A pareto chart data analysis method and system based on structured query language

Abstract

This invention discloses a Pareto chart data analysis method and system based on Structured Query Language (SCL), belonging to the field of data analysis technology. The Pareto chart data analysis method includes the following steps: generating a problem cause statistics table; generating a problem cause statistics table with a descending order ranking field for quantity; generating a cumulative result of the number of problems; generating a problem cause statistics table with a descending order ranking field for quantity and a cumulative percentage field for the number of problems; generating a key cause field using logical judgment statements in SCL according to the Pareto principle; generating Pareto chart data statistics tables for ongoing projects and historical projects; and generating a focus field for the problem causes of ongoing projects based on the Pareto data of historical projects. This invention defines a problem cause focus field by comparing the key cause fields of ongoing and historical projects, enabling focus analysis of the problem causes of ongoing projects based on historical data.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, and more specifically, to a Pareto chart data analysis method and system based on structured query language. Background Technology

[0002] Pareto charts are currently used across various industries both domestically and internationally to analyze quality problems, guiding managers to identify the key causes of the most frequent issues and subsequently resolve major quality problems. Figure 1 A Pareto chart typically consists of a bar chart summarizing the causes of problems and a line chart showing the cumulative percentage of problems. However, quality problem data usually only consists of the causes and descriptions of the problems, often lacking the data needed to create the bar and line charts for a Pareto chart. Typically, the SQL window function OVER(PARTITION BY) in higher versions of various databases is used to analyze quality problem data and obtain Pareto chart data.

[0003] Window functions, also known as OLAP functions (Online Analytical Processing), are SQL functionalities added to implement OLAP. For example, Oracle has offered analytical functions since version 8.1.6. The difference between window functions and aggregate functions is that window functions return multiple rows for each group, while aggregate functions return only one row per group. For example, `GROUP BY` grouping and summarizing changes the number of rows in the table; each row has only one category, retaining only the fields involved in the grouping and the result of the aggregate function. `PARITE BY`, compared to `GROUP BY`, is used to calculate a specific aggregate value based on groups, allowing you to group and sort only certain fields while retaining all data.

[0004] In the existing technology, Pareto Figure 1 A Pareto chart typically consists of a bar chart summarizing the causes of problems and a line chart showing the cumulative percentage of problems. However, quality problem data usually only consists of the causes and descriptions of the problems, often lacking the data needed to create the bar and line charts for a Pareto chart. Furthermore, in older versions of various databases, due to a lack of support for analytical functions, the SQL window function OVER(PARTITION BY) in newer versions cannot be used to analyze quality problem data, thus preventing the generation of Pareto chart data.

[0005] No effective solutions have yet been proposed to address the problems in the relevant technologies. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention proposes a Pareto graph data analysis method and system based on structured query language, solving the problems mentioned in the background section regarding existing Pareto graph methods. Figure 1A Pareto chart typically consists of a bar chart summarizing the causes of problems and a line chart showing the cumulative percentage of problems. However, quality problem data usually only consists of the causes and descriptions of the problems, often lacking the data needed to create the bar and line charts for a Pareto chart. Furthermore, older versions of various databases do not support analytical functions, making it impossible to use the SQL window function OVER(PARTITION BY) in newer versions to analyze quality problem data and thus fail to generate Pareto chart data.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] According to one aspect of the present invention, a Pareto chart data analysis method based on a structured query language is provided, the Pareto chart data analysis method comprising the following steps:

[0009] S1. Using the dataset partitioning statement in Structured Query Language and the counting function in Structured Query Language, count the causes of each problem and the number of problems they cause, and generate a problem cause statistics table;

[0010] S2. Use the sorting clause in the structured query language to sort the generated problem cause statistics table. Combine the preset variables and ranking formula to perform descending order ranking, problem cause and problem quantity field queries to generate a problem cause statistics table with descending order ranking field.

[0011] S3. The problem cause statistics table with descending order of quantity is preset to two sets of identical data. Using the selection criteria clause and aggregate functions in the structured query language, the problem quantity is accumulated one by one to generate the cumulative result of the problem quantity.

[0012] S4. Use the counting function in the structured query language to generate the total number of problems, divide the cumulative result of the number of problems by the total number of problems generated, and perform a query on all fields and the cumulative percentage of the number of problems field on one of the preset sets of data to generate a statistical table of the reasons for problems with descending order of the number of problems and the cumulative percentage of the number of problems field.

[0013] S5. Based on the Pareto principle, generate key reason fields using logical judgment statements in structured query language;

[0014] S6. Based on the project screening criteria, the problem data is filtered into ongoing project data and historical project data, and steps S1 to S5 are executed on the ongoing project data and historical project data to generate Pareto chart data statistics tables for ongoing projects and historical projects.

[0015] S7. Based on the keyword matching statement in the structured query language, the Pareto chart data statistics results of ongoing projects and historical projects are associated by the problem cause field. Combined with the logical judgment statement in the structured query language, the attention field of the problem cause of ongoing projects is generated based on the Pareto data of historical projects.

[0016] Furthermore, by utilizing the dataset partitioning statements in Structured Query Language (SCL) and the counting function in SCL, the causes of each problem and the number of problems they cause are statistically analyzed to generate a problem cause statistics table, including the following steps:

[0017] S11. Preset the structure of the quality problem data table, which includes a problem cause field and a problem description field;

[0018] S12. Apply the dataset partitioning statement in Structured Query Language to the problem cause field, and execute the counting function in Structured Query Language on the problem description field. Set the execution result as the problem quantity field of the problem cause, and generate a problem cause statistics table for quality problem data.

[0019] Furthermore, the generated problem cause statistics table is sorted using the sorting clause in Structured Query Language. Combining preset variables and ranking formulas, queries are performed on the quantity descending order ranking, problem cause, and problem quantity fields to generate a problem cause statistics table with a quantity descending order ranking field. This process includes the following steps:

[0020] S21. Sort the generated problem cause statistics table by the sorting clause in the structured query language according to the rules of descending order of the problem quantity field and ascending order of the problem cause field.

[0021] S22. Set the initial values ​​of preset variables, and use the keyword statement in Structured Query Language to set the descending ranking field of the number of questions as a variable formula;

[0022] S23. Based on the variable formula, perform a query on the fields of quantity descending order ranking, problem cause, and problem quantity, and generate a statistical table of problem causes with a quantity descending order ranking field.

[0023] Furthermore, the variable formula is:

[0024] @id1: = @id1+1;

[0025] In the formula, @id1 represents a variable; := represents assignment; +1 represents incrementing by one.

