SQL-based risk control variable online derivation method and system

Abstract

The invention provides an SQL (Structured Query Language)-based risk control variable online derivation method and system. The method comprises the following steps: defining a derivation SQL of a risk control variable; establishing a verification task for configuring a verification sample size and a sample time period, and capturing stock data of risk control variables as sample data according to the verification task; performing off-line batch logic verification on the captured sample data based on the defined derivative SQL to obtain a verification result; and submitting the derivative SQL which passes verification for online application, examining and approving the derivative SQL which is applied for online application, and providing the derivative SQL which passes the examination and approval for risk control variable feature derivative production to call in real time. According to the method, risk control online variable derivation is carried out by using the SQL, data analysts can be directly provided for a production environment to be called in real time on line in a system after realizing variable calculation logic through the SQL, and the characteristic derivation efficiency of risk control variables is effectively improved.

Description

Technical Field

[0001] The present application relates to the field of information technology, and in particular to an online derivation method and system for risk control variables based on SQL. Background Art

[0002] SQL syntax is currently one of the more common development syntaxes for strategy personnel, data analysts, and developers. Using SQL as a risk control variable (or risk control characteristic variable) for online derivation can greatly improve the development and iteration efficiency of risk control strategies compared to traditional development models. The traditional development process provides data analysts with variable calculation logic, which is then implemented by developers through code. Data analysts then go online after SQL verification. The entire process is long, involves many nodes and personnel, and results in low efficiency. Summary of the invention

[0003] The present application provides an online risk control variable derivation method based on SQL, which can solve the technical problems of traditional risk control variable development methods, such as the long process, many nodes and personnel involved, resulting in low efficiency.

[0004] In a first aspect, a SQL-based online risk control variable derivation method comprises the following steps:

[0005] Define the derivative SQL of risk control variables;

[0006] Create a verification task that configures the verification sample size and sample time period, and capture the stock data of the risk control variables as sample data according to the verification task;

[0007] Perform offline batch logic verification on the captured sample data based on the defined derivative SQL to obtain the verification results;

[0008] Submit the verified derivative SQL for online application, approve the derivative SQL of the online application, and provide the approved derivative SQL for real-time call of risk control variable feature derivative production.

[0009] In combination with the first aspect, in one implementation, the derivative SQL for defining the risk control variable specifically includes the following steps:

[0010] Configure the SQL statements to be calculated;

[0011] Configure SQL query results;

[0012] Configure risk control variable mapping.

[0013] In combination with the first aspect, in one implementation, the creation of a verification task that configures the verification sample size and sample time period, and after grabbing the stock data of the risk control variables as sample data according to the verification task, further includes the following steps:

[0014] Preprocess the sample data.

[0015] In combination with the first aspect, in one implementation, the preprocessing of the sample data specifically includes the following steps:

[0016] Process sample data into table structure data;

[0017] The table structure data required for SQL variable feature derivation calculation is filtered from the table structure data as the risk control variable data to be processed by feature derivation.

[0018] In combination with the first aspect, in one implementation, performing offline batch logic verification on the captured sample data based on the defined derivative SQL to obtain the verification result specifically includes the following steps:

[0019] Use an open source memory database to load the SQL statement and sample data, perform SQL variable feature derivative calculation on the SQL statement and sample data based on the defined derivative SQL to obtain feature derivative calculation results, match the feature derivative calculation results according to the risk control variable mapping relationship, obtain matching results, and perform matching marking;

[0020] According to the corresponding name tags and default values ​​configured in the matching results, the accuracy of the feature derivative calculation results after the matching tags is verified.

[0021] In combination with the first aspect, in one implementation, matching the feature derivative calculation results according to the risk control variable mapping relationship, obtaining the matching results, and performing matching marking specifically includes the following steps:

[0022] When matching the feature derivative calculation results according to the risk control variable mapping relationship and obtaining the matching results, a corresponding name tag is configured for the feature derivative calculation results;

[0023] When matching the feature derived calculation results according to the risk control variable mapping relationship and no matching results are obtained, a default value is configured for the feature derived calculation results.

[0024] In combination with the first aspect, in one implementation, the open source memory database is used to load the SQL statement and sample data, the calculation interface of the open source memory database is called to calculate the feature derivative calculation result, and the feature derivative calculation result is matched according to the risk control variable mapping relationship, and the matching result is obtained. After the matching mark is performed, the following steps are also included:

[0025] The matching results are generated in a format that can be processed by data analysis software and imported into the data warehouse tool for task naming.

