Interface output data generation method and apparatus, device, and medium

Abstract

The present disclosure provides an interface output data generation method. It can be applied to the field of artificial intelligence technology and the field of big data technology. The method comprises the following steps: obtaining target interface input data for field factor sorting, generating a target interface input data field. Obtain b first historical interface input data and c first historical interface output data for field factor sorting, generate d first historical interface input data fields and e first historical interface output data fields. Analyze the relationship between the a target interface input data fields and the d first historical interface input data fields, obtain e first historical interface output data fields corresponding to the a target interface input data fields from the e first historical interface output data fields, and generate e target interface output data fields. The present disclosure also provides an interface output data generation device, equipment, storage medium and program product.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of artificial intelligence, in particular to the technical field of big data, and more particularly to an interface output data generation method and device, equipment, medium and program product. BACKGROUND

[0002] In enterprises including banks, with the increasing complexity of financial industry systems, the mutual calling and dependence of interfaces between systems are increasing. These interfaces connect various financial application programs, including account systems, payment systems, securities trading systems, etc. Since financial transactions involve a large amount of funds and sensitive information, the interfaces between systems must be efficient, reliable and secure.

[0003] Under the traditional interface test mode, all mutually dependent business systems need to be deployed before testing can be carried out. If external system interfaces are involved, the coordination and cooperation of the test environment are more difficult. However, in the agile mode, the iteration cycle is usually very short, and the system function needs to be quickly verified and fed back. The traditional interface test mode cannot meet the timeliness requirements of the system going online quickly, and cannot quickly, accurately and reliably determine the output data corresponding to the input data of the target interface to verify whether the function and interaction of the system meet the expectations. SUMMARY

[0004] In view of the above problems, the present disclosure provides an interface output data generation method, device, equipment, medium and program product.

[0005] According to a first aspect of the present disclosure, a method for generating interface output data is provided. The method comprises: obtaining target interface input data, performing field factor sorting on the target interface input data to generate a target interface input data field, wherein a is an integer and a is greater than or equal to 1; obtaining b first historical interface input data and c first historical interface output data, wherein the b first historical interface input data and the c first historical interface output data have a first mapping relationship, b is an integer and b is greater than or equal to 1, c is an integer and c is greater than or equal to 1; performing field factor sorting on the b first historical interface input data and the c first historical interface output data to generate d first historical interface input data fields and e first historical interface output data fields, wherein d is an integer and d is greater than or equal to b, e is an integer and e is greater than or equal to c; generating a second mapping relationship between the d first historical interface input data fields and the e first historical interface output data fields based on the first mapping relationship; obtaining a first historical interface input data field with the highest degree of association with each target interface input data field from the d first historical interface input data fields to generate a third historical interface input data field; obtaining m first historical interface output data fields having a second mapping relationship with the a third historical interface input data fields from the e first historical interface output data fields to generate m target interface output data fields, wherein m is an integer and m is greater than or equal to 1, and m is less than e; and combining the m target interface output data fields to generate f target interface output data, wherein f is an integer and f is greater than or equal to 1, and f is less than or equal to m.

[0006] According to an embodiment of the present disclosure, performing field factor sorting on the b first historical interface input data and the c first historical interface output data to generate d first historical interface input data fields and e first historical interface output data fields comprises: performing key field extraction on the b first historical interface input data and the c first historical interface output data to generate d second historical interface input data fields and e second historical interface output data fields; and performing field factor sorting on the d second historical interface input data fields and the e second historical interface output data fields to generate d first historical interface input data fields and e first historical interface output data fields.

[0007] According to the embodiment of the present disclosure, the b first historical interface input data and the c first historical interface output data are subjected to key field extraction to generate d second historical interface input data fields and e second historical interface output data fields, including: fieldizing the b first historical interface input data and the c first historical interface output data to generate g first historical interface input data fields and h first historical interface output data fields, where g is an integer and g is greater than or equal to d, and h is an integer and h is greater than or equal to e; performing format conversion on the g first historical interface input data fields and the h first historical interface output data fields to generate g first historical interface input data field key-value pairs and the h first historical interface output data field key-value pairs; calculating the term frequency-inverse document frequency of the values of the g first historical interface input data field key-value pairs and the term frequency-inverse document frequency of the values of the h first historical interface output data field key-value pairs; obtaining the field key-value pairs with the term frequency-inverse document frequency greater than a preset threshold value from the term frequency-inverse document frequency of the values of the g first historical interface input data field key-value pairs and the term frequency-inverse document frequency of the values of the h first historical interface output data field key-value pairs to generate d second historical interface input data field key-value pairs and e second historical interface output data field key-value pairs; and performing inverse conversion on the d second historical interface input data field key-value pairs and the e second historical interface output data field key-value pairs to generate d second historical interface input data fields and e second historical interface output data fields.

[0008] According to the embodiment of the present disclosure, the b first historical interface input data and the c first historical interface output data are subjected to fieldization to generate g first historical interface input data fields and h first historical interface output data fields, including: field splitting the b first historical interface input data and the c first historical interface output data by using a regular expression to generate g first historical interface input data fields and h first historical interface output data fields.

[0009] According to the embodiment of the present disclosure, the d second historical interface input data fields and the e second historical interface output data fields are subjected to field factor sorting to generate d first historical interface input data fields and e first historical interface output data fields, including: obtaining the term frequency-inverse document frequency of the values of the d second historical interface input data field key-value pairs, calculating the mean value of the term frequency-inverse document frequency of the values of the d second historical interface input data field key-value pairs to generate a field sorting factor; and based on the field sorting factor, the d second historical interface input data fields and the e second historical interface output data fields are subjected to field factor sorting to generate d first historical interface input data fields and e first historical interface output data fields.

[0010] According to the embodiment of the present disclosure, the a third historical interface input data fields are generated by: calculating, by using a cosine similarity analysis method, a correlation variable factor between each of the a target interface input data fields and each of the d first historical interface input data fields; and obtaining the a first historical interface input data fields with the maximum correlation variable factor of each of the a target interface input data fields, to generate the a third historical interface input data fields.

[0011] According to the embodiment of the present disclosure, the f target interface output data are generated by: obtaining i preset non-key fields, where i is an integer and i is greater than or equal to 1; and combining the i preset fields and the m target interface output data fields to generate the f target interface output data.

