A visual mining process optimization method

Abstract

The invention discloses a method for visualizing the whole process of mining, and relates to the technical field of mining process optimization. The method comprises analyzing the acquisition time points and visual display time points of various data of various process nodes to obtain the visual time differences of various data of various process nodes; analyzing the transformation relationship between various data and time in various processes, and obtaining more accurate visual display values ​​according to the corresponding transformation relationship analysis by using the visual time difference and the correction coefficient; and visually displaying various data in various processes on a display device according to the visual display values ​​respectively corresponding to various data in various processes, thereby reducing the deviation of the visual display values ​​caused by the time delay between acquiring data and displaying data, and further improving the accuracy of the visual display corresponding to various data in various processes.

Description

Technical Field

[0001] The present invention relates to the technical field of mining process optimization, and in particular to a method for visualizing the whole-process process optimization of mining. Background Art

[0002] Mining engineering has a long history, and the mining technology management involved is also constantly improving with the development of science and technology. Mining engineering design is very important for the safe and efficient mining of mineral resources. The characteristics of mining engineering are analyzed. The actual application of mining technology in mining operations must follow the principles of low-carbon environmental protection and green mining, which can promote the stable development of my country's health industry and kill two birds with one stone. In the process of mining operations, mining workers must follow the basic principles and focus on the pollution problems that may arise in the process. Reasonable use of efficient, environmentally friendly, green ecological modern mining technology to properly solve them can enhance the important position of my country's mining industry.

[0003] However, in the process of visually displaying various data of each process node in the whole mining process, due to a certain time difference between acquiring data and displaying data, there is a certain difference between the displayed values ​​of various data of each process node and the actual real-time values, resulting in inaccurate visualization of various data corresponding to each process. As a result, the visualized values ​​cannot help relevant personnel to monitor and evaluate various data more accurately, which is not conducive to the monitoring of various data of each process node in the whole mining process. Based on the above problems, a visual mining full-process process optimization method is proposed. Summary of the invention

[0004] The purpose of the present invention is to provide a visual mining full-process process optimization method, which solves the technical problem that there is a certain time difference between acquiring data and displaying data, resulting in a certain difference between the displayed values ​​of various data of each process node and the actual real-time values, causing inaccurate visual displays corresponding to various types of data in each process.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A visual mining whole process optimization method includes the following steps:

[0007] Step 1: Obtain the acquisition time points and visualization display time points of various data at each process node;

[0008] Step 2: Analyze the acquisition time points and visualization display time points of various data at each process node to obtain the visualization time difference of various data at each process node;

[0009] Step 3: Analyze the transformation relationship between various types of data and time in each process, and obtain the correction coefficients corresponding to various types of data in each process according to the corresponding transformation relationship analysis;

[0010] Step 4: Use the visualized time difference and correction coefficient to correct the data of each process node to obtain more accurate visual display values ​​and display them.

[0011] As a further solution of the present invention, the specific method of obtaining the visualized time difference of various data of each process node is:

[0012] A1: Select any process node from each process node as the target process;

[0013] A2: Randomly select one type of data from the target process as analysis data;

[0014] In the process of obtaining the analysis data of the target process for n times of visualization, the time difference Cn between the time point of obtaining the analysis data and the time point of visualization is calculated by the discrete value calculation formula to obtain the discrete value U of the time difference Cn, and the discrete value U is analyzed to obtain the visualization time difference of the analysis data of the target process, where n is a preset value, n is a positive integer and n≥1;

[0015] A3: Repeat the above step A2 to obtain the visualized time difference Ki corresponding to each type of data of the target process, where i refers to each type of data of the target process. Bind the visualized time difference Ki corresponding to each type of data of the target process to the process node corresponding to the target process, and then generate a visualized time difference list of data corresponding to the target process;

[0016] A4: Repeat the above steps A1-A3 to obtain the data visualization time difference list Dj corresponding to each process node, where j refers to each process node, and j is the node number corresponding to each process node, and j is a positive integer.

