（来源：衡水日报） 转自：衡水日报 在区块链技术的发展进程中，传统共识算法正遭遇严峻挑战。随着金融高频交易需求爆发、大规模物联网场景落地，区块链对 处理速度和承载能力的要求与日俱增。比特币的工作量证明（PoW）算法，因能耗高、交易确认慢，难以适配高效业务；以太 坊等采用的权益证明（PoS），在大规模节点协同下，也面临共识达成延迟、网络拥堵问题。市场急需一种能突破效率瓶颈、拓 展可扩展性的技术方案，微算法科技（NASDAQ ：MLGO）聚焦检查点方法，为重塑区块链共识算法性能，探索全新路径 。 微算法科技优化区块链共识算法的核心是引入"检查点驱动"机制。该机制结合传统共识算法（如PBFT或Raft）的稳定性，通过 动态设置检查点，将区块链网络划分为多个共识周期。每个周期内，节点通过优化后的共识流程达成数据一致性；周期结束 时，系统生成检查点，固化该阶段数据状态，并作为下一周期的起始基准。检查点不仅用于快速恢复网络状态，还能减少节点 间的冗余通信，在保障安全性的同时，显著提升共识效率与网络可扩展性。 检查点生成后，所有节点将本地账本更新至检查点状态，并删除该周期内的冗余历史数据（如已固化至检查点的临时日志）。 这 ...

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Core Viewpoint - The article discusses the innovative dynamic trust management model developed by Micro Algorithm Technology (NASDAQ: MLGO), which integrates sub-array algorithms with blockchain technology to address the challenges of trust assessment in distributed systems, particularly in the context of IoT, supply chain finance, and decentralized storage. Group 1: Model Overview - The dynamic trust management model utilizes blockchain as the underlying data infrastructure, combined with a distributed computing framework of sub-array algorithms to create a decentralized trust assessment system [1]. - The model divides network nodes into multiple sub-arrays based on geographical location, resource type, or historical behavior characteristics, allowing for independent local trust calculations [1][2]. - The model ensures real-time and reliable trust assessment by dynamically adjusting sub-array members and updating trust values, leveraging blockchain's transparency and immutability for data security [1]. Group 2: Operational Mechanism - The model operates through five core processes: data collection and preprocessing, sub-array division, trust calculation, cross-array consensus, and dynamic updating [2]. - Data is collected from nodes and verified via smart contracts before being stored in a distributed ledger, with key features extracted and historical data weighted down using time decay functions [2]. - Sub-arrays are formed using K-means clustering or geographical hashing algorithms, with dynamic adjustments based on node load and trust value fluctuations [2]. Group 3: Trust Calculation and Consensus - Each sub-array independently runs trust evaluation algorithms to compute local trust values, integrating direct and indirect trust assessments [2][3]. - A modified PBFT consensus mechanism synchronizes trust evaluation results across sub-arrays, reducing communication rounds and computational complexity [3]. - The global trust value is generated by aggregating results from sub-arrays, weighted by their historical reliability [3]. Group 4: Dynamic Updates and Applications - The system triggers trust value updates every 30 seconds, allowing nodes to query their trust scores and adjust interaction strategies accordingly [3]. - The model has applications in various fields, including vehicle networking for enhanced safety and efficiency, e-commerce supply chains for optimized operations, and distributed energy systems for stable energy supply [5]. - As technology evolves, the model is expected to expand into IoT, healthcare, and financial services, integrating with AI and big data to foster innovative trust management solutions [5].

