KEYWORDS: Data mining, Analytical research, Data modeling, Mining, Process modeling, Power supplies, Library classification systems, Data storage, Switches, Solids
The traditional early warning method of power user complaints, the complaint classification management model is not perfect, and the prediction accuracy rate is low. For this reason, an early warning method for power user complaints is designed that integrates GBDT and Logistic. This paper obtains grid customer behavior preference data, focusing on mining potential customers. On this basis, customer satisfaction characteristics are extracted, which promotes customer demand feedback. This paper divides power customers into different categories and builds a complaint classification management model. This paper uses a decision tree as the basic learner, using GBDT and Logistic to establish a complaint early warning mode. The experimental results show that the average prediction accuracy of this method is 83.367% in the 5dB noise environment. The average prediction accuracy of this method is 53.602% in the 10dB noise environment. The prediction accuracy of this method is higher than the two compared methods, which shows that the method in this paper can achieve the purpose of improving the prediction accuracy.
KEYWORDS: Data modeling, Data analysis, Visualization, Data transmission, Visual process modeling, Clouds, Visual analytics, Optimization (mathematics), Performance modeling, Data storage
The current data scheduling retrieval model have the problem that the performance of sub flow path at non bottleneck is restrained, which affects the performance of the model. This paper constructs a visual data scheduling and retrieval model for customer service data analysis platform. Using microservice encapsulation, the overall architecture of the platform is designed, and the corresponding services are called to complete business processing. Considering the fairness of bottleneck, the queuing mechanism under path congestion is established, and the transmission path is allocated according to the number of data packets that need to skip the buffer, which is read and sent according to the path sequence number. Combined with round-trip delay and path congestion, a data scheduling retrieval model is established to schedule and retrieve the path with low congestion. The simulation results show that compared with the models based on service differentiation, scheduling priority allocation, cloud computing and evolutionary multi-objective optimization, the total amount of data transmission, throughput and bandwidth utilization of this model are improved. It has certain transmission advantages in case of network congestion, which is conducive to improving the stability of the platform.
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