[0026] Furthermore, the problem cause statistics table with a descending order ranking field is preset to have two sets of identical data. Using the selection criteria clause and aggregate functions defined in Structured Query Language (SCL), the problem count is accumulated one by one to generate the cumulative problem count result. This involves the following steps:

[0027] S31. Divide the problem cause statistics table with a descending order ranking field into two groups of identical data, a and b.

[0028] S32. Using the selection criteria clause specified in the Structured Query Language, filter out the data of group a where the descending order of the quantity ranking field of group a is less than or equal to the descending order of the quantity ranking field of group b.

[0029] S33. Use aggregate functions in Structured Query Language to generate a cumulative result of the number of problems for each reason in descending order, and use the cumulative result as the numerator of the cumulative percentage of the number of problems.

[0030] Furthermore, the total number of issues is generated using the counting function in Structured Query Language. The cumulative result of the number of issues is divided by the total number of issues generated. Then, a query is performed on one of the preset sets of data for all fields and the cumulative percentage of the number of issues. This generates a statistical table of issues with descending order of quantity and the cumulative percentage of the number of issues. The steps include:

[0031] S41. Use the counting function in Structured Query Language to generate the total number of problems in the problem description field of the problem cause statistics table of the generated quality problem data, and use the total number of problems as the denominator of the cumulative percentage of the number of problems;

[0032] S42. Divide the cumulative number of issues by the total number of issues generated, and define the cumulative percentage of issues field;

[0033] S43. Perform a query on all fields and the cumulative percentage of the number of problems field on the data of group b, and generate a statistical table of the causes of problems with descending order of quantity and the cumulative percentage of the number of problems field.

[0034] Furthermore, according to the Pareto principle, generating key reason fields using logical judgment statements in Structured Query Language includes the following steps:

[0035] S51. According to the Pareto principle, use logical judgment statements in structured query language to determine the key reason field when the cumulative percentage of the number of problems is less than or equal to the threshold.

[0036] S52. Generate a critical cause statistics table that conforms to the Pareto principle, which is used to screen out the critical causes that cause near-threshold problems.

[0037] Furthermore, according to the Pareto principle, the key reason field for determining whether the cumulative percentage of the number of problems is less than or equal to a threshold using logical judgment statements in Structured Query Language includes the following steps:

[0038] S511. Based on the Pareto principle, when the cumulative proportion of the number of problems in descending order is less than or equal to the threshold, the key reason field is assigned the value of "yes"; otherwise, the key reason field is assigned the value of "no".

[0039] S512. Query the fields for problem cause, number of problems, cumulative percentage of problem number, and key cause, and output the filtered results.

[0040] Furthermore, based on the project screening criteria, the problem data is filtered into ongoing project data and historical project data, and steps S1 to S5 are performed on the ongoing project data and historical project data to generate Pareto chart data statistics tables for ongoing projects and historical projects, including the following steps:

[0041] S61. Using report designer software, analyze the Pareto chart data of the quality problem table in the relational database, and divide the problem data into ongoing project data and historical project data based on project screening criteria.

[0042] S62. Perform steps S1 to S5 on the ongoing project data and the historical project data respectively, and finally generate Pareto chart data statistics tables for the ongoing project and the historical project.

[0043] Furthermore, based on the keyword matching statements in Structured Query Language (SCL), the Pareto chart data statistics of ongoing projects and historical projects are correlated according to the issue cause field. Combined with logical judgment statements in SCL, the attention field for issue causes of ongoing projects is generated based on the Pareto data of historical projects, including the following steps:

[0044] S71. Use keywords in Structured Query Language to return the problem cause field for Pareto chart data statistics of ongoing and historical projects related to matching statements;

[0045] S72. Based on logical judgment statements in the structured query language, and by comparing the key cause field values ​​of the problem causes in the ongoing project data and the historical project data, generate the ongoing project problem cause attention field based on the Pareto chart data statistics of the historical projects.

[0046] Furthermore, based on logical judgment statements in Structured Query Language, and by comparing the key cause field values ​​of problem causes in ongoing project data and historical project data, the following steps are taken to generate the ongoing project problem cause attention field based on Pareto chart data statistics:

[0047] S721. If the key reason field values ​​for both ongoing and historical projects are yes, then the issue cause concern field is assigned the value of "highly concerned".

[0048] S722. If the key reason field value of an ongoing project is yes, and the key reason field value of a historical project is no, then the issue cause attention field is assigned the value of increased attention.

[0049] S723. If the key reason field value of an ongoing project is no, and the key reason field value of a historical project is yes, then the issue cause concern field is assigned the value of "keep concerned".

[0050] S724. If the key cause field value of the ongoing project and the key cause field value of the historical project are not as described in steps S721, S722 and S723, then the issue cause concern field is assigned the value of general concern, and an ongoing project issue cause concern field based on Pareto chart data statistics of historical projects is generated.

[0051] According to another aspect of the present invention, a Pareto chart data analysis system based on a structured query language is also provided, the Pareto chart data analysis system comprising:

[0052] The data statistics module is used to utilize the dataset partitioning statement in Structured Query Language and the counting function in Structured Query Language to count the causes of each problem and the number of problems they cause, and generate a problem cause statistics table.

[0053] The data sorting module is used to sort the generated problem cause statistics table using the sorting clause in the structured query language. It combines preset variables and ranking formulas to perform descending order ranking of quantity, query the problem cause and problem quantity fields, and generate a problem cause statistics table with a descending order ranking field.

[0054] The cumulative calculation module is used to pre-set two sets of identical data in the problem cause statistics table with a descending order ranking field for the number of problems. It uses the selection criteria clause and aggregate functions in the structured query language to accumulate the number of problems one by one and generate a cumulative result of the number of problems.

[0055] The cumulative percentage calculation module is used to generate the total number of issues using the counting function in the structured query language, divide the cumulative result of the number of issues by the total number of issues generated, and perform a query on all fields and the cumulative percentage of the number of issues field on one of the preset sets of data to generate a statistical table of the reasons for issues with a descending order of the number of issues and the cumulative percentage of the number of issues field.

[0056] The key reason tagging module is used to generate key reason fields based on the Pareto principle and logical judgment statements in the structured query language.

[0057] The Pareto chart data generation module is used to filter problem data into ongoing project data and historical project data based on project screening criteria, and to perform Pareto chart analysis on the ongoing project data and historical project data to generate Pareto chart data statistics tables for ongoing projects and historical projects.

[0058] The Pareto chart data analysis module is used to return matching statements based on keywords in Structured Query Language (SCL). It associates the Pareto chart data statistics results of ongoing projects and historical projects according to the problem cause field, and combines logical judgment statements in SCL to generate the attention field of the problem cause of ongoing projects based on the Pareto data of historical projects.

