A method and system for automatically generating an interface based on request monitoring

Abstract

The present invention discloses an automatic interface generation method and system based on request monitoring, the method comprising the following steps: constructing an interface probe, the interface probe acquiring interface data; constructing a data structure comparison algorithm, a data structure inclusion judgment algorithm and a data structure merging algorithm; judging whether there is a data structure change in the interface data acquired by the interface probe, and if there is a data structure change, judging the similarity of the data structure by the data structure comparison algorithm; merging or combining the data structures according to the calculated data structure similarity, combining the data inclusion judgment algorithm and the data structure merging algorithm; updating the interface according to the merged and / or combined data structure update project, automatically generating interface request layer code for the problem of missing data structure in the front-end and back-end interaction process, avoiding the problem of interaction failure due to the change of the front-end and back-end codes, and improving the efficiency of front-end and back-end separation web application development.

Description

Technical Field

[0001] The present invention relates to the field of Internet technology, and in particular to a method and system for automatically generating an interface based on request monitoring. Background Art

[0002] With the popularity of TypeScript programming language in front-end development, developers can now use this programming language to declare various data structure types used in the development process. However, in the process of docking with the back-end interface, the front-end cannot perceive the data structure type in the data structure returned by the back-end. In the past, the front-end was required to manually declare the data structure type returned by the back-end, and then use it for subsequent business logic. After using this tool, you can monitor the HTTP protocol of all back-end interfaces in the project, collect its Response after monitoring the HTTP request that meets the specifications, and sort out the correct TypeScript definition, and automatically build the HTTP interface request layer code, and finally output it to the development directory of the project. The above-mentioned prior art may have the technical problem of data structure loss in the development process of web applications with front-end and back-end separation, which may lead to the failure of front-end and back-end interaction and affect the program development process. Summary of the invention

[0003] One of the inventive purposes of the present invention is to provide an automatic interface generation method and system based on request monitoring. The method and system automatically generate interface request layer code to address the problem of missing data structure during the front-end and back-end interaction process, thereby avoiding the problem of interaction failure due to changes in the front-end and back-end codes and improving the efficiency of front-end and back-end separation web application development.

[0004] Another inventive object of the present invention is to provide an automatic interface generation method and system based on request monitoring, which uses JSON data without structural meaning to generate a maintainable TypeScript data structure definition model in a statistical manner, and realizes the maintenance of the model of automatic request layer interface data through comparison of built-in data structures including judgment and merging functions.

[0005] Another inventive object of the present invention is to provide an automatic interface generation method and system based on request monitoring. The method and system use interface probes to obtain interface data, and then determine whether there is a data structure change based on the obtained interface data. If so, the data structure is updated, and the interface definition model is updated to the local project through the probe, thereby achieving the effect of automatically generating, following up and maintaining the back-end interface data structure, reducing the front-end and back-end interaction failures caused by missing data structures.

[0006] In order to achieve at least one of the above-mentioned invention objects, the present invention further provides a method for generating an automatic interface based on request monitoring, the method comprising the following steps:

[0007] Constructing an interface probe, wherein the interface probe acquires interface data;

[0008] Constructing algorithms including data structure comparison algorithm, data structure inclusion judgment algorithm and data structure merging algorithm;

[0009] Determining whether there is a data structure change in the interface data acquired by the interface probe, and if there is a data structure change, determining the similarity of the data structure by using the data structure comparison algorithm;

[0010] According to the calculated data structure similarity, combining the data inclusion judgment algorithm and the data structure merging algorithm to perform data structure merging and / or combining;

[0011] Updates the project implementation interface based on the merged and / or unioned data structure.

[0012] According to one of the preferred embodiments of the present invention, the data structure comparison algorithm, the data structure inclusion judgment algorithm and the data structure merging algorithm respectively include recursive calculations, wherein the data structure comparison algorithm includes a comparison similarity threshold, and after the similarity comparison type is passed in, if the data structure similarity after the recursive calculation according to the data structure comparison algorithm is greater than the preset comparison similarity threshold, the merging operation of the comparison type is performed.

