CN110569418A

CN110569418A - Method and device for verifying academic calendar information

Info

Publication number: CN110569418A
Application number: CN201910672088.4A
Authority: CN
Inventors: 李冰心
Original assignee: Alibaba Group Holding Ltd
Current assignee: Advanced New Technologies Co Ltd; Advantageous New Technologies Co Ltd
Priority date: 2019-07-24
Filing date: 2019-07-24
Publication date: 2019-12-13

Abstract

the application provides a scholarly calendar information verification method and a device, wherein the scholarly calendar information verification method comprises the following steps: acquiring the academic calendar information uploaded by a user; according to the time information contained in the academic calendar information, determining attribute data of the user in a time period corresponding to the time information; analyzing the attribute data in a position dimension, a behavior dimension and a characteristic dimension, and determining data to be verified according to an analysis result; and inputting the data to be verified to a pre-trained verification model, verifying the academic information, and outputting a verification result of the academic information. According to the academic calendar information verification method, according to the academic calendar information uploaded by the user, the data to be checked of the user is determined in different dimensions, and the academic calendar information uploaded by the user is verified by combining the verification model, so that the academic calendar information uploaded by the user is verified in a short time, and the accuracy of the verification result of the academic calendar information is ensured.

Description

method and device for verifying academic calendar information

Technical Field

The application relates to the technical field of internet, in particular to a method for verifying academic calendar information. The application also relates to a device for verifying the academic calendar information, a computing device and a computer readable storage medium.

background

With the development of internet technology, the importance degree of the culture level of a user is higher and higher; no matter the user moves the user to another place or performs a work interview on the user, the academic information of the user plays an important role, however, in the process of applying the academic information of the user, the possibility of false academic information provided by the user is easily encountered, unnecessary loss is caused in both economy and time, and the verification of the academic information of the user is important.

in the prior art, in the process of verifying the academic calendar information provided by a user, the academic calendar information actively uploaded by the user and the attribute information of the user are acquired, the real academic calendar information of the user is inquired in an academic calendar information inquiry website according to the attribute information of the user, and the academic calendar information uploaded by the user is verified by comparing the real academic calendar information with the academic calendar information uploaded by the user.

However, in the process of inquiring the real academic information of the user through the academic information inquiry website and verifying the academic information uploaded by the user through the real academic information, a long time is consumed to finish the verification of the academic information, and in the process of verifying a large amount of academic information, because the cardinality is large, the verification of the academic information of the user through the academic information inquiry website in a short time is difficult to realize.

disclosure of Invention

in view of this, the present application provides a method for verifying academic calendar information. The application also relates to a device for verifying the academic calendar information, a computing device and a computer readable storage medium, which are used for solving the technical defects in the prior art.

according to a first aspect of embodiments of the present application, there is provided a method for verifying academic aptitude information, including:

Acquiring the academic calendar information uploaded by a user;

According to the time information contained in the academic calendar information, determining attribute data of the user in a time period corresponding to the time information;

Analyzing the attribute data in a position dimension, a behavior dimension and a characteristic dimension, and determining data to be verified according to an analysis result;

And inputting the data to be verified to a pre-trained verification model, verifying the academic information, and outputting a verification result of the academic information.

Optionally, when the attribute data includes location data, analyzing the location data in the location dimension, and determining data to be verified in the location dimension according to an analysis result, including:

acquiring text address data of the user and address data contained in the academic information according to the position data;

Calculating the text address overlap ratio and the geographic distance between the text address data and the address data, and determining the query frequency of the user for searching the address data in the time period;

and taking the text address contact ratio, the geographic distance and the query frequency as data to be verified in the position dimension.

Optionally, when the attribute data includes network data, analyzing the network data in the location dimension, and determining data to be verified in the location dimension according to an analysis result, including:

determining a first internet protocol address of the user in the time period according to the network data;

obtaining first longitude and latitude data of the user in the time period by analyzing the first internet protocol address;

Mapping a first area range according to the first longitude and latitude data and the map data;

School data contained in the school calendar information are acquired, and a second internet protocol address of a school is determined according to the school data;

acquiring second longitude and latitude data of the school by analyzing the second internet protocol address;

mapping a second area range of the school according to the second longitude and latitude data and the map data;

and calculating the similarity of the first area range and the second area range, and taking the similarity as the data to be verified in the position dimension.

acquiring the network data of a user terminal connected network uploading the academic calendar information, and determining a service set identifier of the network data;

acquiring school data contained in the school calendar information, and determining a basic service set identifier of a school according to the school data;

Judging whether the basic service set identification contains the service set identification;

if so, taking the basic service set identification as the data to be verified in the position dimension, wherein the basic service set identification comprises the service set identification;

And if not, taking the basic service set identification which does not contain the service set identification as the data to be verified in the position dimension.

optionally, when the attribute data includes social data, determining data to be verified in the behavior dimension according to an analysis result by analyzing the social data in the behavior dimension, including:

determining social network data of the user in the time period according to the social data;

Detecting whether the social network data contains users whose academic information verification is completed;

if yes, determining relationship data between the user and the user after the academic information verification is completed according to the social network data, and taking the relationship data as data to be verified in the behavior dimension.

optionally, when the attribute data includes behavior data, analyzing the behavior data in the behavior dimension, and determining data to be verified in the behavior dimension according to an analysis result, including:

determining credit data of the user in the time period according to the behavior data;

determining a credit level of the user based on the credit data, the credit level being data to be verified in the behavioral dimension.

optionally, when the attribute data includes user feature data, analyzing the user feature data in the feature dimension, and determining data to be verified in the feature dimension according to an analysis result, including:

determining user attribute data and user preference data of the user according to the user characteristic data;

detecting whether the user attribute data and the user preference data contain student attribute data or not;

And if so, determining the student attribute data contained in the user attribute data and the user preference data, and extracting the student attribute data from the user attribute data and the user preference data as the data to be verified in the feature dimension.

optionally, the verification model is trained as follows:

Acquiring the academic information of a user, and determining attribute data corresponding to the academic information and a verification result of the academic information;

Analyzing the attribute data through the position dimension, the behavior dimension and the characteristic dimension, and determining data to be verified according to an analysis result;

taking the data to be verified and the verification result of the academic calendar information as training samples;

and inputting the training sample into the verification model for training, and determining the incidence relation between the data to be verified and the verification result of the academic record information.

