CN113407225B

CN113407225B - Code list generation method and device, computer equipment and storage medium

Info

Publication number: CN113407225B
Application number: CN202110700029.0A
Authority: CN
Inventors: 吴奕浩
Original assignee: Weikun Shanghai Technology Service Co Ltd
Current assignee: Weikun Shanghai Technology Service Co Ltd
Priority date: 2021-06-23
Filing date: 2021-06-23
Publication date: 2022-09-23
Anticipated expiration: 2041-06-23
Also published as: WO2022267181A1; CN113407225A

Abstract

The application relates to the field of artificial intelligence, and provides a code list generation method, a device, computer equipment and a storage medium, wherein the method comprises the following steps: when a code list acquisition request is received, carrying out authentication processing on a user to generate an authentication result, and judging whether the user passes the authentication; if so, extracting the user information from the code list acquisition request, and calling an API (application program interface) provided by JGit to acquire a code list record corresponding to the user information from the Git system; extracting parameter information and judging whether the parameter information meets a preset standard or not; if so, performing parameter assignment on the list generation method based on the parameter information to obtain the list generation method after the assignment; and executing the list generating method after assignment so as to extract the target code list corresponding to the parameter information from the code list record. The method and the device improve the intelligence and the accuracy of code list acquisition. The present application can also be applied to the field of block chains, and the data such as the target code list may be stored on the block chain.

Description

Code list generation method and device, computer equipment and storage medium

Technical Field

The application relates to the field of data artificial intelligence, in particular to a code list generation method and device, computer equipment and a storage medium.

Background

Code quality is an important index for measuring a system, and code review is also called code review and refers to the activity of checking the conformity of a source code and a coding standard and the code quality by reading the code. The code review can not only improve the code quality, but also find defects in the early stage of the project, reduce the loss to the minimum, ensure that developers also carding thought again in the review process, and deepen the understanding of the system.

Current code review meetings involve a listing of code submitted by each developer. Because developers generally submit codes for multiple times, the conventional method for arranging the code lists by the developers usually copies the code lists submitted by the developers one by one, stores the copied code lists in a code list file, and then needs to manually arrange the code lists to obtain the final code list. The manual processing mode is lack of intelligence, time and labor are consumed, and the situation that content is omitted in the code list is easy to occur, so that the obtained code list is low in accuracy.

Disclosure of Invention

The application mainly aims to provide a code list generation method, a code list generation device, computer equipment and a storage medium, and aims to solve the technical problems that an existing code list arrangement mode is lack of intelligence, time and labor are consumed, and the obtained code list is low in accuracy.

The application provides a method for generating a code list, which comprises the following steps:

judging whether a code list acquisition request input by a user is received or not; the code list acquiring request carries user information and parameter information;

if the code list acquisition request is received, carrying out authentication processing on the user based on a preset authorized face image and a face expression recognition model, and generating an authentication result corresponding to the user;

judging whether the identity authentication result is that the identity authentication is passed or not;

if the identity authentication result is that the identity authentication is passed, extracting the user information from the code list acquisition request, and calling an API (application program interface) provided by Jgit to acquire a code list record corresponding to the user information from a Git system;

extracting the parameter information from the code list acquisition request;

judging whether the parameter information meets a preset standard or not;

if the parameter information accords with the preset specification, acquiring a preset list generation method, and performing parameter assignment on the list generation method based on the parameter information to obtain an assigned list generation method;

and executing the list generating method after assignment to extract the target code list corresponding to the parameter information from the code list record.

Optionally, the step of performing authentication processing on the user based on a preset authorized facial image and a facial expression recognition model to generate an authentication result corresponding to the user includes:

acquiring a face image of the user based on a preset camera;

determining a face comparison result corresponding to the face image based on the authorized face image; and the number of the first and second groups,

respectively acquiring expression recognition results corresponding to the face images based on a preset number of pre-trained facial expression recognition models; the preset number is more than 1, and each facial expression recognition model is generated by training based on different training sample data sets;

acquiring target user information corresponding to the face image, and determining an information comparison result between the user information and the target user information;

and generating an identity verification result corresponding to the user based on the face comparison result, the expression recognition result and the information comparison result.

Optionally, the step of generating an authentication result corresponding to the user based on the face comparison result, the expression recognition result, and the information comparison result includes:

judging whether the face comparison result is passed or not;

if the face comparison result is that the face comparison is passed, performing statistical analysis on all the expression recognition results, and screening out a specified expression recognition result with the largest occurrence frequency in all the expression recognition results;

judging whether the designated expression recognition result is a target expression or not;

if the designated expression identification result is not the target expression, judging whether the information comparison result is that the comparison is passed;

if the information comparison result is that the comparison is passed, generating a first authentication result that the authentication is passed;

and if the information comparison result does not pass the comparison, generating a second identity verification result of which the identity verification fails.

Optionally, before the step of obtaining the expression recognition results corresponding to the face images respectively based on a preset number of pre-trained facial expression recognition models, the method includes:

acquiring a preset number of training sample data sets; the training sample of each training sample data set comprises a plurality of sample face images and expression labeling information for labeling the sample face images, and each training sample data set comprises different sample face images;

acquiring a specified training sample data set, taking a sample face image in the specified training sample data set as the input of a preset initial recognition model, and taking expression labeling information corresponding to the sample face image as the output of the initial model to train the initial recognition model and generate a trained initial recognition model; wherein, the specified training sample data set is any one sample data set in all the training sample data sets;

acquiring a preset test sample data set;

verifying the trained initial recognition model based on the test sample data set, and judging whether the verification is passed;

and if the verification is passed, taking the trained initial recognition model as a specified facial expression recognition model corresponding to the specified training sample data set.