[0059] The beneficial effects of this invention are as follows:

[0060] 1. This invention generates statistical data on problem causes, including descending order of quantity, cumulative percentage of problem quantity, and key cause fields, using SQL statements supported by various database versions, on quality problem data composed of problem causes and problem descriptions. Simultaneously, by linking the problem cause field to historical project Pareto chart data within the ongoing project Pareto chart data, and comparing the key cause fields of ongoing and historical projects, a problem cause attention field is defined, enabling attention analysis of problem causes in ongoing projects based on historical data.

[0061] 2. This invention facilitates the direct generation of Pareto charts in project management, using the generated data results to create charts composed of statistical bar charts of problem causes and line charts showing the cumulative percentage of problems. Furthermore, by comparing the key causes of ongoing and historical projects, it enables attention analysis of problem causes in ongoing projects, effectively identifying the key causes generating the most problems and thus resolving the main issues. This method exhibits good instructional versatility and version compatibility, meeting the requirements of high cohesion and low coupling in code. Attached Figure Description

[0062] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0063] Figure 1 This is a flowchart of a Pareto chart data analysis method based on a structured query language according to an embodiment of the present invention;

[0064] Figure 2 This is a schematic diagram of a Pareto chart data analysis system based on a structured query language according to an embodiment of the present invention.

[0065] Figure 3 These are screenshots of quality problem data according to an embodiment of the present invention;

[0066] Figure 4 This is a screenshot showing the statistical causes of problems according to an embodiment of the present invention;

[0067] Figure 5 This is a screenshot showing the quantity in descending order according to an embodiment of the present invention;

[0068] Figure 6 This is a screenshot showing the cumulative number of questions, arranged in descending order, according to an embodiment of the present invention.

[0069] Figure 7 This is a screenshot showing the total number of problems according to an embodiment of the present invention;

[0070] Figure 8 This is a screenshot showing the cumulative percentage of the number of problems accumulated in descending order according to an embodiment of the present invention;

[0071] Figure 9 These are screenshots illustrating key reasons according to embodiments of the present invention;

[0072] Figure 10 This is a screenshot of Pareto chart data statistics for an ongoing project according to an embodiment of the present invention;

[0073] Figure 11 This is a screenshot of historical Pareto chart data statistics according to an embodiment of the present invention;

[0074] Figure 12 This is a screenshot of the attention level of ongoing project issues based on historical projects, according to an embodiment of the present invention.

[0075] In the picture:

[0076] 1. Data statistics module; 2. Data sorting module; 3. Cumulative calculation module; 4. Cumulative percentage calculation module; 5. Key cause marking module; 6. Pareto chart data generation module; 7. Pareto chart data analysis module. Detailed Implementation

[0077] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0078] In the description of this invention, unless otherwise stated, "a plurality of" means two or more. Furthermore, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0079] According to embodiments of the present invention, a Pareto chart data analysis method and system based on structured query language is provided.

[0080] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments, such as... Figure 1 As shown, according to an embodiment of the present invention, a Pareto chart data analysis method based on a structured query language includes the following steps:

[0081] S1. Using the dataset partitioning statement (i.e., the SQL group by statement) and the counting function (i.e., the SQL count function) in the Structured Query Language, we can count the causes of each problem and the number of problems they cause, and generate a problem cause statistics table.

[0082] It needs to be explained that this involves using report designer software to perform Pareto chart data analysis on a quality issue table in a MySQL database (i.e., a relational database management system). The quality issue data table is defined as consisting of issue cause and issue description fields. An SQL group by statement is used on the issue cause field, and the SQL count function is executed on the issue description field to define the number of issues caused by each issue cause. This generates statistics on the causes of quality issues, i.e., the number of issues each cause triggers. For example... Figure 3 As shown, the SQL statement to be executed is as follows:

[0083] SELECT proCATEGORY as Problem Cause, proDetails as Problem Description FROM `qa_problem` ORDER BY proCATEGORY desc

[0084] like Figure 4 As shown, the SQL statement to be executed is as follows:

[0085] SELECT a.proCATEGORY as Problem Reason, COUNT(a.proDetails) as Problem Count FROM `qa_problem` as a group by a.proCATEGORY

[0086] S2. Sort the generated problem cause statistics table using the sorting clause (i.e., the SQL order by clause) in the structured query language. Combine the preset variables and ranking formulas to perform descending order ranking, problem cause and problem quantity field queries to generate a problem cause statistics table with descending order ranking field.

[0087] It needs to be explained that the Pareto chart data analysis of the quality problem table in the MySQL database is implemented using report designer software. The SQL `AS` statement defines the descending order ranking field of the problem quantity as a variable, with the formula `@id1:=@id1+1`, where `@id1:=0`. The SQL `ORDER BY` clause sorts the problem cause statistics table by descending order of the problem quantity field and ascending order of the problem cause field. Queries are then executed on the descending order ranking, problem cause, and problem quantity fields to generate problem classification statistics with the descending order ranking field. This achieves the Pareto chart data for the problem cause.

[0088] like Figure 5 As shown, the SQL statement to be executed is as follows:

[0089] SELECT (@id1 := @id1+1) AS count (descending order), x.reason for problem, x.number of problems

[0090] from(SELECT a.proCATEGORY as Problem Reason, COUNT(a.proDetails) as Problem Count FROM `qa_problem` as a

[0091] group by a.proCATEGORY)as x,(Select(@id1:=0))as z

[0092] order by x.number of issues desc, x.reason for issue asc

[0093] S3. The problem cause statistics table with descending order of quantity is preset to two sets of identical data. Using the selection criteria clause (i.e., SQL where clause) and aggregate functions (i.e. SQL sum function) in the structured query language, the problem quantity is accumulated one by one to generate the cumulative result of the problem quantity.

[0094] S4. Use the counting function in Structured Query Language (i.e., SQL count function) to generate the total number of issues. Divide the cumulative result of the number of issues by the total number of issues generated. Then, perform a query on all fields and the cumulative percentage of the number of issues field on one of the preset sets of data to generate a statistical table of the reasons for issues with a descending order of the number of issues and a cumulative percentage of the number of issues field.

[0095] S5. Based on the Pareto principle, generate key reason fields using logical judgment statements (i.e., SQL case statements) in Structured Query Language;

[0096] S6. Based on the project screening criteria, the problem data is filtered into ongoing project data and historical project data, and steps S1 to S5 are executed on the ongoing project data and historical project data to generate Pareto chart data statistics tables for ongoing projects and historical projects.

