Method for automatic data analysis based on multi-agent cognitive collaboration

Abstract

The invention provides an automatic data analysis method based on multi-agent cognitive collaboration, which comprises the following steps: S1, multiple agents perform modeling processing, and each agent has unique functions and tasks in a multi-agent system; s2, the user side is used for collecting complex problems proposed by a user; s3, the controller coordinates multiple agents to work, wherein the multiple agents comprise a problem analysis module, an SQL module, an OA module and a data analysis module; s4, analyzing a complex problem into a plurality of sub-problems by a problem analysis module; s5, the SQL module searches a table structure according to the sub-problem requirements, compiles an SQL and operates the SQL; s6, the QA module is responsible for checking whether the sql is correct or not; according to the method, the search technology and the large model generation technology are combined, knowledge outside the model can be obtained through the search technology, a tool can be directly operated to solve user problems, and more complex problems can be solved in multiple rounds of interaction.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method for automated data analysis based on multi-agent cognitive collaboration. Background Art

[0002] Multi-agent cognitive collaboration refers to the efficient completion of complex tasks through information exchange, sharing and collaboration among multiple agents. Compared with chat, RAG and single agent, multi-agent collaboration can have more powerful performance.

[0003] The existing method is to directly conduct human-computer dialogue with a large model. The problem with this solution is that it cannot obtain knowledge outside the model, and its search ability is poor. It cannot solve more complex problems in multiple rounds of interactions. Therefore, a method based on multi-agent cognitive collaboration for automated data analysis is proposed. Summary of the invention

[0004] In view of this, an embodiment of the present invention hopes to provide a method for automated data analysis based on multi-agent cognitive collaboration to solve or alleviate the technical problems existing in the prior art, and at least provide a beneficial option.

[0005] The technical solution of the embodiment of the present invention is implemented as follows: a method for automated data analysis based on multi-agent cognitive collaboration, comprising the following steps:

[0006] S1. Multi-agent modeling. In a multi-agent system, each agent has its own unique functions and tasks.

[0007] S2, the user end is used to collect complex questions raised by users;

[0008] S3, the controller coordinates the multi-agents to work, and the multi-agents include problem analysis module, SQL module, OA module and data analysis module;

[0009] S4, problem analysis module, analyzes complex problems into multiple sub-problems;

[0010] S5, SQL module searches the table structure, writes SQL, and runs SQL according to the sub-problem requirements;

[0011] S6, QA module is responsible for checking whether the SQL is correct;

[0012] S7. The data analysis module forms conclusions based on the information returned by SQL.

[0013] In some embodiments, in S5, searching for a table structure is to search for an existing table structure in a database. If the specific requirements are not known, the following SQL statement can be used to view all tables in the database:

[0014] SELECT*FROM information_schema.tables WHERE table_schema='your_database_name';

[0015] Replace 'your_database_name' with the name of your database. To search for a specific table, you can use something like this:

[0016] DESCRIBE your_table_name;

[0017] Or use SHOW CREATE TABLE your_table_name;

[0018] Used to search table structures.

[0019] In some embodiments, in S5, the SQL query statement is written according to the requirements of the sub-problem in S4 to select all records from the table named users, which can be as follows:

[0020] SELECT * FROM users;

[0021] To filter data based on specific conditions, you can use the WHERE clause, which can be used as follows:

[0022] SELECT*FROM users WHERE age>=21;

[0023] Used to write SQL.

[0024] In some embodiments, in S5, running SQL means that after the SQL statement is written, the statement needs to be executed in the database management system. In different environments, the way of running SQL may be different. The SQL statement can be run through a database management tool or using a command line tool.

[0025] In some embodiments, in S6, the specific steps of the QA module to check whether the SQL is correct are as follows:

[0026] S61. Syntax check: ensure that the SQL statement complies with the syntax rules of the database management system;

[0027] S62, logic check: verify that the SQL statement is logically correct;

[0028] S63, Table structure check: Ensure that the tables and fields referenced in the SQL statement exist in the database;

[0029] S64. Data type check: Ensure that the data type used in the SQL statement is consistent with the data type in the table definition;

[0030] S65, Performance check: Evaluate the execution efficiency of SQL statements to ensure that queries can run efficiently;

[0031] S66. Security check: Ensure that SQL statements do not cause security vulnerabilities.