[0012] According to a second aspect of the present disclosure, an interface output data generation apparatus is provided, which comprises: a first generation module configured to obtain target interface input data, perform field factor sorting on the target interface input data, and generate a target interface input data field, wherein a is an integer and a is greater than or equal to 1; a first acquisition module configured to obtain b first historical interface input data and c first historical interface output data, wherein the b first historical interface input data and the c first historical interface output data have a first mapping relationship, b is an integer and b is greater than or equal to 1, and c is an integer and c is greater than or equal to 1; a second generation module configured to perform field factor sorting on the b first historical interface input data and the c first historical interface output data, and generate d first historical interface input data fields and e first historical interface output data fields, wherein d is an integer and d is greater than or equal to b, and e is an integer and e is greater than or equal to c; a third generation module configured to generate a second mapping relationship of the d first historical interface input data fields and the e first historical interface output data fields based on the first mapping relationship; a fourth generation module configured to obtain a first historical interface input data field with the highest degree of association from the a target interface input data fields among the d first historical interface input data fields, and generate a third historical interface input data field; a fifth generation module configured to obtain m first historical interface output data fields having a second mapping relationship with the a third historical interface input data fields from the e first historical interface output data fields, and generate m target interface output data fields, wherein m is an integer and m is greater than or equal to 1, and m is less than e; and a sixth generation module configured to combine the e interface input data fields to generate target interface output data, wherein f is an integer and f is greater than or equal to 1, and f is less than or equal to m.

[0013] According to an embodiment of the present disclosure, the second generation module comprises: a seventh generation module configured to extract a key field from the b first historical interface input data and the c first historical interface output data, and generate d second historical interface input data fields and the e second historical interface output data fields; and an eighth generation module configured to perform field factor sorting on the d second historical interface input data fields and the e second historical interface output data fields, and generate the d first historical interface input data fields and the e first historical interface output data fields.

[0014] According to the embodiment of the present disclosure, the seventh generation module comprises: a ninth generation module configured to fieldize the b first historical interface input data and the c first historical interface output data to generate g first historical interface input data fields and h first historical interface output data fields, wherein g is an integer and g is greater than or equal to d, h is an integer and h is greater than or equal to e; a tenth generation module configured to format convert the g first historical interface input data fields and the h first historical interface output data fields to generate g first historical interface input data field key-value pairs and the h first historical interface output data field key-value pairs; a first calculation module configured to calculate the term frequency-inverse document frequency of the values of the g first historical interface input data field key-value pairs and the term frequency-inverse document frequency of the values of the h first historical interface output data field key-value pairs; an eleventh generation module configured to obtain the field key-value pairs with the term frequency-inverse document frequency greater than a preset threshold value from the term frequency-inverse document frequency of the values of the g first historical interface input data field key-value pairs and the term frequency-inverse document frequency of the values of the h first historical interface output data field key-value pairs to generate d second historical interface input data field key-value pairs and e second historical interface output data field key-value pairs; and a twelfth generation module configured to inverse convert the d second historical interface input data field key-value pairs and the e second historical interface output data field key-value pairs to generate d second historical interface input data fields and e second historical interface output data fields.

[0015] According to the embodiment of the present disclosure, the eighth generation module comprises: a thirteenth generation module configured to obtain the term frequency-inverse document frequency of the values of the d second historical interface input data field key-value pairs, calculate the mean value of the term frequency-inverse document frequency of the values of the d second historical interface input data field key-value pairs to generate a field sorting factor; and a fourteenth generation module configured to sort the d second historical interface input data fields and the e second historical interface output data fields based on the field sorting factor to generate d first historical interface input data fields and e first historical interface output data fields.

[0016] According to the embodiment of the present disclosure, the fourth generation module comprises: a second calculation module configured to calculate the correlation variable factor of each field in the a target interface input data fields and each field in the d first historical interface input data fields by a cosine similarity analysis method; and a fifteenth generation module configured to obtain the a first historical interface input data fields with the maximum correlation variable factor for each field in the a target interface input data fields to generate a third historical interface input data field.

[0017] According to the embodiment of the present disclosure, the sixth generation module comprises a sixteenth generation module configured to acquire i preset non-key fields, wherein i is an integer and i is greater than or equal to 1; and a seventeenth generation module configured to combine the i preset fields with the m target interface output data fields to generate f target interface output data.

[0018] According to a third aspect of the present disclosure, an electronic device is provided, comprising: one or more processors; a storage device configured to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors are caused to perform the interface output data generation method described above.

[0019] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, which stores executable instructions or computer programs, and the instructions or computer programs, when executed by a processor, cause the processor to perform the interface output data generation method described above.

[0020] According to a fifth aspect of the present disclosure, a computer program product is also provided, comprising a computer program, which, when executed by a processor, implements the interface output data generation method described above.

[0021] The method obtains target interface input data and a plurality of historical interface input data and corresponding output data. The correlation variable factor of the target interface input data and the plurality of historical interface input data is calculated, the historical interface input data with the highest correlation degree is screened out, and the output data corresponding to the historical interface input data with the highest correlation degree is obtained as the output data corresponding to the target interface input data. The technical problem that the traditional test mode cannot meet the timeliness requirement of the system rapid online under the agile mode to verify whether the function and interaction of the system meet the expectation is solved. The output data corresponding to the target interface input data can be quickly, accurately and reliably determined, which facilitates to reduce the time and cost of manual writing and execution of test cases, saves time cost, improves work efficiency, and realizes the technical effects of improving the efficiency and reliability of interface output data generation. BRIEF DESCRIPTION OF DRAWINGS

[0022] The above and other objects, features and advantages of the present disclosure will become more apparent from the following description of embodiments of the present disclosure, taken in conjunction with the accompanying drawings, in which:

[0023] Figure 1 An application scenario diagram of the interface output data generation method and device according to the embodiment of the present disclosure is schematically shown;

[0024] Figure 2 A flowchart of the interface output data generation method according to the embodiment of the present disclosure is schematically shown;

[0025] Figure 3 A flowchart illustrating generating d first historical interface input data fields and e first historical interface output data fields based on a field factor ranking in the interface output data generation method according to an embodiment of the present disclosure is shown schematically;

[0026] Figure 4 A flowchart illustrating generating d first historical interface input data fields and e first historical interface output data fields based on a field factor ranking in the interface output data generation method according to an embodiment of the present disclosure is shown schematically;

[0027] Figure 5 A flowchart illustrating generating a plurality of third abnormal data in each access data according to a field ranking factor in the interface output data generation method according to an embodiment of the present disclosure is shown schematically;

[0028] Figure 6 A flowchart illustrating generating a plurality of third abnormal data in each access data according to a field ranking factor in the interface output data generation method according to an embodiment of the present disclosure is shown schematically;

[0029] Figure 7 A flowchart illustrating generating a plurality of third abnormal data in each access data according to a field ranking factor in the interface output data generation method according to an embodiment of the present disclosure is shown schematically;

[0030] Figure 8 A schematic diagram of an interface output data generation system according to an embodiment of the present disclosure is shown schematically;

[0031] Figure 9 A schematic diagram of an interface output data generation system according to an embodiment of the present disclosure is shown schematically; and

[0032] Figure 10 A block diagram of an electronic device suitable for implementing the interface output data generation method according to an embodiment of the present disclosure is shown schematically. DETAILED DESCRIPTION

[0033] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It is to be understood, however, the description is merely exemplary and is not intended to limit the scope of the present disclosure. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. However, it would be apparent that one or more embodiments can be practiced without these specific details. In addition, in the following description, descriptions of well-known structures and techniques are omitted to avoid unnecessarily obscuring the concept of the present disclosure.

