CN115757889A

CN115757889A - Data item processing method and device, computer equipment and storage medium

Info

Publication number: CN115757889A
Application number: CN202211581511.8A
Authority: CN
Inventors: 陈纯
Original assignee: Ping An Property and Casualty Insurance Company of China Ltd
Current assignee: Ping An Property and Casualty Insurance Company of China Ltd
Priority date: 2022-12-08
Filing date: 2022-12-08
Publication date: 2023-03-07

Abstract

The embodiment of the application belongs to the field of artificial intelligence, and relates to a data item processing method, which comprises the following steps: analyzing the description information of the data item from the received data item adding request, and performing similarity analysis on the description information of the data item to generate a similarity analysis result; if the similar analysis result indicates that no data item similar to the target data item exists, displaying an editing interface containing the data model script; generating target codes of the target data items based on modification information input by a user on the editing interface; the target code is evaluated to generate a code evaluation result; and if the code evaluation result is that the evaluation is passed, releasing the target code to a preset production environment. The application also provides a data item processing device, computer equipment and a storage medium. In addition, the application also relates to a block chain technology, and the description information of the data items can be stored in the block chain. The method and the device can effectively avoid the problem of repeated entry of the data items, and improve the accuracy and intelligence of newly-added processing of the data items.

Description

Data item processing method and device, computer equipment and storage medium

Technical Field

The present application relates to the field of artificial intelligence technologies, and in particular, to a method and an apparatus for processing data items, a computer device, and a storage medium.

Background

At present, due to the fact that business departments of financial companies are huge, business lines are various, data information is diversified and the like, the definition of data items (including indexes or dimensions) is easy to be repeatedly input in various development links or business lines. Although standards for formulating data items exist, since a plurality of departments are passed from development to online in the process of requirement proposing, the traditional requirement management does not achieve the purpose of always realizing online from a requirement source end to online, so that the data items are difficult to ensure globally unique in the whole financial company business in practice and are not redefined in each development link. The existing data item construction work is usually mainly in a manual construction mode. Because different people understand knowledge thousands of people, the phenomenon of inputting a plurality of similar data items easily occurs in the business system, and the problem of repeated data item inputting occurs in the business system.

Disclosure of Invention

An embodiment of the present application aims to provide a data item processing method, an apparatus, a computer device, and a storage medium, so as to solve the problem that the existing data item construction work is mainly performed in a manual construction manner. Because different people understand knowledge thousands of people, the phenomenon of inputting a plurality of similar data items easily occurs in the business system, and the technical problem of the situation of repeated inputting of the data items occurs in the business system.

In order to solve the foregoing technical problem, an embodiment of the present application provides a method for processing a data item, which adopts the following technical solutions:

judging whether a data item adding request triggered by a user is received; wherein the data item adding request carries data item description information of a target data item to be added;

if yes, analyzing the data item description information from the data item adding request, and performing similarity analysis on the data item description information based on a preset similarity algorithm and a preset data item database to generate a similarity analysis result corresponding to the target data item;

if the similar analysis result indicates that the data item similar to the target data item does not exist in the data item database, displaying an editing interface containing a preset data model script;

receiving modification information of the data model script input by the user on the editing interface, and generating an object code corresponding to the object data item based on the modification information;

the target code is evaluated to generate a code evaluation result corresponding to the target code;

and if the code evaluation result is that evaluation is passed, releasing the target code to a preset production environment to finish the newly-added processing of the target data item.

Further, the number of the similarity algorithms includes a plurality of similarity algorithms, and the step of performing similarity analysis on the data item description information based on a preset similarity algorithm and a preset data item database to generate a similarity analysis result corresponding to the target data item specifically includes:

determining a designated similarity algorithm from all the similarity algorithms;

similarity calculation is carried out on the data item description information and each data item information stored in the data item database based on the specified similarity calculation method, and a plurality of corresponding similarities are obtained;

judging whether the specified similarity greater than a preset similarity threshold exists in all the similarities;

if the specified similarity larger than the similarity threshold does not exist, generating a first similarity analysis result of the data item which does not exist in the data item database and is similar to the target data item;

and if the specified similarity which is larger than the similarity threshold exists, generating a second similarity analysis result of the data item which is similar to the target data item and exists in the data item database.

Further, the step of determining the specified similarity algorithm from all the similarity algorithms specifically includes:

acquiring a first accuracy rate of each similarity algorithm in a preset time period from a preset algorithm statistical database;

screening out a first similarity algorithm with the accuracy rate larger than a preset accuracy rate threshold value from all the similarity algorithms based on the first accuracy rate;

acquiring second accuracy, calculation efficiency and used times of each first similarity algorithm in the preset time period, and acquiring publishing time information of each first similarity algorithm;

calculating the second accuracy, the calculation efficiency, the used times and the publication time information based on a preset calculation formula to generate a comprehensive evaluation score of each first similarity algorithm;

screening out a second similarity algorithm with the maximum processing evaluation score from all the first similarity algorithms;

and taking the second similarity algorithm as the specified similarity algorithm.