[0032] In some embodiments, in S61, the specific steps of checking the syntax of the SQL statement are as follows:

[0033] S611. Use correct grammatical structure: Make sure that SQL statements follow the grammatical rules of the database management system.

[0034] S612. Check keywords and operators: Make sure that the correct SQL keywords and operators are used.

[0035] S613. Verify data types and expressions: Ensure that the data types of all column names, table names, variables, and expressions are correct;

[0036] S614, Nested queries and clauses: Ensure that nested queries and clauses also comply with grammatical rules;

[0037] S615, Use of quotes and carets: Check that string, column, and table names are correctly quoted;

[0038] S616, statement end symbol: Make sure each SQL statement ends with a semicolon (;);

[0039] S617, Comments and spaces: Check whether the comments and spaces in the SQL statement are placed correctly and will not affect the execution of the statement.

[0040] In some embodiments, in S62, the specific steps of the logic check of the SQL statement are as follows:

[0041] S621, authority verification: ensure that the user who executes the SQL statement has the authority to execute the operation;

[0042] S622, Data consistency: Check whether SQL statements will cause data inconsistency and whether the inserted or updated data complies with database constraints;

[0043] S623, Business rules: Ensure that SQL statements comply with specific business rules and logic;

[0044] S624, Data integrity: Check whether the SQL statement will affect the integrity of the data, whether there will be duplicate records or key data loss;

[0045] S625. Transaction processing: Ensure that SQL statements are executed correctly within a transaction.

[0046] In some embodiments, in S63, the specific steps of checking the table structure of the SQL statement are as follows:

[0047] S631, table existence check: check whether all tables referenced in the SQL statement exist in the database;

[0048] Use the IF EXISTS(SELECT * FROM sys.objects WHERE object_id = OBJECT_ID(N'table name') AND type in(N'U'))(for SQL Server) query to check if the table exists;

[0049] S632, field existence check:

[0050] Check whether all fields referenced in the SQL statement exist in the corresponding table;

[0051] Use a query like SELECT * FROM information_schema.columns WHERE table_name = 'table name' to check if the field exists;

[0052] S633, data type matching:

[0053] Make sure the field data types used in the SQL statement match the definitions in the table structure;

[0054] Use SELECT data_type FROM information_schema.columns WHERE table_name = 'table name' AND column_name = 'field name' to query the data type of the field;

[0055] S634, Constraint Check:

[0056] Check whether the insert or update operation in the SQL statement complies with the table constraints, and use the specific queries provided by the database to check whether the constraints are violated;

[0057] S635, column permission check:

[0058] Make sure the user who executes the SQL statement has the permission to access and operate these columns.

[0059] In some embodiments, in S64, the specific steps of checking the data type of the SQL statement are as follows:

[0060] S641, Data type definition: It is necessary to clearly define the expected data type of each field in the table;

[0061] S642. Check when creating a table structure: Include constraints on data types in the creation statement. In MySQL, the statement to create a user table and ensure that the 'age' field is an integer is as follows:

[0062]

[0063] S643. Use triggers for checking: Before inserting or updating data into a table, set up triggers to perform data type checks. In MySQL, a trigger is as follows:

[0064]

[0065]

[0066] S644. Use database functions to check: Use the functions provided by the database to check the data type in the application. In MySQL, use the DATA_TYPE() function to check the data type of the column.

[0067] S645, Application level check: Before the application submits data to the database, the data type is checked at the application level. This is done by writing code logic, using Python's isinstance() function, or type checking in Java.

[0068] In some embodiments, in S65, the specific steps of the performance check of the SQL statement are as follows:

[0069] S651, Response time: the time from user operation to system response;

[0070] Formula: Response time = operation completion time - operation initiation time;

[0071] S652, Throughput: The amount of work processed by the system per unit time;

[0072] Formula: Throughput = number of completed tasks / time consumed;

[0073] S653, concurrent users: the number of users operating the system at the same time;

[0074] Formula: Number of concurrent users = number of simultaneously active users;

[0075] S654, Resource Utilization: System resource usage;

[0076] Formula: Resource utilization = (actual resources used / total resources) * 100%;

[0077] S655, System load: the workload of the system operation;

[0078] Formula: System load = (number of processes + number of open files) / total number of processes;

[0079] S656. Availability: The ratio of system normal operation time to total time;

[0080] Formula: Availability = (uptime / total time)*100%;

[0081] S657, Error rate: the proportion of errors that occur;

[0082] Formula: Error rate = (number of errors / total number of operations)*100%.

