In order to realize signal modulation through driving optical waveguide switch array operation, a complex multi-channel electrical driving circuit is needed. The main challenge is the complicated voltage calibration process and the imprecision of driving voltage. A control system based on STM32 that could automatically adjust the driving voltage is proposed. It has merits of low cost, real-time detection, fast output control, and high precision of driving voltage, besides it also reduces the complexity of voltage adjustment, and shortens the voltage adjustment time. The system consists of main control module, DAC ( Digital-to-Analog Converter) driver module and optical receiver module. The DAC driver module can drive the optical chip and control the output modulation signal. The functions of optical receiver module include, the adjustable amplified detection, real-time power detection of optical output and the transmission of detected signal to the analog-to-digital converter in the main control module. The main control module can record the output of switch array and adjust the driving voltage accordingly. It consists of the minimum system of single chip microcomputer and the serial screen. The drive control of optical switch is realized through the digital-to-analog converter module, and the feedback of optical switch is received by the optical receiving module. The software provides the function of automatically controlling the voltage within 20 V at an accuracy of 5 mV. The feedbacks from the switch chip are real-time collected to correct the voltage output. The experimental results show that this design works well on the optical switch array driving and has good potentials in optical interconnection, such as the switch array and so on.

When natural disasters such as earthquake and tsunami occur. Base stations on the ground are damaged or satellite signals are interfered, which makes it impossible to provide accurate location information, greatly hinders disaster relief search and post-disaster reconstruction. To solve this problem, a wireless localization scheme for target nodes in the GNSS ( Global Navigation Satellite System) rejection scenario is proposed. In this scheme, the rescue vehicle is selected as the reference node, so that the single UAV (Unmanned Aerial Vehicle) can carry out multi-point wireless ranging around a fixed trajectory centered on the reference point. By using the mobility of the UAV, the traditional multi-UAV floating ranging process is changed to single UAV moving multi-point ranging, which effectively solves the problems of resource waste, difficult clock synchronization and high system complexity. In the ranging process, a two-way ranging method based on weighting is proposed, which can effectively reduce the two-way channel error and clock offset error. Finally, the least square method is used to solve multiple groups of ranging information to obtain the relative position information from the target node to the rescue vehicle. Simulation and experimental results show that the positioning method proposed is reasonable and superior to traditional methods in performance.

Recently, with the rapid improvement of Chinese economy and people's living standards, the demand for the exploitation and transportation of energy is continuously increasing. As one of the important transportation modes, the length of petroleum and gas pipelines in China has increased greatly. Pipeline leakage detection technology is one of the basis for efficient and safe pipeline transportation. This article firstly introduces the development history and existing research achievements of pipeline leakage detection and location technology, and analyzes various common methods. Secondly, the fundamentals and operation manners of numerous routine pipeline leakage detection approaches are described. Then, based on the actual industrial situation, the characteristics and application scenarios of each detection method are described. At last this article analyzes the challenges of the mentioned detection technologies, and looks forward to the development direction of pipeline leak detection technology.

MCR-WPT( Magnetic Coupling Resonant Wireless Power Transfer) is a very important branch of wireless transmission technology. In recent years, its excellent power transmission efficiency in medium distance has attracted great attention at home and abroad, and has made a lot of research results in system characteristics and transmission efficiency. The principle of magnetic resonance radio power transmission is derived and analyzed in detail, and an improved PSO( Particle Swarm Optimization) algorithm is used to optimize the impedance matching of the system, and the superiority of this algorithm is verified by experiments.

The most important parameter in the life prediction of lithium battery is SOH( State Of Health) . Due to the interaction between individual cells in the battery pack, it is difficult to accurately predict the SOH of battery pack. Therefore, we propose a prediction model for SOH of lithium battery pack based on GRU(Gating Recurrent Unit) . Under different connection structures, charge cycle series, battery capacity series and battery temperature series are used to predict the health status of battery pack. The results show that in series and parallel connection mode, the prediction curves of single battery index show better degradation process of batteries, the prediction result is better than that of battery pack index and has lower pridiction error.

