Automatic AI new and old interface data comparison tool and method thereof

Abstract

The invention discloses an automatic AI new and old interface data comparison tool and a method thereof.The automatic AI new and old interface data comparison tool comprises a data preprocessing module, a feature extraction module, a data comparison module, a result display module and a feedback module, and the feedback module is connected with the result display module. According to the method, data preprocessing, feature extraction, data comparison and other work can be completed, the workload of manual comparison is greatly reduced, meanwhile, the comparison accuracy is high, intelligent comparison is performed through an AI algorithm, the difference between data can be accurately recognized, the comparison accuracy is improved, and the method has the advantage of being high in adaptability and high in practicability. The method can be suitable for system upgrading and data migration scenes of different scales and complexities, meanwhile, a friendly user interaction interface and rich result display modes are provided, and a user can understand and use the method conveniently.

Description

Technical Field

[0001] The present application relates to the technical field of interface data comparison tools, in particular to an automated AI new and old interface data comparison tool. Background Art

[0002] With the rise of AI technology and the rapid development of information technology, software systems are being updated and iterated more frequently. As a bridge for interaction between systems, changes in data formats often become a key challenge in system upgrades.

[0003] The traditional manual comparison method is not only time-consuming and labor-intensive, but also prone to errors, and it is difficult to meet the needs of large-scale data processing and rapid response; therefore, it is particularly important to develop a tool that can automatically and accurately compare the new and old interface data; therefore, an automated AI new and old interface data comparison tool is proposed to address the above problems. Summary of the invention

[0004] In this embodiment, an automated AI new and old interface data comparison tool is provided to solve the problem that ordinary manual comparison methods in the prior art are difficult to meet the needs of large-scale data processing and rapid response.

[0005] According to one aspect of the present application, an automated AI new and old interface data comparison tool is provided, and the automated AI new and old interface data comparison tool includes:

[0006] A data preprocessing module, wherein the data preprocessing module is internally provided with a receiving unit, a preprocessing unit, a natural language processing unit and a data mining unit;

[0007] A feature extraction module, wherein the feature extraction module is connected to the data preprocessing module, and a feature extraction unit and a machine learning algorithm unit are arranged inside the feature extraction module;

[0008] A data comparison module, wherein the data comparison module is connected to the feature extraction module, and an AI algorithm unit and an intelligent comparison unit are arranged inside the data comparison module;

[0009] A result display module, which is connected to the data comparison module and has a user interaction interface, a chart display unit and a list display unit disposed therein;

[0010] The feedback module is interconnected with the result display module, and a feedback collection unit, an algorithm optimization unit and an algorithm adjustment unit are arranged inside the feedback module.

[0011] Furthermore, the receiving unit and the pre-processing unit are interconnected, and a format conversion tool and a data cleaning tool are disposed inside the pre-processing unit.

[0012] Furthermore, the natural language processing unit includes a stop word removal unit, a stem extraction unit, a missing value processing unit and an outlier processing unit.

[0013] Furthermore, a key feature recognition unit is provided inside the feature extraction unit.

[0014] Furthermore, a deep learning model is arranged inside the AI ​​algorithm unit.

[0015] Furthermore, the intelligent comparison unit is internally provided with an association rule learning unit, a pattern learning unit, a similarity calculation unit and a classification clustering unit.

[0016] Furthermore, the association rule learning unit, the pattern learning unit, the similarity calculation unit and the classification and clustering unit are interconnected.

[0017] Furthermore, the intelligent comparison unit is also provided with a data difference point identification unit, a data consistency point identification unit and a comparison report generation unit.

[0018] Furthermore, a screening unit and a sorting unit are provided inside the user interaction interface.

