Ash data analysis method, system, and electronic device

Abstract

The embodiment of the present application provides a kind of ash data analysis method, system and electronic equipment, belong to coal washing processing technical field.The method comprises: obtaining the original ash data collected by ash instrument;According to the reliability analysis of the original ash data according to the analysis model constructed, reliable ash data is obtained;Statistical processing is carried out to reliable ash data, and ash change result is obtained.Through analysis model, the operation of ash instrument can be judged in real time by the reliability analysis of original ash data, and reliability and credibility data are obtained, and control personnel can judge whether to correct ash instrument in time.The present application can mine useful information from the original ash data with poor regularity by statistical processing of reliable ash data, and control personnel can easily use these data;And the ash change result obtained can participate in the control of ash.

Description

Technical Field

[0001] This invention relates to the field of coal washing and processing technology, specifically to an ash content data analysis method, an ash content data analysis system, an ash content data analysis electronic device, and a computer-readable storage medium. Background Technology

[0002] Coal ash content is the mass fraction (i.e., weight percentage, denoted as Ad%) of the high atomic number oxide residue after coal has been fully and completely burned at a certain temperature. Coal ash content is one of the important indicators for evaluating coal quality. Strictly controlling the ash content of coal products and improving product stability are important tasks in coal processing and utilization.

[0003] Currently, the quality control laboratory of coal washing plants uses the traditional ignition test method for ash content testing. This method is complex and has a significant time lag in providing test results, making it unsuitable for the needs of product ash content control. Therefore, more and more coal preparation plants are equipping their coal conveyor belts with online ash measuring devices to quickly and accurately detect ash content. Analysis of the raw data provided by the online ash measuring instruments reveals at least the following problems:

[0004] 1. Due to changes in coal quality, online ash content data often shows significant deviations, requiring the control room personnel to calibrate the instruments.

[0005] Second, the raw ash data measured by the online ash analyzer fluctuates frequently and is often disturbed, making it difficult to provide the control personnel with intuitive information on ash changes.

[0006] Third, the raw ash data measured by the online ash analyzer cannot be used as process data and directly affect the ash control system. Summary of the Invention

[0007] The purpose of this invention is to provide an ash content data analysis method, system, and electronic device to at least solve the problem that the raw ash content data measured by online ash content meters in the prior art fluctuates frequently and is often disturbed, making it difficult to provide control personnel with intuitive information on ash content changes.

[0008] To achieve the above objectives, embodiments of the present invention provide a method for ash content data analysis, the method comprising:

[0009] Obtain the raw ash content data collected by the ash analyzer;

[0010] Reliability analysis is performed on the original ash data based on the constructed analysis model to obtain reliable ash data;

[0011] Statistical processing of reliable ash content data yields the results of ash content variation.

[0012] Preferably, the method further includes: constructing an analysis model, including:

[0013] Determine all raw ash data of the ash analyzer within a time period T, wherein the time period T includes multiple sub-time periods t;

[0014] Calculate the average value of multiple raw gray data within each sub-period t to obtain the average data corresponding to each sub-period t;

[0015] M data groups are constructed based on the average data, and each data group includes N average data points arranged in chronological order.

[0016] Each of the data groups is recombined to obtain M. N Group reorganization data;

[0017] Calculate the average value of each group of recombination data to obtain M. N The average number of recombinants.

[0018] Preferably, the method further includes: determining a time period T, including:

[0019] Obtain the timestamp t0 corresponding to the latest ash content measured in the quality control laboratory;

[0020] Obtain the time period T1 before and T2 after timestamp t0 of the ash analyzer;

[0021] The preceding time period T1 and the following time period T2 are combined to form time period T.

[0022] Preferably, the method further includes: performing a reliability analysis on the original ash content data, including:

[0023] Obtain the ash content standard data corresponding to the timestamp t0, wherein the ash content standard data comes from the ash content test value measured by the quality and testing laboratory;

[0024] The ash content standard data are compared with multiple recombination averages to obtain multiple sets of deviations;

[0025] Determine whether the deviation of each group exceeds the set threshold. If it does not exceed the threshold, generate a confidence number and count the number of confidence numbers.