[0034] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the terms "comprises", "comprising", "includes", "including" and the like are, unless otherwise stated, taken to mean 'consisting at least in part of'. While the terms "comprises", "comprising", "includes", "including", and the like specify the presence of stated features, steps or components... but do not preclude the presence or addition of one or more other features, steps, procedures, operations, components, and / or groups thereof.

[0035] All terms used herein including technical and scientific terms have the meanings commonly understood by one of ordinary skill in the art unless otherwise defined. It should be noted that the terms "comprise", "comprising", "comprises" and the like can be used interchangeably with the term "include" or "including". The terms "comprise", "comprising", "comprises" and the like are used herein to permit the presence of the stated features, integers, steps or components but not preclude the presence or addition of one or more other features, integers, steps, components or groups thereof.

[0036] In the case of using expressions like "at least one of A, B, and C, etc.", it generally means to include at least one of A or B or C, or any combination thereof. In the case of using the expression "a or b or c or any combination thereof", it generally means to include at least one of A, B, or C, or any combination thereof.

[0037] Some of the blocks and / or flowcharts in the drawings represent computer program instructions or programs. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to create means for implementing the functions / acts specified in the block diagrams and / or flowchart block or blocks.

[0038] First, the technical terms appearing in this paper are explained as follows:

[0039] JSON format data: a data format composed of key-value pairs, where the key is a string, and the value can be a string, a number, a boolean value, an array, or an object. It is mainly used for data transmission, storage and exchange between the front and back ends, and between systems, which can effectively improve the efficiency of network transmission.

[0040] TF-IDF: (Term Frequency-Inverse Document Frequency), Term Frequency-Inverse Document Frequency, is a weighting technique used in information retrieval and text mining to evaluate the importance of a word to a document in a collection or corpus.

[0041] TF: (term frequency) Term Frequency, which is the ratio of the number of times a word appears in a document to the total number of words in the document.

[0042] IDF: inverse document frequency, the inverse of the frequency of a word in a corpus.

[0043] SORT factor: a sorting factor, mainly used for sorting data, especially in financial data analysis.

[0044] Embodiments of the present disclosure provide an interface output data generation method, which comprises: obtaining target interface input data, performing field factor sorting on the target interface input data, and generating a target interface input data field, wherein a is an integer and a is greater than or equal to 1; obtaining b first historical interface input data and c first historical interface output data, wherein the b first historical interface input data and the c first historical interface output data have a first mapping relationship, wherein b is an integer and b is greater than or equal to 1, c is an integer and c is greater than or equal to 1; performing field factor sorting on the b first historical interface input data and the c first historical interface output data, and generating d first historical interface input data fields and e first historical interface output data fields, wherein d is an integer and d is greater than or equal to b, e is an integer and e is greater than or equal to c; based on the first mapping relationship, generating a second mapping relationship between the d first historical interface input data fields and the e first historical interface output data fields; obtaining a first historical interface input data field with the highest degree of association with each target interface input data field from the d first historical interface input data fields, and generating a third historical interface input data field; obtaining m first historical interface output data fields having a second mapping relationship with the a third historical interface input data fields from the e first historical interface output data fields, and generating m target interface output data fields, wherein m is an integer and m is greater than or equal to 1, m is less than e; and combining the m target interface output data fields to generate f target interface output data, wherein f is an integer and f is greater than or equal to 1, f is less than or equal to m.

[0045] According to the embodiment of the present disclosure, the target interface input data and the plurality of historical interface input data and the corresponding output data are obtained. The correlation variable factor of the target interface input data and the plurality of historical interface input data is calculated, the historical interface input data with the highest correlation degree is screened out, and the output data corresponding to the historical interface input data with the highest correlation degree is obtained as the output data corresponding to the target interface input data. The technical problem that the traditional test mode cannot meet the timeliness requirement of the system rapid online under the agile mode to verify whether the function and interaction of the system meet the expectation is solved. The output data corresponding to the target interface input data can be quickly, accurately and reliably determined, the time and cost of manually writing and executing the test case are reduced, the time cost is saved, the work efficiency is improved, and the technical effects of improving the efficiency and reliability of the interface output data generation are achieved.

[0046] Figure 1 The application scenario diagram of the interface output data generation method and device according to the embodiment of the present disclosure is schematically shown. It should be noted that, Figure 1 The shown is only an example of the scenario to which the embodiment of the present disclosure can be applied, to help those skilled in the art understand the technical content of the present disclosure, but does not mean that the embodiment of the present disclosure cannot be applied to other devices, systems, environments or scenarios.

[0047] As Figure 1 shown, the application scenario 100 according to the embodiment can include the application scenario of the interface output data generation. The network 104 is a medium for providing a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103 and the server 105. The network 104 can include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0048] The user can use the first terminal device 101, the second terminal device 102, the third terminal device 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only as examples).

[0049] The first terminal device 101, the second terminal device 102, the third terminal device 103 can be various electronic devices with display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers and desktop computers, etc.

[0050] Server 105 can be a server that provides various services, such as a backend management server that supports websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103 (this is just an example). The backend management server can analyze and process data such as received user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal devices.

[0051] It should be noted that the interface output data generation method provided in this embodiment can generally be executed by server 105. Correspondingly, the interface output data generation device provided in this embodiment can generally be located in server 105. The interface output data generation method provided in this embodiment can also be executed by a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105. Correspondingly, the interface output data generation device provided in this embodiment can also be located in a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105.

[0052] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0053] The following will be based on Figure 1 The described scene, through Figures 2-8 The interface output data generation method of the disclosed embodiments is described in detail. It should be noted that the above application scenarios are shown only to facilitate understanding of the spirit and principles of this disclosure, and the implementation of this disclosure is not limited in any way. Rather, the implementation of this disclosure can be applied to any applicable scenario.