[0013] According to another preferred embodiment of the present invention, the recursive calculation method includes: determining the data structure type of the interface data obtained by detection, obtaining each field corresponding to the changed data structure type, and obtaining each field corresponding to the same data structure type that has not changed, recursively comparing the changed data structure type fields with the unchanged data structure type fields, accumulating the similarity of each field, and dividing the accumulated similarity by the total number of compared fields to generate an average similarity value as the similarity between the two data structure types.

[0014] According to another preferred embodiment of the present invention, the method for obtaining similarity by comparing fields includes: configuring field name similarity weights and field structure similarity weights, if the field names are the same, the corresponding name similarity value is 1, and the total field similarity is the weighted cumulative value of the field name and field structure similarity.

[0015] According to another preferred embodiment of the present invention, the data structure merging method includes: calculating the similarity between the changed data structure types; if the similarity exceeds a preset comparison similarity threshold, each field of the two compared data structure types is disassembled, and each disassembled field is recursively merged; if the calculated similarity between the changed data structure types is less than or equal to the preset comparison similarity threshold, the merge method is directly called to integrate the two data structure types to form a union type (union) in TypeScript.

[0016] According to another preferred embodiment of the present invention, when the changed data structure type is an array, it is determined that the data structure type to be compared is an array, and the merge operation of the two data structure types to be compared is directly performed; if the data structure type to be compared is a tuple, the comparison similarity between the tuple data structure type and the array data structure type is compared, and when the similarity between the tuple data structure type and the array data structure type is greater than the preset comparison similarity threshold, the merge method is directly called to integrate the tuple data structure type and the array data structure type to form a union type.

[0017] According to another preferred embodiment of the present invention, when the changed data structure type is a union type, the data structure contains a judgment algorithm that is called to determine whether the data structure type to be compared exists in the union type. If so, the data structure similarity algorithm is called to perform a similarity judgment of the corresponding data structure type, and the union type is used to perform a merge and / or union operation on the data structure type to be judged based on the similarity result and the comparison similarity threshold, and the updated union type data is returned.

[0018] According to another preferred embodiment of the present invention, when the interface probe acquires interface data, if a new changed data structure type is found, and the data structure type to be compared is a tuple type or a union type, the similarity between the changed data structure type and the tuple type or the union type is calculated by the data structure comparison algorithm, and the changed data is merged or combined according to the data structure merging algorithm.

[0019] In order to achieve at least one of the above-mentioned invention purposes, the present invention further provides an automatic interface generation system based on request monitoring, and the system executes the above-mentioned automatic interface generation method based on request monitoring.

[0020] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program can be executed by a processor to describe the method for generating an automatic interface based on request monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1Shown is a flow chart of an automatic interface generation method based on request monitoring of the present invention.

[0022] Figure 2 Shown is a schematic diagram of a data structure comparison algorithm table in an automation interface based on request monitoring of the present invention.

[0023] Figure 3 Shown is a table schematic diagram of a data structure judgment algorithm in an automation interface based on request monitoring of the present invention.

[0024] Figure 4 Shown is a table schematic diagram of a data structure merging algorithm in an automation interface based on request monitoring according to the present invention. DETAILED DESCRIPTION

[0025] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art can think of other obvious variations. The basic principles of the present invention defined in the following description can be applied to other embodiments, variations, improvements, equivalents, and other technical solutions that do not deviate from the spirit and scope of the present invention.

[0026] It is to be understood that the term "one" should be understood as "at least one" or "one or more", that is, in one embodiment, the number of an element may be one, while in another embodiment, the number of the element may be multiple, and the term "one" should not be understood as a limitation on the quantity.