[0026] The reliability rate of the raw ash content data of the ash analyzer within the time period T is calculated based on the number of the reliability numbers. The reliability rate is used to characterize the reliability of the raw ash content data.

[0027] Preferably, the method further includes: performing statistical processing on the reliable ash data, including:

[0028] Multiple time periods △T are constructed based on the timestamps corresponding to reliable ash data. nTo obtain sample data for each time period;

[0029] Calculate the maximum, minimum, mean, and variance of the sample data within each time period;

[0030] The divergence value for each time period is calculated based on the variance of each time period and the set variance tolerance factor.

[0031] Based on the time period △T n The divergence value, average value, and set ash tolerance factor are used to determine the time period ΔT. n+1 The ash content change results include ash content increase, ash content decrease, or invalidity.

[0032] The method also includes: displaying the ash content change results using a bar chart.

[0033] The method further includes: based on the ash content change results, pushing the average value of the sample data as a control signal to the ash content control system.

[0034] This invention also provides an ash data analysis system, which is used to implement the above-described ash data analysis method. The system includes:

[0035] The acquisition module is used to acquire the raw ash content data collected by the ash analyzer;

[0036] The analysis module is used to perform reliability analysis on the original ash data according to the constructed analysis model to obtain reliable ash data;

[0037] The statistics module is used to perform statistical processing on reliable ash data to obtain ash change results.

[0038] This invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-described ash data analysis method when executing the computer program.

[0039] This invention also provides a computationally readable storage medium storing a computer program that, when executed by a processor, implements the above-described ash data analysis method.

[0040] The above-mentioned technical solution of the present invention performs reliability analysis on the original ash content data through analysis model, which can judge the operation status of the ash analyzer in real time, obtain reliability and confidence rate data, and enable the central control personnel to promptly determine whether the ash analyzer needs to be calibrated.

[0041] Secondly, by performing statistical processing on reliable ash data, this invention can extract useful information from raw ash data with poor regularity, which can be easily used by central control personnel; and the obtained ash change results can be used to control ash.

[0042] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0043] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0044] Figure 1 This is a flowchart of an ash data analysis method provided by one embodiment of the present invention;

[0045] Figure 2 This is a flowchart of the analysis model construction steps provided in an optional embodiment of the present invention.

[0046] Figure 3 This is a flowchart of the time period determination steps provided in an optional embodiment of the present invention;

[0047] Figure 4 This is a flowchart of the reliability analysis steps for raw ash data provided in an optional embodiment of the present invention;

[0048] Figure 5 This is a flowchart of statistical processing of reliable ash data provided by an optional embodiment of the present invention;

[0049] Figure 6 This is a structural block diagram of an optional embodiment of the present invention for displaying the results of ash content changes;

[0050] Figure 7 This is a block diagram of an ash data analysis system provided in an optional embodiment of the present invention. Detailed Implementation

[0051] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.

[0052] Figure 1 This is a flowchart of an ash data analysis method provided by one embodiment of the present invention, as shown below. Figure 1 As shown, an ash data analysis method includes:

[0053] Step S101: Obtain the raw ash content data collected by the ash analyzer.

[0054] In this embodiment, the raw ash content data is the ash content collected online by the ash analyzer on the coal conveyor belt.

[0055] Step S102: Perform reliability analysis on the original ash data according to the constructed analysis model to obtain reliable ash data.

[0056] As a further optimization of this embodiment, such as Figure 2 As shown, the method further includes: constructing an analysis model, including:

[0057] Step a1: Determine all raw ash data of the ash analyzer within a time period T, where the time period T includes multiple sub-time periods t.

