PID loop correlation analysis method, device, computer equipment and storage medium

Abstract

The present application relates to a PID loop correlation analysis method, device, computer equipment and storage medium. The method comprises: obtaining the automatic control rate data and the stable rate data of the PID loop; generating frequent item sets according to the automatic control rate data and the stable rate data; iterating the frequent item sets to generate candidate item sets; generating association rules according to the candidate item sets. The present invention uses a data mining algorithm to mine the hidden nonlinear relationships and complex patterns in the automatic control rate data and the stable rate data set of the PID system evaluation through a time rolling window mode, and adopts an iterative method for layer-by-layer search, which is mainly used to discover frequent item sets in the data set. New candidate item sets are generated using known frequent item sets, so as to efficiently find frequent item sets and association rules. In order to conduct a more in-depth analysis of high-dimensional data, it can help users improve production processes and fault warning processing according to rules.

Description

Technical Field

[0001] The present application relates to the technical field of PID loop control, and in particular to a PID loop correlation analysis method, device, computer equipment and storage medium. Background Art

[0002] Association rule mining is a data mining technology that is mainly used to discover the correlation between items in a data set. This algorithm can find data patterns in a transaction database, construct data correspondences, and analyze them by calling the algorithm to discover implicit associations and patterns in the data. The purpose of association rule mining is to reveal hidden relationships between data items, which may be of great value in understanding data, predicting trends, or supporting decision-making.

[0003] In the PID loop optimization system, there may be mutual dependence and influence relationships between different loops. Analyzing the correlation between them can help identify the mutual dependence and influence relationships between different loops and understand which factors have an impact on the entire system.

[0004] Existing technical solutions:

[0005] 1. Linear relationship calculation: directly measure the linear relationship between two loops. The disadvantage is that it is only applicable to linear relationships, not suitable for capturing nonlinear correlations, and cannot reveal more complex association rules.

[0006] 2. Heat map and visual analysis: The relationship between the self-control rate and stability rate between the position numbers is directly displayed through heat maps and other graphical visualization methods. The disadvantage is that the visualization results are only superficial phenomena and lack in-depth quantitative analysis. The interpretation of the results still needs to rely on the analyst's experience and insights. Summary of the invention

[0007] Based on this, it is necessary to provide a PID loop correlation analysis method, device, computer equipment, computer-readable storage medium and computer program product that can improve production processes and fault warning processing in response to the above-mentioned technical problems.

[0008] In a first aspect, the present application provides a PID loop correlation analysis method. The method comprises:

[0009] Obtain the automatic control rate data and stability rate data of the PID loop;

[0010] Generate frequent item sets according to the self-control rate data and the stability rate data;

[0011] Iterate the frequent item sets to generate candidate item sets;

[0012] An association rule is generated according to the candidate item set.

[0013] In one of the embodiments, support and confidence are calculated based on the frequent itemsets;

[0014] An association rule is generated according to the support and the confidence.

[0015] In one embodiment, according to the frequent item set, the specific formula for calculating the support is as follows:

[0016] Support(X→Y)=num(X∩Y) / num(N)

[0017] Among them, X and Y represent transactions, num(X∩Y) represents the number of times X and Y appear at the same time, N represents the total transaction set, and num(N) represents the total number of transactions.

[0018] In one embodiment, according to the candidate item set, the specific formula for calculating the confidence is as follows:

[0019] Confidence(X→Y)=num(X∩Y) / num(X)

[0020] Where X and Y represent transactions, num(X∩Y) represents the number of times X and Y appear at the same time, and num(X) represents the number of times X appears alone.

[0021] In one of the embodiments, the self-control rate data and the stability rate data are cleaned to generate cleaned data;

[0022] Converting the cleaning data according to a preset gear to generate discrete data;

[0023] The discrete data is filtered to generate frequent item sets.

[0024] In one embodiment,

[0025] Detecting whether the support of frequent items in the frequent item set is greater than a preset minimum support;

[0026] If the support of the frequent items in the frequent itemset is greater than the preset minimum support, a candidate itemset is generated.

