CN109034662B - Production index visual monitoring system and method based on process flow - Google Patents
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Abstract
The invention discloses a production index visual monitoring system and method based on a process flow, which comprises the following steps: the system comprises a data acquisition module, a production index management module, a production index visual configuration design module, a production index monitoring configuration module, an algorithm management module, a data processing module, a production index visual module and a production index multi-view interaction module; the data acquisition module is used for acquiring production index data of the whole mineral processing production process and storing the data into a local database, and comprises a PLC and a data acquisition sensor; the visual monitoring of the production indexes is realized, the monitoring efficiency of users on the production indexes is improved, and the interactivity and convenience of index monitoring are improved.
Description
Technical Field
The invention belongs to the field of production index monitoring, and particularly relates to a production index visual monitoring system and method based on a process flow.
Background
At present, domestic research and application on a visual monitoring system and method for production indexes of a whole production process are few and have single function. "201310723320.5 (visual beneficiation production full-flow process index optimization decision system)" acquires data required by a beneficiation production full-flow control strategy from a beneficiation production field control system and acquires offline data from local, and encapsulates the algorithm, or modularly modifies the encapsulated algorithm to configure and form the beneficiation production full-flow control strategy. 201610807805.6 (Ore dressing equipment mobile monitoring system and method based on Internet of things and industrial cloud) provides a ore dressing equipment mobile monitoring system and method based on Internet of things and industrial cloud, so that equipment monitoring is not required to be carried out in a fixed place, and monitoring data can be provided for enterprise managers and scientific research personnel at any time and any place. "201711283037. X (a system and a method for visually analyzing mineral processing production indexes)" realizes integration and configuration of indexes of each procedure of mineral processing production and visually analyzes and analyzes the index abnormity, and the patent mainly aims at visually analyzing the abnormal condition of the mineral processing production indexes. However, the above patent does not relate to the configuration and configuration of the visual monitoring of mineral processing production indexes. For the visual monitoring of the production indexes, although the 201711283037.X patent relates to the visualization of the production indexes, the production indexes are only visualized by using a radar map, and different visualization schemes are not adopted according to the actual requirements of the production index monitoring. Furthermore, none of the above patents relate to the combination of production indicator visualization with an indicator monitoring algorithm. Aiming at the problems in the patent, the invention provides a production index visual monitoring system and method based on a process flow chart.
The invention has the following innovation points:
the production index visual configuration design module based on the production process flow is provided, a process flow diagram is drawn in a configuration mode according to the production process, the node function, the trigger event, the alarm information, the prompt information and the process rule of the process flow diagram are defined through a visual interface, and the incidence relation between the process and equipment and between the process and the production index is established, so that the visual monitoring of the production index is realized;
the production indexes are classified according to the procedures and the index types, multi-view monitoring of different classification indexes is realized, and the interactivity and convenience of index monitoring are improved;
the visualization scheme of real-time data, historical data statistical characteristics, multi-index comprehensive comparison analysis and data association relation analysis is supported, and the monitoring function and efficiency of production indexes in the production process are improved; the user-defined visual scheme is supported, the user realizes the personalized customized visual scheme of the production index by means of the visual configuration design module and the monitored production index, and the monitoring efficiency of the user on the production index is improved;
and a production index monitoring algorithm is supported to be configured for monitoring the production index in a visual mode, so that the production index is monitored.
Disclosure of Invention
Aiming at the prior art, the invention provides a production index visual monitoring system and method based on a process flow, which comprises the following steps:
a visual monitoring system of production index based on process flow includes: the system comprises a data acquisition module, a production index management module, a production index visual configuration design module, a production index monitoring configuration module, an algorithm management module, a data processing module, a production index visual module and a production index multi-view interaction module;
the data acquisition module is used for acquiring the production index data of the whole production process of the enterprise and storing the production index data into a local database, and comprises a Programmable Logic Controller (PLC) and a data acquisition sensor;
the data acquisition sensor acquires real-time running state data of equipment from an industrial field;
the PLC is used for storing the real-time production process data acquired by the sensor into a local database;
the production index management module is used for coding the indexes and binding the data sources, and comprises the steps of creating production indexes, editing the production indexes, deleting the production indexes and checking the production indexes;
the production indexes comprise: index codes, index names, index data, acquisition time and index units;
the index code is a unique ID (identification) which is formed by combining a plurality of digits and represents the index;
the index name is the name of the whole process index of the factory;
the index data is a digital value representing the size and height of the index and acquired from a data acquisition module;
the acquisition time is the time when the index data is acquired from an acquisition module;
the index unit is a quantization unit adopted by the index data;
the production index creation is to create and store a vector consisting of index codes, index names, index data, acquisition time and index units in a local database;
the production index editing means that index codes, index names, index data, acquisition time and existing assignments of index units of established indexes in a local database are changed;
the step of deleting the production indexes refers to deleting index codes, index names, index data, acquisition time and existing information of index units related to established indexes in a local database;
the checking of the production index refers to checking of index codes, index names, index data, acquisition time and assigned values of index units of established indexes in a local database;
the production index visual configuration design module is used for drawing a production process into a flow chart in a configuration mode and displaying the flow chart to a user;
the process flow diagram comprises the following steps: visual configuration tool, process node, connecting line, endpoint, anchor point and covering;
the visual configuration tool comprises a function bar, a primitive library and a drawing panel;
the function bar comprises functions of saving, clearing, refreshing, importing and primitive configuring;
the primitive library comprises common shape primitive nodes, and primitive shapes can be customized according to requirements and added into the primitive library;
