CN109034662A - A kind of production target Visualized Monitoring System and method based on process flow - Google Patents

Abstract

The invention discloses a kind of production target Visualized Monitoring System and method based on process flow includes: data acquisition module, production target management module, production target VIS simulation design module, production target monitoring configuration module, algorithm management module, data processing module, production target visualization model and production target multiple view interactive module；Data acquisition module: acquisition mineral processing production whole process production target data, and it is stored to local data base, including PLC and data sampling sensor；The visual control to production target is realized, user is promoted to the monitoring efficiency of production target, promotes the interactivity and convenience of index monitoring.

Description

A production indicator visualization monitoring system and method based on technological process

technical field

The invention belongs to the field of production index monitoring, and in particular relates to a production index visualization monitoring system and method based on a technological process.

Background technique

At present, there are only a few researches and applications on the visualization monitoring system and method of production indicators in the whole production process in China, and the functions are single. "201310723320.5 (visualized mineral processing production process index optimization decision-making system)" obtains the data required for the mineral processing production process control strategy from the mineral processing production site control system and obtains offline data from the local, and encapsulates the algorithm, or performs the packaged algorithm Modular modification and configuration form the whole process control strategy of mineral processing production. This patent is mainly aimed at the optimization of mineral processing production indicators. "201610807805.6 (A mobile monitoring system and method for mineral processing equipment based on the Internet of Things and industrial cloud)" provides a mobile monitoring system and method for mineral processing equipment based on the Internet of Things and industrial cloud, which realizes that the monitoring of the equipment does not have to be in a fixed place It can provide monitoring data anytime and anywhere to enterprise managers and scientific researchers. "201711283037.X (a system and method for visual analysis of mineral processing production indicators)" realizes the integration and configuration of various process indicators of mineral processing production and analyzes and visualizes abnormal indicators. This patent mainly focuses on visual analysis of abnormal mineral processing production indicators . However, the above-mentioned patents do not relate to the configuration and configuration of the visual monitoring of mineral processing production indicators. For the visual monitoring of production indicators, although the 201711283037.X patent involves the visualization of production indicators, it only uses radar charts to visualize production indicators, and does not adopt different visualization solutions according to the actual needs of production indicator monitoring. In addition, none of the above-mentioned patents involves the combination of production indicator visualization and indicator monitoring algorithm. Aiming at the problems existing in the above patents, the present invention proposes a production index visualization monitoring system and method based on the process flow chart.

The present invention has the following innovative points:

Provides a visual configuration design module for production indicators based on the production process, draws the process flow chart in a configuration mode according to the production process, and defines its node functions, trigger events, alarm information, prompt information, and process rules through the visual interface, and establishes The relationship between process and equipment, process and production indicators, so as to realize the visual monitoring of production indicators;

Classify production indicators by process and indicator type, realize multi-view monitoring of different classification indicators, and improve the interactivity and convenience of indicator monitoring;

It supports real-time data, historical data, statistical characteristics of historical data, multi-indicator comprehensive comparative analysis, and visualization solutions for data correlation analysis to improve the monitoring function and efficiency of production indicators in the production process; it supports custom visualization solutions, and users can use visual configuration Design modules and monitored production indicators, realize personalized customization of production indicator visualization solutions, and improve users' monitoring efficiency of production indicators;

It supports the configuration of production index monitoring algorithms for production index monitoring in a visualized way to realize the monitoring of production indexes.

Contents of the invention

For prior art, the present invention provides a kind of production index visual monitoring system and method based on technological process:

A production index visualization monitoring system based on technological process, including: data acquisition module, production index management module, production index visualization configuration design module, production index monitoring configuration module, algorithm management module, data processing module, production index visualization module and Production index multi-view interactive module;

Data acquisition module: collect the production index data of the whole production process of the enterprise, and store it in the local database, including PLC (Programmable Logic Controller, programmable logic controller) and data acquisition sensors;

The data acquisition sensor collects real-time operating status data of the equipment from the industrial site;

The PLC is used to save the real-time data of the production process collected by the sensor to a local database;

Production indicator management module: encode indicators and bind data sources, including creating production indicators, editing production indicators, deleting production indicators, and viewing production indicators;

The production index includes: index code, index name, index data, collection time and index unit;

The index code is the unique ID (identification) of the representative index composed of multiple digits;

The name of the indicator is the factory's naming of the process indicators of the whole process;

The index data is a digital value representing the size and height of the index collected from the data acquisition module;

The collection time is the time when the indicator data is collected from the collection module;

The indicator unit is the quantification unit adopted by the indicator data;

The creation of a production index refers to creating and storing a vector consisting of index code, index name, index data, collection time and index unit in the local database;

The editing of production indicators refers to changing the existing assignments of indicator codes, indicator names, indicator data, collection time and indicator units that have been created in the local database;

The deletion of the production index refers to the deletion of the index code, index name, index data, collection time and existing information of the index unit in the local database about the created index;

The viewing of production indicators refers to viewing the indicator codes, indicator names, indicator data, collection time, and existing assignments of indicator units that have been created in the local database;

Production indicator visualization configuration design module: draw the production process into a flow chart in a configurable way, and display it to the user;

The process flow chart includes: visual configuration tools, process nodes, connection lines, endpoints, anchor points and coverings;

The visual configuration tool includes three parts: a function bar, a graph element library, and a drawing panel;

The function bar includes functions of saving, clearing, refreshing, importing, and graphic element configuration;

The primitive library includes common shape primitive nodes, and custom primitive shapes can be added to the primitive library according to requirements;

The drawing panel is used to draw the process flow. The user drags the nodes in the graphic element library to the drawing panel by dragging the mouse, configures the corresponding attributes of the endpoint and the anchor point on the configuration interface, and performs the connection between each graphic element node through the mouse. Line, the drawn flow chart is saved to the database in a specific format or exported as a text file and saved locally;

The process node refers to the production process with actual physical meaning represented by the graph element in the flowchart, and the process node includes process text information, process state and process event binding;