[0017] As a further solution of the present invention, the specific method of analyzing the discrete value U and obtaining the visualized time difference of the target process analysis data is as follows:

[0018] When the discrete value U is less than or equal to the preset value Q1, the mean of the time difference Cn is used as the visualized time difference of the target process analysis data. When the discrete value U is greater than the preset value Q1, the time difference Cn values ​​that meet the preset condition L1|Cn-Cp|≥Y1 are deleted in descending order, and the discrete value of the remaining time difference is recalculated after each deletion until the discrete value U is less than or equal to the preset value Q1, then the calculation is stopped, and the number of deleted time differences b is recorded. When the number of deletions b is less than the preset value Q2, the mean of the remaining time differences is used as the visualized time difference of the target process analysis data. When the number of deletions b is greater than or equal to the preset value Q2, the mean of the total maximum and minimum values ​​of the remaining time differences is used as the visualized time difference of the target process analysis data, where the preset values ​​Q1, Q2 and Y1 are all preset values, and Q2 is a positive integer.

[0019] As a further solution of the present invention: the specific method of obtaining the correction coefficients corresponding to each type of data in each process is:

[0020] A01: Select the same process node as step A1 as the target process;

[0021] A02: Select the same analytical data as in step A2;

[0022] Preset e time nodes during the working hours of the entire mining process, obtain the numerical values ​​corresponding to the analysis data at e time nodes each day within the preset number of days T, take the numerical average of the analysis data at each node within the preset number of days T as the calculated numerical value Fe corresponding to each time node of the analysis data, sort each time node in ascending order according to the time numerical value corresponding to the time node, and take each time node as the horizontal coordinate, and the calculated numerical value corresponding to each time node as the vertical coordinate, thereby generating each calculated data point of the analysis data, wherein e refers to different time nodes;

[0023] Connect each calculation data point in sequence to generate a calculation curve, connect each two adjacent calculation data points on the calculation curve in sequence, and then generate multiple calculation line segments, and calculate the slope Kf corresponding to each calculation line segment according to the coordinates corresponding to the two calculation data points on each calculation line segment by using the slope calculation formula, where f refers to the number of calculation line segments, f is a positive integer and f≥1;

[0024] Obtain the number v in Kf that is equal to 0 and analyze it. If v≯0.55f, then obtain the correction coefficient X1 of the analysis data through step A03 analysis. Otherwise, mark the correction coefficient X1 of the analysis data as 0.

[0025] A04: Repeat the above steps A02-A03 to obtain the correction coefficients Xi corresponding to each type of data of the target process, bind the correction coefficients Xi corresponding to each type of data of the target process with the process nodes corresponding to the target process, and then generate the correction reference table P1 corresponding to the target process;

[0026] A05: Repeat the above steps A01-A04 to obtain the correction reference table Pj corresponding to each process node.

[0027] As a further solution of the present invention: the specific method of obtaining the correction coefficient of the analysis data through step A03 is:

[0028] The number of positive values ​​Z+ and negative values ​​Z- in all slopes Kf is obtained. When Z+>Z- or Z+<Z-, the analysis data correction coefficient X1 is calculated according to the coordinates of the calculated data points corresponding to the two endpoints on the calculation curve; when Z+≈Z-, it means that the calculated value does not change much or changes irregularly over time, and the analysis data correction coefficient X1 is marked as 0.

[0029] As a further solution of the present invention: when Z+>Z-, the specific method of calculating the correction coefficient of the analytical data according to the coordinates of the calculated data points of the two endpoints on the calculation curve is:

[0030] When Z+>Z-, obtain the ratio of the absolute value of the difference between the ordinates and the absolute value of the difference between the abscissas of the calculated data point coordinates of the two endpoints on the calculated curve, and use it as the correction coefficient X1 of the analysis data.

