同时在连线上配置各工序之间输入输出指标类型。
所述第一工序库子模块用来管理已经构建好的通用基础工序单元，以用于快速构建新的工艺流程图，从而提高基础工序组件的复用性和重用性。
数据探测模块用于探测指标数据之间的关系；包括指标间关联关系探测、指标间时序变化关系探测、指标间主元变量探测和指标数据与维度之间的双向关联关系探测。
所述指标间关联关系探测是指通过皮尔逊相关系数、信息熵分析指标间关联关系，包括输入与输入指标之间、输入与输出指标之间、输出与输出指标之间的关系；所述皮尔逊相关系数用于分析指标间线性关系；所述信息熵用于分析指标间非线性关系。
所述指标间时序变化关系探测是指通过皮尔逊相关系数分析生产指标之间的延迟相关性，即生产指标之间是否间隔一段采样时间而具有相关性。
所述指标间主元变量探测是指通过主成分分析(Principal Component Analysis，即PCA)和基于核函数的主成分分析(Kernel Principal Component Analysis，即KPCA)将影响待测指标的指标集从高维投影到低维，实现对生产指标的降维。所述主成分分析是指通过线性空间变换求取主成分变量，将高维空间变量投影到低维主成分空间；所述基于核函数的主成分分析用于非线性特征提取，通过非线性函数将指标集映射到高维空间再实现PCA。
可视与可视分析模块用于提供生产指标可视化和可视化分析方案，此模块不仅包括实时数据、历史数据的可视化，还支持指标数据对比分析、生产指标关联关系可视分析和生产指标多视图可视化。
所述实时数据用于反应当前生产运行状态是否正常，生产相关指标是否达到预期目的。采用图表、实时数据曲线的方式进行可视化。
所述历史数据及统计特性用于反应指标在一段时间内的历史趋势并对历史数据的统计特性进行可视化，通过时间滑窗的形式灵活的查看不同时间段的历史数据。同时提供交互操作，包括移动、放缩、悬浮提示框、刷新。
所述指标数据对比分析是指同时展示不同量纲的多维数据，采用平行坐标图的形式。所述平行坐标图显示多个维度的数据，每个坐标轴表示一个维度，每个维度表示一个生产指标。
所述生产指标关联关系可视化用于显示生产指标之间的关联关系，输入指标和输入指标、输出和输出指标之间关系使用散点图表示；过程指标与运行指标之间的关联关系，采用图论中的二分图表示。所述二分图是指将过程指标和运行生产指标分别看成两个独立的点集，通过两个点集的映射关系表征两个集合中点的关联关系。
所述生产指标多视图可视化是指根据指标的所属工序和指标类型进行分类，设计多视图可视化方案，提供多视图交互技术。每一个工序和指标类别都对应一个视图，其中工序为主视图，指标分类为子视图，嵌入到工序视图中。操作员可点击具体工序以导航进入该工序查 看详细信息，此时其余工序视图通过缩放技术进行缩小，显示为缩略图；同时在该工序下可以点击具体的子工序导航进入该工序的子工序查看详细信息；在已选择工序或者子工序下，操作员同样可点击具体分类指标，查看该工序或者子工序下该分类指标视图里面的指标详细信息，此时其余指标类别视图通过缩放技术进行缩小，显示为缩略图。
生产指标监控模块用于实现可视监控组态设计环境模块构建完成的工艺流程的可视化监控，结合专家经验、知识和数据探测模块分析得出的指标数据的关系、借助于可视与可视分析模块对指标的可视分析，根据实际需求配置关键指标进行监控，包括第二工序库子模块、生产指标监控与配置子模块、工序监控子模块、报警记录子模块。
所述第二工序库子模块主要显示当前所有的工序流程，包括各级子工序，其目的主要用来帮助用户导航到具体的工序，操作员可通过点击相应的工序，工序监控面板会同步导航到该工序，以显示该工序的工艺流程；
所述生产指标监控与配置子模块用于显示与配置各工序的指标以及对指标配置可视化方案，操作员能够在可视监控组态设计环境模块已配置指标的基础上，根据实际需求过滤出各工序关键指标，并通过配置功能配置各工序关键指标，实现对各工序关键指标的监控；同时对各个指标配置上可视化方案以便进行可视化监控；
所述工序监控子模块用于显示工序库中所点击的工序，触发可视监控组态设计环境模块定义该工序的约束条件、工序事件、工序状态，工序点击事件，操作员可通过双击查看子过程的实时状态；同时能监控该工序已配置监控指标的实时数据曲线和历史数据曲线，并显示该工序下各指标已配置的可视化方案，通过可视分析更高效的监控生产指标。
所述报警记录子模块用于显示当前时段的报警信息，每条报警记录包括报警时间，报警所在的工序名称，故障设备名称和异常指标名称；在设备名称、指标名称前提供符号提示，红色圆圈表示设备停歇、黄色圆圈表示设备温度过高或消耗原料过量、红色上箭头表示指标超上限、绿色下箭头表示指标超下限。