The process text information represents the process name, and by dragging the graphic element node into the drawing panel, an input box pops up for setting the process node text information;

The process status refers to the status notification information of the process node indicating the equipment and production indicators in the process by setting flashing borders of different colors and adding prompt icons;

The process event binding refers to events that can be bound to each process node, including "click" events, "double-click" events, and mouse "hover" events;

The mouse "click" event is to enter the sub-process operation, so that the current interface jumps to the process sub-interface, and draws a more detailed sub-flow chart of the process in the process sub-interface, so that the process flow chart has a nesting function;

The mouse "double-click" event can be set to pop up a dialog box to display prompt information;

The mouse "floating" event can be defined as a pop-up prompt box to display the basic information of the process;

The connecting line refers to the connecting line between nodes, and the different colors of the connecting line indicate whether there is any abnormality in the output of the process node;

The endpoint refers to the starting point of the connection line connecting the process nodes; the shape, size, style and number of endpoints can be customized;

The anchor point indicates the position where the end point appears on the node, and indicates the direction of the connecting line by distinguishing the start anchor point and the end anchor point;

Said overlay refers to the addition of decorations on the connection line, including label text and arrowheads at connection points;

Production index monitoring configuration module: configure the production index that the user wants to monitor and the visualization scheme of the index, support the configuration of the monitoring algorithm for the production index, and realize the diagnosis and trend prediction of the index, including the production index list, process list and visualization plan list;

The production indicator list is used to display all monitorable indicators;

The process list displays all monitorable processes, and the user selects the indicators that need to be monitored in the production index list and configures them to the corresponding processes;

The list of visualization schemes displays all visualization schemes, and the user configures indicators for monitoring by selecting a specific visualization scheme;

The algorithm management module is used for unified management of all the algorithms mentioned by the module, including adding, deleting and modifying functions;

The data processing module is used to calculate and process the collected data, including daily mean value and daily variance;

The production index visualization module is used to display the raw data generated by the data acquisition module and the comparison data and forecast data generated by the data processing module. Including real-time data curve chart, historical data curve chart, daily mean trend line chart, daily variance trend histogram;

The real-time data graph takes time (hour) as the horizontal axis, and the index value is the vertical axis to display real-time data, and the data collection time, index name and index value will be displayed when the mouse hovers over the data point;

The historical data graph takes time (date) as the horizontal axis, and the index value is the vertical axis, and the user can select data within a range of less than two months to view;

The daily average value trend line chart takes time (date) as the horizontal axis, and the daily average value of the index data is displayed on the vertical axis, and the display range is the selected range of historical data, and the date, index name and data daily average value will be displayed when the mouse hovers;

The daily variance trend histogram uses time (date) as the horizontal axis, and the daily variance of indicator data is displayed on the vertical axis. The display range is the selected range of historical data. The date, indicator name and data daily variance value will be displayed when the mouse hovers.

Production indicator multi-view interactive module: display indicators in multiple views, including indicator overview view and indicator classification view;

The overview view is used to display all user-configured monitoring indicators and an overview of all monitoring indicators;

The indicator classification view is used to display indicators of different categories configured by the user, including various indicators classified according to the process and indicator type, so as to realize the display of indicators contained in a specific process or a certain type, so as to facilitate users to view a certain process Or for a certain category of indicators, users can display multiple classification views at the same time, and users can interactively switch between multiple classification views and overview views to improve the convenience for users to monitor indicators. In addition, the monitoring views of different classification indicators provide a good way to meet the monitoring needs of different users, so that users can only select the indicators they care about for monitoring.

Using the production index visualization monitoring system based on the process flow chart to monitor the production index in the whole production process includes the following steps:

Step 1: Collect the production index data of the whole production process of the enterprise and store it in the local database.

Step 2: Coding production indicators and configuring data sources.

Step 2.1: Assign unique index codes to all production indicators in the production process, and manage the production indicators of the entire production process of the enterprise in a unified manner.

Step 2.2: Bind the uniquely encoded production index with the data source collected by the data collection module through the step 1, so that the data source can be viewed by calling the production index code.

Step 3: Perform flow chart configuration on the production process. Show the logical relationship between the front and back processes in the production process, and show the entire production process.

Step 3.1: If there is a production flow chart in the database, skip to step 3.3, otherwise go to the next step.

Step 3.2: Create a new production flow chart.

Step 3.2.1: Build the production process nodes of the whole process of the enterprise by dragging and dropping.

Step 3.2.2: Configure key indicators for process nodes, and set styles, event notifications, alarm upper and lower limits, and connection line status at the same time, so that the flow chart can monitor the real-time status of indicators when it is running, and realize event notification, connection line flashing, and overrun Alarm function.

Step 3.2.3: The drawn flow chart can be packaged into graphic elements to specify input and output. This primitive can then be added as a sub-process to a higher-level flowchart.

Step 3.2.4: According to the logical relationship between the production processes of the whole process of the enterprise, connect the nodes of each production process into a complete production flow chart.

Step 3.3: Add new nodes or delete unnecessary nodes, and adjust the logical relationship in the production flow chart.

Step 3.4: Save the production flow chart to the local database.

Step 4: Manage and configure all algorithms in the system.

Step 5: Configure the algorithm for each production process in the process flow chart, and determine the algorithm used for the monitoring indicators of each process.

Step 5.1: Read the production flow chart from the local database.

Step 5.2: Add algorithms for the production indicators that need to be monitored on the production flow chart and save the configuration.

Step 5.3: Save the data obtained by the algorithm to the local database.

Step 6: Calculate and process the collected data in the local database to obtain the required data.

Step 6.1: Calculate the average value of the daily data based on the historical data of the corresponding indicator in the local database.

Step 6.2: Calculate the variance of each day's data based on the historical data of the corresponding indicator in the local database.

Step 6.3: Save all calculated data.

Step 7: Display all required data to workers through different visualization schemes.