In order to solve the problem that the retrieval results are biased due to the nonstandard keywords and fuzzy semantics in the petroleum drilling literature, an attention pooling method based on the Siamese BERT(Bidirectional Encoder Representation from Transformers) networks model is proposed to improve the accuracy of literature similarity evaluation. Firstly, crawler technology is used to collect and clean the petroleum drilling literature. Then, five evaluation indexes of the petroleum drilling literature data set are used to mark the samples. Finally, combined with the data characteristics of the drilling literature data set, the attention pooling method based on Siamese BERT networks is used to express the overall semantics of multi-feature samples. The experimental results show that compared with the conventional pooling method, this method can improve the effect of similarity measurement of petroleum drilling literature, and has a certain generalization performance.

The frequency of highway congestion is increasing. In order to provide convenient travel path for drivers and slow down road traffic congestion, according to traffic statistics a highway traffic congestion prediction model based on improved genetic algorithm is designed. The fixed and mobile detection technology is used to collect macro traffic flow data such as flow, density and speed. Different identification and processing methods are adopted for abnormal parameters such as redundant data, missing data and error data to obtain effective and complete traffic flow data. The back propagation neural network and support vector machine regression network are used to improve the genetic algorithm. Two sub prediction models are established, and a hybrid prediction model is constructed by weighting the weights of the two models. According to the congestion prediction deviation of the sub prediction model, the weight coefficient of the hybrid prediction model is modified combined with the optimal weight combination strategy. The experimental results show that the design model can divide the traffic congestion level of the target expressway, and predict the congestion status according to the data of traffic flow, speed and occupancy rate, and the model has high prediction accuracy and ideal prediction effectiveness and accuracy.

The current research results of network delay measurement need to be improved in the measurement accuracy and other aspects. Therefore, the measurement technology of Internet of Things packet transmission delay based on edge computing is proposed. The traffic data of the edge position of the network is measured, and the traffic data of each link in the Internet of things is evaluated based on the edge measurement results. Divided into data center and the packet transmission delay deployment planning content delivery network and edge network scale of measurement system, based on the edge of the traffic estimates. By detecting tool further detection in a certain order hop routing link, on the basis of the router address connection by the path of all stripes, access to global connected network topology. Then the routing path between nodes and the corresponding packet transmission delay can be obtained according to the Internet of Things address routing rules. Experimental results show that the measurement technology has high precision and better performance than the current results.

Breast cancer is the most common malignant disease in women endangering women's life and health. Histopathological analysis is the most extensive method for the diagnosis of breast cancer. The diagnosis and classification of numerous pathological images require pathological experts to visually identify histological samples by microscopy, which takes time and effort to identify features. According to the different magnification of BreaKHis data set of breast cancer pathological images, it can be divided into benign and malignant samples for recognition. A breast cancer image recognition method based on DenseNet network and transfer learning is proposed to compare with VGGNet, ResNet, DenseNet and other deep learning models. The proposed model has strong learning ability and the best recognition effect, and the accuracy of the model can reach more than 98% on this dataset.

In order to solve the problem of low embedding capacity of the existing quantum image stegography schemes based on LSB ( Least Significant Bit), a novel color image quantum stegography scheme based on reflected Gray code rule is proposed. The secret information is scrambled first, and then it is divided into 6-bit segments. Finally, the first 3 bits are embedded in the second LSB of the RGB channel of the cover image, and the remaining 3 bits are embedded in the LSB of the RGB channel of the cover image using reflected-Gray code rules. According to the embedding rules, the difference between the embedded secret bit and the LSB of the stego-image is almost as high as 50% , which further guarantees the security of embedded information. Extracting is the inverse process of embedding. A quantum circuit for related operations is designed. The simulation results on the classical computer show that the embedding capacity of this scheme is as high as 6 bits per pixel, which is superior to the existing scheme in terms of embedding capacity, and has higher security. Therefore, it is feasible to implement the research scheme of quantum steganography by using Gray code rule.

