针对金融事件存在论元专业性强、分散度高等特点，传统事件抽取方法难以实现精准抽取问题，提出了一种融合段落局部和文档全局特征的事件抽取方法。该方法首先对金融公告文档分段后并行地对所有段落利用Fin-BERT（Financial Bidirectional Encoder Representation from Transformers）预训练模型、卷积神经网络和自注意力机制获取文档局部特征；然后利用Bi-LSTM（Bi-directional Long-Short Term Memory）对整篇文档的语义信息进行学习获取文档全局特征；最后将段落局部特征与文档全局特征融合，输出事件论元和事件类型。在金融公开数据集ChFinAnn上进行的实验结果表明，该方法获得了平均80.2%的F 1 值，优于基线模型，证明了该方法的有效性。
A football competition risk intelligent warning method based on an improved Copula model is proposed to address the issues of large errors between warning values and actual values,and multiple false alarms in football matches.Based on the fuzzy comprehensive evaluation matrix,the evaluation system for football competition risk indicators is determined.The indicator level status is classified,the Copula function is selected,and an improved Copula football competition risk intelligent warning method is constructed to accurately judge football competition risks and reduce risk losses.The experimental results show that the interference suppression of this method is high,maintained above 20 dB,and have high anti-interference ability.It can effectively suppress interference.This method also reduces the error between the warning value and the actual value,reduces the number of false alarms in the warning,and verifies the practicality and feasibility of this method.
Big data clustering processing has the problem of poor clustering effect and long clustering time for different types of data.Therefore,a big data clustering processing method based on the improved PSO-Means(Particle Swarm Optimization Means)clustering algorithm is proposed.The particle swarm optimization algorithm is used to determine the flight time and direction of unit particles during a cluster,preset the selection range of the initial cluster center,and appropriately adjust the inertia weight of unit particles.It eliminates the clustering defects caused by particle oscillation and successfully obtains the clustering center based on large-scale data.Combined with the spanning tree algorithm,the PSO algorithm is optimized from two aspects:sample skewness and centroid skewness.The optimized clustering center is then input into the k-means clustering algorithm to realize the clustering processing of big data.The experimental results show that the proposed method can effectively cluster different types of data,and the clustering time is only 0.3 s,which verifies that the method has good clustering performance and clustering efficiency.
In order to maintain consistency of parameters such as current, voltage, frequency and phase during wind power grid connection, and improve the safety and stability of wind power grid connection, a control parameter identification method for wind power grid connected converters based on optimization algorithm is proposed. Establish a control model for wind power grid connected converters, and change the power control command of the PI regulator based on the power voltage support situation. Set the control conditions of the PI regulator using differential function equations. Calculate the functional relationship in the complex frequency domain, and clarify the logical relationship of the adjustment integral coefficient. The identifiability of control parameters is obtained through the control transfer function, the control output value and characteristic data of parameters are analyzed, and the optimal identification results of control parameters are finally completed. The experimental results show that the proposed method can complete the identification of control parameters in various environments, with small identification errors and high accuracy.
The traditional methods of manually identifying electrocardiogram signals have problems such as high workload and recognition errors.The existing electrocardiogram monitoring equipment still faces drawbacks such as limited recognition types of electrocardiogram signals,low diagnostic accuracy,and excessive reliance on network services.In order to improve the performance of electrocardiogram monitoring systems,ECG(Electrocardiogram Signals)analysis and detection system is designed based on deep learning technology.SENet-LSTM(Squeeze-and-Excitation Networks-Long Short Term Memory)network model is built to realize automatic diagnosis of seven categories of ECG signals.The model is deployed on an intelligent hardware platform which uses ADS1292R as the ECG acquisition module,STM32F103 as the data processing module,and Raspberry PI as the central processing module.The system uses the integrated high-performance microcomputer Raspberry PI for calculation and analysis,and provides users with offline AI(Artificial Intelligence)services.The preciseness of the model can reach 98.44％,and the accuracy can reach 90.00％,realizing the real-time monitoring and accurate classification of ECG,and providing accurate disease diagnosis for patients.