Financial Performance - Total operating revenues for the six months ending June 30, 2025, were RMB 188,112,127, a decrease of 35.3% compared to RMB 290,441,871 for the same period in 2024[6] - Gross profit for the six months ending June 30, 2025, was RMB 51,571,816, representing a gross margin of 27.4%[6] - Net income attributable to MicroAlgo Inc. for the six months ending June 30, 2025, was RMB 26,470,591, an increase of 62.9% from RMB 16,259,664 in the same period of 2024[6] - Net income for the six months ending June 30, 2025, was RMB 31,736,118, representing a significant increase from RMB 22,176,601 in the same period of 2024, reflecting a growth of approximately 43%[12] - The Company reported a comprehensive income attributable to MicroAlgo Inc. of RMB 18,109,838 for the six months ending June 30, 2025[6] - The Company reported total revenue of RMB 149,090,866 for the year ended December 31, 2024, with no allowance for credit losses on accounts receivable[51] Cash and Liquidity - Cash and cash equivalents increased to RMB 1,813,379,345 as of June 30, 2025, up from RMB 1,035,932,786 as of December 31, 2024[2] - The total cash and cash equivalents at the end of the period on June 30, 2025, reached RMB 1,813,379,345, up from RMB 433,416,702 at the end of the previous period, indicating strong liquidity[12] - The net cash provided by operating activities for the six months ending June 30, 2025, was RMB 31,147,248, a substantial recovery from a cash outflow of RMB 5,063,081 in the same period of 2024[12] - Financing activities generated net cash inflow of RMB 1,079,213,504 for the six months ending June 30, 2025, a significant increase from RMB 127,310,153 in the same period of 2024, driven by stock issuance and convertible debt issuance[12] Assets and Liabilities - Total current liabilities decreased significantly to RMB 71,580,482 as of June 30, 2025, compared to RMB 206,921,631 as of December 31, 2024[2] - Total assets as of June 30, 2025, were RMB 2,368,162,417, reflecting a significant increase from RMB 1,267,134,187 as of December 31, 2024[2] - Accounts receivable, net as of June 30, 2025, was RMB 23,518,435 (USD 3,285,340) with no allowance for credit losses[107] Research and Development - Research and development expenses for the six months ending June 30, 2025, were RMB 33,470,081, a decrease of 56.1% from RMB 75,820,156 in the same period of 2024[6] - The Company’s research and development expenses include salaries, outsourced subcontractors, and related office expenses[74] Investments and Impairments - The company recognized an impairment of RMB 1,102,938 (USD 154,072) for equity method investments due to weak financial conditions[49] - The company disposed of Shenzhen Yiyou Online Technology Co., Ltd. for RMB 10, resulting in a gain of approximately RMB 1,416,187[102] - The total loss on the disposal of Khorgas Weidong Technology Co., Ltd. was approximately RMB 56,134,710[104] - As of June 30, 2025, short-term investments amounted to RMB 472,897,200 (USD 66,060,012) after an investment of RMB 389,427,840 (USD 54,400,000) during the six months ended June 30, 2025[106] Taxation - Under the laws of the Cayman Islands, the Company is not subject to tax on income or capital gain, and no withholding tax is imposed on dividend payments[117] - The applicable tax rate for VIYI Ltd in Hong Kong is 16.5%, but no provisions were made for profit tax as there were no assessable profits since inception[118] - In the PRC, the standard enterprise income tax rate is 25%, with a preferential rate of 15% for High and New Technology Enterprises (HNTEs) that must re-apply for status every three years[119] - As of June 30, 2025, the current income tax expenses amounted to RMB 2,290,252 (approximately USD 319,930) and total income tax expenses were RMB 2,290,252 (USD 319,930)[122] Share Capital and Structure - The Company has undergone multiple share consolidations, with the latest on July 18, 2025, resulting in a share capital of USD 6,000,000,000 divided into 1,000,000,000 shares[140] - The authorized share capital was increased to USD 6,000,000,000 following the share consolidation approved on July 2, 2025[139] - In the first half of 2025, the Company issued 10,503,680 Class A ordinary shares and 1,162,609 Class B ordinary shares, with a total of 10,941,519 Class A and 1,495,942 Class B shares outstanding as of June 30, 2025[144] Revenue Recognition - The Company adopted ASC Topic 606 for revenue recognition, requiring a five-step model to recognize revenue from customer contracts[57] - The Company recognizes revenue from software development contracts over time, with revenue recognized based on progress towards completion[68] - The Company’s performance obligation includes helping customers match consumers and traffic users to increase conversion rates using proprietary data optimization algorithms[64] Geographic Performance - The financial results are reviewed by management based on geographic locations, highlighting the importance of regional performance in revenue generation[167] - Revenues from Mainland PRC amounted to RMB 239,512,194, while Hong Kong revenues were RMB 50,929,677, indicating strong performance in both regions[167]

Company Overview - MicroAlgo Inc. is a technology company based in Shenzhen, China, specializing in custom central processing algorithms that optimize data processing and analysis across hardware and software [1] Business Model - The company focuses on developing software logic that enhances the efficiency of data handling, distinguishing itself from traditional CPU functions [1] Analyst Insights - The analyst emphasizes the importance of understanding macroeconomic trends and their impact on individual companies, suggesting a strategy that combines top-down economic analysis with bottom-up company evaluation [1]