Due to the complexity of geophysical exploration environment, the seismic data collected are often covered by random noise, therefore a convolution based cyclic generation adversarial network (CycleGAN) is introduced into seismic data denoising. The key of the network is to build a good structure of generator and discriminator and optimize the appropriate network parameters. The input data and label data of the network are generated each other to form a ring network structure, so the loss of cyclic consistency can be introduced to improve the accuracy of network parameter training. The non local neural network module is used as the residual link of CycleGAN generator to improve the feature extraction ability. Through the comparison of data visualization, peak signal-to-noise ratio and mean square error, the feasibility of this method is verified. The results show that compared with the general gan network and the residual network denoising effect, this method has better noise performance, higher peak signal-to-noise ratio and can clearer data imaging under different noise levels, which has a certain guiding significance for the actual production work.

The sharp increase of data center network traffic leads to frequent abnormal traffic attacks, which seriously threatens the user data security. Therefore, a data center network abnormal traffic detection algorithm based on SDN( Software Defined Networking) technology is proposed. The data flow transmission process is constructed according to the SDN technology network framework and the time and frequency set method, and then the data flow characteristics are extracted using fuzzy C-means clustering, quadruple, BP(Back Propagation) neural network and other algorithms. The traffic feature subspace is established using the principal component analysis algorithm, and the matrix method is used to project to the subspace. Finally, the set threshold and projection period data vector are used to judge whether there is abnormal traffic in the data center network. The experimental results show that the proposed algorithm is an simple and ensures the accuracy of abnormal traffic detection results, and effectively maintains the stability and security of the data center network.

Most localization algorithms assume that communication region of node is a circular and the ranging accuracy of all anchor nodes is the same. However, in the real environment, due to signal attenuation, shadow and interference, the communication area of the node is not a regular circle. The ranging accuracy from different anchor node is also different. Therefore, PACL (Priority-Anchor node-based Centroid Localization) algorithm is proposed. First, PACL algorithm considers the irregularity of the node communication region in the localization process, and establishes the signal transmission model based on the degree of irregularity. Then, the average hop distance of anchor nodes is calculated, and the priority of anchor nodes is set according to the average hop distance of anchor node, so that the nodes with higher priority take part in the localization process to improve the positioning accuracy. The simulation results show that PACL algorithm can reduce the positioning error compared with the traditional non-ranging positioning algorithm.

The main object of this reseach is to reveal the overall situation of the international cloud computing security technology field, the research status and hot spots of the main participating countries and regions, the main funding institutions, the main hosting institutions, the dominant disciplines, and the statistics and analysis of the main research directions of cloud computing security in China. Taking the scientific research achievements in the field of cloud computing security technology in the global scientific research project database as the research object, this paper uses the bibliometric method to make statistics and analysis on the data of related scientific research projects from 2011 to 2020. The results show that in the past 10 years, cloud computing security research projects show a fluctuating downward trend, the United States and the European Union have the most investment in scientific research, and medical science and information science are the leading direction of scientific research.

Localization algorithm of two-dimensional lightning monitoring system terminal software is simple. It has small amount of data processing, module function limitation and its visualization interactivity is relatively low. To meet the demand of lightning detection data receiving treatment, using C / S architecture and C ++ builder, three-dimensional lightning monitoring data processing center system is developed. By designing the receiving and processing software of the central system, the main functions of receiving data are classified and stored according to date. The three-dimensional positioning results, stored as positioning files, the status data is sent to the monitoring subsystem and storage subsystem, all kinds of statistical information are sent to the monitoring subsystem. The experimental platform is built by using the provincial three-dimensional lightning monitoring network, and the system operation and software function test are carried out. The results show that the algorithm design is accurate, the data processing is smooth, the system has complete functions, the user interface is friendly and convenient for transplantation, meeting the functional requirements of the three-dimensional lightning monitoring and positioning system.