监控结果分析模块用于分析生产指标监控过程的中出现的未被监控到的新的异常和故障，如设备故障、质量指标检验不合格等；同时监控和收集生产指标监控模块中的报警记录，通过逐一分析报警记录信息，追溯设备、指标异常/故障的原因，利用数据探测模块对历史数据进行探测分析并结合机理、专家知识经验重新配置生产指标监控模块中监控指标，从而实现对生产指标监控异常的有效反馈，实现对生产指标监控的动态调整，使得生产指标可视化监控系统具备动态演化能力。
一种基于组态的生产指标可视化监控方法，如图2所示，包括以下步骤：
步骤1、收集、录入选矿三选厂的基础信息，构建该厂基础信息单元并将其存储至数据库，实现该厂对基础信息的管理。具体包括组织结构基础信息管理、工序流程基础信息管理、设备档案基础信息管理、指标档案基础信息管理、计量单位基础信息管理、物料基础信息管理、人员档案基础信息管理。
组织结构基础信息管理是对选矿三选厂的能源、设备、物料、成本等部门之间的层次和职能的管理。
工序流程基础信息管理是对选矿三选厂所有工序及各工序子流程的管理，包括悬浮压滤、空气压缩站、悬浮焙烧炉等；
设备档案基础信息管理是对选矿三选厂所有设备信息的管理，包括高压辊磨机、压滤机、离心空压机等；
指标档案基础信息管理是对选矿三选厂所有指标信息的管理，包括底流泵出口流量、底流泵频率、炉前压滤水分、磁选机作业率等；
计量单位基础信息管理是对选矿三选厂所有设备和指标计量单位的管理，包括％、吨、倍、GJ、kg/t、h等；
物料基础信息管理是对选矿三选厂生产过程涉及的物料信息的管理；
人员档案基础信息管理是对选矿三选厂生产过程中工作人员信息的管理；
步骤2、使用工厂基础信息模块中的选矿三选厂基础信息管理单元，构建该厂生产流程可视监控组态设计环境，具体方法为：
步骤2.1、从图元库中选择悬浮焙烧炉工序的基础图元，点击所选图元将其拖到工艺流程绘制面板；
步骤2.2、根据实际选矿三选厂的生产工艺流程绘制组态界面的流程图，并配置悬浮焙烧炉工序的端点、锚点、工序状态等信息；
步骤2.3、在工序配置子模块配置悬浮焙烧炉工序的基础信息(字体颜色设置为黑色、字号设置为小四、文字字体设置为宋体)、工序事件(鼠标单击表示进入悬浮焙烧炉的子流程、鼠标双击表示选悬浮焙烧炉工序，对该工序进行指标配置、鼠标悬浮显示悬浮焙烧炉工序的相关备注)、约束条件(规则：给矿量65±10t/h、给矿水12±5m3/h、分益浓度50±5％、排矿水48±8m3/h、磨矿浓度80±2％、旋给浓度50±10％、旋给压力125±35Kpa)等属性；
步骤2.4、在指标配置子模块配置悬浮焙烧炉工序的指标，并配置指标类型，包括输入指标、输出指标、输入输出指标、被控量和控制量；
本实施例中，悬浮焙烧炉工序配置的指标包括：强磁精矿产量、选矿综精SiO2、三磁精品位、强磁矿仓、一次一流回收率及弱精品位；
步骤2.5、在悬浮焙烧炉工序已有指标类型的基础上，通过选择框添加算法；
本实施例中，添加的算法为自回归滑动平均模型(Autoregressive moving average model，即ARMA)；
步骤2.6、点击功能栏子模块的保存按钮，将配置完成的悬浮焙烧炉工序保存至数据库，其名称显示到项目工序子模块的目录中，项目工序子模块显示悬浮焙烧炉工序流程，不同的标识颜色表示目前该工序的设计状态，悬浮焙烧炉工序的标识呈红色表示配置完成；
步骤2.7、同时将配置完成的悬浮焙烧炉工序保存至工序库，集中管理已经构建完成的通用基础工序单元，从而提高基础选矿各工序组件的复用性和重用性；
步骤2.8、按照步骤2.1至步骤2.7依次配置选矿三选各个工序，包括悬浮压滤、空气压缩站、除盐水站，并通过鼠标绘制各个工序间的有向连线，表示实际选矿三选生产中的流程，将其保存为JSON格式数据，可将数据保存至数据库或导出为JSON文件保存至本地。
步骤3、在可视监控组态设计环境模块的基础上，应用数据探测模块探测指标数据之间的关系。
本实施例中，选取的生产指标数据如表1所示：
表1 生产指标数据
Figure PCTCN2019077739-appb-000001
步骤3.1、探测指标间关联关系，包括输入与输入指标之间、输入与输出指标之间、输出与输出指标之间的关系。通过皮尔逊相关系数分析指标间线性关联关系，通过互信息分析指标间非线性相关关系。
本实施例中，选取表1中部分生产指标，包括选矿综精产量(湿重)、弱磁精矿产量，综合块矿率、弱精品味、块1#和废石1#矿量。