[0058] In this embodiment, it is preferred to use 1 minute as a sub-period t. Then, the period T can be the period from the 2nd minute to the 10th minute (i.e., the period T includes 8 sub-periods t), or the period from the 5th minute to the 15th minute (i.e., the period T includes 10 sub-periods t). In each sub-period t (i.e. each minute), the ash analyzer detects multiple ash content values, and these ash content values ​​serve as the original ash data in this embodiment.

[0059] Step a2: Calculate the average value of multiple raw gray data within each sub-period t, that is, calculate the average value of all raw gray data within each minute to obtain the average data corresponding to each sub-period t.

[0060] Step a3: Construct M data groups based on the average data, with each data group containing N average data points arranged in chronological order.

[0061] For example, if time period T is the period from minute 1 to minute 12, select three time periods of 3 minutes each from this 11-minute period to construct three data groups. The three time periods do not overlap, and each data group contains three average data points. That is, select three average data points of three consecutive minutes as a data group.

[0062] Step a4: Reassemble the M data sets to obtain M N Group reorganization data.

[0063] In this embodiment, the recombination processing is specifically as follows: Assuming the three data groups are M1(H1,H2,H3), M2(H4,H5,H6), and M3(H7,H8,H9), an average data point is taken from each of the three data groups and combined to form 27 recombined data groups, namely m1(H1,H4,H7), m2(H2,H4,H7), m3(H3,H4,H7), m4(H1,H5,H7), m5(H2,H5,H7), m6(H3,H5,H7), m7(H1,H6,H7), m8(H2,H6,H7), m9(H3,H6,H7), m10(H1,H4,H8), m11 (H2,H4,H8), m12(H3,H4,H8), m13(H1,H5,H8), m14(H2,H5,H8), m15(H 3,H5,H8),m16(H1,H6,H8),m17(H2,H6,H8),m18(H3,H6,H8),m19(H1,H 4,H9), m20(H2,H4,H9), m21(H3,H4,H9), m22(H1,H5,H9), m23(H2,H5,H 9), m24(H3,H5,H9), m25(H1,H6,H9), m26(H2,H6,H9), m27(H3,H6,H9).

[0064] Step a5: Calculate the average value of each group of recombination data to obtain M. N The recombination mean is calculated by taking the average value of each recombination data point from m1 to m27. For example, the recombination mean of recombination data m1 is AVG. m1 = (H1+H4+H7) / 3.

[0065] As a further optimization of this embodiment, when selecting time period T, the detection time in the computer science laboratory is used as a selection reference. For example... Figure 3 As shown, the method further includes: determining a time period T, including:

[0066] Step b1: Obtain the timestamp t0 corresponding to the latest ash content measured in the quality control laboratory;

[0067] Step b2: Obtain the time period T1 before and T2 after timestamp t0 for the ash analyzer.

[0068] In this embodiment, the selection of the next time period T2 can be the lag time of the ash analysis value, while all the original ash data in the previous time period T1 can be reliable data; for example, the previous time period T1 is preferably one minute before the timestamp t0, and the next time period T2 is preferably eleven minutes after the timestamp t0; this method can improve the accuracy of the reliability analysis of the original ash data.

[0069] Step b3: Combine the previous time period T1 and the next time period T2 to form time period T.

[0070] For example, if timestamp t0 is the 2nd minute, then time period T is the time period from the 1st minute to the 13th minute. When constructing a data group, the time period of the data group is divided with timestamp t0 as a reference.

[0071] For example, the first data group M1 is divided with reference to timestamp t0 and two adjacent timestamps t0. The time period of the first data group is from the 1st minute to the 3rd minute. At this time, the average value of all the original gray data in the 1st minute is taken as H1, the average value of all the original gray data in the 2nd minute is taken as H2, and the average value of all the original gray data in the 3rd minute is taken as H3.

