TWI852738B - A system and a method for generating modules - Google Patents

Abstract

容後補呈

Description

Module generation system and method

The present invention relates to a module production method and system, and more particularly to a system and method for producing modules in real time according to user or customer requirements in the industrial field.

In various industrial fields, in order to analyze the operation of various industrial machines, vehicles and images, or to analyze the data captured by various sensing devices, it is often necessary to write customized modules for the data content of each industrial project.

However, since the project situations in various industrial fields are obviously different, even for the data content of the same project, as the user or customer uses it for a longer time, there will be additional needs for modifying the old module. In the knowledge technology, a large number of demand interviews are often required to deal with this modification demand, and system engineers and software engineers are required to modify the entire physical module or virtual module again. This approach often takes a lot of time, manpower and cost. In addition, this modification demand will reappear every once in a while, which will also potentially cost more time, manpower and cost.

Furthermore, although users can fine-tune the input data and calculation structure of known automatic generation tools, their logical structure and the function set required by the program are usually fixed. When the technical problem to be solved is greatly changed or adjusted, it is still inevitable that system engineers and software engineers will need to significantly modify the entire automatic generation tool.

Therefore, in order to overcome the aforementioned problems, the present invention is developed.

The main purpose of the present invention is to provide a module generation method and system, which can allow users to adjust according to their needs, and instantly generate the front-end module and back-end module actually needed by the user, so as to overcome the technical problem of spending a lot of time and cost to modify the entire module in response to subsequent changes in needs. After the generative AI module statistically returns the generated results, keywords with high recurrence rates are adopted to instantly generate the front-end module and back-end module actually needed by the user.

In order to solve the above-mentioned problems and achieve the expected effect, the present invention provides a module generation system, including a display interface, a function group generation unit, a background module generation unit, and a foreground module generation unit. The display interface displays a requirement list for the user to operate the requirement list to generate a confirmed requirement list, and the requirement list includes a plurality of requirements corresponding to the analysis results to be generated by the module to be generated. The function group generation unit is connected to the display interface and is used to generate a first function group according to the steps corresponding to the analysis results to be generated by the module to be generated, and is used to generate a second function group according to the steps required to display the part of the module to be generated that the user needs to view on the display interface among the plurality of data processing steps. The background module generating unit is connected with the function group generating unit and generates the background module through the first function group. The foreground module generating unit is connected with the function group generating unit and generates the foreground module through the second function group.

During implementation, the background operation module is further connected to the database and executes the following steps, including: filtering the original data from the database to generate filtered data, computing the filtered data to generate computed data; and performing statistical analysis on the computed data to generate the analysis result.

During implementation, the requirements list includes the following items: the number of steps, the order of steps, the time unit for calculating steps, the steps that can be skipped, whether to set up check steps or whether the failure of check steps to be executed is considered an exception.

During implementation, the module generation system further includes a setting unit, which is connected to the background module generation unit and the front-end module generation unit, and is used to set the data type of the required input data in the background module generation unit and the front-end module generation unit, the background logic structure corresponding to the background module generation unit, or the front-end logic structure corresponding to the front-end module generation unit according to the multiple steps executed by the corresponding module to be generated.

During implementation, the module generation system further includes a generative AI module, which is connected to the setting unit and provides the setting unit with a function to respectively set the background logic architecture corresponding to the background module generation unit or the front-end logic architecture corresponding to the front-end module generation unit according to a plurality of steps executed corresponding to the module to be generated.

During implementation, the module generation system further includes a generative AI module, which is connected to the background module generation unit to provide the background module generation unit with the first function group, and the generative AI module is connected to the foreground module generation unit to provide the foreground module generation unit with the second function group.

During implementation, the generative AI module is used to receive inquiries of multiple keywords related to the backend logic architecture, the frontend logic architecture, the first function group and the second function group respectively; and is used to receive inquiries of reverse keywords of multiple keywords related to the backend logic architecture, the frontend logic architecture, the first function group and the second function group.

