Advanced Hybrid Information Processing

To improve the accuracy of sports information resource integration, a fuzzy clustering algorithm based method for sports information resource integration is studied. First, classify the sports information resources. According to the classification results of resources, build the sports information resource model. Use different sports concepts as nodes and their relationships as edges to build the concept network model. Based on the concept network model, denoise the sports information data. Based on the denoised sports information data, use the fuzzy clustering algorithm to cluster the sports information cluster analysis, Obtain relevant clusters of data, and then adjust the clustering algorithm parameters accordingly through statistical analysis of the clustering results to obtain accurate and effective integration results of sports information resources. The experimental results show that the accuracy of sports information integration in this method is the highest at 98%, the recall rate is the highest at 96%, the F1 is the highest at 0.97, and the longest time is 3.77 s, indicating the practicality of this method.

Intelligent park energy consumption data monitoring is the basis of effective energy management. By monitoring energy consumption data, you can understand the energy consumption of each equipment, system and region in the park, and analyze and evaluate energy use. This helps identify energy consumption problems, optimize energy use, and develop sound energy management practices. Therefore, a smart park energy consumption data monitoring method based on Internet of Things technology is proposed. The perception layer of the Internet of Things technology is used to control the energy of the smart park through the collection, transmission and control of monitoring data. The energy consumption data of each layer of the large-scale smart park is collected through the sensor network. The host computer uses USB interface to obtain data from the gateway. Based on this, the energy consumption data is preprocessed by using power error data correction and missing data fitting compensation steps. By using Gaussian function to analyze the characteristics of energy consumption sample data of the smart park, a multiple linear regression model is constructed to complete the monitoring of energy consumption data of the smart park. The experimental results show that the smart park energy consumption sequence under the proposed method is more stable in fit degree, more accurate in prediction and shorter in response time.

The multidimensional teaching effect evaluation system has the problem of high CPU usage in the process of actual use. To solve this problem, a multidimensional teaching effect evaluation system based on information integration is designed. Hardware part: choose RC punch and discharge circuit, convert TTL level into PC communication mode as well; software part: identify the characteristics of multidimensional teaching effect evaluation elements, divide them into evaluation subjects, optimize the system software function by using information integration technology, extract and revise the system log intermediate table. Experimental results: The multidimensional teaching effectiveness evaluation system designed this time is lower than the average CPU usage of the other three multidimensional teaching effectiveness evaluation systems: 9.896%, 11.111% and 10.036% respectively, indicating that the performance of the designed multidimensional teaching effectiveness evaluation system is superior after fully integrating information integration technology.

Conventional online education effect evaluation methods in higher vocational education mainly use the random subjective evaluation framework to obtain evaluation factors, which is vulnerable to the impact of micro personalized differences, resulting in low comprehensive evaluation indicators of education effect. Therefore, a new online education effect evaluation method in higher vocational education needs to be designed based on data mining algorithms. That is to say, the online education effect evaluation system is determined, the online education effect evaluation model of higher vocational education is constructed based on the data mining algorithm. The case analysis results show that the designed evaluation method for online education effect of higher vocational education has good evaluation effect, high comprehensive evaluation index, reliability and certain application value, and has made certain contributions to the follow-up optimization and reform of online education of higher vocational education.

With the popularization of civil radar application, it has great development potential in the situations of earthquake disasters and engineering accidents, such as personnel search and rescue, medical detection, and urban anti-terrorism. A civil radar echo signal processing method based on Kalman filter algorithm is designed. The Kalman filter algorithm is used to suppress the noise of the acquired radar echo signal. According to the amount of information obtained for the target being explored in different stages, target detection is regarded as the second stage of civil radar echo signal processing. Based on the Faster R-CNN detection framework, the context information and multi-scale Faster R-CNN target detection method are designed to determine the presence or absence of the target based on the denoised signal. Implement reference signal reconstruction, multipath clutter suppression, target location and tracking, and obtain some basic parameters to determine the target. The test results show that the tracking distance error of this method for stationary target, inching target and moving target is small.