Optionally, the step of determining whether the parameter information meets a preset specification includes:

acquiring the quantity of parameters in the parameter information;

judging whether the number of the parameters is equal to a preset number value or not;

if the parameter quantity is equal to the preset quantity value, judging whether the data format of the parameter information conforms to a preset parameter format;

if the data format of the parameter information conforms to the parameter format, judging that the parameter information conforms to a preset specification;

and if the data format of the parameter information does not accord with the parameter format, judging that the parameter information does not accord with a preset standard.

Optionally, after the step of determining whether the authentication result is that the authentication is passed, the method includes:

if the identity authentication result is not that the identity authentication is passed, limiting the response to the code list acquisition request;

generating corresponding alarm information based on the face image and the user information;

acquiring preset mail login information and acquiring a designated mail address corresponding to a designated user;

logging in a corresponding mail server according to the mail login information;

and sending the alarm information to the specified mail address through the mail server.

Optionally, after the step of determining whether the parameter information meets a preset specification, the method includes:

if the parameter information does not accord with the preset standard, pre-stored parameter filling standard data corresponding to the preset standard are obtained;

generating reminding information corresponding to the parameter filling specification data;

and displaying the reminding information.

The present application further provides a device for generating a code list, including:

the first judgment module is used for judging whether a code list acquisition request input by a user is received or not; the code list acquisition request carries user information and parameter information;

the verification module is used for carrying out identity verification processing on the user based on a preset authorized face image and a face expression recognition model if the code list acquisition request is received, and generating an identity verification result corresponding to the user;

the second judgment module is used for judging whether the identity authentication result is that the identity authentication is passed;

the first obtaining module is used for extracting the user information from the code list obtaining request and calling an API (application program interface) provided by JGit to obtain a code list record corresponding to the user information from the Git system if the identity verification result is that the identity verification is passed;

the extraction module is used for extracting the parameter information from the code list acquisition request;

the third judging module is used for judging whether the parameter information meets the preset specification;

the first generating module is used for acquiring a preset list generating method if the parameter information accords with a preset standard, and performing parameter assignment on the list generating method based on the parameter information to obtain an assigned list generating method;

and the execution module is used for executing the list generation method after the assignment so as to extract the target code list corresponding to the parameter information from the code list record.

The present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

The present application also provides a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the above-mentioned method.

The code list generation method, the code list generation device, the computer equipment and the storage medium have the following beneficial effects:

according to the code list generation method, the code list generation device, the computer equipment and the storage medium, when a code list acquisition request input by a user is received, after the identity verification of the user is judged to be passed, an API (application program interface) interface provided by Jgit is called to acquire a code list record corresponding to user information from a Git system. After the parameter information preset specification carried in the code list acquisition request is detected, parameter assignment is carried out on a preset list generation method based on the parameter information, the list generation method after the assignment is executed, the target code list corresponding to the parameter information is extracted from the code list record, and the target code list required by a user is acquired in an automatic mode, so that manual repeated work can be omitted, the condition of code omission does not need to be worried about, and the intelligence, the integrity, the rapidity and the accuracy of code list acquisition are effectively improved.

Drawings

FIG. 1 is a flowchart illustrating a method for generating a code listing according to an embodiment of the present application;

fig. 2 is a schematic structural diagram of a code list generation apparatus according to an embodiment of the present application;

fig. 3 is a schematic structural diagram of a computer device according to an embodiment of the present application.

The implementation, functional features and advantages of the object of the present application will be further explained with reference to the embodiments, and with reference to the accompanying drawings.

Detailed Description

It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.

It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

Referring to fig. 1, a method for generating a code list according to an embodiment of the present application includes:

s1: judging whether a code list acquisition request input by a user is received; the code list acquisition request carries user information and parameter information;

s2: if the code list acquisition request is received, performing authentication processing on the user based on a preset authorized face image and a face expression recognition model to generate an authentication result corresponding to the user;

s3: judging whether the identity authentication result is that the identity authentication is passed or not;

s4: if the identity authentication result is that the identity authentication is passed, extracting the user information from the code list acquisition request, and calling an API (application program interface) provided by Jgit to acquire a code list record corresponding to the user information from a Git system;

s5: extracting the parameter information from the code list acquisition request;

s6: judging whether the parameter information meets a preset standard or not;

s7: if the parameter information accords with the preset specification, acquiring a preset list generation method, and performing parameter assignment on the list generation method based on the parameter information to obtain an assigned list generation method;

s8: and executing the list generating method after assignment to extract the target code list corresponding to the parameter information from the code list record.

As described in the above steps S1 to S8, the execution subject of the embodiment of the method is a code list generation device. In practical applications, the generating device of the code list may be implemented by a virtual device, such as a software code, or may be implemented by an entity device in which a relevant execution code is written or integrated, and may perform human-computer interaction with a user through a keyboard, a mouse, a remote controller, a touch panel, or a voice control device. The code list generation device in the embodiment can acquire the target code list required by the user in an automatic mode, so that manual repeated work is omitted, the situation of code omission does not need to be worried about, and the intelligence, integrity, rapidness and accuracy of code list acquisition are effectively improved. Specifically, it is first determined whether a code list acquisition request input by a user is received. The code list obtaining request carries user information and parameter information. In addition, the user information may include name information, id information. The parameter information may include a plurality of parameter data including parameter names and parameter values corresponding to the parameter names. The parameter names may include: all file positions gitPath of the code directory, branch name of the code list to be obtained, name of the submitter of the code list, and commit time. And if the code list acquisition request is received, performing authentication processing on the user based on a preset authorized face image and a face expression recognition model, and generating an authentication result corresponding to the user. The face comparison result corresponding to the face image of the user can be determined based on the authorized face image, the expression recognition result corresponding to the face image of the user can be determined based on a pre-trained face expression recognition model, meanwhile, an information comparison result between the user information and target user information corresponding to the face image of the user is determined, and then the identity result of the user is finally determined by combining the face comparison result, the expression recognition result and the information comparison result.