The present invention further provides a module generation method, which includes: A: The user generates a confirmed requirement list by operating the requirement list through the display interface, and the requirement list includes a plurality of requirements corresponding to the analysis results to be generated by the module to be generated; B: The function group generation unit generates a first function group according to the steps corresponding to the analysis results to be generated by the module to be generated, and the function group generation unit generates a second function group according to the steps required to display the part of the analysis result that the user needs to view in the module to be generated on the display interface; C: The background module generation unit generates a background module through the first function group; and D: The foreground module generation unit generates a foreground module through the second function group.

During implementation, step B further includes generating the first function group by the function group generating unit according to the following steps executed corresponding to the module to be generated: filtering the original data to generate filtered data, performing operations on the filtered data to generate calculated data; and performing statistical analysis on the calculated data to generate analysis results.

During implementation, step A further includes: the user confirms the requirement list through the display interface to generate the confirmed requirement list, and the requirement list includes: the number of steps, the order of steps, the time unit for calculating steps, the steps that can be skipped, whether to set the check step or whether the non-execution of the check step is judged as abnormal.

During implementation, step B and step C further include step b: the setting unit determines the data type of each data in the background module generation unit and the background logic architecture corresponding to the background module generation unit according to the multiple steps executed by the module to be generated.

During implementation, step C and step D further include step C: using the setting unit to determine the data type of each data in the foreground module generation unit and the foreground logic architecture corresponding to the foreground module generation unit according to the multiple steps executed by the module to be generated.

During implementation, step B further includes: the function group generating unit generates the first function group according to the generative AI module for the best step corresponding to the analysis result to be generated by the module to be generated, and the function group generating unit generates the second function group according to the generative AI module for the best step required to display the part of the plurality of data processing steps in the aforementioned module to be generated that needs to be viewed by the user on the display interface.

In practice, the step A further includes a step X before the step A. In the step X, the generative AI module is queried with a plurality of keywords related to the backend logic structure, the frontend logic structure, the first function group, and the second function group, and after statistically returning the results, keywords with a high recurrence rate are selected; the generative AI module is queried with the reverse keywords of a plurality of keywords related to the backend logic structure, the frontend logic structure, the first function group, and the second function group. module, thereby removing inappropriate return results; and using detection keywords to respectively check the generation results related to the backend logic framework, the frontend logic framework, the first function group and the second function group, thereby removing inappropriate generation results.

In order to further understand the present invention, the following preferred embodiments are given, and the specific components and effects of the present invention are described in detail with reference to the drawings and figure numbers.

Please refer to FIG. 1 , the present invention provides a module generation system, which includes: a display interface 1, a function group generation unit 2, a background module generation unit 3, and a foreground module generation unit 4. The display interface 1 displays a requirement list for the user to operate the requirement list to generate a confirmed requirement list, the requirement list includes a plurality of requirements corresponding to the module to be generated, for example, the background module described later in this embodiment, and the analysis results to be generated will be described in detail in the embodiment mentioned later. The function group generating unit 2 is connected to the display interface 1 and is used to generate a first function group according to the steps corresponding to the analysis results to be generated by the module to be generated, and to generate a second function group according to the steps required to display the part of the module to be generated that the user needs to watch on the display interface 1 among the multiple data processing steps. The background module generating unit 3 generates a background module through the first function group. The foreground module generating unit 4 generates a front-end module through the second function group.

Please refer to FIG. 2. The present invention further provides a module generation method, which includes: Step A: The user operates the requirement list through the display interface to generate a confirmed requirement list, and the requirement list includes a plurality of requirements corresponding to the analysis results to be generated by the module to be generated; Step B: The function group generation unit generates a first function group according to the steps corresponding to the analysis results to be generated by the module to be generated, and the function group generation unit generates a second function group according to the steps required to display the part of the plurality of data processing steps or analysis results in the display interface that the user needs to view; Step C: The background module generation unit generates a background module through the first function group; and Step D: Generate a foreground module using the foreground module generation unit via the second function set.

The system and method of the present invention are described in detail as follows.

