Software login method and system based on big data

Abstract

The invention provides a software login method and system based on big data, and relates to the technical field of software login, and the method comprises the steps: obtaining a mobile phone screen recorded video, a plurality of pieces of logged-in software, and non-logged-in software; based on the mobile phone screen recorded video and the information of the multiple pieces of logged-in software, using an information processing model to determine the security credibility and the use frequency of each piece of logged-in software; determining a login mode of non-login software based on the security credibility and the use frequency of each piece of logged-in software; and performing verification login on the non-login software based on the login mode of the non-login software. The method can accurately provide a proper login mode for a user.

Description

Technical Field

[0001] The present invention relates to the technical field of software login, and in particular to a software login method and system based on big data. Background Art

[0002] With the rapid expansion of the smartphone application ecosystem, users need to frequently switch and log in to multiple software in daily use, which not only increases the user's operational burden, but also puts higher requirements on the security and convenience of software login. Traditional login methods, such as verification by username and password, can no longer meet the needs of the current complex and changing application environment. Especially when facing software that is not logged in, it is particularly important to dynamically adjust the login strategy based on user behavior habits and security requirements. Existing solutions often rely on a single verification method and lack effective use of user historical behavior data, resulting in an inefficient and insecure login process. Therefore, how to quickly and accurately determine the login method of the software is an urgent problem to be solved. Summary of the invention

[0003] The main technical problem solved by the present invention is to quickly and accurately determine the login mode of the software.

[0004] According to the first aspect, the present invention provides a software login method based on big data, including: obtaining a mobile phone screen recording video, multiple logged-in software, and non-logged-in software; using an information processing model based on the mobile phone screen recording video and the information of multiple logged-in software to determine the security credibility and usage frequency of each logged-in software; determining the login method of the non-logged-in software based on the security credibility and usage frequency of each logged-in software; and verifying the login of the non-logged-in software based on the login method of the non-logged-in software.

[0005] In a possible implementation, the method of determining the login method of the non-logged-in software based on the security credibility and usage frequency of each logged-in software includes: based on the non-logged-in software information, the security credibility and usage frequency of each logged-in software, using a generative adversarial network to generate an estimated security requirement level of the non-logged-in software, an estimated usage frequency, and a similarity between the non-logged-in software and each logged-in software; constructing a graph structure, the graph structure including multiple nodes and multiple edges between the multiple nodes, the multiple nodes including an non-logged-in software node and multiple logged-in software nodes, wherein the non-logged-in software node is a central node, and each non-logged-in software establishes an edge with the non-logged-in software node respectively, the node features of the non-logged-in software node are the estimated security requirement level and estimated usage frequency of the non-logged-in software, the node features of each logged-in software node include the security credibility and usage frequency of the logged-in software, and the edges between nodes are the similarities between the non-logged-in software and the logged-in software; processing the graph structure based on a graph autoencoder to determine the login method of the non-logged-in software.

[0006] In a possible implementation, the login method for the software not logged in includes login through joint verification of face recognition and SMS verification code, SMS verification code verification, password verification login, and direct login without verification.

[0007] In one possible implementation, the information processing model is a long-short term neural network model, the input of the information processing model is the mobile phone screen recording video and information of multiple logged-in software, and the output of the information processing model is the security credibility and usage frequency of each logged-in software.

[0008] According to a second aspect, the present invention provides a software login system based on big data, comprising: The acquisition module is used to obtain the mobile phone screen recording video, multiple logged-in software, and non-logged-in software; An information processing module, used to determine the security credibility and usage frequency of each logged-in software using an information processing model based on the mobile phone screen recording video and information of multiple logged-in software; A login mode confirmation module, used to determine the login mode of the non-logged-in software based on the security credibility and usage frequency of each logged-in software; The login verification module is used to verify the login of the non-logged in software based on the login method of the non-logged in software.

[0009] In a possible implementation, the login method confirmation module is also used to: based on the non-logged in software information, the security credibility and usage frequency of each logged in software, use a generative adversarial network to generate an estimated security requirement level of the non-logged in software, an estimated usage frequency, and a similarity between the non-logged in software and each logged in software; construct a graph structure, the graph structure includes multiple nodes and multiple edges between the multiple nodes, the multiple nodes include an non-logged in software node and multiple logged in software nodes, wherein the non-logged in software node is a central node, and each non-logged in software establishes an edge with the non-logged in software node respectively, the node features of the non-logged in software node are the estimated security requirement level and estimated usage frequency of the non-logged in software, the node features of each logged in software node include the security credibility and usage frequency of the logged in software, and the edges between nodes are the similarities between the non-logged in software and the logged in software; based on the graph autoencoder, the graph structure is processed to determine the login method of the non-logged in software.