optionally, the attribute data includes at least one of:

location data, network data, social data, behavioral data, and user characteristic data;

correspondingly, the analyzing the attribute data in the position dimension, the behavior dimension and the feature dimension, and determining the data to be verified according to the analysis result includes:

analyzing the position data and the network data in the position dimension, analyzing the social data and the behavior data in the behavior dimension, analyzing the user feature data in the feature dimension, determining first data to be verified in the position dimension, second data to be verified in the behavior dimension and third data to be verified in the feature dimension according to an analysis result;

and integrating the first to-be-verified data, the second to-be-verified data and the third to-be-verified data, and determining the to-be-verified data according to an integration result.

optionally, the academic history information includes at least one of the following:

reading time information, reading school name information, reading school address information, reading school handling attribute information and reading school history change information.

optionally, the analyzing the attribute data in the position dimension, the behavior dimension and the feature dimension, and determining data to be verified according to an analysis result includes:

analyzing the attribute data in the position dimension, the behavior dimension and the characteristic dimension to obtain an analysis result in each dimension;

judging whether the analysis results of all dimensions meet the preset threshold of the corresponding dimension;

if not, determining the data to be verified according to the analysis result, executing the verification model which inputs the data to be verified to be trained in advance, verifying the academic calendar information, and outputting the verification result of the academic calendar information;

if yes, determining a prediction verification result of the academic calendar information according to the analysis result, determining the data to be verified according to the analysis result, inputting the data to be verified to a pre-trained verification model, verifying the academic calendar information, and outputting a verification result of the academic calendar information.

optionally, after the step of inputting the data to be verified to a pre-trained verification model, verifying the academic calendar information, and outputting a verification result of the academic calendar information is executed, the method further includes:

judging whether the predicted verification result is consistent with the verification result or not;

if not, taking the data to be verified and the verification result of the academic calendar information as negative training samples, and training the verification model;

And if so, taking the data to be verified and the verification result of the academic calendar information as a positive training sample, and training the verification model.

According to a second aspect of embodiments of the present application, there is provided a academic aptitude information verification apparatus including:

the acquisition academic calendar information module is configured to acquire the academic calendar information uploaded by the user;

the attribute data determining module is configured to determine attribute data of the user in a time period corresponding to the time information according to the time information contained in the academic calendar information;

the data to be verified determining module is configured to analyze the attribute data in a position dimension, a behavior dimension and a characteristic dimension, and determine data to be verified according to an analysis result;

and the verification academic calendar information module is configured to input the data to be verified to a pre-trained verification model, verify the academic calendar information and output a verification result of the academic calendar information.

according to a third aspect of embodiments herein, there is provided a computing device comprising:

A memory and a processor;

the memory is to store computer-executable instructions, and the processor is to execute the computer-executable instructions to:

acquiring the academic calendar information uploaded by a user;

according to a fourth aspect of embodiments of the present application, there is provided a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement any one of the steps of the academic aptitude information verification method.

the academic calendar information verification method comprises the steps of obtaining academic calendar information uploaded by a user, determining attribute data of the user in a time period corresponding to the time information according to the time information contained in the academic calendar information, analyzing the attribute data through position dimension, behavior dimension and characteristic dimension, determining data to be verified according to an analysis result, inputting the data to be verified to a pre-trained verification model, verifying the academic calendar information, and outputting a verification result of the academic calendar information.

In the academic calendar information verification method that this application provided, through confirming user's attribute data to it is right in the different dimensions attribute data carries out the analysis, has realized that can confirm in the different dimensions user's the data of waiting to verify, combine simultaneously the verification model will wait to verify the data and regard as verify the input of model, verify the output of model and do the authenticity of academic calendar information that can confirm user's upload in short time has been realized to through analyzing user's attribute data in the different dimensions, the effectual improvement the accuracy of academic calendar information verification result.

Drawings

fig. 1 is a flowchart of a method for verifying academic aptitude information according to an embodiment of the present application;

fig. 2 is a flowchart illustrating a process of verifying academic aptitude information according to an embodiment of the present application;

Fig. 3 is a schematic structural diagram of an academic calendar information verification apparatus according to an embodiment of the present application;

fig. 4 is a block diagram of a computing device according to an embodiment of the present application.

Detailed Description

in the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. This application is capable of implementation in many different ways than those herein set forth and of similar import by those skilled in the art without departing from the spirit of this application and is therefore not limited to the specific implementations disclosed below.

The terminology used in the one or more embodiments of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the one or more embodiments of the present application. As used in one or more embodiments of the present application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used in one or more embodiments of the present application refers to and encompasses any and all possible combinations of one or more of the associated listed items.

it will be understood that, although the terms first, second, etc. may be used herein in one or more embodiments of the present application to describe various information, these information should not be limited by these terms. These terms are only used to distinguish one type of information from another. For example, a first aspect may be termed a second aspect, and, similarly, a second aspect may be termed a first aspect, without departing from the scope of one or more embodiments of the present application. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.

First, the noun terms to which one or more embodiments of the present invention relate are explained.

LBS: location Based Service is a value-added Service that obtains Location Information (Geographic coordinates or geodetic coordinates) of a Mobile terminal user through a radio communication network (such as a GSM (Global System for Mobile communications) network) or an external Positioning mode (such as a GPS (Global Positioning System)) of a telecommunication Mobile operator, and provides corresponding services for the user with support of a GIS (Geographic Information System) platform.

POI: (Point of Interest, Point of information), in the geographic information system, one POI may be a house, a shop, a mailbox, a bus station, etc., and each POI contains four pieces of facet information, name, category, coordinates, classification.

IP: internet Protocol (ip) addresses are digital labels assigned to Internet Protocol devices used by users to access Internet.

SSID: the SSID technology can divide a wireless local area network into a plurality of sub-networks which need different identity authentication, each sub-network needs independent identity authentication, and only users who pass the identity authentication can enter the corresponding sub-network, thereby preventing unauthorized users from entering the network. And in practical applications, only terminals set to the value of the SSID with the same name can communicate with each other.