[0019] Furthermore, the method for using the automated AI new and old interface data comparison tool includes the following steps:

[0020] a. Receive data input from new and old interfaces through the data preprocessing module, and perform format conversion and data cleaning operations to ensure uniform number and reliable quality. At the same time, use the natural language processing unit and data mining unit to process text data, remove stop words, extract stems, process missing values, and process abnormal values;

[0021] b. Perform feature extraction on the preprocessed data through the feature extraction module. When performing feature extraction, the machine learning algorithm is used to extract key features from the original data, thereby reducing the data dimension and improving the comparison efficiency;

[0022] c. Compare the new and old interface data through the data comparison module, and use the AI ​​algorithm unit and deep learning model for intelligent comparison, which can automatically learn the association rules and patterns between data, identify the differences and consistency points of the data through the similarity calculation unit, classification and clustering unit, data difference point identification unit, and data consistency point identification unit, and finally generate a detailed comparison report through the comparison report generation unit;

[0023] d. The comparison results are displayed to the user through the result display module. The charts and lists can be displayed through the chart display unit and the list display unit. The user interaction interface allows the user to filter and sort the comparison results.

[0024] Through the above technical solution of this application, data preprocessing, feature extraction, data comparison and other tasks can be completed, which greatly reduces the workload of manual comparison. At the same time, the accuracy of the comparison of this application is high. Through the intelligent comparison of AI algorithm, the differences between data can be accurately identified, which improves the accuracy of comparison. This application is also highly adaptable and can be applied to system upgrades and data migration scenarios of different scales and complexities. At the same time, this application provides a friendly user interaction interface and rich result display methods, which are easy for users to understand and use. . BRIEF DESCRIPTION OF THE DRAWINGS

[0025] 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 the description of the prior art 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 labor.

[0026] Figure 1 This is a schematic diagram of the overall architecture of an embodiment of the present application;

[0027] Figure 2 This is a schematic diagram of the structure of a pre-processing unit according to an embodiment of the present application;

[0028] Figure 3 A schematic diagram of the structure of a natural language processing unit according to an embodiment of the present application;

[0029] Figure 4 This is a schematic diagram of the structure of a feature extraction unit according to an embodiment of the present application;

[0030] Figure 5 This is a schematic diagram of the structure of an AI algorithm unit according to an embodiment of the present application;

[0031] Figure 6 This is a schematic diagram of the structure of an intelligent comparison unit according to an embodiment of the present application;

[0032] Figure 7 A structural diagram of a user interaction interface according to an embodiment of the present application.

[0033] In the figure:

[0034] Data preprocessing module 1, receiving unit 101, preprocessing unit 102, format conversion tool 1021, data cleaning tool 1022, natural language processing unit 103, stop word removal unit 1031, stem extraction unit 1032, missing value processing unit 1033, outlier processing unit 1034, data mining unit 104;

[0035] Feature extraction module 2, feature extraction unit 201, key feature identification unit 2011, machine learning algorithm unit 202;

[0036] Data comparison module 3, AI algorithm unit 301, deep learning model 3011, intelligent comparison unit 302, association rule learning unit 3021, pattern learning unit 3022, similarity calculation unit 3023, classification and clustering unit 3024, data difference point identification unit 3025, data consistency point identification unit 3026, comparison report generation unit 3027;

[0037] Result display module 4, user interaction interface 401, screening unit 4011, sorting unit 4012, chart display unit 402, list display unit 403;

[0038] Feedback module 5, feedback collection unit 501, algorithm optimization unit 502, algorithm adjustment unit 503. DETAILED DESCRIPTION

[0039] 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 should fall within the scope of protection of the present application.

[0040] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0041] In the present application, the terms "upper", "lower", "left", "right", "front", "back", "top", "bottom", "inner", "outer", "middle", "vertical", "horizontal", "lateral", "longitudinal" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings. These terms are mainly used to better describe the present application and its embodiments, and are not used to limit the indicated devices, elements or components to have a specific orientation, or to be constructed and operated in a specific orientation.

[0042] In addition, some of the above terms may be used to express other meanings in addition to indicating orientation or positional relationship. For example, the term "on" may also be used to express a certain dependency or connection relationship in some cases. For those of ordinary skill in the art, the specific meanings of these terms in this application can be understood according to specific circumstances.

