In order to improve the operation performance and environmental adaptability of the manipulator, it is essential to control the position and force of the manipulator simultaneously. This paper proposed a hybrid control method of auto-dynamic bit based on bit control to realize flexible grasping of manipulator in unknown environment. The constraint conditions and control types of the hybrid force/bit control are analyzed firstly, and the dynamic model when contacting with the environment is established. Then, an improved position controller based on conventional fuzzy-PID control strategy is introduced to realize the hybrid force/bit control based on the position control by combining constraint estimation method and impedance force control. Finally, the control performance of the force/bit hybrid controller is verified by simulation experiments. The results show that the designed control strategy has relatively ideal force control precision and higher trajectory tracking ability, and the manipulator model has better compliance. 

Self-adjusting force/bit blending control based on quantitative factor-scale factor fuzzy-PID bit control

In order to improve the operation performance and environmental adaptability of the manipulator, it is essential to control the position and force of the manipulator simultaneously. This paper proposed a hybrid control method of auto-dynamic bit based on bit control to realize flexible grasping of manipulator in unknown environment. The constraint conditions and control types of the hybrid force/bit control are analyzed firstly, and the dynamic model when contacting with the environment is established. Then, an improved position controller based on conventional fuzzy-PID control strategy is introduced to realize the hybrid force/bit control based on the position control by combining constraint estimation method and impedance force control. Finally, the control performance of the force/bit hybrid controller is verified by simulation experiments. The results show that the designed control strategy has relatively ideal force control precision and higher trajectory tracking ability, and the manipulator model has better compliance.

Attacks on network systems are becoming more and more common, the current state of increasingly sophisticated attack methods, the emergence of intrusion prevention technology is the inevitable result of the development of computer technology and network technology, and research on intrusion prevention has become a new focus of network security technology research in recent years. In order to ensure the security of computer network confidential information, the authors propose a semisupervised clustering intrusion detection algorithm. An overview of machine learning, followed by an explanation of the theory of cluster analysis, simulation experiments were carried out using the 
 
 K
 
 -means algorithm and the semisupervised clustering algorithm proposed by the author, for 10,000 records, the 
 
 K
 
 -means clustering algorithm and the semisupervised clustering algorithm described in this paper are used, respectively, and intrusion detection data tests were performed. At the same time, different 
 
 K
 
 values were selected, three datasets were selected from “kddcup.newtestdata_10_percent_corrected,” the test data were tested separately, and their average value was taken as the test result. From the simulation results, the detection rate of the semisupervised clustering algorithm is higher than that of the 
 
 K
 
 -means clustering algorithm, and the false alarm rate and 
 
 K
 
 -means algorithms have also been improved. Therefore, the author’s semisupervised algorithm enhances the stability of the system, and the performance of the 
 
 K
 
 -means algorithm is improved to a certain extent. When the value of 
 
 K
 
 gradually increases, the false alarm rate also increases; however, when 
 
 K
 
 is 20, the detection rate is maximized, from this, it can be known that when 
 
 K
 
 is 20, its detection rate reaches 91.76%, and the false alarm rate is 8.54%. The detection rate of the author’s algorithm is significantly higher than the other two algorithms, the false positive rate is slightly higher than 
 
 K
 
 -means, and the false positive rate is lower than that of the other algorithm, proving the superior performance of our algorithm.

Computer Network Confidential Information Security Based on Big Data Clustering Algorithm

The development of big data technology makes the feature selection technology gradually perfect. The advantages of different feature selection technologies are different. Among them, random forest algorithm belongs to integrated feature selection algorithm. Integrated processing of classification results can screen out the most representative feature impact. Based on this background and random forest algorithm, this paper analyzes the evaluation of motion effect. After the measurement, this paper obtains the body data before and after the training. After the calculation, the change data of the body index are determined. The random forest feature selection method is used as the carrier to determine the corresponding index attribute set. In the process of data set input, the corresponding whole input data set is formed through data classification. The completion of training, through the comparative experiment, is conducive to clear the degree of influence of physical indicators and then complete the exercise effect evaluation. The research shows that the random forest algorithm has significant advantages in the evaluation of sports effect and can effectively improve the accuracy of classification.

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Application of Motion Effect Evaluation Algorithm Based on Random Forest

The vigorous development of tourism has made rural tourism a highlight of the new era. In order to better realize the classification of rural tourism features, this paper proposes a knowledge recognition algorithm based on hierarchical clustering analysis. Firstly, the rationality of the optimization of the rural tourism feature algorithm is analyzed in this paper; secondly, the rural tourism feature classification index system is constructed based on the hierarchical clustering analysis; finally, the index weights are clearly divided according to the characteristics through the hierarchical clustering analysis of the knowledge identification algorithm. According to the specialties of hierarchical clustering analysis, the criteria of the algorithm are determined, and the characteristics of rural tourism are carefully classified. Rural tourists can visit different scenic spots in high density and improve the mobility of rural tourism. The experimental results show that: through the analysis of the characteristic classification data of the Rural Tourism College in this paper, it can be seen that the average daily income of the scenic spot is higher than that of the traditional rural scenic spot. The average daily income of rural tourism has increased by more than 261,900 yuan, which has largely promoted the development trend of rural tourism in my country. It is proved that the hierarchical clustering analysis method is helpful for rational zoning and serious thinking about the characteristics of rural tourism. This paper provides a reference for promoting the classification of rural tourism.

Classification of Rural Tourism Features Based on Hierarchical Clustering Analysis Knowledge Recognition Algorithm

Because of multiple advantages such as high bandwidth, high security, and flexible networking, the free space optical communication (FSO) has attracted more and more attention. However, the narrow beam of laser and the constrained access degree of optical node limit the development of mobile FSO networks, especially in the aspect of handover. In this paper, we propose a fuzzy logic control-based handover decision (FLC-HD) algorithm to ensure real-time and reliable communication. First, according to the predictive position and the motion information of mobile nodes obtained by the error correction extend Kalman filter algorithm, we obtain the received signal strength and the relative motion angles at next transmission. Second, we put forward the concept of threshold to ensure the completion of handover operations before the current link breaking. Third, the fuzzy logic control method is designed to select the optimal access point (AP) with three critical parameters, i.e., the received signal strength, the relative motion angles, and the access degrees of nodes. Simulation results show that the FLC-HD algorithm can improve the handover efficiency and relieve the ping-pong effect in mobile FSO networks.

Research on dynamic handover decision algorithm based on fuzzy logic control in mobile FSO networks

The invention discloses a mobile node switching method based on dynamic decision threshold in wireless optical network, solving the problems that the mobile node cannot implement the real-time communication during switching in the wireless optical network, and that the access gateway of the mobile node is overloaded caused by tedious transmission of data packets between the currently accessing gateway of the mobile node and the gateway to be accessed of the mobile node. The invention comprises the following steps: step 1 of predicting motion information of the mobile node in real time; step 2of calculating the decision threshold of the switching distance of the mobile node; step 3 of triggering the decision threshold; step 4 of searching the identification information of the gateway to beaccessed; step 5 of replacing the home agent router information; step 6 of binding the gateway to be accessed; and step 7 of switching the mobile node. The invention is capable of realizing real-timecommunication when switching the mobile node, and has the advantages of small load of the accessing gateway of the mobile node and high switching efficiency of the mobile node.

Ding Guoqing

Papers

Research on dynamic handover decision algorithm based on fuzzy logic control in mobile FSO networks

Mobile node switching method based on dynamic decision threshold in wireless optical network