[0097] S7. Based on the keyword return matching statement in Structured Query Language (i.e., SQL LEFT JOIN statement), the Pareto chart data statistics results of ongoing projects and historical projects are associated according to the problem cause field. Combined with the logical judgment statement in Structured Query Language (i.e. SQL CASE statement), the attention field of the problem cause of ongoing projects is generated based on the Pareto data of historical projects.

[0098] In this optional embodiment, the process of generating a problem cause statistics table by utilizing the dataset partitioning statement in Structured Query Language and the counting function in Structured Query Language to count the number of problems caused by each problem includes the following steps:

[0099] S11. Preset the structure of the quality problem data table, which includes a problem cause field and a problem description field;

[0100] S12. Apply the dataset partitioning statement in Structured Query Language to the problem cause field, and execute the counting function in Structured Query Language on the problem description field. Set the execution result as the problem quantity field of the problem cause, and generate a problem cause statistics table for quality problem data.

[0101] In this optional embodiment, the generated problem cause statistics table is sorted using the sorting clause in Structured Query Language. Combined with preset variables and ranking formulas, queries are performed on the quantity descending order ranking, problem cause, and problem quantity fields to generate a problem cause statistics table with a quantity descending order ranking field. This includes the following steps:

[0102] S21. Sort the generated problem cause statistics table by the sorting clause in the structured query language according to the rules of descending order of the problem quantity field and ascending order of the problem cause field.

[0103] S22. Set the initial values ​​of preset variables, and use the keyword statement in Structured Query Language to set the descending ranking field of the number of questions as a variable formula;

[0104] S23. Based on the variable formula, perform a query on the fields of quantity descending order ranking, problem cause, and problem quantity, and generate a statistical table of problem causes with a quantity descending order ranking field.

[0105] In this optional embodiment, the variable formula is:

[0106] @id1: = @id1+1;

[0107] In the formula, @id1 represents a variable; := represents assignment; +1 represents incrementing by one.

[0108] In this optional embodiment, the problem cause statistics table with a descending order ranking field is preset to have two sets of identical data. Using the selection criteria clause and aggregate functions specified in Structured Query Language (SCL), the number of problems is accumulated one by one to generate the cumulative result of the problem count. This includes the following steps:

[0109] S31. Divide the problem cause statistics table with a descending order ranking field into two groups of identical data, a and b.

[0110] S32. Use the selection criteria clause (i.e., the SQL WHERE clause) specified in the Structured Query Language to filter out the data in group a where the descending order of the quantity ranking field of group a is less than or equal to the descending order of the quantity ranking field of group b.

[0111] S33. Use aggregate functions in Structured Query Language to generate a cumulative result of the number of problems for each reason in descending order, and use the cumulative result as the numerator of the cumulative percentage of the number of problems.

[0112] It needs to be explained that the Pareto chart data analysis of the quality problem table in the MySQL database is implemented using report designer software. The SQL `AS` statement is used to generate a statistical table of problem causes with a descending order of quantity ranking field. This table is defined as two groups of identical data, `a` and `b`. The SQL `WHERE` clause is used to filter group `a` data where the descending order of quantity ranking field is less than or equal to that of group `b`. The SQL `SUM` function is then used on the problem quantity field of group `a` to generate a cumulative result of the problem quantity for each cause, summed in descending order. This result is defined as the numerator of the cumulative problem count percentage.

[0113] like Figure 6 As shown, the SQL statement to be executed is as follows:

[0114] SELECT b.*, (SELECT SUM(a.number of issues) FROM(SELECT(@id2:=@id2+1) AS number in descending order, x.reason for issue, x.number of issues)

[0115] from(SELECT a.proCATEGORY as Problem Reason, COUNT(a.proDetails) as Problem Count FROM `qa_problem` as a

[0116] group by a.proCATEGORY)as x,(Select(@id2:=0))as z

[0117] order by x.number of issues desc, x.reason for issue asc) as a where a.number of issues in descending order <= b.number of issues in descending order) as total number of issues from

[0118] (select(@id1:=@id1+1)as Quantity in descending order, x.Reason for problem, x.Number of problems)

[0119] from(SELECT a.proCATEGORY as Problem Reason, COUNT(a.proDetails) as Problem Count FROM `qa_problem` as a

[0120] group by a.proCATEGORY)as x,(Select(@id1:=0))as z

[0121] order by x.number of problems desc, x.reason for problem asc) as b

[0122] In this optional embodiment, the total number of issues is generated using the SQL count function. The cumulative result of the number of issues is divided by the total number of issues generated. Then, a query is performed on one of the preset sets of data for all fields and the cumulative percentage of the number of issues field to generate an issue cause statistics table with descending order of quantity and cumulative percentage of the number of issues field. The steps include:

[0123] S41. Use the counting function in Structured Query Language to generate the total number of problems in the problem description field of the problem cause statistics table of the generated quality problem data, and use the total number of problems as the denominator of the cumulative percentage of the number of problems;

[0124] It should be explained that the Pareto chart data analysis of the quality problem table in the MySQL database is implemented using report designer software. The SQL COUNT function is used on the problem description field of the quality problem data table defined in step S1 above to generate the total number of problems, which is then defined as the denominator of the cumulative percentage of problems.

[0125] like Figure 7 As shown, the SQL statement to be executed is as follows:

[0126] SELECT COUNT(proDetails) AS Total_Problems FROM `qa_problem`

[0127] S42. Divide the cumulative number of issues by the total number of issues generated, and define the cumulative percentage of issues field;

[0128] S43. Perform a query on all fields and the cumulative percentage of the number of problems field on the data of group b, and generate a statistical table of the causes of problems with descending order of quantity and the cumulative percentage of the number of problems field.

[0129] It needs to be explained that the Pareto chart data analysis of the quality problem table in the MySQL database is implemented using report designer software. The SQL AS statement defines the cumulative percentage of problem count as the SQL statement resulting from step S3 divided by the SQL statement resulting from step S4. Then, queries are performed on all fields and the cumulative percentage of problem count field for the data group b defined in step S3. This generates a statistical analysis of the causes of problems with descending order of quantity and the cumulative percentage of problem count field. This completes the Pareto chart of problem cause statistics (bar chart and line chart of cumulative percentage of problem count), including: descending order of quantity (generated in step S2), problem causes (original data), problem count (generated in step S1), and cumulative percentage of problem count (generated in step S4).