步骤3.1.1、使用皮尔逊相关系数分析指标间线性关联关系，如果分析结果表明指标间线性关系强，则认为指标间存在线性相关性，如果分析结果表明指标间不存在线性关系，转到步骤3.1.2，探测指标间是否存在非线性关系；
步骤3.1.2、通过互信息分析指标间非线性相关关系。
本实施例中，表1中选取的指标经线性关联关系探测，得出结果如表2所示。选矿综精产量(湿重)与弱磁精矿产量的皮尔逊相关系数为0.926，表示两指标有极强的正相关；综合块矿率与弱磁精矿产量的皮尔逊相关系数为-0.023，表示两指标有较弱的负相关。
表2 生产指标间的线性关联关系探测结果
Figure PCTCN2019077739-appb-000002
Figure PCTCN2019077739-appb-000003
步骤3.2、探测指标间时序变化关系，通过皮尔逊相关系数分析生产指标之间的延迟相关性，即生产指标之间是否间隔一段采样时间而具有相关性。
本实施例中，选取表1部分生产指标，包括选矿综精产量和选矿综精SiO 2。经延时相关关系探测，选矿综精SiO 2延时选矿综精产量t时刻的相关系数，探测结果如表3所示。当选矿综精SiO 2延时t＝5时，皮尔逊相关系数为0.54786，选矿综精产量与选矿综精SiO 2相关性最大。
表3 生产指标之间的延迟相关性的探测结果
t＝0 t＝1 t＝2 t＝3 t＝4 t＝5 t＝6 t＝7 t＝8
0.42886 0.01841 0.27133 0.11379 0.37944 0.54786 0.20034 0.31005 0.42422
步骤3.3、探测指标间的潜变量，通过潜变量代替原始的指标变量，实现对指标的降维。使用主成分分析PCA和基于核函数的主成分分析KPCA方法分别探测线性指标间潜变量和非线性指标间的潜变量。
本实施例中，选取的生产指标包括3-1球磨机电流、3-1旋流器给矿压力、3-1旋流器给矿浓度、3-1旋流器给矿流量、3-1#泵运转频率、3-1球磨排矿水阀门开度、3-1球磨给矿水阀门开度和3-2旋流器给矿压力。生产指标数据如表4所示。通过步骤3.1，得出指标数据间是线性关系，相关系数矩阵如表5所示，进行以下线性指标间潜变量的探测。
表4 生产指标数据
Figure PCTCN2019077739-appb-000004
Figure PCTCN2019077739-appb-000005
表5 相关系数矩阵
Figure PCTCN2019077739-appb-000006
进行主成分PCA分析，得到特征值，从大到小依次为：145.56、21.53、5.44、3.66、1.03、0.67、0.28、-5.41，前两个特征值(145.56+21.53/(145.56+21.53+5.44+3.66+1.03+0.67+0.28-5.41)>0.9)，表明该组变量之间线性相关性非常强，可以用前两个特征值(145.56、21.53)对应的主元来代替原始的8个指标，实现对指标的降维。
步骤3.4、将经过数据探测模块分析得到的结果通过可视与可视分析模块中的可视化功能以更直观的方式展示出来。
步骤4、以可视监控组态设计环境模块已配置的生产指标为基础，借助数据探测模块分析得出的指标间各类关系，利用可视与可视分析模块对指标数据进行可视分析，辅助人们从可视的角度去理解选矿生产指标数据间的关系，具体方法为：
步骤4.1、采用图表或实时数据曲线的方式表示实时数据，反应当前生产运行状态是否正常，生产相关指标是否达到预期目的；
本实施例中，实时数据选取综合生产指标中的选矿综精产量(湿重)；
步骤4.2、采用历史数据曲线的方式表示历史数据，并通过时间滑窗的形式灵活的查看不同时间段的历史数据趋势，同时提供交互操作，包括移动、放缩、悬浮提示框、刷新；
本实施例中，历史数据选取综合生产指标中的选矿综精产量(湿重)，数据时间选择2018-5-1至2018-5-7。
步骤4.3、采用平行坐标图表示指标数据对比分析，能够同时展示不同量纲的多维数据；
本实施例中，选取指标选矿综精产量(湿重)、综合块矿率、选矿综精水分、选矿综精 SiO 2、测算烧结矿品味和选矿综精CaO进行指标数据对比分析。
步骤4.3.1、通过平行坐标图显示多个维度的数据，每个坐标轴表示一个维度；
步骤4.3.2、选矿综精产量(湿重)、综合块矿率、选矿综精水分、选矿综精SiO2、测算烧结矿品味、选矿综精CaO各表示一个维度，通过对指标设置不同单位来描述不同数量级的数据；
步骤4.3.3、每个维度显示该指标数据的当前值，通过设置上下限，反应该指标当前的运行状态；
本实施例中，选矿综精产量(湿重)的指标范围为260t至350t，在2018-4-20 19:28指标数据值为355t有超上限报警提示，选矿综精产量(湿重)所在的坐标轴显示为红色；