BSSID: (Basic Service Set Identifier) it is a binary Identifier with a length of 48 bits to identify different BSSs (Basic Service Set), and its main advantage is that it can be used for filtering.

in the present application, a scholars information verification method is provided, and the present application relates to a scholars information verification apparatus, a computing device, and a computer-readable storage medium, which are described in detail one by one in the following embodiments.

fig. 1 shows a flowchart of a method for verifying academic aptitude information, which includes steps 102 to 108.

step 102: and acquiring the academic calendar information uploaded by the user.

in one embodiment of the present application, the user is a user who needs to perform the verification of the study information, the study information is the educational history of the user, and in one or more embodiments of this embodiment, the study information may include information such as reading time information, reading school name information, reading school address information, reading school transaction attribute information, and reading school history change information of the user, where the reading time information is the educational time of the user, and the reading school history change information is specific change information that the school that the user reads appears during the reading period of the user or after graduation, for example, the change information such as school name change, school address change, school splitting or merging is the history change information of the reading school.

here, the method for verifying the academic form information is described by taking the user as an applicant as an example; based on the above, in the process of applying for the user, in order to prevent the user from providing false academic information, the academic information provided by the user needs to be verified, in the process, the authenticity of the academic information provided by the user is verified, the credit of the user can be confirmed, and whether the user is qualified for the post of applying for the user can be determined through the academic information, so that the application company can be ensured to apply for high-quality employees.

the application provides a academic record information verification method, under the condition that can guarantee the academic record information accuracy of user of verification, can also improve the verification efficiency of academic record information, through confirming user's attribute data, and it is right at different dimensions attribute data carry out the analysis, realized can confirming at different dimensions the user wait to verify the data, combine simultaneously the verification model will wait to verify the data and regard as the input of verification model, the output of verification model does the authenticity of the academic record information that can confirm the user and upload in short time has been realized to through analyzing the attribute data of user at different dimensions, effectual improvement the accuracy of academic record information verification result.

based on the above, in the service platform for verifying the user's academic information, schools such as high schools, specialty schools, research institutes, adult education institutes and the like are integrated, the attributive region, the study attribute and the education level information of each school are integrated to determine the attribute information of each school, and the POI information, the longitude and latitude information and the text address information of each school are acquired to determine the domain name information of each school, so that a verification basis is provided in the service platform for verifying the user's academic information, and the authenticity of the academic information provided by the verification user can be effectively improved.

in addition, in some feature scenarios, a large amount of user's academic information may need to be verified. For example, credit points of users are improved through real academic information, and under the condition, the authenticity of the academic information of a large number of users can be effectively improved through the academic information verification method provided by the application, so that the effect of verifying the academic information efficiently is achieved.

Step 104: and determining attribute data of the user in a time period corresponding to the time information according to the time information contained in the academic record information.

specifically, on the basis of obtaining the academic information uploaded by the user, further, according to the time information included in the academic information, attribute data of the user in a time period corresponding to the time information is determined, where the time information specifically refers to reading time recorded in the academic information uploaded by the user in a reading school, and the attribute data specifically refers to attribute data of the user in the reading time period, including location data of the user, network data of a network frequently used by the user, social data of social relationships of the user, behavior data of favorite movement and shopping conditions of the user, and feature data of personal features of the user.

Based on the position information, the position data of the user in the reading time is determined to specifically mean the position information of the user with the most positions in the learning period; the network data of the user in the reading time specifically refers to the network information which is used by the user most frequently during the learning period; the social data of the user in the reading time specifically refers to social network information of the user in the school period; the behavior data of the user in the reading time specifically refers to the motion information and online shopping information of the user in the learning period; the characteristic data of the user in the reading time specifically refers to age information and education information of the user.

in addition, the network data may further include data such as user authentication mailbox data and user network time data, the behavior data may further include data such as user asset condition, performance condition, transfer record, financial loan, and the like, and the specific content may be confirmed according to an actual application scenario, which is not limited herein.

For example, the calendar information uploaded by the user includes: university A graduates, education is professionally accepted at a local computer in 7/1/2014 to 2018/7/1/year, and according to the reading time 2014/7/1/year to 2018/7/1/month, the position data, the network data, the behavior data, the social data and the feature data of the user in 4 years are determined, namely the position information and the network information which are used most frequently by the user in 4 years, the user motion information and the shopping information, the user social relationship information and the user personal information are determined.

besides, the attribute data of the user in the time period can be determined, the real-time attribute data of the user can be determined, whether the user is a student at school can be further judged by performing subsequent processing on the real-time attribute data of the user, and the academic calendar information verification method provided by the application plays an important role in determining whether the user is a service of the student at school.

Step 106: analyzing the attribute data in a position dimension, a behavior dimension and a characteristic dimension, and determining data to be verified according to an analysis result.

Specifically, on the basis of determining the attribute data of the user in the time period, further, according to the data included in the attribute data, the data included in the attribute data are analyzed in the corresponding dimension respectively in the position dimension, the behavior dimension and the characteristic dimension, and the data to be verified, which needs to be input into the verification model, is determined according to the analysis result.

Based on this, when the attribute data includes location data, network data, social data, behavior data, and user feature data, the location data and the network data need to be analyzed in the location dimension, the social data and the behavior data need to be analyzed in the behavior dimension, and the user feature data need to be analyzed in the feature dimension.

the location data specifically refers to location information of a user and a school on a map, the network data specifically refers to network information used by the user and network information covering the school, the social data specifically refers to social network data of the user (namely attribute categories to which friends of the user belong), the behavior data specifically refers to a mode of frequent movement of the user and types of articles bought by the user through online shopping or shopping, and the user feature data specifically refers to feature information of the user.

in one or more embodiments of this embodiment, in a case that the attribute data includes location data, network data, social data, behavior data, and user feature data, a process of analyzing the location data, the network data, the social data, the behavior data, and the user feature data in each dimension is specifically implemented as follows:

And integrating the first data to be verified, the second data to be verified and the third data to be verified, and determining the data to be verified according to an integration result.

specifically, the position data is analyzed in the position dimension to obtain position data to be verified; analyzing the network data in the position dimension to obtain network data to be verified, and integrating the position data to be verified and the network data to be verified to obtain first data to be verified; analyzing the social data in the behavior dimension to obtain social data to be verified; analyzing the behavior data in the behavior dimension to obtain behavior to-be-verified data, and integrating the social to-be-verified data with the behavior to-be-verified data to obtain second to-be-verified data; and analyzing the user feature data in the feature dimension to obtain feature data to be verified, and taking the feature data to be verified as the third data to be verified.

on the basis, integrating the first to-be-verified data, the second to-be-verified data and the third to-be-verified data to obtain the to-be-verified data; the data to be verified comprises data to be verified corresponding to different dimensions.

the data to be verified of the position can be the actual distance between the frequently-applied position information of the user in the time period corresponding to the time information and the position information of the school in the academic calendar information; the data to be verified of the network can be whether the IP address frequently applied by the user is the same as the IP address of the school in the academic information; forming the first to-be-verified data by using data corresponding to whether the actual distance and the IP address are the same, namely, preprocessing the obtained to-be-verified data according to the academic calendar information and the attribute data before inputting the to-be-verified data into the verification model, obtaining the to-be-verified data of the corresponding dimensionality of the data contained in the attribute data according to the preprocessing, and at the moment, preliminarily determining the data in the to-be-verified data;

for example, if the IP address frequently used by the user is the same as the IP address of the school in the academic story information, the same result is obtained, the data to be verified comprises the data corresponding to the IP address frequently applied by the user and the IP address of the school in the academic information, at the moment, the data to be verified comprising the data corresponding to the IP address frequently applied by the user and the IP address of the school in the academic information are input into the verification model, and the model can output the result of whether the academic information is true or not, the method comprises the steps of obtaining the probability of the academic form information in different dimensions, obtaining the probability of the academic form information in the different dimensions, obtaining the probability of the academic form information in.

similarly, the data to be verified of the social contact can be data of friends held by the user in a time period corresponding to the time information, and whether the relationship between friends held by the user and the user is a classmate relationship is determined according to the data of the friends; the data to be verified can be credit data of the user in a time period corresponding to the time information, credit points of the user on a credit platform are determined according to the credit data, and whether the user is a credit user is determined; judging whether the relationship between friends and users handed by the users is a classmate relationship or not by the behavior dimension to obtain a result, judging whether the user is a credit user or not to obtain a result, and inputting data corresponding to the result into the verification model as to-be-verified data.

