Monitoring equipment stationing optimization method and device based on Pearson correlation analysis

Abstract

The invention provides a monitoring equipment stationing optimization method and device based on Pearson correlation analysis. The method comprises the following steps: normalizing a plurality of obtained water quality parameter sequences to obtain a plurality of target water quality parameter sequences; based on the number of the historical water quality parameter values and the historical water quality parameter values in each target water quality parameter sequence, performing Pearson correlation analysis on the multiple target water quality parameter sequences to obtain a correlation coefficient of the Pearson correlation analysis; determining a plurality of significance parameter groups based on the correlation coefficient and a preset coefficient threshold; determining a to-be-optimized parameter based on a sequence result and a fitting rule which are obtained by sequentially arranging the parameters in the saliency parameter group and average data of correlation coefficients of other parameters in the saliency parameter group; the monitoring device corresponding to the to-be-optimized parameter is the to-be-optimized device. In the mode, the redundancy of the parameters is effectively determined, so that the maintenance cost of monitoring equipment is reduced, and the operation efficiency of a sewage treatment plant is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment layout optimization, and in particular to a monitoring equipment layout optimization method and device based on Pearson correlation analysis. Background Art

[0002] The deployment of monitoring equipment in traditional sewage treatment plants usually relies on the experience and judgment of engineers, which may not only lead to the existence of monitoring blind spots, but also waste resources due to unreasonable distribution of monitoring points. With the development of Internet of Things technology and big data, more and more real-time monitoring data are being collected, and how to effectively use this data to optimize monitoring points has become a new challenge.

[0003] In related technologies, the covariance analysis method is usually used to reveal the correlation between parameter levels of different monitoring devices. However, the covariance analysis method not only needs to assume that the data follows a normal distribution, which limits the data type, but also cannot effectively identify the linear correlation between parameters, and thus cannot handle the linear relationship between parameters. Furthermore, it is impossible to determine the monitoring equipment corresponding to the redundant parameters based on the linear relationship between the parameters, which increases the maintenance cost of the monitoring equipment and reduces the operating efficiency of the sewage treatment plant. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a monitoring equipment layout optimization method and device based on Pearson correlation analysis, which can effectively determine the redundancy of parameters, thereby reducing the maintenance cost of monitoring equipment and improving the operating efficiency of sewage treatment plants.

[0005] In a first aspect, an embodiment of the present invention provides a monitoring equipment deployment optimization method based on Pearson correlation analysis, which is applied to a sewage treatment plant, and the method comprises: obtaining a plurality of water quality parameter sequences of the sewage treatment plant; wherein the water quality parameter sequence comprises a plurality of historical water quality parameters obtained at a preset time interval, and each water quality parameter sequence corresponds to a parameter; normalizing the plurality of water quality parameter sequences to obtain a plurality of target water quality parameter sequences; the target water quality parameter sequence comprises a plurality of historical water quality parameter values; if the plurality of target water quality parameter sequences all conform to a normal distribution, then based on the historical water quality parameters in each target water quality parameter sequence, The number of values ​​and the historical water quality parameter values ​​in each target water quality parameter sequence are analyzed, and the multiple target water quality parameter sequences are subjected to Pearson correlation analysis in pairs to obtain the correlation coefficient of the Pearson correlation analysis; multiple significance parameter groups are determined based on the correlation coefficient and a preset coefficient threshold; wherein each significance parameter group includes at least two parameters; based on the sequential results obtained after sequentially arranging the average data of the correlation coefficient between each parameter in each significance parameter group and other parameters in the significance parameter group and the preset fitting regulations, the parameters to be optimized are determined; the monitoring equipment corresponding to the parameters to be optimized is the equipment to be optimized.

[0006] In a preferred embodiment of the present invention, the above-mentioned Pearson correlation analysis is performed on multiple target water quality parameter sequences in pairs based on the number of historical water quality parameter values ​​in each target water quality parameter sequence and the historical water quality parameter values ​​in each target water quality parameter sequence to obtain the correlation coefficient of the Pearson correlation analysis, including: grouping the multiple target water quality parameter sequences in pairs to obtain multiple data groups; each data group contains two groups of historical water quality parameter values; determining the respective average values ​​of the two groups of historical water quality parameter values ​​in each data group; determining the covariance and standard deviation based on the average values ​​and the respective numbers of the two groups of historical water quality parameter values ​​in each data group; and determining the correlation coefficient based on the covariance and the standard deviation.

[0007] In a preferred embodiment of the present invention, the above-mentioned determination of the covariance and standard deviation based on the average value and the respective number of two sets of historical water quality parameter values ​​in each data group includes: determining the covariance based on the average value and the respective number of two sets of historical water quality parameter values ​​in each data group by the following formula: Among them, Cov(X, Y) represents covariance, X represents a set of historical water quality parameter values, Y represents another set of historical water quality parameter values, x i represents the i-th historical water quality parameter value in a set of historical water quality parameter values, Represents the average value of a set of historical water quality parameter values, y i represents the i-th historical water quality parameter value in another set of historical water quality parameter values, represents the average value of another set of historical water quality parameter values, n represents the sum of the number of the two sets of historical water quality parameter values; the standard deviation is determined based on the average value and the number of the two sets of historical water quality parameter values ​​in each data group by the following formula: Among them, s x represents the standard deviation of a set of historical water quality parameter values, S y represents the standard deviation of another set of historical water quality parameter values, x i represents the i-th historical water quality parameter value in a set of historical water quality parameter values, Represents the average value of a set of historical water quality parameter values, y i represents the i-th historical water quality parameter value in another set of historical water quality parameter values, represents the average value of another set of historical water quality parameter values, n1 represents the number of one set of historical water quality parameter values, and n2 represents the number of another set of historical water quality parameter values.

