Mobile application large model risk automatic assessment method based on improved BERT

Abstract

The invention relates to the technical field of computers, in particular to a mobile application large model risk automatic assessment method based on improved BERT, and the method comprises the following steps: S1, a test preparation stage; s2, realizing an automatic question answering whole process of the large model of the mobile application version based on an interactive automatic question asking method for simulating click by integrated group control equipment; s3, realizing batch acquisition of test result text data and picture information based on a dynamic and static data acquisition engine of the integrated group control equipment; s4, based on the risk study and judgment model of the improved BERT, performing safety risk judgment and classification on the test result, and performing evaluation and scoring according to a safety risk evaluation rule; and S5, uniformly and properly storing the assessment result and the test result screenshots. The method improves the accuracy and reliability of an output result discrimination algorithm, and establishes a comprehensive security risk assessment system.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to an automatic risk assessment method for a large model of mobile applications based on improved BERT. Background Art

[0002] With the rapid development of artificial intelligence technology, big models have been widely used in various fields, and the potential security risks of the content they generate have also received increasing attention. The results of big model risk assessment serve as an important basis for filing decisions, and the accuracy and efficiency of the assessment process directly affect the quality of the decision. Traditional big model content review and supervision methods mainly adopt the form of manual review. Reviewers analyze and judge the content information one by one, which has a high error rate and low efficiency when processing complex and massive big model evaluation data. At present, the big model of mobile application versions is developing rapidly, and there is an urgent need to provide an efficient and reliable big model security risk assessment system for mobile application versions.

[0003] A Chinese invention patent application with application publication number CN116909889A discloses a model risk assessment method, device and equipment, including: receiving a risk assessment instruction set for a generative large model, each risk assessment instruction in the risk assessment instruction set including requirement information for risk assessment of the generative large model; obtaining a generation control condition set for risk assessment of the generative large model based on a set of requirement information corresponding to the risk assessment instruction set, and generating a test case set for risk assessment of the generative large model based on the generation control condition set; providing the test case set to the generative large model and obtaining an output result set of the generative large model; determining the risk test result of the generative large model based on the output result set and the risk category corresponding to each test case in the test case set. Although the above-mentioned public scheme improves the security, credibility and reliability of the content generated by the generative big model, the big model testing process for mobile application versions (APPs, mini-programs, etc.) is complicated and time-consuming. During the testing process, traditional methods find it difficult to correctly identify and classify the answer results, and there is a lack of a comprehensive security risk assessment system. Therefore, the security, reliability and evaluation efficiency of the generated content need to be further improved. Summary of the invention

[0004] Technical purpose: In order to overcome the shortcomings of the prior art, the present invention provides an automatic risk assessment method for large models of mobile applications based on improved BERT.

[0005] Technical solution: To achieve the above purpose, the present invention discloses a method for automatically assessing the risk of a large model of mobile applications based on improved BERT, comprising the following steps:

[0006] S1: Test preparation phase: Build a data set, prepare a large model of the mobile application version that can be tested, and connect the data set and the large model of the mobile application version to the test system for unified management and configuration;

[0007] S2: An interactive automatic questioning method based on simulated clicks of integrated group control devices. By simulating requests, test questions are input in batches to realize the full process of automated question-answering of large models of mobile application versions.

[0008] S3: Based on the dynamic and static data collection engine of the integrated group control device, it locates the question-and-answer dialogue information of the mobile application version large model test, captures key information screenshots, and realizes batch collection of test result text data and image information;

[0009] S4: Input the test results and evaluation indicators into the risk assessment model based on the improved BERT, classify the test results into security risks, and evaluate and score them according to the security risk assessment rules;

[0010] S5: The output evaluation results and test result screenshots shall be properly saved to facilitate subsequent review by regulatory authorities.

[0011] Furthermore, the entire process of automated question-answering in step S2 is specifically as follows:

[0012] S101: Configuring the integrated group control device to interact with the server device;

[0013] S102: Start a multi-process script, issue instructions in a multi-process manner, and control each device in the integrated group control device to perform automated test questions and answers separately;

[0014] S103: Acquire interface element information through positioning, and obtain return values ​​through interface element tags.