步骤4.3.4、在正常工况下，选矿综精产量(湿重)、综合块矿率、选矿综精水分、选矿综精SiO 2、测算烧结矿品味、选矿综精CaO应均在上下限范围内，平行坐标图的整体轮廓应大致相同，若整体轮廓出现异常形状，表示工况异常，操作员可通过观察图形的整体轮廓，判断生产运行情况；
步骤4.4、借助二分图方法，采用桑基图的方式表示生产指标关联关系；
步骤4.4.1、在二分图中，将过程指标和运行指标分别看成两个独立的点集，通过两个点集的映射关系表征两个集合中点的关联关系；
步骤4.4.2、将二分图中的映射关系平移到桑基图中，左侧颜色条代表过程指标，右侧颜色条代表影响过程指标的运行指标；
本实施例中，选取指标：选矿综精CaO、选矿综精SiO 2、测算烧结矿品味、综合块矿率、选矿综精水分为过程指标；选取指标一次溢流回收率、平环尾矿品位、弱尾品位、强精SiO 2、弱磁精品位为运行指标。
步骤4.4.3、通过对左右两侧的颜色条设置不同的颜色来区分不同的指标；左侧各个颜色条流向右侧，表示右侧指标影响左侧指标的因素；
步骤4.4.4、根据步骤3筛选出的关键指标以及各个过程指标与运行指标间关系，计算各运行指标对过程指标影响的贡献率，确定图中各个运行指标的比例关系；
步骤4.4.5、提供交互操作，当鼠标悬浮于左侧选矿综精SiO 2所在区域时，单独显示影响该指标的运行指标；
本实施例中，弱精品位的贡献率为57％、强精SiO 2的贡献率为8％、山磁精品位的贡献率为7％、一次溢流回收率的贡献率为7％。
步骤4.5、采用多视图交互技术实现选矿生产指标的多视图可视化；
本实施例中，涉及的工序包括悬浮压滤、空气压缩站、悬浮焙烧炉、酿造间、悬浮磨选车间等；指标分类包括设备指标、质量指标、工艺指标、成本指标、计量指标、能源指标；
步骤4.5.1、将每一个工序对应一个主视图，每一个指标分类对应一个子视图；
步骤4.5.2、将指标分类视图嵌入到工序视图中；
步骤4.5.3、操作员点击悬浮焙烧炉时能够导航进入该工序，该工序视图通过缩放技术进行放大并显示该工序下已配置指标的实时数据和历史数据，此时其余工序视图通过缩放技术进行缩小，显示为缩略图移到界面最右侧；
步骤4.5.4、在悬浮焙烧炉工序中，选择设备指标分类的同时筛选出该工序下已配置 指标中的设备指标，该分类视图通过缩放技术进行放大并显示筛选出的设备指标的实时数据和历史数据，此时其余工序视图通过缩放技术进行缩小，显示为缩略图移到子界面的最右侧；
步骤5、在生产指标监控模块中配置选矿三选工艺流程监控运行参数，具体方法为：
步骤5.1、参考步骤3和步骤4分析得出的选矿生产指标数据间的关系，借助步骤4中各可视化方案所呈现的信息，直观显示选矿生产指标间关联关系和各指标的重要程度，为配置监控指标提供依据；
步骤5.2、结合专家知识经验，补充因系统分析不全面所遗漏的指标，配置生产指标监控模块的参数；
本实施例中，根据步骤3得出强精SiO 2对选矿综精SiO 2的贡献率为8％，但是在步骤2.6并没有配置该指标，在此将强精SiO 2配置到悬浮焙烧炉工序中；
步骤5.3、构建选矿生产指标监控运行环境；
步骤5.3.1、通过第二工序库子模块显示选矿三选厂各工序流程，包括各级子流程；
步骤5.3.2、点击第二工序库子模块中的悬浮焙烧炉工序，工序监控子模块同步导航到该工序，并显示该工序的工艺流程；
选中悬浮焙烧炉工序时，触发可视监控组态设计环境模块定义的该工序的约束条件、工序事件、工序状态，工序点击事件；
步骤5.3.3、根据实际需求在生产指标监控与配置子模块中对悬浮焙烧炉工序配置所要监控的关键指标，以及对该工序已配置的指标配置可视化方案；
本实施例中，配置的指标为强精SiO 2、强磁精矿产量、选矿综精SiO 2、三磁精品位、强磁矿仓、一次一流回收率、弱精品位，配置的可视化方案为实时数据和历史数据可视化方案；
步骤5.3.4、通过双击悬浮焙烧炉工序查看子过程的实时状态；
能够显示悬浮焙烧炉工序已配置的所有指标，包括强精SiO 2、强磁精矿产量、选矿综精SiO 2、三磁精品位、强磁矿仓、一次一流回收率、弱精品位，显示各指标的实时数据曲线、历史数据曲线；同时显示各个可视化方案，便于监控分析已配置指标；
步骤5.3.5、通过报警记录子模块显示当前时段的报警信息，每条信息包括故障时间、故障工序名称、故障设备名称、异常指标名称；并分析出设备故障类型和指标异常类型，如设备停歇、指标超上限、指标超下限；
本实施例中，报警记录显示在2018-09-22，悬浮压滤工序中1-3-1磁选机设备的#6电流数据在11:38分超指标上限。