[0043] In addition, the terms "installed", "set", "provided with", "connected", "connected", and "socketed" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral structure; it can be a mechanical connection, or an electrical connection; it can be a direct connection, or an indirect connection through an intermediate medium, or it can be an internal connection between two devices, elements, or components. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0044] See also Figure 1-3 As shown, an automated AI new and old interface data comparison tool, the automated AI new and old interface data comparison tool includes:

[0045] A data preprocessing module 1, wherein the data preprocessing module 1 is provided with a receiving unit 101, a preprocessing unit 102, a natural language processing unit 103 and a data mining unit 104;

[0046] A feature extraction module 2, wherein the feature extraction module 2 is connected to the data preprocessing module 1, and a feature extraction unit 201 and a machine learning algorithm unit 202 are provided inside the feature extraction module 2;

[0047] A data comparison module 3, wherein the data comparison module 3 is connected to the feature extraction module 2, and an AI algorithm unit 301 and an intelligent comparison unit 302 are provided inside the data comparison module 3;

[0048] A result display module 4, which is connected to the data comparison module 3, and has a user interaction interface 401, a chart display unit 402 and a list display unit 403;

[0049] A feedback module 5, wherein the feedback module 5 is interconnected with the result display module 4, and a feedback collection unit 501, an algorithm optimization unit 502 and an algorithm adjustment unit 503 are provided inside the feedback module 5;

[0050] The receiving unit 101 is connected to the pre-processing unit 102, and a format conversion tool 1021 and a data cleaning tool 1022 are provided inside the pre-processing unit 102;

[0051] The natural language processing unit 103 includes a stop word removal unit 1031, a stem extraction unit 1032, a missing value processing unit 1033 and an abnormal value processing unit 1034;

[0052] The feature extraction unit 201 is internally provided with a key feature recognition unit 2011;

[0053] The AI ​​algorithm unit 301 is internally provided with a deep learning model 3011 .

[0054] The intelligent comparison unit 302 is internally provided with an association rule learning unit 3021, a pattern learning unit 3022, a similarity calculation unit 3023 and a classification clustering unit 3024;

[0055] The association rule learning unit 3021, the pattern learning unit 3022, the similarity calculation unit 3023 and the classification and clustering unit 3024 are interconnected;

[0056] The intelligent comparison unit 302 is also provided with a data difference point identification unit 3025, a data consistency point identification unit 3026 and a comparison report generation unit 3027;

[0057] The user interaction interface 401 is internally provided with a screening unit 4011 and a sorting unit 4012 .

[0058] The method for using the automated AI new and old interface data comparison tool includes the following steps:

[0059] a. Receive data input from new and old interfaces through the data preprocessing module 1, and perform format conversion and data cleaning operations to ensure uniform number and reliable quality. At the same time, use the natural language processing unit 103 and the data mining unit 104 to process the text data, remove stop words, extract stems, process missing values, and process abnormal values;

[0060] b. Perform feature extraction on the preprocessed data through feature extraction module 2. When performing feature extraction, a machine learning algorithm is used to extract key features from the original data, thereby reducing the data dimension and improving the comparison efficiency;

[0061] c. Compare the new and old interface data through the data comparison module 3, and use the AI ​​algorithm unit 301 and the deep learning model 3011 for intelligent comparison, which can automatically learn the association rules and patterns between data, identify the difference points and consistency points of the data through the similarity calculation unit 3023, the classification and clustering unit 3024, the data difference point identification unit 3025, and the data consistency point identification unit 3026, and finally generate a detailed comparison report through the comparison report generation unit 3027;

[0062] d. The comparison results are displayed to the user through the result display module 4. The chart display unit 402 and the list display unit 403 can display charts and lists. The user interaction interface 401 allows the user to filter and sort the comparison results.

[0063] This application has a high degree of automation and can complete data preprocessing, feature extraction, data comparison and other tasks, which greatly reduces the workload of manual comparison. At the same time, the comparison accuracy of this application is high. Through AI algorithm for intelligent comparison, it can accurately identify the differences between data and improve the accuracy of comparison. This application is also highly adaptable and can be used for system upgrades and data migration scenarios of different scales and complexities. At the same time, this application provides a friendly user interaction interface and rich result display methods to facilitate user understanding and use.

