The current multi-source heterogeneous sensory data fusion lacks the application of user feedback, resulting in low user satisfaction of the data fusion results. In this regard, a multi-source heterogeneous perceptual data fusion method for user feedback is proposed. A multi-source heterogeneous perceptual data storage model is built to complete the acquisition and storage of data. Uniform semantic annotation of the stored data. Obtain user relevance feedback. Based on the feedback information, the dynamic feedback fusion of multi-source heterogeneous perceptual data is completed. The experiments show that the average user satisfaction of the method is 0.9534, which is a significant improvement and has high practical application value.
The current method of power data consistency test based on gray correlation analysis verifies the consistency of data by measuring the shape and distance between data sequences, which leads to low accuracy of the test due to high data redundancy. In this regard, the consistency check of power fault multi-source heterogeneous big data under common factor structure is proposed. The Kalman filter algorithm is used to reduce the redundancy of power fault data, and the consistency discriminant criterion is established to realize the consistency test of power fault multi-source heterogeneous data by discriminating the cofactor relationship between the data. In the experiments, the proposed method is verified for testing accuracy. The analysis of the experimental results shows that the proposed method is used to test the consistency of power fault data, and its test error is low and has a high test accuracy.
KEYWORDS: Power consumption, Data modeling, Data communications, Data storage, Matrices, Mathematical optimization, Data processing, Data analysis, Statistical modeling, Statistical analysis
In the context of the problem of excessive MAPE values in the method of filling in missing big data of electricity consumption information, a method of filling in missing big data of electricity consumption information based on variational self-encoder is designed. Optimising the electricity information pre-processing model, store and manage the various types of raw and application data collected in a classified manner, define the objective function as the algebraic sum of the squared measurement errors, construct an electricity big data tensor filling model, treat missing values as variables, and design a missing filling method based on a variational self-encoder. Experimental results: The mean value of MAPE of the big data missing fill method for electricity consumption information in the paper is: 38.514%, indicating that the performance of the designed big data missing fill method for electricity consumption information is better after fusing the variational self-encoder.
KEYWORDS: Analytical research, Statistical analysis, Data analysis, Data modeling, Data mining, Power grids, Distributed computing, Data processing, Parallel computing
In view of the increasing data volume and the increasingly difficult data analysis in the power industry, an intelligent and efficient analysis and mining framework for power big data is designed to quickly obtain valuable information. Analyze the overall framework of the power big data center, mainly including the service layer, verification layer, data source layer, and feature analysis layer. In addition, through analyzing the process of data mining, it is found that the business needs to be strengthened And realize expansion. The framework design of power big data intelligent analysis and mining technology mainly includes power market demand, customer analysis, high-performance data analysis, service system, data security governance and other modules. Through the analysis of an example of intelligent power big data mining, the analysis results show that the intelligent power data mining has good analysis effect and high mining accuracy
KEYWORDS: Data modeling, Computer security, Data conversion, Data processing, Education and training, Data centers, Performance modeling, Process modeling, Evolutionary algorithms, Random forests
The distributed big data security risk control model achieves the control of big data security risk by distributed training of data feature vectors. The lack of processing of encrypted data leads to weak generalization ability. In this regard, a big data security risk control model based on federal learning algorithm is proposed. The heterogeneous data is formatted and the original data is preprocessed by data discretization and data scaling. The optimized federation learning algorithm is used to match the encrypted data, and the big data security risk control model is constructed to improve the generalization ability of the model. In the experiments, the proposed model is tested for its generalization ability. The analysis of the experimental results shows that the big data security risk control model constructed by using the proposed method has high data generalization ability.
Some intelligent detection methods for ultra-dense network attacks are likely to generate false alarms in the application process. In order to improve the security in ultra-dense network, an intelligent detection method based on edition learning is designed. Considering the SRP change rate, different thresholds are set, the node switching structural features of ultra-dense networks are extracted, the function sets that can effectively control error detection are selected, the host recognition algorithm is designed, the function field selection model based on joint learning is constructed, the iteration points are created in the feasible domain, real-time network traffic is collected, and the doattack intelligent detection model is optimized. Experimental results: in the paper, the average probability of non-intelligent detection methods for attacks in ultra-dense networks is 24.864%, which shows that when combined with federated learning algorithm, it has more advantages in practical performance.
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