[0008] In a preferred embodiment of the present invention, the above-mentioned determination of the correlation coefficient based on the covariance and the standard deviation includes: determining the correlation coefficient based on the covariance and the standard deviation by the following formula: Among them, r represents the correlation coefficient, Cov(X, Y) represents the covariance, and s xRepresents the standard deviation of a set of historical water quality parameter values, s y represents the standard deviation of another set of historical water quality parameter values, x i represents the i-th historical water quality parameter value in a set of historical water quality parameter values, Represents the average value of a set of historical water quality parameter values, y i represents the i-th historical water quality parameter value in another set of historical water quality parameter values, represents the average value of another set of historical water quality parameter values, and n represents the sum of the two sets of historical water quality parameter values.

[0009] In a preferred embodiment of the present invention, before the above-mentioned normalization processing is performed on multiple water quality parameter sequences to obtain multiple target water quality parameter sequences, the method also includes: cleaning the multiple water quality parameter sequences according to a predetermined cleaning strategy; the cleaning strategy is: filling missing values ​​after eliminating historical water quality parameters that do not meet three times the standard deviation in the water quality parameter sequence.

[0010] In a preferred embodiment of the present invention, the above-mentioned parameter grouping based on the correlation coefficient and the pre-set coefficient threshold obtains multiple significant parameter groups, including: taking two target water quality parameter sequences whose correlation coefficients are greater than the coefficient threshold as relevant groups; if there is a target water quality parameter sequence to be grouped, and the correlation coefficients with the two target water quality parameter sequences are both greater than the coefficient threshold, then the target water quality parameter sequence to be grouped is added to the relevant group to obtain a significant parameter group.

[0011] In a preferred embodiment of the present invention, based on the sequential results obtained after sequentially arranging the average data of the correlation coefficient of each parameter in each significance parameter group with other parameters in the significance parameter group and the pre-set fitting regulations, the parameters to be optimized are determined, including: for each significance parameter group, determining the first average data of the correlation coefficient of the first parameter in the significance parameter group and the correlation coefficient of the first other parameter; the first other parameter is all parameters in the significance parameter group except the first parameter; determining the second average data of the correlation coefficient of the second parameter in the significance parameter group and the correlation coefficient of the second other parameter; the second other parameter is all parameters in the significance parameter group except the second parameter; arranging the first average data and the second average data in order from large to small to obtain a sequential result; determining the parameters to be optimized based on the sequential results and the pre-set fitting regulations, and determining the parameters to be optimized.

[0012] In a second aspect, an embodiment of the present invention further provides a monitoring equipment deployment optimization device based on Pearson correlation analysis for application in a sewage treatment plant, the device comprising: a water quality parameter sequence acquisition module, for acquiring multiple water quality parameter sequences of the sewage treatment plant; wherein the water quality parameter sequence comprises multiple historical water quality parameters acquired at preset time intervals, and each water quality parameter sequence corresponds to one parameter; a normalization processing module, for performing normalization processing on the multiple water quality parameter sequences respectively to obtain multiple target water quality parameter sequences; the target water quality parameter sequence comprises multiple historical water quality parameter values; a correlation analysis module, for, if the multiple target water quality parameter sequences all conform to the normal distribution, then based on the historical values ​​in each target water quality parameter sequence, The number of water quality parameter values ​​and the historical water quality parameter values ​​in each target water quality parameter sequence, and performing Pearson correlation analysis on multiple target water quality parameter sequences in pairs to obtain the correlation coefficient of the Pearson correlation analysis; a significant parameter group determination module is used to determine multiple significant parameter groups based on the correlation coefficient and a preset coefficient threshold; wherein each significant parameter group includes at least two parameters; a parameter determination module to be optimized is used to determine the parameter to be optimized based on the sequence result obtained after sequentially arranging the average data of the correlation coefficient between each parameter in each significant parameter group and other parameters in the significant parameter group and the preset fitting regulations; the monitoring device corresponding to the parameter to be optimized is the device to be optimized.

[0013] In a third aspect, an embodiment of the present invention further provides an electronic device, comprising a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the monitoring equipment layout optimization method based on Pearson correlation analysis of the first aspect mentioned above.

[0014] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the monitoring equipment layout optimization method based on Pearson correlation analysis of the first aspect mentioned above.

[0015] The embodiments of the present invention bring the following beneficial effects:

[0016] The embodiment of the present invention provides a monitoring equipment deployment optimization method and device based on Pearson correlation analysis, by obtaining multiple water quality parameter sequences of a sewage treatment plant; wherein the water quality parameter sequence includes multiple historical water quality parameters obtained at preset time intervals, and each water quality parameter sequence corresponds to a parameter; the multiple water quality parameter sequences are respectively normalized to obtain multiple target water quality parameter sequences; the target water quality parameter sequence includes multiple historical water quality parameter values; if the multiple target water quality parameter sequences all conform to the normal distribution, then based on the number of historical water quality parameter values ​​in each target water quality parameter sequence and the historical water quality parameter values ​​in each target water quality parameter sequence, the multiple target water quality parameter sequences are subjected to Pearson correlation analysis in pairs to obtain the correlation coefficient of the Pearson correlation analysis; multiple significant parameter groups are determined based on the correlation coefficient and a preset coefficient threshold; wherein each significant parameter group includes at least two parameters; based on the sequence result obtained after the average data of the correlation coefficient between each parameter in each significant parameter group and other parameters in the significant parameter group are sequentially arranged and the pre-set fitting regulations, the parameter to be optimized is determined; the monitoring equipment corresponding to the parameter to be optimized is the equipment to be optimized. In this way, the redundancy of parameters is effectively determined, thereby reducing the maintenance cost of monitoring equipment and improving the operating efficiency of the sewage treatment plant.

[0017] Other features and advantages of the present disclosure will be set forth in the following description, or some features and advantages may be inferred or unambiguously determined from the description, or may be learned by implementing the above-mentioned technology of the present disclosure.