the drawing panel is used for drawing a process flow, a user drags nodes in the primitive library to the drawing panel in a mouse dragging mode, corresponding attributes of endpoints and anchor points are configured on a configuration interface, connection among the primitive nodes is carried out through a mouse, and the drawn flow chart is stored in a database in a specific format or is exported to be a text file and stored to the local;
the process nodes are production processes which are represented by graphic elements in the flow chart and have actual physical significance, and the process nodes comprise process text information, process states and process event binding;
the process text information represents a process name, and an input box is popped up to set process node text information by dragging a primitive node to a drawing panel;
the process state refers to state notification information of equipment and production indexes in the process which is represented by the process nodes through setting flashing frames with different colors and adding prompt icons;
the process event binding refers to bindable events for each process node, and comprises a click event, a double click event and a mouse suspension event;
the mouse click event is the operation of entering a sub-process, so that the current interface jumps to a process sub-interface, a more detailed sub-flow chart of the process is drawn in the process sub-interface, and the process flow chart has a nesting function;
the mouse double-click event can be set as a pop-up dialog box display prompt message;
the mouse 'suspension' event can be defined as basic information of a display procedure of a floating prompt box;
the connecting line is a connecting line between nodes, and whether the output of the process node is abnormal or not is indicated by different colors of the connecting line;
the end point refers to the starting point of a connecting line connecting the process nodes; defining the shape, size, style and number of the end points by user;
the anchor points indicate positions of endpoints appearing on the nodes, and the trend of the connecting line is indicated by distinguishing starting anchor points and ending anchor points;
the covering is formed by adding decorations on the connecting line, and comprises a label text and an arrow of the connecting point;
the production index monitoring configuration module is used for configuring the production indexes and the visual schemes of the indexes which are required to be monitored by a user, supporting the configuration of a monitoring algorithm for the production indexes, and realizing the diagnosis and trend prediction of the indexes, and comprises a production index list, a process list and a visual scheme list;
the production index list is used for displaying all monitorable indexes;
the process list displays all the monitorable processes, and a user selects the indexes needing to be monitored in the production index list and configures the indexes to the corresponding processes;
the visualization scheme list displays all visualization schemes, and a user configures monitored indexes by selecting a specific visualization scheme;
the algorithm management module is used for uniformly managing all algorithms mentioned by the module, and comprises functions of adding, deleting and modifying;
the data processing module is used for calculating and processing the acquired data, including a daily mean value and a daily square difference;
the production index visualization module is used for displaying the original data generated by the data acquisition module and the comparison data and the prediction data generated by the data processing module. The method comprises a real-time data curve graph, a historical data curve graph, a daily mean trend line graph and a daily variance trend bar graph;
the real-time data curve graph takes time (hours) as a horizontal axis and index values as a vertical axis to display real-time data, and the mouse can display data acquisition time, index names and the index values when the mouse is suspended at the data points;
the historical data graph takes time (date) as a horizontal axis and an index value as a vertical axis, and a user can select data in a range less than two months to view;
the day-average trend line graph is displayed by taking time (date) as a horizontal axis, the index data day-average is displayed by taking an index data day-average as a vertical axis, the display range is a range selected by historical data, and the date, the index name and the data day-average are displayed by a mouse in a suspension mode;
the daily variance trend histogram is displayed by taking time (date) as a horizontal axis, the daily variance of the index data as a vertical axis, the display range is a range selected by the historical data, and the date, the index name and the daily variance of the index data are displayed by the mouse in a floating mode.
The production index multi-view interaction module is used for carrying out multi-view display on indexes, wherein the multi-view display comprises an index overview view and an index classification view;
the overview view is used for displaying all monitoring indexes configured by the user and overviewing all the monitoring indexes;
the index classification view is used for displaying different classification indexes configured by a user, and comprises various indexes classified according to processes and index types, the indexes contained in a specific process or a certain type are displayed, so that the user can conveniently check the indexes of a certain process or a certain type, the user can simultaneously display a plurality of classification views, the user can interactively switch between the plurality of classification views and the overview view, and the convenience of the user in index monitoring is improved. In addition, different classification index monitoring views provide a good way for monitoring requirements of different users, so that the users can only select the index concerned by themselves for monitoring.
The production index visual monitoring system based on the process flow chart is adopted to monitor the production index of the whole production flow, and comprises the following steps:
step 1: and collecting the production index data of the whole production process of the enterprise and storing the data in a local database.
Step 2: and encoding the production index and configuring a data source.
Step 2.1: and distributing a unique index code for all production indexes in the production process, and uniformly managing the production indexes of the whole production process of the enterprise.
Step 2.2: and binding the production index of the unique code with the data source acquired by the data acquisition module through the step 1, so that the data source can be checked by calling the production index code.
And step 3: and carrying out flow diagram configuration on the production process. And displaying the logical relationship between the front and the rear working procedures in the production process and displaying the whole production process flow.
Step 3.1: if the production flow diagram already exists in the database, jump to step 3.3, otherwise execute the next step.
Step 3.2: and (5) establishing a production flow chart.
Step 3.2.1: and establishing the nodes of the whole-flow production process of the enterprise by dragging.
Step 3.2.2: key indexes are configured for the process nodes, and styles, event notifications, upper and lower alarm limits and states of connecting lines are set at the same time, so that the real-time states of the indexes can be monitored in the running state of the flow chart, and the functions of event notification, connecting line flicker and out-of-limit alarm are realized.
Step 3.2.3: the drawn flow chart can be packaged into a primitive to specify input and output. This primitive can then be added as a sub-process to a higher level flowchart.
Step 3.2.4: and connecting the nodes of all the production processes into a complete production flow chart according to the logical relationship among the production processes of the enterprise in the whole process.
Step 3.3: and adding new nodes or deleting unnecessary nodes, and adjusting the logical relationship in the production flow chart.
Step 3.4: and saving the production flow chart to a local database.
And 4, step 4: all algorithms in the system are managed and configured.
And 5: and (4) carrying out algorithm configuration on each production process in the process flow chart, and determining an algorithm adopted by indexes monitored by each process.
Step 5.1: the production flow diagram is read from a local database.
Step 5.2: and adding an algorithm for the production indexes needing to be monitored on the production flow chart and storing the configuration.