Further, the step of performing review processing on the target code and generating a code review result corresponding to the target code specifically includes:

calling a preset code review model;

inputting the object code into the code review model;

and auditing the target code through the code review model to generate a code review result corresponding to the target code.

Further, the step of issuing the target code to a preset production environment specifically includes:

performing smoking test on the target code to obtain a smoking test result corresponding to the target code;

if the smoking test result is that the test is passed, performing gray level test on the target code to obtain a gray level test result corresponding to the target code;

and if the gray test result is that the test is passed, the target code is issued to the production environment.

Further, the step of performing a smoking test on the target code to obtain a corresponding smoking test result specifically includes:

calling a preset universal data test library;

acquiring a target smoking test case corresponding to the target code from the universal data test library;

and carrying out smoking test on the target code based on the target smoking test case, and generating the smoking test result corresponding to the target code.

Further, after the step of issuing the object code to a preset production environment, the method further includes:

acquiring a preset information notification template;

inputting the target data item into the information notification template, and generating target push information corresponding to the target data item;

and displaying the target push information.

In order to solve the foregoing technical problem, an embodiment of the present application further provides a data item processing apparatus, which adopts the following technical solutions:

the judging module is used for judging whether a data item adding request triggered by a user is received or not; wherein the data item adding request carries data item description information of a target data item to be added;

the analysis module is used for analyzing the data item description information from the data item adding request if the data item adding request is positive, and carrying out similarity analysis on the data item description information based on a preset similarity algorithm and a preset data item database to generate a similarity analysis result corresponding to the target data item;

the first display module is used for displaying an editing interface containing a preset data model script if the similarity analysis result indicates that the data item similar to the target data item does not exist in the data item database;

the first generation module is used for receiving modification information of the data model script input by the user in the editing interface and generating an object code corresponding to the object data item based on the modification information;

the second generation module is used for evaluating the target code and generating a code evaluation result corresponding to the target code;

and the issuing module is used for issuing the target code to a preset production environment to finish the newly added processing of the target data item if the code evaluation result is that the evaluation is passed.

In order to solve the above technical problem, an embodiment of the present application further provides a computer device, which adopts the following technical solutions:

if so, analyzing the data item description information from the data item adding request, and performing similarity analysis on the data item description information based on a preset similarity algorithm and a preset data item database to generate a similarity analysis result corresponding to the target data item;

In order to solve the above technical problem, an embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solutions:

judging whether a data item adding request triggered by a user is received; the data item adding request carries data item description information of a target data item to be added;

and if the code evaluation result is that the evaluation is passed, releasing the target code to a preset production environment to finish the newly added processing of the target data item.

Compared with the prior art, the embodiment of the application mainly has the following beneficial effects:

when a data item adding request triggered by a user is received, analyzing the data item description information from the data item adding request, and performing similarity analysis on the data item description information based on a preset similarity algorithm and a preset data item database to generate a similarity analysis result corresponding to the target data item; if the similar analysis result indicates that the data item similar to the target data item does not exist in the data item database, displaying an editing interface containing a preset data model script; then receiving modification information of the data model script input by the user on the editing interface, and generating an object code corresponding to the object data item based on the modification information; subsequently, the target code is evaluated to generate a code evaluation result corresponding to the target code; and if the code evaluation result is that evaluation is passed, releasing the target code to a preset production environment to finish the newly-added processing of the target data item. According to the method and the device, the similarity analysis result corresponding to the target data item can be accurately and quickly generated based on the similarity algorithm and the data item database, whether the data item similar to the target data item exists in the electronic equipment can be accurately judged according to the similarity analysis result, and only when the data item similar to the target data item does not exist, the target data item is used as a new data item and corresponding new processing is executed, so that the problem of repeated entry of the data item can be effectively avoided, and the accuracy and the intelligence of new processing of the data item are improved.

Drawings

In order to more clearly illustrate the solution of the present application, the drawings needed for describing the embodiments of the present application will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and that other drawings can be obtained by those skilled in the art without inventive effort.

FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;

FIG. 2 is a flow diagram of one embodiment of a method of processing data items according to the present application;

FIG. 3 is a schematic block diagram of one embodiment of a data item processing apparatus according to the present application;

FIG. 4 is a schematic block diagram of one embodiment of a computer device according to the present application.

Detailed Description

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "including" and "having," and any variations thereof in the description and claims of this application and the description of the figures above, are intended to cover non-exclusive inclusions. The terms "first," "second," and the like in the description and claims of this application or in the above-described drawings are used for distinguishing between different objects and not for describing a particular order.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.

