All Modern information technology has been integrated into various fields, which can not only achieve efficient storage of a large amount of information data, but also become more and more stable. However, the security vulnerabilities in computer software are becoming more and more complex and difficult to deal with, which requires more advanced detection technology to quickly find the causes of vulnerabilities, timely remedy security defects, and ensure that computer software is always in normal operation. Based on this, this paper discusses the characteristics of security vulnerabilities in computer software, analyze computer software security vulnerability detection technology, the application of security vulnerability detection technology in computer software is proposed.

With the popularity of open source software, software security problems have become increasingly prominent. Traditional vulnerability detection methods are no longer able to cope with today’s complex virus vulnerabilities, and the detection results often have certain limitations. Therefore, an open source software security vulnerability detection method based on graph attention network is proposed. Firstly, the graph attention network model is constructed, and the source code detection is used to identify and detect the graph attention vulnerability. In order to verify the performance of the graph attention network model in the proposed method, the paper compares the proposed model with Drebin algorithm. The results show that the graph attention network model can effectively detect the number of security vulnerabilities in open source software, and its maximum error value is 4. Compared with Drebin algorithm, the security vulnerability detection method has higher accuracy and efficiency.

With the continuous development of information technology, various SQL injection attack tools emerge one after another, and the types of attacks are varied. SQL injection has always been the main problem of network security. Therefore, this paper proposes an intrusion detection method based on N-Gram and tfidf(term frequency inverse document frequency). The core idea is using N-Gram to select feature words in the preprocessing stage and TFIDF to vectorize SQL sentences. Then on the basis of this data set the SVM classifier is trained. Finally, the classification effect is tested by comparing with existing research. Experimental results show that, compared with using predefined feature vectors directly, this method can improve the accuracy on the basis of ensuring the recall rate.

In response to the typical optimization problem of economic load dispatch(ELD)in power systems,this paper proposes a combination of adaptive weight updating strategy and differential evolution random mutation strategy in the whale optimization algorithm(ADWOA).Firstly,an adaptive weight is introduced into the WO A algorithm to enhance its capturing ability,allowing the algorithm to perform fine-grained global search in the early stages and precise local search in the later stages,thus accelerating the iteration of the optimization algorithm.Simultaneously,due to the random mutation strategy,positions are updated again.Then,the best position is selected from the updated results to expedite the convergence of the population.Finally,multiple bio-inspired algorithms are compared for solving the ELD problem.The results validate that the ADWOA algorithm can better solve the ELD problem,quickly find the optimal solution,and achieve low-cost load allocation.

The goal of full awareness of the power grid's state implies the widespread application of internet of things(IoT)technology in the power grid,where a large number of IoT terminals will be connected to the network through sensing,communication,and computer technologies.Power IoT terminals are characterized by a large quantity,wide geographical distribution,and complex data collection,making them susceptible to breaches and intrusions by attackers.Additionally,traditional centralized authentication systems suffer from issues like single-point failure and performance bottlenecks.This paper,based on blockchain technology,develops and applies technologies such as decentralized identifiers(DID)digital identity based on terminal identifiers,a blockchain-adaptive DID resolver,zero-knowledge proofs based on terminal credential information,and device management based on machine learning algorithms.These technologies culminate in the design of a blockchain-based security authentication system for power Internet of Things terminals.The system has been successfully applied in the power grid,achieving decentralized authentication of IoT devices,reducing network security risks associated with device access,lowering the costs of centralized infrastructure construction and maintenance,and enhancing the efficiency of operations and maintenance personnel.

为及时了解用电状况,减少电力设备故障,利用Python编程,实现对用户用电量、报装容量和故障工单的数据分析,查找到高负荷用电用户,并及时将其用电信息自动发送给供电管理人员和用电设备主人.实际数据分析表明高负荷用电与用电故障存在较高的关联,通过本系统的数据分析与信息的及时发送能有效保障供电的安全,提高供电的效率.