First, please refer to FIG. 2. In step A, the user operates the requirement list through the display interface 1 to generate the confirmed requirement list. The display interface 1 may be a display interface 1 of a personal computer, an industrial computer, a tablet or a smart phone. In another embodiment, in step A, the requirement list includes a plurality of requirements corresponding to the analysis results to be generated by the module to be generated. For example, the requirement list includes the following items: the number of steps to be executed, the order of the steps to be executed, the time unit of the calculation steps to be executed, whether there are steps that can be skipped, whether to set a check step, and whether the failure of the check step to be executed is determined to be abnormal. Please continue to refer to Figure 1. The background operation module is connected to the database 6 and executes the following steps: in the step B, the original data from the database 6 is filtered to generate filtered data, the filtered data is calculated to generate calculated data; and the calculated data is statistically analyzed to generate the analysis result.

In another embodiment, please refer to FIG. 3A , which is to filter the original data to generate filtered data, and the filtering condition is the data of steps 1 to 3 from 3:00 to 4:00 p.m. on June 12, 2023; then, the multiple data in steps 1 to 3 are averaged to obtain the values in steps 1 to 3 in FIG. 3A . The calculated data is subjected to statistical analysis, such as ANOVA-test, to generate an analysis result of whether there is a significant difference in the operation time of steps 1 to 3.

Please refer to the embodiment of FIG. 3A of the present invention, which is a table of the filling time of each step of the tank truck filling operation and whether to execute the specific action of each step. In this embodiment, the requirement list displayed by the display device 1 includes confirmation of the following items: whether a database connection is required, if the user selects yes, the database connection information needs to be provided, for example, 127.0.0.1:3066/root/1234), the number of steps is 3, the order of the steps is steps 1 to 3, the time unit for calculating the steps is seconds (sec), whether there are steps that can be skipped (no), whether to set the check step (all three steps need to be checked) and whether the failure of the check step is judged as abnormal (yes). In another embodiment, the step sequence of the aforementioned tank truck loading operation can be adjusted according to the actual situation, the time unit of the calculation step can also be adjusted to minutes (min), whether there are steps that can be skipped can be adjusted to yes, whether to set the inspection step can be adjusted to only check one of them, and whether the inspection step is judged as abnormal if it is not executed can be set to no. The above adjustments are also within the scope of the present invention.

In step B, please refer to FIG. 3B , the function group generating unit 2 generates the first function group according to the step corresponding to the analysis result to be generated by the module to be generated, that is, for example, function group 1: use the database 6 to connect, the address is set to: 127.0.0.1:3066, account: root/password: 3569; function group 2: a total of 3 action fields, counted from left to right, if there is a step time of 0 in the middle, indicating a step jump, it is determined to be abnormal; function group 3: the time unit of each action field is sec, and each data must be processed to the unit of seconds; function group 4: a total of 3 check fields, which are used to check each step, if not checked, it is determined to be abnormal. The function group generating unit 2 generates the second function group according to the steps required for displaying the part of the module to be generated that the user needs to view on the display interface 1 among the plurality of data processing steps executed by the function group 1, the function group 2, the function group 3 and the function group 4. In other words, referring to FIG. 3B , the display interface 1 displays the part of the module to be generated that the user needs to view, i.e., the table on the display interface 1 shown in FIG. 3B , and the steps required for the user to confirm shown in FIG. 3A are performed to generate the second function group. Next, in step C, the background module generation unit 3 is used to generate the background module through the aforementioned function group 1-4 executed by the first function group. The code (only part of it is disclosed) is as follows: Db = open_database(127.0.0.1,3066,root, 3569) Alarm = False For(int I = 0; I ＜３; i++) if( getValue(col[i] == 0) Alarm = True break For(int I = 3; i＜ 6; i++) if(getValue(col[i] == 0) Alram = True break.

Please continue to refer to FIG. 4A . In another embodiment, the original data is filtered to generate filtered data. The filtering condition is the data of steps 1 to 3 from 3:00 to 3:30 pm on June 12, 2023. Then, the multiple data in steps 1 to 3 are averaged to obtain the values in steps 1 to 3 in FIG. 4A . Then, the calculated data is subjected to statistical analysis, such as ANOVA-test, to generate an analysis result of whether there is a significant difference in the operation time of steps 1 to 3.