And then judging whether the identity authentication result is that the identity authentication is passed or not. And if the identity authentication result is that the identity authentication is passed, extracting the user information from the code list acquisition request, and calling an API (application program interface) provided by JGit to acquire a code list record corresponding to the user information from the Git system. JGit is a lightweight class library of pure Java, which is used for realizing access of a version control system of the Git and providing a core version control algorithm. The main modules of JGit are: org. eclipse. jgit: core implementations including git commands, protocols, etc.; org, eclipse, jgit, archive, supporting the export of various compressed formats; org. eclipse. jgit. http. server: a server supporting http protocol. The main subjects are: repository, AnyObjectId, Ref, RevWalk, RevCommit, RevTag, RevTree. The overall design of Git is divided into 2 layers, porcelain and plumbing respectively. The plombing layer provides a set of commands to process the underlying tasks. Above that, the porcelain layer implements a command set (e.g., checkout, branch, commit, etc.) that interacts directly with the user. Likewise, JGit is also divided into two layers. The command API emulates the command line of Git, and for each Git verb (commit, branch, checkout, etc.), JGit provides a corresponding API in the org. The JGit usage scenario looks just like the plumbing layer using Git. In addition, a jgit jar toolkit provided by eclipse can be used for providing a calling interface mode to operate the Git system, so that a code list record corresponding to the user information is obtained from the Git system, and the code list record comprises all code lists of the user. The code listing refers to the file name of the code file that modified the original code.

And then extracting the parameter information from the code list acquisition request. The parameter information may include a plurality of parameter data, and the parameter data includes a parameter name and a parameter value corresponding to the parameter name. The parameter names may include: all file positions gitPath of the code directory, branch name of the code list to be obtained, name of the submitter of the code list, and commit time. In addition, the parameter information is information for filtering the obtained code list records, so as to subsequently filter the target code list related to the parameter information from the code list records. And judging whether the parameter information meets a preset standard. Judging whether the parameter information conforms to the preset specification or not refers to judging whether the number of parameters in the parameter information is equal to a preset number value or not, simultaneously judging whether the data format of the parameter information conforms to the preset parameter format or not, and judging that the parameter information conforms to the preset specification if the number of parameters in the parameter information and the data format of the parameter information conform to the preset parameter format.

And if the parameter information accords with the preset specification, acquiring a preset list generation method, and performing parameter assignment on the list generation method based on the parameter information to obtain the list generation method after the assignment. The list generating method is an interface method corresponding to processing logic for extracting a code list corresponding to parameter data from a code list record obtained by Git based on preset parameter data and written by a developer. In addition, the parameter assignment process comprises the following steps: and traversing the parameter information, determining parameter values matched with the parameter names of the parameters in the list generation method from the parameter information, and assigning the parameter values to the parameters one to complete parameter assignment. And finally, executing the list generating method after assignment to extract a target code list corresponding to the parameter information from the code list record. After the parameter assignment of the list generation method is completed, the list generation method after the assignment can be called to process the obtained code list record so as to extract the target code list corresponding to the parameter information from the code list record, namely, the required target code list corresponding to the parameter information is automatically and quickly filtered from the code list record based on the list generation method after the assignment. In addition, the list generating method also has a data deduplication function, namely the obtained target code list is subjected to data deduplication processing, so that the accuracy of the generated target code list can be improved.

In this embodiment, when a code list acquisition request input by a user is received, after the user is identified to pass the authentication, an API interface provided by JGit is called to acquire a code list record corresponding to user information from the Git system. After the parameter information preset specification carried in the code list acquisition request is detected, parameter assignment is carried out on a preset list generation method based on the parameter information, the list generation method after the assignment is executed, the target code list corresponding to the parameter information is extracted from the code list record, and the target code list required by a user is acquired in an automatic mode, so that manual repeated work can be omitted, the condition of code omission does not need to be worried about, and the intelligence, the integrity, the rapidity and the accuracy of code list acquisition are effectively improved.

Further, in an embodiment of the present application, the step S2 includes:

s200: acquiring a face image of the user based on a preset camera;

s201: determining a face comparison result corresponding to the face image based on the authorized face image; and the number of the first and second groups,

s202: respectively acquiring expression recognition results corresponding to the face images based on a preset number of pre-trained facial expression recognition models; the preset number is more than 1, and each facial expression recognition model is generated by training based on different training sample data sets;

s203: acquiring target user information corresponding to the face image, and determining an information comparison result between the user information and the target user information;

s204: and generating an identity verification result corresponding to the user based on the face comparison result, the expression recognition result and the information comparison result.