[0083] The embodiment of the present invention has the following advantages due to the adoption of the above technical solution:

[0084] The present invention combines search technology and large model generation technology through this method, can obtain knowledge beyond the model through search technology, can directly operate tools to solve user problems, and can solve more complex problems in multiple rounds of interaction.

[0085] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present invention will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0087] Figure 1 is a flow chart of the present invention;

[0088] Figure 2 It is a framework diagram of the present invention. DETAILED DESCRIPTION

[0089] In the following, only some exemplary embodiments are briefly described. As those skilled in the art will appreciate, the described embodiments may be modified in various ways without departing from the spirit or scope of the present invention. Therefore, the drawings and descriptions are considered to be exemplary and non-restrictive in nature.

[0090] It should be noted that the terms "first", "second", "symmetrical", "array", etc. are only used to distinguish between description and position description purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the definition of "first", "symmetrical", etc. can explicitly or implicitly include one or more of these features; similarly, when the quantity of certain features is not limited in the form of words such as "two" or "three", it should be noted that this feature also explicitly or implicitly includes one or more feature quantities;

[0091] In the present invention, unless otherwise clearly specified and limited, the terms such as "installation", "connection", "fixation" and the like should be understood in a broad sense; for example, it can be a fixed connection, a detachable connection, or an integral molding; it can be a mechanical connection, a direct connection, welding, or an indirect connection through an intermediate medium, or it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood based on the description and drawings combined with specific circumstances.

[0092] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0093] like Figure 1-Figure 2 As shown, an embodiment of the present invention provides a method for automated data analysis based on multi-agent cognitive collaboration, comprising the following steps:

[0094] S1. Multi-agent modeling. In a multi-agent system, each agent has its own unique functions and tasks.

[0095] S2, the user end is used to collect complex questions raised by users;

[0096] S3, the controller coordinates the multi-agents to work, and the multi-agents include problem analysis module, SQL module, OA module and data analysis module;

[0097] S4, problem analysis module, analyzes complex problems into multiple sub-problems;

[0098] S5, SQL module searches the table structure, writes SQL, and runs SQL according to the sub-problem requirements;

[0099] S6, QA module is responsible for checking whether the SQL is correct;

[0100] S7. The data analysis module forms conclusions based on the information returned by SQL.

[0101] In this embodiment, specifically, in S5, searching the table structure is to search for the structure of an existing table in the database. If the specific requirements are not determined, the following SQL statement can be used to view all tables in the database:

[0102] SELECT*FROM information_schema.tables WHERE table_schema='your_database_name';

[0103] Replace 'your_database_name' with the name of your database. To search for a specific table, you can use something like this:

[0104] DESCRIBE your_table_name;

[0105] Or use SHOW CREATE TABLE your_table_name;

[0106] Used to search table structures.

[0107] In this embodiment, specifically, in S5, SQL is written according to the requirements of the sub-problem in S4, and a corresponding SQL query statement is written to select all records from a table named users, which can be used as follows:

[0108] SELECT * FROM users;

[0109] To filter data based on specific conditions, you can use the WHERE clause, which can be used as follows:

[0110] SELECT*FROM users WHERE age>=21;

[0111] Used to write SQL.

[0112] In this embodiment, specifically, in S5, running SQL means that after the SQL statement is written, the statement needs to be executed in the database management system. In different environments, the way of running SQL may be different. The SQL statement can be run through a database management tool or using a command line tool.