In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

As shown in fig. 1, the system architecture 100 may include

terminal devices

101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the

terminal devices

101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

The user may use the

terminal devices

101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The

terminal devices

101, 102, 103 may have various communication client applications installed thereon, such as a web browser application, a shopping application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like.

The

terminal devices

101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to a smart phone, a tablet computer, an e-book reader, an MP3 player (Mov I ng features Experts Group Aud I o Layer I, motion picture Experts compression standard audio Layer 3), an MP4 player (Mov I ng features Experts Group Aud I o Layer I V, motion picture Experts compression standard audio Layer 4), a laptop portable computer, a desktop computer, and the like.

The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the

terminal devices

101, 102, 103.

It should be noted that the processing method for the data item provided in the embodiments of the present application is generally executed by a server/terminal device, and accordingly, the processing apparatus for the data item is generally disposed in the server/terminal device.

It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for an implementation.

With continued reference to FIG. 2, a flow diagram of one embodiment of a method of processing data items is shown, in accordance with the present application. The data item processing method comprises the following steps:

step S201, judging whether a data item adding request triggered by a user is received; wherein the data item adding request carries data item description information of a target data item to be added.

In this embodiment, an electronic device (for example, a server/terminal device shown in fig. 1) on which the processing method of the data item is executed is built in the electronic device, and a data item cloud system is built in the electronic device. The data item addition request may be obtained through a wired connection or a wireless connection. It should be noted that the wireless connection means may include, but is not limited to, a 3G/4G/5G connection, a wifi connection, a bluetooth connection, a wimax connection, a Z i gbee connection, a UWB (u l t ra W i deband) connection, and other wireless connection means now known or developed in the future. The data item may include an index or a dimension, and the data item description information may include basic information of the target data item, for example, may include service definition information of the target data item, or may indicate information such as a name of the data item, a service attribution, and the like.

Step S202, if yes, analyzing the data item description information from the data item adding request, and performing similarity analysis on the data item description information based on a preset similarity algorithm and a preset data item database to generate a similarity analysis result corresponding to the target data item.

In this embodiment, the data item description information may be parsed from the data item addition request by performing parsing processing on the data item addition request. In the present application, a detailed implementation process of performing similarity analysis on the data item description information based on a preset similarity algorithm and a preset data item database to generate a similarity analysis result corresponding to the target data item is described in further detail in subsequent specific embodiments, which is not set forth herein. In addition, the description information of the data items can be analyzed through AI intelligent semantics, existing indexes or dimension information with high similarity can be automatically matched, and the indexes or the dimension information is sorted according to the similarity for the reference of business personnel, if the business personnel think that the indexes or the dimension information do not meet the requirements, the indexes/the dimensions can be newly established, the mode avoids the business personnel from repeatedly inputting the indexes to a certain extent, and the data items with different names or different synonyms can be avoided to the greatest extent.

Step S203, if the similarity analysis result indicates that there is no data item similar to the target data item in the data item database, displaying an editing interface including a preset data model script.

In this embodiment, the data model script is a script template that is pre-constructed according to the code generation requirement and is used for generating codes of different data items. In the editing interface, the user can edit and modify the data model script.

Step S204, receiving modification information of the data model script input by the user in the editing interface, and generating an object code corresponding to the object data item based on the modification information.

In this embodiment, the modified information of the data model script input by the user on the editing interface is acquired, the modified position corresponding to the modified information is determined from the data model script, and then the modified information is used to perform one-to-one corresponding replacement processing on the original information in the modified position, so as to obtain a processed data model script, and the processed data model script is used as the target code.

In further embodiments, the process of target code corresponding to the target data item may further include: for the request of adding the new index/dimension, the basic information of the newly-built data item can be obtained from the data item cloud system through the interface during the new creation of the index, if a data model script needs to be newly developed, service personnel, namely a user, is required to fill in a required data table structure, and because the data item is defined by the developer in the former mode, the developer does not understand the meaning of the service and cannot define the exact data item. The data item cloud system calls a table building assistant and a data standardization library of the developer platform, so that table building sentences are automatically generated, business personnel can simply write the processing logic of the data items to be added, and a system background automatically generates processing codes according to the processing logic and generates target codes corresponding to the target data items. The developer does not have the right to modify the table building statement, so that the developer is prevented from modifying the data item carelessly, and a large amount of development labor cost is saved;

step S205, performing review processing on the target code, and generating a code review result corresponding to the target code.

In this embodiment, the target code may be reviewed based on a preset code review model to generate a code review result corresponding to the target code, and a specific implementation process is described in further detail in subsequent specific embodiments, which is not described herein. In addition, the evaluation processing of the target code can also be performed in a manual evaluation mode.

Step S206, if the code evaluation result is that the evaluation is passed, releasing the target code to a preset production environment to complete the new processing of the target data item.