步骤6、通过监控结果分析模块分析生产指标监控过程的中出现的未处理的异常和故障。
步骤6.1、通过监控结果分析模块分析正在监控的工艺流程中未知故障，如设备停歇、指标质量检验不合格等；
步骤6.2、监控和收集生产指标监控模块中的报警记录，通过逐一分析报警记录信息，追溯设备、指标异常/故障的原因；
步骤6.3、利用数据探测模块对历史数据进行探测分析；
步骤6.4、结合机理、专家知识经验重新配置生产指标监控模块中监控指标；
步骤6.5、最终实现对生产指标监控异常的有效反馈，实现对生产指标监控的动态调整。
最后应说明的是：以上实施例仅用以说明本发明的技术方案，而非对其限制；尽管参照 前述实施例对本发明进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述实施例所记载的技术方案进行修改，或者对其中部分或者全部技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本发明权利要求所限定的范围。

Claims (10)

  1. 一种基于组态的生产指标可视化监控系统，其特征在于：包括工厂基础信息模块、可视监控组态设计环境模块、数据探测模块、可视与可视分析模块、生产指标监控模块及监控结果分析模块；所述工厂基础信息模块用于对工厂基础信息进行建模，实现工厂对基础信息的管理；所述可视监控组态设计环境模块用于构建基于工厂基础信息模块的各个工艺流程，利用生产工艺流程的逻辑关系，构建各工序的流程及其子流程，并配置各工序及其子工序的设备、生产指标、报警事件、生产工艺规则约束、专家经验知识和算法约束；所述可视与可视分析模块用于提供生产指标可视化和可视化分析方案，此模块不仅包括实时数据、历史数据及统计特性的可视化，还支持指标数据对比分析、生产指标关联关系的可视化分析及生产指标多视图的可视化；所述数据探测模块用于探测生产指标数据之间的关系；所述生产指标监控模块用于实现可视监控组态设计环境模块构建完成的工艺流程的可视化监控，结合专家经验、知识和数据探测模块分析得出的生产指标数据之间的关系、借助于可视与可视分析模块对指标的可视分析，根据实际需求对配置关键指标进行监控；所述监控结果分析模块用于分析生产指标监控过程中出现的未被监控到的新的异常和故障，同时监控和收集生产指标监控模块中的报警记录，通过逐一分析报警记录信息，追溯设备、指标异常或故障的原因，利用数据探测模块对历史数据进行探测分析，并结合机理、专家知识经验重新配置生产指标监控模块中的监控指标，从而实现对生产指标监控异常的有效反馈，实现对生产指标监控的动态调整，使得生产指标可视化监控系统具备动态演化能力；
    所述可视监控组态设计环境模块包括图元库子模块、绘制面板子模块、功能栏子模块、项目工序子模块、配置子模块和第一工序库子模块；
    所述生产指标监控模块包括第二工序库子模块、生产指标监控与配置子模块、工序监控子模块、报警记录子模块。
  2. 根据权利要求1所述的一种基于组态的生产指标可视化监控系统，其特征在于：所述工厂基础信息模块包括组织结构基础信息管理单元、工序流程基础信息管理单元、设备档案基础信息管理单元、指标档案基础信息管理单元、计量单位基础信息管理单元、物料基础信息管理单元和人员档案基础信息管理单元；
    所述组织结构基础信息管理单元用于管理工厂内部各个部门之间层次和职能结构信息；
    所述工序流程基础信息管理单元用于管理各个工艺流程以及其子工序流程；
    所述设备档案基础信息管理单元用于管理各个工艺流程中所涉及的所有设备信息；
    所述指标档案基础信息管理单元用于管理各个工艺流程中所涉及的所有指标信息；
    所述计量单位基础信息管理单元用于管理设备、指标的度量单位；
    所述物料基础信息管理单元用于管理生产过程中涉及的物料信息；
    所述人员档案基础信息管理单元用于管理生产过程中涉及的人员信息。
  3. 根据权利要求1所述的一种基于组态的生产指标可视化监控系统，其特征在于：
    所述图元库子模块包括常见图元节点和连接线的形状，及根据需求自定义的图元和连接线形状；
    所述绘制面板子模块通过鼠标拖拽方式将图元库中节点拖至绘制面板中，并配置端点、 锚点及工序状态；
    所述功能栏子模块包括保存、导入、后退、前进、清除、缩放和刷新功能；所述保存功能用于将新构建的工艺流程或子流程保存到数据库或以文本格式保存到本地；所述导入功能用于将本地文件转换为文本格式导入到绘制面板；所述后退功能用于后退到上一个操作时的界面状态；所述前进功能用于恢复后退功能的操作；所述清除功能用于清空当前绘制面板；所述缩放功能用于放大或缩小当前绘制面板，既能概览整个的工艺流程，也能放大局部具体查看各个子流程；所述刷新功能用于初始化整个绘制面板；