[0031] As a further solution of the present invention: when Z+<Z-, the specific method of calculating the correction coefficient of the analytical data according to the coordinates of the calculated data points of the two endpoints on the calculation curve is:

[0032] When Z+<Z-, obtain the ratio of the absolute value of the difference between the ordinates and the absolute value of the difference between the abscissas of the calculated data point coordinates of the two endpoints on the calculated curve, and use the product of the ratio and -1 as the correction coefficient X1 of the analysis data.

[0033] As a further solution of the present invention, the specific method of performing deviation correction processing on the data of each process node to obtain a more accurate visual display value is as follows:

[0034] Through BC ij=Wi j×(1+M ij), the visual display value XS ij corresponding to each type of data in each process is calculated, where Wi j is the real-time acquired value corresponding to each type of data in each process, Mij is the correction coefficient corresponding to each type of data in each process, where i refers to each type of data in the target process, j refers to each process node, and j is the node number corresponding to each process node, and i and j are positive integers.

[0035] Beneficial effects of the present invention:

[0036] The present invention obtains the visualization time difference corresponding to each type of data at each process node by analyzing the acquisition time point and the visualization display time point of each type of data at each process node, analyzes the transformation relationship between each type of data and time in each process, obtains the correction coefficient corresponding to each type of data in each process according to the corresponding transformation relationship analysis, performs correction processing on the data of each process node by using the visualization time difference and the correction coefficient, obtains more accurate visual display values, and visualizes the various types of data in each process on a display device according to the visual display values ​​corresponding to each type of data in each process, thereby reducing the deviation of the visual display values ​​caused by the time delay between acquiring data and displaying data, and further improving the accuracy of the visualization corresponding to each type of data in each process. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The present invention will be further described below in conjunction with the accompanying drawings.

[0038] Figure 1 It is a schematic diagram of the framework structure of a method for visualizing the entire mining process optimization of the present invention. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] Embodiment 1

[0041] See also Figure 1 As shown, the present invention is a visual mining full-process process optimization method, comprising the following steps:

[0042] Step 1: Obtain visual display data of various data of each process node. The visual display data of various data of each process node includes the acquisition time point and visual display time point of various data of each process node:

[0043] Step 2: Analyze the visualized display data of various data at each process node, and obtain the visualized time difference of various data at each process node according to the analysis results. The specific method is as follows:

[0044] A1: Select any process node from each process node as the target process;

[0045] A2: Randomly select one type of data from the target process as analysis data;

[0046] In the process of obtaining the analytical data of the target process for n times of visualization, the time difference between the time point of obtaining the analytical data and the time point of visualization is marked as Cn, where n is a preset value, n is a positive integer and n≥1;

[0047] Calculate the discrete value formula: Calculate and obtain the discrete value U of the time difference Cn, where Cp is the mean value of Cn, a is a positive integer, and n≥a≥1;

[0048] The discrete value U is compared and analyzed with the preset value Q1. When the discrete value U is less than or equal to the preset value Q1, it means that the discrete degree of the time difference Cn is small, and its mean value Cp is representative. Then Cp is used as the visualized time difference K1 of the target process analysis data. When the discrete value U is greater than the preset value Q1, it means that the discrete degree of the time difference Cn is large, and its mean value is not representative. The time difference Cn values ​​that meet the preset condition L1 are deleted in descending order, and the discrete values ​​of the remaining time differences are recalculated after each deletion until the discrete value U is less than or equal to the preset value Q1, then the calculation is stopped, and the number b of the deleted time differences is recorded at the same time;

[0049] The preset condition L1 is specifically: satisfying |Cn-Cp|≥Y1, where Y1 is a preset value. The specific value is determined by relevant personnel based on actual needs and application scenarios;

[0050] The number of deleted time differences b and the preset value Q2 are compared and analyzed. When the number of deleted time differences b is less than the preset value Q2, the mean of the remaining time differences is used as the visualization time difference K1 of the target process analysis data; when the number of deleted time differences b is greater than or equal to the preset value Q2, the mean of the total maximum and minimum values ​​of the remaining time differences is used as the visualization time difference K1 of the target process analysis data, wherein the preset values ​​Q1 and Q2 are both preset values, and the specific values ​​are formulated by relevant personnel according to actual conditions, and Q2 is a positive integer;