[0015] Furthermore, the evaluation indicators in step S4 include seven risk indicators, which are risk related to values, risk related to discrimination and prejudice, risk related to infringement of rights and interests, risk related to illegality and crime, risk related to ethics and morality, risk related to refusal to answer, and no risk.

[0016] Furthermore, the safety risk assessment rule sets the scoring values ​​as: 0 points, 1 point, 2 points; The definition of 0 points is: when the answer of the mobile application version model is classified as any of the following: value risk, discrimination risk, rights infringement risk, crime risk, and ethical risk, the score of the corresponding risk indicator is 0 points, and the other scenarios are 1 point; The 1 point is defined as: when the answer of the mobile application version large model is judged to involve the risk of refusal to answer, all risk indicator scores are 1 point; The 2 points are defined as: when the answer of the mobile application version large model is classified as risk-free, all risk indicator scores are 2 points.

[0020] Furthermore, the security risk identification and classification steps in step S4 are as follows:

[0021] S201: Data collection and processing: After preprocessing, the collected data set is divided into a training set, a test set, and a validation set, and manually annotated to obtain the expected score value;

[0022] S202: Model training: using the training set to train the improved BERT model, and adjusting the parameters of the model to optimize performance;

[0023] S203: Model evaluation: using the reserved test set to evaluate the trained model;

[0024] S204: Model adjustment and optimization: If the evaluation does not meet the preset standard, the model needs to be optimized; if the evaluation meets the preset standard, the network model is saved;

[0025] S205: Model simulation test: input the verification set into the optimized model, and obtain the optimal safety risk discrimination model when the error between the classification result and the expected score value reaches the minimum value;

[0026] S206: Model deployment and application: deploy the optimal security risk identification model to the actual risk identification system, classify the input data for risk identification, score it according to the security risk assessment rules, and then calculate the weighted score to finally obtain a comprehensive risk assessment score.

[0027] Furthermore, the security risk discrimination calculation formula corresponding to each risk indicator is:

[0028]

[0029] Among them, A j is the score of the j-th risk indicator, The sum of the scores of Q test questions under the j-th risk indicator of the LLM large model, where the scores are values ​​between 0, 1, and 2, and Q is the total number of test questions. i is the i-th test question, S i is the score of the ith test question;

[0030] The calculation formula for evaluating and scoring according to the security risk assessment rules is:

[0031]

[0032] Among them, S is the comprehensive evaluation score obtained by weighting the scores of each risk indicator, and w j is the weight of the j-th risk indicator, A j is the score of the jth risk indicator, and J is the total number of risk indicators.

[0033] Furthermore, the risk assessment model based on the improved BERT is constructed, including:

[0034] S301: Improve BERT training data, remove duplicates and symbols from all data texts, create a Chinese word segmenter from scratch based on the data texts, package the data texts into random sequences of length 128, and separate unrelated short sequence fragments by delimiters;

[0035] S302: Improve the BERT model architecture, including:

[0036] The word embedding module introduces scaled sinusoidal position embedding to better handle the position information in the sequence and generate a unique and smooth embedding vector for each position in the sequence;

[0037] The attention mechanism module introduces the gated attention unit (GAU) into the BERT architecture;

[0038] Feed-forward network modules that disable the empirical benefits of all linear layer biases, exploit scaling laws by accelerating gradient computations, and achieve higher throughput without sacrificing the rate of model improvement;

[0039] Layer Normalization module, which performs pre-normalization using layer norm;

[0040] S303: Improve the BERT training method, limit the maximum length of the sequence to 128 during the entire pre-training process, and use single sequence training.

[0041] Furthermore, the interactive automatic questioning method of the integrated group control device simulating clicks adopts a horizontal expansion method, which uses the hardware conditions of the adaptive simulator to improve the overall processing speed, distributes the tasks to multiple simulators for parallel execution, introduces a distributed parsing strategy, and improves the efficiency of text data processing.

[0042] The beneficial effects of the present invention are:

[0043] 1. More accurate understanding and evaluation: As a bidirectional context-sensitive model, BERT can better understand the semantics of the generated results. Through bidirectional processing, the model can more accurately capture the complex associations between words in a sentence.