[0113] In this embodiment, specifically, in S6, the QA module is responsible for checking whether the SQL is correct in the following specific steps:

[0114] S61. Syntax check: ensure that the SQL statement complies with the syntax rules of the database management system;

[0115] S62, logic check: verify that the SQL statement is logically correct;

[0116] S63, Table structure check: Ensure that the tables and fields referenced in the SQL statement exist in the database;

[0117] S64. Data type check: Ensure that the data type used in the SQL statement is consistent with the data type in the table definition;

[0118] S65, Performance check: Evaluate the execution efficiency of SQL statements to ensure that queries can run efficiently;

[0119] S66. Security check: Ensure that SQL statements do not cause security vulnerabilities.

[0120] In this embodiment, specifically, in S61, the specific steps of checking the syntax of the SQL statement are as follows:

[0121] S611. Use correct grammatical structure: Make sure that SQL statements follow the grammatical rules of the database management system.

[0122] S612. Check keywords and operators: Make sure that the correct SQL keywords and operators are used.

[0123] S613. Verify data types and expressions: Ensure that the data types of all column names, table names, variables, and expressions are correct;

[0124] S614, Nested queries and clauses: Ensure that nested queries and clauses also comply with grammatical rules;

[0125] S615, Use of quotes and carets: Check that string, column, and table names are correctly quoted;

[0126] S616, statement end symbol: Make sure each SQL statement ends with a semicolon (;);

[0127] S617, Comments and blanks: Check whether the comments and blanks in the SQL statement are placed correctly and will not affect the execution of the statement. The above setting clauses include SELECT, FROM, WHERE, GROUP BY and HAVING.

[0128] In this embodiment, specifically, in S62, the specific steps of the logic check of the SQL statement are as follows:

[0129] S621, authority verification: ensure that the user who executes the SQL statement has the authority to execute the operation;

[0130] S622, Data consistency: Check whether SQL statements will cause data inconsistency and whether the inserted or updated data complies with database constraints;

[0131] S623, Business rules: Ensure that SQL statements comply with specific business rules and logic;

[0132] S624, Data integrity: Check whether the SQL statement will affect the integrity of the data, whether there will be duplicate records or key data loss;

[0133] S625. Transaction processing: Ensure that SQL statements are executed correctly within a transaction.

[0134] In this embodiment, specifically, in S63, the specific steps of checking the table structure of the SQL statement are as follows:

[0135] S631, table existence check: check whether all tables referenced in the SQL statement exist in the database;

[0136] Use the IF EXISTS(SELECT * FROM sys.objects WHERE object_id = OBJECT_ID(N'table name') AND type in(N'U'))(for SQL Server) query to check if the table exists;

[0137] S632, field existence check:

[0138] Check whether all fields referenced in the SQL statement exist in the corresponding table;

[0139] Use a query like SELECT * FROM information_schema.columns WHERE table_name = 'table name' to check if the field exists;

[0140] S633, data type matching:

[0141] Make sure the field data types used in the SQL statement match the definitions in the table structure;

[0142] Use SELECT data_type FROM information_schema.columns WHERE table_name = 'table name' AND column_name = 'field name' to query the data type of the field;

[0143] S634, Constraint Check:

[0144] Check whether the insert or update operation in the SQL statement complies with the table constraints, and use the specific queries provided by the database to check whether the constraints are violated;

[0145] S635, column permission check:

[0146] Make sure the user who executes the SQL statement has the permission to access and operate these columns.

[0147] In this embodiment, specifically, in S64, the specific steps of checking the data type of the SQL statement are as follows:

[0148] S641, Data type definition: It is necessary to clearly define the expected data type of each field in the table;

[0149] S642. Check when creating a table structure: Include constraints on data types in the creation statement. In MySQL, the statement to create a user table and ensure that the 'age' field is an integer is as follows:

[0150]

[0151] S643. Use triggers for checking: Before inserting or updating data into a table, set up triggers to perform data type checks. In MySQL, a trigger is as follows:

[0152]

[0153] S644. Use database functions to check: Use the functions provided by the database to check the data type in the application. In MySQL, use the DATA_TYPE() function to check the data type of the column.

[0154] S645, Application level check: Before the application submits data to the database, the data type is checked at the application level by writing code logic, using Python's isinstance() function, or type checking in Java. The data types set above include integers (INT), floating point numbers (FLOAT), strings (VARCHAR), or dates (DATETIME), etc.