[0010] In a possible implementation, the login method for the software not logged in includes login through joint verification of face recognition and SMS verification code, SMS verification code verification, password verification login, and direct login without verification.

[0011] In one possible implementation, the information processing model is a long-short term neural network model, the input of the information processing model is the mobile phone screen recording video and information of multiple logged-in software, and the output of the information processing model is the security credibility and usage frequency of each logged-in software.

[0012] According to the third aspect, an embodiment of the present invention provides an electronic device, comprising: a processor; a memory; and a computer program; wherein the computer program is stored in the memory and is configured to be executed by the processor to implement the method as described above, the method comprising: obtaining a mobile phone screen recording video, multiple logged-in software, and non-logged-in software; using an information processing model based on the information of the mobile phone screen recording video and multiple logged-in software to determine the security credibility and usage frequency of each logged-in software; determining a login method for the non-logged-in software based on the security credibility and usage frequency of each logged-in software; and verifying the login of the non-logged-in software based on the login method of the non-logged-in software.

[0013] According to the fourth aspect, this embodiment provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned big data-based software login method, the method comprising: obtaining a mobile phone screen recording video, multiple logged-in software, and non-logged-in software; using an information processing model based on the mobile phone screen recording video and the information of multiple logged-in software to determine the security credibility and usage frequency of each logged-in software; determining a login method for the non-logged-in software based on the security credibility and usage frequency of each logged-in software; and verifying the login of the non-logged-in software based on the login method of the non-logged-in software.

[0014] The present invention provides a software login method and system based on big data, the method comprising obtaining a mobile phone screen recording video, multiple logged-in software, and non-logged-in software; using an information processing model based on the mobile phone screen recording video and the information of multiple logged-in software to determine the security credibility and usage frequency of each logged-in software; determining the login method of the non-logged-in software based on the security credibility and usage frequency of each logged-in software; and verifying the login of the non-logged-in software based on the login method of the non-logged-in software. The method can quickly and accurately determine the login method of the software. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A schematic diagram of an application scenario of a software login method based on big data provided by an embodiment of the present invention; Figure 2 A flowchart of a software login method based on big data provided by an embodiment of the present invention; Figure 3A flowchart of determining a login method for a non-logged-in software provided by an embodiment of the present invention; Figure 4 A schematic diagram of a software login system based on big data provided by an embodiment of the present invention; Figure 5 A schematic diagram of an electronic device provided by an embodiment of the present invention; Figure 6 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0016] The present invention is further described in detail below by specific embodiments in conjunction with the accompanying drawings. Wherein similar elements in different embodiments adopt associated similar element numbers. In the following embodiments, many detailed descriptions are for making the present invention better understood. However, those skilled in the art can easily recognize that some features can be omitted in different situations, or can be replaced by other elements, materials, methods. In some cases, some operations related to the present invention are not shown or described in the specification, this is to avoid the core part of the present invention being overwhelmed by too much description, and for those skilled in the art, it is not necessary to describe these related operations in detail, and they can fully understand the related operations according to the description in the specification and the general technical knowledge in the art.

[0017] Figure 1 A schematic diagram of an application scenario of a software login method based on big data provided in an embodiment of the present invention. Figure 1 The application scenario of the software login method based on big data may include a server 11, a network 12, a terminal 13 and a storage device 14.

[0018] In some embodiments, the server 11 may be a single server or a server group. The server 11 may access information and / or data stored in the terminal 13 or the storage device 14 through the network 12. In some embodiments, the server 11 may be used to execute Figure 2 The software login method based on big data shown in.

[0019] The network 12 may facilitate the exchange of information and / or data. In some embodiments, the network 12 may be any form of wired or wireless network, or any combination thereof.

[0020] Terminal 13 may refer to one or more terminal devices used by a user. In some embodiments, terminal 13 may include one or more combinations of a mobile device, a tablet computer, a laptop computer, and the like.

[0021] The storage device 14 may store data and / or instructions. For example, the storage device 14 may store data instructions of a software login method based on big data.

