A method for removing and supplementing abnormal conductivity data in water simulation

Abstract

This invention belongs to the field of steel smelting technology and discloses a method for removing and supplementing abnormal data of simulated water conductivity. The method includes: acquiring raw simulated water conductivity data and performing data preprocessing; analyzing the regularity of the raw simulated water conductivity data; if a regularity is determined, setting data removal and supplementation rules, determining the upper and lower limits of non-abnormal simulated water conductivity data, the deviation value of abnormal simulated water conductivity data, and the supplementation rules for abnormal simulated water conductivity data, and judging and processing the abnormal simulated water conductivity data; judging the expected data of the processed abnormal simulated water conductivity data; if it meets the expected data, outputting the data; otherwise, reprocessing the abnormal simulated water conductivity data until it meets the expected data. This invention effectively removes and supplements abnormal data by setting data removal and supplementation rules, improving data analysis efficiency.

Description

Technical Field

[0001] This invention relates to the field of steel smelting technology, and in particular to a method for removing and supplementing abnormal data of simulated water conductivity. Background Technology

[0002] Water simulation technology is a core supporting tool for steelmaking process research. By replacing the high-temperature, difficult-to-observe molten steel with transparent water at room temperature, and combining it with monitoring technologies such as conductivity meters, it solves the technical bottleneck of direct observation of molten steel under high-temperature conditions, effectively optimizing the steelmaking process. Data processing is the key to the analysis of water simulation results, which directly affects the accuracy and application value of the results. Existing outlier removal methods include the quartile interval method, the isolated forest algorithm, the sliding window mean method, and the standard deviation method. However, these methods have low data removal quality when processing data from water simulation technology. Summary of the Invention

[0003] To address some or all of the technical problems existing in the prior art, the present invention provides a method for removing and supplementing abnormal conductivity data in water simulation.

[0004] The method for removing and supplementing abnormal conductivity data in water simulation provided by this invention includes: The raw data of simulated water conductivity were obtained and preprocessed to analyze the regularity of the raw data. If it is determined that the original data of water simulated conductivity has a regularity, then data elimination and supplementation rules are set, the upper and lower limits of non-abnormal data of water simulated conductivity, the deviation value of abnormal data of water simulated conductivity, and the supplementation rules for abnormal data of water simulated conductivity are determined, and abnormal data of water simulated conductivity are judged and processed. The abnormal water conductivity data after processing is judged according to the expected data. If the abnormal water conductivity data after processing is judged to meet the expected data, the data is output. Otherwise, the abnormal water conductivity data is processed again until it meets the expected data.

[0005] Furthermore, in the above method for removing and supplementing abnormal water conductivity data, if it is determined that the original water conductivity data does not exhibit any regularity, the data processing is terminated directly.

[0006] Furthermore, in the above-mentioned method for removing and supplementing abnormal data of water simulation conductivity, the upper and lower limits of non-abnormal data of water simulation conductivity are determined based on the physical principles of water simulation experiments or historical experimental data.

[0007] Furthermore, in the above methods for removing and supplementing abnormal water conductivity data, the analysis of the regularity of the original water conductivity data includes: The raw data of simulated water conductivity obtained by the conductivity meter are analyzed based on the experience of the testing experts. If the experience of the experts is available, the raw data of simulated water conductivity is analyzed for regularity based on the experience of the experts. If no expert experience is available, the raw data of simulated water conductivity is preprocessed to obtain the mean and standard deviation of the raw data. If the standard deviation is not higher than the acquisition accuracy of the conductivity meter, it is determined that the raw data of simulated water conductivity has a regularity. If the standard deviation is higher than the acquisition accuracy of the conductivity meter, differential sequence analysis is performed on the raw data of water simulated conductivity to obtain the differential sequence and standard deviation of the differential sequence of the raw data of water simulated conductivity, and to determine the relative fluctuation ratio of the raw data of water simulated conductivity. If the relative fluctuation ratio is not higher than the preset fluctuation threshold, it is determined that the original data of water simulation conductivity has regularity. If the relative fluctuation ratio is higher than the preset fluctuation threshold, the autocorrelation function test is performed on the original data of water simulated conductivity to obtain the autocorrelation coefficient of the original data of water simulated conductivity. If the autocorrelation coefficient is not lower than the preset autocorrelation threshold, it is determined that the original data of water simulated conductivity has regularity. If the autocorrelation coefficient is lower than the preset autocorrelation threshold, it is determined that the original data of water simulated conductivity does not have a regularity.