[0051] A3: Repeat the above step A2 to obtain the visualized time difference Ki corresponding to each type of data of the target process, where i refers to each type of data of the target process and i is the class label of each type of data of the target process. Bind the visualized time difference Ki corresponding to each type of data of the target process with the process node corresponding to the target process, and then generate the data visualized time difference list D1 corresponding to the target process, where i is a positive integer;

[0052] A4: Repeat the above steps A1-A3 to obtain the visualized time difference corresponding to each type of data of each process node, and bind the visualized time difference corresponding to each type of data of each process node to the corresponding process node, and then generate the data visualized time difference list Dj corresponding to each process node, where j refers to each process node, and j is the node number corresponding to each process node, and j is a positive integer;

[0053] By obtaining the visualized time difference corresponding to each type of data at each process node, the time delay in the display of various types of data in each process of the entire mining process can be identified, which is convenient for relevant personnel to monitor and judge various types of data in each process more accurately according to the visualized time difference corresponding to various types of data at each process node during the monitoring of the entire mining process, thereby further improving the monitoring accuracy of various types of data in each process of the entire mining process.

[0054] Embodiment 2

[0055] As the second embodiment of the present invention, when the present application is implemented, compared with the first embodiment, the technical solution of the present embodiment is different from that of the first embodiment only in the present embodiment;

[0056] Analyze the transformation relationship between various types of data and time in each process, and obtain the correction coefficients corresponding to various types of data in each process according to the corresponding transformation relationship analysis. The specific method is as follows:

[0057] A01: Select the same process node as step A1 as the target process;

[0058] A02: Select the same analytical data as in step A2;

[0059] Preset e time nodes during the working hours of the entire mining process, and obtain the corresponding values ​​of the analytical data at e time nodes every day within the preset number of days T. The e time nodes are different time points during the working hours of the entire mining process. The time intervals between the e time nodes can be uniform or uneven. Here, the default working hours of the entire mining process are 9 am to 5 pm. The default mining process is non-stop during the working hours. e is a positive integer, and the specific value is formulated by relevant personnel according to actual needs and application scenarios;

[0060] The average value of the analytical data at each node within the preset number of days T is used as the calculated value Fe corresponding to the analytical data at each time node, where e refers to different time nodes;

[0061] Sort each time node in ascending order according to the time value corresponding to the time node, and use each time node as the horizontal axis and the calculated value corresponding to each time node as the vertical axis, so as to generate each calculated data point of the analysis data, and connect each calculated data point in sequence to generate a calculation curve;

[0062] Connect each two adjacent calculation data points on the calculation curve in sequence to generate multiple calculation line segments. According to the coordinates corresponding to the two calculation data points on each calculation line segment, the slope corresponding to each calculation line segment is calculated by the slope calculation formula and marked as Kf, where f refers to the number of calculation line segments, f is a positive integer, and f≥1;

[0063] The slope calculation formula is as follows: slope = (y2-y1) / (x2-x1), y2 and x2 are the ordinate and abscissa of the next calculated data point on the calculated line segment, and y1 and x1 are the ordinate and abscissa of the previous calculated data point on the calculated line segment;

[0064] Get the number v of Kf that is equal to 0. When v≯0.55f, it means that the number of zeros in the slope Kf is small, and then proceed to step A03. Otherwise, it means that the number of zeros in the slope Kf is large, and the correction coefficient X1 of the analysis data is marked as 0.