[0018] In order to make the above-mentioned objectives, features and advantages of the present disclosure more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 A flowchart of a monitoring equipment deployment optimization method based on Pearson correlation analysis provided by an embodiment of the present invention;

[0021] Figure 2 A flowchart of another monitoring equipment deployment optimization method based on Pearson correlation analysis provided by an embodiment of the present invention;

[0022] Figure 3A structural diagram of a monitoring equipment point optimization device based on Pearson correlation analysis provided by an embodiment of the present invention;

[0023] Figure 4 A schematic diagram of the structure of a monitoring equipment layout optimization device based on Pearson correlation analysis provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are 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.

[0025] The deployment of monitoring equipment in traditional sewage treatment plants usually relies on the experience and judgment of engineers, which may not only lead to the existence of monitoring blind spots, but also waste resources due to unreasonable distribution of monitoring points. With the development of Internet of Things technology and big data, more and more real-time monitoring data are being collected, and how to effectively use this data to optimize monitoring points has become a new challenge.

[0026] In related technologies, the covariance analysis method is usually used to reveal the correlation between parameter levels of different monitoring devices. However, the covariance analysis method not only needs to assume that the data follows a normal distribution, which limits the data type, but also cannot effectively identify the linear correlation between parameters, and thus cannot handle the linear relationship between parameters. Furthermore, it is impossible to determine the monitoring equipment corresponding to the redundant parameters based on the linear relationship between the parameters, which increases the maintenance cost of the monitoring equipment and reduces the operating efficiency of the sewage treatment plant.

[0027] Based on this, an embodiment of the present invention provides a monitoring equipment layout optimization method and device based on Pearson correlation analysis, which can obtain multiple water quality parameter sequences of a sewage treatment plant; wherein the water quality parameter sequence includes multiple historical water quality parameters obtained at preset time intervals, and each water quality parameter sequence corresponds to a parameter; the multiple water quality parameter sequences are respectively normalized to obtain multiple target water quality parameter sequences; the target water quality parameter sequence includes multiple historical water quality parameter values; if the multiple target water quality parameter sequences all conform to the normal distribution, then based on the number of historical water quality parameter values ​​in each target water quality parameter sequence and the historical water quality parameter values ​​in each target water quality parameter sequence, the multiple target water quality parameter sequences are subjected to Pearson correlation analysis in pairs to obtain the correlation coefficient of the Pearson correlation analysis; multiple significant parameter groups are determined based on the correlation coefficient and a preset coefficient threshold; wherein each significant parameter group includes at least two parameters; based on the sequence result obtained after the average data of the correlation coefficient between each parameter in each significant parameter group and other parameters in the significant parameter group are sequentially arranged and the pre-set fitting regulations, the parameter to be optimized is determined; the monitoring equipment corresponding to the parameter to be optimized is the equipment to be optimized. In this way, the redundancy of parameters is effectively determined, thereby reducing the maintenance cost of monitoring equipment and improving the operating efficiency of the sewage treatment plant.

[0028] To facilitate understanding of this embodiment, a monitoring equipment layout optimization method based on Pearson correlation analysis disclosed in an embodiment of the present invention is first introduced in detail.

[0029] Example 1

[0030] The embodiment of the present invention provides a monitoring equipment deployment optimization method based on Pearson correlation analysis. Figure 1 The flowchart of a monitoring equipment deployment optimization method based on Pearson correlation analysis provided by an embodiment of the present invention. Figure 1 As shown, the monitoring equipment layout optimization method based on Pearson correlation analysis may include the following steps:

[0031] Step S101, obtaining multiple water quality parameter sequences of a sewage treatment plant.

[0032] The water quality parameter sequence includes a plurality of historical water quality parameters acquired at preset time intervals, and each water quality parameter sequence corresponds to one parameter.

[0033] Among them, historical water quality parameters may include: inlet chemical oxygen demand, ammonia nitrogen, total nitrogen, total phosphorus, suspended solids, biochemical pool dissolved oxygen, biochemical pool suspended solids, biochemical pool aeration volume, transmembrane pressure difference, and transmembrane flow.

[0034] Step S102: normalize the multiple water quality parameter sequences respectively to obtain multiple target water quality parameter sequences.

[0035] Among them, the target water quality parameter sequence includes multiple historical water quality parameter values.

[0036] Furthermore, before normalizing multiple water quality parameter sequences to obtain multiple target water quality parameter sequences, it can also include: cleaning the multiple water quality parameter sequences according to a predetermined cleaning strategy; the cleaning strategy is: filling missing values ​​after eliminating historical water quality parameters that do not meet three times the standard deviation in the water quality parameter sequence.

[0037] Specifically, historical water quality parameters that do not meet three times the standard deviation in the water quality parameter sequence can be eliminated to obtain multiple target historical water quality parameters.

[0038] Among them, the standard deviation range is the range of plus or minus 3 times the average value of historical water quality parameters. The calculation of the standard deviation is the conventional standard deviation calculation formula. After calculating the standard deviation of each historical water quality parameter, the historical water quality parameters outside the range of plus or minus 3 times the standard deviation of the average value are eliminated.

[0039] The calculation formula of standard deviation can be:

[0040] Where s represents the standard deviation, N represents the number of time points, and x i represents the historical water quality parameter at the i-th time point in the water quality parameter sequence, represents the average value of historical water quality parameters at all time points, Represents the square of the difference between the historical water quality parameter and the average value at each time point.

[0041] Specifically, in, represents the average value of the historical water quality parameters at all time points, N represents the number of time points, x i Represents the historical water quality parameters at the i-th time point in the water quality parameter sequence.