[0072] The second data group M2 is divided with reference to the timestamp t0+5min and the two time points adjacent to the timestamp t0+5min, that is, the 7th minute and the two time points adjacent to the timestamp 7th minute. The time period of the second data group is from the 6th minute to the 8th minute. At this time, the average value of all the original gray data within the 6th minute is taken as H4, the average value of all the original gray data within the 7th minute is taken as H5, and the average value of all the original gray data within the 8th minute is taken as H6.

[0073] The third data group M3 is divided with reference to the timestamp t0+10min and two adjacent timestamps t0+10min, i.e., the 12th minute and two adjacent timestamps. The time period of the third data group is from the 11th minute to the 13th minute. At this time, the average value of all raw gray data within the 11th minute is taken as H7, the average value of all raw gray data within the 12th minute is taken as H8, and the average value of all raw gray data within the 13th minute is taken as H9.

[0074] As a further optimization of this embodiment, such as Figure 4 As shown, the method further includes: performing a reliability analysis on the original ash data, including:

[0075] Step c1: Obtain the ash standard data corresponding to the timestamp t0. The ash standard data comes from the ash test value measured by the quality and testing laboratory.

[0076] Because the ash content test results measured by the quality control laboratory have high detection accuracy, but the time lag in providing the test results is significant, the ash content test results are used as a standard reference to conduct reliability analysis on the online detection values ​​of the ash analyzer and to judge the operating status of the ash analyzer in real time.

[0077] Step c2: Compare the ash content standard data with M respectively. NBy comparing the recombinant means, multiple sets of deviations were obtained.

[0078] A total of 27 recombination averages (AVG) were obtained in steps a1 to a5. m1 ~AVG m27 At this point, 27 sets of deviations can be obtained.

[0079] Step c3: Determine whether the deviation of each group exceeds the set threshold. If it does not exceed the threshold, generate a confidence number and count the number of confidence numbers.

[0080] The 27 sets of deviations obtained in step c2 are compared with the set threshold, for example, the set threshold is 0.5. When a deviation is determined to be within ±0.5, the confidence number is incremented by 1. After all deviations are judged, the total number of confidence numbers is counted.

[0081] Step c4: Calculate the reliability rate of the raw ash data of the ash analyzer within the time period T based on the number of reliability numbers. The reliability rate is used to characterize the reliability of the raw ash data.

[0082] Specifically, the confidence rate kx_l = (kx / 27) * 100%, where kx is the total number of confidence numbers. Therefore, during the analysis process, the analysis model updates the detection period of the ash analyzer based on the latest time point of the ash analysis value measured by the quality control laboratory. It can judge the operation status of the ash analyzer in real time, obtain the reliability and confidence rate data of the ash analyzer, and the central control personnel can promptly determine whether the ash analyzer needs to be calibrated.

[0083] Step S103: Perform statistical processing on the reliable ash content data to obtain the ash content change results.

[0084] As a further optimization of this embodiment, such as Figure 5 As shown, the method further includes: performing statistical processing on the reliable ash data, including:

[0085] Step d1: Construct multiple time periods ΔT n To obtain sample data for each time period.

[0086] In this embodiment, the reliable gray data is the original gray data analyzed in step S102. The timestamps corresponding to the reliable gray data are divided into time periods, for example, every 15 minutes is a time period. At the same time, the average value of all reliable gray data in each minute within the time period is calculated. At this time, 15 sample data can be obtained. The sample data includes the average value of the reliable gray data and the timestamp corresponding to the average value of the reliable gray data. Therefore, the 15 sample data of the first time period △T1 are as follows: (t1,H1), (t2,H2), (t3,H3), (t4,H4), (t5,H5), (t6,H6), (t7,H7), (t8,H8), (t9,H9), (t10,H10), (t11,H11), (t12,H12), (t13,H13), (t14,H14), (t15,H15).

[0087] Step d2: Calculate the mean and variance of the sample data for each time period.

[0088] In this embodiment, the maximum and minimum values ​​of the sample data within each time period are also calculated; for example, in the first time period △T1, the maximum value MAX1, minimum value MIN1, average value AVG1, and variance Var1 of 15 sample data are calculated.