[0064] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. Automated AI new and old interface data comparison tool, featuring: The automated AI new and old interface data comparison tool includes: A data preprocessing module (1), wherein the data preprocessing module (1) is internally provided with a receiving unit (101), a preprocessing unit (102), a natural language processing unit (103) and a data mining unit (104); A feature extraction module (2), wherein the feature extraction module (2) and the data preprocessing module (1) are interconnected, and a feature extraction unit (201) and a machine learning algorithm unit (202) are arranged inside the feature extraction module (2); A data comparison module (3), wherein the data comparison module (3) and the feature extraction module (2) are connected to each other, and an AI algorithm unit (301) and an intelligent comparison unit (302) are arranged inside the data comparison module (3); A result display module (4), wherein the result display module (4) is connected to the data comparison module (3), and the result display module (4) is provided with a user interaction interface (401), a chart display unit (402) and a list display unit (403); A feedback module (5), wherein the feedback module (5) is interconnected with the result display module (4), and the feedback module (5) is internally provided with a feedback collection unit (501), an algorithm optimization unit (502) and an algorithm adjustment unit (503).

2. The automated AI new and old interface data comparison tool according to claim 1, characterized in that: The receiving unit (101) and the pre-processing unit (102) are interconnected, and a format conversion tool (1021) and a data cleaning tool (1022) are arranged inside the pre-processing unit (102).

3. The automated AI new and old interface data comparison tool according to claim 1, characterized in that: The natural language processing unit (103) includes a stop word removal unit (1031), a stem extraction unit (1032), a missing value processing unit (1033) and an abnormal value processing unit (1034).

4. The automated AI new and old interface data comparison tool according to claim 1, characterized in that: The feature extraction unit (201) is provided with a key feature recognition unit (211) inside.

5. The automated AI new and old interface data comparison tool according to claim 1, characterized in that: The AI ​​algorithm unit (301) is internally provided with a deep learning model (3011).

6. The automated AI new and old interface data comparison tool according to claim 1, characterized in that: The intelligent comparison unit (302) is internally provided with an association rule learning unit (3021), a pattern learning unit (3022), a similarity calculation unit (3023) and a classification clustering unit (3024).

7. The automated AI new and old interface data comparison tool according to claim 6, characterized in that: The association rule learning unit (3021), the pattern learning unit (3022), the similarity calculation unit (3023) and the classification and clustering unit (3024) are interconnected.

8. The automated AI new and old interface data comparison tool according to claim 1, characterized in that: The intelligent comparison unit (302) is also provided with a data difference point identification unit (3025), a data consistency point identification unit (3026) and a comparison report generation unit (3027).

9. The automated AI new and old interface data comparison tool according to claim 1, characterized in that: The user interaction interface (401) is provided with a screening unit (4011) and a sorting unit (4012) therein.

10. A method for using the automated AI new and old interface data comparison tool according to any one of claims 1 to 9, characterized in that: The method for using the automated AI new and old interface data comparison tool includes the following steps: a. Receive data input from new and old interfaces through a data preprocessing module (1), and perform format conversion and data cleaning operations to ensure uniformity of number and reliable quality. At the same time, use a natural language processing unit (103) and a data mining unit (104) to process text data, remove stop words, extract stems, process missing values, and process abnormal values; b. extracting features from the preprocessed data using a feature extraction module (2). When extracting features, a machine learning algorithm is used to extract features, thereby extracting key features from the original data, thereby reducing data dimensions and improving comparison efficiency; c. Comparing the new and old interface data through the data comparison module (3), and using the AI ​​algorithm unit (301) and the deep learning model (3011) for intelligent comparison, which can automatically learn the association rules and patterns between the data, identify the differences and consistency points of the data through the similarity calculation unit (3023), the classification and clustering unit (3024), the data difference point identification unit (3025), and the data consistency point identification unit (3026), and finally generate a detailed comparison report through the comparison report generation unit (3027); d. The comparison results are displayed to the user through the result display module (4). The chart display unit (402) and the list display unit (403) can display charts and lists. The user interaction interface (401) allows the user to filter and sort the comparison results.

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