Step 5.3: and storing the data obtained by the algorithm into a local database.
Step 6: and calculating the collected data in the local database to obtain the required data.
Step 6.1: and calculating the average value of the data every day according to the historical data of the corresponding indexes in the local database.
Step 6.2: and calculating the variance of the data every day according to the historical data of the corresponding indexes in the local database.
Step 6.3: all the calculation data is saved.
And 7: all required data are displayed to the staff through different visualization schemes.
Step 7.1: and selecting the configured production index, reading the real-time data of the production index from the database, and monitoring the real-time data.
Step 7.1.1: and selecting an index overview view to monitor all indexes configured by the user and to overview all monitoring indexes.
Step 7.1.2: selecting an index classification view, classifying according to the process and the index type, and monitoring the indexes contained in a specific process or a certain type.
Step 7.1.3: the multiple views can be monitored simultaneously by selecting different view modes, and interactive switching can be performed between the multiple classification views and the overview view.
Step 7.2: and selecting different visualization schemes to realize historical data viewing, comparative analysis and correlation analysis.
Step 7.2.1: the visualization scheme is configured for the index, so that in the subsequent steps, a corresponding visualization scheme, such as historical data, comparative analysis, correlation analysis, and a custom scheme, can be selected for the index.
Step 7.2.2: and (4) specifying a certain production index needing to be checked, and generating a historical data chart.
Step 7.2.2.1: and reading the historical data of the production index from a local database, selecting a time range through a time selector, and generating a historical data curve.
Step 7.2.2.2: and reading the daily mean data of the production index from a local database to generate a daily data mean trend broken line.
Step 7.2.2.3: and reading the daily variance data of the production index from a local database to generate a daily data variance trend histogram.
Step 7.2.3: and selecting a plurality of production indexes for comparative analysis to generate a radar chart.
Step 7.2.3.1: and selecting indexes.
Step 7.2.3.2: and setting an upper index limit and a lower index limit.
Step 7.2.3.3: and selecting historical time, and generating an index radar map of the specified time.
Step 7.2.4: and generating a process index and production index association relation graph.
Step 7.2.4.1: selecting a few main influence indexes from a plurality of process production indexes influencing the comprehensive production indexes, and calculating the contribution rate of each process index to the comprehensive production indexes.
Step 7.2.4.2: and determining the proportional relation of each index according to the contribution rate of each process index on the influence of the comprehensive production index. And distinguishing different indexes according to different colors to generate a process index and production index association relation graph. Has the advantages that: in summary, the invention provides a production index visual monitoring system and method based on a process flow diagram, aiming at the characteristics of long process, multiple processes and multiple production indexes of a complex industrial process, and combining with the technical requirements of data visualization applied in the process industry. The tool is provided in a configuration mode, so that the tool can be quickly applied to other process industries, and the configuration of the visual monitoring of the production indexes is realized. For monitoring production indexes in a complex process, a process flow diagram can be used for expressing the logical relationship of the process flow and displaying the running state of equipment, equipment alarm information, index abnormal information and production notification information in the process, so that the visual monitoring of the whole production process is realized. Secondly, classifying the production indexes according to the working procedures, and designing a visual scheme for real-time data, historical data statistical characteristics, multi-index comprehensive comparison analysis and data association relation analysis on the basis of the classified production indexes and aiming at improving the monitoring function and efficiency of the production indexes in the production process so as to realize visual monitoring of the production indexes.
Drawings
FIG. 1 is a functional block diagram of the system of the present invention
FIG. 2 is a flow chart of the method steps of the present invention
FIG. 3 is a graphical element connection diagram of a visual configuration tool
FIG. 4 is a graphical representation of a tool node hint information for a visual configuration
FIG. 5 mineral processing comprehensive concentrates S historical data curve chart (2018/7/4-2018/8/4)
FIG. 6 dressing heald essence S day mean value variation trend curve chart (2018/7/4-2018/8/4)
FIG. 7 is a histogram of changes of the square difference S of the mineral processing heald essence (2018/7/4-2018/8/4)
Detailed Description
The following is a detailed description of specific embodiments of the invention.
The embodiment applies the production index visual monitoring system and method based on the process flow chart to the monitoring of the mineral processing comprehensive production index and the procedure production index of the mineral processing industrial flow.
As shown in fig. 1; the ore dressing production index visual monitoring system based on the process flow diagram comprises a data acquisition module, a production index management module, a production index visual configuration design module, a production index configuration module, an algorithm management module, a data processing module and a visual module;
the data acquisition module is used for acquiring production index data of the whole process of mineral separation production and storing the production index data into a local database, and comprises a Programmable Logic Controller (PLC) and a data acquisition sensor. The data acquisition sensor is used for acquiring real-time running state data of the equipment from an industrial field; and the PLC is used for storing the real-time running state data of the equipment acquired by the sensor to a local database.
In this embodiment, typical OPC industry standards are used for the data acquisition sensors.
In this embodiment, the monitoring equipment includes a ball mill, a shaft furnace, a filter, a strong magnetic separator, a weak magnetic separator, a high gradient magnetic separator, and a high frequency fine screen.
The production index management module is used for encoding the indexes and binding the data sources. Including create, edit, delete, view operations.
The production indexes specifically comprise: index code, index name, index data, acquisition time, deactivation flag, and index unit.
The index code is coded by 12 decimal digits, for example, the index ID of the mineral processing comprehensive refining S is 020206000104 (shown in the table 1).
The index name is the name of the whole-process beneficiation process index by the beneficiation plant (as shown in table 1).
The indicator data is the value of the indicator collected from the data collection module (see table 1).
The acquisition time is a time when the index data is acquired from the acquisition module.
In this embodiment, the acquisition time is represented in the format "2018/8/413: 00: 00" (see table 1).
The deactivation flag is a deactivation status of the indicator.
In this embodiment, the disable flag is 0 for disable and 1 for enable.