    所述项目工序子模块用于显示当前所配置的项目的各工序及子工序，并在各工序名称后面用不同颜色的五角星表示目前各工序的设计状态；
    所述配置子模块包括工序配置子模块和指标配置子模块，用于配置选定工序的基础信息、工序事件、工序指标、约束条件，并通过鼠标进行各个工序节点间的连线，同时在连线上配置各工序之间输入输出指标类型；
    所述第一工序库子模块用来管理已经构建好的通用基础工序单元，以用于快速构建新的工艺流程图，从而提高基础工序组件的复用性和重用性。
  4. 根据权利要求1所述的一种基于组态的生产指标可视化监控系统，其特征在于：所述数据探测模块用于探测指标数据之间的关系，具体包括指标间关联关系的探测、指标间时序变化关系的探测、指标间主元变量的探测及指标数据与维度之间的双向关联关系的探测；
    所述指标间关联关系的探测通过皮尔逊相关系数和信息熵分析指标间的关联关系，包括输入与输入指标之间、输入与输出指标之间、输出与输出指标之间的关系；所述皮尔逊相关系数用于分析指标间线性关系；所述信息熵用于分析指标间非线性关系；
    所述指标间时序变化关系的探测是指通过皮尔逊相关系数分析生产指标之间的延迟相关性，即生产指标之间是否间隔一段采样时间而具有相关性；
    所述指标间主元变量的探测通过主成分分析及基于核函数的主成分分析将影响待测指标的指标集从高维投影到低维，实现对生产指标的降维。
  5. 根据权利要求1所述的一种基于组态的生产指标可视化监控系统，其特征在于：所述实时数据可视化用于反应当前生产运行状态是否正常，生产相关指标是否达到预期目的；采用图表、实时数据曲线的方式进行可视化；
    所述历史数据及统计特性可视化用于反应指标在一段时间内的历史趋势并对历史数据的统计特性进行可视化，并通过时间滑窗的形式灵活的查看不同时间段的历史数据；同时提供移动、放缩、悬浮提示框、刷新的交互操作；
    所述指标数据对比分析可视化通过同时展示不同量纲的多维数据，采用平行坐标图的形式进行表示；所述平行坐标图显示多个维度的数据，每个坐标轴表示一个维度，每个维度表示一个生产指标；
    所述生产指标关联关系可视化用于显示生产指标之间的关联关系，输入指标和输入指标、输出和输出指标之间关系使用散点图表示；过程指标与运行指标之间的关联关系，采用图论中的二分图表示；所述二分图是指将过程指标和运行生产指标分别看成两个独立的点集，通 过两个点集的映射关系表征两个集合中点的关联关系；
    所述生产指标多视图可视化根据指标的所属工序和指标类型进行分类，设计多视图可视化方案，提供多视图交互技术；每一个工序和指标类别都对应一个视图，其中，工序为主视图，指标分类为子视图，嵌入到工序视图中；操作员点击具体工序以导航进入该工序查看详细信息，此时，其余工序视图通过缩放技术进行缩小，显示为缩略图；同时在该工序下点击具体的子工序导航进入该工序的子工序查看详细信息；在已选择工序或者子工序下，操作员点击具体分类指标，查看该工序或者子工序下该分类指标视图里面的指标详细信息，此时其余指标类别视图通过缩放技术进行缩小，显示为缩略图。
  6. 根据权利要求1所述的一种基于组态的生产指标可视化监控系统，其特征在于：
    所述第二工序库子模块用于显示当前所有的工序流程，包括各级子工序，用来帮助用户导航到具体的工序；操作员通过点击相应的工序，工序监控子模块会同步导航到该工序，以显示该工序的工艺流程；
    所述生产指标监控与配置子模块用于显示与配置各工序的指标以及对指标配置可视化方案，操作员能够在可视监控组态设计环境模块已配置指标的基础上，根据实际需求过滤出各工序关键指标，并通过配置功能配置各工序关键指标，实现对各工序关键指标的监控；同时对各个指标配置可视化方案以便进行可视化监控；
    所述工序监控子模块用于显示工序库子模块中所点击的工序，触发可视监控组态设计环境模块定义该工序的约束条件、工序事件、工序状态，工序点击事件，操作员通过双击查看子过程的实时状态；同时监控该工序已配置监控指标的实时数据曲线和历史数据曲线，并显示该工序下各指标已配置的可视化方案，通过可视分析更高效的监控生产指标；
    所述报警记录子模块用于显示当前时段的报警信息，每条报警记录包括报警时间，报警所在的工序名称，故障设备名称和异常指标名称；在设备名称、指标名称前提供符号提示。