As described in the foregoing steps S200 to S204, the step of performing authentication processing on the user based on the preset authorized facial image and the facial expression recognition model to generate an authentication result corresponding to the user may specifically include: firstly, a face image of the user is obtained based on a preset camera. The face image may be a face image with the highest definition among a plurality of face images currently captured by a camera associated with the apparatus. And then determining a face comparison result corresponding to the face image based on the authorized face image. The similarity between the face image of the user and the authorized face image can be determined by adopting a face recognition technology, and for example, a local feature analysis method, a feature face method, a perceptual hash algorithm and the like can be adopted. And extracting the target similarity with the maximum value from all the similarities, judging whether the target similarity is greater than a preset similarity threshold, if so, comparing the human face, and otherwise, not comparing the human face. Respectively acquiring expression recognition results corresponding to the face images based on a preset number of pre-trained facial expression recognition models; the preset number is greater than 1, and the specific number can be set according to actual requirements, such as 3 or 4. And each facial expression recognition model is generated by training based on different training sample data sets. In addition, the number of the acquired training sample data sets is the same as the number of the expression recognition models, that is, the training sample sets and the expression recognition models are in one-to-one correspondence relationship. The labeling information of the sample face images included in each of the specified number of training sample data sets may be specifically labeled based on different labeling modes. Specifically, the different labeling modes may be labeled for different people based on subjective judgment. Because the labeling information of the face image in each training sample data set is generated by labeling different people based on subjective judgment, different people label the face expression in similar sample face images, and different expression labeling information can be possibly obtained, so that the expression labeling information in the sample face images can be more comprehensive. In addition, for the facial expression presented by the same one of the facial images, the expression recognition results recognized by the different expression recognition models to indicate that the facial expression presented by the one of the facial images may not be completely the same. When the expression recognition model is used for carrying out expression recognition on the face image, the accuracy rate of the recognized expression information is low. The facial expressions represented by the facial image are respectively subjected to expression recognition by utilizing the specified number of expression recognition models, so that the accuracy of the expression types of the users in the obtained facial image can be improved. And then acquiring target user information corresponding to the face image, and determining an information comparison result between the user information and the target user information. And judging whether the user information and the target user information are the same information, if so, comparing the information, and otherwise, not comparing the information. And finally, generating an identity verification result corresponding to the user based on the face comparison result, the expression recognition result and the information comparison result. According to the method and the device, the user is authenticated in multiple dimensions corresponding to face comparison, expression recognition and information comparison, the accuracy of authentication is improved, an illegal user can be prevented from stealing important resources, namely code list data of a target user, by counterfeiting the target user, the data safety in the processing process of a code list acquisition request is ensured, and adverse consequences caused by responding to the code list acquisition request submitted by the illegal user are effectively avoided.

Further, in an embodiment of the application, the step S204 includes:

s2040: judging whether the face comparison result is passed or not;

s2041: if the face comparison result is that the face comparison is passed, performing statistical analysis on all the expression recognition results, and screening out the designated expression recognition result with the largest occurrence frequency in all the expression recognition results;

s2042: judging whether the specified expression recognition result is a target expression or not;

s2043: if the designated expression identification result is not the target expression, judging whether the information comparison result is that the comparison is passed;

s2044: if the information comparison result is that the comparison is passed, generating a first identity verification result that the identity verification is passed;

s2045: and if the information comparison result does not pass the comparison, generating a second identity verification result of which the identity verification fails.

As described in the foregoing steps S2040 to S2045, the step of generating an authentication result corresponding to the user based on the face comparison result, the expression recognition result, and the information comparison result may specifically include: firstly, judging whether the face comparison result is passed through comparison. And if the face comparison result is that the face comparison is passed, performing statistical analysis on all the expression recognition results, and screening out the designated expression recognition result with the largest occurrence frequency in all the expression recognition results. The facial image is subjected to expression recognition by using a preset number of facial expression recognition models, expression recognition results returned by the facial expression recognition models are collected, and the designated expression recognition result corresponding to the expression recognition result with the largest occurrence frequency is used as the expression type of the user, so that the phenomenon that the recognition error is too large due to the fact that only one expression recognition model is used for carrying out expression recognition on the facial image can be avoided, the accuracy of the expression recognition on the facial image is effectively improved, and the accuracy of identity verification of the user by using the facial expression recognition models is further improved. And then judging whether the specified expression recognition result is the target expression. The target expressions refer to expressions which are easily revealed when a user needs to acquire a corresponding target code list through a code list acquisition request, for example, the target expressions include expressions such as fear and surprise. And if the specified expression identification result is not the target expression, judging whether the information comparison result is that the comparison is passed. And if the information comparison result is that the comparison is passed, generating a first identity verification result that the identity verification is passed. And if the information comparison result does not pass the comparison, generating a second identity verification result of which the identity verification fails. And when the current user has any authentication mode which is not authenticated, the authentication is regarded as that the user has failed in authentication. According to the embodiment, the user is authenticated in multiple dimensions corresponding to face comparison, expression recognition and information comparison, so that the accuracy of authentication is improved, an illegal user can be prevented from stealing important resources, namely code list data of a target user, by counterfeiting the target user, the data security in the processing process of a code list acquisition request is ensured, and adverse consequences caused by responding to the code list acquisition request submitted by the illegal user are effectively avoided.

Further, in an embodiment of the present application, before the step S202, the method includes:

s2020: acquiring a preset number of training sample data sets; the training sample of each training sample data set comprises a plurality of sample face images and expression labeling information for labeling the sample face images, and each training sample data set comprises different sample face images;

s2021: acquiring a specified training sample data set, taking a sample face image in the specified training sample data set as the input of a preset initial recognition model, and taking expression labeling information corresponding to the sample face image as the output of the initial model to train the initial recognition model and generate a trained initial recognition model; wherein, the specified training sample data set is any one sample data set in all the training sample data sets;

s2022: acquiring a preset test sample data set;

s2023: verifying the trained initial recognition model based on the test sample data set, and judging whether the verification is passed;

s2024: and if the verification is passed, taking the trained initial recognition model as a specified facial expression recognition model corresponding to the specified training sample data set.