Due to the relative radial movement between the transmitter and receiver of marine radar, the frequency of radar sampling signal is prone to deviation, which reduces the quality of radar sampling signal. In order to ensure the effective transmission of radar signals, a frequency offset estimation method of marine radar sampling signals in X-band based on phase difference is proposed. The AD9225 chip is selected to acquire the marine radar signal, and the undersampling theorem is used to determine the marine radar signal sampling frequency, so as to prevent the radar signal from mixing. After digital down conversion processing, two baseband signals are obtained, and the phase information of the radar sampling signal is extracted. Based on the Midamble code, the frequency offset estimation program of marine radar sampling signal is designed. The frequency offset estimation result of the signal can be obtained by executing the established procedure, and the frequency offset estimation of the sampling signal of the X-band marine radar can be realized. Experimental data show that after the application of the proposed method, the minimum signal to noise ratio of radar sampling signal is 4 dB, the minimum mean square error of frequency offset estimation is 4%, and the minimum time of frequency offset estimation is 2 s, which fully confirms that the proposed method has better application performance.

In order to solve the problem that the terrain echo signal of marine radar is affected by noise during transmission, which leads to poor enhancement effect, a terrain echo signal enhancement technology of marine radar based on generalized filtering is proposed. Design the graphic processing pipeline of programmable GPU, and draw the terrain echo image of marine radar. The generalized weighted median filter and Wiener filter are used to process high and low frequency signals to avoid some useful signals being filtered out. According to the high and low frequency signal processing results of the echo, the polynomial fitting sliding window is used to obtain the least square error fitting results to smooth the radar echo data. The echo signal enhancement structure is constructed, and the echo signal gain is processed to achieve the purpose of pseudo signal attenuation. Call the OpenGL read pixel function, and complete the echo signal enhancement processing according to the linear mapping relationship between the echo map and the coordinates of the DEM when processing in the GPU segment. From the experimental results, it can be seen that the echo gain effect of this technology is actually consistent, and there is only a maximum error of 1 dB between the echo signal strength and the actual data.

Fault detection can ensure the safe operation of airborne ocean radar. In order to improve the fault detection performance of airborne ocean radar, the design and improvement of the fault detection algorithm for airborne ocean radar is proposed. Based on the current and voltage values of stable operation, the fault area is determined. The fault information is decomposed by wavelet transform, the fault information is reconstructed, and the fault location is determined. Through the preprocessing of the fault data, the feature matching degree of the fault data is defined, and the features of the fault data are extracted by using the information state function of the fault data. Calculate the average trajectory of the observation vector of the operating state, and combine the operating trajectories of the fault data variables at different times to detect the faults of airborne ocean radar. The experimental results show that the algorithm in this paper has certain effectiveness in detecting the faults of airborne ocean radar, and has better performance in terms of missed detection rate, false detection rate and signal-to-noise ratio of fault signal acquisition.

Aiming at the problem of fixed period sampling control of radar signal and event triggered variable period sampling control, there are few research results at present, so a DSP based automatic control algorithm for civil radar signal sampling timing is designed. Using orthogonal intermittent sampling to implement sampling modulation on radar signals, combined with improved UNet3+network and sequence data recognition method, the modulation signal recognition is completed. Based on the recognition results, a DSP processor is designed with TTA technology as the core, integrating phase-locked loop synchronization and timing sampling technology to achieve automatic control of the signal synchronization sampling process. The test results show that the time control error of the algorithm is low, the control performance is good, and the control stability is higher than 95%. With the extension of the sampling period, the control stability does not show a downward trend.

In order to ensure the stability of drone flight and improve control performance, a constant speed, variable pitch, and heavy-duty multi-axis drone control system design based on LiDAR technology is proposed. In the hardware design of the system, the power supply circuit for drone control is designed based on the principle of lithium battery charging and discharging circuit and the working principle of SX1308; Based on the mathematical model of drone position control, a control law for the horizontal position outer loop and the horizontal position inner loop was designed. Based on the closed-loop structure diagram of the drone position controller, a drone position controller was designed to ensure that the drone can accurately hover, return, and waypoint flight. In the software design of the system, LiDAR technology is used to extract drone trajectory features, and combined with drone control algorithm design, drone control is achieved. The system testing results show that the system in the article can achieve the expected design goals and improve the control accuracy to over 90%.