[0054] Figure 2 A flowchart illustrating an interface output data generation method according to an embodiment of the present disclosure is shown.

[0055] like Figure 2 As shown, the method 200 includes steps S201 to S207.

[0056] Step S201: Obtain target interface input data, sort the target interface input data by field factor, and generate a target interface input data fields, where a is an integer and a is greater than or equal to 1.

[0057] For example, the target interface input data can be fielded to generate n first interface input data fields, where n is an integer and n is greater than or equal to 1. The n first interface input data fields are converted to generate n first interface input data field key-value pairs. The term frequency-inverse document frequency of the values of the n first interface input data field key-value pairs is calculated, the field key-value pairs with a value greater than a preset threshold in the term frequency-inverse document frequency of the values of the n first interface input data field key-value pairs are obtained, a second interface input data field key-value pair is generated, and a is an integer, and n is greater than or equal to a.

[0058] In step S202, b first historical interface input data and c first historical interface output data are obtained, where the b first historical interface input data and the c first historical interface output data have a first mapping relationship, b is an integer and b is greater than or equal to 1, and c is an integer and c is greater than or equal to 1.

[0059] For example, the b first historical interface input data and the c first historical interface output data of the interface in a period of time can be collected by interface flow recording, application log table and other methods. The b first historical interface input data and the c first historical interface output data have a first mapping relationship, and the distribution probability interval of the b first historical interface input data and the c first historical interface output data is generated.

[0060] In step S203, the b first historical interface input data and the c first historical interface output data are sorted by field factor to generate d first historical interface input data fields and e first historical interface output data fields, where d is an integer and d is greater than or equal to b, and e is an integer and e is greater than or equal to c.

[0061] Figure 3 A flowchart for generating d first historical interface input data fields and e first historical interface output data fields based on field factor sorting in the interface output data generation method according to an embodiment of the present disclosure is schematically shown.

[0062] As shown in Figure 3 The method 300 includes steps S301-S302.

[0063] In step S301, the b first historical interface input data and the c first historical interface output data are extracted to generate d second historical interface input data fields and e second historical interface output data fields.

[0064] Figure 4A flowchart schematically showing a method for generating d second historical interface input data fields and e second historical interface output data fields in the interface output data generation method according to an embodiment of the present disclosure is shown.

[0065] As shown in Figure 4 The method 400 includes steps S401-S405.

[0066] In step S401, the b first historical interface input data and the c first historical interface output data are fielded to generate g first historical interface input data fields and h first historical interface output data fields, where g is an integer and g is greater than or equal to d, and h is an integer and h is greater than or equal to e.

[0067] For example, the b first historical interface input data and the c first historical interface output data can be fielded by a regular expression to generate g first historical interface input data fields and h first historical interface output data fields. Fielding the b first historical interface input data and the c first historical interface output data by a regular expression to generate g first historical interface input data fields and h first historical interface output data fields can improve the accuracy of generating fields.

[0068] In step S402, the g first historical interface input data fields and the h first historical interface output data fields are format-converted to generate g first historical interface input data field key-value pairs and h first historical interface output data field key-value pairs.

[0069] For example, the g first historical interface input data fields and the h first historical interface output data fields can be format-converted to a standard JSON format data representation, with each field of the input and output data represented in the form of K:V (KEY:VALUE).

[0070] In step S403, the term frequency-inverse document frequency of the values of the g first historical interface input data field key-value pairs and the term frequency-inverse document frequency of the values of the h first historical interface output data field key-value pairs are calculated.

[0071] For example, the term frequency-inverse document frequency (i.e., TF-IDF) of the values of the g first historical interface input data field key-value pairs and the term frequency-inverse document frequency of the values of the h first historical interface output data field key-value pairs can be calculated according to a TF-IDF method.

[0072] The TF-IDF calculation method is as follows:

[0073] The term frequency (TF) calculation formula (1) is as follows:

[0074] (1)

[0075] Wherein: is the word frequency of a certain V value in the input and output data, is the frequency of the V value in the interface input and output data, and the denominator is the sum of the frequencies of all V values in the interface input and output data.

[0076] Inverse document frequency (IDF) is a measure of the general importance of a word, and the calculation formula (2) is:

[0077] (2)

[0078] Wherein: the numerator is the total number of interface input and output data, and the denominator is the number of input and output data containing V values. The larger the calculation result is, the more distinguishing the vocabulary is.

[0079] The TF-IDF value is the product of the TF word frequency and the IDF inverse document frequency. The results of the above two formulas are multiplied to obtain the TF-IDF value.

[0080] Step S404, obtain the field key value pair whose word frequency inverse document frequency of the value of the g first historical interface input data field key value pair and the word frequency inverse document frequency of the value of the h first historical interface output data field key value pair are greater than a preset threshold value, generate d second historical interface input data field key value pair and e second historical interface output data field key value pair.

[0081] For example, the TF-IDF value higher than the preset threshold value is a key interface field. The preset threshold value can be customized according to the application scenario.

[0082] Step S405, inverse conversion is performed on the d second historical interface input data field key value pair and the e second historical interface output data field key value pair to generate d second historical interface input data field and e second historical interface output data field.

[0083] By formatting the key value pair and calculating the word frequency inverse document frequency to screen the key field, non-key fields can be effectively filtered out, and the efficiency of obtaining the key field is improved.

[0084] Referring back to Figure 3 In step S302, field factor sorting is performed on the d second historical interface input data field and the e second historical interface output data field to generate d first historical interface input data field and e first historical interface output data field.

[0085] Figure 5A flowchart of generating a plurality of third abnormal data in each access data according to a field sorting factor in an interface output data generation method according to an embodiment of the present disclosure is shown.

[0086] As shown in Figure 5 The method 500 includes steps S501-S502.

[0087] In step S501, the TF-IDF of the values of the d second historical interface input data field key-value pairs is obtained, the mean of the TF-IDF of the values of the d second historical interface input data field key-value pairs is calculated, and a field sorting factor is generated.

[0088] For example, the TF-IDF mean of the values of all key-value pairs of each key input field can be calculated to obtain the field SORT factor (i.e., the field sorting factor).

[0089] In step S502, based on the field sorting factor, the d second historical interface input data fields and the e second historical interface output data fields are sorted by field factor to generate d first historical interface input data fields and e first historical interface output data fields.