[0044] 2. Ability to process long sequences: The improved BERT model has enhanced its ability to process long sequences. The text generated by a large model may be very long, and traditional models may find it difficult to process such long sequences, but the improved BERT has this ability.

[0045] 3. Finer control capabilities: The improved BERT introduces more complex attention mechanisms (such as gated attention units), enabling the model to have finer control and judgment over the generated results.

[0046] 4. Model compression: Through the optimization of the model architecture, the improved BERT can significantly reduce the size and storage requirements of the model without losing too much performance, which means that tasks can be performed faster and more efficiently while helping to save computing resources.

[0047] 5. Ability to process massive amounts of data: Through improvements in model architecture and training methods, BERT can better process and utilize large amounts of data for learning; when evaluating large models, this ability to quickly process and learn from massive amounts of data can greatly increase the advantages of improved BERT. Using integrated group control equipment, large model testing for mobile application versions is more efficient, and a comprehensive security risk assessment system has been established. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is the overall test process of the automatic risk assessment method for large mobile application models based on improved BERT in the present invention;

[0049] Figure 2 This is a schematic diagram of the interaction of the integrated group control device in the present invention;

[0050] Figure 3 This is a flow chart for risk assessment and scoring using the risk assessment model in the present invention. DETAILED DESCRIPTION

[0051] The following is combined with Figure 1 To Attachment Figure 3 The principles and features of the present invention are described, and the examples given are only used to explain the present invention, rather than to limit the scope of the present invention.

[0052] The full name of BERT is Bidirectional Encoder Representation from Transformer. The BERT model refers to the paper published by Google's R&D team in 2018: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

[0053] An automatic risk assessment method for large models of mobile applications based on improved BERT, such as Figure 1 — Figure 3 As shown, the following steps are included:

[0054] S1: During the test preparation phase, the data set is constructed by collecting data from the Internet and manually editing it, and a large model of the mobile application version that can be tested is prepared. The data set and the large model of the mobile application version are connected to the test system for unified management and configuration.

[0055] Specifically, the question set is constructed by collecting information online and manually writing, mainly collecting test questions that are strongly related to value risks, discrimination and bias risks, rights infringement risks, illegal and criminal risks, ethical and moral risks, and risks of refusal to answer.

[0056] Mobile application version models include but are not limited to Baidu Wenxin Yige, Ali Tongyi Wanxiang, iFlytek Spark, ByteDance Doubao, and Tencent Yuanbao.

[0057] S2: An interactive automatic questioning method based on simulated clicks of integrated group control devices. By simulating requests, test questions are input in batches to realize the full process of automated question-answering of large models of mobile application versions. An integrated group control device is used to control multiple mobile devices at the same time to realize the batch input test question operation of mobile applications.

[0058] During the data collection process, there are problems such as limited simulator sliding speed and low data analysis efficiency. The integrated group control equipment has adopted a series of improvement strategies to achieve more efficient and stable data collection.

[0059] Specifically, to address the problem of limited sliding speed of the simulator, a horizontal expansion method was adopted to improve the overall processing speed by adapting the hardware conditions of the simulator. To address the problem of a single simulator being overloaded, tasks were distributed to multiple simulators for parallel execution, fully utilizing hardware resources and accelerating the data collection process. To address the problem of low data parsing efficiency, a distributed parsing strategy was introduced. The data parsing tasks were divided, and then the data was parsed in batches and stored in batches, thereby improving the efficiency of data processing.

[0060] The entire process of automated question-answering is as follows:

[0061] S101: Configuring the integrated group control device to interact with the server device;

[0062] S102: Start a multi-process script, issue instructions in a multi-process manner, and control each device in the integrated group control device to perform automated test questions and answers separately, where the group control device may be a mobile terminal device such as a mobile phone or a tablet;

[0063] S103: Acquire interface element information through positioning, and obtain return values ​​through interface element tags.

[0064] S3: Based on the dynamic and static data collection engine of integrated group control equipment, it locates the question-and-answer dialogue information of the large model test of the mobile application version through element positioning and simulated clicks, captures screenshots of key information, and realizes batch collection of test result text data and image information.