In response to the characteristics of railway frequency shift signals, transient electromagnetic radar is used to start from two aspects: carrier frequency and low frequency. The carrier frequency uses the same frequency signal detection principle, while the low frequency uses a step sequence that can cover the entire low frequency band to detect unknown low frequencies. The detection principle and steps of carrier frequency and low frequency are given, and a simulation model is built and verified. Finally, accurate frequency shift signals are detected. Finally, the accurate detection of railway frequency shift signal is also realized under the condition of in band harmonic interference and white noise interference, and the bit error rate is analyzed under different signal to noise ratios. Compared with the traditional railway frequency shift signal measurement methods, the threshold of signal to noise ratio based on transient electromagnetic radar can be lower when detecting railway frequency shift signal.

Conventional multi-target tracking methods for rail transit crossings mainly use the Deep SORT tracking detector to measure the tracking Mahalanobis distance, which is vulnerable to the dynamic change of target tracking state, resulting in low accuracy of multi-target tracking. Therefore, a new multi-target tracking method for rail transit crossings needs to be designed based on transient electromagnetic radar. That is to say, the transient electromagnetic radar is used to collect the multi-target tracking data of rail transit crossings, build the multi-target tracking model of rail transit crossings, and design the multi-target tracking algorithm of rail transit crossings, thus realizing the multi-target tracking of rail transit crossings. The experimental results show that the designed multi-target tracking method based on transient electromagnetic radar has high accuracy, which proves that the designed multi-target tracking method for rail transit crossings has good tracking effect, accuracy, and certain application value, and has made certain contributions to improving the safety of rail transit crossings.

In order to improve the effectiveness of e-commerce potential customer data mining and processing, a method based on Apriori association rule algorithm for e-commerce potential customer data mining and processing is proposed. Innovatively adopting a multidimensional tree structure to improve the Apriori association rule algorithm, using frequent itemsets as candidate itemsets, and further expanding on this basis by adding judgment conditions to reduce the frequency of scanning the database; The Vector space model is used to calculate the similarity between e-commerce potential customers, and the similarity is used as a scalar value to complete the accurate calculation. The e-commerce potential customers at different levels in customer transaction data are divided. Obtain a sticky evaluation system for potential e-commerce customers from the perspectives of perceived usefulness, perceived ease of use, perceived service, perceived security, and perceived interest, as the basic indicators for subsequent mining and processing. The Quicksort method is used to sort each data dimension in the e-commerce customer data set, and the improved Apriori association rule algorithm is used to realize data mining and processing of e-commerce potential customers through high-density grid. The experimental results demonstrate that the method innovatively utilizes the improved Apriori association rule algorithm to mine three types of customer behavior data with an accuracy of over 80%, which is in line with the actual situation. It improves the effectiveness of e-commerce potential customer data mining and processing, effectively mining e-commerce potential customers, and providing good basic data for e-commerce platforms to adjust marketing strategies.

In order to increase the number of real-time online people on the education platform and realize the comprehensive promotion of English mobile online education, an English mobile online education platform based on GPRS/CDMA and the Internet is designed. Set up online education resource push module, mobile partner search module and English vocabulary learning module respectively to improve the hardware design of English mobile online education platform. On this basis, configure the GPRS/CDMA model, analyze the system’s education needs by driving the Internet serial port, realize online education services at all levels, and combine relevant application components to complete the design of English mobile online education platform based on GPRS/CDMA and the Internet. The experimental results show that under the influence of GPRS/CDMA and the Internet system, the number of real-time online people on the education platform has significantly increased, which is in line with the practical application needs of comprehensively promoting English mobile online education.

In order to meet the functional and performance requirements of users for online cultural education mobile terminals, the application research of artificial intelligence technology on online cultural education mobile terminals is proposed. The overall architecture of online culture and education mobile terminal is designed using artificial intelligence technology. Through communication connection design and identity verification design, the communication function of mobile terminal is designed. Combining the curriculum center module, evaluation module, terminal user information acquisition module and mobile terminal load balance module, the functional module of online culture and education mobile terminal is designed. Realize the application of artificial intelligence technology online cultural and educational mobile terminals. The test results show that the authentication module, course center module and evaluation module of the mobile terminal in this paper can meet the needs of users. In terms of user information acquisition delay and load balancing, they can also meet the performance requirements of users for mobile terminals.