[0027] Please combine Figure 1-Figure 4 The present invention discloses an automatic interface generation method and system based on request monitoring, the method comprising: firstly, it is necessary to configure an interface probe at the data interface of the web front end and the back end, the interface probe is used to obtain interface data, and further analyze the data structure in the interface data, wherein the data structure includes but is not limited to field name, field type and field combination mode. By setting a data structure comparison algorithm in the back end program to determine whether the data structure has changed, further combining the data structure inclusion judgment algorithm and the data structure merging algorithm to perform data structure and report merging and / or union.

[0028] The present invention preferably uses json format data for data interaction between the front-end and back-end, and the json format data structure types include JSON boolean value = TypeScript boolean, JSON string = TypeScript string, JSON number = TypeScript Number, JSON object = TypeScript interface (using an interface to declare a certain object data structure), JSON array = TypeScript array, JSONnull = TypeScript null, JSON date = TypeScript Date. The interface probe obtains interface data including array types. If the element types in the interface data are different, the tuple in TypeScript is introduced to perform data processing. When the acquired data format is not json format data, the undefined value is processed by introducing the undefined type in TypeScript to process data that is not recognized as json format data.

[0029] After the interface probe obtains the interface data, when it determines that the interface data has changed, it determines whether the data structure in the interface data has changed, and determines whether to choose to merge the data structure or separate the data structure as the union type in TypeScript according to the size of the data structure change. Specifically, the interface probe obtains the interface data and determines the data structure type of the interface data, wherein the data structure type of the original corresponding interface input is used as the data structure to be compared, and determines whether the interface data has changed. If the interface data has changed, it determines whether the data structure in the interface data is of the same type according to the data structure comparison algorithm, and further combines the data structure inclusion algorithm and the data structure merging algorithm to perform merging and / or union operations. For example: if the incoming changed data structure type is an object type, then each field in the object type data is obtained, and the unchanged data structure obtained by the probe last time is obtained as the data structure to be compared; if the data structure type to be compared is also an object type, then each field of the object type to be compared is further obtained, and each field of the newly obtained object type is recursively compared with each field of the object type to be compared, and the similarity of each compared field is accumulated to obtain the total similarity of the two compared data structure types, and the total similarity of the comparison is divided by the total number of compared fields to produce the average similarity of the compared data structure types, and the average similarity is used as the similarity value of the two compared data structure types.

[0030] It is worth mentioning that the field comparison method includes: configuring the similarity weights of the field name and the field structure, wherein the similarity weight of the field name can be configured as 0.4, and the similarity weight of the field structure is configured as 0.6, and calculating the similarity of the names between the compared fields, wherein if all the characters in the field name are the same, it indicates that the name similarity of the two compared fields is 1, and if the field names are different, the field name similarity is 0. The method for calculating the field structure similarity includes: judging the basic type between the compared fields, wherein the basic type includes Boolean value, string, number, object, array, null and date, etc., when the field basic type obtained from the data interface is different from the basic type of the field to be compared, the field structure similarity is 0, if the same, the field structure similarity is 1, and further weighted accumulation of the field name similarity and the field structure similarity, and the weighted accumulation result is used as the similarity value of the comparison between the two fields. It should be noted that the above-mentioned method for calculating the field similarity comparison is part of the data structure comparison algorithm.

[0031] Furthermore, taking the above-mentioned object type as an example, after calculating the similarity values ​​of all fields of the newly acquired changed object type and all fields of the object type to be compared, the similarity values ​​of all fields are recursively accumulated to obtain the total similarity value of all fields, and further the total similarity value of all fields is divided by the total number of fields to be compared to obtain the average similarity value, and the average similarity value is used as the similarity value of the received new changed object type and the object type to be compared. When the incoming data structure type is not an object type, it is directly determined that the received new data structure type and the data structure type to be compared have no similarity, and the field similarity comparison and object type similarity comparison are combined to form a complete data structure comparison algorithm.