In this embodiment, in step B, please refer to FIG. 4B , the function group generating unit 2 generates the first function group according to the step corresponding to the analysis result to be generated by the module to be generated, that is, for example, function group 1: use the database 6 to connect, the address is set to: 127.0.0.1:3066, account: root/password: 8172; function group 2: a total of 3 action fields , counting from left to right, if there is a step time of 0 in the middle, it means skipping the step, and it is determined to be abnormal; Function group 3: if the number of steps 2 and 3 is less than the number of steps 1, it is also determined to be abnormal; Function group 4: the time unit of each action field is sec, and each data needs to be processed to the unit of seconds; Function group 5: there are 3 check fields in total, which are used to check whether each step has been checked. If not checked, it is determined to be abnormal. The function group generation unit 2 generates the second function group according to the steps required for displaying the part of the module to be generated that the user needs to watch in the display interface 1 among the multiple data processing steps executed by the function group 1, function group 2, function group 3, function group 4 and function group 5. In other words, please refer to Figure 4B, the display interface 1 displays the part of the module to be generated that the user needs to view, that is, the table on the display interface 1 shown in Figure 4B, and the required steps required for user confirmation shown in Figure 4A are generated to generate the second function set. Next, in step C, the background module generation unit 3 is used to generate the background module through the aforementioned function group 1-5 executed by the first function group. The code of this module (only part of it is disclosed) is as follows: Db = open_database(127.0.0.1,3066,root, 1234) Alarm = False For(int I = 0; I ＜3; i++) if( getValue(col[i] == 0) Alarm = True break For(int I = 4; i＜ 6; i++) if(getValue(col[i] ＜ getValue(col[3])) Alram = True break.

Then, in step D, the foreground module generating unit 4 generates a foreground module through the second function group, and the screen displayed by the foreground module is shown in FIG4B. In another embodiment, step B further includes a step of displaying the information required to be viewed by the user according to the plurality of steps executed by the background operation module executed by the corresponding module to be generated, thereby generating the second function group.

In another embodiment, please refer to Figure 1. The system of the present invention further includes a setting unit 5, which is connected to the background module generation unit 3 and the foreground module generation unit 4, and is used to set the data type of the required input data in the background module generation unit 3 and the foreground module generation unit 4 according to the multiple steps executed by the corresponding module to be generated. The data type includes: integer type, floating point type, character type, string type, Boolean type, etc., depending on the needs of data analysis, which will not be elaborated here. In another embodiment, step B and step C further include step b, in which the setting unit 5 determines the data type of each data in the background module generating unit 3 and the background logic architecture corresponding to the background module generating unit 3 according to the multiple steps executed by the corresponding module to be generated. In another embodiment, step C and step D further include step c, in which the setting unit 5 determines the data type of each data in the foreground module generating unit 4 and the background logic architecture corresponding to the foreground module generating unit 4 according to the multiple steps executed by the corresponding module to be generated.

Furthermore, the setting unit 5 is also used to set the background logic structure corresponding to the background module generating unit 3 or the foreground logic structure corresponding to the foreground module generating unit 4 according to the multiple steps executed by the corresponding module to be generated. That is, please refer to the embodiment of Figure 3A. The background logic structure corresponding to the background module generating unit 3 is a logic structure that operates function groups 1 to 4 and outputs the required function groups 1 to 4 according to the user's analysis requirements; similarly, the setting unit 5 sets the foreground logic structure according to the foreground logic structure corresponding to the foreground module generating unit 4 according to the user's requirements and the content that the user needs to view on the display interface 1 corresponding to the background logic structure.