Step 7.1: Select the configured production index, read the real-time data of the production index from the database, and monitor the real-time data.

Step 7.1.1: Select the indicator overview view to monitor all user-configured indicators and get an overview of all monitoring indicators.

Step 7.1.2: Select the indicator classification view, classify according to the process and indicator type, and monitor the indicators contained in a specific process or a certain type.

Step 7.1.3: Select different view modes to monitor multiple views at the same time, and switch between multiple classification views and overview views interactively.

Step 7.2: Select different visualization schemes to realize historical data viewing, comparative analysis and correlation analysis.

Step 7.2.1: Configure visualization schemes for indicators, so that in subsequent steps, corresponding visualization schemes can be selected for indicators, such as historical data, comparative analysis, correlation analysis, and custom schemes.

Step 7.2.2: Specify a certain production indicator to be viewed, and generate a historical data chart.

Step 7.2.2.1: Read the historical data of the production index from the local database, select the time range through the time selector, and generate the historical data curve.

Step 7.2.2.2: Read the daily average value data of the production index from the local database, and generate the daily average value trend line.

Step 7.2.2.3: Read the daily variance data of the production index from the local database, and generate a histogram of the daily variance trend of the data.

Step 7.2.3: Select multiple production indicators for comparative analysis and generate a radar chart.

Step 7.2.3.1: Perform indicator selection.

Step 7.2.3.2: Set the upper and lower limits of the indicator.

Step 7.2.3.3: Select the historical time and generate the indicator radar chart at the specified time.

Step 7.2.4: Generate a relationship diagram between process indicators and production indicators.

Step 7.2.4.1: Select a small number of main influencing indicators from among the many process production indicators that affect the comprehensive production indicator, and calculate the contribution rate of each process indicator to the comprehensive production indicator.

Step 7.2.4.2: According to the contribution rate of each process index to the comprehensive production index, determine the proportional relationship of each index. Different indicators are distinguished according to different colors, and a relationship diagram between process indicators and production indicators is generated. Beneficial effects: To sum up, the present invention aims at the characteristics of long complex industrial processes, many procedures, and many production indicators, combined with the technical requirements for the application of data visualization in the process industry, and proposes a visual monitoring system for production indicators based on process flow charts and method. Firstly, a production indicator visualization configuration tool based on the production process flow chart is designed. It can draw the process flow chart in a configuration mode according to the production process, and can customize its node functions, trigger events, prompt information, and index analysis through the visual interface. Algorithm, so as to realize the visual monitoring of production indicators. At the same time, because the tool is provided in configuration mode, it can be quickly applied to other process industries to realize the configuration of visual monitoring of production indicators. For the monitoring of complex process production indicators, the process flow chart can not only be used to express the logical relationship of the process flow, but also can be used to display the equipment operation status, equipment alarm information, index abnormal information, and production notification information in the process, and then realize Visual monitoring of the entire production process. Secondly, the production indicators are classified according to the process, based on the classified production indicators, aiming at improving the monitoring function and efficiency of the production indicators in the production process, the real-time data, historical data, statistical characteristics of historical data, and multi-indicators are designed. Comprehensive comparative analysis and visualization scheme of data correlation analysis to realize visual monitoring of production indicators.

Instructions attached

Fig. 1 system functional block diagram of the present invention

Fig. 2 method flow chart of the present invention

Figure 3 Visual configuration design tool graphic element connection diagram

Figure 4 Visual configuration design tool node prompt information diagram

Figure 5 S historical data curve of comprehensive mineral processing (2018/7/4-2018/8/4)

Figure 6 Trend curve of the daily average value of mineral processing comprehensive fine S (2018/7/4-2018/8/4)

Figure 7 Histogram of daily variance change trend of mineral processing comprehensive fine S (2018/7/4-2018/8/4)

Detailed ways

Specific embodiments of the present invention will be described in detail below.

This implementation mode is to apply the visual monitoring system and method of production indicators based on the process flow chart to the monitoring of the comprehensive production indicators and process production indicators of the mineral processing industrial process.

As shown in Figure 1; the mineral processing production index visualization monitoring system based on the process flow chart of the present embodiment includes a data acquisition module, a production index management module, a production index visualization configuration design module, a production index configuration module, an algorithm management module, and a data collection module. processing module and visualization module;

The data acquisition module is used to collect the production index data of the whole process of beneficiation production and store it in the local database, including PLC (Programmable Logic Controller, programmable logic controller) and data acquisition sensors. The data acquisition sensor is used to collect the real-time operation status data of the equipment from the industrial site; the PLC is used to save the real-time operation status data of the equipment collected by the sensor to the local database.

In this embodiment, the data acquisition sensor adopts a typical OPC industry standard.

In this embodiment, the monitored equipment includes ball mills, shaft furnaces, filters, strong magnetic separators, weak magnetic separators, high-gradient magnetic separators, and high-frequency fine screens.

The production indicator management module is used to encode indicators and bind data sources. Including create, edit, delete, view operations.

Production indicators specifically include: indicator code, indicator name, indicator data, collection time, deactivation mark and indicator unit.

The index code is coded with 12 decimal numbers, for example, the index ID of "Comprehensive Concentration S" is "020206000104" (see Table 1).

The index name is the naming of the mineral processing plant for the whole process mineral processing process index (as shown in Table 1).

The index data is the value of the index collected from the data acquisition module (as shown in Table 1).

The collection time is the time when the index data is collected from the collection module.

In this embodiment, the collection time is expressed in the format of "2018/8/4 13:00:00" (as shown in Table 1).

The disabled flag is the disabled status of the metric in question.

In this embodiment, the disabled flag uses 0 to indicate disabled, and 1 to indicate enabled.

The indicator unit is the quantitative unit adopted by the indicator data.

In this embodiment, the index units include percentage (%), ton (t), kilowatt-hour (kwh), cubic meter (m3), hour (h), joule (GJ), meter (m), hertz (HZ), etc. .

The data source is the name of the database table used to save the indicator data.