Core Viewpoint - The rapid development of big data and artificial intelligence has highlighted data security and privacy issues, with traditional data transmission methods posing significant risks. The introduction of blockchain technology offers new solutions, exemplified by MicroAlgorithm Technology's innovative BlockFL architecture, which ensures secure, efficient, and privacy-protecting data transmission [1][6]. Group 1: BlockFL Architecture - BlockFL architecture utilizes blockchain networks to achieve efficient data exchange and synchronization in federated learning, allowing devices to upload local model updates and download global model updates quickly and effectively [2]. - The decentralized nature and high concurrency of blockchain ensure that all devices receive the same global model updates, maintaining consistency and accuracy in model training [2]. Group 2: Process Overview - Initialization involves the system administrator creating an initial model and broadcasting it to all participating nodes while the blockchain records metadata of the federated learning activity [4]. - Each node trains the model on its local dataset without exposing original data, thus protecting data privacy [4]. - Nodes upload encrypted model parameters to the blockchain, where smart contracts validate their effectiveness and integrity, preventing malicious actions [4]. - Once verified, a central server or designated aggregation node extracts parameters from the blockchain, averages them, and generates a new version of the global model [4]. - The updated global model is then broadcasted to all nodes for the next training round, with the blockchain ensuring traceability of all operations [4]. - An incentive and penalty mechanism is integrated into BlockFL to encourage participation and quality data contribution, with smart contracts automatically executing rewards and penalties [4]. Group 3: Applications and Future Prospects - BlockFL architecture can be applied across various sectors, including healthcare, financial risk control, smart manufacturing, and smart cities, facilitating data collaboration while maintaining security and privacy [5]. - In healthcare, BlockFL enables hospitals to collaboratively train diagnostic models while protecting patient privacy; in finance, it allows institutions to identify fraud without sharing sensitive information; in smart manufacturing, it promotes collaboration between factories; and in smart cities, it supports inter-departmental cooperation without compromising sensitive data [5]. - The combination of blockchain and federated learning in BlockFL addresses traditional data transmission challenges, enhancing efficiency and accuracy in model training, positioning it as a significant technological support in data transmission and machine learning in the future [6].

Core Insights - Quantum computing is evolving with the exploration of non-standard quantum oracles, which aim to overcome the limitations of standard quantum oracles in addressing complex computational needs [1][4] - Microalgorithm Technology (NASDAQ MLGO) is focusing on the development of non-standard quantum oracles to enhance quantum computing capabilities and provide new solutions for complex problems [1][5] Group 1: Non-Standard Quantum Oracles - Non-standard quantum oracles offer greater flexibility and can perform more complex logical operations compared to traditional quantum oracles, which are limited to specific tasks [1][3] - These oracles can dynamically adjust their computational logic based on different application scenarios, enhancing the efficiency and applicability of quantum algorithms [1][3] Group 2: Quantum Gate Operations - Microalgorithm Technology integrates new quantum interaction mechanisms into its design, creating innovative quantum gate combinations that allow for precise information processing [3] - The unique handling of environmental interactions by non-standard quantum oracles transforms noise and decoherence into beneficial elements for computation, improving efficiency and accuracy [3] Group 3: Applications and Potential - Non-standard quantum oracles can tackle complex mathematical problems and physical system simulations that standard quantum oracles struggle with, leading to new problem-solving approaches [4] - In cryptography, these oracles can enhance encryption algorithms, making them more resistant to quantum attacks and improving information security [4] - They also have the potential to optimize logistics and resource allocation, as well as improve machine learning model training, thereby advancing artificial intelligence [4] Group 4: Future Prospects - The research on non-standard quantum oracles by Microalgorithm Technology holds significant promise for overcoming current technical bottlenecks, moving from theoretical research to practical applications [5]

Core Viewpoint - MicroAlgo Inc. has developed a Grover-based quantum algorithm aimed at finding pure Nash equilibria in graphical games, marking a significant advancement in quantum algorithm research and game theory applications [1][6]. Group 1: Algorithm Development - The Grover search algorithm is utilized for efficient searching in unstructured databases, achieving a time complexity of the square root of the number of elements [1]. - The algorithm transforms the oracle in graphical games into a Boolean satisfiability problem, encoding game states and strategies as quantum states [2]. - A method has been designed to convert Boolean expressions into quantum gate operations, ensuring the quantum circuit reflects strategy choices and payoff feedback [3]. Group 2: Implementation and Efficiency - Adjustments were made to the Grover algorithm to address efficiency bottlenecks in multi-objective or multi-dimensional problems, employing a stepwise iterative approach to improve search efficiency [4]. - The algorithm's iterative process maximizes the amplitude of the target state based on oracle feedback, enhancing the success rate of finding pure Nash equilibria [4]. Group 3: Experimental Validation - Extensive experiments on random graphical game instances using a quantum simulator demonstrated the algorithm's effectiveness, showing significant improvements in speed and accuracy compared to traditional methods [5]. - The algorithm exhibited a higher success rate and shorter computation time across multiple iterations in complex gaming environments [5]. Group 4: Future Implications - The Grover-based quantum algorithm is expected to play a key role in practical business decision-making, market analysis, and multi-party game scenarios, equipping decision-makers with advanced tools for complex competitive environments [7]. - MicroAlgo aims to expand the application boundaries of this technology through collaboration with academia and industry, potentially driving scientific progress and business innovation [8].