[0042] Among them, based on the distance between adjacent target historical water quality parameters, missing values ​​can be filled in according to a preset linear missing value filling rule to obtain an initial water quality parameter sequence, and then the initial water quality parameter sequence is normalized to obtain a target water quality parameter sequence.

[0043] Among them, the linear filling missing value rule can be:

[0044] If the distance characterizing the target historical water quality parameter has non-missing values ​​only on the left side, the values ​​on the left are used to fill the missing values; if the distance characterizing the target historical water quality parameter has non-missing values ​​only on the right side, the values ​​on the right are used to fill the missing values; if the distance characterizing the target historical water quality parameter has non-missing values ​​on both sides, linear interpolation is performed based on the non-missing values ​​on both sides to obtain the estimated value of the missing value, so as to fill the missing value with the estimated value.

[0045] Specifically, the estimated value is calculated using the following formula:

[0046] Among them, y i Represents the estimated value of the missing value; t0 represents the position of the non-missing value on the left, and its corresponding value is x0; t1 represents the position of the non-missing value on the right, and its corresponding value is x1.

[0047] Among them, for normalization processing, the maximum and minimum values ​​of the values ​​in the initial water quality parameter sequence can be determined, and then the normalization processing is performed based on the maximum, minimum and original values ​​of the data in the initial water quality parameter sequence by the following formula:

[0048] Step S103, if the multiple target water quality parameter sequences all conform to the normal distribution, then based on the number of historical water quality parameter values ​​in each target water quality parameter sequence and the historical water quality parameter values ​​in each target water quality parameter sequence, the multiple target water quality parameter sequences are subjected to Pearson correlation analysis in pairs to obtain the correlation coefficient of the Pearson correlation analysis.

[0049] Among them, the kstest function can be imported into python to perform a Kolmogorov-Smirnov normal distribution test on each target water quality parameter sequence to determine whether the target water quality parameter sequence conforms to the normal distribution.

[0050] Among them, the target water quality parameter sequence that meets the Pearson correlation analysis can be screened out based on the number of historical water quality parameter values ​​in each target water quality parameter sequence and the historical water quality parameter values ​​in each target water quality parameter sequence.

[0051] Specifically, the same value ratio corresponding to each target detection data sequence can be determined based on multiple historical water quality parameter values ​​and the number of historical water quality parameter values; and screening is performed based on the same value ratio and a preset ratio threshold.

[0052] Specifically, determining the proportion of identical values ​​corresponding to each target detection data sequence based on multiple historical water quality parameter values ​​and the number of historical water quality parameter values ​​may include: determining the number of data with the same numerical value in multiple historical water quality parameter values; if the number of historical water quality parameter values ​​is less than a preset quantity threshold, then taking the ratio of the number of data to the number of historical water quality parameter values ​​as the proportion of identical values.

[0053] The quantity threshold may be 1000.

[0054] Specifically, if the proportion of identical values ​​corresponding to each target detection data sequence is not greater than a preset proportion threshold, the Spearman rank correlation analysis is met.

[0055] The ratio threshold may be 20%.

[0056] Step S104: determining a plurality of significance parameter groups based on the correlation coefficient and a preset coefficient threshold.

[0057] Each significance parameter group includes at least two parameters.

[0058] Specifically, grouping parameters based on the correlation coefficient and a pre-set coefficient threshold to obtain multiple significant parameter groups can include: taking two target water quality parameter sequences whose correlation coefficients are greater than the coefficient threshold as relevant groups; if there is a target water quality parameter sequence to be grouped, and its correlation coefficients with the two target water quality parameter sequences are greater than the coefficient threshold, then adding the target water quality parameter sequence to be grouped to the relevant group to obtain a significant parameter group.

[0059] The correlation coefficient threshold may be 0.8.

[0060] For example, the first target water quality parameter sequence and the second target water quality parameter sequence whose correlation coefficients are greater than the coefficient threshold are taken as related groups. If the correlation coefficients based on the Pearson correlation analysis corresponding to the first target water quality parameter sequence and the third target water quality parameter sequence are greater than the correlation coefficient threshold, and the correlation coefficients based on the Pearson correlation analysis corresponding to the second target water quality parameter sequence and the third target water quality parameter sequence are greater than the correlation coefficient threshold, then the third target water quality parameter sequence is added to the related groups as a group of significant parameter groups.

[0061] Step S105, determining the parameters to be optimized based on the order results obtained by sequentially arranging the average data of the correlation coefficients of each parameter in each significant parameter group with other parameters in the significant parameter group and the preset fitting regulations.

[0062] Among them, the monitoring device corresponding to the parameter to be optimized is the device to be optimized.

[0063] Specifically, based on the sequential results obtained after sequentially arranging the average data of the correlation coefficients of each parameter in each significance parameter group with other parameters in the significance parameter group and the pre-set fitting regulations, determining the parameters to be optimized can include: for each significance parameter group, determining the first average data of the correlation coefficient of the first parameter in the significance parameter group and the correlation coefficient of the first other parameter; the first other parameter is all parameters in the significance parameter group except the first parameter; determining the second average data of the correlation coefficient of the second parameter in the significance parameter group and the correlation coefficient of the second other parameter; the second other parameter is all parameters in the significance parameter group except the second parameter; arranging the first average data and the second average data in order from large to small to obtain the sequential results; determining the parameters to be optimized based on the sequential results and the pre-set fitting regulations, and determining the parameters to be optimized.

[0064] For example, in a significance parameter group, there are 4 parameters, namely parameter A, parameter B, parameter C and parameter D. The correlation coefficient between parameter A and parameter B is 0.9, the correlation coefficient between parameter A and parameter C is 0.91, the correlation coefficient between parameter A and parameter D is 0.92, the correlation coefficient between parameter B and parameter C is 0.86, the correlation coefficient between parameter B and parameter D is 0.88, and the correlation coefficient between parameter C and parameter D is 0.85. Then, for the first parameter being parameter A, the first average data is the result of (0.9+0.91+0.92) / 3, for the second parameter being parameter B, the second average data is the result of (0.9+0.86+0.88) / 3, and the second parameter being C and parameter D is similar. The obtained results are then sorted to obtain the sequential results.