In this embodiment, the data sources include SAPDATA, PIKDATA, STRAPDATAHOUR, REALTIMEDATAHOUR, ENERGYDATAHOUR, INDEXRUNTIME, REPORTCELLDATA.

The production indicator visualization configuration design module is used to draw the production process into a flow chart in a configurable way and display it to the user. The process flow chart includes: visual configuration tools, process nodes, connecting lines, endpoints, anchor points and coverings; as shown in Figure 3;

Visual configuration tool, including three parts: function bar, graphic element library, and drawing panel.

In this embodiment, the user drags the nodes in the graph element library to the drawing panel by dragging the mouse, configures the endpoint and anchor point attributes on the configuration interface, and connects the graph element nodes through the mouse. The drawn flow chart is saved to the database in json format or exported as a text file and saved locally.

The function bar includes save, clear, refresh, import, and primitive configuration functions.

The primitive library includes common shape primitive nodes, and custom primitive shapes can be added to the primitive library according to requirements.

In this embodiment, by default, common primitive nodes include square, rectangle, rhombus and circle.

The process node refers to the production process with actual physical meaning represented by the graph element in the flow chart. The process node includes process text information, process state and process event binding.

In this embodiment, the user drags the primitive node to the drawing panel, and an input box pops up for setting the text information of the process node.

In this embodiment, the process status refers to the status notification information of the process node indicating the equipment and production indicators in the process by setting blinking borders of different colors and adding prompt icons. When the process node is normal, the border does not flash, and when there is a prompt, it is displayed with a yellow border, when there is a warning, it is displayed with a purple border, and when an abnormality occurs, it is displayed with a red border. The above prompt information is divided into three types: notification, equipment, and indicator, which are displayed in the upper right corner of the node with a red background and white font to remind the operator prominently, as shown in Figure 4.

Process event binding refers to events that can be bound to each process node, including "click" event, "double click" event, and mouse "hover" event.

In this embodiment, the mouse "click" event is to enter the sub-process operation, so that the current interface jumps to the process sub-interface, and draws a more detailed sub-flow chart of the process in the process sub-interface as described above, thereby realizing the process flow Diagram nesting function; the mouse "double-click" event can be set to pop up a dialog box to display prompt information; the mouse "hover" event can be defined as a pop-up prompt box to display the basic information of the process.

The connection line refers to the connection between nodes, and the different colors of the connection line indicate whether there is any abnormality in the output of the process node.

In this embodiment, when the connecting line is gray, it indicates that the output index is normal, and when the connecting line is red, it indicates that the output index of the process is abnormal.

An endpoint is the starting point of a connecting line connecting process nodes. The shape, size, style and number of endpoints can be customized.

Anchor points indicate where the end point appears on the node, and indicate the direction of the connecting line by distinguishing the start anchor point and the end anchor point.

Covering refers to the addition of decorations on the connecting wires.

In this embodiment, the user can add label text and arrows for connection points to the connection line.

The production index monitoring configuration module is used to configure the production index that the user wants to monitor, and also supports inserting algorithms for the production index, and predicts and diagnoses the index. The production index monitoring configuration module includes a production index list and a process list, and the user selects the index to be monitored in the production index list and configures it to the corresponding process. For example, select ore dressing comprehensive concentrate S, ore dressing comprehensive concentrate CaO, ore dressing comprehensive concentrate output (wet weight), ore beneficiation comprehensive concentrate burning loss Ig, and configure it to the mineral processing comprehensive index process.

In this embodiment, the production indicator list is used to display all monitorable indicators (see Table 2).

In this embodiment, the process list displays all monitorable processes (see Table 3).

The algorithm management module is used for unified management of all the algorithms mentioned in the module, including adding, deleting and modifying functions.

In this embodiment, the algorithm includes principal component analysis (PCA) (Principal Component Analysis) and the like.

The data processing module is used to calculate and process the collected data.

In this embodiment, the data to be calculated and processed includes daily mean value and daily variance. For example, the daily average value of SiO2 in "2018/8/3" is 8.134, and the daily variance is 0.016338 (see Table 4).

The visualization module is used to display the original data generated by the data acquisition module and the comparison data and forecast data generated by the data processing module. Including real-time data graphs, historical data graphs, daily average trend line graphs, and daily variance trend histograms.

In this embodiment, the real-time data graph takes time (hours) as the horizontal axis, and the vertical axis displays real-time data with index values. When the mouse hovers over a data point, the data collection time, index name, and index value will be displayed.

In this embodiment, the historical data graph takes time (date) as the horizontal axis and the index value as the vertical axis, and the user can select data within a range less than two months to view.

In this embodiment, the daily average value trend line chart takes time (date) as the horizontal axis, and the daily average value of the indicator data is displayed on the vertical axis. The display range is the range selected by the historical data, and the date, indicator name and daily average value of the data will be displayed when the mouse hovers.

In this embodiment, the daily variance trend histogram takes time (date) as the horizontal axis, and the daily variance of the indicator data is displayed on the vertical axis. The display range is the range selected by the historical data, and the date, indicator name and data daily variance will be displayed when the mouse hovers value.

The multi-view interaction module of production indicators is used to display indicators in multiple views, including indicator overview view and indicator classification view.

In this embodiment, the overview view is used to display all monitoring indicators configured by the user (as shown in Table 2), so as to realize an overview of all monitoring indicators.

The indicator classification view is used to display indicators of different categories configured by the user, including indicators classified by process and indicator type.

In this embodiment, according to the process (see Table 3), the indicators can be divided into comprehensive production indicators, raw ore information indicators, screening process indicators, fine ore indicators, lump ore indicators, shaft furnace roasting indicators, strong magnetic grinding indicators, and weak ore indicators. Magnetic grinding index, waste rock index, strong magnetic separation index, weak magnetic separation index, middle ore concentration index, reverse flotation index, concentrate concentration index, tailings concentration index, concentrate filtration index.

