Data correlation analysis method and system, readable storage medium and computer

Abstract

The invention provides a data correlation analysis method and system, a readable storage medium and a computer, and the method comprises the steps: carrying out the filtering and smoothing of monitoring data, and carrying out the data aggregation of the monitoring data after the filtering and smoothing, and obtaining an optimized data set; grouping the monitoring data according to a pairwise combination mode to obtain grouped data; performing data correlation analysis on the grouped data to obtain a corresponding data correlation analysis result; sorting according to the data correlation analysis result to obtain ranking differences of the grouped data, and calculating Spearman level correlation coefficients corresponding to the grouped data according to the ranking differences of the grouped data; and performing correlation judgment on the corresponding grouped data according to each Spearman level correlation coefficient to output a data judgment result. According to the method, relevance judgment can be performed on a pair of data, unified processing and analysis can be performed on multiple items of data, and powerful decision support is provided for data analysts.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a data correlation analysis method, system, readable storage medium and computer. Background Art

[0002] With the development of the data monitoring industry, the amount of data generated by monitoring equipment is increasing. Among many different monitoring items, the hidden relationships between data items can be discovered through association analysis. By analyzing the correlation between the data, it helps to determine whether the changes in the monitoring factor data are affected by external factors.

[0003] At present, when performing correlation analysis on large quantities of data, the existence of individual abnormal data points often leads to the inaccuracy of the analysis results of the overall data. In addition, when performing correlation analysis on large quantities of data, most of the data correlation analysis relies on the data within the device. When performing correlation analysis on the data of two devices under different monitoring devices, the missing data of different devices and the inconsistent collection granularity will lead to large deviations in the correlation analysis results. Summary of the invention Based on this, the purpose of the present invention is to provide a data correlation analysis method, system, readable storage medium and computer to solve the deficiencies in the above-mentioned technology.

[0004] The present invention proposes a data correlation analysis method, comprising: Acquire a number of monitoring data, perform filtering and smoothing processing on each of the monitoring data, and perform data aggregation on the monitoring data after filtering and smoothing processing to obtain an optimized data set; Grouping the monitoring data in pairs to obtain corresponding grouped data; Performing data correlation analysis on each of the grouped data to obtain corresponding data correlation analysis results; Sorting the data according to the data correlation analysis results to obtain the ranking difference of each grouping data, and calculating the Spearman rank correlation coefficient corresponding to each grouping data according to the ranking difference of each grouping data; According to each of the Spearman rank correlation coefficients, correlation judgment is performed on the corresponding grouped data to output a data judgment result.

[0005] Furthermore, the step of filtering and smoothing each of the monitoring data includes: Performing Z-score statistics on each of the monitoring data to perform filtering and screening processing to obtain the mean and standard deviation of each of the monitoring data; The Z score of each observation value of each monitoring data is calculated, and the obtained Z score is compared with a preset score threshold to screen out monitoring data that meets the requirements.

[0006] Furthermore, the calculation formula for the mean of each monitoring data is: ; In the formula, Indicates the monitoring data Observations, represents the number of observations; The calculation formula of the standard deviation of each monitoring data is: ; The calculation formula of the Z score of each observation value of each monitoring data is: .

[0007] Furthermore, the step of performing data aggregation on the monitoring data after filtering and smoothing to obtain an optimized data set includes: Aggregate the monitoring data that meets the requirements, set continuous time windows, and calculate the median of the monitoring data that meets the requirements in each of the time windows; The medians of the data are aggregated and the corresponding time window information is retained to obtain an optimized data set.

[0008] Further, the step of sorting according to the data correlation analysis result to obtain the ranking difference of each grouped data includes: Sorting the data according to the same timestamp according to the data correlation analysis result to calculate the ranking difference of each grouped data; Calculate the square value of the ranking difference of each grouped data, and accumulate all the obtained square values, and calculate the accumulated square values ​​using the Spearman rank correlation coefficient algorithm to obtain the Spearman rank correlation coefficient corresponding to each grouped data .

[0009] Further, the step of performing correlation judgment on the corresponding grouped data according to each of the Spearman rank correlation coefficients to output the data judgment result includes: like , then the data in the corresponding grouped data have complete correlation; like , then the correlation of the data in the corresponding grouped data is the first level; like , then the correlation of the data in the corresponding grouped data is the second level; like , then the correlation of the data in the corresponding grouped data is the third level; like , then the correlation of the data in the corresponding grouped data is the fourth level; like , then the data in the corresponding grouped data have no correlation; Among them, the correlations corresponding to the first level, the second level, the third level, and the fourth level decrease in sequence.