As described in the foregoing steps S2020 to S2024, before the step of obtaining the expression recognition results corresponding to the face images respectively based on the pre-trained facial expression recognition models with a preset number is executed, a process of creating the facial expression recognition models may also be included. Specifically, a preset number of training sample data sets are first obtained. The training sample of each training sample data set comprises a plurality of sample face images and expression labeling information for labeling the sample face images, and each training sample data set comprises different sample face images. The labeling information of the sample face images included in each of the specified number of training sample data sets may be specifically labeled based on different labeling modes. Specifically, the different labeling modes may be labeled based on subjective judgment for different people. Because the labeling information of the face image in each training sample data set is generated by labeling different people based on subjective judgment, different people label the face expression in similar sample face images, and different expression labeling information can be possibly obtained, so that the expression labeling information in the sample face images can be more comprehensive. And then acquiring a specified training sample data set, taking a sample face image in the specified training sample data set as the input of a preset initial recognition model, taking expression labeling information corresponding to the sample face image as the output of the initial model, training the initial recognition model, and generating a trained initial recognition model. Wherein the specified training sample data set is any one of all the training sample data sets. In addition, various existing convolutional neural network structures can be used as the initial recognition model for training, but the training is not limited to the convolutional neural network, and a specific model structure can be set according to actual requirements. Specifically, the training process of the initial recognition model may include: and inputting the face images of the appointed samples in the appointed training sample data set to a feature extraction layer of the initial recognition model to be trained so as to obtain image features. The feature extraction layer may include a convolutional layer, a pooling layer, and the like. The image features may include features indicating the location of facial eyebrows, eyes, mouth shape, ears, etc. that are presented. And then inputting the obtained image characteristics to a full-connection layer of the initial recognition model, thereby outputting the probability value that the sample facial image belongs to the presented facial expression as the labeled facial expression. And finally, determining whether the preset loss function is converged or not based on the obtained probability value corresponding to the sample face image. And if the preset loss function is converged, determining that the training of the initial facial expression recognition model is finished. The predetermined loss function may be a softmax loss function. The resulting probability value can be substituted into the softmax loss function to determine whether the softmax loss function converges. The convergence is that the predetermined loss function reaches a predetermined loss value. And if the preset loss function is not converged, adjusting the parameters of the initial recognition model to be trained, and continuing to execute the training steps by using a back propagation algorithm until the loss function is converged, thereby completing the training process. And then acquiring a preset test sample data set. The test sample data set may be pre-collected data different from the training sample data set. And finally, verifying the trained initial recognition model based on the test sample data set, and judging whether the verification is passed. And if the verification is passed, taking the trained initial recognition model as a specified facial expression recognition model corresponding to the specified training sample data set. In the embodiment, the preset number of facial expression recognition models are generated by training based on the preset number of training sample data sets, so that the expression types contained in the facial images of the user can be accurately recognized subsequently by using the preset number of facial expression recognition models, and then the identity of the user is accurately verified according to the expression recognition result corresponding to the user, which is beneficial to improving the accuracy of the identity verification of the user.

Further, in an embodiment of the present application, the step S6 includes:

s600: acquiring the number of parameters in the parameter information;

s601: judging whether the parameter quantity is equal to a preset quantity value or not;

s602: if the parameter quantity is equal to the preset quantity value, judging whether the data format of the parameter information conforms to a preset parameter format;

s603: if the data format of the parameter information accords with the parameter format, judging that the parameter information accords with a preset specification;

s604: and if the data format of the parameter information does not conform to the parameter format, judging that the parameter information does not conform to a preset specification.

As described in the foregoing steps S600 to S604, the step of determining whether the parameter information meets the preset specification may specifically include: firstly, the parameter number in the parameter information is obtained. The parameter number refers to the number of parameter data included in the parameter information. And then judging whether the parameter quantity is equal to a preset quantity value or not. The value of the preset quantity value is not particularly limited, and can be set according to actual requirements. For example, if the predefined standard parameter data includes: all the file positions gitPath of the code directory, the branch name of the code list to be obtained, the name authorame of the submitter of the code list to be obtained, and the submittal start time commit time are required, and the corresponding preset quantity value is 4. And if the parameter quantity is equal to the preset quantity value, judging whether the data format of the parameter information conforms to a preset parameter format. The above parameter format is not particularly limited, and may be set according to actual requirements. For example, the parameter format is in the form of parameter name + parameter value. And if the data format of the parameter information conforms to the parameter format, judging that the parameter information conforms to a preset specification. And if the data format of the parameter information does not accord with the parameter format, judging that the parameter information does not accord with a preset specification. In this embodiment, after the parameter information is extracted from the code list acquisition request, it is further determined whether the parameter information meets a preset specification, and a subsequent procedure of calling a preset list generation method to extract a target code list corresponding to the parameter information from the code list record is executed only after the parameter information meets the preset specification, otherwise, the response processing on the code list acquisition request is directly stopped, thereby effectively avoiding the situation of idle work consumption, and improving the intelligence in the data processing process.

Further, in an embodiment of the application, after the step S3, the method includes:

s300: if the identity authentication result is not that the identity authentication is passed, limiting the response to the code list acquisition request;

s301: generating corresponding alarm information based on the face image and the user information;

s302: acquiring preset mail login information and acquiring a designated mail address corresponding to a designated user;

s303: logging in a corresponding mail server according to the mail login information;

s304: and sending the alarm information to the specified mail address through the mail server.