[0022] In an embodiment of the present invention, there is provided Figure 2 A software login method based on big data is shown, and the software login method based on big data includes steps S1 to S4: Step S1, obtaining a mobile phone screen recording video, multiple logged-in software, and non-logged-in software; Mobile screen recording videos are multiple images of consecutive time points that record the entire process of operations on the mobile phone screen through specific software or functions. Mobile screen recording videos include but are not limited to actions such as clicking and sliding and their corresponding interface changes. Mobile screen recording videos can be used to capture browsing behavior in applications.

[0023] Multiple logged-in software information records relevant data of each logged-in software, such as name, version number, last login time, usage frequency, authentication method, etc.

[0024] Non-logged-in software refers to software that the user needs to log in to but has not yet logged in.

[0025] Step S2, determining the security credibility and usage frequency of each logged-in software based on the mobile phone screen recording video and the information of multiple logged-in software using the information processing model; The information processing model is a long-short term neural network model, the input of the information processing model is the mobile phone screen recording video and the information of multiple logged-in software, and the output of the information processing model is the security credibility and usage frequency of each logged-in software.

[0026] The information processing model is obtained by training the existing long-term and short-term neural network model. In some embodiments, the information processing model can be obtained by training the existing long-term and short-term neural network model based on the training sample by the gradient descent method. The sample input of the training sample is the mobile phone screen recording video and the information usage information of multiple logged-in software, and the sample output of the training sample is the security credibility and usage frequency of each logged-in software.

[0027] The long-term and short-term neural network model includes a long-term and short-term neural network (LSTM, Long Short-Term Memory). The long-term and short-term neural network model can process the mobile phone screen recording video of a continuous time period, can better capture the relationship in the time series of the mobile phone screen recording video, and can output the features that comprehensively consider the correlation relationship of the mobile phone screen recording video at each time point, so that the output features are more accurate and comprehensive.

[0028] The long-short term neural network model can effectively capture the long-term dependencies in these sequences, understand the user's interaction patterns, and determine the security credibility and usage frequency of each logged-in software based on the mobile phone screen recording video and information of multiple logged-in software.

[0029] Mobile phone screen recording videos are used to analyze user behavior. The information processing model analyzes the mobile phone screen recording videos to count the number of times each logged-in software is opened, the duration of each use, and other data, thereby directly quantifying the usage frequency of multiple logged-in software. The information processing model can determine the specific interaction steps between the user and the logged-in software through the mobile phone screen recording videos, such as entering a password, performing identity authentication, and other operations, to determine whether the logged-in software has good security measures. The information processing model can also further understand the functions of the logged-in software and the types of operations involved by extracting features such as icons and interface elements from the mobile phone screen recording videos, thereby determining the purpose and potential risks of the logged-in software.

[0030] The security credibility of each logged-in software is a measure of whether each software is safe and reliable during its use. For example, if the logged-in software uses multi-factor authentication and has no abnormal login records, the information processing model determines that the security credibility of the logged-in software is high. For another example, if the logged-in software is only logged in by password, the information processing model can determine that the security credibility of the logged-in software is low.

[0031] The frequency of use of logged-in software is the number of times or duration of use of the logged-in software by the user within a certain period of time. The information processing model can process the frequency of use of the logged-in software based on the mobile phone screen recording video and the information of the multiple logged-in software.

[0032] In some embodiments, the information processing model may include a segmentation layer, a logged-in software information determination layer, and a security credibility determination layer. The input of the segmentation layer is the mobile phone screen recording video and the information of multiple logged-in software. The output of the segmentation layer is the mobile phone screen recording video of each logged-in software and the usage frequency of each logged-in software. The input of the logged-in software information determination layer is the mobile phone screen recording video of each logged-in software. The output of the logged-in software information determination layer is the click degree, sliding degree, sharing degree, and jump degree of each logged-in software. The input of the security credibility determination layer is the click degree, sliding degree, sharing degree, and jump degree of each logged-in software. The output of the security credibility determination layer is the security credibility of each logged-in software.

[0033] Step S3, determining a login method for the non-logged-in software based on the security credibility and usage frequency of each logged-in software; In some embodiments, Figure 3A flowchart of determining a login method for a non-logged-in software provided by an example of the present invention, wherein the determining a login method for a non-logged-in software comprises steps S21 to S23: Step S21, based on the unregistered software information, the security credibility and usage frequency of each registered software, using a generative adversarial network to generate an estimated security requirement level of the unregistered software, an estimated usage frequency, and a similarity between the unregistered software and each registered software; The input of the generative adversarial network is the unregistered software information, the security credibility and usage frequency of each registered software, and the output of the generative adversarial network is the estimated security requirement level of the unregistered software, the estimated usage frequency, and the similarity between the unregistered software and each registered software.