[0090] For example, the input-output fields corresponding to the values of the key interface field key-value pairs obtained above can be re-extracted, and non-key interface field data can be removed. And reordering from large to small according to the field SORT factor.

[0091] The field sorting factor can achieve efficient sorting, which facilitates subsequent comparison of target input fields and historical input fields, and improves comparison accuracy and reliability. Through key field extraction and field factor sorting, the data is fielded. The range of input and output fields can be refined, noise fields can be cleaned up, CPU utilization can be achieved, computing resources can be saved, and computing efficiency can be improved.

[0092] Referring back to Figure 2 In step S204, based on the first mapping relationship, a second mapping relationship of d first historical interface input data fields and e first historical interface output data fields is generated.

[0093] For example, according to the first mapping relationship and the distribution probability interval of the b first historical interface input data and the c first historical interface output data, the second mapping relationship of the d first historical interface input data fields and the e first historical interface output data fields and the distribution probability interval of the d first historical interface input data fields and the e first historical interface output data fields can be generated.

[0094] Step S205: obtaining a first historical interface input data field with the highest degree of association with each target interface input data field from the d first historical interface input data fields, and generating a third historical interface input data field.

[0095] Figure 6 A flowchart of generating a third historical interface input data field in the interface output data generation method according to an embodiment of the present disclosure is schematically shown.

[0096] As shown in the method 600, the method 600 includes steps S601-S602. Figure 6

[0097] Step S601: calculating, by a cosine similarity analysis method, an association variable factor of each field in the a target interface input data fields and each field in the d first historical interface input data fields.

[0098] For example, the cosine similarity analysis method evaluates the similarity by the cosine value of the included angle of two vectors. The a target interface input data fields and the d first historical interface input data fields can be vectorized to generate a word vector of the value of each field key-value pair in the a target interface input data fields and a word vector of the value of each field key-value pair in the d first historical interface input data fields. The two are combined into a word segmentation list, and the calculation formulas (3) and (4) are as follows:

[0099] F(A) = {f A1 ,f A2 ,…,f Ak} (3)

[0100] F(B) = {f B1 ,f B2 ,…,f Bk} (4)

[0101] Wherein, f represents the frequency of the keyword appearing in the text.

[0102] Then, the cosine value is calculated, and the calculation formula (5) is as follows:

[0103] (5)

[0104] The above cosine value is the association variable factor.

[0105] Step S602: obtaining a first historical interface input data field with the largest association variable factor from each field in the a target interface input data fields, and generating a third historical interface input data field.

[0106] The association variable factor is determined by the cosine similarity analysis method, which can improve the accuracy, reliability and robustness of the comparison between the target input field and the historical input field.​

[0107] Referring back Figure 2 In step S206, m first historical interface output data fields that have a second mapping relationship with a third historical interface input data field are obtained from the e first historical interface output data fields, and m target interface output data fields are generated, where m is an integer greater than or equal to 1 and less than e.

[0108] In step S207, the m target interface output data fields are combined to generate f target interface output data, where f is an integer greater than or equal to 1 and less than or equal to m.

[0109] Figure 7 A flowchart for generating f target interface output data in the interface output data generation method according to an embodiment of the present disclosure is schematically shown.

[0110] As shown in Figure 7 , the method 700 includes steps S701-S702.

[0111] In step S701, i preset non-critical fields are obtained, where i is an integer greater than or equal to 1.

[0112] For example, for a non-critical field, if there is a preset method, the corresponding value is returned according to the preset method, such as time, serial number; otherwise, the corresponding field value is randomly matched from the pre-prepared data.

[0113] In step S702, the i preset non-critical fields and the m target interface output data fields are combined to generate f target interface output data.

[0114] Through the preset non-critical data, the target interface output data information can be further improved, and the reliability of the target interface output data can be improved.

[0115] Figure 8 A schematic diagram of an interface output data generation system according to an embodiment of the present disclosure is schematically shown.

[0116] As shown in Figure 8 , the system 800 includes a data preparation subsystem 801, a consistency preset subsystem 802, a data analysis subsystem 803, and an output data subsystem 804.

[0117] The data preparation subsystem 801 can first collect input and output data of a production interface for a period of time through interface flow recording, application log table, etc.

[0118] The consistency preset subsystem 802 can convert the input and output data into a standard JSON format data representation, with each field of the input and output data represented in the form of K:V (KEY:VALUE) to describe the structure and field information of the data.

[0119] The data analysis subsystem 803 can analyze the key interface fields in the input and output data according to the TF-IDF method for each key value. A key interface field is one with a TF-IDF value higher than a threshold value. The threshold value can be customized. The TF-IDF average of all input key values of each key input field is calculated to obtain a field SORT factor. The input and output fields corresponding to the key interface field key values obtained above are re-extracted, and non-key interface field data is removed. The input and output fields are reordered according to the field SORT factor from large to small. For each group of key input field key values, the frequency of all key output field key values under the key input field key value is calculated to obtain the distribution probability interval of the input and output data.

[0120] The output data subsystem 804 can remove non-key interface fields from new input data to be simulated, and sort the key interface fields according to the field SORT factor. The cosine similarity analysis method is used to compare the new input data with each group of key input field key values obtained by the data analysis module above to obtain an association variable factor. The association variable factor is obtained by calculating the cosine similarity. The key output field key values that are hit are obtained according to the distribution probability interval of the input and output data corresponding to the hit key input field key values. For non-key output fields, if there is a preset method, the corresponding value is returned according to the preset method, such as time or serial number; otherwise, the corresponding field value is randomly matched from the pre-prepared data. The final JSON format data is converted into output data in the corresponding format according to the interface specification.

[0121] Figure 9 A structural block diagram of an interface output data generation apparatus according to an embodiment of the present disclosure is schematically shown.

[0122] As shown in Figure 9 the apparatus 900 includes a first generation module 901, a first acquisition module 902, a second generation module 903, a third generation module 904, a fourth generation module 905, a fifth generation module 906, and a sixth generation module 907.

[0123] The first generation module 901 is configured to acquire target interface input data, perform field factor sorting on the target interface input data, and generate a target interface input data field a, where a is an integer and a is greater than or equal to 1. In an embodiment, the first generation module 901 can be configured to perform the step S201 described above, and thus details are not repeated here.

[0124] The first obtaining module 902 is configured to obtain b first historical interface input data and c first historical interface output data, where the b first historical interface input data and the c first historical interface output data have a first mapping relationship, b is an integer and b is greater than or equal to 1, and c is an integer and c is greater than or equal to 1. In an embodiment, the first obtaining module 902 can be configured to perform the step S202 described above, and details are not repeated here.