In the power Internet of Things, user-side data is in a relatively isolated position, which makes it more difficult to mine data association rules. Therefore, a deep mining method of user-side data in the power Internet of Things based on association rule mapping is proposed. Based on the directed graph structure of user-side data mesh topology, the association mapping relationship among data sets is analyzed according to the association attribute group. And the association rules among data sets are mined using the correlation matrix. Extreme value standardization strategy and radial basis function neural network are introduced, and the dimensionless method and discrete clustering method are built. Through the hidden layer neural network is obtained. According to the K-means clustering process, data preprocessing, data types according to the different users of dominant and recessive side matrix, score matrix and users-project scale, deep data mining is realized. Experimental results show that this method can complete the mining task in a relatively short time, the processing effect of different data sets is better, and the data depth mining can be completed in a small memory space.

In order to solve the interactive problem of children's electronic picture books and realize the design and production for children's reading, based on the dual coding theory, a picture book design method an IOS (Input Output System) platform technology is proposed, which introduces digital media, animation and a variety of production methods such as sound effects realizing a new type of electronic picture book with better interactivity. Through the mobile device based on the IOS system, the visual display and intelligent recommendation of children's picture book knowledge are realized. Through verification and analysis, the system solves the problem of lack of interactivity of children's electronic picture books, and improves the accuracy of intelligent recommendation of children's electronic picture books. It can be used as a reference for the design and development of children's picture books in the future.

In order to solve the problem of high power consumption and low efficiency of information storage caused by the enlargement of large data environment, a data cloud storage optimization algorithm based on LSM (Log Structured Merge) tree is proposed. According to the difference of the quantity and size of data storage, network bandwidth and link length, the judgement model of data fragmentation storage is established, and the time delay of data sending and receiving is calculated. For the data that need to be stored in segments, the server power consumption, static power consumption and dynamic power consumption under each node are determined by the time delay threshold, and the data with high average power consumption are transmitted by category to achieve storage and energy saving optimization. Simulation results show that the proposed method can reduce the amount of redundant data in cloud storage environment, and the processing stability is strong, the average consumption is lower than the set threshold.

Traditional methods for the sharing of teaching resources often have problems such as unclear resource classification and low sharing efficiency. For this reason, a design scheme of teaching resource sharing platform based on the VEM ( Variational Expectation Maximization) framework is proposed. The platform consists of 4 levels, which are the preprocessing, feature extraction, classification and sharing of teaching resources for collaborative work. In the preprocessing stage, redundant information is deleted to reduce the system storage space. The feature extraction module uses the semantic adjacency matrix to calculate the feature value of the vocabulary in the resource, and selects the vocabulary with a larger feature value. In the classification module, the upper and lower relationship is used to construct a classification framework, calculate the strength of the relationship between each word pair, and realize the classification according to the different strength. The resource number of the completed classification is stored separately, and the teacher can directly find and complete the resource sharing. The experimental results show that the classification accuracy of the teaching resource sharing platform is 98. 6% , and the average time for resource sharing is 1.15 s, proving that the designed teaching resource sharing platform is accurate and efficient.

When using the current algorithm to schedule cloud computing QoS(Quality of Service) tasks, the scheduling time is long and the user satisfaction is low. Therefore, based on the user needs, the production function is introduced to build the cloud computing QoS task scheduling objective function. DPSO( Discrete Particle Swarm Optimization) algorithm is used to establish fitness function and obtain the optimal value through scheduling operation and deadline constrained search space. By solving the objective function to obtain the optimal scheduling scheme, the cloud computing QoS task scheduling is realized. Experimental results show that the proposed algorithm has high user satisfaction and scheduling efficiency.