[0065] It should be noted that the order results obtained by arranging the results actually represent the arrangement order of the parameters corresponding to the results. In the subsequent parameter fitting, the parameters are fitted based on the arrangement order.

[0066] Wherein, the parameters to be optimized are determined based on the sequential results and the pre-set fitting regulations, and the determination of the parameters to be optimized may include: fitting the first-ranked parameter in the sequential results with the parameters of the parameter group with the same significance after the first ranking in the order corresponding to the sequential results, to obtain a first fitting result; if the first fitting result contains a third parameter whose coefficient of determination with the first-ranked parameter is greater than a pre-set coefficient threshold, the third parameter is used as the parameter to be optimized and the third parameter is eliminated from the sequential results; fitting the next-ranked parameter in the sequential results with the parameters of the parameter group with the same significance after the next ranking in the order corresponding to the sequential results after the third parameter is eliminated, to obtain a second fitting result; if the second fitting result contains a fourth parameter whose coefficient of determination with the next-ranked parameter is greater than the pre-set coefficient threshold, the fourth parameter is used as the parameter to be optimized and the fourth parameter is eliminated from the sequential results after the third parameter is eliminated.

[0067] Among them, a scatter plot can be drawn for multiple monitoring data sequences based on the order corresponding to the sequential results, so as to determine whether the multiple monitoring data sequences are linearly correlated through the scatter plot. If they are linearly correlated, the parameter fitting is carried out by using the linear function in the sklearn.linear_model toolbox imported by python, including but not limited to simple linear regression.

[0068] The monitoring equipment deployment optimization method based on Pearson correlation analysis provided by the embodiment of the present invention can be obtained by obtaining multiple water quality parameter sequences of a sewage treatment plant; wherein the water quality parameter sequence includes multiple historical water quality parameters obtained at preset time intervals, and each water quality parameter sequence corresponds to a parameter; the multiple water quality parameter sequences are respectively normalized to obtain multiple target water quality parameter sequences; the target water quality parameter sequence includes multiple historical water quality parameter values; if the multiple target water quality parameter sequences all conform to the normal distribution, then based on the number of historical water quality parameter values ​​in each target water quality parameter sequence and the historical water quality parameter values ​​in each target water quality parameter sequence, the multiple target water quality parameter sequences are subjected to Pearson correlation analysis in pairs to obtain the correlation coefficient of the Pearson correlation analysis; multiple significant parameter groups are determined based on the correlation coefficient and a preset coefficient threshold; wherein each significant parameter group includes at least two parameters; based on the sequence result obtained after sequentially arranging the average data of the correlation coefficient between each parameter in each significant parameter group and other parameters in the significant parameter group and the preset fitting regulations, the parameter to be optimized is determined; the monitoring equipment corresponding to the parameter to be optimized is the equipment to be optimized. In this way, the redundancy of parameters is effectively determined, thereby reducing the maintenance cost of monitoring equipment and improving the operating efficiency of the sewage treatment plant.

[0069] Example 2

[0070] An embodiment of the present invention also provides another monitoring equipment layout optimization method based on Pearson correlation analysis; this method is implemented on the basis of the method in the above embodiment; this method focuses on describing the specific implementation method of performing Pearson correlation analysis on multiple target water quality parameter sequences in pairs based on the number of historical water quality parameter values ​​in each target water quality parameter sequence and the historical water quality parameter values ​​in each target water quality parameter sequence to obtain the correlation coefficient of the Pearson correlation analysis.

[0071] Figure 2 A flowchart of another monitoring equipment deployment optimization method based on Pearson correlation analysis provided by an embodiment of the present invention is shown in FIG. Figure 2 As shown, based on the number of historical water quality parameter values ​​in each target water quality parameter sequence and the historical water quality parameter values ​​in each target water quality parameter sequence, multiple target water quality parameter sequences are subjected to Pearson correlation analysis in pairs to obtain the correlation coefficient of the Pearson correlation analysis, which may include the following steps:

[0072] Step S201, grouping multiple target water quality parameter sequences in pairs to obtain multiple data groups.

[0073] Each data set contains two sets of historical water quality parameter values.

[0074] Step S202, determining the respective average values ​​of two groups of historical water quality parameter values ​​in each data group.

[0075] The sum result is obtained by adding up the historical water quality parameter values, and the sum result is divided by the number of historical water quality parameter values ​​to obtain the average value.

[0076] Step S203, determining the covariance and standard deviation based on the mean value and the respective numbers of the two groups of historical water quality parameter values ​​in each data group.

[0077] Specifically, determining the covariance and standard deviation based on the mean value and the respective numbers of two sets of historical water quality parameter values ​​in each data set may include:

[0078] The covariance is determined based on the mean and the number of each of the two sets of historical water quality parameter values ​​in each data set using the following formula:

[0079] Among them, Cov(X, Y) represents covariance, X represents a set of historical water quality parameter values, Y represents another set of historical water quality parameter values, x i represents the i-th historical water quality parameter value in a set of historical water quality parameter values, Represents the average value of a set of historical water quality parameter values, y i represents the i-th historical water quality parameter value in another set of historical water quality parameter values, represents the average value of another set of historical water quality parameter values, and n represents the sum of the two sets of historical water quality parameter values.