[0032] Please refer to Figure 2 The data structure comparison algorithm table is displayed. When the above data structure comparison algorithm is satisfied, the value in the table of the corresponding data structure type is 1, and when it is not satisfied, it is 0. Figure 2 In the table, "Calculation" indicates that there is a recursive similarity calculation for the data structure type of this group of comparisons.

[0033] After completing the similarity judgment of the data structure type, further perform the merging and / or union operation of the data structure type according to the similarity of the compared data structure type. The specific method includes the following steps: setting a comparison similarity threshold for the data structure type comparison in the back-end system, if the similarity value between the data structure type obtained from the data interface and the data structure type to be compared is greater than the comparison similarity threshold, it means that there is a high similarity in the group of data structure types, and then further perform the merging operation of the data structure type, wherein the merging operation is to recursively merge each field in the data structure type, merge two identical fields into one, and save different fields as different fields. If the similarity between the data structure type obtained from the data structure and the data structure type to be compared is less than the comparison similarity threshold, then directly call the Merge method, and integrate the two compared object types into a union type through the subclass in the Merge method. The above-mentioned Merge method itself is a prior art, and the present invention will not be described in detail.

[0034] It is worth mentioning that the data structure comparison algorithm also includes comparison of array types, tuple types and union types, wherein the array type data comparison method includes: obtaining the elements in the two compared arrays to construct an array similarity comparison matrix, calculating the similarity between each element in the array, and placing the similarity value between each element in the matrix in the corresponding position, such as the first array is (A, B, C), and the second array is (A1, B1, C1). According to the data structure comparison algorithm, each element in the first array is compared with each element in the second array to obtain a 3*3 size similarity comparison matrix. In one of the preferred embodiments of the present invention, by calculating the inner product of each element in the similarity comparison matrix, by setting the inner product threshold, when the calculated inner product of the elements in the similarity comparison matrix is ​​greater than the inner product threshold, the array is summarized as an array of a certain type. In another preferred embodiment of the present invention, by calculating the average value of all elements in the similarity comparison matrix, and setting the average value threshold, if the average value threshold of the similarity matrix is ​​greater than the average value threshold, the array type is summarized as a corresponding type. For example: the first array is (1,2,3), the second array is (4,5,6), according to the comparison algorithm of the data structure, the average value of the similarity matrix elements between the first array and the second array can be calculated to be 1 (the field name is an integer, and the field structure is a numerical value). The values ​​of all similarity matrix elements constructed by the first array and the second array are 1, so the average value is 1. The preset comparison similarity threshold is 0.6, then it can be determined that the first array and the second array meet the comparison similarity threshold, and the first array and the second array can be aggregated to form a new integer array (1,2,3,4,5,6). The above field name can be used to aggregate and classify two data structure types. For another example: the third array is (1, "Hello"), the fourth array is (2, "Hello"), the field names of the third array and the fourth array include integers (1, 2) and text (Hello, Hello), and the corresponding field structures are numerical values ​​(1, 2) and strings (Hello, Hello). According to the data structure comparison algorithm, if the mean similarity of the elements of the similarity matrix constructed by the third array and the fourth array is less than the comparison similarity threshold, the third array and the fourth array cannot be classified as "integer" array types. The third array and the fourth array can only be summarized as "tuples". In another preferred embodiment of the present invention, a similarity comparison matrix can be directly constructed for itself from the obtained array type to determine whether the array itself belongs to an "array" or a "tuple".