[0065] A03: Obtain the number of positive and negative values ​​of all slopes Kf and mark them as Z+ and Z- respectively;

[0066] When Z+>Z-, it means that in most calculation segments, the calculated values ​​of analytical data increase with time, that is, the overall trend is upward;

[0067] When Z+<Z-, it means that in most of the calculation segments, the calculated values ​​decrease with the increase of time, that is, the overall trend is downward;

[0068] When Z+>Z- or Z+<Z-, the specific method of calculating the analysis data correction coefficient X1 is as follows:

[0069] When Z+>Z-, obtain the ratio of the absolute value of the difference between the ordinates and the absolute value of the difference between the abscissas of the calculated data point coordinates of the two endpoints on the calculated curve, and use it as the correction coefficient X1 of the analysis data;

[0070] When Z+<Z-, obtain the ratio of the absolute value of the difference between the ordinates and the absolute value of the difference between the abscissas of the calculated data point coordinates of the two endpoints on the calculated curve, and use the product of the ratio and -1 as the correction coefficient X1 of the analysis data;

[0071] When Z+≈Z-, it means that the calculated value does not change much or changes irregularly over time, and the analysis data correction coefficient X1 is marked as 0;

[0072] A04: Repeat the above steps A02-A03 to obtain the correction coefficients Xi corresponding to each type of data of the target process, bind the correction coefficients Xi corresponding to each type of data of the target process with the process nodes corresponding to the target process, and then generate the correction reference table P1 corresponding to the target process;

[0073] A05: Repeat the above steps A01-A04 to obtain the deviation correction reference table Pj corresponding to each process node;

[0074] According to the data visualization time difference list and correction reference table corresponding to each process node, the visual correction calculation formula corresponding to each type of data in each process is obtained. According to the visual correction calculation formula corresponding to each type of data in each process, the visual display value corresponding to each type of data in each process is obtained. The specific method is as follows:

[0075] By BC ij=Wi j×(1+M ij), the visual display values ​​XS ij corresponding to each type of data in each process are calculated, wherein Wi ij is the real-time acquired value corresponding to each type of data in each process, and Mi ij is the correction coefficient corresponding to each type of data in each process;

[0076] According to the visual display values ​​corresponding to each type of data in each process, each type of data in each process is visualized on the display device, reducing the deviation of the visual display values ​​caused by the time delay between acquiring data and displaying data, and further improving the accuracy of the visual display corresponding to each type of data in each process, which is significantly helpful for improving the accuracy and efficiency of process monitoring.

[0077] Embodiment 3

[0078] As the third embodiment of the present invention, when the present application is specifically implemented, compared with the first and second embodiments, the technical solution of this embodiment is to combine the solutions of the first and second embodiments mentioned above for implementation.

[0079] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0080] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A visual mining full-process process optimization method, characterized in that: The following steps are involved: Step 1: Obtain the acquisition time points and visualization display time points of various data at each process node; Step 2: Analyze the acquisition time points and visualization display time points of various data at each process node to obtain the visualization time difference of various data at each process node; Step 3: Analyze the transformation relationship between various types of data and time in each process, and obtain the correction coefficients corresponding to various types of data in each process according to the corresponding transformation relationship analysis; Step 4: Use the visualized time difference and correction coefficient to correct the data of each process node to obtain more accurate visual display values ​​and display them; The specific method of obtaining the visualized time difference of various data at each process node is: A1: Select any process node from each process node as the target process; A2: Randomly select one type of data from the target process as analysis data; In the process of obtaining the analysis data of the target process for n times of visualization, the time difference Cn between the time point of obtaining the analysis data and the time point of visualization is calculated by the discrete value calculation formula to obtain the discrete value U of the time difference Cn, and the discrete value U is analyzed to obtain the visualization time difference of the analysis data of the target process, where n is a preset value, n is a positive integer and n≥1; A3: Repeat the above step A2 to obtain the visualized time difference Ki corresponding to each type of data of the target process, where i refers to each type of data of the target process. Bind the visualized time difference Ki corresponding to each type of data of the target process to the process node corresponding to the target process, and then generate a visualized time difference list of data corresponding to the target process; A4: Repeat the above steps A1-A3 to obtain the data visualization time difference list Dj corresponding to each process node, where j refers to each process node, and j is the node number corresponding to each process node, and j is a positive integer; The specific method of analyzing the discrete value U and obtaining the visualized time difference of the target process analysis data is as follows: When the discrete value U is less than or equal to the preset value Q1, the mean of the time difference Cn is used as the visualization time difference of the target process analysis data. When the discrete value U is greater than the preset value Q1, the time difference Cn values ​​that meet the preset condition L1: |Cn-Cp|≥Y1 will be deleted in order from large to small, and the discrete value of the remaining time difference will be recalculated after each deletion until the discrete value U is less than or equal to the preset value Q1, then the calculation is stopped, and the number of deleted time differences b is recorded. When the number of deletions b is less than the preset value Q2, the mean of the remaining time differences is used as the visualization time difference of the target process analysis data. When the number of deletions b is greater than or equal to the preset value Q2, the mean of the total maximum and minimum values ​​of the remaining time differences is used as the visualization time difference of the target process analysis data, where the preset values ​​Q1, Q2 and Y1 are all preset values, Q2 is a positive integer, and Cp is the mean of Cn.