[0130] like Figure 8 As shown, the SQL statement to be executed is as follows:

[0131] SELECT b.*, (SELECT SUM(a.number of issues) FROM(SELECT(@id2:=@id2+1) AS number in descending order, x.reason for issue, x.number of issues)

[0132] from(SELECT a.proCATEGORY as Problem Reason, COUNT(a.proDetails) as Problem Count FROM `qa_problem` as a

[0133] group by a.proCATEGORY)as x,(Select(@id2:=0))as z

[0134] order by x.number of problems desc, x.reason of problem asc) as a where a.number of problems (descending order) <= b.number of problems (descending order) / (select count(proDetails) from `qa_problem`) as cumulative percentage of problem count from

[0135] (select(@id1:=@id1+1)as Quantity in descending order, x.Reason for problem, x.Number of problems)

[0136] from(SELECT a.proCATEGORY as Problem Reason, COUNT(a.proDetails) as Problem Count FROM `qa_problem` as a

[0137] group by a.proCATEGORY)as x,(Select(@id1:=0))as z

[0138] order by x.number of problems desc, x.reason for problem asc) as b

[0139] In this optional embodiment, generating the key reason field using logical judgment statements in a structured query language, according to the Pareto principle, includes the following steps:

[0140] S51. According to the Pareto principle, use logical judgment statements in structured query language to determine the key reason field when the cumulative percentage of the number of problems is less than or equal to the threshold.

[0141] S52. Generate a critical cause statistics table that conforms to the Pareto principle, which is used to screen out the critical causes that cause near-threshold problems.

[0142] It's important to explain that this involves using report designer software to perform Pareto chart data analysis on the quality problem table in a MySQL database. The Pareto principle, also known as the 80 / 20 rule, states that 80% of problems are caused by 20% of the underlying causes. Pareto charts are primarily used in project management to identify the critical causes that generate the majority of problems, thus helping to resolve most issues. According to the Pareto principle, when the cumulative percentage of problems (in descending order) is ≤0.8, the corresponding causes account for approximately 80% of the problems. Therefore, the "critical cause" field is defined using an SQL CASE statement on the "cumulative percentage of problems" field from step S4 above. When the cumulative percentage is ≤0.8, the critical cause field is assigned the value "Yes"; otherwise, it's assigned "No," facilitating the direct filtering of the critical causes that generate approximately 80% of the problems.

[0143] like Figure 9 As shown, the SQL statement to be executed is as follows:

[0144] SELECT c.*, (CASE WHEN c.Cumulative Percentage of Problems>0.8 THEN'NO'ELSE'Yes'END) AS Key Reason FROM

[0145] (select b.*,(select sum(a.number of issues)from(select(@id2:=@id2+1)as number in descending order,x.reason for issue,x.number of issues)

[0146] from(SELECT a.proCATEGORY as Problem Reason, COUNT(a.proDetails) as Problem Count FROM `qa_problem` as a

[0147] group by a.proCATEGORY)as x,(Select(@id2:=0))as z

[0148] order by x.number of problems desc, x.reason of problem asc) as a where a.number of problems (descending order) <= b.number of problems (descending order) / (select count(proDetails) from `qa_problem`) as cumulative percentage of problem count from

[0149] (select(@id1:=@id1+1)as Quantity in descending order, x.Reason for problem, x.Number of problems)

[0150] from(SELECT a.proCATEGORY as Problem Reason, COUNT(a.proDetails) as Problem Count FROM `qa_problem` as a

[0151] group by a.proCATEGORY)as x,(Select(@id1:=0))as z

[0152] order by x.number of problems desc, x.reason for problem asc)as b)as c

[0153] In this optional embodiment, according to the Pareto principle, determining the key reason field when the cumulative percentage of the number of questions is less than or equal to a threshold using logical judgment statements in a structured query language includes the following steps:

[0154] S511. Based on the Pareto principle, when the cumulative proportion of the number of problems in descending order is less than or equal to the threshold, the key reason field is assigned the value of "yes"; otherwise, the key reason field is assigned the value of "no".

[0155] S512. Query the fields for problem cause, number of problems, cumulative percentage of problem number, and key cause, and output the filtered results.

[0156] In this optional embodiment, based on the project screening criteria, the problem data is filtered into ongoing project data and historical project data, and steps S1 to S5 are performed on the ongoing project data and historical project data to generate Pareto chart data statistics tables for ongoing projects and historical projects, including the following steps:

[0157] S61. Using report designer software, analyze the Pareto chart data of the quality problem table in the relational database, and divide the problem data into ongoing project data and historical project data based on project screening criteria.

[0158] S62. Perform steps S1 to S5 on the ongoing project data and the historical project data respectively, and finally generate Pareto chart data statistics tables for the ongoing project and the historical project.

[0159] It should be explained that the Pareto chart data analysis of the quality problem table in the MySQL database is implemented using report designer software. The SQL WHERE clause filters data for ongoing and historical projects. Steps S1 to S7 above are then executed on the filtered data to generate Pareto chart statistics for both ongoing and historical projects.

[0160] like Figure 10 and Figure 11 As shown, when WHERE projectID = 136, it represents ongoing project data; when WHERE projectID ! = 136, it represents historical project data. The SQL statements executed are as follows:

[0161] ① Pareto chart data statistics for ongoing projects

[0162] SELECT c.*, (CASE WHEN c.Cumulative Percentage of Problems>0.8 THEN'NO'ELSE'Yes'END) AS Key Reason FROM

[0163] (select b.*,(select sum(a.number of issues)from(select(@id2:=@id2+1)as number in descending order,x.reason for issue,x.number of issues)

[0164] from(SELECT a.proCATEGORY as Problem Reason, count(a.proDetails) as Problem Count FROM(SELECT * from `qa_problem` where projectID=136) as a

[0165] group by a.proCATEGORY)as x,(Select(@id2:=0))as z

[0166] order by x.number of problems desc, x.reason of problem asc) as a where a.number of problems (desc) <= b.number of problems (desc) / (select count(a.proDetails) from (SELECT * from `qa_problem` where projectID = 136) as a) as the cumulative percentage of problem count from

[0167] (select Project ID, (@id1:=@id1+1) as Quantity (descending order), x.Reason for Problem, x.Number of Problems)

[0168] From(SELECT a.projectID as Project ID, a.proCATEGORY as Problem Reason, count(a.proDetails) as Problem Count FROM(SELECT * from `qa_problem` where projectID=136) as a

[0169] group by a.proCATEGORY)as x,(Select(@id1:=0))as z

[0170] order by x.number of problems desc, x.reason for problem asc)as b)as c

[0171] ②Statistical analysis of Pareto chart data for historical projects

[0172] SELECT c.*, (CASE WHEN c.Cumulative Percentage of Problems>0.8 THEN'NO'ELSE'Yes'END) AS Key Reason FROM

[0173] (select b.*,(select sum(a.number of issues)from(select(@id2:=@id2+1)as number in descending order,x.reason for issue,x.number of issues)