[0155] In this embodiment, specifically, in S65, the specific steps of the performance check of the SQL statement are as follows:

[0156] S651, Response time: the time from user operation to system response;

[0157] Formula: Response time = operation completion time - operation initiation time;

[0158] S652, Throughput: The amount of work processed by the system per unit time;

[0159] Formula: Throughput = number of completed tasks / time consumed;

[0160] S653, concurrent users: the number of users operating the system at the same time;

[0161] Formula: Number of concurrent users = number of simultaneously active users;

[0162] S654, Resource Utilization: System resource usage;

[0163] Formula: Resource utilization = (actual resources used / total resources) * 100%;

[0164] S655, System load: the workload of the system operation;

[0165] Formula: System load = (number of processes + number of open files) / total number of processes;

[0166] S656. Availability: The ratio of system normal operation time to total time;

[0167] Formula: Availability = (uptime / total time)*100%;

[0168] S657, Error rate: the proportion of errors that occur;

[0169] Formula: Error rate = (number of errors / total number of operations) * 100%, through the above settings system resource usage, such as CPU, memory, disk I / O, etc.

[0170] In this embodiment, specifically, the agent uses prompt word definitions, which have a certain flexibility, and its essence is to guide the large language model to play a certain role and output the result according to the specified role;

[0171] Each agent has a separate memory, which is associated with skills. The memory is saved to ES through text, using keywords and vectors as indexes;

[0172] The agent's memory includes but is not limited to examples, rules, and database schemas. A single agent is similar to a RAG system in a specific domain.

[0173] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of various changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for automated data analysis based on multi-agent cognitive collaboration, characterized in that: The following steps are involved: S1. Multi-agent modeling. In a multi-agent system, each agent has its own unique functions and tasks. S2, the user end is used to collect complex questions raised by users; S3, the controller coordinates the multi-agents to work, and the multi-agents include problem analysis module, SQL module, OA module and data analysis module; S4, problem analysis module, analyzes complex problems into multiple sub-problems; S5, SQL module searches the table structure, writes SQL, and runs SQL according to the sub-problem requirements; S6, QA module is responsible for checking whether the SQL is correct; S7. The data analysis module forms conclusions based on the information returned by SQL.

2. The method for automated data analysis based on multi-agent cognitive collaboration according to claim 1, characterized in that: In S5, searching for table structure is to find the structure of an existing table in the database. If the specific requirements are not known, the following SQL statement can be used to view all tables in the database: SELECT*FROM information_schema.tables WHERE table_schema='your_database_name'; Replace 'your_database_name' with the name of your database. To search for a specific table, you can use something like this: DESCRIBE your_table_name; Or use SHOW CREATE TABLE your_table_name; Used to search table structures.

3. The method for automated data analysis based on multi-agent cognitive collaboration according to claim 1, characterized in that: In S5, write SQL according to the requirements of the sub-problem in S4, write the corresponding SQL query statement, select all records from the table named users, which can be used as follows: SELECT * FROM users; To filter data based on specific conditions, you can use the WHERE clause, which can be used as follows: SELECT*FROM users WHERE age>=21; Used to write SQL.

4. The method for automated data analysis based on multi-agent cognitive collaboration according to claim 1, characterized in that: In S5, running SQL means that after the SQL statement is written, the statement needs to be executed in the database management system. In different environments, the way of running SQL may be different. The SQL statement can be run through a database management tool or using a command line tool.

5. The method for automated data analysis based on multi-agent cognitive collaboration according to claim 1, characterized in that: In S6, the specific steps of the QA module to check whether the SQL is correct are as follows: S61. Syntax check: ensure that the SQL statement complies with the syntax rules of the database management system; S62, logic check: verify that the SQL statement is logically correct; S63, Table structure check: Ensure that the tables and fields referenced in the SQL statement exist in the database; S64. Data type check: Ensure that the data type used in the SQL statement is consistent with the data type in the table definition; S65, Performance check: Evaluate the execution efficiency of SQL statements to ensure that queries can run efficiently; S66. Security check: Ensure that SQL statements do not cause security vulnerabilities.