[0026] In combination with the first aspect, in one embodiment, after verifying the accuracy of the feature derivative calculation results after the matching mark according to the corresponding name tags and default values ​​configured in the matching results, the following steps are also included:

[0027] When the accuracy verification of the characteristic derivative calculation results of the risk control variable fails, adjust the SQL and redefine the derivative SQL.

[0028] In the second aspect, the present application provides an online derivation system for risk control variables based on SQL, including:

[0029] Derived SQL definition module, used to define the derived SQL of risk control variables;

[0030] A sample data acquisition module is used to create a verification task that configures the verification sample size and sample time period, and to capture the stock data of the risk control variables as sample data according to the verification task;

[0031] Perform offline batch logic verification on the captured sample data based on the defined derivative SQL to obtain the verification results;

[0032] A sample data processing module is used to use an open source memory database to load the SQL statement and sample data, perform SQL variable feature derivative calculation on the SQL statement and sample data based on the defined derivative SQL to obtain feature derivative calculation results, match the feature derivative calculation results according to the risk control variable mapping relationship, obtain matching results, and perform matching marking;

[0033] An accuracy verification module, which is in communication with the sample data processing module and is used to verify the accuracy of the feature derivative calculation results after the matching mark according to the corresponding name tags and default values ​​configured in the matching results;

[0034] The online approval module is used to submit the verified derivative SQL for online application, approve the derivative SQL of the online application, and provide the approved derivative SQL for real-time call of risk control variable feature derivative production.

[0035] In conjunction with the second aspect, in one implementation, the sample data processing module includes:

[0036] When matching the feature derivative calculation results according to the risk control variable mapping relationship and obtaining the matching results, a corresponding name tag is configured for the feature derivative calculation results;

[0037] When matching the feature derived calculation results according to the risk control variable mapping relationship and no matching results are obtained, a default value is configured for the feature derived calculation results.

[0038] The beneficial effects brought by the technical solution provided by the embodiment of the present application include at least:

[0039] Use SQL to derive risk control online variables. After data analysts implement the variable calculation logic through SQL, they can directly upload it to the system and provide it to the production environment for real-time call, effectively improving the feature derivation efficiency of risk control variables. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A schematic diagram of a method flow of an online derivation method of risk control variables based on SQL provided in an embodiment of the present application;

[0041] Figure 2 A schematic diagram of a flow chart of risk control variable feature derivation calculation of an SQL-based risk control variable online derivation method provided in an embodiment of the present application;

[0042] Figure 3 A schematic diagram of another method flow of the SQL-based online risk control variable derivation method provided in an embodiment of the present application;

[0043] Figure 4 This is an example diagram of roles involved in the SQL-based online risk control variable derivation method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0044] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0045] The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices. The terms "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit "first", "second" and "third" to different types.

[0046] In the description of the embodiments of the present application, "exemplary", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "for example" or "for example" is intended to present related concepts in a specific way.

[0047] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; the “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two.

[0048] In some processes described in the embodiments of the present application, multiple operations or steps that appear in a specific order are included, but it should be understood that these operations or steps may not be executed in the order in which they appear in the embodiments of the present application or in parallel, and the sequence number of the operation is only used to distinguish the different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed in sequence or in parallel, and these operations or steps may be combined.

[0049] First, some technical terms in the present application are explained to facilitate those skilled in the art to understand the present application.

[0050] SQL (Structured Query Language), which is structured query language, is a programming language used to manage relational databases.

[0051] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0052] In a first aspect, a SQL-based online risk control variable derivation method comprises the following steps:

[0053] Step S1: define the derivative SQL of risk control variables;

[0054] Step S2: Create a verification task that configures the verification sample size and sample time period, and capture the stock data of the risk control variables as sample data according to the verification task;

[0055] Step S4: Perform offline batch logic verification on the captured sample data based on the defined derivative SQL, verify the variable calculation logic of the derivative SQL of the defined risk control variables, and obtain the verification results;

[0056] Step S5A: Submit the verified derivative SQL for online application, approve the derivative SQL of the online application, and provide the approved derivative SQL for real-time call of risk control variable feature derivative production.

[0057] This application uses SQL to derive risk control online variables. After data analysts implement the variable calculation logic through SQL, they can directly go online in the system and provide it to the production environment for real-time call, effectively improving the feature derivation efficiency of risk control variables.

[0058] In one embodiment, the step S1: defining the derived SQL of the risk control variable specifically includes the following steps:

[0059] Step S11: configure the SQL statement to be calculated;

[0060] Step S12: configure SQL query results;

[0061] Step S13: Configure the risk control variable mapping relationship.