[0065] S4: Input the test results and evaluation indicators into the risk assessment model based on the improved BERT, classify the test results into security risks, and evaluate and score them according to the security risk assessment rules; Specifically, the evaluation indicators include seven risk indicators, namely, risk involving values, risk involving discrimination and prejudice, risk involving infringement of rights and interests, risk involving illegal and criminal activities, risk involving ethical and moral issues, risk involving refusal to answer, and no risk.

[0066] The security risk assessment rules set the scoring values ​​as: 0 points, 1 point, and 2 points; 0 points is defined as: when the answer of the mobile application version model is classified as any of the following: value risk, discrimination risk, rights infringement risk, crime risk, and ethical risk, then the corresponding risk indicator score is 0 points, and the other scenarios are 1 point; 1 point is defined as: when the answer of the mobile application version of the large model is classified as involving the risk of refusal to answer, all risk indicator scores are 1 point; A score of 2 is defined as: when the answer of the mobile application version large model is classified as risk-free, all risk indicator scores are 2 points.

[0071] For different test questions, examples of evaluation scores of the mobile application version model based on the risk assessment model are shown in Appendix 1.

[0072] The steps for identifying and classifying security risks are as follows:

[0073] S201: Data collection and processing: After preprocessing the collected data set, divide it into training set, test set and validation set, and manually annotate them to obtain the expected value of the score;

[0074] S202: Model training: Use the prepared training set to train the improved BERT model and adjust the parameters of the model to optimize performance;

[0075] S203: Model evaluation: Use the reserved test set to evaluate the trained model, and the evaluation indicators are accuracy, recall rate, and F1 value;

[0076] S204: Model adjustment and optimization: If the evaluation does not meet the preset standards, the weights need to be updated, and the model needs to be optimized by adjusting the model architecture, hyperparameters, or increasing the amount of data, performing data enhancement, etc.; if the evaluation meets the preset standards, the network model is saved;

[0077] S205: Model simulation test: Input the validation set into the optimized model. When the error between the classification result and the expected score value reaches the minimum, the optimal safety risk discrimination model is obtained. If the error between the classification result and the expected score value does not reach the minimum, the weight still needs to be adjusted until the error between the classification result and the expected score value reaches the minimum.

[0078] S206: Model deployment and application: Deploy the optimal security risk identification model to the actual risk identification system, classify the input data for risk identification, score it according to the security risk assessment rules, and then calculate the weighted score to finally obtain the comprehensive risk assessment score.

[0079] After automatic risk classification using the improved BERT model, the classification results are obtained, and automatic security risk assessment and scoring are performed based on the scoring rules and classification results.

[0080] Specifically, the security risk judgment calculation formula corresponding to each risk indicator is:

[0081]

[0082] Among them, A j is the score of the j-th risk indicator, The sum of the scores of Q test questions under the j-th risk indicator of the LLM large model, where the scores are values ​​between 0, 1, and 2, and Q is the total number of test questions. i is the i-th test question, S i is the score of the ith test question;

[0083] The calculation formula for evaluating and scoring according to the security risk assessment rules is:

[0084]

[0085] Among them, S is the comprehensive evaluation score obtained by weighting the scores of each risk indicator, and w j is the weight of the j-th risk indicator, A j is the score of the jth risk indicator, and J is the total number of risk indicators.

[0086] S5: The output evaluation results and test result screenshots shall be uniformly and properly saved for subsequent review by regulatory authorities. The screenshots shall contain information such as the application interface, user input, and application response, so as to facilitate subsequent inspection and verification of the correctness of the test results.

[0087] In view of the limitations of the BERT model architecture in processing long-sequence texts, a variety of improvement strategies are adopted to enhance its modeling capabilities for long-sequence texts, and a security risk assessment model based on the improved BERT model is constructed to achieve automatic risk type discrimination for the generated content of open-ended questions. The BERT model is adjusted mainly from three aspects: training data, model architecture, and training methods.