In this embodiment, the above-mentioned specific implementation process of issuing the object code to the preset production environment will be described in further detail in the following specific embodiments, which are not set forth herein too much.

In some optional implementations, the number of similarity algorithms includes a plurality, and step S202 includes the following steps:

and determining the appointed similarity algorithm from all the similarity algorithms.

In the present embodiment, the similarity calculation method may be various existing similarity calculations, and may include, for example, a cosine similarity calculation method, a hamming distance-based semantic similarity calculation method, a vector space model-based calculation method, and the like. In addition, the above-mentioned specific implementation process of determining the specified similarity algorithm from all the similarity algorithms is further described in detail in the following specific embodiments, and is not set forth herein in any detail.

And performing similarity calculation on the data item description information and each data item information stored in the data item database based on the specified similarity calculation method to obtain a plurality of corresponding similarities.

In this embodiment, through a parallel computing instruction, similarity computation may be performed on the data item description information and each data item information stored in the data item database by using a specified similarity computation method, so as to obtain a plurality of corresponding similarities, so as to improve processing efficiency of computing the similarity. The parallel computing instruction may specifically refer to a single instruction stream multiple data (S I ng l e nst r uct I on mu l t I p l e data, S I MD) instruction.

And judging whether the specified similarity greater than a preset similarity threshold exists in all the similarities.

In this embodiment, the value of the similarity threshold is not specifically limited, and may be set according to an actual service usage requirement, for example, may be set to 0.95.

And if the specified similarity larger than the similarity threshold does not exist, generating a first similarity analysis result of the data item which does not exist in the data item database and is similar to the target data item.

The method comprises the steps that an appointed similarity algorithm is determined from all similarity algorithms; then, similarity calculation is carried out on the data item description information and each data item information stored in the data item database based on the specified similarity calculation method, and a plurality of corresponding similarities are obtained; subsequently judging whether the specified similarity greater than a preset similarity threshold exists in all the similarities; if the specified similarity which is larger than the similarity threshold does not exist, generating a first similarity analysis result of the data item which does not exist in the data item database and is similar to the target data item; and if the specified similarity which is larger than the similarity threshold exists, generating a second similarity analysis result of the data item which is similar to the target data item and exists in the data item database. According to the method and the device, the specified similarity algorithm with high comprehensive evaluation is determined from all the similarity algorithms, and similarity calculation is carried out on the data item description information and each data item information stored in the data item database based on the specified similarity algorithm, so that the accuracy of the obtained multiple similarities can be effectively guaranteed.

In some optional implementations of this embodiment, determining the specified similarity algorithm from all the similarity algorithms includes:

and acquiring a first accuracy of each similarity algorithm in a preset time period from a preset algorithm statistical database.

In this embodiment, the algorithm statistical database is a pre-constructed database storing algorithm test statistical data of a plurality of similarity algorithms, and the algorithm test statistical data may be data obtained by performing data tests on the various similarity algorithms by related developers. Wherein the algorithm test statistic data at least comprises accuracy, calculation efficiency, used times and publication time information.

And screening out the first similarity algorithms with the accuracy rate larger than a preset accuracy rate threshold value from all the similarity algorithms based on the first accuracy rate.

In this embodiment, the value of the accuracy threshold is not specifically limited, and may be set according to actual service usage requirements.

And acquiring second accuracy, calculation efficiency and used times of each first similarity algorithm in the preset time period, and acquiring publishing time information of each first similarity algorithm.

In this embodiment, the second accuracy, the calculation efficiency and the number of times of use of each first similarity algorithm in the preset time period may be obtained from an algorithm statistical database, and publication time information of each first similarity algorithm may be obtained. The publication time information may include publication age information, which is a difference between a current year and a publication year of the similarity algorithm. If the publication year of the similarity algorithm a is 2008 and the current publication year is 2022, the publication year limit information of the similarity algorithm a is a difference 14 of 2022 to 2008.

And calculating the second accuracy, the calculation efficiency, the used times and the publication time information based on a preset calculation formula to generate a comprehensive evaluation score of each first similarity algorithm.

In this embodiment, the calculation formula may specifically be: soc re = (Z × a + S × b + C × C)/(F × d), where Socre is a comprehensive evaluation score, Z is accuracy, a is a first weight of accuracy, S is calculation efficiency, b is a second weight of calculation efficiency, C is the number of used times, C is a third weight of the number of used times, F is publication time information, and d is a fourth weight of publication time information.

And screening out a second similarity algorithm with the maximum processing evaluation score from all the first similarity algorithms.