[0008] Furthermore, in the above method for removing and supplementing abnormal conductivity data in water simulation, the data removal and supplementation rules include: Based on the upper and lower limits of the non-abnormal data of water simulated conductivity, the original data of water simulated conductivity that exceeds the upper and lower limits are removed once. Set the deviation value for abnormal data of water simulation conductivity; Based on the data change value of adjacent data, the data removal results of the first data removal are judged for data removal. If the data change value between the original data of the simulated conductivity of the current data point and the original data of the simulated conductivity of the previous data point is not lower than the deviation value of the abnormal data of the simulated conductivity, the original data of the simulated conductivity of the current data point is determined to be abnormal data of the simulated conductivity, and a second data removal is performed. At the same time, the original data of the simulated conductivity of the previous data point is used to supplement the data. If the result of the secondary data removal of the current data point is not lower than the mean of the preset neighborhood, then the result of the secondary data removal of the current data point is subjected to a third data removal, and the data is supplemented according to the algorithm of the preset neighborhood mean.

[0009] Furthermore, in the above method for removing and supplementing abnormal water conductivity data, the data from the previous data point is used first for data supplementation. If the data from the previous data point is abnormal water conductivity data, the data is directly supplemented according to the preset neighborhood mean algorithm.

[0010] Furthermore, in the above method for removing and supplementing abnormal water conductivity data, if the processed abnormal water conductivity data is between the upper and lower limits of the data, and the data change value between the processed abnormal water conductivity data and the original water conductivity data of the previous data point is lower than the deviation value of the abnormal water conductivity data and lower than the mean value of the preset neighborhood, then it is determined to meet the data expectation.

[0011] Furthermore, in the above method for removing and supplementing abnormal water conductivity data, a maximum number of reprocessing times is set. If the number of times the abnormal water conductivity data is reprocessed exceeds the maximum number of reprocessing times, the data is removed.

[0012] The main advantages of the technical solution of this invention are as follows: The present invention provides a method for removing and supplementing abnormal data in simulated water conductivity. This method involves acquiring raw simulated water conductivity data and performing data preprocessing to analyze the regularity of the raw data. If a regularity is determined in the raw simulated water conductivity data, data removal and supplementation rules are set, including determining the upper and lower limits of non-abnormal simulated water conductivity data, the deviation value of abnormal simulated water conductivity data, and rules for supplementing abnormal simulated water conductivity data. Abnormal simulated water conductivity data is then judged and processed. The processed abnormal simulated water conductivity data undergoes a data expectation determination. If the processed abnormal simulated water conductivity data meets the data expectation, the data is output; otherwise, the abnormal simulated water conductivity data is reprocessed until it meets the data expectation. By setting data removal and supplementation rules, the present invention effectively removes and supplements abnormal data, improving data analysis efficiency. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for further understanding of the embodiments of the present invention and constitute a part of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings: Figure 1 A flowchart illustrating the method for removing and supplementing abnormal conductivity data in water simulation provided in this embodiment of the invention; Figure 2 A flowchart illustrating the method for removing and supplementing abnormal conductivity data in water simulation provided in this embodiment of the invention; Figure 3 A plot of the raw data of simulated water conductivity provided in an embodiment of the present invention; Figure 4The image provided in this embodiment of the invention shows the result of removing and supplementing abnormal data of water simulated conductivity after removing data from the original data. Figure 5 This is a plotted graph provided by an embodiment of the present invention after removing data from the original data of simulated water conductivity using the quartile spacing method; Figure 6 This is a plotted image provided by an embodiment of the present invention after data removal from the original data of simulated water conductivity using the isolated forest algorithm; Figure 7 This is a plotted graph provided in an embodiment of the method after removing data from the original data of simulated water conductivity using the sliding window mean method; Figure 8 This is a plotted graph provided by an embodiment of the present invention after removing data from the original data of simulated water conductivity using the standard deviation method; Figure 9 This is a schematic diagram illustrating the quantity and removal ratio of abnormal data using different methods, provided for embodiments of the present invention. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0015] refer to Figure 1 The method for removing and supplementing abnormal conductivity data in water simulation provided in this embodiment of the invention includes the following steps: The raw data of simulated water conductivity were obtained and preprocessed to analyze the regularity of the raw data. If it is determined that the original data of water simulated conductivity has a regularity, then data elimination and supplementation rules are set, the upper and lower limits of non-abnormal data of water simulated conductivity, the deviation value of abnormal data of water simulated conductivity, and the supplementation rules for abnormal data of water simulated conductivity are determined, and abnormal data of water simulated conductivity are judged and processed. The abnormal water conductivity data after processing is judged according to the expected data. If the abnormal water conductivity data after processing is judged to meet the expected data, the data is output. Otherwise, the abnormal water conductivity data is processed again until it meets the expected data.