As described in the above steps S300 to S304, when the authentication result is not the authentication pass, after the step of determining whether the authentication result is the authentication pass is performed, a process of generating and transmitting alarm information corresponding to the face image and the user information may be further included. Specifically, if the authentication result does not pass the authentication, the response to the code list acquisition request is limited first. And then generating corresponding alarm information based on the face image and the user information. Wherein the alarm information at least comprises the face image and the user information. And then acquiring preset mail login information and acquiring a specified mail address corresponding to a specified user. The designated user can be related personnel of the data security department. And subsequently logging in a corresponding mail server according to the mail login information. And finally, sending the alarm information to the specified mail address through the mail server. After the authentication result corresponding to the user is generated and the authentication result is judged not to pass, namely the authentication of the user fails, the method and the system can intelligently generate the alarm information corresponding to the face image of the user and the user information and send the alarm information to the appointed mail address corresponding to the appointed user, so that the appointed user can timely know the information that the current user has the risk of illegally acquiring the code list based on the alarm information, and can timely make corresponding processing, thereby effectively ensuring the data security in the request processing process.

Further, in an embodiment of the present application, after the step S6, the method includes:

s610: if the parameter information does not accord with the preset standard, pre-stored parameter filling standard data corresponding to the preset standard are obtained;

s611: generating reminding information corresponding to the parameter filling specification data;

s612: and displaying the reminding information.

As described in the foregoing steps S610 to S612, when the parameter information does not meet the preset specification, after the step of determining whether the parameter information meets the preset specification is completed, a process of generating and displaying a reminder corresponding to the preset specification may be further included. Specifically, if the parameter information does not meet the preset specification, pre-stored parameter filling specification data corresponding to the preset specification is obtained first. The parameter filling specification data is pre-programmed data at least comprising parameter data quantity values of standard parameters and parameter format description data of the standard parameters. In addition, the data can be stored locally or in the cloud according to actual requirements. For example, if the local storage space is larger than the preset capacity value, the parameter filling specification data is stored locally, otherwise, the parameter filling specification data is stored in the cloud, so as to realize intelligent storage of the parameter filling specification data. And then generating reminding information corresponding to the parameter filling specification data. The reminding information at least carries the parameter filling specification data. And finally, displaying the reminding information. The display mode of the reminding information is not limited, and can be set according to actual requirements, for example, the reminding mode can be a desktop popup reminding mode, a webpage link reminding mode, and the like. According to the embodiment, when the user passes the identity authentication but the input parameter information does not accord with the preset specification, the parameter filling specification data can be intelligently called out to generate the corresponding reminding information and display the reminding information, so that the user can input the parameter information in the correct format based on the reminding information, convenience is provided for the user, and the use experience of the user is improved.

The method for generating a code list in the embodiment of the present application may also be applied to the field of block chains, for example, data such as the target code list is stored on a block chain. By storing and managing the object code list using a block chain, the security and the non-tamper property of the object code list can be effectively ensured.

The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism and an encryption algorithm. A block chain (Blockchain), which is essentially a decentralized database, is a string of data blocks associated by using a cryptographic method, 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 a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.

The block chain underlying platform can comprise processing modules such as user management, basic service, intelligent contract and operation monitoring. The user management module is responsible for identity information management of all blockchain participants, and comprises public and private key generation maintenance (account management), key management, user real identity and blockchain address corresponding relation maintenance (authority management) and the like, and under the authorization condition, the user management module supervises and audits the transaction condition of certain real identities and provides rule configuration (wind control audit) of risk control; the basic service module is deployed on all block chain node equipment and used for verifying the validity of the service request, recording the service request to storage after consensus on the valid request is completed, for a new service request, the basic service firstly performs interface adaptation analysis and authentication processing (interface adaptation), then encrypts service information (consensus management) through a consensus algorithm, transmits the service information to a shared account (network communication) completely and consistently after encryption, and performs recording and storage; the intelligent contract module is responsible for registering and issuing contracts, triggering the contracts and executing the contracts, developers can define contract logics through a certain programming language, issue the contract logics to a block chain (contract registration), call keys or other event triggering and executing according to the logics of contract clauses, complete the contract logics and simultaneously provide the function of upgrading and canceling the contracts; the operation monitoring module is mainly responsible for deployment, configuration modification, contract setting, cloud adaptation in the product release process and visual output of real-time states in product operation, such as: alarm, monitoring network conditions, monitoring node equipment health status, and the like.

Referring to fig. 2, an embodiment of the present application further provides a device for generating a code list, including:

the first judging module 1 is used for judging whether a code list acquiring request input by a user is received or not; the code list acquiring request carries user information and parameter information;

the verification module 2 is used for performing authentication processing on the user based on a preset authorized face image and a face expression recognition model if the code list acquisition request is received, and generating an authentication result corresponding to the user;

the second judging module 3 is used for judging whether the identity authentication result passes the identity authentication;

the first obtaining module 4 is configured to, if the authentication result is that the authentication is passed, extract the user information from the code list obtaining request, and call an API interface provided by JGit to obtain a code list record corresponding to the user information from a Git system;

an extracting module 5, configured to extract the parameter information from the code list obtaining request;

a third judging module 6, configured to judge whether the parameter information meets a preset specification;

the first generating module 7 is configured to, if the parameter information conforms to a preset specification, obtain a preset list generating method, and perform parameter assignment on the list generating method based on the parameter information to obtain an assigned list generating method;

and the execution module 8 is configured to execute the list generation method after the assignment, so as to extract the target code list corresponding to the parameter information from the code list record.

In this embodiment, the implementation processes of the functions and actions of the first determining module 1, the verifying module 2, the second determining module 3, the first obtaining module 4, the extracting module 5, the third determining module 6, the first generating module 7, and the executing module 8 in the device for generating a code list are specifically detailed in the implementation processes corresponding to steps S1 to S8 in the method for generating a code list, and are not described herein again.