6. The method for automated data analysis based on multi-agent cognitive collaboration according to claim 5, characterized in that: In S61, the specific steps of checking the syntax of the SQL statement are as follows: S611. Use correct grammatical structure: Make sure that SQL statements follow the grammatical rules of the database management system. S612. Check keywords and operators: Make sure that the correct SQL keywords and operators are used. S613. Verify data types and expressions: Ensure that the data types of all column names, table names, variables, and expressions are correct; S614, Nested queries and clauses: Ensure that nested queries and clauses also comply with grammatical rules; S615, Use of quotes and carets: Check that string, column, and table names are correctly quoted; S616, statement end symbol: Make sure each SQL statement ends with a semicolon (;); S617, Comments and spaces: Check whether the comments and spaces in the SQL statement are placed correctly and will not affect the execution of the statement.

7. The method for automated data analysis based on multi-agent cognitive collaboration according to claim 5, characterized in that: In S62, the specific steps of the logic check of the SQL statement are as follows: S621, authority verification: ensure that the user who executes the SQL statement has the authority to execute the operation; S622, Data consistency: Check whether SQL statements will cause data inconsistency and whether the inserted or updated data complies with database constraints; S623, Business rules: Ensure that SQL statements comply with specific business rules and logic; S624, Data integrity: Check whether the SQL statement will affect the integrity of the data, whether there will be duplicate records or key data loss; S625. Transaction processing: Ensure that SQL statements are executed correctly within a transaction.

8. The method for automated data analysis based on multi-agent cognitive collaboration according to claim 5, characterized in that: In S63, the specific steps of checking the table structure of the SQL statement are as follows: S631, table existence check: check whether all tables referenced in the SQL statement exist in the database; Use IF EXISTS(SELECT*FROM sys.objects WHERE object_id = OBJECT_ID (N'table name') AND type in (N'U')) (for SQL Server) query to check if the table exists; S632, field existence check: Check whether all fields referenced in the SQL statement exist in the corresponding table; Use a query like SELECT * FROM information_schema.columns WHERE table_name = 'table name' to check if the field exists; S633, data type matching: Make sure the field data types used in the SQL statement match the definitions in the table structure; Use SELECT data_type FROM information_schema.columns WHERE table_name = 'table name' AND column_name = 'field name' to query the data type of the field; S634, Constraint Check: Check whether the insert or update operation in the SQL statement complies with the table constraints, and use the specific queries provided by the database to check whether the constraints are violated; S635, column permission check: Make sure the user who executes the SQL statement has the permission to access and operate these columns.

9. The method for automated data analysis based on multi-agent cognitive collaboration according to claim 5, characterized in that: In the S64, the specific steps of checking the data type of the SQL statement are as follows: S641, Data type definition: It is necessary to clearly define the expected data type of each field in the table; S642. Check when creating a table structure: Include constraints on data types in the creation statement. In MySQL, the statement to create a user table and ensure that the 'age' field is an integer is as follows: S643. Use triggers for checking: Before inserting or updating data into a table, set up triggers to perform data type checks. In MySQL, a trigger is as follows: S644. Use database functions to check: Use the functions provided by the database to check the data type in the application. In MySQL, use the DATA_TYPE() function to check the data type of the column. S645, Application level check: Before the application submits data to the database, the data type is checked at the application level. This is done by writing code logic, using Python's isinstance() function, or type checking in Java.

10. The method for automated data analysis based on multi-agent cognitive collaboration according to claim 5, characterized in that: In S65, the specific steps of the performance check of the SQL statement are as follows: S651, Response time: the time from user operation to system response; Formula: Response time = operation completion time - operation initiation time; S652, Throughput: The amount of work processed by the system per unit time; Formula: Throughput = number of completed tasks / time consumed; S653, concurrent users: the number of users operating the system at the same time; Formula: Number of concurrent users = number of simultaneously active users; S654, Resource Utilization: System resource usage; Formula: Resource utilization = (actual resources used / total resources) * 100%; S655, System load: the workload of the system operation; Formula: System load = (number of processes + number of open files) / total number of processes; S656. Availability: The ratio of system normal operation time to total time; Formula: Availability = (uptime / total time)*100%; S657, Error rate: the proportion of errors that occur; Formula: Error rate = (number of errors / total number of operations)*100%.

Images