[0016] In this embodiment of the invention, if it is determined that the original data of water simulated conductivity does not exhibit any regularity, the data processing is terminated directly.

[0017] In this embodiment of the invention, the upper and lower limits of non-abnormal data of water simulation conductivity are determined based on the physical principles of water simulation experiments or historical experimental data.

[0018] In this embodiment of the invention, the raw water conductivity data refers to the unprocessed data obtained by a conductivity meter during the water simulation experiment.

[0019] In this embodiment of the invention, data preprocessing refers to the process of performing regularity analysis on the raw data of simulated water conductivity.

[0020] In this embodiment of the invention, the regularity of the raw data of water simulated conductivity refers to the predictable variability of the raw data of water simulated conductivity over time, such as the regularity of steady fluctuations.

[0021] In this embodiment of the invention, the data elimination and supplementation rules refer to the rules for eliminating and supplementing the original data of water simulated conductivity, including the upper and lower limits of non-abnormal data of water simulated conductivity, the deviation value of abnormal data of water simulated conductivity, and the rules for supplementing abnormal data of water simulated conductivity.

[0022] In this embodiment of the invention, non-abnormal data of water simulated conductivity refers to data in the original data of water simulated conductivity that does not contain any abnormal data.

[0023] In this embodiment of the invention, the upper and lower limits of the data refer to the reasonable range of values ​​of the original data of water simulated conductivity during normal experiments.

[0024] For example, the upper limit of non-abnormal data for water simulated conductivity is set at 200, and the lower limit is set at 0.

[0025] In this embodiment of the invention, the deviation value of the water simulated conductivity abnormal data refers to the value used to determine the water simulated conductivity abnormal data.

[0026] For example, the deviation value of abnormal water conductivity data is set to 5. If the change value between the original water conductivity data of the current data point and the original water conductivity data of the previous data point is not lower than the deviation value of abnormal water conductivity data, then the original water conductivity data of the current data point is determined to be abnormal water conductivity data.

[0027] In this embodiment of the invention, abnormal data in simulated water conductivity refers to data in the original simulated water conductivity data that contains abnormalities.

[0028] In this embodiment of the invention, the data is abnormal, for example, exceeding the upper or lower limits of the data, or exceeding the abnormal deviation value of the simulated conductivity of water.

[0029] In this embodiment of the invention, the rule for supplementing abnormal water conductivity data refers to the rule for supplementing data after removing abnormal water conductivity data.

[0030] In this embodiment of the invention, data expectation determination refers to the determination of abnormal data of the processed water simulated conductivity based on data expectation, which is used to ensure that the abnormal data of the processed water simulated conductivity is effectively removed and supplemented.

[0031] In this embodiment of the invention, data expectation refers to the expectation of judging and processing abnormal data of water simulated conductivity.

[0032] In this embodiment of the invention, the analysis of the regularity of the original data of simulated water conductivity includes: The raw data of simulated water conductivity obtained by the conductivity meter are analyzed based on the experience of the testing experts. If the experience of the experts is available, the raw data of simulated water conductivity is analyzed for regularity based on the experience of the experts. If no expert experience is available, the raw data of simulated water conductivity is preprocessed to obtain the mean and standard deviation of the raw data. If the standard deviation is not higher than the acquisition accuracy of the conductivity meter, it is determined that the raw data of simulated water conductivity has a regularity. If the standard deviation is higher than the acquisition accuracy of the conductivity meter, differential sequence analysis is performed on the raw data of water simulated conductivity to obtain the differential sequence and standard deviation of the differential sequence of the raw data of water simulated conductivity, and to determine the relative fluctuation ratio of the raw data of water simulated conductivity. If the relative fluctuation ratio is not higher than the preset fluctuation threshold, it is determined that the original data of water simulation conductivity has regularity. If the relative fluctuation ratio is higher than the preset fluctuation threshold, the autocorrelation function test is performed on the original data of water simulated conductivity to obtain the autocorrelation coefficient of the original data of water simulated conductivity. If the autocorrelation coefficient is not lower than the preset autocorrelation threshold, it is determined that the original data of water simulated conductivity has regularity. If the autocorrelation coefficient is lower than the preset autocorrelation threshold, it is determined that the original data of water simulated conductivity does not have a regularity.