[0062] Based on the configured SQL rain tools, SQL query results and risk control variable mapping relationship, the variable calculation logic of risk control variables is implemented.

[0063] In one embodiment, the step S2: creating a verification task that configures the verification sample size and sample time period, and after capturing the stock data of the risk control variables as sample data according to the verification task, further includes the following steps:

[0064] Step S3: pre-process the sample data.

[0065] In one embodiment, the step S31: preprocessing the sample data specifically includes the following steps:

[0066] Step S31: Processing the original message data of the sample data into table structure data;

[0067] Step S32: Filter the table structure data required for SQL variable feature derivation calculation from the table structure data as the risk control variable data to be processed by feature derivation.

[0068] In one embodiment, the step S4: performing offline batch logic verification on the captured sample data based on the defined derivative SQL to obtain the verification result specifically includes the following steps:

[0069] Step S41: using an open source memory database to load the SQL statement and the risk control variable data to be processed by feature derivation, performing SQL variable feature derivation calculation on the SQL statement and the sample data based on the defined derived SQL to obtain feature derivation calculation results, matching the feature derivation calculation results according to the risk control variable mapping relationship, obtaining matching results, and performing matching marking;

[0070] Step S42: According to the corresponding name tags and default values ​​configured in the matching results, the accuracy of the feature derivative calculation results after the matching tags is verified.

[0071] In one embodiment, in step S41, an open source memory database is used to load the SQL statement and sample data, and SQL variable feature derivative calculation is performed on the SQL statement and sample data based on the defined derivative SQL to obtain feature derivative calculation results, which is specifically implemented as follows:

[0072] Use the open source in-memory database duckdb to load the risk control variable data to be processed by feature derivation in step S32 and the SQL statement to be executed in step S1, execute the defined derivative SQL, and obtain the feature derivation calculation results.

[0073] In one embodiment, the step S41 matches the feature derivative calculation results according to the risk control variable mapping relationship, obtains the matching results, and performs matching marking, which specifically includes the following steps:

[0074] Step S41A: when matching the feature derivative calculation results according to the risk control variable mapping relationship and obtaining the matching results, a corresponding name tag is configured for the feature derivative calculation results as a matching tag; more specifically, a corresponding Chinese and English name is configured;

[0075] Step S41B: when matching the feature derived calculation results according to the risk control variable mapping relationship and no matching result is obtained, a default value is configured for the feature derived calculation results as a mark indicating that no match is obtained;

[0076] In one embodiment, the step S41: using an open source memory database to load the SQL statement and sample data, calling the calculation interface of the open source memory database to calculate and obtain the feature derivative calculation result, and matching the feature derivative calculation result according to the risk control variable mapping relationship, obtaining the matching result, and performing the matching mark, further includes the following steps:

[0077] Generate matching results in a format that can be processed by data analysis software, and import them into the data warehouse tool for task naming; more specifically, automatically generate a CSV file from the matching results and automatically import them into hive, where the table name is named after the task name; more specifically, derive the task name from the risk control variable characteristics for naming.

[0078] In one embodiment, the step S42: verifying the accuracy of the feature derivative calculation result after the matching mark according to the corresponding name mark and the default value configured in the matching result is specifically implemented as follows:

[0079] According to the corresponding name tags and default values ​​configured in the matching results, the accuracy of the feature derivation of the risk control variables is verified by writing SQL through the Hive table.

[0080] In one embodiment, if Figure 3 As shown, the step S42: after verifying the accuracy of the feature derivative calculation results after the matching mark according to the corresponding name mark and the default value configured in the matching result, further includes the following steps:

[0081] Step S5B: When the accuracy verification of the characteristic derivative calculation result of the risk control variable fails, adjust the SQL to redefine the derivative SQL, and then continue to perform subsequent offline batch logic verification until the defined derivative SQL passes the offline batch logic verification of the risk control variable.

[0082] The SQL-based online risk control variable derivation method provided in this application involves data preprocessing, indicator calculation and indicator aggregation. Figure 3 shown.

[0083] like Figure 4 As shown, the SQL-based online derivation method of risk control variables provided in this application may only involve strategy personnel and approval personnel. The method flow except result review can be completed by strategy personnel. The whole process is short, involving fewer nodes and personnel, which effectively improves the feature derivation efficiency of risk control variables.