The method comprises the steps that a first accuracy rate of each similarity algorithm in a preset time period is obtained from a preset algorithm statistical database; screening out first similarity algorithms with accuracy rates larger than a preset accuracy rate threshold value from all the similarity algorithms based on the first accuracy rates; then, acquiring a second accuracy, a calculation efficiency and the number of used times of each first similarity algorithm in the preset time period, and acquiring publishing time information of each first similarity algorithm; then, calculating the second accuracy, the calculation efficiency, the used times and the publication time information based on a preset calculation formula to generate a comprehensive evaluation score of each first similarity algorithm; and subsequently screening a second similarity algorithm with the maximum processing evaluation score from all the first similarity algorithms, and taking the second similarity algorithm as the specified similarity algorithm. The specified similarity algorithm has the advantages of high accuracy and calculation efficiency, advanced release time and high use frequency, so that similarity analysis is performed on the data item description information based on the specified similarity algorithm and a data item database subsequently to generate a similar analysis result corresponding to the target data item, and the accuracy of the obtained multiple similar analysis results can be effectively ensured.

In some alternative implementations, step S205 includes the following steps:

and calling a preset code review model.

In this embodiment, the code review model may be generated by training an initial model based on pre-collected code data samples. The initial model may specifically adopt an NLP model. The training generation process of the code review model can comprise the following steps: obtaining code data samples in a historical time period; the code data sample comprises a large number of correct code sets developed by the code data sample, all the problems encountered in the code review process and the improved codes are used as error code sets, and the correct code sets and the error code sets are used; dividing the code data sample into training data and testing data according to a preset proportion; wherein, the value of the preset proportion is not particularly limited; inputting the training data to the initial model to cause the initial model to automatically learn; when the code review model is trained, the initial model inputs the error code, the position of the error code with the problem and the modified code into the initial model, and the initial model adopts an algorithm to automatically learn by a machine according to the error code, the position of the error code with the problem and the modified code. The process of machine learning is a process only focusing on results before and after code modification, and only needs to know what is needed without knowing why; inputting the test data into the initial model after automatic learning. Obtaining a test code evaluation result of the test data; judging whether the initial model meets expected conditions of code review or not based on the test code review result; the expected conditions comprise error identification accuracy of error review in the codes, and can also comprise corresponding conditions for judging whether errors frequently occurring in the process of verifying the codes according to manual experience can be identified or not; the value of the error identification accuracy is not specifically limited, and may be set according to the actual service use requirement, for example, may be set to 0.95; if the initial model meets the expected condition of code review, finishing the training of the initial model, and taking the initial model as the code review model; and if the initial model does not meet the expected condition of code review, repeating the training process by adopting a larger number of test samples until the initial model meets the expected condition.

Inputting the object code into the code review model.

And auditing the target code through the code review model to generate the code review result corresponding to the target code.

In this embodiment, the code review result includes whether the audit is passed or not passed.

The method comprises the steps of calling a preset code review model; and then inputting the target code into the code review model, and auditing the target code through the code review model to generate the code review result corresponding to the target code. According to the method and the device, the code review result corresponding to the target code can be quickly and accurately generated by using the code review model, and the accuracy of the generated code review result is ensured.

In some alternative implementations, step S206 includes the steps of:

and carrying out smoking test on the target code to obtain a smoking test result corresponding to the target code.

In this embodiment, the specific implementation process of performing the smoking test on the target code to obtain the smoking test result corresponding to the target code is described in further detail in the following specific embodiments, and will not be described herein.

And if the smoking test result is that the test is passed, performing gray level test on the target code to obtain a gray level test result corresponding to the target code.

In this embodiment, the gray test is that the program corresponding to the target code is developed and completed, and after the test personnel pass all tests, the program corresponding to the target code is relatively stable at this time, the development team will only open the upgrading function of the target code to some users, some bugs will appear in the use process of the some users, the detection function of the program will report bug logs to the development team, the development personnel will modify the bug logs, and after the modification is completed, an upgrading notification is sent to all users, which is called the gray test.

The method comprises the steps of carrying out smoking test on the target code to obtain a smoking test result corresponding to the target code; if the smoking test result is that the test is passed, performing gray level test on the target code to obtain a gray level test result corresponding to the target code; and if the gray test result is that the test is passed, the target code is issued to the production environment. According to the method and the device, after the target code corresponding to the target data item is generated, the target code can be subjected to smoking test and gray level test intelligently, and only when the target code successfully passes the smoking test and the gray level test, the target code can be issued to a preset production environment subsequently, so that the newly added processing of the target data item is completed, and the normalization and the accuracy of the newly added processing of the target data item are guaranteed.

In some optional implementation manners of this embodiment, the performing a smoking test on the target code to obtain a corresponding smoking test result includes the following steps:

and calling a preset universal data test library.

In this embodiment, the generic database is a database that is constructed in advance and stores a smoking test case for performing a smoking test on a code. The smoking test case can be compiled and generated by developers according to the test requirements of the actual smoking function.

And acquiring a target smoking test case corresponding to the target code from the universal data test library.