[0033] In this embodiment of the invention, the acquisition accuracy refers to the accuracy of the conductivity meter in acquiring the raw data of the simulated conductivity of water.

[0034] In this embodiment of the invention, differential sequence analysis refers to analyzing the original water conductivity data based on the differential sequence and the standard deviation of the differential sequence, in order to analyze whether there is a regularity in the original water conductivity data.

[0035] In this embodiment of the invention, the relative fluctuation ratio refers to the degree of fluctuation of the original water simulated conductivity data in the time series, which is determined based on the ratio of the standard deviation of the difference series of the original water simulated conductivity data to the standard deviation.

[0036] In this embodiment of the invention, the preset fluctuation threshold refers to a preset threshold used to determine whether there is a regularity in the data trend of the original data of water simulated conductivity.

[0037] In this embodiment of the invention, the autocorrelation function test refers to the test that obtains the autocorrelation coefficient of the original data of simulated water conductivity through the autocorrelation function and then determines whether there is a regularity in the original data of simulated water conductivity. It is used to detect whether there is a regularity in the data within a period.

[0038] In this embodiment of the invention, the preset autocorrelation threshold refers to a preset threshold used to determine whether there is a regularity in the data within a period.

[0039] For example, the autocorrelation coefficient of the raw data of simulated water conductivity is calculated: ; in, The autocorrelation coefficient represents the raw data of simulated water conductivity. Indicates the first Raw data of water simulation conductivity for each data point; express The raw data for the simulated electrical conductivity of water at the next data point; This represents the amount of raw data for the simulated conductivity of water.

[0040] In embodiments of the present invention, such as Figure 2 As shown, the data removal and supplementation rules include: Based on the upper and lower limits of the non-abnormal data of water simulated conductivity, the original data of water simulated conductivity that exceeds the upper and lower limits are removed once. Set the deviation value for abnormal data of water simulation conductivity; Based on the data change value of adjacent data, the data removal results of the first data removal are judged for data removal. If the data change value between the original data of the simulated conductivity of the current data point and the original data of the simulated conductivity of the previous data point is not lower than the deviation value of the abnormal data of the simulated conductivity, the original data of the simulated conductivity of the current data point is determined to be abnormal data of the simulated conductivity, and a second data removal is performed. At the same time, the original data of the simulated conductivity of the previous data point is used to supplement the data. If the result of the secondary data removal of the current data point is not lower than the mean of the preset neighborhood, then the result of the secondary data removal of the current data point is subjected to a third data removal, and the data is supplemented according to the algorithm of the preset neighborhood mean.

[0041] In this embodiment of the invention, a single data removal refers to the data removal process performed on the original data of water simulated conductivity based on the upper and lower limits of the non-abnormal data of water simulated conductivity.

[0042] In this embodiment of the invention, secondary data removal refers to the data removal process performed on the original water simulated conductivity data after primary data removal based on the deviation value of abnormal water simulated conductivity data.

[0043] For example, if the data satisfies Then for Perform data removal and based on right Perform data replacement; in, This indicates the first data removal step. Original data of water simulation conductivity; express The raw data for the simulated electrical conductivity of water at the next data point; .

[0044] In this embodiment of the invention, the preset neighborhood refers to a preset domain of neighboring data points of the current data, such as the area of ​​the previous data points of the current data. Individual, after A domain of data points.

[0045] In this embodiment of the invention, the third data removal refers to further removing data from the second data removal result based on the mean of a preset neighborhood, in order to further ensure the quality of data removal.

[0046] In this embodiment of the invention, the preset neighborhood mean algorithm refers to a preset algorithm used for data supplementation.