[0010] The present invention also provides a data correlation analysis system, comprising: A data processing module, used for acquiring a number of monitoring data, performing filtering and smoothing processing on each of the monitoring data, and performing data aggregation on the monitoring data after filtering and smoothing processing to obtain an optimized data set; A data grouping module, used for grouping the monitoring data in pairs to obtain corresponding grouped data; A correlation analysis module, used for performing data correlation analysis on each of the grouped data to obtain corresponding data correlation analysis results; A correlation coefficient calculation module is used to sort the data according to the data correlation analysis results to obtain the ranking difference of each grouping data, and calculate the Spearman rank correlation coefficient corresponding to each grouping data according to the ranking difference of each grouping data; The correlation judgment module is used to perform correlation judgment on the corresponding grouped data according to each Spearman rank correlation coefficient to output a data judgment result.

[0011] Furthermore, the data processing module includes: A score statistics unit, used to perform Z score statistics on each of the monitoring data to perform filtering and screening processing to obtain the mean and standard deviation of each of the monitoring data; The data screening unit is used to calculate the Z score of each observation value of each monitoring data, and compare the obtained Z score with a preset score threshold to screen out monitoring data that meets the requirements.

[0012] Furthermore, the data processing module also includes: A data aggregation unit, used for performing aggregation processing on the monitoring data that meets the requirements, setting continuous time windows, and calculating the data median of the monitoring data that meets the requirements in each of the time windows; The data aggregation unit is used to aggregate the medians of the data and retain the corresponding time window information to obtain an optimized data set.

[0013] Furthermore, the correlation coefficient calculation module includes: A data sorting unit, used for sorting the data according to the same timestamp according to the data correlation analysis result, so as to calculate the ranking difference of each grouped data; The correlation coefficient calculation unit is used to calculate the square value of the ranking difference of each grouped data, and accumulate all the obtained square values, and calculate the accumulated square values ​​using the Spearman rank correlation coefficient algorithm to obtain the Spearman rank correlation coefficient corresponding to each grouped data. .

[0014] The present invention also provides a readable storage medium on which a computer program is stored, and when the program is executed by a processor, the above-mentioned data correlation analysis method is implemented.

[0015] The present invention also proposes a computer, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned data association analysis method when executing the computer program.

[0016] The data correlation analysis method, system, readable storage medium and computer of the present invention filter and smooth the monitoring data and aggregate the data, use the Z-score method to clean abnormal data for the data initially judged to be correlated, extract data eigenvalues ​​based on the data aggregation, perform Spearman correlation analysis based on the eigenvalues ​​to obtain corresponding results, judge the results, conduct further in-depth analysis based on the given results, quickly judge the correlation between data through preset rules, exclude occasional abnormal data, not only can make a correlation judgment on a pair of data, but also can uniformly process and analyze multiple data, provide strong decision-making support for data analysts, greatly improve the accuracy of judging the rationality of data changes, and provide a strong data judgment basis for data analysis in the monitoring industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a flow chart of a data association analysis method in a first embodiment of the present invention; Figure 2 for Figure 1 Detailed flow chart of step S101; Figure 3 for Figure 1 A detailed flowchart of another implementation of step S101; Figure 4 for Figure 1 Detailed flow chart of step S104; Figure 5 is a structural block diagram of a data relevance analysis system in a second embodiment of the present invention; Figure 6 FIG. 4 is a structural block diagram of a computer in a third embodiment of the present invention.

[0018] The following specific implementation manner will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0019] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0021] Embodiment 1 See also Figure 1 , which shows a data association analysis method in a first embodiment of the present invention, and the method specifically includes steps S101 to S104: S101, acquiring a number of monitoring data, performing filtering and smoothing processing on each of the monitoring data, and performing data aggregation on the monitoring data after filtering and smoothing processing to obtain an optimized data set; For further information, please refer to Figure 2 , the step S101 specifically includes steps S1011~S1012: S1011, performing Z-score statistics on each of the monitoring data to perform filtering and screening processing to obtain the mean and standard deviation of each of the monitoring data; S1012, calculating the Z score of each observation value of each monitoring data, and comparing the obtained Z score with a preset score threshold to screen out monitoring data that meets the requirements.

[0022] In the specific implementation, multiple data that need to be analyzed are uploaded to the access system in a fixed format. After the input starts processing, the data is processed and the results are output through the data analysis system. The Z score statistics of the acquired monitoring data are filtered and screened, and the corresponding mean is calculated according to the following formula: ; In the formula, Indicates the monitoring data Observations, represents the number of observations; Furthermore, the standard deviation of each monitoring data is calculated: ; Calculate the Z score for each observation of each monitoring data, for each value in the data set: .