[0035] In one of the preferred embodiments of the present invention, when the original saved data structure type is obtained as an array, it is further calculated whether the data structure type obtained from the data interface is an array. If it is an array, a similarity comparison matrix is ​​generated for the saved array and the newly generated array, and the average value of the elements between each similarity comparison matrix is ​​calculated. If it is greater than the preset comparison similarity threshold, the newly obtained array can be merged into the original saved array. If the new data structure type obtained from the data interface is a "tuple", wherein the tuple can be understood as a data structure type containing more non-array types. Then the similarity matrix between the corresponding fields of the tuple and the corresponding fields of the saved array is calculated, and the Math.min of the calling program is used to calculate the minimum value of the elements in the similarity matrix and multiply it by the preset natural similarity weight as the similarity value between the tuple and the array. For example, the minimum value between the tuple and the array calculated is P. Since there is a natural similarity of 0.05, the natural similarity weight is set to 0.95, and the similarity value between the tuple and the array is P*0.95.

[0036] That is to say, when it is detected that both data structure types to be merged are arrays, the data structure inclusion judgment algorithm can be called to obtain the saved data structure type inclusion and the newly acquired data structure type, and then the arrays can be directly merged to merge the same array element contents into 1, and different element contents into the same array.

[0037] When the similarity between the tuple and the array obtained by the data structure comparison algorithm is less than the comparison similarity threshold, the union in TypeScript is directly called to generate a union type. The union type indicates that the two data structure types are saved in a split manner.

[0038] In one of the preferred embodiments of the present invention, when the saved data structure is a union type and a new data structure type is obtained from the data interface, the Optimize method of the TsMerger is further called. The Optimize method can be used to determine which part of the union type the new data structure type belongs to, and the new data structure type is further embedded in the list of corresponding union types for similarity calculation. Based on the similarity calculation result, it is determined whether a merge or union operation is required, and the updated union type is returned to the front end.

[0039] After the comparison, merging, and inclusion judgment between all data structure types are completed, the newly generated data structure needs to be optimized. For the combination type, an algorithm is used to calculate the number of types required for the combination type in different situations. The case with a small number of types is optimized, and the case with a large number of types is abandoned.

[0040] The optimization method optimizes arrays, tuples, or unions. After passing in the member type list (members) of the array or tuple and union type, the merger is called to try to merge the list (the similarities between lists will be exhausted in the merger algorithm, and the ones with high similarity will be merged first). After the merger is completed, the optimized list can be obtained. If the length of this list is 1, this type is directly returned, otherwise a union type is generated.

[0041] It should be noted that the present invention uses Socket.IO to communicate data with the back-end server, and uses an asymmetric encryption algorithm to generate a Token authentication connection to the back-end server. When the front-end developer runs his own front-end development environment, the probe will automatically run and monitor the HTTP interface request issued by the project, and submit it to the server for analysis after a simple HASH verification and judgment locally. If the interface changes, the latest data structure information of the detected interface is updated to the database. And notify each probe via WebSocket. After receiving the notification, the probe will update the interface request definition model in the local project. Then achieve the purpose of automatically generating, following up, and maintaining the changes in the back-end interface data structure. Among them Figure 2 The value 1 in the table indicates similarity, and 0 indicates dissimilarity. Figure 3 In the table, TRUE indicates that the data structure type is judged to be included under the data structure inclusion judgment algorithm, and FALSE indicates that the data structure type is judged to be not included under the data structure inclusion judgment algorithm. Figure 4 In the table, TRUE indicates the data structure type that performs the merge operation under the data structure merge algorithm, and FALSE indicates the data structure type that performs the join operation under the data structure merge algorithm.

[0042] In particular, according to the embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part, and / or installed from a removable medium. When the computer program is executed by the central processing unit (CPU), the above-mentioned functions defined in the method of the present application are executed. It should be noted that the above-mentioned computer-readable medium of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, but not limited to, a system, device or device of an electrical, magnetic, optical, electromagnetic, infrared segment, or semiconductor, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection with one or more wire segments, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, electrical wire, optical cable, RF, etc., or any suitable combination of the foregoing.

[0043] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0044] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are only examples and do not limit the present invention. The objectives of the present invention have been fully and effectively achieved. The functional and structural principles of the present invention have been demonstrated and explained in the embodiments. Without departing from the principles, the implementation methods of the present invention may be deformed or modified in any way.