The index unit is a quantization unit employed for the index data.
In the present embodiment, the index unit includes percentage (%), ton (t), kilowatt-hour (kwh), cubic meter (m3), hour (h), joule (GJ), meter (m), Hertz (HZ), and the like.
The data source is a database table name used to store the index data.
In this embodiment, the data source includes SAPDATA, piddata, strapdatashor, realimedata, endergydatashor, indexrunt, and reporttceldata.
The production index visual configuration design module is used for drawing the production process into a flow chart in a configuration mode and displaying the flow chart to a user. The process flow chart comprises the following steps: visual configuration tool, process node, connecting line, endpoint, anchor point and covering; as shown in fig. 3;
the visual configuration tool comprises a function bar, a primitive library and a drawing panel.
In this embodiment, a user drags nodes in the primitive library to the drawing panel in a mouse dragging mode, configures endpoints and anchor point attributes on the configuration interface, and connects the primitive nodes through a mouse. And storing the drawn flow chart into a database in a json format or exporting the flow chart as a text file and storing the text file to the local.
The function column comprises functions of saving, clearing, refreshing, importing and primitive configuration.
The primitive library comprises common shape primitive nodes, and the primitive shapes can be added into the primitive library according to requirements in a self-defining mode.
In this embodiment, the default common primitive nodes include squares, rectangles, diamonds, and circles.
The process nodes are production processes which are represented by graphic elements in the flow chart and have actual physical significance, and the process nodes comprise process text information, process states and process event binding.
In this embodiment, the user drags the primitive node to the drawing panel, and pops up the input box to set the process node text information.
In the present embodiment, the process status refers to status notification information indicating equipment and production indexes in the process by setting a frame with different colors and flashing and adding a prompt icon in the process node. When the process node is normal, the frame does not flicker, the frame is displayed by a yellow frame when a prompt is given, the frame is displayed by a purple frame when a warning is given, and the frame is displayed by a red frame when an abnormity appears. The prompt information is divided into three types: the notifications, devices, and indicators are displayed in red-background-white font in the upper right corner of the node to prominently prompt the operator, as shown in fig. 4.
Process event binding refers to the binding of events to each process node, including "single click" events, "double click" events, mouse "hover" events.
In the embodiment, a mouse click event is a sub-process operation, so that the current interface jumps to a process sub-interface, and a more detailed sub-flow chart of the process is drawn in the process sub-interface in the manner described above, thereby realizing the process flow chart nesting function; the event of double-click of the mouse can be set as a pop-up dialog box to display prompt information; the mouse "hover" event may be defined as a pop-up prompt to show process essential information.
The connecting line is a connecting line between the nodes, and whether the output of the process node is abnormal or not is indicated by different colors of the connecting line.
In the present embodiment, when the connecting line is gray, the output index is normal, and when the connecting line is red, the output index of the process is abnormal.
The end point refers to a start point of a connection line connecting the process nodes. The shape, size, style and number of endpoints can be customized.
The anchor points indicate positions of endpoints appearing on the nodes, and the connecting lines are indicated by distinguishing the starting anchor points from the ending anchor points.
The covering is that ornaments are added on the connecting lines.
In this embodiment, the user may add a label text and an arrow of the connection point to the connection line.
The production index monitoring and configuring module is used for configuring production indexes which a user wants to monitor, and also supports a production index inserting algorithm to predict and diagnose the indexes. The production index monitoring configuration module comprises a production index list and an engineering list, and a user selects an index needing to be monitored in the production index list and configures the index to a corresponding engineering. For example, the beneficiation combined concentrate S, the beneficiation combined concentrate CaO, the beneficiation combined concentrate yield (wet weight), and the beneficiation combined concentrate burnout Ig are selected and configured to the beneficiation combined index process.
In this embodiment, the production index list is used to display all the monitorable indexes (see table 2).
In the present embodiment, the process list displays all the monitorable processes (see table 3).
The algorithm management module is used for uniformly managing all algorithms mentioned by the module, and comprises functions of adding, deleting and modifying.
In the present embodiment, the algorithm includes principal Component analysis (pca).
The data processing module is used for calculating and processing the acquired data.
In the present embodiment, the data subjected to the calculation processing includes the daily average value and the daily variance. For example, the mean value of the ore dressing concentrate SiO2 in the day of "2018/8/3" is 8.134, and the difference in the day is 0.016338 (see Table 4).
The visualization module is used for displaying the original data generated by the data acquisition module and the comparison data and the prediction data generated by the data processing module. The method comprises a real-time data curve graph, a historical data curve graph, a daily mean trend line graph and a daily variance trend bar graph.
In the present embodiment, the real-time data graph displays real-time data with time (hours) as the horizontal axis and index values as the vertical axis, and displays data acquisition time, index names, and index values when the mouse is suspended at the data points.
In the present embodiment, the historical data graph has time (date) as the horizontal axis and the index value as the vertical axis, and the user can select and view data in a range of less than two months.
In this embodiment, the daily average trend line graph is displayed with time (date) as the horizontal axis, the index data daily average as the vertical axis, the display range is the range selected by the history data, and the date, the index name, and the data daily average are displayed by the mouse float.
In the present embodiment, the daily variance trend histogram is displayed with time (date) as the horizontal axis, the index data daily variance as the vertical axis, the display range is the range selected by the history data, and the date, the index name, and the data daily variance value are displayed by the mouse float.
The production index multi-view interaction module is used for carrying out multi-view display on indexes, and comprises an index overview view and an index classification view.
In this embodiment, the overview view is used to display all the monitoring indicators configured by the user (as in table 2), so as to realize an overview of all the monitoring indicators.
The index classification view is used for displaying indexes of different classifications configured by a user, and the indexes comprise indexes classified according to processes and index types.