[0027] In a second aspect, the present application also provides a PID loop correlation analysis device. The device comprises:

[0028] Data acquisition module, used to obtain the automatic control rate data and stability rate data of the PID loop;

[0029] An item set generation module, used for generating frequent item sets according to the self-control rate data and the stability rate data;

[0030] An item set iteration module, used for iterating the frequent item sets to generate candidate item sets;

[0031] The association rule module is used to generate association rules according to the candidate item set.

[0032] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0033] Obtain the automatic control rate data and stability rate data of the PID loop;

[0034] Generate frequent item sets according to the self-control rate data and the stability rate data;

[0035] Iterate the frequent item sets to generate candidate item sets;

[0036] An association rule is generated according to the candidate item set.

[0037] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0038] Obtain the automatic control rate data and stability rate data of the PID loop;

[0039] Generate frequent item sets according to the self-control rate data and the stability rate data;

[0040] Iterate the frequent item sets to generate candidate item sets;

[0041] An association rule is generated according to the candidate item set.

[0042] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0043] Obtain the automatic control rate data and stability rate data of the PID loop;

[0044] Generate frequent item sets according to the self-control rate data and the stability rate data;

[0045] Iterate the frequent item sets to generate candidate item sets;

[0046] An association rule is generated according to the candidate item set.

[0047] The above-mentioned PID loop correlation analysis method, device, computer equipment and storage medium obtain the automatic control rate data and stable rate data of the PID loop; generate frequent item sets based on the automatic control rate data and the stable rate data; iterate the frequent item sets to generate candidate item sets; generate association rules based on the candidate item sets. The present invention uses a data mining algorithm to mine the hidden nonlinear relationships and complex patterns in the automatic control rate data and stable rate data sets of the PID system evaluation through a time rolling window mode, and adopts an iterative method for layer-by-layer search, which is mainly used to discover frequent item sets in the data set. Use known frequent item sets to generate new candidate item sets, so as to efficiently find frequent item sets and association rules. In order to conduct a more in-depth analysis of high-dimensional data, it can help users improve production processes and fault warning processing according to rules. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 An application environment diagram of a PID loop correlation analysis method in one embodiment;

[0049] Figure 2 A schematic diagram of a flow chart of a PID loop correlation analysis method in one embodiment;

[0050] Figure 3 A structural block diagram of a PID loop correlation analysis device in one embodiment;

[0051] Figure 4 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0053] The PID loop correlation analysis method provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The present invention uses a data mining algorithm to mine the hidden nonlinear relationships and complex patterns in the PID system evaluation self-control rate data and the stability rate data set through a time rolling window mode, and adopts an iterative method for layer-by-layer search, which is mainly used to discover frequent item sets in the data set. New candidate item sets are generated using known frequent item sets, so as to efficiently find frequent item sets and association rules. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, Internet of Things devices and portable wearable devices, and the Internet of Things devices can be instruments, sensor devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented with an independent server or a server cluster consisting of multiple servers.

[0054] In one embodiment, Figure 2 As shown in the figure, a PID loop correlation analysis method is provided, which can be applied to Figure 1 Taking the server 104 in the example as an example, the following steps are included:

[0055] Step 202, obtaining the automatic control rate data and the stability rate data of the PID loop.

[0056] Specifically, the automatic control rate data and the stability rate data of the selected loop in a certain time period in the PID loop optimization system are found.

[0057] Step 204: Generate frequent item sets according to the self-control rate data and the stability rate data.

[0058] Specifically, the self-control rate data and the stability rate data are cleaned to generate cleaned data; the cleaned data are converted according to a preset gear to generate discrete data; and the discrete data are filtered to generate frequent item sets.

[0059] More specifically, the missing values ​​in the self-control rate data and the stability rate data are filled with 0 as default values, and repeated values ​​and abnormal values / noise values ​​are processed to generate cleaned data.

[0060] The cleaned data is converted into discrete value types according to the preset gear: four categories: excellent, good, medium, and poor, that is, discrete data.

[0061] The minimum support (Min Support) and the minimum confidence (Min Confidence) are set, and the discrete data is filtered by the minimum support / minimum confidence to generate a discrete item set.