Similarly, the feature data to be verified can be motion information with the most motion mode of the user in a time period corresponding to the time information and type information of an article bought by the user through online shopping or shopping, the motion data frequently moved by the user and the shopping type data of the user are determined in the feature dimension and are used as the data to be verified to be input into the verification model, the preprocessing process of the feature dimension is similar to the preprocessing process of the position dimension, and details are not repeated in the application.

The data contained in the attribute data are analyzed in different dimensions, the data to be verified are determined in different dimensions respectively, the data to be verified in different dimensions are integrated and determined as the data to be verified input into the verification model, preprocessing of the attribute data in different dimensions is achieved, the preprocessed attribute data are verified through the verification model, the verification result of the academic information can be determined, and the accuracy of verification of the academic information is improved to a great extent.

in one or more implementations of this embodiment, when the attribute data includes location data, the location data is analyzed in the location dimension, and data to be verified in the location dimension is determined according to an analysis result, where a specific implementation manner of determining the data to be verified in the location dimension is as follows:

specifically, according to the position data included in the attribute data, the text address data of the user is acquired, and meanwhile, the address data of the school included in the academic calendar information is acquired, wherein the text address data of the user can be determined through LBS service, that is, the text name of the real-time address of the user and the text name of the address of the school included in the academic calendar information are acquired; determining a relationship degree between a user and a school included in the academic calendar information by calculating a text address coincidence degree between the text address data and the address data, wherein the relationship degree is actually determined by an editing distance between a text name of a user real-time address and a text name of an address of the school in the process of calculating the text address coincidence degree, namely, between two character strings, and the minimum number of editing operations required for converting the character string of the text name of the user real-time address into the character string of the text name of the address of the school can be used as the text address coincidence degree (the smaller the number of editing operations, the higher the text address coincidence degree, and otherwise, the larger the number of editing operations, the lower the text address coincidence degree);

determining the geographic distance according to the text address data and the address data, determining the specific position of the user according to the text address data, determining the specific position of a school contained in a school calendar according to the text address data, calculating the actual distance between the position of the user and the position of the school, and determining the actual distance as the geographic distance; and meanwhile, determining the query frequency of the user using the school address in the time period, namely determining the frequency of searching the school address by the user, the frequency of applying the school address by the address of the departure place or the destination added by the user through the online taxi appointment, and taking the text address contact ratio, the geographic distance and the query frequency as the data to be verified in the position dimension.

based on the above, the number of times of editing the text name of the real-time address of the user and the text name of the address of the school, the geographic details of the user position and the school position, and the frequency of using the school address by the user are used as data to be verified in the position dimension, and then the data are input into the verification model.

for example, when a user is at university B, the academic information is uploaded on a certain service platform to obtain more services, the user uploads the academic information of the user to the service platform, the academic information includes university B name and university B address, the service platform determines that the address of the user is the same as the university B address through LBS service, and the user usually visits through a way of network appointment, then determines that the text address overlap ratio of the user and university B is 100%, the geographic distance is 0, the frequency of querying the university B address is greater in daily life than the frequency of using other addresses, and the proportion of querying the university B address by the user is greater, that is, the proportion of the text address overlap ratio of the user and university B is 100%, the geographic distance is 0, and the proportion of querying the university B address by the user is used as data to be verified in the location dimension.

in one or more embodiments of this embodiment, when the attribute data includes network data, the network data is analyzed in the location dimension, and the data to be verified in the location dimension is determined according to an analysis result, where a specific implementation manner of determining the data to be verified in the location dimension is as follows:

specifically, according to network data included in the attribute data, a first internet protocol address (i.e., an IP address) of the user in the on-reading time period is determined, the IP address is analyzed, and first latitude and longitude data of the user in the on-reading time period are obtained, wherein the first latitude and longitude data are obtained by analyzing the IP address and determining latitude and longitude data of an area range to which the IP address belongs, the first latitude and longitude data are determined according to the latitude and longitude data to which the IP address belongs, the first area range is mapped according to the first latitude and longitude data and map data, and an area range enclosed on a map by an area range covered by the IP address of the user is determined as the first area range;

acquiring the school data contained in the school calendar information, wherein the school data comprises a school name, determining an IP address of the school according to the school name, and analyzing the IP address of the school to obtain second longitude and latitude data of the school, wherein the second longitude and latitude data are longitude and latitude data of a region range to which the school IP address belongs determined by analyzing the school IP address, the second longitude and latitude data are determined according to the longitude and latitude data to which the school IP address belongs, and a second region range is mapped according to the second longitude and latitude data and the map data, so that a region range covered by the school IP address is defined on a map and is determined as the second region range of the school;

comparing the determined first area range of the user with a second area range of the school, determining the similarity between the first area range and the second area range, and taking the similarity as data to be verified in the position dimension; for example, if the first area coverage is an area coverage of 2 square kilometers in the city a in the area a, and the second area coverage is an area coverage of 2.1 square kilometers in the city a in the area a, the similarity between the first area coverage and the second area coverage is 88%.

in practical application, taking the IP address of the user as 255.255.0.0 and the IP address of university C in the academic calendar information uploaded by the user as 255.255.0.1 as an example, the IP address of the user as 255.255.0.0 is analyzed to determine that the first area range of the user is a circular area range of 3 square kilometers in the city B, the IP address of the university C as 255.255.0.1 is analyzed to determine that the second area range of the university C is a square area range of 2 square kilometers in the city B, and the similarity between the first area range and the second area range is determined as 61% by calculating the similarity between the first area range and the second area range, that is, the similarity between the first area range determined according to the IP address of the user and the second area range determined according to the IP address of university C is 61% as data to be verified in the location dimension.