In this embodiment, the indexes can be classified into a comprehensive production index, a raw ore information index, a screening process index, a fine ore index, a lump ore index, a shaft furnace roasting index, a strong magnetic grinding index, a weak magnetic grinding index, a waste rock index, a strong magnetic sorting index, a weak magnetic sorting index, a middling concentration index, a reverse flotation index, a concentrate concentration index, a tailing concentration index, and a concentrate filtration index according to the process (see table 3).
In the present embodiment, the index may be classified into a planned production index, an equipment index, a plant-wide production index, a real-time index, a chemical examination index, and the like according to the index type.
The beneficiation production index visual monitoring system and method based on the process flow chart are adopted to monitor the beneficiation comprehensive production index and the process production index of the beneficiation industrial flow, as shown in figure 2; the specific implementation steps are as follows:
step 1: collecting production index data of the whole mineral processing production process and storing the data in a local database.
Step 2: and encoding the production index and configuring a data source.
Step 2.1: and a unique index code is distributed to all production indexes in the mineral separation process, so that the production indexes of the whole mineral separation process can be managed uniformly, and the mineral separation comprehensive S code 020206000104 is obtained.
Step 2.2: and binding the production index of the unique code with the data acquisition module through the data source acquired in the step 1, so that the data source can be checked by calling the production index code, and the beneficiation complex refining S configures the data source to be SAPDATA.
And step 3: and carrying out flow diagram configuration on the production process. And indicating the logical relationship between the front and the back working procedures in the production process and showing the whole production process flow.
Step 3.1: if the production flow diagram already exists in the database, jump to step 3.3, otherwise execute the next step.
Step 3.2: and (5) establishing a production flow chart.
Step 3.2.1: and establishing a full-flow production process node of mineral separation by dragging.
Step 3.2.2: key indexes are configured for the process nodes, and styles, event notifications, upper and lower alarm limits and states of connecting lines are set at the same time, so that the real-time states of the indexes can be monitored in the running state of the flow chart, and the functions of event notification, connecting line flicker and out-of-limit alarm are realized.
Step 3.2.3: the drawn flow chart can be packaged into a primitive to specify input and output. This primitive can then be added as a sub-process to a higher level flowchart.
Step 3.2.4: and connecting the nodes of all the production procedures into a complete production flow chart according to the logical relationship among the production procedures of the whole mineral dressing flow.
Step 3.3: and adding new nodes or deleting unnecessary nodes, and adjusting the logical relationship in the production flow chart.
Step 3.4: and saving the production flow chart to a local database.
And 4, step 4: all algorithms in the system are managed and configured.
Step 4.1: the algorithm is used by the user as a default, and the user can add, delete and modify the algorithm.
Step 4.2: the applicable production index is configured for the newly added algorithm, so that the user can choose to add the algorithm under the relevant production index through step 5.3.
And 5: and (4) carrying out algorithm configuration on each production process in the process flow chart, namely determining an algorithm which is required to be adopted for the monitored indexes of each process.
Step 5.1: the production flow diagram is read from a local database.
Step 5.2: and adding an algorithm for the production indexes needing to be monitored on the production flow chart and storing the configuration.
Step 5.3: and storing the data obtained by the algorithm into a local database.
Step 6: and calculating the collected data in the local database to obtain the required data.
Step 6.1: and calculating the average value of the daily data according to historical data of corresponding indexes in the local database, wherein the daily average value of the beneficiation concentrates S at 2018/8/1 is 0.277.
Step 6.2: and calculating the variance of the data every day according to the historical data of the corresponding indexes in the local database, wherein the daily variance of the mineral processing comprehensive essence S at 2018/8/1 is 0.003536.
Step 6.3: all the calculation data is saved.
And 7: and displaying the required data to the staff through different visualization schemes.
Step 7.1: and selecting the configured production index, reading the real-time data of the production index from the database, and monitoring the real-time data.
Step 7.1.1: and selecting an index overview view to monitor all indexes configured by the user, so as to realize the overview of all monitoring indexes.
Step 7.1.2: and selecting an index classification view, and classifying according to the process and the index type to realize the monitoring of the indexes contained in a specific process or a certain type.
Step 7.1.3: and selecting an overview view, a process classification view and an index type classification view, monitoring a plurality of views simultaneously, and interactively switching between the plurality of classification views and the overview view by a user.
Step 7.2: different visualization schemes are selected.
Step 7.2.1: and configuring a visualization scheme for the index, so that a corresponding visualization scheme including historical data, comparative analysis, correlation analysis and a custom scheme can be selected for the index in the subsequent step.
Step 7.2.2: and (4) specifying the production indexes to be checked, selecting a time range and generating a historical data chart.
Step 7.2.2.1: historical data of the production index is read from a local database, a time range is selected through a time selector, and a historical data curve is generated (the historical data curve of the beneficiation complex S from 7-month and 4-day in 2018 to 8-month and 4-day in 2018, see fig. 5).
Step 7.2.2.2: the daily mean data of the production index are read from a local database, and a daily data mean trend broken line is generated (a daily data mean trend broken line graph of the beneficiation concentrates S from 7-month and 4-day in 2018 to 8-month and 4-day in 2018 is shown in FIG. 6).
Step 7.2.2.3: the daily variance data of the production index is read from a local database, and a daily data variance trend histogram (the daily data variance trend histogram of the beneficiary plan S from 7/4/2018 to 8/4/2018, see FIG. 7) is generated.
Step 7.2.3: selecting ore dressing comprehensive refined grade (Tfe), ore dressing comprehensive refined water, ore dressing comprehensive refined burning loss Ig, ore dressing comprehensive refined S, ore dressing comprehensive refined CaO, ore dressing comprehensive refined SiO2, measuring and calculating a plurality of production indexes of sintered ore level, and carrying out contrastive analysis to generate a radar chart.
Step 7.2.3.1: selecting ore dressing comprehensive refined grade (Tfe), ore dressing comprehensive refined moisture, ore dressing comprehensive refined burning loss Ig, ore dressing comprehensive refined S, ore dressing comprehensive refined CaO, ore dressing comprehensive refined SiO2, and measuring and calculating the grade of sinter.