Further, in an embodiment of the present application, the verification module 2 includes:

the first acquisition unit is used for acquiring a face image of the user based on a preset camera;

the first determining unit is used for determining a face comparison result corresponding to the face image based on the authorized face image; and the number of the first and second groups,

the second acquisition unit is used for respectively acquiring expression recognition results corresponding to the face images based on a preset number of pre-trained face expression recognition models; the preset number is more than 1, and each facial expression recognition model is generated by training based on different training sample data sets;

a third obtaining unit, configured to obtain target user information corresponding to the face image, and determine an information comparison result between the user information and the target user information;

and the generating unit is used for generating an identity verification result corresponding to the user based on the face comparison result, the expression identification result and the information comparison result.

In this embodiment, the implementation processes of the functions and functions of the first obtaining unit, the first determining unit, the second obtaining unit, the third obtaining unit and the generating unit in the generating device of the code list are specifically detailed in the implementation processes corresponding to steps S200 to S204 in the generating method of the code list, and are not described again here.

Further, in an embodiment of the present application, the generating unit includes:

the first judging subunit is used for judging whether the face comparison result is that the comparison is passed;

the analysis subunit is configured to perform statistical analysis on all the expression recognition results if the face comparison result is that the face comparison is passed, and screen out a specified expression recognition result that appears most frequently in all the expression recognition results;

the second judgment subunit is used for judging whether the specified expression recognition result is the target expression;

a third judging subunit, configured to, if the specified expression identification result is not the target expression, judge whether the information comparison result passes the comparison;

the first generation subunit is used for generating a first authentication result which passes the authentication if the information comparison result is that the comparison is passed;

and the second generation subunit is used for generating a second identity verification result of which the identity verification fails if the information comparison result is not that the comparison is passed.

In this embodiment, the implementation processes of the functions and actions of the first determining subunit, the analyzing subunit, the second determining subunit, the third determining subunit, the first generating subunit and the second generating subunit in the device for generating a code list are specifically detailed in the implementation processes corresponding to steps S2040 to S2045 in the method for generating a code list, and are not described herein again.

the fourth acquisition unit is used for acquiring training sample data sets with preset quantity; the training sample of each training sample data set comprises a plurality of sample face images and expression labeling information for labeling the sample face images, and each training sample data set comprises different sample face images;

the training unit is used for acquiring a specified training sample data set, taking a sample face image in the specified training sample data set as the input of a preset initial recognition model, taking expression labeling information corresponding to the sample face image as the output of the initial model, training the initial recognition model and generating a trained initial recognition model; wherein, the specified training sample data set is any one sample data set in all the training sample data sets;

a fifth obtaining unit, configured to obtain a preset test sample data set;

the verification unit is used for verifying the trained initial identification model based on the test sample data set and judging whether the verification is passed;

and if the verification is passed, the trained initial recognition model is used as a specified facial expression recognition model corresponding to the specified training sample data set.

In this embodiment, the implementation processes of the functions and functions of the fourth obtaining unit, the training unit, the fifth obtaining unit, the verification unit and the second determining unit in the device for generating a code list are specifically detailed in the implementation processes corresponding to steps S2020 to S2024 in the method for generating a code list, and are not described herein again.

Further, in an embodiment of the present application, the third determining module 6 includes:

a sixth obtaining unit, configured to obtain the number of parameters in the parameter information;

the first judging unit is used for judging whether the parameter quantity is equal to a preset quantity value or not;

a second determining unit, configured to determine whether a data format of the parameter information conforms to a preset parameter format if the parameter number is equal to the preset number value;

the first judging unit is used for judging that the parameter information conforms to a preset specification if the data format of the parameter information conforms to the parameter format;

and the second judging unit is used for judging that the parameter information does not accord with the preset standard if the data format of the parameter information does not accord with the parameter format.

In this embodiment, the implementation processes of the functions and functions of the sixth obtaining unit, the first determining unit, the second determining unit, the first determining unit and the second determining unit in the device for generating a code list are specifically detailed in the implementation processes corresponding to steps S600 to S604 in the method for generating a code list, and are not described again here.

Further, in an embodiment of the present application, the apparatus for generating a code list includes:

the processing module is used for limiting the response to the code list acquisition request if the identity authentication result does not pass the identity authentication;

the second generation module is used for generating corresponding alarm information based on the face image and the user information;

the second acquisition module is used for acquiring preset mail login information and acquiring a designated mail address corresponding to a designated user;

the login module is used for logging in a corresponding mail server according to the mail login information;

and the sending module is used for sending the alarm information to the specified mail address through the mail server.

In this embodiment, the implementation processes of the functions and functions of the processing module, the second generating module, the second obtaining module, the login module, and the sending module in the device for generating a code list are specifically detailed in the implementation processes corresponding to steps S300 to S304 in the method for generating a code list, and are not described herein again.

the third acquisition module is used for acquiring prestored parameter filling standard data corresponding to the preset standard if the parameter information does not accord with the preset standard;

the third generation module is used for generating reminding information corresponding to the parameter filling specification data;

and the display module is used for displaying the reminding information.

In this embodiment, the implementation processes of the functions and functions of the third obtaining module, the third generating module and the displaying module in the device for generating the code list are specifically detailed in the implementation processes corresponding to steps S610 to S612 in the method for generating the code list, and are not described herein again.