In addition, in one or more embodiments of this embodiment, another description content for analyzing network data in a location dimension is provided, and the specific description content is as follows: under the condition that the attribute data contains network data, analyzing the network data in the position dimension, and determining to-be-verified data in the position dimension according to an analysis result, wherein a specific implementation manner of determining the to-be-verified data in the position dimension is as follows:

specifically, the network data connected to the user terminal that uploads the academic calendar information is acquired, which can be understood as that the user terminal needs to be connected to a network when the user uploads the academic calendar information through the user terminal, and the attribute information of the connected network is the network data, wherein the user terminal can be a mobile phone or a computer and other terminal devices; determining a service set identifier (namely SSID) of the user terminal according to the network data, and acquiring school data contained in the school calendar information, wherein the school data can be network data of a school and a basic service set identifier (namely BSSID) set by a computer in the school;

based on this, by judging whether the basic service set identifier of the school contains the service set identifier of the user, the judgment result is used as the data to be verified in the position dimension, wherein the judgment result comprises that if the basic service set identifier contains the service set identifier, the containing relation is used as the data to be verified in the position dimension; and if the basic service set identification does not contain the service set identification, taking the basic service set identification and the service set identification which do not have the inclusion relationship as the data to be verified in the position dimension.

In one or more embodiments of this embodiment, when the attribute data includes social data, the social data is analyzed in the behavior dimension, and the data to be verified in the behavior dimension is determined according to the analysis result, where a specific implementation manner of determining the data to be verified in the behavior dimension is as follows:

if yes, determining relationship data between the user and the user after the academic information verification is completed according to the social network data, and taking the relationship data as data to be verified in the behavior dimension;

If not, taking the data corresponding to the verification completion of the academic form information which does not exist in the social network data of the user as the data to be verified in the action dimension.

Specifically, the social network data in the reading time period included in the academic information of the user is determined according to the social network data, and the social network data specifically refers to the relationship between friends and the user of the user in the reading time period, for example, if the user a hands over the user B by playing basketball, and the user a and the user B are both team members of a basketball school in university D, it can be determined that the user a and the user B are alumni; taking this as an example, determining the social network data of the user through social data of the user in the reading time period;

on the basis, whether the user completes the academic record information verification or not is detected in the social network data of the user in the reading time period, if yes, the fact that the user completes the academic record information verification is indicated in the social network data of the user, specifically, the user with the relation of the same school exists in friends of the user is indicated, and the relation data of the user with the academic record information verification is used as the data to be verified in the behavior dimension; if not, the social network data of the user does not contain the user who has finished the academic information verification, specifically, the user who does not have a relation of classmates in the friends of the user, and the data corresponding to the user who does not have the academic information verification completion in the social network data is taken as the data to be verified in the action dimension.

For example, if the user a has 1000 users with a friend relationship, including 500 users with a classmate relationship, 200 users with a neighbor relationship, and 200 users with a colleague relationship, it may be determined that 20% of the users among the friends of the user a have completed the verification of the academic information, and then the data corresponding to the 20% users who have completed the verification of the academic information in the social relationship data of the user a is used as the data to be verified in the behavioral dimension.

In one or more implementations of this embodiment, when the attribute data includes behavior data, the behavior data is analyzed in the behavior dimension, and data to be verified in the behavior dimension is determined according to an analysis result, and a specific implementation manner of determining the data to be verified of the behavior data is as follows:

Specifically, determining credit data of the user in the reading time period according to the behavior data, specifically, that the user has a credit account on a credit platform, and the credit platform records in the credit account corresponding to the user according to the credit event of the user, so that the credit level of the user can be determined, wherein if the credit of the user is poor, the credit level is low, and if the credit of the user is good, the credit level is high; based on the above, on the basis of determining the credit level of the user, the credit level of the user is used as the data to be verified in the behavior dimension.

In addition, before the credit level of the user is determined, the credit point of the user can be determined according to the credit data of the user, the credit point can be increased when the user has a credit event, the credit point can be decreased when the user has a loss event, and the credit level of the user can be further determined according to the credit point condition of the user.

In one or more embodiments of this embodiment, when the attribute data includes user feature data, the user feature data is analyzed in the feature dimension, data to be verified in the feature dimension is determined according to an analysis result, and a specific implementation manner of determining the data to be verified in the feature dimension is as follows:

If yes, determining student attribute data contained in the user attribute data and the user preference data, and extracting the student attribute data from the user attribute data and the user preference data as data to be verified in the feature dimension;

and if not, taking the attribute data of the students which are not contained in the user attribute data and the user preference data as the data to be verified in the characteristic dimension.

Specifically, the user attribute data and the user preference data of the user are determined according to the feature data, wherein the user attribute data can be data such as the age, the sex, the address and the like of the user, the user preference data can be data such as the sport liked by the user or the game liked by the user, and the data to be verified in the feature dimension is determined according to the detection result by detecting whether the user attribute data and the user preference data contain student attribute data;

Based on this, if the user attribute data and the user preference data contain student attribute data, which indicates that the probability that the user is possibly a student is high, the student attribute data contained in the user attribute data and the user preference data are determined, and the student attribute data are extracted from the user attribute data and the user preference data and serve as data to be verified in the feature dimension; if the user attribute data and the user preference data do not contain student attribute data, the probability that the user is a student is low, and the user attribute data and the user preference data do not contain student attribute data and serve as data to be verified in the feature dimension.

In practical application, taking the age of the user as 20 years, and liking playing basketball and network games as examples, describing the data to be verified determined in the characteristic dimension, according to the fact that the age of the user is determined to be in accordance with the age of the college, the preferred sports are basketball, and the network games are also favored, judging that the probability that the user is the college is higher, and taking the age of the user as 20 years, and liking playing basketball and network games as the data to be verified in the characteristic dimension.

in addition, the data to be verified can be determined in different dimensions at the same time, then the data to be verified in different dimensions are integrated, and the integrated result is used as the data to be verified to be input into the verification model, so that the academic calendar information can be verified in different dimensions at the same time, and the verification accuracy is improved.

by analyzing the position data, the network data, the behavior data, the social data and the user feature data in the position dimension, the behavior dimension and the feature dimension respectively, the verification of the academic record information uploaded by the user in different dimensions is realized, the possibility of false academic record information is avoided, and the corresponding data to be verified is determined in different dimensions respectively to be used as the data to be verified of the input verification model, so that the verification result of the academic record information output by the subsequent verification model is more accurate.