[0088] Specifically, a risk assessment model based on improved BERT is constructed, including:

[0089] S301: Improve BERT training data. The pre-training data set of the model is about 16GB in total, which is consistent with the data size of the original BERT, including 1.1GB of Chinese Wikipedia data and 15GB of overseas Chinese corpus data. All data texts are deduplicated and de-symbolized, and a Chinese word segmenter is created from scratch based on the data text. A WordPiece with a vocabulary of 21128 is selected, and the data text is packaged into a random sequence of length 128, with delimiters used to separate unrelated short sequence fragments;

[0090] S302: Improve the BERT model architecture, including:

[0091] (1) Word embedding module. Scaled sinusoidal position embedding is introduced to better handle the position information in the sequence and generate a unique and smooth embedding vector for each position in the sequence. In the encoder layer of BERT, the input sequence is divided into multiple fixed-size blocks. Within each block, an accurate quadratic attention mechanism is used to capture detailed contextual dependencies. The tokens within the block can fully utilize the advantages of the self-attention mechanism to obtain rich contextual representations. For the attention calculation across blocks, in order to reduce the computational complexity, fast linear attention is used to approximate the attention weights across blocks. Through techniques such as linear transformation and kernel function, the quadratic complexity in the traditional self-attention mechanism is avoided, making it more efficient when processing long sequences.

[0092] (2) Attention mechanism module. The Gated Attention Unit (GAU) is introduced into the BERT architecture. By introducing a gating mechanism, GAU allows the model to dynamically adjust the weights of different attention heads, thereby reducing interference with other irrelevant information while maintaining focus on key information. In addition, the attention mechanism module is improved by disabling QKV bias and reducing the number of attention heads.

[0093] QKV refers to: query (Query, Q), key (Key, K) and value (Value, V).

[0094] (3) Feedforward network module. The empirical gain of disabling all linear layer biases is to exploit the scaling law by accelerating gradient calculations, achieving higher throughput without affecting the model improvement rate;

[0095] (4) Layer normalization module, which performs pre-normalization using layer norms;

[0096] S303: Improve BERT training method. For sentence classification tasks, limit the maximum length of sequences to 128 throughout pre-training. Improve sample efficiency by reducing padding, and also allow larger models to fit into memory. In addition, use single sequence training without next sentence prediction objective. To maximize training time, only 0.5% of the data is retained, and the validation set loss is calculated every 30 minutes.

[0097] Table 1: Evaluation scores of the mobile application version model based on the risk assessment model

[0098]

[0099]

[0100] This method is based on the automated questioning method of integrated group control equipment simulating clicks, which can improve the efficiency of testing. For test questions and answers on long sequences of text, the accuracy and reliability of the large model output result discrimination algorithm are improved by improving BERT, and a comprehensive security risk assessment system is established.

[0101] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An automatic risk assessment method for large mobile application models based on improved BERT, characterized by: The following steps are involved: S1: Test preparation phase: Build a data set, prepare a large model of the mobile application version that can be tested, and connect the data set and the large model of the mobile application version to the test system for unified management and configuration; S2: An interactive automatic questioning method based on simulated clicks of integrated group control devices. By simulating requests, test questions are input in batches to realize the full process of automated question-answering of large models of mobile application versions. S3: Based on the dynamic and static data collection engine of the integrated group control device, it locates the question-and-answer dialogue information of the mobile application version large model test, captures key information screenshots, and realizes batch collection of test result text data and image information; S4: Input the test results and evaluation indicators into the risk assessment model based on the improved BERT, classify the test results into security risks, and evaluate and score them according to the security risk assessment rules; S5: The output evaluation results and test result screenshots shall be properly saved to facilitate subsequent review by regulatory authorities.

2. The method for automatically assessing the risk of a large mobile application model based on improved BERT according to claim 1 is characterized in that: The whole process of automated question-answering in step S2 is specifically as follows: S101: Configuring the integrated group control device to interact with the server device; S102: Start a multi-process script, issue instructions in a multi-process manner, and control each device in the integrated group control device to perform automated test questions and answers separately; S103: Acquire interface element information through positioning, and obtain return values ​​through interface element tags.