2. A visual mining full-process process optimization method according to claim 1, characterized in that: The specific method of obtaining the correction coefficients corresponding to each type of data in each process is: A01: Select the same process node as step A1 as the target process; A02: Select the same analytical data as in step A2; Preset e time nodes during the working hours of the entire mining process, obtain the numerical values ​​corresponding to the analysis data at e time nodes each day within the preset number of days T, take the numerical average of the analysis data at each node within the preset number of days T as the calculated numerical value Fe corresponding to each time node of the analysis data, sort each time node in ascending order according to the time numerical value corresponding to the time node, and take each time node as the horizontal coordinate, and the calculated numerical value corresponding to each time node as the vertical coordinate, thereby generating each calculated data point of the analysis data, wherein e refers to different time nodes; Connect each calculation data point in sequence to generate a calculation curve, connect each two adjacent calculation data points on the calculation curve in sequence, and then generate multiple calculation line segments, and calculate the slope Kf corresponding to each calculation line segment according to the coordinates corresponding to the two calculation data points on each calculation line segment by using the slope calculation formula, where f refers to the number of calculation line segments, f is a positive integer and f≥1; Obtain the number v in Kf that is equal to 0 and analyze it. If v≯0.55f, then obtain the correction coefficient X1 of the analysis data through step A03 analysis. Otherwise, mark the correction coefficient X1 of the analysis data as 0. A04: Repeat the above steps A02-A03 to obtain the correction coefficients Xi corresponding to each type of data of the target process, bind the correction coefficients Xi corresponding to each type of data of the target process with the process nodes corresponding to the target process, and then generate the correction reference table P1 corresponding to the target process; A05: Repeat the above steps A01-A04 to obtain the correction reference table Pj corresponding to each process node; The specific method of obtaining the correction coefficient of the analysis data through step A03 is: The number of positive values ​​Z+ and negative values ​​Z- in all slopes Kf is obtained. When Z+>Z- or Z+<Z-, the analysis data correction coefficient X1 is calculated according to the coordinates of the calculated data points corresponding to the two endpoints on the calculation curve; when Z+≈Z-, it means that the calculated value does not change much or changes irregularly over time, and the analysis data correction coefficient X1 is marked as 0.

3. A visual mining full-process process optimization method according to claim 2, characterized in that: When Z+>Z-, the specific method of calculating the correction coefficient of the analytical data according to the coordinates of the calculated data points at the two endpoints on the calculation curve is: When Z+>Z-, obtain the ratio of the absolute value of the difference between the ordinates and the absolute value of the difference between the abscissas of the calculated data point coordinates of the two endpoints on the calculated curve, and use it as the correction coefficient X1 of the analysis data.

4. A visual mining full-process process optimization method according to claim 2, characterized in that: When Z+<Z-, the specific method of calculating the correction coefficient of the analytical data according to the coordinates of the calculated data points at the two endpoints on the calculation curve is: When Z+<Z-, obtain the ratio of the absolute value of the difference between the ordinates and the absolute value of the difference between the abscissas of the calculated data point coordinates of the two endpoints on the calculated curve, and use the product of the ratio and -1 as the correction coefficient X1 of the analysis data.

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