[0023] The threshold of the Z score is defined as ±3. When the absolute value of the screened Z score is less than 3, the data is retained. When the absolute value of the screened Z score is greater than or equal to 3, the data point is marked as an outlier or removed. In this way, the monitoring data that meets the requirements is screened out from the acquired monitoring data.

[0024] For further information, please refer to Figure 3 In some optional embodiments, the step S101 further includes steps S1111-S1112: S1111, performing aggregation processing on the monitoring data that meets the requirements, setting continuous time windows, and calculating the data median of the monitoring data that meets the requirements in each of the time windows; S1112, summarizing the medians of the data and retaining the corresponding time window information to obtain an optimized data set.

[0025] In the specific implementation, the filtered data is aggregated and a time window is set. Generally, the data within 1 hour is taken, and then the median of the n data in each time window is taken. Taking the data set [3, 1, 4, 1, 5, 9] as an example, the data set is arranged in order from small to large. The sorted data set is [1, 1, 3, 4, 5, 9]. Calculate the number of values ​​in the data set (n).

[0026] n = 6. If the number of data sets is an odd number (n is an odd number), the median is the (n+1) / 2th number. If the number of data sets is an even number (n is an even number), the median is the average of the n / 2th number and the (n / 2)+1th number. In this example, n = 6 is an even number, and the average of the 6 / 2th number and the 6 / 2+1th number is calculated. The third number is 3, and the fourth number is 4. Median = (3+4) / 2 = 3.5. According to the above rules, record the calculated median for each 1-hour time window and retain the corresponding time window information. Aggregate the median results of all time windows into a new data set, which will show the median data within each hour.

[0027] S102, grouping the monitoring data in pairs to obtain corresponding grouped data; S103, performing data correlation analysis on each of the grouped data to obtain corresponding data correlation analysis results; S104, sorting according to the data correlation analysis result to obtain the ranking difference of each grouping data, and calculating the Spearman rank correlation coefficient corresponding to each grouping data according to the ranking difference of each grouping data; For further information, see Figure 4 , the step S104 specifically includes steps S1041-S1042: S1041, sorting the data according to the same timestamp according to the data correlation analysis result to calculate the ranking difference of each grouped data; S1042, calculating the square value of the ranking difference of each grouped data, and accumulating all the obtained square values, and calculating the accumulated square values ​​using the Spearman rank correlation coefficient algorithm to obtain the Spearman rank correlation coefficient corresponding to each grouped data .

[0028] In the specific implementation, the two sets of monitoring data are sorted according to the same timestamp, and for each pair of observations, their ranking difference (d) in the two sets of data is calculated. d=R(x i )−R(y i ), where R(x i ) is the rank of the i-th observation in the first monitoring data variable, R(y i ) is the rank of the ith observation in the second monitoring data variable. Calculate the square of the rank difference: ; Add up the squares of all rank differences: , where n is the number of observations, applying the Spearman rank correlation coefficient formula: ; Where: ρ is the Spearman rank correlation coefficient. n is the number of observations. ∑d2 is the sum of squares of all ranking differences. The Spearman rank correlation coefficient of the two sets of monitoring data is obtained .

[0029] S105, performing correlation judgment on the corresponding grouped data according to each Spearman rank correlation coefficient to output a data judgment result.

[0030] In the specific implementation, according to the Spearman rank correlation coefficient obtained above The corresponding conclusion is drawn from the size of: Specifically, if , then the data in the corresponding grouped data have complete correlation; like , then the correlation of the data in the corresponding grouped data is the first level; like , then the correlation of the data in the corresponding grouped data is the second level; like , then the correlation of the data in the corresponding grouped data is the third level; like , then the correlation of the data in the corresponding grouped data is the fourth level; like , then the data in the corresponding grouped data have no correlation; Among them, the correlations corresponding to the first level, the second level, the third level, and the fourth level decrease in sequence.

[0031] This embodiment uses preset rules and methods, and the data processing method can quickly make an accurate judgment on the correlation between data. Ordinary correlation methods will reduce the accuracy of the analysis results of the overall data due to the existence of individual abnormal data points. This method can exclude occasional abnormal data. Ordinary correlation methods will be unable to perform correlation analysis due to missing data from different devices and inconsistent collection granularity, which will lead to the inability to align data. Inconsistent collection granularity makes it impossible to aggregate data, and this method effectively solves this problem and makes accurate judgments on various monitoring data with correlation. Secondly, during the project operation and maintenance process, this method can not only make a correlation judgment on a pair of data, but also uniformly process and analyze multiple data, provide powerful decision-making support for data analysts, and greatly improve the accuracy of judging the rationality of data changes. It provides a strong basis for data judgment for data analysis in the monitoring industry.