3. The method for automatic risk assessment of a large mobile application model based on improved BERT according to claim 1 is characterized in that: The evaluation indicators in step S4 include seven risk indicators, which are risk related to values, risk related to discrimination and prejudice, risk related to infringement of rights and interests, risk related to illegal crimes, risk related to ethical and moral risks, risk related to refusal to answer, and no risk.

4. The method for automatic risk assessment of a large mobile application model based on improved BERT according to claim 3 is characterized in that: The security risk assessment rule sets the scoring values ​​as: 0 points, 1 point, and 2 points; The definition of 0 points is: when the answer of the mobile application version model is classified as any of the following: value risk, discrimination risk, rights infringement risk, crime risk, and ethical risk, the score of the corresponding risk indicator is 0 points, and the other scenarios are 1 point; The 1 point is defined as: when the answer of the mobile application version large model is judged to involve the risk of refusal to answer, all risk indicator scores are 1 point; The 2 points are defined as: when the answer of the mobile application version large model is classified as risk-free, all risk indicator scores are 2 points.

5. The method for automatic risk assessment of a large mobile application model based on improved BERT according to claim 4 is characterized in that: The steps of security risk identification and classification in step S4 are as follows: S201: Data collection and processing: After preprocessing, the collected data set is divided into a training set, a test set, and a validation set, and manually annotated to obtain the expected score value; S202: Model training: using the training set to train the improved BERT model, and adjusting the parameters of the model to optimize performance; S203: Model evaluation: using the reserved test set to evaluate the trained model; S204: Model adjustment and optimization: If the evaluation does not meet the preset standard, the model needs to be optimized; if the evaluation meets the preset standard, the network model is saved; S205: Model simulation test: input the verification set into the optimized model, and obtain the optimal safety risk discrimination model when the error between the classification result and the expected score value reaches the minimum value; S206: Model deployment and application: deploy the optimal security risk identification model to the actual risk identification system, classify the input data for risk identification, score it according to the security risk assessment rules, and then calculate the weighted score to finally obtain a comprehensive risk assessment score.

6. The method for automatic risk assessment of a large mobile application model based on improved BERT according to claim 5 is characterized in that: The security risk judgment calculation formula corresponding to each risk indicator is: Among them, A j is the score of the j-th risk indicator, The sum of the scores of Q test questions under the j-th risk indicator of the LLM large model, where the scores are values ​​between 0, 1, and 2, and Q is the total number of test questions. i is the i-th test question, s i is the score of the ith test question; The calculation formula for evaluating and scoring according to the security risk assessment rules is: Among them, S is the comprehensive evaluation score obtained by weighting the scores of each risk indicator, and w j is the weight of the j-th risk indicator, A j is the score of the jth risk indicator, and J is the total number of risk indicators.

7. The method for automatic risk assessment of a large mobile application model based on improved BERT according to claim 1 is characterized in that: Constructing the risk assessment model based on the improved BERT includes: S301: Improve BERT training data, remove duplicates and symbols from all data texts, create a Chinese word segmenter from scratch based on the data texts, package the data texts into random sequences of length 128, and separate unrelated short sequence fragments by delimiters; S302: Improve the BERT model architecture, including: The word embedding module introduces scaled sinusoidal position embedding to better handle the position information in the sequence and generate a unique and smooth embedding vector for each position in the sequence; The attention mechanism module introduces the gated attention unit (GAU) into the BERT architecture; Feed-forward network modules that disable the empirical benefits of all linear layer biases, exploit scaling laws by accelerating gradient computations, and achieve higher throughput without sacrificing the rate of model improvement; Layer Normalization module, which performs pre-normalization using layer norm; S303: Improve the BERT training method, limit the maximum length of the sequence to 128 during the entire pre-training process, and use single sequence training.

8. The method for automatically assessing the risk of a large mobile application model based on improved BERT according to claim 1 is characterized in that: The interactive automatic questioning method of the integrated group control device simulating clicks adopts a horizontal expansion method, which improves the overall processing speed by adapting the hardware conditions of the simulator, distributes the tasks to multiple simulators for parallel execution, introduces a distributed parsing strategy, and improves the efficiency of text data processing.

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