Step 7.2.3.2: and setting an index upper limit and an index lower limit [ a, b ], wherein a represents the index lower limit and b represents the index upper limit.
The method comprises the following steps of ore dressing fully refined grade (Tfe) (50.00% -61.40%), ore dressing fully refined moisture (11.20% -15.60%), ore dressing fully refined burning loss Ig (9.00% -9.80%), ore dressing fully refined S (0.23% -0.31%), ore dressing fully refined CaO (1.80% -1.98%), ore dressing fully refined SiO2 (7.10% -8.40%), and measuring and calculating sintered ore grade (48.00% -51.00%).
Step 7.2.3.3: and selecting 20 days in 7 months and 4 days in 8 months in 2018 from historical time to generate an index radar map of the specified time.
Step 7.2.4: and generating a correlation between the process indexes and the production indexes.
Step 7.2.4.1: a few main influence indexes, namely primary overflow recovery rate, high-intensity magnetic separation theoretical metal recovery rate, high-intensity magnetic ball mill feeding amount, low-intensity magnetic separation theoretical metal recovery rate, swirler feeding flow rate, swirler feeding pressure, flotation separation ratio and comprehensive tailing grade, are selected from a plurality of process production indexes influencing the comprehensive concentrate grade, and the contribution rate of each process index to the comprehensive concentrate grade is calculated and is shown in a table 5.
Step 7.2.4.2: and determining the proportional relation of each index according to the contribution rate of each process index on the influence of the comprehensive production index. And distinguishing different indexes according to different colors to generate a process index and production index association relation graph.
Table 1 mineral processing comprehensive concentrates S index historical data (2018/7/4-2018/8/4)
| Index ID | Index name | Time of acquisition | Data of |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/8/4 1:00:00 | 0.29 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/8/3 13:00:00 | 0.292 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/8/2 13:00:00 | 0.27 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/8/2 1:00:00 | 0.271 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/8/1 13:00:00 | 0.282 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/8/1 1:00:00 | 0.272 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/31 13:00:00 | 0.267 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/30 13:00:00 | 0.268 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/30 1:00:00 | 0.255 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/29 13:00:00 | 0.254 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/29 1:00:00 | 0.262 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/28 13:00:00 | 0.268 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/28 1:00:00 | 0.288 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/27 13:00:00 | 0.278 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/27 1:00:00 | 0.281 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/26 13:00:00 | 0.275 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/26 1:00:00 | 0.276 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/25 13:00:00 | 0.286 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/24 13:00:00 | 0.292 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/24 1:00:00 | 0.276 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/23 13:00:00 | 0.274 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/23 1:00:00 | 0.268 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/22 13:00:00 | 0.26 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/22 1:00:00 | 0.256 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/21 13:00:00 | 0.273 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/21 1:00:00 | 0.292 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/20 13:00:00 | 0.277 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/20 1:00:00 | 0.254 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/19 13:00:00 | 0.252 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/19 1:00:00 | 0.26 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/18 13:00:00 | 0.255 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/17 13:00:00 | 0.28 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/17 1:00:00 | 0.274 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/16 13:00:00 | 0.274 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/15 13:00:00 | 0.267 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/14 13:00:00 | 0.249 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/13 13:00:00 | 0.26 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/13 1:00:00 | 0.27 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/11 13:00:00 | 0.276 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/10 13:00:00 | 0.27 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/9 13:00:00 | 0.277 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/9 1:00:00 | 0.272 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/8 13:00:00 | 0.277 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/7 13:00:00 | 0.274 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/6 13:00:00 | 0.28 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/6 1:00:00 | 0.274 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/5 13:00:00 | 0.286 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/5 1:00:00 | 0.31 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/4 13:00:00 | 0.3 |
| 020206000104 | Mineral dressing comprehensive refining S | 2018/7/3 1:00:00 | 0.308 |
Table 2 monitorable index list (part)
| Index ID | Index name |
| 20204000106 | Grade of weak essence |
| 20204000107 | Grade of weak tail |
| 20204000108 | Grade of weak magnetic tail |
| 20204000109 | Three magnetic fine position |
| 20204000110 | Grade qualification rate of weak concentrate |
| 20204000111 | Flotation feed grade |
| 20204000112 | Flotation feeding SiO2 |
| 20204000113 | Weak magnetic floating tail grade |
| 20204000114 | Weak magnetic suspension SiO2 |
| 20203000101 | High magnetic grinding grade |
| 20203000102 | Grade of high grade |
| 20203000103 | Comprehensive grade of tailings by strong magnetism |
| 20203000104 | Flat ring concentrate grade |
| 20203000106 | High gradient concentrate grade |
| 20203000108 | High grade of fine material |
| 20203000109 | Strong fine SiO2 |
| 20203000702 | High magnetic particle size |
| 20203000703 | Concentration of strong magnetic |
| 20203000704 | 1-2 cyclone overflow particle size (-200 mesh) |
| 20203000705 | 1-2 vortex overflow concentration |
| 20203000707 | 2-2 vortex overflow concentration |
| 20203000708 | Weak magnetic cubic particle size (-300 mesh) |
| 20203000709 | Triple concentration of weak magnetic field |
| 20206000117 | Magnetic separation tailing grade |
| 20206000118 | Concentration of magnetic separation tailings |
| 20106000105 | Choose No. 3 |
| 20106000106 | Block 1# |
| 20106000107 | Powder 2# |
| 20106000108 | Finished product of 2# ore |
| 20106000109 | Waste rock-1 # ore content |
| 20106000110 | 2X sperm 4# |
| …… | …… |
Table 3 process list
| Process ID | Name of procedure |
| 200001 | Comprehensive production index |
| 201001 | Information of raw ore |
| 201002 | Screening process |
| 201003 | Fine ore |
| 201004 | Lump ore |
| 201006 | Roasting in shaft furnace |
| 201005 | Strong magnetic grinding ore |
| 201007 | Weak magnetic grinding ore |
| 201008 | Waste stone |
| 201009 | Strong magnetic sorting |
| 201010 | Weak magnetic sorting |
| 201011 | Concentrating the middlings |
| 201012 | Reverse flotation |
| 201013 | Concentrate concentration |
| 201014 | Concentration of tailings |
| 201015 | Concentrate filtration |
Table 4 beneficiation heald essence SiO2 index daily mean and variance (2018/8/3)
Table 5 major index contribution rate affecting the overall concentrate grade
| Index name | Rate of contribution |
| Recovery rate of primary overflow | 19.30％ |
| Theoretical metal recovery rate of strong magnetic separation | 18.40％ |
| Ore feeding quantity of strong magnetic ball mill | 3.20％ |