[0080] The standard deviation is determined based on the mean and the number of each of the two sets of historical water quality parameter values ​​in each data set using the following formula:

[0081] Among them, S x Represents the standard deviation of a set of historical water quality parameter values, s y represents the standard deviation of another set of historical water quality parameter values, x i represents the i-th historical water quality parameter value in a set of historical water quality parameter values, Represents the average value of a set of historical water quality parameter values, y i represents the i-th historical water quality parameter value in another set of historical water quality parameter values, represents the average value of another set of historical water quality parameter values, n1 represents the number of one set of historical water quality parameter values, and n2 represents the number of another set of historical water quality parameter values.

[0082] Step S204: determining a correlation coefficient based on the covariance and the standard deviation.

[0083] Specifically, determining the correlation coefficient based on the covariance and the standard deviation may include:

[0084] The correlation coefficient is determined based on the covariance and standard deviation using the following formula:

[0085] Among them, r represents the correlation coefficient, Cov(X, Y) represents the covariance, S x represents the standard deviation of a set of historical water quality parameter values, S y represents the standard deviation of another set of historical water quality parameter values, x i represents the i-th historical water quality parameter value in a set of historical water quality parameter values, Represents the average value of a set of historical water quality parameter values, y i represents the i-th historical water quality parameter value in another set of historical water quality parameter values, represents the average value of another set of historical water quality parameter values, and n represents the sum of the two sets of historical water quality parameter values.

[0086] Example 3

[0087] Corresponding to the above method embodiment, the embodiment of the present invention provides a monitoring equipment point optimization device based on Pearson correlation analysis, Figure 3 A schematic diagram of a monitoring equipment layout optimization device based on Pearson correlation analysis provided by an embodiment of the present invention is shown in FIG. Figure 3 As shown, the monitoring equipment layout optimization device based on Pearson correlation analysis may include:

[0088] The water quality parameter sequence acquisition module 301 is used to acquire multiple water quality parameter sequences of the sewage treatment plant; wherein the water quality parameter sequence includes multiple historical water quality parameters acquired at preset time intervals, and each water quality parameter sequence corresponds to one parameter.

[0089] The normalization processing module 302 is used to perform normalization processing on multiple water quality parameter sequences to obtain multiple target water quality parameter sequences; the target water quality parameter sequences include multiple historical water quality parameter values.

[0090] The correlation analysis module 303 is used to perform Pearson correlation analysis on the multiple target water quality parameter sequences in pairs based on the number of historical water quality parameter values ​​in each target water quality parameter sequence and the historical water quality parameter values ​​in each target water quality parameter sequence if the multiple target water quality parameter sequences all conform to the normal distribution, and obtain the correlation coefficient of the Pearson correlation analysis.

[0091] The significance parameter group determination module 304 is used to determine a plurality of significance parameter groups based on the correlation coefficient and a preset coefficient threshold; wherein each significance parameter group includes at least two parameters.

[0092] The module 305 for determining the parameters to be optimized is used to determine the parameters to be optimized based on the sequence results obtained after sequentially arranging the average data of the correlation coefficients between each parameter in each significance parameter group and other parameters in the significance parameter group and the pre-set fitting regulations; the monitoring device corresponding to the parameter to be optimized is the device to be optimized.

[0093] The monitoring equipment deployment optimization device based on Pearson correlation analysis provided by the embodiment of the present invention can obtain multiple water quality parameter sequences of a sewage treatment plant; wherein the water quality parameter sequence includes multiple historical water quality parameters obtained at preset time intervals, and each water quality parameter sequence corresponds to a parameter; the multiple water quality parameter sequences are respectively normalized to obtain multiple target water quality parameter sequences; the target water quality parameter sequence includes multiple historical water quality parameter values; if the multiple target water quality parameter sequences all conform to the normal distribution, then based on the number of historical water quality parameter values ​​in each target water quality parameter sequence and the historical water quality parameter values ​​in each target water quality parameter sequence, the multiple target water quality parameter sequences are subjected to Pearson correlation analysis in pairs to obtain the correlation coefficient of the Pearson correlation analysis; multiple significant parameter groups are determined based on the correlation coefficient and a preset coefficient threshold; wherein each significant parameter group includes at least two parameters; based on the sequence result obtained after the average data of the correlation coefficient between each parameter in each significant parameter group and other parameters in the significant parameter group are sequentially arranged and the pre-set fitting regulations, the parameter to be optimized is determined; the monitoring equipment corresponding to the parameter to be optimized is the equipment to be optimized. In this way, the redundancy of parameters is effectively determined, thereby reducing the maintenance cost of monitoring equipment and improving the operating efficiency of the sewage treatment plant.

[0094] In some embodiments, the correlation analysis module is also used to group multiple target water quality parameter sequences in pairs to obtain multiple data groups; each data group contains two groups of historical water quality parameter values; determine the average value of the two groups of historical water quality parameter values ​​in each data group; determine the covariance and standard deviation based on the average value and the respective number of the two groups of historical water quality parameter values ​​in each data group; determine the correlation coefficient based on the covariance and standard deviation.

[0095] In some embodiments, the correlation analysis module is further used to determine the covariance based on the average value and the respective numbers of the two sets of historical water quality parameter values ​​in each data group by the following formula: Among them, Cov(X, Y) represents covariance, X represents a set of historical water quality parameter values, Y represents another set of historical water quality parameter values, x i represents the i-th historical water quality parameter value in a set of historical water quality parameter values, Represents the average value of a set of historical water quality parameter values, y i represents the i-th historical water quality parameter value in another set of historical water quality parameter values, represents the average value of another set of historical water quality parameter values, n represents the sum of the number of the two sets of historical water quality parameter values; the standard deviation is determined based on the average value and the number of the two sets of historical water quality parameter values ​​in each data group by the following formula: Among them, S x Represents the standard deviation of a set of historical water quality parameter values, s yrepresents the standard deviation of another set of historical water quality parameter values, x i represents the i-th historical water quality parameter value in a set of historical water quality parameter values, Represents the average value of a set of historical water quality parameter values, y i represents the i-th historical water quality parameter value in another set of historical water quality parameter values, represents the average value of another set of historical water quality parameter values, n1 represents the number of one set of historical water quality parameter values, and n2 represents the number of another set of historical water quality parameter values.