In this embodiment, the target code may be analyzed to obtain a basic function corresponding to the target code, and then the smoking test case corresponding to the basic function may be obtained from the universal data test library as the target smoking test case. The target smoking test case comprises the content of the function data item corresponding to the basic function of the target code.

And performing a smoking test on the target code based on the target smoking test case, and generating the smoking test result corresponding to the target code.

In this embodiment, the smoking test result includes a test pass or a test fail.

The method comprises the steps of calling a preset universal data test library; then obtaining a target smoking test case corresponding to the target code from the universal data test library; and then, performing a smoking test on the target code based on the target smoking test case, and generating the smoking test result corresponding to the target code. In the embodiment, based on the use of the universal data test library and the target smoking test case, the target code can be subjected to smoking test rapidly and intelligently to obtain a corresponding smoking test result, so that the accuracy of the generated smoking test result is ensured.

In some optional implementations of this embodiment, after step S206, the electronic device may further perform the following steps:

and acquiring a preset information notification template.

In this embodiment, the information notification template is a template file created in advance according to the service usage requirement of the actual information notification. For example, the information notification template may include: XXX data items have been added successfully, as noted.

And inputting the target data item into the information notification template to generate target push information corresponding to the target data item.

In this embodiment, the target data item may be input to a filling position corresponding to the data item in the information notification template, so as to obtain the information notification template after information filling, and the information notification template after information filling is used as the target push information.

And displaying the target push information.

In this embodiment, the display of the target push information is not limited, and for example, a mode of displaying the target push information on a current interface of the electronic device may be adopted.

After the addition processing of the target data item is completed, a preset information notification template is obtained; then inputting the target data item into the information notification template to generate target push information corresponding to the target data item; and then displaying the target push information to remind the user that the successful adding processing of the target data item is completed in the electronic equipment, so that the user can find the target data item newly built in the data item cloud system, and the use experience of the user is improved.

It is emphasized that, in order to further ensure the privacy and security of the data item description information, the data item description information may also be stored in a node of a block chain.

The block chain referred by the application is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. The block chain (B l ockcha i n), which is essentially a decentralized database, is a string of data blocks associated by using cryptography, and each data block contains information of a batch of network transactions, which is used for verifying the validity (anti-counterfeiting) of the information and generating the next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.

The embodiment of the application can acquire and process related data based on an artificial intelligence technology. The artificial intelligence (Art I f I c I a l I nte l I gene, AI) is a theory, method, technique and application system for simulating, extending and expanding human intelligence by using a digital computer or a machine controlled by a digital computer, sensing environment, acquiring knowledge and obtaining the best result by using the knowledge.

The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above may be implemented by hardware that is configured to be instructed by computer-readable instructions, which can be stored in a computer-readable storage medium, and when executed, the programs may include the processes of the embodiments of the methods described above. The storage medium may be a non-volatile storage medium such as a magnetic disk, an optical disk, a Read-only Memory (ROM), or a Random Access Memory (RAM).

It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless otherwise indicated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of execution is not necessarily sequential, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

With further reference to fig. 3, as an implementation of the method shown in fig. 2, the present application provides an embodiment of an apparatus for processing data items, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and the apparatus may be specifically applied to various electronic devices.

As shown in fig. 3, the data item processing apparatus 300 according to the present embodiment includes: the system comprises a judging module 301, an analyzing module 302, a first showing module 303, a first generating module, a second generating module 305 and a publishing module 306. Wherein:

a judging module 301, configured to judge whether a data item addition request triggered by a user is received; wherein the data item adding request carries data item description information of a target data item to be added;

an analysis module 302, configured to, if yes, parse the data item description information from the data item addition request, perform similarity analysis on the data item description information based on a preset similarity algorithm and a preset data item database, and generate a similarity analysis result corresponding to the target data item;

a first display module 303, configured to display an editing interface including a preset data model script if the similarity analysis result indicates that a data item similar to the target data item does not exist in the data item database;

a first generating module 304, configured to receive modification information, which is input by the user in the editing interface, for the data model script, and generate an object code corresponding to the target data item based on the modification information;

a second generating module 305, configured to perform review processing on the target code, and generate a code review result corresponding to the target code;

the issuing module 306 is configured to issue the target code to a preset production environment if the code review result is that the review passes, so as to complete the new addition processing on the target data item.

In this embodiment, the operations executed by the modules or units respectively correspond to the steps of the data item processing method of the foregoing embodiment one by one, and are not described herein again.

In some optional implementations of this embodiment, the number of similarity algorithms includes a plurality, and the analysis module 302 includes:

the determining submodule is used for determining a specified similarity algorithm from all the similarity algorithms;

the calculation submodule is used for carrying out similarity calculation on the data item description information and each data item information stored in the data item database based on the specified similarity calculation method to obtain a plurality of corresponding similarities;

the judgment submodule is used for judging whether the specified similarity greater than a preset similarity threshold exists in all the similarities;

a first generation submodule, configured to generate a first similarity analysis result of a data item, which is not similar to the target data item, in the data item database if there is no specified similarity greater than the similarity threshold;

and the second generation submodule is used for generating a second similarity analysis result of the data item which is similar to the target data item in the data item database if the specified similarity which is greater than the similarity threshold exists.