[0062] Filter out items in discrete item sets that are greater than the minimum support / minimum confidence and generate frequent item sets.

[0063] Step 206: iterate the frequent item sets to generate candidate item sets.

[0064] Specifically, support and confidence are calculated according to the frequent item sets; and association rules are generated according to the support and confidence.

[0065] The specific formula for calculating support is as follows:

[0066] Support(X→Y)=num(X∩Y) / num(N)

[0067] Among them, X and Y represent transactions, num(X∩Y) represents the number of times X and Y appear at the same time, N represents the total transaction set, and num(N) represents the total number of transactions.

[0068] The specific formula for calculating confidence is as follows:

[0069] Confidence(X→Y)=num(X∩Y) / num(X)

[0070] Where X and Y represent transactions, num(X∩Y) represents the number of times X and Y appear at the same time, and num(X) represents the number of times X appears alone.

[0071] The iterating the frequent item set to generate a candidate item set includes: detecting whether the support of the frequent items in the frequent item set is greater than the preset minimum support; if the support of the frequent items in the frequent item set is greater than the preset minimum support, generating a candidate item set.

[0072] Wherein, the frequent item set includes multiple frequent items;

[0073] Specifically, if the support of a frequent item in the frequent item set is less than or equal to the preset minimum support, the frequent item is removed. Then the support of each item in the frequent candidate set is calculated to detect whether it is less than the preset minimum support, and the items with a support greater than the preset minimum support are screened out. The support is calculated repeatedly until all the screened items are less than the preset minimum support, and these items are generated into a candidate item set. It can be understood that the screening criterion can also be a minimum confidence.

[0074] Step 208, generating association rules based on the candidate item set.

[0075] In one embodiment, a rule is generated from the frequent item set {A loop self-control rate is high, B loop self-control rate is high}: A loop self-control rate is high->B loop self-control rate is high.

[0076] Furthermore, by selecting rules with high confidence and support for analysis, the relationship between specific bit number data and changes in automatic control rate can be found. For example, when the automatic control rate of a certain loop reaches a certain threshold, the probability of the automatic control rate of another loop increasing is higher.

[0077] In the above PID loop correlation analysis method, the automatic control rate data and the stable rate data of the PID loop are obtained; frequent item sets are generated according to the automatic control rate data and the stable rate data; the frequent item sets are iterated to generate candidate item sets; and association rules are generated according to the candidate item sets. The present invention uses a data mining algorithm to mine the hidden nonlinear relationships and complex patterns in the automatic control rate data and the stable rate data set of the PID system evaluation through a time rolling window mode, and adopts an iterative method for layer-by-layer search, which is mainly used to discover frequent item sets in the data set. New candidate item sets are generated using known frequent item sets, so as to efficiently find frequent item sets and association rules. In order to conduct a more in-depth analysis of high-dimensional data, it can help users improve production processes and fault warning processing according to rules.

[0078] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0079] Based on the same inventive concept, the embodiment of the present application also provides a PID loop correlation analysis device for implementing the PID loop correlation analysis method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more PID loop correlation analysis device embodiments provided below can refer to the limitations of the PID loop correlation analysis method above, and will not be repeated here.

[0080] In one embodiment, Figure 3 As shown, a PID loop correlation analysis device is provided, including: a data acquisition module 410, an item set generation module 420, an item set iteration module 430 and an association rule module 440, wherein:

[0081] The data acquisition module 410 is used to acquire the automatic control rate data and the stability rate data of the PID loop.

[0082] The item set generation module 420 is used to generate frequent item sets according to the self-control rate data and the stability rate data.

[0083] The item set iteration module 430 is used to iterate the frequent item sets to generate candidate item sets.

[0084] The association rule module 440 is used to generate association rules according to the candidate item set.

[0085] The item set iteration module 430 is further used to calculate support and confidence according to the frequent item sets;

[0086] An association rule is generated according to the support and the confidence.

[0087] The item set iteration module 430 is also used to calculate the support degree according to the frequent item sets, and the specific formula is as follows:

[0088] Support(X→Y)=num(X∩Y) / num(N)

[0089] Among them, X and Y represent transactions, num(X∩Y) represents the number of times X and Y appear at the same time, N represents the total transaction set, and num(N) represents the total number of transactions.