[0034] Generative Adversarial Network (GAN) includes a generator and a discriminator. The generator and the discriminator compete with each other and continuously optimize to achieve the purpose of generating realistic data. Generative adversarial networks can learn the characteristic representation of data through the training process. The generator is responsible for generating the estimated security requirement level of the unregistered software, the estimated usage frequency, and the similarity between the unregistered software and each registered software, while the discriminator is used to determine the probability that the data generated by the generator is real data. The generator tries to generate more realistic data to confuse the discriminator, while the discriminator strives to improve its ability to distinguish. The two continuously improve their performance through adversarial training, and ultimately make the data generated by the generator more realistic.

[0035] Based on its powerful data distribution learning ability, the Generative Adversarial Network can learn the distribution law from the security credibility, usage frequency and other data of the logged-in software through adversarial training of the generator and the discriminator, and generate estimated data with a highly similar distribution to the real data, thereby calculating the similarity between the unregistered software and each logged-in software. The Generative Adversarial Network can also generate the estimated security requirement level and estimated usage frequency of the unregistered software based on the unregistered software information and the features of the logged-in software.

[0036] The estimated security requirement level is the security level required for the unregistered software based on the prediction of the generative adversarial network. The higher the estimated security requirement level of the unregistered software, the higher the security verification level required for the unregistered software, and vice versa.

[0037] The estimated usage frequency is based on the prediction of the usage frequency of the software without logging in in a certain period of time in the future. For example, the estimated usage frequency can be expressed as the number of times it is used per day or per week.

[0038] The similarity between the unregistered software and each logged-in software is a measure of the similarity between the unregistered software and other logged-in software. For example, the more similar the unregistered software is to the logged-in software in function, the higher the similarity generated by the generative adversarial network.

[0039] The unregistered software information, the security credibility of each registered software, and the usage frequency provide the key data foundation and feature basis for the generative adversarial network, so that the generative adversarial network can generate high-quality estimation results. The unregistered software information contains the basic attributes of the unregistered software, such as functional category, user group, etc., and the usage frequency reflects the user's usage habits. By learning the unregistered software information, the security credibility of each registered software, and the distribution law of usage frequency, the generative adversarial network can capture the correlation between the registered software and the unregistered software, calculate the estimated security requirement level of the unregistered software, the estimated usage frequency, and the similarity between the unregistered software and each registered software, and provide a basis for the selection of the login method.

[0040] Step S22, constructing a graph structure, wherein the graph structure includes a plurality of nodes and a plurality of edges between the plurality of nodes, wherein the plurality of nodes include an unregistered software node and a plurality of registered software nodes, wherein the unregistered software node is a central node, and each unregistered software respectively establishes an edge with the unregistered software node, wherein the node features of the unregistered software node are the estimated security requirement degree and estimated usage frequency of the unregistered software, and the node features of each registered software node include the security credibility and usage frequency of the registered software, and the edges between the nodes are the similarities between the unregistered software and the registered software; A graph is a data structure that represents the relationship between objects. The graph structure consists of nodes (vertices) and edges (edges). In this context, the graph structure is used to represent the relationship between unregistered software and other registered software. Each node represents an unregistered software or a registered software. The edge represents the similarity between unregistered software and registered software.

[0041] Step S23, processing the graph structure based on the graph autoencoder to determine the login method of the non-logged-in software.

[0042] The input of the graph autoencoder is the graph structure, and the output of the graph autoencoder is the login method of the non-logged-in software.

[0043] The login method of the non-logged-in software includes face recognition and SMS verification code collaborative verification login, SMS verification code verification, password verification login, and direct login without verification. Graph autoencoder is a neural network model specially used to process graph structure data. Graph autoencoder reconstructs the graph structure by learning the representation of nodes and edges, thereby extracting useful feature information. Graph autoencoder captures its features and relationships by converting nodes in the graph into low-dimensional embeddings, including the similarity between non-logged-in software and logged-in software. Graph autoencoder can reconstruct the graph structure and analyze the similarity between nodes, so as to recommend a login method suitable for non-logged-in software.