In this embodiment, the operations executed by the modules or units respectively correspond to the steps of the data item processing method in the foregoing embodiment one by one, and are not described herein again.

In some optional implementations of this embodiment, the determining the sub-module includes:

the first acquisition unit is used for acquiring a first accuracy rate of each similarity algorithm in a preset time period from a preset algorithm statistical database;

the first screening unit is used for screening out a first similarity algorithm with the accuracy rate larger than a preset accuracy rate threshold value from all the similarity algorithms on the basis of the first accuracy rate;

the second acquisition unit is used for acquiring a second accuracy, calculation efficiency and used times of each first similarity algorithm in the preset time period and acquiring publishing time information of each first similarity algorithm;

the calculating unit is used for calculating the second accuracy, the calculating efficiency, the used times and the publishing time information based on a preset calculating formula and generating a comprehensive evaluation score of each first similarity algorithm;

the second screening unit is used for screening out a second similarity algorithm with the maximum processing evaluation score from all the first similarity algorithms;

a determining unit, configured to use the second similarity algorithm as the specified similarity algorithm.

In this embodiment, the operations executed by the modules or units respectively correspond to the steps of the data item processing method in the foregoing embodiment one to one, and are not described herein again.

In some optional implementations of the present embodiment, the first generating module 304 includes:

the calling sub-module is used for calling a preset code review model;

the input sub-module is used for inputting the target code into the code review model;

and the third generation sub-module is used for auditing the target code through the code review model and generating the code review result corresponding to the target code.

In some optional implementations of this embodiment, the publishing module 306 includes:

the first test sub-module is used for carrying out smoking test on the target code to obtain a smoking test result corresponding to the target code;

the second testing submodule is used for carrying out gray level testing on the target code if the smoking testing result is that the test is passed, so as to obtain a gray level testing result corresponding to the target code;

and the issuing sub-module is used for issuing the target code to the production environment if the gray test result is that the test is passed.

In some optional implementations of this embodiment, the first testing sub-module includes:

the calling unit is used for calling a preset universal data test library;

a third obtaining unit, configured to obtain a target smoking test case corresponding to the target code from the universal data test library;

and the test unit is used for carrying out smoking test on the target code based on the target smoking test case and generating the smoking test result corresponding to the target code.

In some optional implementations of this embodiment, the data item processing apparatus further includes:

the acquisition module is used for acquiring a preset information notification template;

the third generation module is used for inputting the target data item into the information notification template and generating target push information corresponding to the target data item;

and the second display module is used for displaying the target push information.

In order to solve the technical problem, an embodiment of the present application further provides a computer device. Referring to fig. 4 in particular, fig. 4 is a block diagram of a basic structure of a computer device according to the embodiment.

The computer device 4 comprises a memory 41, a processor 42, a network interface 43 communicatively connected to each other via a system bus. It is noted that only computer device 4 having components 41-43 is shown, but it is understood that not all of the shown components are required to be implemented, and that more or fewer components may be implemented instead. AS will be understood by those skilled in the art, the computer device herein is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware includes, but is not limited to, a microprocessor, an application specific integrated circuit (App I cat I on Spec I C I integrated C I rcu I, AS ic), a programmable Gate array (F I l D-programmable ab l Gate Ar ray, FPGA), a digital Processor (D I ta l S I gna l Processor, DSP), an embedded device, and the like.

The computer device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The computer equipment can carry out man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch panel or voice control equipment and the like.

The memory 41 includes at least one type of readable storage medium including flash memory, hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), random Access Memory (RAM), static Random Access Memory (SRAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), programmable Read Only Memory (PROM), magnetic memory, magnetic disks, optical disks, etc. In some embodiments, the memory 41 may be an internal storage unit of the computer device 4, such as a hard disk or a memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, a Smart Memory Card (SMC), a secure digital (Secu re D i g i ta l, SD) Card, a flash memory Card (F l ash Card), and the like, provided on the computer device 4. Of course, the memory 41 may also include both internal and external storage devices of the computer device 4. In this embodiment, the memory 41 is generally used for storing an operating system installed in the computer device 4 and various types of application software, such as computer readable instructions of a data item processing method. Further, the memory 41 may also be used to temporarily store various types of data that have been output or are to be output.

The processor 42 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is configured to execute computer readable instructions stored in the memory 41 or to process data, such as computer readable instructions for executing the data item processing method.