[0174] from(SELECT a.proCATEGORY as Problem Reason, count(a.proDetails) as Problem Count FROM(SELECT * from `qa_problem` where projectID!=136) as a

[0175] group by a.proCATEGORY)as x,(Select(@id2:=0))as z

[0176] order by x.number of problems desc, x.reason of problem asc) as a where a.number of problems (desc) <= b.number of problems (desc) / (select count(a.proDetails) from (SELECT * from `qa_problem` where projectID!=136) as a) as cumulative percentage of problem number from

[0177] (select(@id1:=@id1+1)as Quantity in descending order, x.Reason for problem, x.Number of problems)

[0178] from(SELECT a.proCATEGORY as Problem Reason, count(a.proDetails) as Problem Count FROM(SELECT * from `qa_problem` where projectID!=136) as a

[0179] group by a.proCATEGORY)as x,(Select(@id1:=0))as z

[0180] order by x.number of problems desc, x.reason for problem asc)as b)as c

[0181] In this optional embodiment, based on keyword matching statements in Structured Query Language, the Pareto chart data statistics of ongoing projects and historical projects are correlated according to the issue cause field. Combined with logical judgment statements in Structured Query Language, the following steps are taken to generate the attention field for issue causes of ongoing projects based on the Pareto data of historical projects:

[0182] S71. Use keywords in Structured Query Language to return the problem cause field for Pareto chart data statistics of ongoing and historical projects related to matching statements;

[0183] S72. Based on logical judgment statements in the structured query language, and by comparing the key cause field values ​​of the problem causes in the ongoing project data and the historical project data, generate the ongoing project problem cause attention field based on the Pareto chart data statistics of the historical projects.

[0184] It needs to be explained that the Pareto chart data analysis of the quality problem table in the MySQL database is performed using report designer software. The SQL LEFT JOIN statement is used to join the problem cause field from the Pareto chart data statistics of ongoing projects and historical projects in the results of step 8. Then, using an SQL CASE statement, based on the comparison of the key cause field values ​​in these two sets of data, the problem cause attention field is assigned the value of "highly concerned" when both the ongoing and historical project key cause field values ​​are "yes"; when the ongoing project key cause field value is "yes" and the historical project key cause field value is "no", the problem cause attention field is assigned the value of "intensified concern"; when the ongoing project key cause field value is "no" and the historical project key cause field value is "yes", the problem cause attention field is assigned the value of "maintain concern"; otherwise, the field is assigned the value of "general concern", generating the ongoing project problem cause attention field based on the historical project Pareto chart data statistics.

[0185] like Figure 12 As shown, the SQL statement to be executed is as follows:

[0186] SELECT a.*, b.keyreason AS historicalkeyreason, (CASE WHEN a.keyreason = 'Yes' AND b.keyreason = 'Yes' THEN 'Focus on key' WHEN a.keyreason = 'Yes' AND b.keyreason = 'No' THEN 'Focus on key' WHEN a.keyreason = 'No' AND b.keyreason = 'Yes' THEN 'Maintain focus' Else 'General focus' END) AS problemkeyreasonsfocusfrom

[0187] (select c.*,(case when c.accumulated percentage of problems>0.8then'no'else'yes'end)as key reason from

[0188] (select b.*,(select sum(a.number of issues)from(select(@id2:=@id2+1)as number in descending order,x.reason for issue,x.number of issues)

[0189] from(SELECT a.proCATEGORY as Problem Reason, count(a.proDetails) as Problem Count FROM(SELECT * from `qa_problem` where projectID=136) as a

[0190] group by a.proCATEGORY)as x,(Select(@id2:=0))as z

[0191] order by x.number of problems desc, x.reason of problem asc) as a where a.number of problems (desc) <= b.number of problems (desc) / (select count(a.proDetails) from (SELECT * from `qa_problem` where projectID = 136) as a) as the cumulative percentage of problem count from

[0192] (select Project ID, (@id1:=@id1+1) as Quantity (descending order), x.Reason for Problem, x.Number of Problems)

[0193] From(SELECT a.projectID as Project ID, a.proCATEGORY as Problem Reason, count(a.proDetails) as Problem Count FROM(SELECT * from `qa_problem` where projectID=136) as a

[0194] group by a.proCATEGORY)as x,(Select(@id1:=0))as z

[0195] order by x. Number of problems desc, x. Reason for the problem asc) as b) as c) a

[0196] left join

[0197] (select c.*,(case when c.accumulated percentage of problems>0.8then'no'else'yes'end)as key reason from

[0198] (select b.*,(select sum(a.number of issues)from(select(@id2:=@id2+1)as number in descending order,x.reason for issue,x.number of issues)

[0199] from(SELECT a.proCATEGORY as Problem Reason, count(a.proDetails) as Problem Count FROM(SELECT * from `qa_problem` where projectID!=136) as a

[0200] group by a.proCATEGORY)as x,(Select(@id2:=0))as z

[0201] order by x.number of problems desc, x.reason of problem asc) as a where a.number of problems (desc) <= b.number of problems (desc) / (select count(a.proDetails) from (SELECT * from `qa_problem` where projectID!=136) as a) as cumulative percentage of problem number from

[0202] (select(@id1:=@id1+1)as Quantity in descending order, x.Reason for problem, x.Number of problems)

[0203] from(SELECT a.proCATEGORY as Problem Reason, count(a.proDetails) as Problem Count FROM(SELECT * from `qa_problem` where projectID!=136) as a

[0204] group by a.proCATEGORY)as x,(Select(@id1:=0))as z

[0205] order by x. Number of problems desc, x. Reason for the problem asc) as b) as c) b

[0206] a. Reason for the problem = b. Reason for the problem

[0207] In this optional embodiment, based on logical judgment statements in Structured Query Language, and by comparing the key cause field values ​​of problem causes in ongoing project data and historical project data, generating the ongoing project problem cause attention field based on Pareto chart data statistics includes the following steps:

[0208] S721. If the key reason field values ​​for both ongoing and historical projects are yes, then the issue cause concern field is assigned the value of "highly concerned".

[0209] S722. If the key reason field value of an ongoing project is yes, and the key reason field value of a historical project is no, then the issue cause attention field is assigned the value of increased attention.

[0210] S723. If the key reason field value of an ongoing project is no, and the key reason field value of a historical project is yes, then the issue cause concern field is assigned the value of "keep concerned".

[0211] S724. If the key cause field value of the ongoing project and the key cause field value of the historical project are not as described in steps S721, S722 and S723, then the issue cause concern field is assigned the value of general concern, and an ongoing project issue cause concern field based on Pareto chart data statistics of historical projects is generated.