  7. 采用权利要求1所述的一种基于组态的生产指标可视化监控系统进行生产指标可视化监控的方法，其特征在于：包括以下步骤：
    步骤1、通过工厂基础信息模块收集、录入工厂的生产基础信息，构建基础信息单元并将其存储至数据库，实现工厂对基础信息的管理；所述基础信息的管理包括组织结构基础信息管理、工序流程基础信息管理、设备档案基础信息管理、指标档案基础信息管理、计量单位基础信息管理、物料基础信息管理、人员档案基础信息管理。
    步骤2、通过使用工厂基础信息模块中的基础信息管理单元，构建各生产流程可视监控组态设计环境；
    步骤3、在可视监控组态设计环境模块的基础上，应用数据探测模块探测指标数据之间的关系；
    步骤4、以可视监控组态设计环境模块中已配置的生产指标为基础，借助数据探测模块分析得出的指标间各类关系，利用可视与可视分析模块对生产指标数据进行可视分析，以辅助人们从可视的角度去理解指标数据间的关系；
    步骤5、在生产指标监控模块中配置监控运行参数，具体方法为：
    步骤5.1、参考数据探测模块和可视与可视分析模块分析得出的指标数据间的关系，借助可视与可视分析模块中各可视化方案所呈现的信息，直观显示生产指标间关联关系和重要程度，为配置监控指标提供依据；
    步骤5.2、结合专家知识经验，补充因系统分析不全面所遗漏的指标，配置生产指标监控模块的参数；
    步骤5.3、在生产指标监控模块中构建生产指标监控运行环境，配置监控运行参数；
    步骤5.3.1、通过第二工序库子模块显示当前所有的工序流程，包括各级子工序；
    步骤5.3.2、通过点击第二工序库子模块中的工序，工序监控子模块同步导航到该工序，显示该工序的工艺流程；
    选中相应工序时，触发可视监控组态设计环境模块定义的该工序的约束条件、工序事件、工序状态，工序点击事件；
    步骤5.3.3、根据实际需求在生产指标监控与配置子模块中对各工序配置所要监控的关键指标，以及对该工序已配置的指标配置可视化方案；
    步骤5.3.4、通过双击查看该工序的子过程的实时状态；显示该工序已配置的所有指标，指标的实时数据曲线、历史数据曲线；同时显示各个可视化方案，便于监控分析已配置指标；
    步骤5.3.5、通过报警记录子模块显示当前时段的报警信息，每条信息包括故障时间、故障工序名称、故障设备名称及异常指标名称；并分析出设备故障类型和指标异常类型，如设备停歇、指标超上限、指标超下限；
    步骤6、通过监控结果分析模块分析生产指标监控过程的中出现的未处理的异常和故障；
    步骤6.1、通过监控结果分析模块分析正在监控的工艺流程中未知故障；
    步骤6.2、同时监控和收集生产指标监控模块中的报警记录，通过逐一分析报警记录信息，追溯设备、指标异常或故障的原因；
    步骤6.3、利用数据探测模块对历史数据进行探测分析；
    步骤6.4、结合机理、专家知识经验重新配置生产指标监控模块中监控指标；
    步骤6.5、最终实现对生产指标监控异常的有效反馈，实现对生产指标监控的动态调整。
  8. 根据权利要求7所述的一种基于组态的生产指标可视化监控方法，其特征在于：所述步骤2的具体方法为：
    步骤2.1、从图元库中选择需要构建的基础图元，点击所选图元将其拖到工艺流程绘制面板；
    步骤2.2、根据实际生产工艺流程绘制组态界面，并配置各个工序的端点、锚点、工序状态信息；
    步骤2.3、在工序配置子模块配置工序基础信息、工序事件、约束条件属性，并通过鼠标绘制各个工序间的有向连线，表示实际生产中的流程；
    步骤2.4、在指标配置子模块为各工序配置指标、并配置指标类型，包括输入指标、输出指标、输入输出指标、被控量和控制量；
    步骤2.5、在已有指标类型的基础上添加算法，并通过选择框的形式选择不同的算法进行 建模；
    步骤2.6、通过功能栏子模块的保存功能钮，将配置完成的工序保存至本地数据库并显示到项目工序子模块，项目工序子模块显示当前项目工序流程，不同的标识颜色表示目前该工序的设计状态，红色表示配置完成，绿色表示配置未完成，黄色表示未配置；
    步骤2.7、同时将配置完成的工序保存至工序库，集中管理已经构建完成的通用基础工序单元，从而提高基础工序组件的复用性和重用性；
    步骤2.8、将绘制并配置完成的流程图保存为文本格式数据，然后将数据保存至本地数据库或导出为文本文件保存至本地。
  9. 根据权利要求7所述的一种基于组态的生产指标可视化监控方法，其特征在于：所述步骤3的具体方法为：