[0044] Each node contains rich feature information, such as security credibility, usage frequency, etc. These features help evaluate the security requirements of the application. The edge represents the similarity between the unregistered software and the registered software. High similarity means that the two have something in common in terms of function, purpose, permission request, etc. The graph structure constructs a complex relationship network through nodes and edges. This relationship network allows the graph autoencoder to better understand and evaluate the security requirements of the unregistered software and recommend appropriate login methods. Based on the features of the nodes and edges in the graph structure, the graph autoencoder can provide accurate login method recommendations for unregistered software.

[0045] Step S4, verifying the login of the non-logged-in software based on the login method of the non-logged-in software.

[0046] When the login method of the non-logged-in software is determined, the mobile phone number currently used by the user is obtained, and the login method of the non-logged-in software is verified and logged in to the non-logged-in software based on the mobile phone number currently used by the user.

[0047] Based on the same inventive concept, Figure 4 A schematic diagram of a software login system based on big data provided by an embodiment of the present invention, the software login system based on big data includes: An acquisition module 41 is used to acquire a mobile phone screen recording video, multiple logged-in software, and non-logged-in software; An information processing module 42 is used to determine the security credibility and usage frequency of each logged-in software based on the mobile phone screen recording video and the information of multiple logged-in software using an information processing model; A login mode confirmation module 43, used to determine the login mode of the non-logged-in software based on the security credibility and usage frequency of each logged-in software; The login verification module 44 is used to verify the login of the non-logged in software based on the login method of the non-logged in software.

[0048] Based on the same inventive concept, an embodiment of the present invention provides an electronic device, such as Figure 5 As shown, including: It includes: a processor 51; a memory 52; and a computer program; wherein the computer program is stored in the memory 52 and is configured to be executed by the processor 51 to implement the software login method based on big data as provided above, the method including: obtaining a mobile phone screen recording video, multiple logged-in software, and non-logged-in software; using an information processing model based on the mobile phone screen recording video and the information of multiple logged-in software to determine the security credibility and usage frequency of each logged-in software; determining the login method of the non-logged-in software based on the security credibility and usage frequency of each logged-in software; and verifying the login of the non-logged-in software based on the login method of the non-logged-in software.

[0049] Based on the same inventive concept, this embodiment provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by the processor 51, the software login method based on big data provided above is implemented, and the method includes: obtaining a mobile phone screen recording video, multiple logged-in software, and non-logged-in software; using an information processing model based on the information of the mobile phone screen recording video and multiple logged-in software to determine the security credibility and usage frequency of each logged-in software; determining the login method of the non-logged-in software based on the security credibility and usage frequency of each logged-in software; and verifying the login of the non-logged-in software based on the login method of the non-logged-in software.

[0050] The big data-based software login method provided in the embodiments of the present application can be applied to terminal devices (such as mobile phones), tablet computers, laptops, ultra-mobile personal computers (UMPCs), handheld computers, netbooks, personal digital assistants (PDAs), wearable devices (such as smart watches, smart glasses or smart helmets, etc.), augmented reality (AR) and virtual reality (VR) devices, smart home devices, car computers and other electronic devices, and the embodiments of the present application do not impose any restrictions on this.

[0051] Taking the mobile phone 100 as an example of the electronic device, Figure 6 A schematic structural diagram of the mobile phone 100 is shown.

[0052] like Figure 6As shown, the mobile phone 100 may include a processing module 110, an external memory interface 120, an internal memory 121, a universal serial bus (USB) interface 130, a charging management module 140, a power management module 141, a battery 142, an antenna 1, an antenna 2, a mobile communication module 150, a wireless communication module 160, an audio module 170, a speaker 170A, a receiver 170B, a microphone 170C, an earphone interface 170D, a sensor module 180, a button 190, a motor 191, an indicator 192, a camera 193, a display screen 194, and a subscriber identification module (SIM) card interface 195, etc.

[0053] The processing module 110 can be used to: obtain a mobile phone screen recording video, multiple logged-in software, and non-logged-in software; determine the security credibility and usage frequency of each logged-in software based on the information of the mobile phone screen recording video and multiple logged-in software using an information processing model; determine the login method of the non-logged-in software based on the security credibility and usage frequency of each logged-in software; and verify the login of the non-logged-in software based on the login method of the non-logged-in software.