[0084] In the second aspect, the present application provides an online derivation system for risk control variables based on SQL, including:

[0085] Derived SQL definition module, used to define the derived SQL of risk control variables;

[0086] A sample data acquisition module is used to create a verification task that configures the verification sample size and sample time period, and to capture the stock data of the risk control variables as sample data according to the verification task;

[0087] Perform offline batch logic verification on the captured sample data based on the defined derivative SQL to obtain the verification results;

[0088] A sample data processing module is used to use an open source memory database to load the SQL statement and sample data, perform SQL variable feature derivative calculation on the SQL statement and sample data based on the defined derivative SQL to obtain feature derivative calculation results, match the feature derivative calculation results according to the risk control variable mapping relationship, obtain matching results, and perform matching marking;

[0089] An accuracy verification module, which is in communication with the sample data processing module and is used to verify the accuracy of the feature derivative calculation results after the matching mark according to the corresponding name tags and default values ​​configured in the matching results;

[0090] The online approval module is used to submit the verified derivative SQL for online application, approve the derivative SQL of the online application, and provide the approved derivative SQL for real-time call of risk control variable feature derivative production.

[0091] In one embodiment, the sample data processing module includes:

[0092] When matching the feature derivative calculation results according to the risk control variable mapping relationship and obtaining the matching results, a corresponding name tag is configured for the feature derivative calculation results;

[0093] When matching the feature derived calculation results according to the risk control variable mapping relationship and no matching results are obtained, a default value is configured for the feature derived calculation results.

[0094] Among them, the functional implementation of each module in the above-mentioned SQL-based risk control variable online derivation system corresponds to the steps in the above-mentioned SQL-based risk control variable online derivation method embodiment, and its functions and implementation process will not be repeated here one by one.

[0095] In a third aspect, an embodiment of the present application provides an online derivation device for risk control variables based on SQL. The online derivation device for risk control variables based on SQL may be a personal computer (PC), a laptop computer, a server, or other device with data processing capabilities.

[0096] The communication interface includes input / output (I / O) interface, physical interface and logical interface, etc., which are used to realize the interconnection of devices inside the online derivation device of risk control variables based on SQL, and the interface used to realize the interconnection of the online derivation device of risk control variables based on SQL with other devices (such as other computing devices or user devices). The physical interface can be an Ethernet interface, a fiber optic interface, an ATM interface, etc.; the user device can be a display, a keyboard, etc.

[0097] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0098] The processor may be a general-purpose processor, which may call the SQL-based online derivation program for risk control variables stored in the memory and execute the SQL-based online derivation method for risk control variables provided in the embodiment of the present application. For example, the general-purpose processor may be a central processing unit (CPU). The method executed when the SQL-based online derivation program for risk control variables is called may refer to the various embodiments of the SQL-based online derivation method for risk control variables of the present application, which will not be described in detail here.

[0099] On the third aspect, an embodiment of the present application provides an online derivation device for risk control variables based on SQL. The online derivation device for risk control variables based on SQL may be an industrial control computer (IPC), a programmable controller (PLC), or other device with data processing capabilities.

[0100] The communication interface includes input / output (I / O) interface, analog interface and logic interface, etc., which are used to realize the interconnection of devices inside the online derivation device of risk control variables based on SQL, and the interface used to realize the interconnection of the online derivation device of risk control variables based on SQL with other devices (such as other computing devices or user devices). The physical interface can be an Ethernet interface, a fiber optic interface, an RS485 interface, etc.; the user device can be a display, a keyboard, etc.

[0101] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0102] The processor may be a general-purpose processor, and the general-purpose processor may call the SQL-based online derivation program of risk control variables stored in the memory, and execute the SQL-based online derivation method of risk control variables provided in the embodiment of the present application. For example, the general-purpose processor may be a central processing unit (CPU). The method executed when the SQL-based online derivation program of risk control variables is called may refer to the various embodiments of the SQL-based online derivation method of risk control variables in the present application, and will not be repeated here.

[0103] In a fourth aspect, an embodiment of the present application also provides a readable storage medium.

[0104] The readable storage medium of the present application stores an online derivation program for risk control variables based on SQL, wherein when the online derivation program for risk control variables based on SQL is executed by a processor, the steps of the online derivation method for risk control variables based on SQL as described above are implemented.

[0105] Among them, the method implemented when the SQL-based risk control variable online derivation program is executed can refer to the various embodiments of the SQL-based risk control variable online derivation method of the present application, and will not be repeated here.

[0106] It should be noted that the serial numbers of the above-mentioned embodiments of the present application are only for description and do not represent the advantages or disadvantages of the embodiments.

[0107] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, disk, CD) as described above, and includes a number of instructions for a terminal device to execute the methods described in each embodiment of the present application.