On the basis of determining the data to be verified, further, in one or more embodiments of this embodiment, before the verification model is input, prediction may be performed according to the analysis result, and a specific implementation manner is as follows:

Specifically, the attribute data are analyzed in the position dimension, the behavior dimension and the feature dimension to respectively obtain an analysis result in the position dimension, and the analysis result in the behavior dimension and the analysis result in the feature dimension judge whether the analysis result in the position dimension meets a preset threshold value of the position dimension, whether the analysis result in the behavior dimension meets a preset threshold value of the behavior dimension, and whether the analysis result in the feature dimension meets a preset threshold value of the feature dimension;

based on this, if the analysis result of any one of the position dimension, the behavior dimension and the feature dimension does not meet the corresponding preset threshold, which indicates that the academic history information may be unreal, further verification needs to be performed through the verification model; if the analysis results in the position dimension, the behavior dimension and the characteristic dimension all meet the corresponding preset threshold values, the probability that the academic record information is possibly true is high, the prediction verification result of the academic record is determined, meanwhile, the data to be verified is input to the model for further verification, and the accuracy of verifying the authenticity of the academic record information is high.

in specific implementation, the preset threshold of the location dimension may be a geographic distance less than 20 kilometers, that is, it may be determined that the probability that the verification of the academic information uploaded by the user in the location dimension is true is higher, the preset threshold of the behavior dimension may be that the credit score of the user is greater than 700 scores, that is, it may be determined that the probability that the verification of the academic information uploaded by the user in the behavior dimension is true is higher, the preset threshold of the characteristic dimension may be that the age of the user is less than 25 years, that is, it may be determined that the verification of the academic information uploaded by the user in the characteristic dimension is true, the preset thresholds of different dimensions may be set according to an actual application scenario, and the present application is not limited herein.

for example, the geographic distance of the user to the position data in the position dimension is 0, the IP addresses of the user to the network data in the position dimension are the same, the social data in the action dimension are more users with the same relation, the behavior data in the action dimension are analyzed to be good in credit, the user feature data in the feature dimension are analyzed to be age 20, the probability that the verification results of the user in different dimensions are both true through determining preset thresholds of different dimensions is higher, the predicted verification result of the academic information uploaded by the user can be determined to be true, and then the verification model is used for further verification.

before the data to be verified is input into the verification model, analysis results can be determined according to analysis of the attribute data in different dimensions, the truth and falseness of the academic record information uploaded by the user can be preliminarily judged, and the accuracy of verifying the authenticity of the academic record information can be further improved.

Step 108: and inputting the data to be verified to a pre-trained verification model, verifying the academic information, and outputting a verification result of the academic information.

specifically, on the basis that the attribute data is analyzed and the data to be verified is determined according to the position dimension, the behavior dimension and the feature dimension, the data to be verified is further input to the verification model, the academic calendar information is verified, and the verification model outputs a verification result of the academic calendar information, wherein the verification result may be that the academic calendar information is true or that the academic calendar information is false.

in specific implementation, the verification model can be a two-class model, and by adopting the two-class model as the verification model, the prediction result of the model can be output as a real result, so that the result output by the model is avoided from being analyzed, and the verification efficiency of verifying the academic calendar information is improved to a great extent; in addition, the binary model can be trained and fitted by using various algorithms, which is not described herein in detail.

based on the above verification of the academic aptitude information by the verification model, in one or more embodiments of this embodiment, the verification model is trained as follows:

Specifically, under the condition of training the verification model, a large number of training samples are needed, a verification result is obtained by collecting the academic information of the user and determining the academic information, on the basis, the attribute data is analyzed in the position dimension, the behavior dimension and the characteristic dimension, the data to be verified is determined according to the analysis result, meanwhile, the verification model is trained by adding a real label to the data to be verified corresponding to the academic information which is true, adding a false label to the data to be verified corresponding to the academic information which is false, and taking the data to be verified to which the label is added and the verification result of the corresponding academic information as the training samples, so that the incidence relation between the data to be verified and the verification result of the academic information is determined.

here, the verification process of the verification model may be understood as setting a threshold in the verification model, comparing the data of each dimension with the threshold, determining the value of each dimension according to the comparison result, adding the values of each dimension to obtain a total value, comparing the total value with the threshold, and determining that the academic information is true if the total value is greater than or equal to the threshold, and determining that the academic information is false if the total value is less than the threshold.

in specific implementation, under the condition of training the verification model, the authenticity information of the academic calendar information in the training sample can be inquired on the academic calendar information inquiry website, so that the authenticity of the training sample can be ensured, and the accuracy of the output result of the verification model is improved.

On the basis of determining the academic record information prediction verification result, further, in one or more embodiments of this embodiment, because the prediction verification result is a preliminary judgment result, and the accuracy is not high, the verification needs to be performed again through the verification model, and further, the accuracy of the academic record information verification result can be determined, which is specifically implemented as follows:

Specifically, the determined prediction verification result is compared with a verification result output by the model, if the prediction verification result is consistent with the verification result, it is indicated that the prediction verification result and the verification result are both accurate, then the to-be-verified data with the prediction verification result consistent with the verification result and the verification result of the academic information are taken as positive training samples to train the verification model, and if the prediction verification result is inconsistent with the verification result, it is indicated that the verification may be inaccurate in the prediction verification result and the verification result, then the to-be-verified data with the prediction verification result inconsistent with the verification result and the verification result of the academic information are taken as negative training samples to train the verification model; in the process, the model is optimized through the positive and negative training samples, so that the output result of the verification model is more accurate.

In the academic calendar information verification method provided by the application, by determining the attribute data of the user and analyzing the attribute data in different dimensions, the to-be-verified data of the user can be determined in different dimensions, meanwhile, the verification model is combined and the to-be-verified data is taken as the input of the verification model, the output of the verification model is the verification result of the academic calendar information, the authenticity of the academic calendar information uploaded by the user can be determined in a short time, the accuracy of the academic calendar information verification result is effectively improved by analyzing the attribute data of the user in different dimensions, and the preliminary verification of the academic calendar information is realized by obtaining the prediction verification result through the analysis of the attribute data in each dimension before the to-be-verified data is input into the verification model, and the predicted verification result is compared with the verification result, so that the accuracy of the academic information verification result is further improved.

the academic record information verification method provided by the present application is further described below with reference to fig. 2, taking the verification application of the academic record information verification method to the academic record in the user application process as an example. Fig. 2 shows a processing flow chart of a process of verifying academic aptitude information according to an embodiment of the present application, and specific steps include step 202 to step 214.

Step 202: and acquiring the academic information of the user U.

Specifically, when the user U applies for the post of the company A, academic information needs to be provided, and the company A judges whether the user U is suitable for the post according to the academic information provided by the user U;

based on the above, the study information of the user U comprises that the reading school is S university, the reading time is from 2012 to 2016, the name of the school is S university, the longitude and latitude data of the school address in the C district of the C city is 125 degrees and 50 minutes of east longitude and 44 degrees and 20 minutes of north latitude, and the IP address of the S university is determined according to the name of the school.

step 204: the location data, network data, behavior data and feature data of the user U during the reading time are determined.