[0047] For example, if the data exceeds the previous Individual, after The average of the data, then for Data is removed and the results are calculated based on a preset neighborhood mean algorithm. right Supplement the data; in, ; Used to ensure that there is enough data in the neighborhood; Indicates the previous one in the preset neighborhood The first of them One data point; Indicates the predefined neighborhood after The first of them Data points.

[0048] In this embodiment of the invention, the data from the previous data point is used first for data supplementation. If the data from the previous data point is abnormal data of water simulated conductivity, the data is directly supplemented according to the preset neighborhood mean algorithm.

[0049] In this embodiment of the invention, if the processed abnormal water conductivity data is between the upper and lower limits of the data, and the data change value between the processed abnormal water conductivity data and the original water conductivity data of the previous data point is lower than the deviation value of the abnormal water conductivity data and lower than the mean value of the preset neighborhood, then it is determined that the data meets the expectation.

[0050] In this embodiment of the invention, a maximum number of reprocessing times is set. If the number of times the abnormal conductivity data of water simulation is reprocessed exceeds the maximum number of reprocessing times, the data is discarded.

[0051] In this embodiment of the invention, the maximum number of reprocessing times refers to the maximum number of times the simulated water conductivity abnormal data is reprocessed when the processed data does not meet the expected data.

[0052] For example, refer to Figure 4 , Figure 9 The original water conductivity data was analyzed based on the method of removing and supplementing abnormal data. A total of 9,837 abnormal data were removed, with a removal rate of 40.99%.

[0053] For example, refer to Figure 9 The raw data of simulated water conductivity were analyzed using the quartile interval method, isolated forest algorithm, sliding window mean method, and standard deviation method, and the cumulative number of rejections and rejection ratios for each method were obtained.

[0054] For example, refer to Figure 3 The graph was drawn based on the original data of the simulated conductivity of water.

[0055] For example, refer to Figure 5 The raw data of simulated water conductivity were analyzed using the interquartile range method. Calculate the 25th percentile (Q1 = -202.667 mS / cm) and 75th percentile (Q3 = 28.524 mS / cm) of the data. Calculate the interquartile range: IQR = Q3 - Q1 = 231.191 mS / cm; Set outlier detection boundaries: lower bound = Q1 - 1.5 × IQR = -549.454 mS / cm, upper bound = Q3 + 1.5 × IQR = 375.310 mS / cm; Retain data within the boundaries and remove outliers outside the boundaries.

[0056] For example, refer to Figure 6 The raw data of water conductivity simulation were analyzed using the isolated forest algorithm. Construct an isolated forest model with 100 decision trees to balance accuracy and computational efficiency, and isolate outliers by random splitting; random_state=42 to ensure reproducibility of results; max_samples=auto, the number of samples used per tree, the default is min(256, the total number of samples).

[0057] Calculate the "isolation score" (abnormality level) for each sample; the higher the score, the more likely it is to be an outlier. The "auto" mode automatically calculates the proportion of outliers and marks outlier samples (-1) and normal samples (1). Keep normal samples and remove abnormal samples.

[0058] For example, refer to Figure 7 The raw data of simulated water conductivity were analyzed using the sliding window mean method. Set the sliding window size to 50 to balance local trend capture and smoothness; deviation_threshold=2 to control outlier sensitivity and iterate through all data points. Calculate the local mean (moving mean) and local standard deviation for each window; Set anomaly criteria: the deviation of the data from the moving mean is greater than 2 × local standard deviation; Data with deviations within the threshold are retained, while local outliers exceeding the threshold are removed.

[0059] For example, refer to Figure 8 The raw data of simulated water conductivity were analyzed using the standard deviation method. Set the standard deviation coefficient σ=3; Calculate the overall mean (μ=-24.437mS / cm) and overall standard deviation (σ=277.037 mS / cm) of the data. Set outlier detection boundaries to cover 99.73% of normal data: lower bound = μ - 3σ = -855.544 mS / cm, upper bound = μ + 3σ = 806.670 mS / cm). Retain data within the boundaries and remove extreme outliers outside the boundaries.

[0060] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Additionally, the terms "front," "back," "left," "right," "upper," and "lower" in this document refer to the placement shown in the accompanying drawings.