[0096] In some embodiments, the correlation analysis module is further used to determine the correlation coefficient based on the covariance and the standard deviation by the following formula: Among them, r represents the correlation coefficient, Cov(X, Y) represents the covariance, S x represents the standard deviation of a set of historical water quality parameter values, S y represents the standard deviation of another set of historical water quality parameter values, x i represents the i-th historical water quality parameter value in a set of historical water quality parameter values, Represents the average value of a set of historical water quality parameter values, y i represents the i-th historical water quality parameter value in another set of historical water quality parameter values, represents the average value of another set of historical water quality parameter values, and n represents the sum of the two sets of historical water quality parameter values.

[0097] In some embodiments, the normalization processing module is also used to clean multiple water quality parameter sequences according to a pre-set cleaning strategy; the cleaning strategy is: remove historical water quality parameters that do not meet three times the standard deviation in the water quality parameter sequence and then fill in missing values.

[0098] In some embodiments, the significance parameter group determination module is also used to treat two target water quality parameter sequences whose correlation coefficients are greater than a coefficient threshold as relevant groups; if there is a target water quality parameter sequence to be grouped, and its correlation coefficients with the two target water quality parameter sequences are both greater than the coefficient threshold, then the target water quality parameter sequence to be grouped is added to the relevant group to obtain a significance parameter group.

[0099] In some embodiments, the module for determining parameters to be optimized is also used to determine, for each significance parameter group, a first average data of a correlation coefficient between a first parameter in the significance parameter group and a correlation coefficient between a first other parameter; the first other parameter is all parameters in the significance parameter group except the first parameter; determine a second average data of a correlation coefficient between a second parameter in the significance parameter group and a correlation coefficient between a second other parameter; the second other parameter is all parameters in the significance parameter group except the second parameter; arrange the first average data and the second average data in order from large to small to obtain a sequential result; determine the parameters to be optimized based on the sequential result and a pre-set fitting regulation, and determine the parameters to be optimized.

[0100] The device provided in the embodiment of the present invention has the same implementation principle and technical effects as those of the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference may be made to the corresponding contents in the aforementioned method embodiment.

[0101] Example 4

[0102] The embodiment of the present invention further provides an electronic device for executing the above-mentioned monitoring equipment deployment optimization method based on Pearson correlation analysis; Figure 4 A structural schematic diagram of an electronic device is shown, which includes a memory 400 and a processor 401, wherein the memory 400 is used to store one or more computer instructions, and the one or more computer instructions are executed by the processor 401 to implement the above-mentioned monitoring equipment layout optimization method based on Pearson correlation analysis.

[0103] Further, Figure 4 The electronic device shown further includes a bus 402 and a communication interface 403 , and the processor 401 , the communication interface 403 and the memory 400 are connected via the bus 402 .

[0104] The memory 400 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk storage. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 403 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used. The bus 402 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0105] The processor 401 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 401. The above processor 401 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in conjunction with the embodiments of the present invention can be directly embodied as a hardware decoding processor for execution, or a combination of hardware and software modules in the decoding processor for execution. The software module may be located in a storage medium mature in the art, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 400, and the processor 401 reads the information in the memory 400 and completes the steps of the method of the above embodiment in combination with its hardware.

[0106] An embodiment of the present invention also provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the above-mentioned monitoring equipment layout optimization method based on Pearson correlation analysis. The specific implementation can be found in the method embodiment, which will not be repeated here.

[0107] The computer program product for the monitoring equipment layout optimization method based on Pearson correlation analysis provided in the embodiment of the present invention includes a computer-readable storage medium storing a non-volatile program code executable by a processor, and the instructions included in the program code can be used to execute the method described in the previous method embodiment. The specific implementation can be found in the method embodiment, which will not be repeated here.

[0108] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0109] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0110] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0111] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0112] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0113] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-mentioned embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-mentioned embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A monitoring equipment layout optimization method based on Pearson correlation analysis, characterized in that: Applied to a sewage treatment plant, the method comprises: Acquire multiple water quality parameter sequences of the sewage treatment plant; wherein the water quality parameter sequences include multiple historical water quality parameters acquired at preset time intervals, and each of the water quality parameter sequences corresponds to one parameter; Normalizing the plurality of water quality parameter sequences respectively to obtain a plurality of target water quality parameter sequences; the target water quality parameter sequences include a plurality of historical water quality parameter values; If the plurality of target water quality parameter sequences all conform to the normal distribution, then based on the number of historical water quality parameter values ​​in each of the target water quality parameter sequences and the historical water quality parameter values ​​in each of the target water quality parameter sequences, the plurality of target water quality parameter sequences are subjected to Pearson correlation analysis in pairs to obtain a correlation coefficient of the Pearson correlation analysis; Determining a plurality of significance parameter groups based on the correlation coefficient and a preset coefficient threshold; wherein each of the significance parameter groups includes at least two parameters; Based on the sequential results obtained by sequentially arranging the average data of the correlation coefficients of each parameter in each of the significant parameter groups with other parameters in the significant parameter group and the pre-set fitting regulations, the parameters to be optimized are determined; the monitoring equipment corresponding to the parameters to be optimized is the equipment to be optimized.

2. The method according to claim 1, characterized in that Based on the number of historical water quality parameter values ​​in each target water quality parameter sequence and the historical water quality parameter value in each target water quality parameter sequence, performing Pearson correlation analysis on multiple target water quality parameter sequences in pairs to obtain a correlation coefficient of the Pearson correlation analysis includes: Grouping the plurality of target water quality parameter sequences in pairs to obtain a plurality of data groups; each of the data groups includes two sets of historical water quality parameter values; determining an average of each of the two sets of historical water quality parameter values ​​in each of the data sets; determining a covariance and a standard deviation based on the mean and the respective numbers of two sets of historical water quality parameter values ​​in each of the data sets; The correlation coefficient is determined based on the covariance and the standard deviation.