Core Viewpoint - The integration of Explainable AI (XAI) technology by Micro Algorithm Technology (NASDAQ: MLGO) significantly enhances threat detection capabilities in blockchain networks, providing transparency and understanding in decision-making processes [1][5]. Group 1: Technology and Methodology - Micro Algorithm Technology has developed an intelligent threat detection system that combines deep learning models with an explainability module, allowing for automatic learning of attack patterns from large volumes of network traffic data [1][3]. - The system employs data collection and preprocessing techniques to ensure data quality and consistency, followed by feature extraction and selection using machine learning algorithms [3]. - The threat detection model is trained and evaluated using explainable machine learning algorithms, ensuring high accuracy and interpretability through iterative optimization [3]. Group 2: Applications and Impact - The application of Micro Algorithm Technology's XAI in blockchain networks has led to significant improvements in threat detection accuracy and efficiency, particularly in identifying anomalous transactions and malicious nodes [4]. - The technology effectively detects unusual transaction patterns, such as large or frequent transactions, which may indicate fraudulent activities, enabling rapid response to mitigate losses [4]. - In smart contract auditing, the technology analyzes code logic to identify potential security vulnerabilities, thereby preventing attacks due to contract flaws [4]. Group 3: Future Prospects - As technology continues to evolve, Micro Algorithm Technology's solutions are expected to find broader applications, contributing to the creation of a more secure and intelligent network environment [5].

Core Viewpoint - The article discusses the increasing security issues in blockchain technology, particularly focusing on DDoS attacks and how quantum convolutional neural networks (QCNN) developed by Micro Algorithm Technology (NASDAQ: MLGO) can enhance detection and response capabilities against these threats [1][7]. Group 1: Quantum Convolutional Neural Network (QCNN) Development - Micro Algorithm Technology has innovatively improved QCNN for detecting DDoS attacks in blockchain networks by optimizing quantum bit initialization and control methods, enhancing stability and reliability [1][7]. - The structure of QCNN has been adjusted to better handle blockchain transaction data and network status information, making it more suitable for the specific characteristics of blockchain data [1][7]. - Specialized quantum state reading and parsing technologies have been developed to accurately extract features related to DDoS attacks from quantum computation results [1][7]. Group 2: Data Collection and Preprocessing - Data collection involves gathering various types of data from the blockchain network, including transaction data, node status information, and network traffic data, using APIs and monitoring tools [3]. - Preprocessing of collected data is crucial for the effective operation of QCNN, involving data cleaning, noise reduction, and standardization to ensure data quality [3]. - Feature extraction is performed to identify characteristics related to DDoS attacks, such as transaction frequency and network traffic changes, which serve as inputs for the QCNN [3]. Group 3: Quantum Operations - Quantum bit initialization ensures that quantum bits are in a stable initial state, balancing the number of quantum bits with computational complexity [4]. - Quantum convolution operations utilize the properties of quantum bits to extract features and recognize patterns from input data through a series of quantum gate operations [4]. - Quantum pooling operations reduce data dimensions while retaining important features, employing a measurement-based pooling method to select the most probable quantum states [5]. Group 4: Classification and Output - After quantum convolution and pooling, a quantum fully connected layer processes the low-dimensional quantum state for DDoS attack classification and detection [6]. - The output from the quantum fully connected layer is a quantum state representing classification results, which is converted into a readable format using specialized quantum state reading techniques [6]. - If the probability distribution indicates a high likelihood of a DDoS attack, alerts are generated to notify network administrators for appropriate defensive actions [6]. Group 5: Applications and Future Prospects - The QCNN developed by Micro Algorithm Technology can monitor blockchain networks in real-time, promptly detecting signs of DDoS attacks and issuing alerts for immediate defensive measures [7]. - This technology can be integrated with other security measures, such as encryption and access control, to create a more secure blockchain environment [7]. - As quantum computing technology advances, the application prospects for QCNN in detecting DDoS attacks will expand, potentially enhancing computational power and accuracy [7].