[0125] The second generating module 903 is configured to perform field factor sorting on the b first historical interface input data and the c first historical interface output data, to generate d first historical interface input data fields and e first historical interface output data fields, where d is an integer and d is greater than or equal to b, and e is an integer and e is greater than or equal to c. In an embodiment, the second generating module 903 can be configured to perform the step S203 described above.

[0126] The second generating module 903 includes a seventh generating module and an eighth generating module.

[0127] The seventh generating module is configured to perform key field extraction on the b first historical interface input data and the c first historical interface output data, to generate d second historical interface input data fields and the e second historical interface output data fields. In an embodiment, the seventh generating module can be configured to perform the step S301 described above.

[0128] The seventh generating module includes a ninth generating module, a tenth generating module, a first calculating module, an eleventh generating module and a twelfth generating module.

[0129] The ninth generating module is configured to perform fieldization on the b first historical interface input data and the c first historical interface output data, to generate g first historical interface input data fields and h first historical interface output data fields, where g is an integer and g is greater than or equal to d, and h is an integer and h is greater than or equal to e. In an embodiment, the ninth generating module can be configured to perform the step S401 described above, and details are not repeated here.

[0130] The tenth generating module is configured to perform format conversion on the g first historical interface input data fields and the h first historical interface output data fields, to generate g first historical interface input data field key-value pairs and h first historical interface output data field key-value pairs. In an embodiment, the tenth generating module can be configured to perform the step S402 described above, and details are not repeated here.

[0131] The first calculating module is configured to calculate the term frequency-inverse document frequency of the values of the g first historical interface input data field key-value pairs and the term frequency-inverse document frequency of the values of the h first historical interface output data field key-value pairs.

[0132] The eleventh generating module is configured to obtain the field key-value pairs with the term frequency-inverse document frequency greater than a preset threshold value from the term frequency-inverse document frequency of the values of the g first historical interface input data field key-value pairs and the term frequency-inverse document frequency of the values of the h first historical interface output data field key-value pairs, and generate the d second historical interface input data field key-value pairs and the e second historical interface output data field key-value pairs.

[0133] The twelfth generating module is configured to perform inverse conversion on the d second historical interface input data field key-value pairs and the e second historical interface output data field key-value pairs to generate the d second historical interface input data fields and the e second historical interface output data fields.

[0134] The eighth generating module is configured to perform field factor sorting on the d second historical interface input data fields and the e second historical interface output data fields to generate the d first historical interface input data fields and the e first historical interface output data fields.

[0135] The eighth generating module includes a thirteenth generating module and a fourteenth generating module.

[0136] The thirteenth generating module is configured to obtain the term frequency-inverse document frequency of the values of the d second historical interface input data field key-value pairs, calculate the mean value of the term frequency-inverse document frequency of the values of the d second historical interface input data field key-value pairs, and generate a field sorting factor.

[0137] The fourteenth generating module is configured to perform field factor sorting on the d second historical interface input data fields and the e second historical interface output data fields based on the field sorting factor to generate the d first historical interface input data fields and the e first historical interface output data fields.

[0138] The third generation module 904 is configured to generate a second mapping relationship between the d first historical interface input data fields and the e first historical interface output data fields based on the first mapping relationship. In an embodiment, the third generation module 904 can be configured to perform the step S204 described above, and details are not repeated here.

[0139] The fourth generation module 905 is configured to obtain a first historical interface input data field with the highest degree of association with each target interface input data field from the d first historical interface input data fields, and generate a third historical interface input data field. In an embodiment, the fourth generation module 905 can be configured to perform the step S205 described above.

[0140] The fourth generation module 905 includes a second calculation module and a fifteenth generation module.

[0141] The second calculation module is configured to calculate an association variable factor of each field in the a target interface input data fields and each field in the d first historical interface input data fields by using a cosine similarity analysis method. In an embodiment, the second calculation module can be configured to perform the step S601 described above, and details are not repeated here.

[0142] The fifteenth generation module is configured to obtain a first historical interface input data field with the largest association variable factor from each field in the a target interface input data fields, and generate a third historical interface input data field. In an embodiment, the fifteenth generation module can be configured to perform the step S602 described above, and details are not repeated here.

[0143] The fifth generation module 906 is configured to obtain a first historical interface output data field that has a second mapping relationship with the a third historical interface input data field from the e first historical interface output data fields, and generate a target interface output data field, where m is an integer and m is greater than or equal to 1 and m is less than e. In an embodiment, the fifth generation module 906 can be configured to perform the step S206 described above, and details are not repeated here.

[0144] The sixth generation module 907 is configured to combine the m target interface output data fields to generate f target interface output data, where f is an integer and f is greater than or equal to 1 and f is less than or equal to m. In an embodiment, the sixth generation module 907 can be configured to perform the step S207 described above.

[0145] The sixth generation module 907 includes a sixteenth generation module and a seventeenth generation module.

[0146] The sixteenth generating module is configured to acquire i preset non-key fields, where i is an integer and i is greater than or equal to 1. In an embodiment, the sixteenth generating module can be configured to perform the step S701 described above, and details are not repeated here.

[0147] The seventeenth generating module is configured to perform data combination on the i preset non-key fields and the m target interface output data fields to generate f target interface output data. In an embodiment, the seventeenth generating module can be configured to perform the step S702 described above, and details are not repeated here.

[0148] According to embodiments of the present disclosure, any multiple of the first generating module 901, the first acquiring module 902, the second generating module 903, the third generating module 904, the fourth generating module 905, the fifth generating module 906 and the sixth generating module 907 can be combined in one module, or any one of them can be split into multiple modules. Alternatively, at least part of the function of one or more of these modules can be combined with at least part of the function of other modules, and implemented in one module. According to embodiments of the present disclosure, at least one of the first generating module 901, the first acquiring module 902, the second generating module 903, the third generating module 904, the fourth generating module 905, the fifth generating module 906 and the sixth generating module 907 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on chip, a system on board, a system on package, an application specific integrated circuit (ASIC), or any other reasonable way of hardware or firmware that can be integrated or packaged, or any one of software, hardware and firmware or any appropriate combination of several of them. Alternatively, at least one of the first generating module 901, the first acquiring module 902, the second generating module 903, the third generating module 904, the fourth generating module 905, the fifth generating module 906 and the sixth generating module 907 can be at least partially implemented as a computer program module which can perform corresponding functions when running.

[0149] Figure 10 The block diagram of the electronic device suitable for implementing the interface output data generating method according to embodiments of the present disclosure is schematically shown.