Please continue to refer to Figure 1B. In another embodiment, the module generation system of the present invention further includes a generative AI module 7, which is connected to the background module generation unit 3 to provide the background module generation unit 3 with the first function set, and the generative AI module 7 is connected to the foreground module generation unit 4 to provide the foreground module generation unit 4 with the second function set. In another embodiment, the step B further includes: the function group generating unit generates the first function group according to the generative AI module for the best step corresponding to the analysis result to be generated by the module to be generated, and the function group generating unit generates the second function group according to the generative AI module for the best step required to display the part of the plurality of data processing steps in the aforementioned module to be generated that needs to be viewed by the user on the display interface 1. Please refer to FIG. 1B , in another embodiment, the generative AI module 7 is connected to the setting unit 5 and provides the setting unit 5 with the plurality of steps corresponding to the module to be generated and respectively sets the background logic architecture corresponding to the background module generation unit 3 or the front-end logic architecture corresponding to the front-end module generation unit 4. The description of the front-end logic architecture and the background logic architecture of the present invention is as described above and will not be repeated here. In one embodiment, the generative AI module 7 of the present invention can generate the aforementioned front-end and back-end logical structures. The generative AI module 7 of the present invention is also a generative AI module for generating program code. The present invention widely uses the GPT model. The GPT model performs adversarial training and reinforcement training on the function set of the background module that has been learned in advance and the logical structure of the background module. Similarly, the GPT model performs adversarial training and reinforcement training on the function set of the background module that has been learned in advance. The function group and the logical structure of the foreground module are subjected to confrontational training and reinforcement training; thereby, the first function group is generated for the best step corresponding to the analysis result to be generated by the module to be generated, and the function group generating unit generates the second function group according to the generative AI module for the best step required to display the part of the aforementioned module to be generated that needs to be viewed by the user in the display interface 1 among the multiple data processing steps.

In another embodiment, the step A further includes a step X, in which the generative AI module 7 is queried with multiple keywords related to the backend logic architecture, the frontend logic architecture, the first function group, and the second function group, and after the return results are statistically analyzed, keywords with high recurrence rates are adopted. In another embodiment, the aforementioned multiple keywords include, for example, data filtering date, selection step, page query, etc. Then, the generative AI module is queried with reverse keywords of multiple keywords related to the backend logic architecture, the frontend logic architecture, the first function group, and the second function group, respectively, so as to remove inappropriate return results. In another embodiment, the reverse keywords of the aforementioned multiple keywords include, for example, no time selection, no multiple page selection steps, no one query segmentation, etc. Then, the detection keywords are used to respectively check the generation results related to the background logic architecture, the front-end logic architecture, the first function group and the second function group, so as to remove inappropriate generation results. The key point of using the detection keywords is, for example, whether to use the My Connector connector, whether to use the connection port 3306, whether to use the specific library version 5.5 or above, etc., so as to remove inappropriate generation modules, that is, modules that cannot generate the expected goals.

Please continue to refer to FIG. 5. In another embodiment, the module generation method and system of the present invention can also be applied to the field of image recognition. In step A, the user operates the requirement list through the display interface 1 to generate the confirmed requirement list. The requirement list includes a plurality of requirements corresponding to the analysis results to be generated by the module to be generated. For example, the requirement list includes the following items: the number of objects in the image, the name of each object, and the relative position relationship of each object. The background operation module 3 is connected to the database 6 and executes the following steps, including: filtering the original image data from the database 6 to generate filtered data, and performing operations on the filtered data to generate calculated data. Taking this embodiment as an example, the calculated data is the position characteristics of each object, that is, mobile phone: x:200/y10/w:100/h:300; charging head: x:50/y:200/w:100/h:200; charging cable: x:220/y:300/w:50/h:100.

Then, in step B, the function group generating unit 2 generates the first function group according to the analysis result corresponding to the module to be generated, that is, identifying the meaning of each object in this image: a mobile phone is charging, there is a charging head plugged into the socket, and there is a charging cable on the charging head, that is, for example, function group 1: used to identify the mobile phone; function group 2: used to identify the charging head; function group 3: used to identify the charging cable; function group 4: used to identify the relative position of the aforementioned objects; function group 5: used to identify the meaning of each object. Next, the function group generating unit 2 generates the second function group according to the steps required for displaying the part of the module to be generated that the user needs to view on the display interface 1 among the plurality of data processing steps executed by the function groups 1 to 5. In other words, referring to FIG5 , the second function group displays the part of the module to be generated that the user needs to view on the display interface 1, that is, the meaning of displaying each object on the original image shown in FIG5 : a mobile phone is being charged (box A), a charging head is plugged into a socket (box B), and a charging cable is on the charging head (box C).