| Metal recovery rate by weak magnetic separation theory | 12.50％ |
| Feeding flow of cyclone | 6.00％ |
| Cyclone ore feeding pressure | 11.40％ |
| Flotation separation ratio | 18.40％ |
| Comprehensive tailing grade | 10.80％ |
Claims (3)
1. A visual monitored control system of production index based on process flow, its characterized in that includes: the system comprises a data acquisition module, a production index management module, a production index visual configuration design module, a production index monitoring configuration module, an algorithm management module, a data processing module, a production index visual module and a production index multi-view interaction module;
the data acquisition module is used for acquiring the production index data of the whole production process of the enterprise and storing the production index data into a local database, and comprises a PLC and a data acquisition sensor;
the production index management module is used for coding the indexes and binding the data sources, and comprises the steps of creating production indexes, editing the production indexes, deleting the production indexes and checking the production indexes;
the production index visual configuration design module is used for drawing a production process into a flow chart in a configuration mode and displaying the flow chart to a user;
an algorithm management module: the module is used for carrying out unified management on all algorithms mentioned by the module, including functions of addition, deletion and modification;
a data processing module: calculating and processing the acquired data, including a daily mean value and a daily square difference;
a production index visualization module: displaying the original data generated by the data acquisition module and the comparison data and the prediction data generated by the data processing module; the method comprises a real-time data curve graph, a historical data curve graph, a daily mean trend line graph and a daily variance trend bar graph;
the data acquisition sensor acquires real-time running state data of equipment from an industrial field; the PLC is used for storing the real-time production process data acquired by the sensor into a local database;
the production indexes comprise: index codes, index names, index data, acquisition time and index units;
the index code is a unique ID which is formed by combining a plurality of digits and represents an index;
the index name is the name of the whole process index of the factory;
the index data is a digital value representing the size and height of an index acquired from a data acquisition module;
the acquisition time is the time when the index data is acquired from an acquisition module;
the index unit is a quantization unit adopted by the index data;
the production index creation is to create and store a vector consisting of index codes, index names, index data, acquisition time and index units in a local database;
the production index editing means that index codes, index names, index data, acquisition time and existing assignments of index units of established indexes in a local database are changed;
the step of deleting the production indexes refers to deleting index codes, index names, index data, acquisition time and existing information of index units related to established indexes in a local database;
the checking of the production index refers to checking of index codes, index names, index data, acquisition time and assigned values of index units of established indexes in a local database;
the process flow chart comprises the following steps: visual configuration tool, process node, connecting line, endpoint, anchor point and covering;
the visual configuration tool comprises a function bar, a primitive library and a drawing panel;
the function bar comprises functions of saving, clearing, refreshing, importing and primitive configuring;
the primitive library comprises common shape primitive nodes, and primitive shapes can be customized according to requirements and added into the primitive library;
the drawing panel is used for drawing a process flow, a user drags nodes in the primitive library to the drawing panel in a mouse dragging mode, corresponding attributes of endpoints and anchor points are configured on a configuration interface, connection among the primitive nodes is carried out through a mouse, and the drawn flow chart is stored in a database in a specific format or is exported to be a text file and stored to the local;
the process nodes are production processes which are represented by graphic elements in the flow chart and have actual physical significance, and the process nodes comprise process text information, process states and process event binding;
the process text information represents a process name, and an input box is popped up to set process node text information by dragging a primitive node to a drawing panel;
the process state refers to state notification information of equipment and production indexes in the process which is represented by the process nodes through setting flashing frames with different colors and adding prompt icons;
the process event binding refers to bindable events for each process node, and comprises a click event, a double click event and a mouse suspension event;
the mouse click event is the operation of entering a sub-process, so that the current interface jumps to a process sub-interface, a more detailed sub-flow chart of the process is drawn in the process sub-interface, and the process flow chart has a nesting function;
the mouse double-click event can be set as a pop-up dialog box display prompt message;
the mouse 'suspension' event can be defined as basic information of a display procedure of a floating prompt box;
the connecting line is a connecting line between nodes, and whether the output of the process node is abnormal or not is indicated by different colors of the connecting line;
the end point refers to the starting point of a connecting line connecting the process nodes; defining the shape, size, style and number of the end points by user;
the anchor points indicate positions of endpoints appearing on the nodes, and the trend of the connecting line is indicated by distinguishing starting anchor points and ending anchor points;
the covering is formed by adding decorations on the connecting line, and comprises a label text and an arrow of the connecting point;
the production index monitoring configuration module is used for configuring the production indexes and the visual schemes of the indexes which are required to be monitored by a user, supporting the configuration of a monitoring algorithm for the production indexes, and realizing the diagnosis and trend prediction of the indexes, and comprises a production index list, a process list and a visual scheme list;
the production index list is used for displaying all monitorable indexes;
the process list displays all the monitorable processes, and a user selects the indexes needing to be monitored in the production index list and configures the indexes to the corresponding processes;
the visualization scheme list displays all visualization schemes, and a user configures monitored indexes by selecting a specific visualization scheme;
the production index multi-view interaction module is used for carrying out multi-view display on indexes, wherein the multi-view display comprises an index overview view and an index classification view;
the overview view is used for displaying all monitoring indexes configured by the user and overviewing all the monitoring indexes;
the index classification view is used for displaying indexes of different classifications configured by a user, and comprises various indexes classified according to processes and index types, so that the indexes contained in a specific process or a certain type are displayed, the indexes of a certain process or a certain type are checked, a plurality of classification views can be displayed at the same time, and interactive switching is performed between the plurality of classification views and the overview view.