3. The method according to claim 2, characterized in that The determining of the covariance and the standard deviation based on the mean value and the respective numbers of the two sets of historical water quality parameter values ​​in each of the data sets comprises: The covariance is determined based on the mean and the respective number of two sets of historical water quality parameter values ​​in each of the data sets by the following formula: Among them, Cov(X, Y) represents covariance, X represents a set of historical water quality parameter values, Y represents another set of historical water quality parameter values, x i represents the i-th historical water quality parameter value in a set of historical water quality parameter values, Represents the average value of a set of historical water quality parameter values, y i represents the i-th historical water quality parameter value in another set of historical water quality parameter values, represents the average value of another set of historical water quality parameter values, and n represents the sum of the numbers of the two sets of historical water quality parameter values; The standard deviation is determined based on the mean and the respective number of two sets of historical water quality parameter values ​​in each of the data sets by the following formula: Among them, S x represents the standard deviation of a set of historical water quality parameter values, S y represents the standard deviation of another set of historical water quality parameter values, x i represents the i-th historical water quality parameter value in a set of historical water quality parameter values, Represents the average value of a set of historical water quality parameter values, y i represents the i-th historical water quality parameter value in another set of historical water quality parameter values, represents the average value of another set of historical water quality parameter values, n1 represents the number of one set of historical water quality parameter values, and n2 represents the number of another set of historical water quality parameter values.

4. The method according to claim 2, characterized in that: The determining the correlation coefficient based on the covariance and the standard deviation comprises: The correlation coefficient is determined based on the covariance and the standard deviation by the following formula: Among them, r represents the correlation coefficient, Cov(X, Y) represents the covariance, S x represents the standard deviation of a set of historical water quality parameter values, S y represents the standard deviation of another set of historical water quality parameter values, x i represents the i-th historical water quality parameter value in a set of historical water quality parameter values, Represents the average value of a set of historical water quality parameter values, y i represents the i-th historical water quality parameter value in another set of historical water quality parameter values, represents the average value of another set of historical water quality parameter values, and n represents the sum of the two sets of historical water quality parameter values.

5. The method according to claim 1, characterized in that Before respectively normalizing the plurality of water quality parameter sequences to obtain a plurality of target water quality parameter sequences, the method further includes: Cleaning the plurality of water quality parameter sequences respectively according to a pre-set cleaning strategy; The cleaning strategy is: remove the historical water quality parameters that do not meet three times the standard deviation in the water quality parameter sequence and then fill in the missing values.

6. The method according to claim 5, characterized in that The parameter grouping based on the correlation coefficient and the preset coefficient threshold to obtain a plurality of significance parameter groups includes: The two target water quality parameter sequences whose correlation coefficient is greater than the coefficient threshold are taken as related groups; If there is a target water quality parameter sequence to be grouped, and the correlation coefficients with the two target water quality parameter sequences are both greater than the coefficient threshold, the target water quality parameter sequence to be grouped is added to the related group to obtain the significance parameter group.

7. The method according to claim 1, characterized in that The determining of the parameters to be optimized based on the sequence results obtained by sequentially arranging the average data of the correlation coefficients of each parameter in each of the significant parameter groups with other parameters in the significant parameter group and the pre-set fitting regulations comprises: For each significance parameter group, determining first average data of a correlation coefficient of a first parameter in the significance parameter group and a correlation coefficient of a first other parameter; the first other parameter is all parameters in the significance parameter group except the first parameter; Determining second average data of the correlation coefficient of the second parameter in the significance parameter group and the correlation coefficient of the second other parameter; the second other parameter is all parameters in the significance parameter group except the second parameter; Arrange the first average data and the second average data in descending order to obtain a sequence result; The parameters to be optimized are determined based on the sequence results and the preset fitting regulations.

8. A monitoring equipment layout optimization device based on Pearson correlation analysis, characterized in that: Applied to a sewage treatment plant, the device comprises: A water quality parameter sequence acquisition module, used to acquire multiple water quality parameter sequences of the sewage treatment plant; wherein the water quality parameter sequence includes multiple historical water quality parameters acquired at preset time intervals, and each of the water quality parameter sequences corresponds to one parameter; A normalization processing module, used for performing normalization processing on the plurality of water quality parameter sequences to obtain a plurality of target water quality parameter sequences; the target water quality parameter sequences include a plurality of historical water quality parameter values; A correlation analysis module, for performing Pearson correlation analysis on the plurality of target water quality parameter sequences in pairs based on the number of historical water quality parameter values ​​in each target water quality parameter sequence and the historical water quality parameter value in each target water quality parameter sequence if the plurality of target water quality parameter sequences all conform to a normal distribution, to obtain a correlation coefficient of the Pearson correlation analysis; A significance parameter group determination module, used to determine a plurality of significance parameter groups based on the correlation coefficient and a preset coefficient threshold; wherein each of the significance parameter groups includes at least two parameters; The module for determining parameters to be optimized is used to determine the parameters to be optimized based on the sequence results obtained after sequentially arranging the average data of the correlation coefficients of each parameter in each of the significant parameter groups with other parameters in the significant parameter group and the pre-set fitting regulations; the monitoring device corresponding to the parameter to be optimized is the device to be optimized.

9. An electronic device, characterized in that: It includes a processor and a memory, the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the monitoring equipment layout optimization method based on Pearson correlation analysis as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are called and executed by the processor, the computer-executable instructions prompt the processor to implement the monitoring equipment layout optimization method based on Pearson correlation analysis as described in any one of claims 1 to 7.

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