In order to solve the problem of poor targeted teaching ability of college psychological mobile education and improve the mental health level of college students, a college psychological mobile education system based on GPRS/CDMA and the Internet was designed. On the basis of the distributed service framework, the IIS mechanism of the education system is set up, and then the mobile psychological learning module is combined to complete the hardware operation scheme design of the college psychological mobile education system. Perfect the VPN workflow in the GPRS/CDMA network, determine the layout form of the database organization with the help of the PPP Internet connection protocol, realize various technical functions in the mobile education system, and complete the design of the college psychological mobile education system based on GPRS/CDMA and the Internet in combination with the relevant hardware application structure. The experimental results show that the application of GPRS/CDMA and the Internet system can achieve accurate matching of college psychological mobile education in student terminals, effectively solve the problem of poor targeted teaching ability of psychological education, and can better improve the mental health level of college students, in line with the actual application needs.

Aiming at many problems brought by the complex running environment, controller performance and obstacles of garbage cleaning robot, this paper puts forward a path planning method of garbage cleaning robot based on mobile communication network. Jud that initial signal rate of the mobile communication network, constructing a motion model of the clean robot, and planning the robot grasping trajectory according to the model; According to the straight path planning and turning path planning, the planning method is studied. The experimental results show that the navigation deviation of the proposed method is small, which can avoid obstacles and effectively plan the path of the garbage cleaning robot.

The current methods for monitoring the status of electrical equipment are prone to interference from the external environment, resulting in low accuracy of monitoring results and longer monitoring time. Therefore, this study proposes a method for monitoring the status of electrical equipment based on wireless communication technology. Firstly, a low-power wireless transceiver module is established based on RF transceivers. After selecting a suitable wireless communication receiving device, a wireless monitoring framework is established. Then, Fourier transform technology is used to collect electrical equipment status monitoring signal data, and wavelet analysis technology is used to organize the collected signals. Finally, neural network technology is used to evaluate the real-time status of electrical equipment. Through data mining, conduct in-depth analysis of signal data to obtain the final monitoring results. The experimental results show that this method can effectively improve the accuracy of monitoring results and shorten the output time of monitoring results.

With the rapid development of mobile technology, the coverage of Wlan, 3G and 4G networks is expanding day by day, and intelligent mobile terminal assisted English teaching and learning has become a hot research field. This study explores the application of intelligent mobile terminals in mixed listening and speaking teaching of college English from three aspects. The first aspect analyzes the application of mobile terminals in the collection of listening and speaking teaching resources. The second aspect analyzes the application of mobile terminals in the recommendation of listening and speaking teaching resources. The third aspect analyzes the application of intelligent mobile terminals in listening and speaking teaching scoring: intelligent mobile terminals extract the relevant features of students’ input voice, and use SVR to give students’ listening and speaking practice scores, which are presented on the mobile terminal learning page. The results show that the average absolute error is less than 1, indicating that the application of intelligent mobile terminals in the recommendation of college English mixed listening and speaking teaching resources is better. The correlation degree is more than 0.5, which indicates that the accuracy of the evaluation results is high, and the resource recommendation time is always below 80ms, proves the application effect of intelligent mobile terminals in college English mixed listening and speaking teaching.

The current channel allocation method does not consider the user transmission power problem, which leads to the problems of high user power consumption and low average transmission capacity, so a new anti-interference dynamic allocation algorithm for social communication channel resources in heterogeneous cellular networks is proposed. Determining a reusable set of channel resources for social network users; Under the premise that a given social network user reuses an arbitrary set of resources, the transmission power of the user is adjusted to measure the throughput of each network user on different channel resource sets. At the same time, the undirected graph theory in graph theory and ant colony genetic algorithm are used to cluster social network users, and as heterogeneous network scenarios change, the undirected graph will dynamically change, forming a new clustering scheme. Using intra cluster orthogonal inter cluster multiplexing as a criterion, auction method is used to allocate channel resources for social network users to reduce inter user interference. According to the selfishness of user behavior, a non cooperative game model is established, which combines fixed point theory and iterative algorithms to allocate power to users who complete channel allocation, maximizing user energy efficiency. The experimental results show that the proposed algorithm can reduce the power consumption and greatly increase the average user data amount.