In this embodiment, the indicators can be divided into planned production indicators, equipment indicators, plant-wide production indicators, real-time indicators, chemical inspection indicators, etc. according to the indicator types.

The above-mentioned visual monitoring system and method for mineral processing production indicators based on the process flow chart are used to monitor the comprehensive mineral processing production indicators and process production indicators of the mineral processing industrial process, as shown in Figure 2; the specific implementation steps are as follows:

Step 1: Collect the production index data of the whole process of beneficiation production and store it in the local database.

Step 2: Coding production indicators and configuring data sources.

Step 2.1: Allocate a unique index code for all production indicators in the mineral processing process, so as to facilitate the unified management of the production indicators in the whole process of mineral processing.

Step 2.2: Bind the uniquely encoded production index with the data source collected by the data acquisition module through the step 1, so that the data source can be viewed by calling the production index code.

Step 3: Perform flow chart configuration on the production process. Indicate the logical relationship between the front and back processes in the production process, and show the entire production process.

Step 3.1: If there is a production flow chart in the database, skip to step 3.3, otherwise go to the next step.

Step 3.2: Create a new production flow chart.

Step 3.2.1: Build the production process nodes of the whole process of beneficiation by dragging and dropping.

Step 3.2.2: Configure key indicators for process nodes, and set styles, event notifications, alarm upper and lower limits, and connection line status at the same time, so that the flow chart can monitor the real-time status of indicators when it is running, and realize event notification, connection line flashing, and overrun Alarm function.

Step 3.2.3: The drawn flow chart can be packaged into graphic elements to specify input and output. This primitive can then be added as a sub-process to a higher-level flowchart.

Step 3.2.4: According to the logical relationship between the production processes of the whole mineral processing process, connect each production process node into a complete production flow chart.

Step 3.3: Add new nodes or delete unnecessary nodes, and adjust the logical relationship in the production flow chart.

Step 3.4: Save the production flow chart to the local database.

Step 4: Manage and configure all algorithms in the system.

Step 4.1: There are algorithms for users to use by default, and users can also add, delete and modify algorithms.

Step 4.2: Configure the applicable production index for the new algorithm, so that the user can choose to add the algorithm through step 5.3 under the relevant production index.

Step 5: Configure the algorithm for each production process in the process flow chart, that is, determine the algorithm that needs to be used for the monitoring indicators of each process.

Step 5.1: Read the production flow chart from the local database.

Step 5.2: Add algorithms for the production indicators that need to be monitored on the production flow chart and save the configuration.

Step 5.3: Save the data obtained by the algorithm to the local database.

Step 6: Calculate and process the collected data in the local database to obtain the required data.

Step 6.1: Calculate the average value of the daily data based on the historical data of the corresponding indicators in the local database. The daily average value of Mineral Concentration S in "2018/8/1" is 0.277.

Step 6.2: Calculate the variance of the daily data based on the historical data of the corresponding indicators in the local database. The daily variance of Mineral Processing Comprehensive S is 0.003536 on "2018/8/1".

Step 6.3: Save all calculated data.

Step 7: Display the required data to the staff through different visualization schemes.

Step 7.1: Select the configured production index, read the real-time data of the production index from the database, and monitor the real-time data.

Step 7.1.1: Select the indicator overview view to monitor all user-configured indicators to achieve an overview of all monitoring indicators.

Step 7.1.2: Select the index classification view, and classify according to the process and index type to realize the monitoring of specific processes or indicators contained in a certain type.

Step 7.1.3: Select the overview view, process classification view, and indicator type classification view to monitor multiple views at the same time, and the user can interactively switch between multiple classification views and overview views.

Step 7.2: Choose a different visualization scheme.

Step 7.2.1: Configure visualization schemes for indicators, so that in subsequent steps, corresponding visualization schemes can be selected for indicators, including historical data, comparative analysis, correlation analysis, and custom schemes.

Step 7.2.2: Specify the production indicators to be viewed, select the time range, and generate historical data charts.

Step 7.2.2.1: Read the historical data of the production indicator from the local database, select the time range through the time selector, and generate the historical data curve (the historical data of Mineral Concentration S from July 4, 2018 to August 4, 2018 Data curve, see Figure 5).

Step 7.2.2.2: Read the daily average value data of the production index from the local database, and generate a daily average data trend line (the daily data average trend line chart of Mineral Processing Synthesis S from July 4, 2018 to August 4, 2018 , see Figure 6).

Step 7.2.2.3: Read the daily variance data of the production index from the local database, and generate a histogram of the daily data variance trend (the daily data variance trend column of Mineral Processing Synthesis S from July 4, 2018 to August 4, 2018 Figure, see Figure 7).

Step 7.2.3: Select the comprehensive fine grade of mineral dressing (Tfe), the water content of mineral processing comprehensive fine, the burning loss Ig of mineral processing comprehensive fine, the mineral processing comprehensive fine S, the mineral processing comprehensive fine CaO, the mineral processing comprehensive fine SiO2, and the comparison of multiple production indicators of the sintered ore grade Analyze and generate radar charts.

Step 7.2.3.1: Select production indicators such as mineral processing fine grade (Tfe), mineral processing fine water, mineral processing fine burning loss Ig, mineral processing fine S, mineral processing fine CaO, ore dressing SiO2, and calculate sinter grade.

Step 7.2.3.2: Set the upper and lower limits of the indicator [a,b], where a indicates the lower limit of the indicator, and b indicates the upper limit of the indicator.

Mineral processing comprehensive fine grade (Tfe) [50.00%-61.40%], mineral processing comprehensive fine moisture [11.20%-15.60%], mineral processing comprehensive fine burning loss Ig [9.00%-9.80%], mineral processing comprehensive fine S [0.23%-0.31 %], comprehensive mineral processing concentrate CaO [1.80%-1.98%], mineral processing comprehensive concentration SiO2 [7.10%-8.40%], estimated sinter grade [48.00%-51.00%].