[0150] As Figure 10As shown, the electronic device 1000 according to embodiments of the present disclosure includes a processor 1001 that can perform various appropriate actions and processes according to programs stored in a read only memory (ROM) 1002 or loaded from a storage section 1008 into a random access memory (RAM) 1003. The processor 1001 can include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor, and / or a related chip set, and / or a special purpose microprocessor (e.g., an application specific integrated circuit (ASIC)), and so on. The processor 1001 can also include an on-board memory for cache use. The processor 1001 can include a single processing unit or multiple processing units to perform the various actions of the method processes according to embodiments of the present disclosure.

[0151] In the RAM 1003, various programs and data required for the operation of the electronic device 1000 are stored. The processor 1001, the ROM 1002, and the RAM 1003 are connected to each other via a bus 1004. The processor 1001 performs various operations of the method processes according to embodiments of the present disclosure by executing the programs in the ROM 1002 and / or the RAM 1003. Note that the programs can also be stored in one or more memories other than the ROM 1002 and the RAM 1003. The processor 1001 can also perform various operations of the method processes according to embodiments of the present disclosure by executing the programs stored in the one or more memories.

[0152] According to embodiments of the present disclosure, the electronic device 1000 can also include an input / output (I / O) interface 1005, which is also connected to the bus 1004. The electronic device 1000 can also include one or more of the following components connected to the I / O interface 1005: an input section 1006 including a keyboard, a mouse, etc.; an output section 1007 including a display such as a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 1008 including a hard disk, etc.; and a communication section 1009 including a network interface card such as a LAN card, a modem, etc. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to the I / O interface 1005 as necessary. A removable medium 1011 such as a magnetic disk, a magneto-optical disk, a semiconductor memory, etc. is attached to the drive 1010 as necessary, so that a computer program read out therefrom is installed into the storage section 1008 as necessary.

[0153] The present disclosure also provides a computer readable storage medium, which can be included in the device / apparatus / system described in the above embodiments, or exist separately without being assembled into the device / apparatus / system. The above computer readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of the present disclosure.

[0154] According to the embodiments of the present disclosure, the computer readable storage medium can be a non-volatile computer readable storage medium, which can include, but is not limited to, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any appropriate combination thereof. In the present disclosure, the computer readable storage medium can be any tangible medium that contains or stores a program, which can be used by or in connection with an instruction execution system, apparatus, or device. For example, according to the embodiments of the present disclosure, the computer readable storage medium can include one or more memories of the ROM 1002 and / or the RAM 1003 described above and / or one or more memories other than the ROM 1002 and the RAM 1003.

[0155] The embodiments of the present disclosure also include a computer program product, which includes a computer program containing program codes for executing the method shown in the flowcharts. When the computer program product is run in a computer system, the program codes are used to make the computer system implement the interface output data generation method provided by the embodiments of the present disclosure.

[0156] The above functions defined in the system / apparatus of the embodiments of the present disclosure are performed when the computer program is executed by the processor 1001. According to the embodiments of the present disclosure, the system, apparatus, module, unit, etc. described above can be implemented by computer program modules.

[0157] In one embodiment, the computer program can rely on a tangible storage medium such as an optical storage device, a magnetic storage device, etc. In another embodiment, the computer program can also be transmitted, distributed, and downloaded in the form of a signal via a network medium, and be downloaded and installed via the communication part 1009 and / or installed from the detachable medium 1011. The program codes contained in the computer program can be transmitted via any appropriate network medium, including but not limited to wireless, wired, etc., or any appropriate combination thereof.

[0158] In such embodiments, the computer program can be downloaded and installed from the network through the communication section 1009, and / or installed from the removable media 1011. When the computer program is executed by the processor 1001, the above-described functions defined in the system of the embodiments of the present disclosure are executed. According to the embodiments of the present disclosure, the system, device, apparatus, module, unit, and the like described above can be implemented by computer program modules.

[0159] According to the embodiments of the present disclosure, the program code for executing the computer program provided by the embodiments of the present disclosure can be written in any combination of one or more programming languages, and specifically, these computer programs can be implemented using high-level procedural and / or object-oriented programming language, and / or assembly / machine language. The programming language includes, but is not limited to, such as Java, C++, python, "C" language, or similar programming language. The program code can be executed entirely on the user computing device, partially on the user device, partially on a remote computing device, or entirely on a remote computing device or server. In the case involving a remote computing device, the remote computing device can be connected to the user computing device through any kind of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, connected to the Internet through an Internet service provider).

[0160] The flowcharts and block diagrams in the drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowcharts or block diagrams can represent a module, a segment, or a portion of code, which contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur in a different order than that shown in the figures. For example, two blocks noted in succession can actually be executed substantially concurrently, or they can sometimes be executed in reverse order, depending on the functionality involved. It should also be noted that each block in the flowcharts or block diagrams, and combinations of blocks in the flowcharts or block diagrams, can be implemented by dedicated hardware-based systems that perform the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0161] Those skilled in the art can understand that the features described in various embodiments of the present disclosure and / or claims can be combined or / and integrated, even if such combinations or integrations are not explicitly described in the present disclosure. In particular, the features described in various embodiments of the present disclosure and / or claims can be combined and / or integrated in various combinations, without departing from the spirit and teachings of the present disclosure. All these combinations and / or integrations fall within the scope of the present disclosure.

[0162] The above described embodiments of the present disclosure. However, these embodiments are merely for illustrative purposes, and are not intended to limit the scope of the present disclosure. Although each embodiment is described above separately, this does not mean that the measures in each embodiment cannot be advantageously used in combination. The scope of the present disclosure is defined by the appended claims and their equivalents. Those skilled in the art can make various substitutions and modifications without departing from the scope of the present disclosure, and all such substitutions and modifications shall fall within the scope of the present disclosure.