Specifically, the location data of the user U refers to address data used most frequently in 2012 to 2016 at the time of reading, the network data of the user U refers to an IP address used most frequently in 2012 to 2016 at the time of reading, the behavior data of the user U refers to sports liked in 2012 to 2016 at the time of reading, and the feature data of the user U refers to the age of the user.

step 206: the first data to be verified of the position dimension is obtained by analyzing the position data and the network data of the user U in the position dimension.

Specifically, the location data of the user U is analyzed in the location dimension, the address with the most frequent use of the user U from 2012 to 2016 is determined to be an S university address, and the network data of the user U is analyzed in the location dimension, and the IP address with the most frequent use of the user U from 2012 to 2016 is determined to be an S university IP address;

based on this, the first to-be-authenticated data of the location dimension is obtained as the user U often uses the S university address, and the IP address coincides with the S university IP address.

Step 208: and analyzing the behavior data of the user U in the behavior dimension to obtain second data to be verified of the behavior dimension.

specifically, the behavior data of the user U is analyzed in the behavior dimension, and the user U is determined to play basketball frequently in 2012 to 2016;

based on the above, the second data to be verified of the behavior dimension is obtained, and the user U likes to play basketball.

Step 210: and analyzing the feature data of the user U in the feature dimension to obtain third data to be verified of the feature dimension.

specifically, the age of the user U is determined to be 24 years by analyzing the feature data of the user U in the feature dimension;

based on this, the third data to be verified of the feature dimension is obtained as the user U age 24.

step 212: and integrating the first to-be-verified data, the second to-be-verified data and the third to-be-verified data to be used as the to-be-verified data of the input verification model.

Step 214: and inputting the data to be verified into the verification model, verifying the learning information of the user U, and determining that the learning information of the user U is true.

Specifically, the user U is determined to frequently use the address of the university S in the position dimension, the IP address of the user U is consistent with the IP address of the university S, the user U is determined to like playing basketball in the behavior dimension, the age of the user U is determined to be 24 years in the characteristic dimension, the data are input into the verification model as data to be verified, the data of the user U in each dimension are determined to be in accordance with the academic calendar information provided by the user U, and then the academic calendar information of the user U can be determined to be in accordance with the user and the academic calendar is real.

according to the academic calendar information verification method, the position data, the network data, the behavior data and the characteristic data of the user are determined, and the position data, the network data, the behavior data and the characteristic data are analyzed in different dimensions, so that the to-be-verified data of the user can be determined in different dimensions, meanwhile, the verification model is combined, the to-be-verified data is used as the input of the verification model, the output of the verification model is the verification result of the academic calendar information, the authenticity of the academic calendar information uploaded by the user can be determined in a short time, and the position data, the network data, the behavior data and the characteristic data of the user are analyzed in different dimensions, so that the accuracy of the academic calendar information verification result is effectively improved.

corresponding to the above method embodiment, the present application further provides an embodiment of a academic calendar information verification apparatus, and fig. 3 shows a schematic structural diagram of the academic calendar information verification apparatus provided in an embodiment of the present application. As shown in fig. 3, the apparatus includes:

an acquisition study information module 302 configured to acquire study information uploaded by a user;

an attribute data determining module 304, configured to determine, according to the time information included in the academic calendar information, attribute data of the user in a time period corresponding to the time information;

A data to be verified determining module 306 configured to analyze the attribute data in a position dimension, a behavior dimension, and a feature dimension, and determine data to be verified according to an analysis result;

and the academic calendar information verification module 308 is configured to input the data to be verified to a pre-trained verification model, verify the academic calendar information, and output a verification result of the academic calendar information.

In an optional embodiment, when the attribute data includes location data, determining data to be verified in the location dimension according to an analysis result by analyzing the location data in the location dimension, including:

in an optional embodiment, when the attribute data includes network data, determining data to be verified in the location dimension according to an analysis result by analyzing the network data in the location dimension, including:

In an optional embodiment, in a case that the attribute data includes social data, determining data to be verified in the behavior dimension according to an analysis result by analyzing the social data in the behavior dimension includes:

In an optional embodiment, when the attribute data includes behavior data, determining data to be verified in the behavior dimension according to an analysis result by analyzing the behavior data in the behavior dimension, including:

in an optional embodiment, when the attribute data includes user feature data, determining data to be verified in the feature dimension according to an analysis result by analyzing the user feature data in the feature dimension, includes:

in an alternative embodiment, the verification model is trained by:

the system comprises a collecting unit, a verification unit and a verification unit, wherein the collecting unit is configured to collect the academic information of a user and determine attribute data corresponding to the academic information and a verification result of the academic information;

The determining unit is configured to analyze the attribute data in the position dimension, the behavior dimension and the characteristic dimension, and determine data to be verified according to an analysis result;

determining a training sample unit configured to use the data to be verified and the verification result of the academic form information as training samples;

and the incidence relation determining unit is configured to input the training sample to the verification model for training, and determine the incidence relation between the data to be verified and the verification result of the academic record information.

in an alternative embodiment, the attribute data comprises at least one of:

Correspondingly, the module 306 for determining data to be verified includes:

A data to be verified unit configured to analyze the location data and the network data in the location dimension, analyze the social data and the behavior data in the behavior dimension, analyze the user feature data in the feature dimension, determine first data to be verified in the location dimension, second data to be verified in the behavior dimension, and third data to be verified in the feature dimension according to an analysis result;

and the integration unit is configured to integrate the first data to be verified, the second data to be verified and the third data to be verified, and determine the data to be verified according to an integration result.

In an optional embodiment, the academic record information comprises at least one of the following items:

In an optional embodiment, the module 306 for determining data to be verified includes:

A determining analysis result unit configured to obtain analysis results in respective dimensions by analyzing the attribute data in the position dimension, the behavior dimension, and the feature dimension;

the judging unit is configured to judge whether the analysis results of all the dimensions meet preset thresholds of corresponding dimensions;

If not, determining the data to be verified according to the analysis result, and operating the verification academic calendar information module 308;

if yes, determining a prediction verification result of the academic calendar information according to the analysis result, determining the data to be verified according to the analysis result, and operating the academic calendar information verification module 308.

In an optional embodiment, the academic calendar information verification apparatus further includes:

A determination module configured to determine whether the predicted verification result is consistent with the verification result;

If not, operating the first training module;

the first training module is configured to train the verification model by taking the data to be verified and the verification result of the academic form information as negative training samples;

if yes, operating a second training module;

the second training module is configured to train the verification model by taking the data to be verified and the verification result of the academic form information as a positive training sample.