[0089] In the second time period △T2, the maximum value MAX2, minimum value MIN2, mean value AVG2, and variance Var2 of 15 sample data are calculated.

[0090] In the second time period △T3, the maximum value MAX3, minimum value MIN3, mean value AVG3, and variance Var3 of 15 sample data are statistically analyzed.

[0091] In the nth time period △T n In the above, calculate the maximum value MAXn, minimum value MINn, mean value AVGn, and variance Varn of 15 sample data.

[0092] Step d3: Calculate the divergence value for each time period based on the variance of each time period and the set variance tolerance factor.

[0093] Specifically, for example, in the first time period ΔT1, when Var1 > Var_dev, Dif1 = 1; when Var1 ≤ Var_dev, Dif1 = 0; where Var_dev is the variance tolerance factor and Dif1 is the divergence value of the first time period. Similarly, the nth time period ΔT can be calculated. n The divergence value Difn.

[0094] Step d4: Based on the time period △T nThe divergence value, average value, and set ash tolerance factor are used to determine the time period ΔT. n+1 The ash content change results include ash content increase, ash content decrease, or invalidity.

[0095] In this embodiment, starting from the second time period △T2, the changes in the raw ash content data of the ash analyzer are statistically analyzed:

[0096] When Dif1 = 0, Dif2 = 0, and AVG2 - AVG1 > Hdev, the ash content change result is an increase in ash content, where Hdev is the ash tolerance factor.

[0097] When Dif1 = 0, Dif2 = 0, and AVG2 - AVG1 < -Hdev, the ash content change result is a decrease in ash content, where Hdev is the ash tolerance factor.

[0098] Other judgments are invalid.

[0099] In this embodiment, the statistical processing method for the online raw data of the ash analyzer described above can extract useful information from raw data with poor regularity, and the control personnel can easily use this data.

[0100] As a further optimization of this embodiment, the method also includes: displaying the ash content change results using a bar chart.

[0101] Specifically, such as Figure 6 As shown, each time period ΔT n The statistical data is expressed using a three-element format: an outer bar chart (showing bar 1), an inner bar chart (showing bar 2), and a point (showing point 2). The x-coordinate of the center point of each of the three elements is △T. n / 2 locations, of which:

[0102] The lower limit of the outer bar chart is MINn, the upper limit is MAXn, and the width is approximately 10-50 pixels; the inner fill color changes as follows: red when gray content increases, green when gray content decreases, and gray when invalid.

[0103] The lower limit of the inner bar chart is MINn, the upper limit is MINn+Varn, and the width is approximately 6-50 pixels; the inner fill color changes as follows: when Difn=0, it is orange, and when Difn=1, it is blue.

[0104] The x-coordinate of the point is the center point of the bar chart, the y-coordinate is AVGn, and the color is black.

[0105] Therefore, users can directly observe the changes in the original gray data, which improves the user-friendliness of human-computer interaction.

[0106] As a further optimization of this embodiment, the method further includes: based on the ash content change result, pushing the average value of the sample data as a control signal to the ash content control system; for example: when it is determined that the ash content is rising or falling, pushing AVGn and AVGn-1 to the ash content control system as the two ash content change values. The ash content control system is an existing system, and its specific system configuration is not described in detail in this embodiment.

[0107] Figure 7 An optional embodiment of the ash content data analysis system provided by the present invention, such as Figure 7 As shown, the system is used to implement the above-mentioned ash data analysis method, and the system includes:

[0108] The acquisition module is used to acquire the raw ash content data collected by the ash analyzer;

[0109] The analysis module is used to perform reliability analysis on the original ash data according to the constructed analysis model to obtain reliable ash data;

[0110] The statistics module is used to perform statistical processing on reliable ash data to obtain ash change results.

[0111] The ash data analysis system of this invention can perform reliability analysis on the raw ash data through the analysis model, and can judge the operation status of the ash analyzer in real time to obtain reliability and confidence rate data. The central control personnel can promptly determine whether the ash analyzer needs to be calibrated.