2. The visual production index monitoring system based on the process flow as claimed in claim 1, wherein the real-time data graph displays real-time data with time as a horizontal axis and an index value as a vertical axis, and a mouse can display data acquisition time, an index name and an index value when the mouse is suspended at a data point;
the historical data curve graph takes time as a horizontal axis and index values as a vertical axis, and a user can select data in a range less than two months to view;
the time is taken as a horizontal axis, the index data daily average value is taken as a vertical axis to be displayed, the display range is a range selected by historical data, and the date, the index name and the data daily average value are displayed by mouse suspension;
the daily variance trend histogram is displayed by taking time as a horizontal axis, the daily variance of the index data as a vertical axis, the display range is a range selected by the historical data, and the date, the index name and the daily variance of the index data are displayed by mouse suspension.
3. A visual monitoring method of production index based on process flow is characterized by comprising the following steps:
step 1: collecting the production index data of the whole production process of an enterprise and storing the data in a local database;
step 2: encoding the production index and configuring a data source;
step 2.1: distributing a unique index code for all production indexes in the production process, and uniformly managing the production indexes of the whole production process of an enterprise;
step 2.2: binding the production index of the unique code with the data source acquired by the data acquisition module through the step 1, so that the data source can be checked by calling the production index code;
and step 3: carrying out flow diagram configuration on the production process; displaying the logical relationship between the front and the back procedures in the production process, and displaying the whole production process flow;
step 3.1: if the production flow chart exists in the database, jumping to the step 3.3, otherwise, executing the next step;
step 3.2: newly building a production flow chart;
step 3.2.1: establishing an enterprise full-flow production process node through dragging;
step 3.2.2: key indexes are configured for process nodes, and styles, event notifications, upper and lower alarm limits and states of connecting lines are set at the same time, so that the real-time states of the indexes can be monitored in a flow chart in an operating state, and the functions of event notification, connecting line flicker and over-limit alarm are realized;
step 3.2.3: the drawn flow chart can be packaged into a primitive, and input and output are regulated; then the graphics primitive can be taken as a sub-process and added to a higher level flow chart;
step 3.2.4: connecting all production process nodes into a complete production flow chart according to the logical relationship among the enterprise full-flow production processes;
step 3.3: adding new nodes or deleting unnecessary nodes, and adjusting the logical relationship in the production flow chart;
step 3.4: saving the production flow chart to a local database;
and 4, step 4: managing and configuring all algorithms in the system;
and 5: carrying out algorithm configuration on each production process in the process flow chart, and determining an algorithm adopted by indexes monitored by each process;
step 5.1: reading a production flow diagram from a local database;
step 5.2: adding an algorithm for the production indexes needing to be monitored on the production flow chart and storing configuration;
step 5.3: storing the data obtained by the algorithm into a local database;
step 6: calculating the collected data in the local database to obtain the required data;
step 6.1: calculating the average value of data every day according to historical data of corresponding indexes in a local database;
step 6.2: calculating the variance of the data every day according to the historical data of the corresponding indexes in the local database;
step 6.3: storing all the calculation data;
and 7: displaying the required data to the staff through different visualization schemes;
step 7.1: selecting configured production indexes, reading real-time data of the production indexes from a database, and monitoring the real-time data;
step 7.1.1: selecting an index overview view to monitor all indexes configured by a user and overview all monitoring indexes;
step 7.1.2: selecting an index classification view, classifying according to processes and index types, and monitoring indexes contained in a specific process or a certain type;
step 7.1.3: a plurality of views can be monitored simultaneously by selecting different view modes, and interactive switching is carried out between a plurality of classification views and overview views;
step 7.2: selecting different visualization schemes to realize historical data viewing, comparative analysis and correlation analysis;
step 7.2.1: configuring a visualization scheme for the index, so that a corresponding visualization scheme, such as historical data, comparative analysis, association analysis and a custom scheme, can be selected for the index in subsequent steps;
step 7.2.2: appointing a certain production index to be checked, and generating a historical data chart;
step 7.2.2.1: reading historical data of the production index from a local database, selecting a time range through a time selector, and generating a historical data curve;
step 7.2.2.2: reading the daily mean data of the production index from a local database to generate a daily data mean trend broken line;
step 7.2.2.3: reading the daily variance data of the production index from a local database to generate a daily data variance trend histogram;
step 7.2.3: selecting a plurality of production indexes for comparative analysis to generate a radar chart;
step 7.2.3.1: selecting indexes;
step 7.2.3.2: setting an upper index limit and a lower index limit;
step 7.2.3.3: selecting historical time, and generating an index radar map of specified time;
step 7.2.4: generating a process index and production index association relation graph;
step 7.2.4.1: selecting a few main influence indexes from a plurality of process production indexes influencing the comprehensive production indexes, and calculating the contribution rate of each process index to the comprehensive production indexes;
step 7.2.4.2: determining the proportional relation of each index according to the contribution rate of each process index on the influence of the comprehensive production index; and distinguishing different indexes according to different colors to generate a process index and production index association relation graph.
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| CN113342327A (en) * | 2020-03-02 | 2021-09-03 | 中国科学院沈阳自动化研究所 | Method for generating visual industrial configuration interactive graph |
| CN111611301B (en) * | 2020-06-16 | 2023-12-08 | 武汉虹信科技发展有限责任公司 | KPI index representing method, calculating method and system of wireless communication equipment |
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