[0061] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for water analog conductivity anomaly data editing and supplementing, characterized in that, The method comprises the following steps: acquiring water simulation conductivity raw data and performing data preprocessing, and analyzing the regularity of the water simulation conductivity raw data; if it is determined that the water simulation conductivity raw data has regularity, setting data rejection and supplement rules, determining the upper and lower limits of the water simulation conductivity non-anomalous data, the water simulation conductivity anomalous data deviation value, and the water simulation conductivity anomalous data supplement rule, and determining and processing the water simulation conductivity anomalous data; performing data expectation determination on the processed water simulation conductivity anomalous data, if it is determined that the processed water simulation conductivity anomalous data meets the data expectation, performing data output, otherwise, reprocessing the water simulation conductivity anomalous data until the data expectation is met.

2. The method of water analog conductivity anomaly editing and supplementing according to claim 1, wherein, if it is determined that the water simulation conductivity raw data has no regularity, directly ending the data processing.

3. The method of water analog conductivity anomaly editing of claim 1, wherein, The upper and lower limits of the water simulation conductivity non-anomalous data are determined according to the physical principle of the water simulation experiment or historical experimental data.

4. The method of water analog conductivity anomaly editing and filling of claim 1, wherein, The regularity of the water simulation conductivity raw data comprises the following steps: acquiring the water simulation conductivity raw data from the conductivity meter, detecting expert experience, if there is expert experience, analyzing the regularity of the water simulation conductivity raw data according to the expert experience; if there is no expert experience, performing data preprocessing on the water simulation conductivity raw data, acquiring the mean value and the standard deviation of the water simulation conductivity raw data, if the standard deviation is not higher than the collection accuracy of the conductivity meter, it is determined that the water simulation conductivity raw data has regularity; if the standard deviation is higher than the collection accuracy of the conductivity meter, performing difference sequence analysis on the water simulation conductivity raw data, acquiring the difference sequence and the difference sequence standard deviation of the water simulation conductivity raw data, and determining the relative fluctuation ratio of the water simulation conductivity raw data; if the relative fluctuation ratio is not higher than the preset fluctuation threshold, it is determined that the water simulation conductivity raw data has regularity; if the relative fluctuation ratio is higher than the preset fluctuation threshold, performing autocorrelation function test on the water simulation conductivity raw data, and acquiring the autocorrelation coefficient of the water simulation conductivity raw data; if the autocorrelation coefficient is not lower than the preset autocorrelation threshold, it is determined that the water simulation conductivity raw data has regularity; if the autocorrelation coefficient is lower than the preset autocorrelation threshold, it is determined that the water simulation conductivity raw data has no regularity.

5. The method of water analog conductivity anomaly editing and filling of claim 1, wherein, Setting the data rejection and supplement rules comprises the following steps: performing one-time data rejection on the water simulation conductivity raw data that exceeds the upper and lower limits of the water simulation conductivity non-anomalous data according to the upper and lower limits of the water simulation conductivity non-anomalous data; setting the water simulation conductivity anomalous data deviation value; performing data rejection determination on the one-time data rejection result based on the data change value of adjacent data, if the data change value of the current data point and the previous data point of the water simulation conductivity raw data is not lower than the water simulation conductivity anomalous data deviation value, it is determined that the current data point of the water simulation conductivity raw data is water simulation conductivity anomalous data, and secondary data rejection is performed, and the previous data point of the water simulation conductivity raw data is used for data supplement. If the result of the secondary data removal of the current data point is not lower than the mean of the preset neighborhood, then the result of the secondary data removal of the current data point is subjected to a third data removal, and the data is supplemented according to the algorithm of the preset neighborhood mean.

6. The method of water simulation conductivity anomaly data rejection and supplementation according to claim 5, characterized in that, Priority is given to using the data from the previous data point for data supplementation. If the data from the previous data point is abnormal data of water simulation conductivity, then data supplementation is performed directly according to the preset neighborhood mean algorithm.

7. The method of water analog conductivity anomaly editing of claim 1, wherein, If the processed abnormal water conductivity data is between the upper and lower limits of the data, and the change value of the original water conductivity data from the previous data point is lower than the deviation value of the abnormal water conductivity data and lower than the mean value of the preset neighborhood, then it is determined that it meets the data expectation.

8. The method of water analog conductivity anomaly editing and filling of claim 1, wherein, Set a maximum number of reprocessing attempts. If the number of times the abnormal conductivity data of the simulated water is reprocessed exceeds the maximum number of reprocessing attempts, the data will be discarded.

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