Therefore, the present invention has the following advantages: 1. For users in the learning technology, they may have different requirements for data analysis. The module generation method and system of the present invention can effectively carry out real-time customized module development, saving the cost of system engineers and software engineers to re-modify the entire physical module and virtual module at a large cost. 2. For the automatic generation tool of the learning, the module generation method and system of the present invention can effectively carry out the development of the front-end module and the back-end module at a large cost, and also save the cost of system engineers and software engineers to re-adjust the automatic generation tool at a large cost. 3. Conventional module development requires project analysts to conduct demand interviews, write demand specifications, engineers to write design specifications, and engineers to develop programs. With the module and system of the present invention, the module requirements are confirmed by users in real time, effectively saving the time and cost of project analysts conducting demand interviews and writing demand specifications; and through the function group generation unit, the setting unit for the background module generation unit and the front-end module generation unit, or through the function group generation unit, the setting unit for the background module generation unit and the front-end module generation unit and the generative AI module, the time and cost of engineers writing design specifications and developing programs are effectively saved. 4. By respectively querying the generative AI module with a plurality of keywords related to the backend logic framework, the frontend logic framework, the first function group and the second function group, and respectively querying the generative AI module with the reverse keywords of a plurality of keywords related to the backend logic framework, the frontend logic framework, the first function group and the second function group, the operating performance of the generative AI module is optimized.

In summary, according to the contents disclosed above, the present invention can achieve the expected purpose and provide a module generation method and system, which can effectively save the time and cost of engineers writing design specifications and developing programs by using a function group generation unit and a setting unit to generate a background module generation unit and a front-end module generation unit, or by using a function group generation unit and a setting unit to generate a background module generation unit and a front-end module generation unit in collaboration with a generative AI module, so as to ensure that the generated module can achieve the expected planned function, and has great value for industrial use. Therefore, a patent application for the invention is filed in accordance with the law.

Although the present invention discloses preferred specific embodiments to achieve the above-mentioned purpose, it is not intended to limit the structural features of the present invention. Any person with ordinary knowledge in the technical field should know that within the technical spirit of the present invention, any easily conceivable changes or modifications are possible and are all covered by the scope of the patent application of the present invention.

1: Display interface

2: Function group generation unit

3: Background module generation unit

4: Front-end module generation unit

5: Setting unit

6: Database

7: Generative AI module

A, B, C, D, a, b, c, d, X: steps

FIG. 1A is a structural block diagram of an embodiment of a module generation system of the present invention.

FIG. 1B is a structural block diagram of another embodiment of the module generation system of the present invention.

FIG. 2 is a flow chart of an embodiment of the module generation method of the present invention.

FIG. 3A is a diagram showing an embodiment of the module generation system and method of the present invention, wherein raw data is filtered to generate filtered data.

FIG. 3B is a table diagram showing the portion of the module to be generated that the user needs to view on the display interface in an embodiment of the module generation system and method of the present invention.

FIG. 4A is a schematic diagram of another embodiment of the module generation system and method of the present invention, in which raw data is filtered to generate a table of filtered data.

FIG. 4B is a schematic diagram of another embodiment of the module generation system and method of the present invention, showing the display interface displaying a table of the portion of the module to be generated that the user needs to view.

FIG. 5 is a schematic diagram of image analysis of another embodiment of the module generation system and method of the present invention.

1: Display interface

2: Function group generation unit

3: Background module generation unit

4: Front-end module generation unit

5: Setting unit

6: Database

7: Generative AI module

Claims (13)