Because the teaching effect of online teaching is influenced by teaching means and teaching content, the accuracy of online teaching effect evaluation of physical education is low. Therefore, a multi-index evaluation method based on fuzzy K-means clustering algorithm is designed. Firstly, the multi-index evaluation model of online sports teaching effect is constructed, the key elements that affect online sports teaching quality are divided, the index weights are solved, and the evaluation index parameters of online sports teaching effect are clustered and merged by fuzzy K-means clustering method, so that the quantitative recursive results can be obtained after the parameters of convergence index with nonlinear time series characteristics are constrained, avoiding the interference of teaching environment and completing the high-precision online sports teaching effect evaluation. The experimental results show that the proposed evaluation method can improve the operation speed on the basis of ensuring the high-precision evaluation of online teaching effect of physical education.

The data chain of ground to air communication plays an important role in the information transmission between the ground and the air. In order to analyze the information exchange of the data chain accurately, a new algorithm of data chain signal feature recognition based on particle swarm optimization and subtraction clustering is proposed. The envelope change is regarded as the basis of data chain signal division, and the basic features of signal carrier frequency, bandwidth and symbol speed are extracted using the strategy of square spectrum. The energy spectrum of signal is defined by wavelet transform, and the feature properties of signal are analyzed by the concept of energy fusion point. The frequency band with obvious energy spectrum difference is selected to create the feature quantity model of data chain signal. The particle swarm and subtraction fusion are integrated with Class method to search and reconstruct constellation. The best solution of particle and global population is defined. The fitness function of particle swarm is constructed, the cluster radius of the best subtraction method of constellationis calculated and the best cluster as the final signal feature recognition result is output. The experimental results show that the proposed method has high precision and strong ability of anti-interference, and can realize the real-time information interaction of VoIP ( Voice over Internet Protocol ) for ground to air communication data chain.

In order to solve the intelligent transformation of existing civil elevators in a wide range and high efficiency, an externally installed voice interactive key system based on STM32 is designed. The key system takes stm32f103rct6 as the control core, and realizes the non-contact key in the lift car by combining the voice interaction part and the mechanical key part. It is an external independent auxiliary key device, which ensures that the “person object person冶 cross infection pathway off without dismantling the lift car for transformation and affecting the use of the manual keys of the original elevator. The practice shows that the system has the advantages of simple and fast installation and low production cost. It can accurately realize the speech recognition and floor selection in the elevator car, and can meet the relevant applications of elevator transformation and car epidemic prevention.

Aerial crane work is flexible, convenient, practical, safe, reliable, and suitable for a variety of complex construction environment. Thus, it is an integral part of modern industrial construction. However, most of the existing aerial working platforms can only move in the vertical direction, and their efficiency is greatly limited. To complete an overhead operation, the basket needs to be adjusted in position several times in order to cover the entire working area. It consumes high energy, and takes a long time. Aiming at this problem, a two-wire vertical space moving system is designed and two-wire vertical working basket which can be moved horizontally is put forward. The distance between the two ropes and the two points on the roof is calculated using trigonometric function, which is transformed into the rotation angle of the hoist, enabling the basket to move flexibly in the vertical space, both horizontally and vertically. And the two-dimensional movement of the basket in the vertical space is realized through theoretical calculation and simulation. The reliability of the algorithm and the rationality of the device are tested. The experimental results show that the device has the characteristics of low cost, stable and efficient greatly improving work efficiency while guaranteeing the safety factor.

In order to enable users with different identities to access the specified information resources and refuse unauthorized illegal users to access the network resources, so as to protect the information security of network participants, aiming at the problem that identity authentication and key agreement protocols are difficult to achieve user anonymity, three-factor security and perfect forward security, a new three-factor authentication and key agreement protocol are proposed. The protocol can be applied to multi-server network environment. The security proof and performance analysis of the new protocol show that the new protocol is safe, practical and can effectively resist all kinds of known attacks.