[0032] In summary, the data correlation analysis method in the above embodiment of the present invention performs filtering and smoothing processing and data aggregation on the monitoring data, performs the Z-score method on the data preliminarily judged to be correlated to clean abnormal data, extracts data eigenvalues ​​according to the data aggregation, performs Spearman correlation analysis according to the eigenvalues ​​to obtain corresponding results, judges the results, conducts further in-depth analysis based on the given results, and quickly judges the correlation between data through preset rules to exclude occasional abnormal data. It can not only make a correlation judgment on a pair of data, but also perform unified processing and analysis on multiple data, provide strong decision-making support for data analysts, greatly improve the accuracy of judging the rationality of data changes, and provide a strong data judgment basis for data analysis in the monitoring industry.

[0033] Embodiment 2 Another aspect of the present invention is to provide a data correlation analysis system. Figure 5 , which shows a data association analysis system in a second embodiment of the present invention, the system comprises: The data processing module 11 is used to obtain a number of monitoring data, filter and smooth each of the monitoring data, and perform data aggregation on the monitoring data after the filtering and smoothing process to obtain an optimized data set; Furthermore, the data processing module 11 includes: A score statistics unit, used to perform Z score statistics on each of the monitoring data to perform filtering and screening processing to obtain the mean and standard deviation of each of the monitoring data; The data screening unit is used to calculate the Z score of each observation value of each monitoring data, and compare the obtained Z score with a preset score threshold to screen out monitoring data that meets the requirements.

[0034] Furthermore, the data processing module 11 also includes: A data aggregation unit, used for performing aggregation processing on the monitoring data that meets the requirements, setting continuous time windows, and calculating the data median of the monitoring data that meets the requirements in each of the time windows; The data aggregation unit is used to aggregate the medians of the data and retain the corresponding time window information to obtain an optimized data set.

[0035] The data grouping module 12 is used to group the monitoring data in pairs to obtain corresponding grouped data; A correlation analysis module 13 is used to perform data correlation analysis on each of the grouped data to obtain corresponding data correlation analysis results; The correlation coefficient calculation module 14 is used to sort the data according to the data correlation analysis results to obtain the ranking difference of each grouping data, and calculate the Spearman rank correlation coefficient corresponding to each grouping data according to the ranking difference of each grouping data; Furthermore, the correlation coefficient calculation module 14 includes: A data sorting unit, used for sorting the data according to the same timestamp according to the data correlation analysis result, so as to calculate the ranking difference of each grouped data; The correlation coefficient calculation unit is used to calculate the square value of the ranking difference of each grouped data, and accumulate all the obtained square values, and calculate the accumulated square values ​​using the Spearman rank correlation coefficient algorithm to obtain the Spearman rank correlation coefficient corresponding to each grouped data. .

[0036] The correlation judgment module 15 is used to perform correlation judgment on the corresponding grouped data according to each Spearman rank correlation coefficient to output a data judgment result.

[0037] The functions or operation steps implemented when the above modules and units are executed are generally the same as those in the above method embodiments, and will not be repeated here.

[0038] The data correlation analysis system 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 system embodiment, reference may be made to the corresponding contents in the aforementioned method embodiment.

[0039] Embodiment 3 The present invention also provides a computer, see Figure 6 , shown is a computer in the third embodiment of the present invention, including a memory 10, a processor 20, and a computer program 30 stored in the memory 10 and executable on the processor 20. When the processor 20 executes the computer program 30, the above-mentioned data correlation analysis method is implemented.

[0040] The memory 10 includes at least one type of readable storage medium, which includes a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., an SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 10 may be an internal storage unit of a computer, such as a hard disk of the computer. In other embodiments, the memory 10 may also be an external storage device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card, etc. Further, the memory 10 may also include both an internal storage unit of the computer and an external storage device. The memory 10 may be used not only to store application software and various types of data installed in the computer, but also to temporarily store data that has been output or is to be output.

[0041] Among them, in some embodiments, the processor 20 can be an electronic control unit (Electronic Control Unit, abbreviated as ECU, also known as a vehicle computer), a central processing unit (Central Processing Unit, CPU), a controller, a microcontroller, a microprocessor or other data processing chip, used to run the program code stored in the memory 10 or process data, such as executing access restriction programs, etc.

[0042] It should be pointed out that Figure 6 The structure shown does not constitute a limitation on the computer. In other embodiments, the computer may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0043] The embodiment of the present invention further provides a readable storage medium on which a computer program is stored. When the program is executed by a processor, the data correlation analysis method as described above is implemented.