Claims

1. A method for generating an automatic interface based on request monitoring, characterized in that: The method comprises the following steps: Constructing an interface probe, wherein the interface probe acquires interface data; Constructing algorithms including data structure comparison algorithm, data structure inclusion judgment algorithm and data structure merging algorithm; Determining whether there is a data structure change in the interface data acquired by the interface probe, and if there is a data structure change, determining the similarity of the data structure by using the data structure comparison algorithm; According to the calculated data structure similarity, combining the data inclusion judgment algorithm and the data structure merging algorithm to perform data structure merging and / or combining; updating the project execution interface according to the merged and / or joined data structure; The data structure merging method includes: calculating the similarity between the changed data structure types; if the similarity exceeds a preset comparison similarity threshold, each field of the two compared data structure types is disassembled, and each disassembled field is recursively merged; if the calculated similarity between the changed data structure types is less than or equal to the preset comparison similarity threshold, the merge method is directly called to integrate the two data structure types to form a union type in TypeScript.

2. The method for generating an automatic interface based on request monitoring according to claim 1, characterized in that: The data structure comparison algorithm, the data structure inclusion judgment algorithm and the data structure merging algorithm respectively include recursive calculations, wherein the data structure comparison algorithm includes a comparison similarity threshold. After the similarity comparison type is input, if the data structure similarity after the recursive calculation according to the data structure comparison algorithm is greater than the preset comparison similarity threshold, the merging operation of the comparison type is performed.

3. The method for generating an automatic interface based on request monitoring according to claim 2, characterized in that: The recursive calculation method includes: determining the data structure type of the detection interface data, obtaining each field corresponding to the changed data structure type, and obtaining each field corresponding to the same data structure type that has not changed, recursively comparing the changed data structure type field with the unchanged data structure type field, accumulating the similarity of each field, and dividing the accumulated similarity by the total number of compared fields to generate an average similarity value as the similarity between the two data structure types.

4. The method for generating an automatic interface based on request monitoring according to claim 3, characterized in that: The method for obtaining total similarity by comparing fields includes: configuring field name similarity weights and field structure similarity weights, if the field names are the same, the corresponding name similarity value is 1, and the total field similarity is the weighted cumulative value of the field name and field structure similarities.

5. The method for generating an automatic interface based on request monitoring according to claim 1, characterized in that: When the changed data structure type is an array, the data structure type to be compared is determined to be an array, and the merge operation of the two data structure types to be compared is directly performed; if the data structure type to be compared is a tuple, the comparison similarity of the tuple data structure type and the array data structure type is compared. When the similarity between the tuple data structure type and the array data structure type is greater than the preset comparison similarity threshold, the merge method is directly called to integrate the tuple data structure type and the array data structure type to form a union type.

6. The method for generating an automatic interface based on request monitoring according to claim 1, characterized in that: When the changed data structure type is a union type, the data structure contains a judgment algorithm that is called to determine whether the data structure type to be compared exists in the union type. If so, the data structure similarity algorithm is called to perform a similarity judgment of the corresponding data structure type, and the union type is used to perform a merge and / or union operation on the data structure type to be judged based on the similarity result and the comparison similarity threshold, and the updated union type data is returned.

7. The method for generating an automatic interface based on request monitoring according to claim 1, characterized in that: When the interface probe acquires interface data, if a new changed data structure type is found, and the data structure type to be compared is a tuple type or a union type, the similarity between the changed data structure type and the tuple type or the union type is calculated by the data structure comparison algorithm, and the changed data is merged and / or combined according to the data structure merging algorithm.

8. An automatic interface generation system based on request monitoring, characterized in that: The system executes an automatic interface generation method based on request monitoring as described in any one of claims 1-7.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program can be executed by a processor to implement the method for generating an automatic interface based on request monitoring as described in any one of claims 1 to 7.

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