[0212] According to another embodiment of the invention, such as Figure 2 As shown, a Pareto chart data analysis system based on a structured query language is also provided, which includes:

[0213] Data statistics module 1 is used to use the dataset partitioning statement in Structured Query Language and the counting function in Structured Query Language to count the causes of each problem and the number of problems they cause, and generate a problem cause statistics table.

[0214] Data sorting module 2 is used to sort the generated problem cause statistics table using the sorting clause in the structured query language. It combines preset variables and ranking formulas to perform descending order ranking of quantity, query the problem cause and problem quantity fields, and generate a problem cause statistics table with a descending order ranking field.

[0215] The cumulative calculation module 3 is used to pre-set the problem cause statistics table with a descending order ranking field of quantity as two sets of identical data, and use the selection criteria clause and aggregate functions in the structured query language to accumulate the number of problems one by one to generate the cumulative result of the number of problems;

[0216] The cumulative percentage calculation module 4 is used to generate the total number of problems using the counting function in the structured query language, divide the cumulative result of the number of problems by the total number of problems generated, and perform a query on all fields and the cumulative percentage of the number of problems field on one of the preset sets of data to generate a statistical table of the reasons for problems with a descending order of the number of problems and the cumulative percentage of the number of problems field.

[0217] Key reason marking module 5 is used to generate key reason fields based on Pareto principle and logical judgment statements in structured query language;

[0218] The Pareto chart data generation module 6 is used to filter the problem data into ongoing project data and historical project data based on the project screening conditions, and to perform Pareto chart analysis on the ongoing project data and historical project data (i.e., to execute steps S1 to S5) to generate Pareto chart data statistics tables for ongoing projects and historical projects.

[0219] The Pareto chart data analysis module 7 is used to return matching statements based on keywords in the structured query language, associate the Pareto chart data statistics results of ongoing projects and historical projects according to the problem cause field, and combine logical judgment statements in the structured query language to generate the attention field of the problem cause of ongoing projects based on the Pareto data of historical projects.

[0220] In summary, by utilizing the above-mentioned technical solution of this invention, this invention generates statistical data on problem causes—including descending order of quantity, cumulative percentage of problem quantity, and key cause fields—from quality problem data composed of problem causes and problem descriptions using SQL statements supported by various database versions. This data is necessary for drawing Pareto charts. Simultaneously, in the Pareto chart data of ongoing projects, the problem cause field is linked to the Pareto chart data of historical projects. By comparing the key cause fields of ongoing and historical projects, a problem cause attention field is defined, enabling attention analysis of problem causes in ongoing projects based on historical data. This invention facilitates the direct generation of Pareto charts composed of problem cause statistical bar charts and cumulative percentage of problem quantity line charts in project management. Furthermore, by comparing the key causes of ongoing and historical projects, attention analysis of problem causes in ongoing projects is achieved, effectively focusing on the key causes that generate the most problems, thereby resolving major issues. This method has good instruction versatility and version compatibility, meeting the requirements of high cohesion and low coupling in code.

[0221] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A Pareto chart data analysis method based on Structured Query Language, characterized in that, This Pareto chart data analysis method includes the following steps: S1. Using the dataset partitioning statement in Structured Query Language and the counting function in Structured Query Language, count the causes of each problem and the number of problems they cause, and generate a problem cause statistics table; S2. Use the sorting clause in the structured query language to sort the generated problem cause statistics table. Combine the preset variables and ranking formula to perform descending order ranking, problem cause and problem quantity field queries to generate a problem cause statistics table with descending order ranking field. S3. The problem cause statistics table with descending order of quantity is preset to two sets of identical data. Using the selection criteria clause and aggregate functions in the structured query language, the problem quantity is accumulated one by one to generate the cumulative result of the problem quantity. S4. Use the counting function in the structured query language to generate the total number of problems, divide the cumulative result of the number of problems by the total number of problems generated, define the cumulative percentage of the number of problems field, and perform a query on all fields and the cumulative percentage of the number of problems field on one of the preset sets of data to generate a statistical table of the causes of problems with descending order of quantity and the cumulative percentage of the number of problems field. S5. Based on the Pareto principle, generate key reason fields using logical judgment statements in structured query language; S6. Based on the project screening criteria, the problem data is filtered into ongoing project data and historical project data, and steps S1 to S5 are executed on the ongoing project data and historical project data to generate Pareto chart data statistics tables for ongoing projects and historical projects. S7. Based on the keyword matching statement in the structured query language, the Pareto chart data statistics results of ongoing projects and historical projects are associated by the problem cause field. Combined with the logical judgment statement in the structured query language, the attention field of the problem cause of ongoing projects is generated based on the Pareto data of historical projects.

2. The Pareto chart data analysis method based on structured query language according to claim 1, characterized in that, The process of using the dataset partitioning statement in Structured Query Language (SCL) and the counting function in SCL to count the causes of each problem and the number of problems they cause, and generating a problem cause statistics table, includes the following steps: S11. Preset the structure of the quality problem data table, which includes a problem cause field and a problem description field; S12. Apply the dataset partitioning statement in Structured Query Language to the problem cause field, and execute the counting function in Structured Query Language on the problem description field. Set the execution result as the problem quantity field of the problem cause, and generate a problem cause statistics table for quality problem data.

3. The Pareto chart data analysis method based on structured query language according to claim 1, characterized in that, The process of sorting the generated problem cause statistics table using the sorting clause in Structured Query Language, and combining preset variables and ranking formulas to perform descending order ranking, problem cause, and problem quantity field queries to generate a problem cause statistics table with a descending order ranking field includes the following steps: S21. Sort the generated problem cause statistics table by the sorting clause in the structured query language according to the rules of descending order of the problem quantity field and ascending order of the problem cause field. S22. Set the initial values ​​of preset variables, and use the keyword statement in Structured Query Language to set the descending ranking field of the number of questions as a variable formula; S23. Based on the variable formula, perform a query on the fields of quantity descending order ranking, problem cause, and problem quantity, and generate a statistical table of problem causes with a quantity descending order ranking field.

4. The Pareto chart data analysis method based on structured query language according to claim 3, characterized in that, The formula for the variable is: @id1:=@id1+1; In the formula, @id1 represents a variable; := represents assignment; +1 represents incrementing by one.