In the academic calendar information verification device that this application provided, through confirming user's attribute data to it is right at different dimensions attribute data carries out the analysis, has realized that can confirm at different dimensions user's the data of waiting to verify, combine simultaneously the verification model will wait to verify the data and regard as verify the input of model, verify the output of model and do the authenticity of academic calendar information that can confirm user's upload in short time has been realized to through analyzing user's attribute data at different dimensions, the effectual improvement the accuracy of academic calendar information verification result.

the above is a schematic configuration of the academic calendar information verification apparatus of the present embodiment. It should be noted that the technical solution of the academic calendar information verification apparatus and the technical solution of the academic calendar information verification method described above belong to the same concept, and details of the technical solution of the academic calendar information verification apparatus, which are not described in detail, can be referred to the description of the technical solution of the academic calendar information verification method described above.

fig. 4 shows a block diagram of a computing device 400 provided according to an embodiment of the present application. The components of the computing device 400 include, but are not limited to, a memory 410 and a processor 420. Processor 420 is coupled to memory 410 via bus 430 and database 450 is used to store data.

computing device 400 also includes access device 440, access device 440 enabling computing device 400 to communicate via one or more networks 460. Examples of such networks include the Public Switched Telephone Network (PSTN), a Local Area Network (LAN), a Wide Area Network (WAN), a Personal Area Network (PAN), or a combination of communication networks such as the internet. The access device 440 may include one or more of any type of network interface (e.g., a Network Interface Card (NIC)) whether wired or wireless, such as an IEEE802.11 Wireless Local Area Network (WLAN) wireless interface, a worldwide interoperability for microwave access (Wi-MAX) interface, an ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a bluetooth interface, a Near Field Communication (NFC) interface, and so forth.

in one embodiment of the present application, the other components of the computing device 400 described above and not shown in FIG. 4 may also be connected to each other, such as by a bus. It should be understood that the block diagram of the computing device architecture shown in FIG. 4 is for purposes of example only and is not limiting as to the scope of the present application. Those skilled in the art may add or replace other components as desired.

computing device 400 may be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., tablet, personal digital assistant, laptop, notebook, netbook, etc.), mobile phone (e.g., smartphone), wearable computing device (e.g., smartwatch, smartglasses, etc.), or other type of mobile device, or a stationary computing device such as a desktop computer or PC. Computing device 400 may also be a mobile or stationary server.

Wherein processor 420 is configured to execute the following computer-executable instructions:

acquiring the academic calendar information uploaded by a user;

Optionally, the verification model is trained as follows:

Optionally, the attribute data includes at least one of:

optionally, after the data to be verified is input to a verification model trained in advance, the academic calendar information is verified, and a verification result instruction of the academic calendar information is output to be executed, the processor 420 is further configured to execute the following computer-executable instructions:

The above is an illustrative scheme of a computing device of the present embodiment. It should be noted that the technical solution of the computing device and the technical solution of the aforementioned academic calendar information verification method belong to the same concept, and details that are not described in detail in the technical solution of the computing device can be referred to the description of the technical solution of the aforementioned academic calendar information verification method.

an embodiment of the present application further provides a computer-readable storage medium storing computer instructions that, when executed by a processor, are configured to:

acquiring the academic calendar information uploaded by a user;

Optionally, the verification model is trained as follows:

optionally, the attribute data includes at least one of:

the above is an illustrative scheme of a computer-readable storage medium of the present embodiment. It should be noted that the technical solution of the storage medium and the technical solution of the aforementioned academic calendar information verification method belong to the same concept, and for details that are not described in detail in the technical solution of the storage medium, reference may be made to the description of the technical solution of the aforementioned academic calendar information verification method.

The foregoing description of specific embodiments of the present application has been presented. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

the computer instructions comprise computer program code which may be in the form of source code, object code, an executable file or some intermediate form, or the like. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.

It should be noted that, for the sake of simplicity, the above-mentioned method embodiments are described as a series of acts or combinations, but those skilled in the art should understand that the present application is not limited by the described order of acts, as some steps may be performed in other orders or simultaneously according to the present application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.

In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.

the preferred embodiments of the present application disclosed above are intended only to aid in the explanation of the application. Alternative embodiments are not exhaustive and do not limit the invention to the precise embodiments described. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the application and its practical applications, to thereby enable others skilled in the art to best understand and utilize the application. The application is limited only by the claims and their full scope and equivalents.

Claims

1. a method for verifying academic calendar information is characterized by comprising the following steps:

acquiring the academic calendar information uploaded by a user;

2. The academic calendar information verification method according to claim 1, wherein in a case where the attribute data contains location data, determining data to be verified in the location dimension from an analysis result by analyzing the location data in the location dimension comprises:

3. the academic calendar information verification method according to claim 1, wherein in a case where the attribute data contains network data, determining data to be verified in the location dimension from an analysis result by analyzing the network data in the location dimension comprises:

4. the academic calendar information verification method according to claim 1, wherein in a case where the attribute data contains network data, determining data to be verified in the location dimension from an analysis result by analyzing the network data in the location dimension comprises:

5. The academic information verification method according to claim 1, wherein in a case where the attribute data includes social data, determining data to be verified in the behavioral dimension according to an analysis result by analyzing the social data in the behavioral dimension comprises:

6. the academic calendar information verification method according to claim 1, wherein in a case where the attribute data contains behavior data, determining data to be verified in the behavior dimension according to an analysis result by analyzing the behavior data in the behavior dimension comprises:

7. the academic calendar information verification method according to claim 1, wherein in a case where the attribute data contains user feature data, determining data to be verified in the feature dimension according to an analysis result by analyzing the user feature data in the feature dimension comprises:

8. The academic information verification method according to claim 1, wherein the verification model is trained by:

9. The academic calendar information verification method according to claim 1, wherein the attribute data includes at least one of:

10. The academic information verification method according to claim 1, wherein the academic information contains at least one of:

11. The academic calendar information verification method according to claim 1, wherein the determining of the data to be verified according to the analysis result by analyzing the attribute data in a position dimension, a behavior dimension and a feature dimension comprises:

12. the academic information verification method according to claim 11, wherein after the step of inputting the data to be verified to a verification model trained in advance, verifying the academic information, and outputting the verification result of the academic information is executed, the method further comprises:

13. an academic calendar information verification apparatus, comprising:

14. a computing device, comprising:

A memory and a processor;

acquiring the academic calendar information uploaded by a user;

15. a computer readable storage medium storing computer instructions which, when executed by a processor, carry out the steps of the scholarly calendar information verification method of any one of claims 1 to 12.