[0112] Secondly, the ash data analysis system of this invention can extract useful information from raw ash data with poor regularity by statistically processing reliable ash data. The data can be easily used by the central control personnel; and the obtained ash change results can be used to control ash.

[0113] This invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-described ash data analysis method when executing the computer program.

[0114] This invention also provides a computationally readable storage medium storing a computer program that, when executed by a processor, implements the above-described ash data analysis method.

[0115] The optional embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details in the above embodiments. Within the scope of the technical concept of the embodiments of the present invention, various simple modifications can be made to the technical solutions of the embodiments of the present invention, and these simple modifications all fall within the protection scope of the embodiments of the present invention.

[0116] It should also be noted that the various specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the embodiments of the present invention will not describe the various possible combinations separately.

[0117] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a microcontroller, chip, or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0118] Furthermore, various different implementations of the present invention can be combined arbitrarily, as long as they do not violate the spirit of the present invention, they should also be regarded as the content disclosed in the present invention.

Claims

1. A method for analyzing ash content data, characterized in that, The method includes: Obtain the raw ash content data collected by the ash analyzer; Reliability analysis is performed on the original ash data based on the constructed analysis model to obtain reliable ash data; Statistical processing is performed on reliable ash content data to obtain the ash content change results; The method further includes: constructing an analysis model, including: Determine all raw ash data of the ash analyzer within a time period T, wherein the time period T includes multiple sub-time periods t; Calculate the average value of multiple raw gray data within each sub-period t to obtain the average data corresponding to each sub-period t; M data groups are constructed based on the average data, and each data group includes N average data points arranged in chronological order. Each of the data groups is recombined to obtain M. N Group reorganization data; Calculate the average value of each group of recombination data to obtain M. N The average number of recombinant units; The method further includes: determining the time period T, including: Obtain the timestamp t0 corresponding to the latest ash content measured in the quality control laboratory; Obtain the time period T1 before and T2 after timestamp t0 of the ash analyzer; The preceding time period T1 and the following time period T2 are combined to form time period T; Reliability analysis was performed on the raw ash data, including: Obtain the ash content standard data corresponding to the timestamp t0, wherein the ash content standard data comes from the ash content test value measured by the quality and testing laboratory; The ash content standard data are compared with multiple recombination averages to obtain multiple sets of deviations; Determine whether the deviation of each group exceeds the set threshold. If it does not exceed the threshold, generate a confidence number and count the number of confidence numbers. The reliability rate of the raw ash data of the ash analyzer within the time period T is calculated based on the number of the reliability numbers. The reliability rate is used to characterize the reliability of the raw ash data. Statistical processing of reliable ash data includes: Multiple time periods △T are constructed based on the timestamps corresponding to reliable ash data. n To obtain sample data for each time period; Calculate the mean and variance of the sample data within each time period; The divergence value for each time period is calculated based on the variance of each time period and the set variance tolerance factor. Based on the time period △T n The divergence value, average value, and set ash tolerance factor are used to determine the time period ΔT. n+1 The ash content change results include ash content increase, ash content decrease, or invalidity.

2. The method according to claim 1, characterized in that, The method also includes: displaying the ash content change results using a bar chart.

3. The method according to claim 1, characterized in that, The method further includes: based on the ash content change results, pushing the average value of the sample data as a control signal to the ash content control system.

4. An ash data analysis system, said system being used to implement the ash data analysis method according to any one of claims 1-3, characterized in that, The system includes: The acquisition module is used to acquire the raw ash content data collected by the ash analyzer; The analysis module is used to perform reliability analysis on the original ash data according to the constructed analysis model to obtain reliable ash data; The statistics module is used to perform statistical processing on reliable ash data to obtain ash change results.

5. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the ash data analysis method according to any one of claims 1-3.

6. A computationally readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the ash data analysis method according to any one of claims 1-3.

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