5. The Pareto chart data analysis method based on structured query language according to claim 1, characterized in that, The step of pre-setting the problem cause statistics table with a descending order ranking field as two sets of identical data, and using the selection criteria clause and aggregate functions specified in Structured Query Language (SCL) to accumulate the problem count one by one to generate the cumulative result of the problem count includes the following steps: S31. Divide the problem cause statistics table with a descending order ranking field into two groups of identical data, a and b. S32. Using the selection criteria clause specified in the Structured Query Language, filter out the data of group a where the descending order of the quantity ranking field of group a is less than or equal to the descending order of the quantity ranking field of group b. S33. Use aggregate functions in Structured Query Language to generate a cumulative result of the number of problems for each reason in descending order, and use the cumulative result as the numerator of the cumulative percentage of the number of problems.

6. The Pareto chart data analysis method based on structured query language according to claim 5, characterized in that, The process of generating a total number of issues using a counting function in Structured Query Language, dividing the cumulative number of issues by the total number of issues generated, and then performing queries on all fields and the cumulative percentage of issues field on a preset set of data to generate an issue cause statistics table with descending order of number and cumulative percentage of issues field includes the following steps: S41. Use the counting function in Structured Query Language to generate the total number of problems in the problem description field of the problem cause statistics table of the generated quality problem data, and use the total number of problems as the denominator of the cumulative percentage of the number of problems; S42. Divide the cumulative number of issues by the total number of issues generated, and define the cumulative percentage of issues field; S43. Perform a query on all fields and the cumulative percentage of the number of problems field on the data of group b, and generate a statistical table of the causes of problems with descending order of quantity and the cumulative percentage of the number of problems field.

7. The Pareto chart data analysis method based on structured query language according to claim 1, characterized in that, The process of generating key reason fields using logical judgment statements in Structured Query Language based on the Pareto principle includes the following steps: S51. According to the Pareto principle, use logical judgment statements in structured query language to determine the key reason field when the cumulative percentage of the number of problems is less than or equal to the threshold. S52. Generate a critical cause statistics table that conforms to the Pareto principle, which is used to screen out the critical causes that cause near-threshold problems.

8. The Pareto chart data analysis method based on structured query language according to claim 7, characterized in that, The process of determining the key reason field when the cumulative percentage of the number of problems is less than or equal to a threshold, based on the Pareto principle and using logical judgment statements in Structured Query Language, includes the following steps: S511. Based on the Pareto principle, when the cumulative proportion of the number of problems in descending order is less than or equal to the threshold, the key reason field is assigned the value of "yes"; otherwise, the key reason field is assigned the value of "no". S512. Query the fields for problem cause, number of problems, cumulative percentage of problem number, and key cause, and output the filtered results.

9. The Pareto chart data analysis method based on structured query language according to claim 1, characterized in that, The process of filtering problem data into ongoing project data and historical project data based on project screening criteria, and then performing steps S1 to S5 on the ongoing project data and historical project data to generate Pareto chart data statistics tables for ongoing projects and historical projects, includes the following steps: S61. Using report designer software, analyze the Pareto chart data of the quality problem table in the relational database, and divide the problem data into ongoing project data and historical project data based on project screening criteria. S62. Perform steps S1 to S5 on the ongoing project data and the historical project data respectively, and finally generate Pareto chart data statistics tables for the ongoing project and the historical project.

10. The Pareto chart data analysis method based on structured query language according to claim 1, characterized in that, The process of using keyword-based matching statements in Structured Query Language (SCL) to correlate Pareto chart data statistics of ongoing and historical projects by the issue cause field, and combining this with logical judgment statements in SCL to generate the attention field for issue causes of ongoing projects based on Pareto data of historical projects, includes the following steps: S71. Use keywords in Structured Query Language to return the problem cause field for Pareto chart data statistics of ongoing and historical projects related to matching statements; S72. Based on logical judgment statements in the structured query language, and by comparing the key cause field values ​​of the problem causes in the ongoing project data and the historical project data, generate the ongoing project problem cause attention field based on the Pareto chart data statistics of the historical projects.

11. The Pareto chart data analysis method based on structured query language according to claim 10, characterized in that, The process of generating the "Attention Field for Problem Causes in Ongoing Projects" based on Pareto chart data statistics from historical projects, using logical judgment statements in Structured Query Language and comparing the key cause field values ​​of problem causes in ongoing project data and historical project data, includes the following steps: S721. If the key reason field values ​​for both ongoing and historical projects are yes, then the issue cause concern field is assigned the value of "highly concerned". S722. If the key reason field value of an ongoing project is yes, and the key reason field value of a historical project is no, then the issue cause attention field is assigned the value of increased attention. S723. If the key reason field value of an ongoing project is no, and the key reason field value of a historical project is yes, then the issue cause concern field is assigned the value of "keep concerned". S724. If the key cause field value of the ongoing project and the key cause field value of the historical project are not as described in steps S721, S722 and S723, then the issue cause concern field is assigned the value of general concern, and an ongoing project issue cause concern field based on Pareto chart data statistics of historical projects is generated.

12. A Pareto chart data analysis system based on Structured Query Language, used to implement the Pareto chart data analysis method based on Structured Query Language as described in any one of claims 1-11, characterized in that, This Pareto chart data analysis system includes: The data statistics module is used to utilize the dataset partitioning statement in Structured Query Language and the counting function in Structured Query Language to count the causes of each problem and the number of problems they cause, and generate a problem cause statistics table. The data sorting module is used to sort the generated problem cause statistics table using the sorting clause in the structured query language. It combines preset variables and ranking formulas to perform descending order ranking of quantity, query the problem cause and problem quantity fields, and generate a problem cause statistics table with a descending order ranking field. The cumulative calculation module is used to pre-set two sets of identical data in the problem cause statistics table with a descending order ranking field for the number of problems. It uses the selection criteria clause and aggregate functions in the structured query language to accumulate the number of problems one by one and generate a cumulative result of the number of problems. The cumulative percentage calculation module is used to generate the total number of issues using the counting function in the structured query language, divide the cumulative result of the number of issues by the total number of issues generated, and perform a query on all fields and the cumulative percentage of the number of issues field on one of the preset sets of data to generate a statistical table of the reasons for issues with a descending order of the number of issues and the cumulative percentage of the number of issues field. The key reason tagging module is used to generate key reason fields based on the Pareto principle and logical judgment statements in the structured query language. The Pareto chart data generation module is used to filter problem data into ongoing project data and historical project data based on project screening criteria, and to perform Pareto chart analysis on the ongoing project data and historical project data to generate Pareto chart data statistics tables for ongoing projects and historical projects. The Pareto chart data analysis module is used to return matching statements based on keywords in Structured Query Language (SCL). It associates the Pareto chart data statistics results of ongoing projects and historical projects according to the problem cause field, and combines logical judgment statements in SCL to generate the attention field of the problem cause of ongoing projects based on the Pareto data of historical projects.

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