Referring to fig. 3, an embodiment of the present application further provides a computer device, where the computer device may be a server, and an internal structure of the computer device may be as shown in fig. 3. The computer device comprises a processor, a memory, a network interface, a display screen, an input device and a database which are connected through a system bus. Wherein the processor of the computer device is designed to provide computing and control capabilities. The memory of the computer device comprises a storage medium and an internal memory. The storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operating system and the computer programs in the storage medium to run. The database of the computer device is used for storing user information, parameter information, authorized face images, facial expression recognition models, identity verification results, code list records, list generation methods and object code lists. The network interface of the computer device is used for communicating with an external terminal through a network connection. The display screen of the computer equipment is an indispensable graphic output equipment in the computer and is used for converting digital signals into optical signals so that characters and graphics are displayed on the screen of the display screen. The input device of the computer equipment is the main device for information exchange between the computer and the user or other equipment, and is used for transmitting data, instructions, some mark information and the like to the computer. The computer program is executed by a processor to implement a method of generating a code listing.

The processor executes the steps of the code list generation method:

judging whether a code list acquisition request input by a user is received; the code list acquisition request carries user information and parameter information;

extracting the parameter information from the code list acquisition request;

judging whether the parameter information meets a preset standard or not;

Those skilled in the art will appreciate that the structure shown in fig. 3 is only a block diagram of a part of the structure related to the present application, and does not constitute a limitation to the apparatus and the computer device to which the present application is applied.

An embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, where when the computer program is executed by a processor, the method for generating a code list is implemented, and specifically:

if the identity authentication result is that the identity authentication is passed, extracting the user information from the code list acquisition request, and calling an API (application program interface) provided by Jgit to acquire a code list record corresponding to the user information from the Git system;

extracting the parameter information from the code list acquisition request;

judging whether the parameter information meets a preset standard or not;

In summary, according to the code list generation method, apparatus, computer device, and storage medium provided in this embodiment of the present application, when a code list acquisition request input by a user is received, after the user is identified that the user passes authentication, an API interface provided by JGit is called to acquire a code list record corresponding to user information from a Git system. After the parameter information preset specification carried in the code list acquisition request is detected, parameter assignment is carried out on a preset list generation method based on the parameter information, the list generation method after the assignment is executed, the target code list corresponding to the parameter information is extracted from the code list record, and the target code list required by a user is acquired in an automatic mode, so that manual repeated work can be omitted, the condition of code omission does not need to be worried about, and the intelligence, the integrity, the rapidity and the accuracy of code list acquisition are effectively improved.

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 can be implemented by a computer program, which can be stored in a computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the computer program is executed. Any reference to memory, storage, database, or other medium provided herein and used in the examples may include non-volatile and/or volatile memory. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), double-rate SDRAM (SSRSDRAM), Enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, apparatus, article, or method that includes the element.

The above description is only a preferred embodiment of the present application, and not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings of the present application, or which are directly or indirectly applied to other related technical fields, are also included in the scope of the present application.

Claims

1. A method for generating a code list, comprising:

judging whether a code list acquisition request input by a user is received; the code list acquiring request carries user information and parameter information;

extracting the parameter information from the code list acquisition request;

judging whether the parameter information meets a preset standard or not;

executing the list generating method after assignment to extract a target code list corresponding to the parameter information from the code list record;

the method comprises the following steps of carrying out authentication processing on the user based on a preset authorized face image and a face expression recognition model, and generating an authentication result corresponding to the user, wherein the steps comprise:

acquiring a face image of the user based on a preset camera;

generating an identity verification result corresponding to the user based on the face comparison result, the expression recognition result and the information comparison result;

after the step of judging whether the identity authentication result is passed, the method comprises the following steps:

logging in a corresponding mail server according to the mail login information;

2. The method for generating a code list according to claim 1, wherein the step of generating an authentication result corresponding to the user based on the face comparison result, the expression recognition result, and the information comparison result includes:

judging whether the face comparison result is passed or not;

if the face comparison result is that the face comparison is passed, performing statistical analysis on all the expression recognition results, and screening out the designated expression recognition result with the largest occurrence frequency in all the expression recognition results;

3. The method for generating a code list according to claim 1, wherein before the step of obtaining the expression recognition results corresponding to the facial images based on a preset number of pre-trained facial expression recognition models, the method comprises:

acquiring a specified training sample data set, taking a sample face image in the specified training sample data set as the input of a preset initial recognition model, and taking expression labeling information corresponding to the sample face image as the output of the initial model to train the initial recognition model and generate a trained initial recognition model; wherein the specified training sample data set is any one of all the training sample data sets;

acquiring a preset test sample data set;

4. The method for generating a code list according to claim 1, wherein the step of determining whether the parameter information meets a preset specification comprises:

acquiring the number of parameters in the parameter information;

and if the data format of the parameter information does not conform to the parameter format, judging that the parameter information does not conform to a preset specification.

5. The method for generating a code list according to claim 1, wherein the step of determining whether the parameter information meets a preset specification comprises:

if the parameter information does not accord with the preset standard, acquiring prestored parameter filling standard data corresponding to the preset standard;

and displaying the reminding information.

6. An apparatus for generating a code list, comprising:

the first judgment module is used for judging whether a code list acquisition request input by a user is received or not; the code list acquiring request carries user information and parameter information;

the execution module is used for executing the list generation method after the assignment so as to extract a target code list corresponding to the parameter information from the code list record;

the verification module comprises:

the second acquisition unit is used for respectively acquiring expression recognition results corresponding to the face images based on a preset number of pre-trained face expression recognition models; the preset number is larger than 1, and each facial expression recognition model is generated by training based on different training sample data sets;

the generating unit is used for generating an identity verification result corresponding to the user based on the face comparison result, the expression identification result and the information comparison result;

the processing module is used for limiting the response to the code list acquisition request if the identity authentication result is not that the identity authentication is passed;

7. A computer arrangement comprising a memory and a processor, the memory having a computer program stored therein, characterized in that the processor, when executing the computer program, is adapted to carry out the steps of the method according to any of claims 1 to 5.

8. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 5.