Claims

1. An interface output data generation method characterized by, The method comprises: obtaining target interface input data, performing field factor sorting on the target interface input data to generate a target interface input data field, wherein a is an integer and a is greater than or equal to 1; obtaining b first historical interface input data and c first historical interface output data, wherein the b first historical interface input data and the c first historical interface output data have a first mapping relationship, b is an integer and b is greater than or equal to 1, and c is an integer and c is greater than or equal to 1; performing field factor sorting on the b first historical interface input data and the c first historical interface output data to generate d first historical interface input data fields and e first historical interface output data fields, wherein d is an integer and d is greater than or equal to b, and e is an integer and e is greater than or equal to c; based on the first mapping relationship, generating a second mapping relationship of the d first historical interface input data fields and the e first historical interface output data fields; obtaining a from the d first historical interface input data fields highest degree of association with each target interface input data field first historical interface input data field, generating a third historical interface input data field; obtaining m first historical interface output data fields from the e first historical interface output data fields that have a second mapping relationship with the a third historical interface input data fields, generating m target interface output data fields, wherein m is an integer and m is greater than or equal to 1, and m is less than e; and combining the m target interface output data fields to generate f target interface output data, wherein f is an integer and f is greater than or equal to 1, and f is less than or equal to m, wherein performing field factor sorting on the target interface input data to generate a target interface input data field comprises: fielding the target interface input data to generate n first interface input data fields, wherein n is an integer and n is greater than or equal to 1; performing format conversion on the n first interface input data fields to generate n first interface input data field key-value pairs; calculating the term frequency-inverse document frequency of the values of the n first interface input data field key-value pairs; obtaining the field key-value pairs with a term frequency-inverse document frequency greater than a preset threshold value from the n first interface input data field key-value pairs to generate a second interface input data field key-value pair; and performing inverse conversion on the a second interface input data field key-value pairs to generate a target interface input data field, wherein a is less than or equal to n.

2. The method of claim 1, wherein, performing field factor sorting on the b first historical interface input data and the c first historical interface output data to generate d first historical interface input data fields and e first historical interface output data fields comprises: performing key field extraction on the b first historical interface input data and the c first historical interface output data to generate d second historical interface input data fields and e second historical interface output data fields; and The d second historical interface input data fields and the e second historical interface output data fields are field factor sorted to generate d first historical interface input data fields and e first historical interface output data fields.

3. The method of claim 2, wherein, The b first historical interface input data and the c first historical interface output data are key field extracted to generate d second historical interface input data fields and e second historical interface output data fields, including: The b first historical interface input data and the c first historical interface output data are fielded to generate g first historical interface input data fields and h first historical interface output data fields, where g is an integer and g is greater than or equal to d, and h is an integer and h is greater than or equal to e; The g first historical interface input data fields and h first historical interface output data fields are format converted to generate g first historical interface input data field key-value pairs and h first historical interface output data field key-value pairs; Term frequency-inverse document frequencies of values of the g first historical interface input data field key-value pairs and the h first historical interface output data field key-value pairs are calculated; G first historical interface input data field key-value pairs and h first historical interface output data field key-value pairs with term frequency-inverse document frequencies greater than a preset threshold are obtained to generate d second historical interface input data field key-value pairs and e second historical interface output data field key-value pairs; and The d second historical interface input data fields and the e second historical interface output data fields are inverse converted to generate d second historical interface input data fields and e second historical interface output data fields.

4. The method of claim 3, wherein, The b first historical interface input data and the c first historical interface output data are fielded to generate g first historical interface input data fields and h first historical interface output data fields, including: The b first historical interface input data and the c first historical interface output data are fielded to generate g first historical interface input data fields and h first historical interface output data fields, including:

5. The method of claim 3, wherein, The d second historical interface input data fields and the e second historical interface output data fields are field factor sorted to generate d first historical interface input data fields and e first historical interface output data fields, including: Term frequency-inverse document frequencies of values of d second historical interface input data field key-value pairs are obtained, and a mean value of the term frequency-inverse document frequencies of the values of the d second historical interface input data field key-value pairs is calculated to generate a field sorting factor; and Based on the field sorting factor, the d second historical interface input data fields and the e second historical interface output data fields are field factor sorted to generate d first historical interface input data fields and e first historical interface output data fields.

6. The method of claim 1, wherein, The a third historical interface input data fields are generated by: The a target interface input data fields are calculated by a cosine similarity analysis method, and the correlation variable factors of each field in the a target interface input data fields and each field in the d first historical interface input data fields are calculated. The a third historical interface input data fields are generated by obtaining the a first historical interface input data fields with the maximum correlation variable factors of each field in the a target interface input data fields.

7. The method according to any one of claims 1 to 6, characterized in that, The m target interface output data fields are combined to generate f target interface output data, including: i preset non-key fields are obtained, where i is an integer and i is greater than or equal to 1; and The i preset non-key fields and the m target interface output data fields are combined to generate f target interface output data.

8. An interface output data generating apparatus characterized by comprising: The device comprises: The first generation module is configured to obtain target interface input data, sort field factors of the target interface input data, and generate a target interface input data field, where a is an integer and a is greater than or equal to 1; The first acquisition module is configured to obtain b first historical interface input data and c first historical interface output data, where the b first historical interface input data and the c first historical interface output data have a first mapping relationship, b is an integer and b is greater than or equal to 1, and c is an integer and c is greater than or equal to 1; The second generation module is configured to sort field factors of the b first historical interface input data and the c first historical interface output data, and generate d first historical interface input data fields and e first historical interface output data fields, where d is an integer and d is greater than or equal to b, and e is an integer and e is greater than or equal to c; The third generation module is configured to generate a second mapping relationship of the d first historical interface input data fields and the e first historical interface output data fields based on the first mapping relationship; The fourth generation module is configured to obtain a target interface input data field with the highest correlation degree in the d first historical interface input data fields, and generate a third historical interface input data field; The fifth generation module is configured to obtain m first historical interface output data fields that have a second mapping relationship with the a third historical interface input data fields from the e first historical interface output data fields, and generate m target interface output data fields, where m is an integer and m is greater than or equal to 1, and m is less than e; and The sixth generation module is configured to combine the m target interface output data fields to generate f target interface output data, where f is an integer and f is greater than or equal to 1, and f is less than or equal to m, The target interface input data is sorted by field factors to generate a target interface input data field, including: The target interface input data is fielded to generate n first interface input data fields, where n is an integer and n is greater than or equal to 1; perform format conversion on the n first interface input data field to generate n first interface input data field key-value pairs; calculate the term frequency-inverse document frequency of the values of the n first interface input data field key-value pairs; obtain the field key-value pairs with the term frequency-inverse document frequency greater than a preset threshold value from the values of the n first interface input data field key-value pairs to generate a second interface input data field key-value pairs; and perform inverse conversion on the a second interface input data field key-value pairs to generate a target interface input data field, wherein a is less than or equal to n. 9.An electronic device, comprising: one or more processors; a memory for storing one or more computer programs, characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1-7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program or instruction is executed by the processor to implement the steps of the method according to any one of claims 1-7.

11. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method according to any one of claims 1-7. The computer program is executed by the processor to implement the steps of the method according to any one of claims 1-7.

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