The current numerical simulation of dual laterolog response is mostly unidirectional, and the efficiency of numerical simulation is low, resulting in high resistivity and potential safety hazards when conducting potential processing. Therefore, the design and verification research of the numerical simulation of dual laterolog response based on wireless communication technology are proposed. Firstly, the feature extraction of numerical simulation is carried out, and the multi-level method is adopted to improve the efficiency of numerical simulation, realize multi-level grid generation of dual laterolog, establish a wireless communication numerical simulation model, and implement numerical simulation by boundary constraint processing. The final test results show that the measured resistivity is well controlled below 7 in combination with the electrical coefficient of each set point through numerical simulation processing at the depths of 1.1 m, 1.3 m, 1.5 m, 1.8 m, 2 m, 2.3 m, 2.5 m and 3 m, which indicates that this numerical simulation has relatively large coverage and strong pertinence, it has practical application value to optimize the unit simulation structure, improve the efficiency and quality of the overall numerical simulation, and minimize the difference in the simulation process.

Due to the large amount of data and complicated information processing of online physical education teaching resources, the reliability of traditional teaching resources sharing method is difficult to guarantee, and it is easy to collapse when multiple users run together. In order to improve the transmission efficiency of online educational resources sharing, SOA architecture and wireless network are introduced, and an online physical education teaching resource sharing method is designed. Design an overall architecture of data sharing based on SOA architecture, and establish an online physical education teaching resource base according to this framework; On this basis, the entity E-R diagrams of different modular teaching resources are established, and the data information is converted into DBMS records. By dividing the attributes of physical education teaching resources and classifying teaching resources, the effective transmission of resource sharing can be realized. Complete the sharing of online physical education teaching resources in higher vocational colleges in the data exchange center. The experimental results show that the proposed method has a good effect in practical application, which can improve the network load rate, ensure the transmission speed of resource sharing at a higher level, and have higher reliability and better resource sharing performance.

The conventional teaching method of building construction training is mainly desktop real-time data interaction. Although the investment cost is low, its immersion is poor, which affects the effectiveness of practical teaching courses. Therefore, the application of intelligent mobile terminal in virtual building construction training teaching is studied. Establish the function module of virtual building construction training teaching, and improve the immersion function of the training teaching course. Simplify the teaching task of virtual building construction training based on intelligent mobile terminals, reduce other costs to the minimum, increase the investment in interactive equipment, and meet the immersive experience of students in practical teaching. Manage the virtual building construction training report, and conduct the whole life cycle training management for students in the whole training teaching process, so as to achieve the immersion and effectiveness of the training teaching. The simulation experiment proves that the average benefit function of this teaching method is 24.6, and its intelligent mobile terminal has low latency and energy consumption, which has high terminal efficiency.

The current method is faced with the interference of logging signals caused by mud invasion, which cannot meet the real-time requirements. In order to effectively solve geological problems and improve the ability to solve complex formations, it is necessary to carry out numerical simulation of swept frequency dielectric logging response. Therefore, a numerical simulation model of swept frequency dielectric logging response based on wireless communication is proposed. By defining directional signals, the inclined transmitter coil structure has the ability of formation evaluation and geosteering azimuth detection, and a variety of formation models are used for numerical simulation of logging tools. The experimental results show that the numerical simulation model is effective.

Incorrect actions not only affect the training effect, but also cause certain harm to the athlete’s body. A sports athlete incorrect action recognition system based on wireless communication network is proposed. Build a wireless communication network architecture, obtain sports athletes’ sports videos, extract key frames, determine athletes’ positions, extract sports athletes’ action characteristics (STIP characteristics, Cuboids characteristics, enhanced dense trajectory characteristics and covariance characteristics), based on the extraction of sports athletes’ action characteristics, build a Hyperplane based on support vector mechanism, and finally realize the recognition of sports athletes’ wrong actions. The experimental data shows that after the application of the system in this article, the maximum recognition rate of incorrect movements of sports athletes obtained is 96.35%.