[0054] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements and corrections to this specification. Such modifications, improvements and corrections are suggested in this specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

[0055] At the same time, this specification uses specific words to describe the embodiments of this specification. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures or characteristics in one or more embodiments of this specification can be appropriately combined.

[0056] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this specification, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0057] Similarly, it should be noted that in order to simplify the description disclosed in this specification and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this specification, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0058] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A software login method based on big data, characterized in that: include: Get mobile phone screen recording videos, multiple logged-in apps, and logged-out apps; Determine the security credibility and usage frequency of each logged-in software based on the mobile phone screen recording video and the information of multiple logged-in software using the information processing model; Determine the login method of the non-logged-in software based on the security credibility and usage frequency of each logged-in software; The non-logged-in software is authenticated and logged in based on the login method of the non-logged-in software.

2. The software login method based on big data according to claim 1, characterized in that: The method of determining the login method of the non-logged-in software based on the security credibility and usage frequency of each logged-in software includes: Based on the unregistered software information, the security credibility and usage frequency of each registered software, using a generative adversarial network to generate an estimated security requirement level, an estimated usage frequency, and a similarity between the unregistered software and each registered software; Constructing a graph structure, wherein the graph structure includes a plurality of nodes and a plurality of edges between the plurality of nodes, wherein the plurality of nodes include an unregistered software node and a plurality of registered software nodes, wherein the unregistered software node is a central node, and each unregistered software respectively establishes an edge with the unregistered software node, wherein the node features of the unregistered software node are an estimated safety requirement degree and an estimated usage frequency of the unregistered software, and the node features of each registered software node include a safety credibility and a usage frequency of the registered software, and the edges between the nodes are similarities between the unregistered software and the registered software; The graph structure is processed based on the graph autoencoder to determine a login method for the non-logged-in software.

3. The software login method based on big data as claimed in claim 2, characterized in that: The login methods for the non-logged-in software include face recognition and SMS verification code collaborative verification login, SMS verification code verification, password verification login, and direct login without verification.

4. The software login method based on big data according to claim 1, characterized in that: The information processing model is a long-short term neural network model, the input of the information processing model is the mobile phone screen recording video and the information of multiple logged-in software, and the output of the information processing model is the security credibility and usage frequency of each logged-in software.

5. A software login system based on big data, characterized in that: include: The acquisition module is used to obtain the mobile phone screen recording video, multiple logged-in software, and non-logged-in software; An information processing module, used to determine the security credibility and usage frequency of each logged-in software using an information processing model based on the mobile phone screen recording video and information of multiple logged-in software; A login mode confirmation module, used to determine the login mode of the non-logged-in software based on the security credibility and usage frequency of each logged-in software; The login verification module is used to verify the login of the non-logged in software based on the login method of the non-logged in software.

6. The software login system based on big data as claimed in claim 5, characterized in that: The login mode confirmation module is also used for: Based on the unregistered software information, the security credibility and usage frequency of each registered software, using a generative adversarial network to generate an estimated security requirement level, an estimated usage frequency, and a similarity between the unregistered software and each registered software; Constructing a graph structure, wherein the graph structure includes a plurality of nodes and a plurality of edges between the plurality of nodes, wherein the plurality of nodes include an unregistered software node and a plurality of registered software nodes, wherein the unregistered software node is a central node, and each unregistered software respectively establishes an edge with the unregistered software node, wherein the node features of the unregistered software node are an estimated safety requirement degree and an estimated usage frequency of the unregistered software, and the node features of each registered software node include a safety credibility and a usage frequency of the registered software, and the edges between the nodes are similarities between the unregistered software and the registered software; The graph structure is processed based on the graph autoencoder to determine a login method for the non-logged-in software.

7. The software login system based on big data as claimed in claim 5, characterized in that: The login methods for the non-logged-in software include face recognition and SMS verification code collaborative verification login, SMS verification code verification, password verification login, and direct login without verification.

8. The software login system based on big data as claimed in claim 5, characterized in that: The information processing model is a long-short term neural network model, the input of the information processing model is the mobile phone screen recording video and the information of multiple logged-in software, and the output of the information processing model is the security credibility and usage frequency of each logged-in software.

9. An electronic device, characterized in that: include: processor; Memory; And a computer program; wherein, the computer program is stored in the memory and is configured to be executed by the processor to implement the big data-based software login method as described in any one of claims 1 to 4.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the software login method based on big data as described in any one of claims 1 to 4 is implemented.

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