[0090] The item set iteration module 430 is also used to calculate the confidence level according to the candidate item set. The specific formula is as follows:

[0091] Confidence(X→Y)=num(X∩Y) / num(X)

[0092] Where X and Y represent transactions, num(X∩Y) represents the number of times X and Y appear at the same time, and num(X) represents the number of times X appears alone.

[0093] The item set generation module 420 is also used to clean the self-control rate data and the stability rate data to generate cleaned data;

[0094] Converting the cleaning data according to a preset gear to generate discrete data;

[0095] The discrete data is filtered to generate frequent item sets.

[0096] The item set generation module 420 is also used to detect whether the support of the frequent items in the frequent item set is greater than a preset minimum support;

[0097] If the support of the frequent items in the frequent itemset is greater than the preset minimum support, a candidate itemset is generated.

[0098] Each module in the above PID loop correlation analysis device can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0099] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 4 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store association rule data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a PID loop correlation analysis method is implemented.

[0100] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0101] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, any one of the PID loop correlation analysis methods in the above embodiments is implemented.

[0102] Obtain the automatic control rate data and stability rate data of the PID loop;

[0103] Generate frequent item sets according to the self-control rate data and the stability rate data;

[0104] Iterate the frequent item sets to generate candidate item sets;

[0105] An association rule is generated according to the candidate item set.

[0106] In one embodiment, when the processor executes the computer program, the following steps are further implemented: calculating support and confidence according to the frequent item set;

[0107] An association rule is generated according to the support and the confidence.

[0108] In one embodiment, when the processor executes the computer program, the following steps are further implemented: According to the frequent item set, the specific formula for calculating the support is as follows:

[0109] Support(X→Y)=num(X∩Y) / num(N)

[0110] Among them, X and Y represent transactions, num(X∩Y) represents the number of times X and Y appear at the same time, N represents the total transaction set, and num(N) represents the total number of transactions.

[0111] In one embodiment, when the processor executes the computer program, the following steps are further implemented: according to the candidate item set, the specific formula for calculating the confidence is as follows:

[0112] Confidence(X→Y)=num(X∩Y) / num(X)

[0113] Where X and Y represent transactions, num(X∩Y) represents the number of times X and Y appear at the same time, and num(X) represents the number of times X appears alone.

[0114] In one embodiment, when the processor executes the computer program, the following steps are further implemented: cleaning the automatic control rate data and the stability rate data to generate cleaned data;

[0115] Converting the cleaning data according to a preset gear to generate discrete data;

[0116] The discrete data is filtered to generate frequent item sets.

[0117] In one embodiment, when the processor executes the computer program, the following steps are further implemented: detecting whether the support of the frequent items in the frequent item set is greater than a preset minimum support;

[0118] If the support of the frequent items in the frequent itemset is greater than the preset minimum support, a candidate itemset is generated.

[0119] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, any one of the PID loop correlation analysis methods in the above embodiments is implemented.

[0120] Obtain the automatic control rate data and stability rate data of the PID loop;

[0121] Generate frequent item sets according to the self-control rate data and the stability rate data;

[0122] Iterate the frequent item sets to generate candidate item sets;

[0123] An association rule is generated according to the candidate item set.

[0124] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: calculating support and confidence according to the frequent item set;

[0125] An association rule is generated according to the support and the confidence.

[0126] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: According to the frequent item set, the specific formula for calculating the support is as follows:

[0127] Support(X→Y)=num(X∩Y) / num(N)

[0128] Among them, X and Y represent transactions, num(X∩Y) represents the number of times X and Y appear at the same time, N represents the total transaction set, and num(N) represents the total number of transactions.

[0129] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: according to the candidate item set, the specific formula for calculating the confidence is as follows:

[0130] Confidence(X→Y)=num(X∩Y) / num(X)

[0131] Where X and Y represent transactions, num(X∩Y) represents the number of times X and Y appear at the same time, and num(X) represents the number of times X appears alone.

[0132] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: cleaning the automatic control rate data and the stability rate data to generate cleaned data;

[0133] Converting the cleaning data according to a preset gear to generate discrete data;

[0134] The discrete data is filtered to generate frequent item sets.