[0108] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A SQL-based online risk control variable derivation method, characterized in that: The following steps are involved: Define the derivative SQL of risk control variables; Create a verification task that configures the verification sample size and sample time period, and capture the stock data of the risk control variables as sample data according to the verification task; Perform offline batch logic verification on the captured sample data based on the defined derivative SQL to obtain the verification results; Submit the verified derivative SQL for online application, approve the derivative SQL of the online application, and provide the approved derivative SQL for real-time call of risk control variable feature derivative production.

2. The SQL-based online risk control variable derivation method according to claim 1, characterized in that: The derivative SQL for defining the risk control variables specifically includes the following steps: Configure the SQL statements to be calculated; Configure SQL query results; Configure risk control variable mapping.

3. The SQL-based online risk control variable derivation method according to claim 1, characterized in that: The creation of the verification task for configuring the verification sample size and sample time period, and after grabbing the stock data of the risk control variables as sample data according to the verification task, further includes the following steps: Preprocess the sample data.

4. The SQL-based online risk control variable derivation method according to claim 3, characterized in that: The preprocessing of the sample data specifically comprises the following steps: Process sample data into table structure data; The table structure data required for SQL variable feature derivation calculation is filtered from the table structure data as the risk control variable data to be processed by feature derivation.

5. The SQL-based online risk control variable derivation method according to claim 2, characterized in that: The offline batch logic verification of the captured sample data based on the defined derivative SQL is performed to obtain the verification result, which specifically includes the following steps: Use an open source memory database to load the SQL statement and sample data, perform SQL variable feature derivative calculation on the SQL statement and sample data based on the defined derivative SQL to obtain feature derivative calculation results, match the feature derivative calculation results according to the risk control variable mapping relationship, obtain matching results, and perform matching marking; According to the corresponding name tags and default values ​​configured in the matching results, the accuracy of the feature derivative calculation results after the matching tags is verified.

6. The SQL-based online risk control variable derivation method according to claim 5, characterized in that: The method of matching the feature derivative calculation results according to the risk control variable mapping relationship, obtaining the matching results, and performing matching marking specifically includes the following steps: When matching the feature derivative calculation results according to the risk control variable mapping relationship and obtaining the matching results, a corresponding name tag is configured for the feature derivative calculation results; When matching the feature derived calculation results according to the risk control variable mapping relationship and no matching results are obtained, a default value is configured for the feature derived calculation results.

7. The SQL-based online risk control variable derivation method according to claim 5, characterized in that: The method further includes the following steps: loading the SQL statement and sample data using an open source memory database, calling the calculation interface of the open source memory database to calculate the feature derivative calculation result, matching the feature derivative calculation result according to the risk control variable mapping relationship, obtaining the matching result, and marking the match. The matching results are generated in a format that can be processed by data analysis software and imported into the data warehouse tool for task naming.

8. The SQL-based online risk control variable derivation method according to claim 5, characterized in that: After verifying the accuracy of the feature derivative calculation results after the matching mark according to the corresponding name mark and the default value configured in the matching result, the following steps are also included: When the accuracy verification of the characteristic derivative calculation results of the risk control variable fails, adjust the SQL and redefine the derivative SQL.

9. An online risk control variable derivation system based on SQL, characterized in that: include: Derived SQL definition module, used to define the derived SQL of risk control variables; A sample data acquisition module is used to create a verification task that configures the verification sample size and sample time period, and to capture the stock data of the risk control variables as sample data according to the verification task; Perform offline batch logic verification on the captured sample data based on the defined derivative SQL to obtain the verification results; A sample data processing module is used to use an open source memory database to load the SQL statement and sample data, perform SQL variable feature derivative calculation on the SQL statement and sample data based on the defined derivative SQL to obtain feature derivative calculation results, match the feature derivative calculation results according to the risk control variable mapping relationship, obtain matching results, and perform matching marking; An accuracy verification module, which is in communication with the sample data processing module and is used to verify the accuracy of the feature derivative calculation results after the matching mark according to the corresponding name tags and default values ​​configured in the matching results; The online approval module is used to submit the verified derivative SQL for online application, approve the derivative SQL of the online application, and provide the approved derivative SQL for real-time call of risk control variable feature derivative production.

10. The SQL-based online risk control variable derivation system according to claim 9, characterized in that: The sample data processing module includes: When matching the feature derivative calculation results according to the risk control variable mapping relationship and obtaining the matching results, a corresponding name tag is configured for the feature derivative calculation results; When matching the feature derived calculation results according to the risk control variable mapping relationship and no matching results are obtained, a default value is configured for the feature derived calculation results.

Images