    步骤3.1、探测指标间的关联关系，包括输入与输入指标之间、输入与输出指标之间、输出与输出指标之间的关系；通过皮尔逊相关系数分析指标间的线性关联关系，通过互信息分析指标间的非线性相关关系；
    步骤3.1.1、使用皮尔逊相关系数分析指标间线性关联关系，如果分析结果表明指标间线性关系强，则指标间存在线性相关性，如果分析结果表明指标间不存在线性关系，转到步骤3.1.2，探测指标间是否存在非线性关系；
    步骤3.1.2、通过互信息分析指标间非线性相关关系；
    步骤3.2、探测指标间时序变化关系，通过皮尔逊相关系数分析生产指标之间的延迟相关性，即生产指标之间是否间隔一段采样时间而具有相关性；
    步骤3.3、探测指标间的潜变量，从而通过使用潜变量代替原始的指标变量，实现对指标的降维；使用主成分分析和基于核函数的主成分分析方法分别探测线性指标间潜变量和非线性指标间的潜变量；利用步骤3.1的结论，如果指标数据间是线性关系，则转到步骤3.3.1，如果指标数据间是非线性关系，则转到步骤3.3.2；
    步骤3.3.1、利用主成分分析探测指标间的潜变量，利用主成分分析求解出指标数据的特征值并按其值从大到小进行排列，选择其中最大的k个，使得这k个主成分占全部主成分的百分比超过设定的阈值，使用这k个特征值对应的主元即潜变量代替原始指标，实现对原始指标数据的降维；
    步骤3.3.2、利基于核函数的主成分分析提取非线性特征，通过非线性函数将指标集映射到高维线性特征空间，然后在高维空间中使用主成分分析方法计算其主元成分，实现对原始指标数据的降维；
    步骤3.4、将经过数据探测模块分析得到的结果通过可视与可视分析模块中可视化功能以更直观的方式展示出来。
  10. 根据权利要求7所述的一种基于组态的生产指标可视化监控方法，其特征在于：所述步骤3的具体方法为：
    具体方法为：
    步骤4.1、采用图表或实时数据曲线的方式表示实时数据，反应当前生产运行状态是否正 常，生产相关指标是否达到预期目的；
    步骤4.2、采用历史数据曲线的方式表示历史数据，并通过时间滑窗的形式灵活的查看不同时间段的历史数据趋势，同时提供交互操作，包括移动、放缩、悬浮提示框、刷新；
    步骤4.3、采用平行坐标图表示指标数据对比分析，同时展示不同量纲的多维数据；
    步骤4.3.1、通过平行坐标图显示多个维度的数据，每个坐标轴表示一个维度；
    步骤4.3.2、每个维度表示一个生产指标，通过对每个维度设置不同单位来描述不同数量级的数据；
    步骤4.3.3、每个维度显示该指标数据的当前值，通过设置上下限，反应该指标当前的运行状态，若超出限制值有报警提示，异常指标数据所在的坐标轴显示为红色；
    步骤4.3.4、在正常工况下，每个生产指标均在上下限范围内，平行坐标图的整体轮廓大致相同，若整体轮廓出现异常形状，表示工况异常；操作员通过观察图形的整体轮廓，判断生产运行情况；
    步骤4.4、借助二分图的方法，采用桑基图的方式表示生产指标关联关系；
    步骤4.4.1、在二分图中，将过程指标和运行指标分别看成两个独立的点集，通过两个点集的映射关系表征两个集合中点的关联关系；
    步骤4.4.2、将二分图中的映射关系平移到桑基图中，左侧颜色条代表过程指标，右侧颜色条代表影响过程指标的运行指标；
    步骤4.4.3、通过对左右两侧的颜色条设置不同的颜色来区分不同的指标；
    左侧各个颜色条流向右侧，表示右侧指标影响左侧指标的因素；
    步骤4.4.4、根据步骤3筛选出的关键指标以及各个过程指标与运行指标间关系，计算各工序指标对过程指标影响的贡献率，确定图中各个指标的比例关系；
    步骤4.4.5、提供交互操作，当鼠标悬浮于左侧某个过程指标所在区域时，单独显示影响该指标的左侧指标，并显示百分比以表示对该指标的影响程度；
    步骤4.5、采用多视图交互技术实现指标多视图监控的可视；
    步骤4.5.1、每一个工序对应一个主视图，每一个指标分类对应一个子视图；
    步骤4.5.2、将指标分类视图嵌入到工序视图中；
    步骤4.5.3、操作员点击具体工序导航进入该工序，该工序视图通过缩放技术进行放大并显示详细信息，此时其余工序视图通过缩放技术进行缩小，显示为缩略图；
    步骤4.5.4、在该工序中，点击具体分类指标，该分类视图通过缩放技术进行放大并显示该类别指标，此时其余工序视图通过缩放技术进行缩小，显示为缩略图。
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