Step 7.2.3.3: Select the historical time from July 20, 2018 to August 4, 2018, and generate an indicator radar chart for the specified time.

Step 7.2.4: Generate the relationship between process indicators and production indicators.

Step 7.2.4.1: Select a few of the main influencing indicators among the many process production indicators that affect the comprehensive concentrate grade. The recovery rate, cyclone feeding flow rate, cyclone feeding pressure, flotation ratio, comprehensive tailings grade, and the contribution rate of each process index to the comprehensive concentrate grade are calculated, as shown in Table 5.

Step 7.2.4.2: According to the contribution rate of each process index to the comprehensive production index, determine the proportional relationship of each index. Different indicators are distinguished according to different colors, and a relationship diagram between process indicators and production indicators is generated.

Table 1 Historical data of S indicators of comprehensive mineral processing (2018/7/4-2018/8/4)

Indicator ID Indicator name collection time data 020206000104 Mineral dressing concentrate S 2018/8/4 1:00:00 0.29 020206000104 Mineral dressing concentrate S 2018/8/3 13:00:00 0.292 020206000104 Mineral dressing concentrate S 2018/8/2 13:00:00 0.27 020206000104 Mineral dressing concentrate S 2018/8/2 1:00:00 0.271 020206000104 Mineral dressing concentrate S 2018/8/1 13:00:00 0.282 020206000104 Mineral dressing concentrate S 2018/8/1 1:00:00 0.272 020206000104 Mineral dressing concentrate S 2018/7/31 13:00:00 0.267 020206000104 Mineral dressing concentrate S 2018/7/30 13:00:00 0.268 020206000104 Mineral dressing concentrate S 2018/7/30 1:00:00 0.255 020206000104 Mineral dressing concentrate S 2018/7/29 13:00:00 0.254 020206000104 Mineral dressing concentrate S 2018/7/29 1:00:00 0.262 020206000104 Mineral dressing concentrate S 2018/7/28 13:00:00 0.268 020206000104 Mineral dressing concentrate S 2018/7/28 1:00:00 0.288 020206000104 Mineral dressing concentrate S 2018/7/27 13:00:00 0.278 020206000104 Mineral dressing concentrate S 2018/7/27 1:00:00 0.281 020206000104 Mineral dressing concentrate S 2018/7/26 13:00:00 0.275 020206000104 Mineral dressing concentrate S 2018/7/26 1:00:00 0.276 020206000104 Mineral dressing concentrate S 2018/7/25 13:00:00 0.286 020206000104 Mineral dressing concentrate S 2018/7/24 13:00:00 0.292 020206000104 Mineral dressing concentrate S 2018/7/24 1:00:00 0.276 020206000104 Mineral dressing concentrate S 2018/7/23 13:00:00 0.274 020206000104 Mineral dressing concentrate S 2018/7/23 1:00:00 0.268 020206000104 Mineral dressing concentrate S 2018/7/22 13:00:00 0.26 020206000104 Mineral dressing concentrate S 2018/7/22 1:00:00 0.256 020206000104 Mineral dressing concentrate S 2018/7/21 13:00:00 0.273 020206000104 Mineral dressing concentrate S 2018/7/21 1:00:00 0.292 020206000104 Mineral dressing concentrate S 2018/7/20 13:00:00 0.277 020206000104 Mineral dressing concentrate S 2018/7/20 1:00:00 0.254 020206000104 Mineral dressing concentrate S 2018/7/19 13:00:00 0.252 020206000104 Mineral dressing concentrate S 2018/7/19 1:00:00 0.26 020206000104 Mineral dressing concentrate S 2018/7/18 13:00:00 0.255 020206000104 Mineral dressing concentrate S 2018/7/17 13:00:00 0.28 020206000104 Mineral dressing concentrate S 2018/7/17 1:00:00 0.274 020206000104 Mineral dressing concentrate S 2018/7/16 13:00:00 0.274 020206000104 Mineral dressing concentrate S 2018/7/15 13:00:00 0.267 020206000104 Mineral dressing concentrate S 2018/7/14 13:00:00 0.249 020206000104 Mineral dressing concentrate S 2018/7/13 13:00:00 0.26 020206000104 Mineral dressing concentrate S 2018/7/13 1:00:00 0.27 020206000104 Mineral dressing concentrate S 2018/7/11 13:00:00 0.276 020206000104 Mineral dressing concentrate S 2018/7/10 13:00:00 0.27 020206000104 Mineral dressing concentrate S 2018/7/9 13:00:00 0.277 020206000104 Mineral dressing concentrate S 2018/7/9 1:00:00 0.272 020206000104 Mineral dressing concentrate S 2018/7/8 13:00:00 0.277 020206000104 Mineral dressing concentrate S 2018/7/7 13:00:00 0.274 020206000104 Mineral dressing concentrate S 2018/7/6 13:00:00 0.28 020206000104 Mineral dressing concentrate S 2018/7/6 1:00:00 0.274 020206000104 Mineral dressing concentrate S 2018/7/5 13:00:00 0.286 020206000104 Mineral dressing concentrate S 2018/7/5 1:00:00 0.31 020206000104 Mineral dressing concentrate S 2018/7/4 13:00:00 0.3 020206000104 Mineral dressing concentrate S 2018/7/3 1:00:00 0.308

Table 2 list of monitorable indicators (partial)

Indicator ID Indicator name 20204000106 weak bit 20204000107 weak tail grade 20204000108 Weak magnetic tail grade 20204000109 Three magnets 20204000110 Weak fine grade pass rate 20204000111 Flotation Feed Grade 20204000112 Flotation Feed SiO2 20204000113 Weak magnetic floating tail grade 20204000114 Weak magnetic levitation refined SiO2 20203000101 Strong magnetic grinding grade 20203000102 Strong quality 20203000103 Strong magnetic comprehensive tailings grade 20203000104 Pinghuan Concentrate Grade 20203000106 High Gradient Concentrate Grade 20203000108 Strong quality grade pass rate 20203000109 Strong Refined SiO2 20203000702 Strong magnetic particle size 20203000703 Strong magnetic concentration 20203000704 1-2 Spin overflow particle size (-200 mesh) 20203000705 1-2 Spiral concentration 20203000707 2-2 Spiral concentration 20203000708 Weak magnetic tertiary particle size (-300 mesh) 20203000709 Third concentration of weak magnetic field 20206000117 Magnetic Separation Tailings Grade 20206000118 Magnetic separation tailings concentration 20106000105 Choose 3# 20106000106 Block 1# 20106000107 Powder 2# 20106000108 Finished product - 2# ore quantity 20106000109 Waste rock-1# ore quantity 20106000110 2X Fine 4# ... ...