The network interface 43 may comprise a wireless network interface or a wired network interface, and the network interface 43 is generally used for establishing communication connection between the computer device 4 and other electronic devices.

in the embodiment of the application, when a data item adding request triggered by a user is received, the data item description information is firstly analyzed from the data item adding request, similarity analysis is carried out on the data item description information on the basis of a preset similarity algorithm and a preset data item database, and a similarity analysis result corresponding to the target data item is generated; if the similar analysis result indicates that the data item similar to the target data item does not exist in the data item database, displaying an editing interface containing a preset data model script; then receiving modification information of the data model script input by the user on the editing interface, and generating an object code corresponding to the object data item based on the modification information; subsequently, the target code is evaluated to generate a code evaluation result corresponding to the target code; and if the code evaluation result is that evaluation is passed, releasing the target code to a preset production environment to finish the newly-added processing of the target data item. According to the embodiment of the application, the similarity analysis result corresponding to the target data item can be accurately and quickly generated based on the similarity algorithm and the data item database, whether the data item similar to the target data item exists in the electronic equipment can be accurately judged according to the similarity analysis result, and only when the data item similar to the target data item does not exist, the target data item is used as a new data item and corresponding additional processing is executed, so that the problem of repeated entry of the data item can be effectively avoided, and the accuracy and the intelligence of the additional processing of the data item are improved.

The present application further provides another embodiment, which is a computer-readable storage medium storing computer-readable instructions executable by at least one processor to cause the at least one processor to perform the steps of the method of processing data items as described above.

in the embodiment of the application, when a data item adding request triggered by a user is received, the data item description information is firstly analyzed from the data item adding request, similarity analysis is carried out on the data item description information based on a preset similarity algorithm and a preset data item database, and a similarity analysis result corresponding to the target data item is generated; if the similar analysis result indicates that the data item similar to the target data item does not exist in the data item database, displaying an editing interface containing a preset data model script; then receiving modification information of the data model script input by the user on the editing interface, and generating an object code corresponding to the object data item based on the modification information; subsequently, the target code is evaluated to generate a code evaluation result corresponding to the target code; and if the code evaluation result is that evaluation is passed, releasing the target code to a preset production environment to finish the newly-added processing of the target data item. According to the embodiment of the application, the similarity analysis result corresponding to the target data item can be accurately and quickly generated based on the similarity algorithm and the data item database, whether the data item similar to the target data item exists in the electronic equipment can be accurately judged according to the similarity analysis result, and the target data item can be used as a new data item and corresponding additional processing is executed only when the data item similar to the target data item does not exist in the electronic equipment, so that the problem of repeated entry of the data item can be effectively avoided, and the accuracy and the intelligence of the new processing of the data item are improved.

Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present application.

It should be understood that the above-described embodiments are merely exemplary of some, and not all, embodiments of the present application, and that the drawings illustrate preferred embodiments of the present application without limiting the scope of the claims appended hereto. This application is capable of embodiments in many different forms and is provided for the purpose of enabling a thorough understanding of the disclosure of the application. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to one skilled in the art that the present application may be practiced without modification or with equivalents of some of the features described in the foregoing embodiments. All equivalent structures made by using the contents of the specification and the drawings of the present application are directly or indirectly applied to other related technical fields and are within the protection scope of the present application.

Claims

1. A method of processing a data item, comprising the steps of:

2. The method for processing the data item according to claim 1, wherein the number of the similarity algorithms includes a plurality, and the step of performing similarity analysis on the description information of the data item based on a preset similarity algorithm and a preset data item database to generate a similarity analysis result corresponding to the target data item specifically includes:

3. The method for processing a data item according to claim 2, wherein the step of determining a specified similarity algorithm from all the similarity algorithms comprises:

screening out first similarity algorithms with accuracy rates larger than a preset accuracy rate threshold value from all the similarity algorithms based on the first accuracy rates;

4. The method for processing the data item according to claim 1, wherein the step of performing review processing on the object code to generate a code review result corresponding to the object code specifically includes:

calling a preset code review model;

inputting the object code into the code review model;

5. The method for processing the data item according to claim 1, wherein the step of issuing the object code to a preset production environment specifically includes:

and if the gray level test result is that the test is passed, the target code is issued to the production environment.

6. The method for processing a data item according to claim 5, wherein the step of performing a smoking test on the target code to obtain a corresponding smoking test result specifically includes:

calling a preset universal data test library;

7. The method of claim 1, wherein after the step of releasing the object code to a preset production environment, the method further comprises:

acquiring a preset information notification template;

and displaying the target push information.

8. A device for processing data items, comprising:

the second generation module is used for carrying out review processing on the target code and generating a code review result corresponding to the target code;

9. A computer device comprising a memory having computer readable instructions stored therein and a processor which when executed implements the steps of a method of processing data items as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that it has computer-readable instructions stored thereon, which, when executed by a processor, implement the steps of a method of processing data items according to any one of claims 1 to 7.