[0135] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: detecting whether the support of the frequent items in the frequent item set is greater than a preset minimum support;

[0136] If the support of the frequent items in the frequent itemset is greater than the preset minimum support, a candidate itemset is generated.

[0137] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0138] Obtain the automatic control rate data and stability rate data of the PID loop;

[0139] Generate frequent item sets according to the self-control rate data and the stability rate data;

[0140] Iterate the frequent item sets to generate candidate item sets;

[0141] An association rule is generated according to the candidate item set.

[0142] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: calculating support and confidence according to the frequent item set;

[0143] An association rule is generated according to the support and the confidence.

[0144] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: According to the frequent item set, the specific formula for calculating the support is as follows:

[0145] Support(X→Y)=num(X∩Y) / num(N)

[0146] Among them, X and Y represent transactions, num(X∩Y) represents the number of times X and Y appear at the same time, N represents the total transaction set, and num(N) represents the total number of transactions.

[0147] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: according to the candidate item set, the specific formula for calculating the confidence is as follows:

[0148] Confidence(X→Y)=num(X∩Y) / num(X)

[0149] Where X and Y represent transactions, num(X∩Y) represents the number of times X and Y appear at the same time, and num(X) represents the number of times X appears alone.

[0150] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: cleaning the automatic control rate data and the stability rate data to generate cleaned data;

[0151] Converting the cleaning data according to a preset gear to generate discrete data;

[0152] The discrete data is filtered to generate frequent item sets.

[0153] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: detecting whether the support of the frequent items in the frequent item set is greater than a preset minimum support;

[0154] If the support of the frequent items in the frequent itemset is greater than the preset minimum support, a candidate itemset is generated.

[0155] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0156] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0157] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above 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.

[0158] The above-described 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 present application. 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 present application shall be subject to the attached claims.

Claims

1. A PID loop correlation analysis method, characterized in that: The method comprises: Obtain the automatic control rate data and stability rate data of the PID loop; Generate frequent item sets according to the self-control rate data and the stability rate data; Iterate the frequent item sets to generate candidate item sets; An association rule is generated according to the candidate item set.

2. The method according to claim 1, characterized in that: The iterating the frequent itemsets to generate candidate itemsets comprises: According to the frequent item sets, calculate support and confidence; An association rule is generated according to the support and the confidence.

3. The method according to claim 2, characterized in that The calculating support and confidence according to the frequent item set includes: According to the frequent item set, the specific formula for calculating support is as follows: Support(X→Y)=num(X∩Y) / num(N) Among them, X and Y represent transactions, num(X∩Y) represents the number of times X and Y appear at the same time, N represents the total transaction set, and num(N) represents the total number of transactions.

4. The method according to claim 2, characterized in that: The calculating support and confidence according to the frequent item set includes: According to the candidate item set, the specific formula for calculating the confidence is as follows: Confidence(X→Y)=num(X∩Y) / num(X) Where X and Y represent transactions, num(X∩Y) represents the number of times X and Y appear at the same time, and num(X) represents the number of times X appears alone.

5. The method according to claim 1, characterized in that: The generating of frequent item sets according to the self-control rate data and the stability rate data comprises: Cleaning the self-control rate data and the stability rate data to generate cleaned data; Converting the cleaning data according to a preset gear to generate discrete data; The discrete data is filtered to generate frequent item sets.

6. The method according to claim 1, characterized in that The frequent item sets are iterated to generate candidate item sets. include: Wherein, the frequent item set includes multiple frequent items; Detecting whether the support of frequent items in the frequent item set is greater than a preset minimum support; If the support of the frequent items in the frequent itemset is greater than the preset minimum support, a candidate itemset is generated.

7. A PID loop correlation analysis device, characterized in that: The device comprises: Data acquisition module, used to obtain the automatic control rate data and stability rate data of the PID loop; An item set generation module, used for generating frequent item sets according to the self-control rate data and the stability rate data; An item set iteration module, used for iterating the frequent item sets to generate candidate item sets; The association rule module is used to generate association rules according to the candidate item set.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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