Form 3 Process List

Process ID Process name 200001 Comprehensive production index 201001 Raw ore information 201002 screening process 201003 fine ore 201004 lump ore 201006 Shaft furnace roasting 201005 Strong magnetic grinding 201007 Weak magnetic grinding 201008 waste rock 201009 Strong magnetic sorting 201010 Weak magnetic sorting 201011 Medium ore concentrate 201012 Anti-flotation 201013 Concentrate 201014 Tailings thickening 201015 Concentrate Filtration

Table 4 The daily average value and variance of SiO2 indicators in comprehensive mineral processing (2018/8/3)

Table 5 Contribution rate of main indicators affecting comprehensive concentrate grade

Indicator name Contribution rate Primary overflow recovery 19.30% Strong Magnetic Separation Theoretical Metal Recovery Rate 18.40% Feed rate of strong magnetic ball mill 3.20% Weak Magnetic Separation Theoretical Metal Recovery Rate 12.50% Cyclone feed flow 6.00% Cyclone feed pressure 11.40% Flotation ratio 18.40% Comprehensive tailings grade 10.80%

Claims (6)

1. a kind of production target Visualized Monitoring System based on process flow characterized by comprising data acquisition module, Production target management module, production target VIS simulation design module, production target monitor configuration module, algorithm management mould Block, data processing module, production target visualization model and production target multiple view interactive module；

Data acquisition module: acquisition enterprise produces whole process production target data, and is stored to local data base, including PLC and data sampling sensor；

Production target management module: carrying out coding to index and data source bound, including creation production target, editor's production refer to It marks, deletes production target, checks production target；

Production target VIS simulation designs module: production process being depicted as flow chart in a manner of configuration, shows use Family；

Algorithm management module: for being managed collectively to all algorithms that the module is mentioned, including increase, deletion, modification Function；

Data processing module: calculation processing data collected, including annual average and day variance；

Production target visualization model: the comparison that the initial data and data processing module that display data acquisition module generates generate Data, prediction data；Including real-time data curve figure, historical data curve graph, annual average trend line chart, day variance trend column Shape figure.

2. a kind of production target Visualized Monitoring System based on process flow according to claim 1, which is characterized in that The data sampling sensor acquires equipment real-time running state data from industry spot；The PLC, for acquiring sensor Production process real time data save to local data base.

3. a kind of production target Visualized Monitoring System based on process flow according to claim 1, which is characterized in that The production target includes: index coding, index name, achievement data, acquisition time and index unit；

The index coding is the unique ID for the representative index that long number is composed；

The index name is name of the factory to full-flow process index；

The achievement data is the representative index size, the digital value of height come from data collecting module collected；

The acquisition time is the time when achievement data is collected from acquisition module；

The index unit is quantization unit used by the achievement data；

The creation production target refer in the local database create one by index coding, index name, achievement data, adopt Collect vector and the storage of time and index unit；

Editor's production target refer to created in local data base the index coding of index, index name, achievement data, The existing assignment of acquisition time and index unit is changed；

The deletion production target, which refers to, deletes in local data base about having created the index coding of index, index name, referred to It marks data, acquisition time and index unit and has information；

It is described check production target refer to created in local data base the index coding of index, index name, achievement data, Acquisition time and index unit have assignment and are checked.

4. a kind of production target Visualized Monitoring System based on process flow according to claim 1, which is characterized in that The process flow chart includes: VIS simulation tool, procedure-node, connecting line, endpoint, anchor point and covering；

The VIS simulation tool, including function bar, pel library, drafting three parts of panel；

The function bar includes saving, removing, refreshing, importing, pel configuration feature；

The pel library, including Common Shape pel node, and customized primitive shapes can be added in pel library according to demand；

The drafting panel is used for draw craft process, and pel library interior joint is dragged to drafting face by mouse drag mode by user In plate, endpoint, anchor point respective attributes is configured in configuration interface, and the line between each pel node is carried out by mouse, drawn Good flow chart saves in the specific format to database or exports as text file and save to local；

The procedure-node refers to the production process for having actual physical meaning indicated in flow chart with pel, and procedure-node includes Process text information, working procedure states and process event binding；

The process text information indicates process title, and by dragging pel node to drawing in panel, pop-up input frame is used for Setting process node text information；

The working procedure states refer to that procedure-node passes through in setting flashing different colours frame and addition prompt icon representation process The state notification information of equipment, production target；

The process event binding, which refers to, can bind event, including " clicking " event, " double-click " event, mouse to each procedure-node " suspension " event of marking；

The mouse " clicks " event to enter sub- process operations, so that current interface is jumped to process sub-interface, on the sub- boundary of process The more detailed sub-process figure of process is drawn in face, and process flow chart is made to have the function of nesting；

Mouse " double-click " event may be configured as pop-up dialog box and show prompt information；

Mouse " suspension " event may be defined as emersion prompting frame and show process essential information；

The connecting line refers to the line between node and node, indicates that procedure-node output is by connecting line different colours It is no to there is exception；

The endpoint refers to the starting point of the connecting line of connection procedure-node；Customized endpoint shape, size, pattern and endpoint Number；

The anchor point indicates the position that endpoint occurs on node, indicates connecting line by distinguishing starting anchor point and terminating anchor point Trend；

The covering, which refers to, adds ornament on connecting line, the arrow including label text and tie point；

Production target monitors configuration module: configuration user wants the production target of monitoring and the visualization scheme of index, support pair Production target configuration monitoring algorithm, it can be achieved that diagnosis and trend prediction to index, including production target list, process list and Visualization scheme list；

The production target list for show it is all can monitor control index；

The process list display is all to monitor process, and user is by choosing the finger for needing to monitor in the production target list Mark, is allocated to corresponding process；

All visualization schemes of visualization scheme list display, user is by choosing specific visualization scheme to be allocated to prison The index of control.