A module generation system includes: a display interface, which displays a requirement list confirmed by a user, the requirement list including a plurality of requirements corresponding to the analysis results to be generated by the module to be generated; a function group generation unit, which is connected to the display interface and is used to generate a first function group according to a generative AI module for the best step corresponding to the analysis result to be generated by the module to be generated; and a function group generation unit, which is connected to the display interface and is used to generate a first function group according to the best step corresponding to the analysis result to be generated by the module to be generated. The formula AI module generates a second function group for displaying the best of the steps required for the user to view the part of the module to be generated on the display interface among multiple data processing steps; a background module generation unit, which is connected to the function group generation unit and generates a background module through the first function group; and a foreground module generation unit, which is connected to the function group generation unit and generates a foreground module through the second function group. The module generation system as described in claim 1, wherein the background operation module is further connected to a database and performs the following steps, including: filtering the original data from the database to generate a filtered data, computing the filtered data to generate a computed data; and performing statistical analysis on the computed data to generate the analysis result. A module generation system as described in claim 1, wherein the requirement list includes the following items: the number of steps, the order of steps, the time unit for calculating steps, the steps that can be skipped, whether to set a check step or whether the failure of the check step to be executed is considered abnormal. The module generation system as described in claim 1 further includes a setting unit, which is connected to the background module generation unit and the foreground module generation unit, and is used to set the data type of the required input data in the background module generation unit and the foreground module generation unit, a background logic framework corresponding to the background module generation unit, or a foreground logic framework corresponding to the foreground module generation unit according to the multiple steps executed by the corresponding module to be generated; wherein the foreground logic framework is used to set each of the second function group according to the content viewed by the user on the display interface corresponding to the background logic framework; wherein the background logic framework is used to set each of the first function group according to the analysis requirements of the user. The module generation system as described in claim 4, wherein the generative AI module is connected to the setting unit to provide the setting unit with a plurality of steps corresponding to the module to be generated to respectively set the background logic architecture corresponding to the background module generation unit or the front-end logic architecture corresponding to the front-end module generation unit. A module generation system as described in claim 4, wherein the generative AI module is connected to the background module generation unit to provide the background module generation unit with the first function set, and the generative AI module is connected to the foreground module generation unit to provide the foreground module generation unit with the second function set. A module generation system as described in claim 5 or 6, wherein the generative AI module is used to receive inquiries of multiple keywords related to the backend logic architecture, the frontend logic architecture, the first function group and the second function group; and is used to receive inquiries of reverse keywords of multiple keywords related to the backend logic architecture, the frontend logic architecture, the first function group and the second function group. A module generation method, comprising: A: receiving a requirement list confirmed by a user, the requirement list including a plurality of requirements corresponding to the analysis results to be generated by the module to be generated; B: using a function group generation unit to generate a first function group according to a generative AI module for the best step corresponding to the analysis results to be generated by the module to be generated, and using the function group generation unit to generate a second function group according to the generative AI module for the best step required to display the part of the module to be generated that the user needs to view on the display interface among the plurality of data processing steps; C: using a background module generation unit to generate a background module via the first function group; and D: using a foreground module generation unit to generate a foreground module via the second function group. The module generation method as described in claim 8, wherein the step B further includes generating the first function group by the function group generation unit according to the following steps executed corresponding to the module to be generated: filtering the original data to generate a filtered data, operating the filtered data to generate an operated data; and performing statistical analysis on the operated data to generate an analysis result. The module generation method as described in claim 8, wherein the requirement list in step A includes: the number of steps, the order of steps, the time unit for calculating steps, the steps that can be skipped, whether to set a check step or whether the failure of the check step to be executed is considered abnormal. The module generation method as described in claim 8, wherein the step B and the step C further include a step b: using a setting unit to determine the data type of each data in the background module generation unit and a background logic architecture corresponding to the background module generation unit according to the multiple steps executed corresponding to the module to be generated. The module generation method as described in claim 8, wherein the step C and the step D further include a step C: using a setting unit to determine the data type of each data in the foreground module generation unit and a foreground logic architecture corresponding to the foreground module generation unit according to the multiple steps executed corresponding to the module to be generated. The module generation method as described in claim 8, wherein the step A further includes a step X: querying the generative AI module with a plurality of keywords related to the backend logic architecture, the frontend logic architecture, the first function group, and the second function group, and taking the keywords with a high recurrence rate after statistically returning the results; querying the generative AI module with a plurality of keywords related to the backend logic architecture, the frontend logic architecture, the first function group, and the second function group, respectively; The reverse keyword query of multiple keywords related to the second function group is used to query the generative AI module, so as to remove the generated modules that fail to achieve the expected goals in the returned results; the generated results related to the backend logic structure, the frontend logic structure, the first function group and the second function group are respectively tested using the detection keywords, so as to remove the generated modules that fail to achieve the expected goals in the returned results.

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