In order to improve the accuracy of adaptive detection of security vulnerabilities in mobile social networks and achieve the ideal effect of high-precision adaptive detection of vulnerabilities, static analysis is introduced and an adaptive detection algorithm design of security vulnerabilities in mobile social networks based on static analysis is developed. Use plug-in technology to scan mobile social network ports, databases, operating systems, Web, security baselines, weak passwords, and industrial control systems to obtain network data. The abnormal data propagation rules are used to preprocess the scanned data and extract the network abnormal data. The static analysis of the extracted abnormal data defines the corresponding rules of network security vulnerabilities by building an abstract simulation of network applications, extracts the corresponding relationship between abnormal data and network security vulnerabilities, calculates the final score of network security vulnerabilities according to the basic evaluation utilization factor, and identifies and detects the security vulnerabilities of mobile social networks. The experimental analysis results show that the designed algorithm has a vulnerability detection rate of more than 90% with and without security protection mechanism, and the adaptive vulnerability detection rate is high.

In order to improve the efficiency of dynamic mining for wireless network information transmission security vulnerabilities and improve the accuracy of mining results, this paper proposes a dynamic mining method for wireless network information transmission security vulnerabilities based on the spatiotemporal dimension. Firstly, collect data on security vulnerabilities in wireless network data transmission; Secondly, wavelet transform is introduced to filter and process wireless network information transmission security vulnerability data; Then, in the deep neural network architecture, the instruction level word embedding method based on Word2vec obtains the feature attributes of wireless network information transmission security vulnerabilities; Finally, dynamically mine wireless network information transmission security vulnerabilities based on the spatiotemporal dimension. The experimental results show that the vulnerability dynamic mining method proposed in this paper takes 25.8 s, with an accuracy of 99.0% and a recall rate of 98.1%, which can improve the effectiveness of vulnerability dynamic mining.

Due to the problems of low recognition accuracy and long recognition time in traditional wireless visual sensing network identity feature recognition methods, a convolutional neural network-based wireless visual senscto the operation results, the global threshold method is used to obtain the binary image sequence and perform morphological processing. Based on the processing results, Extract target regions from video image sequences of wireless visual sensing networks, detect human targets, and construct a Softmax classifier using convolutional neural networks to classify human targets in video image sequences of wireless visual sensing networks, in order to identify identity features. The simulation results show that the proposed method has high accuracy and short recognition time for identity feature recognition in wireless visual sensing networks.

To enhance the precision and clarity of graphic and image depictions, we propose a super-resolution image reconstruction method driven by the power of deep learning. This method initiates by obtaining the reconstruction object from graphics and images, subsequently simulating their degradation process. The preprocessing of initial images is accomplished via registration and expansion, setting a solid foundation for the subsequent stages. Deep learning algorithms are employed to interrogate and dissect the inherent features of the graphics and images. Subsequently, a lineup of techniques including feature fusion and bilinear interpolation are deployed to gain super-resolution reconstruction results of the graphics and images. Upon examining and juxtaposing our deep learning-based method with conventional techniques, we discerned a noticeable advantage of the former. Intriguingly, the resolution deviation within the image reconstruction results derived via our idealized strategy has been remarkably minimized. Concurrently, peak signal-to-noise ratio and structural similarity attributes have been substantially augmented. This unique confluence of improvements as embodied in our approach places it squarely as a potential game-changer in the domain of super-resolution image reconstruction.

In response to the problems of low accuracy and long time consumption in traditional hyperspectral remote sensing image classification methods, this paper proposes a hyperspectral remote sensing image classification method based on a three-dimensional convolutional neural network model. Firstly, the image data is preprocessed and normalized. Based on this, a three-dimensional convolutional neural network is introduced into the learning of image data. On this basis, by optimizing the overall connectivity parameters of the convolutional kernel function, hyperspectral remote sensing image classification based on the convolutional kernel function was achieved. Experiments have shown that the algorithm proposed in this article can accurately classify hyperspectral images and achieve good results.

To improve the feature visualization effect of texture images, a texture image feature enhancement processing method based on visual saliency model is proposed. After collecting texture images, use soft and hard threshold denoising algorithms to denoise the texture images. Extract and decompose the features of the denoised image based on the visual saliency model. Based on the results of feature decomposition, the resolution of the texture image is reconstructed using deep learning technology, and then the texture image is described using shear wave transformation method to enhance the expression of the image’s feature information. According to the experiment, it can be seen that after applying this method, the distortion coefficient of the texture image is smaller and the clarity is higher, indicating the feasibility of this method.