5. a kind of production target Visualized Monitoring System based on process flow according to claim 1, which is characterized in that For the real-time data curve figure using the time as horizontal axis, index value is that the longitudinal axis shows real time data, meeting when mouse is suspended in data point Show data acquisition time, index name and index value；

For the historical data curve graph using the time as horizontal axis, index value is the longitudinal axis, and user may be selected to be less than bimestrial one section of model Data in enclosing are checked；

For the annual average trend line chart using the time as horizontal axis, achievement data annual average is that the longitudinal axis shows that indication range is history Range selected by data, mouse suspension can show date, index name and data annual average；

For the day variance trend histogram using the time as horizontal axis, achievement data day variance is that the longitudinal axis shows that indication range is history Range selected by data, mouse suspension can show date, index name and data Japanese side difference；

Production target multiple view interactive module: Multi-view display, including index overview view and index classification view are carried out to index Figure；

The overview view is used to show the monitor control index of all user configurations, the general view to all monitor control indexes；

The index classification view is used to show the index of the different classifications of user configuration, including according to process and pointer type All kinds of indexs of classification, realize the display to specific process perhaps the included index of a certain type check a certain process or certain A kind of other index, can show multiple classification views simultaneously, interact switching between multiple classification view and overview views.

6. carrying out enterprise's production whole process production using the production target Visualized Monitoring System based on process flow chart Index monitoring, includes the following steps:

Step 1: acquisition enterprise production whole process production target data are simultaneously stored to local data base；

Step 2: coding being carried out to production target and data source configures；

Step 2.1: distributing sole indicator coding for all production targets of production process, enterprise is produced into full-range production target It is managed collectively；

Step 2.2: the production target of unique encodings and data acquisition module are carried out by the collected data source of the step 1 Binding, so that calling production target coding that can check data source；

Step 3: flow diagram configuration is carried out to production process；Logical relation between process, shows whole before and after showing in production process A technological process of production；

Step 3.1: if existing flow sheet in database, skips to step 3.3, otherwise perform the next step；

Step 3.2: newly-built flow sheet；

Step 3.2.1: enterprise's whole process production process node is built by pulling；

Step 3.2.2: configuring key index for procedure-node, while pattern, event notice, alarm bound, connecting line is arranged State allows flow chart to monitor index real-time status in run mode, and realization event notice, connecting line flashing, transfinite report Alert function；

Step 3.2.3: the flow chart drawn can be packaged into pel, it is specified that input and output；Then this pel can be taken as Sub- process is added in more higher leveled flow chart；

Step 3.2.4: according to the logical relation between enterprise's whole process production process, each production process node is connected into one Complete flow sheet；

Step 3.3: increasing new node or delete unnecessary node, adjust logical relation in flow sheet；

Step 3.4: saving flow sheet to local data base；

Step 4: all algorithms in management and configuration system；

Step 5: algorithm configuration being carried out to per pass production process in process flow chart, determines and each process institute's monitoring index is used Algorithm；

Step 5.1: reading flow sheet from local data base；

Step 5.2: the production target to need to monitor on flow sheet adds algorithm and saves configuration；

Step 5.3: algorithm the data obtained is saved to local data base；

Step 6: calculation processing being carried out to the data acquired in local data base, obtains required data；

Step 6.1: the mean value of every day data is calculated according to the historical data of corresponding index in local data base；

Step 6.2: the variance of every day data is calculated according to the historical data of corresponding index in local data base；

Step 6.3: saving all calculating data；

Step 7: required data are shown to staff by different visualization schemes；

Step 7.1: selecting configured production target, the real time data of the production target is read from database, monitor number in real time According to；

Step 7.1.1: selective goal overview view is monitored the index of all user configurations, to the general of all monitor control indexes It lookes at；

Step 7.1.2: selective goal classification view is classified according to process and pointer type, to specific process or a certain type The monitoring of included index；

Step 7.1.3: selecting different pictorial representations that can monitor multiple views simultaneously, in multiple classification views and overview view Between interact switching；

Step 7.2: select different visualization scheme realize historical data check, comparative analysis and association analysis；

Step 7.2.1: it for index allocation visualization scheme, is visualized so that in the next steps can be that index selection is corresponding Scheme, such as historical data, comparative analysis, association analysis and customized scheme；

Step 7.2.2: specified some production target for needing to check generates historical data chart；

Step 7.2.2.1: reading the historical data of the production target from local data base, selects time model by time gate It encloses, generates historical data curve；

Step 7.2.2.2: reading the average daily Value Data of the production target from local data base, generates day data mean value trend folding Line；

Step 7.2.2.3: reading Japanese side's difference data of the production target from local data base, generates day data variance trend column Figure；

Step 7.2.3: selecting multiple production target comparative analyses, generates radar map；

Step 7.2.3.1: index selection is carried out；

Step 7.2.3.2: setting target bound；

Step 7.2.3.3: selection historical time generates the index radar map of specified time；

Step 7.2.4: generation process index and production target incidence relation figure；

Step 7.2.4.1: selecting a small number of main influence indexs in the numerous process production targets for influencing comprehensive production index, And each process index is calculated to the contribution rate of comprehensive production index；

Step 7.2.4.2: the contribution rate influenced according to each process index on comprehensive production index determines that the ratio of each index is closed System；Different indexs, generation process index and production target incidence relation figure are distinguished according to different colours.