[0044] Those skilled in the art will appreciate that the logic and / or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable instructions for implementing logical functions, and may be specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For purposes of this specification, "computer-readable medium" may be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0045] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0046] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or a combination thereof: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0047] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0048] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A data correlation analysis method, characterized in that: include: Acquire a number of monitoring data, perform filtering and smoothing processing on each of the monitoring data, and perform data aggregation on the monitoring data after filtering and smoothing processing to obtain an optimized data set; Grouping the monitoring data in pairs to obtain corresponding grouped data; Performing data correlation analysis on each of the grouped data to obtain corresponding data correlation analysis results; Sorting the data according to the data correlation analysis results to obtain the ranking difference of each grouping data, and calculating the Spearman rank correlation coefficient corresponding to each grouping data according to the ranking difference of each grouping data; According to each of the Spearman rank correlation coefficients, correlation judgment is performed on the corresponding grouped data to output a data judgment result.

2. The data correlation analysis method according to claim 1, characterized in that: The steps of filtering and smoothing each of the monitoring data include: Performing Z-score statistics on each of the monitoring data to perform filtering and screening processing to obtain the mean and standard deviation of each of the monitoring data; The Z score of each observation value of each monitoring data is calculated, and the obtained Z score is compared with a preset score threshold to screen out monitoring data that meets the requirements.

3. The data association analysis method according to claim 2, characterized in that: The calculation formula for the mean of each monitoring data is: ； In the formula, Indicates the monitoring data Observations, represents the number of observations; The calculation formula of the standard deviation of each monitoring data is: ； The calculation formula of the Z score of each observation value of each monitoring data is: 。 4. The data association analysis method according to claim 2, characterized in that: The steps of performing data aggregation on the monitoring data after filtering and smoothing to obtain an optimized data set include: Aggregate the monitoring data that meets the requirements, set continuous time windows, and calculate the median of the monitoring data that meets the requirements in each of the time windows; The medians of the data are aggregated and the corresponding time window information is retained to obtain an optimized data set.

5. The data association analysis method according to claim 2, characterized in that: The step of sorting according to the data correlation analysis result to obtain the ranking difference of each grouped data includes: Sorting the data according to the same timestamp according to the data correlation analysis result to calculate the ranking difference of each grouped data; Calculate the square value of the ranking difference of each grouped data, and accumulate all the obtained square values, and calculate the accumulated square values ​​using the Spearman rank correlation coefficient algorithm to obtain the Spearman rank correlation coefficient corresponding to each grouped data .

6. The data association analysis method according to claim 5, characterized in that: The step of performing correlation judgment on the corresponding grouped data according to each of the Spearman rank correlation coefficients to output the data judgment result comprises: like , then the data in the corresponding grouped data have complete correlation; like , then the correlation of the data in the corresponding grouped data is the first level; like , then the correlation of the data in the corresponding grouped data is the second level; like , then the correlation of the data in the corresponding grouped data is the third level; like , then the correlation of the data in the corresponding grouped data is the fourth level; like , then the data in the corresponding grouped data have no correlation; Among them, the correlations corresponding to the first level, the second level, the third level, and the fourth level decrease in sequence.

7. A data correlation analysis method, characterized in that: include: A data processing module, used for acquiring a number of monitoring data, performing filtering and smoothing processing on each of the monitoring data, and performing data aggregation on the monitoring data after filtering and smoothing processing to obtain an optimized data set; A data grouping module, used for grouping the monitoring data in pairs to obtain corresponding grouped data; A correlation analysis module, used to perform data correlation analysis on each of the grouped data to obtain corresponding data correlation analysis results; A correlation coefficient calculation module is used to sort the data according to the data correlation analysis results to obtain the ranking difference of each grouping data, and calculate the Spearman rank correlation coefficient corresponding to each grouping data according to the ranking difference of each grouping data; The correlation judgment module is used to perform correlation judgment on the corresponding grouped data according to each Spearman rank correlation coefficient to output a data judgment result.

8. The data correlation analysis system according to claim 7, characterized in that: The data processing module comprises: A score statistics unit, used for performing Z score statistics on each of the monitoring data to perform filtering and screening processing to obtain the mean and standard deviation of each of the monitoring data; The data screening unit is used to calculate the Z score of each observation value of each monitoring data, and compare the obtained Z score with a preset score threshold to screen out monitoring data that meets the requirements.

9. A readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the data association analysis method as described in any one of claims 1 to 6 is implemented.

10. A computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the data association analysis method according to any one of claims 1 to 6 is implemented.

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