For a specific temperature system, although most control methods can achieve good control effect,they also increase a lot of parameter adjustment process and other preliminary work. A generally applicable adaptive PID ( Proportion Integration Differentiation) control method combined with model predictive control is proposed. This method includes a simple modeling method. The predicted system output is obtained through the model, and the parameter change of PID controller is guided by the principle of minimizing the sum of square error between the predicted system output and expectation in the future. The method is implemented on the industrial controller PLC(Programmable Logic Controller). It is verified that compared with the traditional PID,due to the parameter adaptation guided by the model prediction, its control accuracy is higher, and its maximum error does not exceed the minimum resolution of the sensor after the output is stable. The convergence speed is faster, the average adjustment time is shortened by about 30% , and the average rise time is shortened by 20% ~40% . It has strong stability, and the maximum dynamic deviation is the same order as PID. Compared with
other model prediction methods, this method gives full play to the robustness of PID method, and has the characteristics of easy implementation and universal application.

In order to further design and analyze the discrete control system so that the new closed-loop system is asymptotically stable and meets the expected performance index, a static output feedback controller is designed for singularly perturbed uncertain control systems in the case of delay dependence and delay independence. And a new quadratic summation type L-K functional is constructed, the functional difference process is amplified by means of the cross-term defined method, and the uncertainty functionof the system is eliminated by lemmas, the sufficient criterion of the static output feedback controller with time delays is obtained, which enlarges the control range of the controller. The effectiveness and feasibility of the derived conclusions are verified by the selected example, and the controller is more superior in times comparing the corresponding literature.

An electronic vacuum booster braking control system and method are proposed for solving the problems of intelligent driving braking, driverless braking or automatic emergency braking. The controller system controls the solenoid valve of the electronic vacuum booster to realize the active and passive pressurization, and realize the active braking and passive braking of the vehicle. The temperature model is used to solve the problem of insufficient braking force caused by the heating of the drive chip and the long-time braking. It provides a control principle and method for controlling electronic vacuum booster.

In order to give full play to the overall combat superiorities of the aircraft cluster to obtain optimal target allocation plan, we use an improved wolf pack algorithm to solve the battlefield situation model. In order to improve the global optimization ability of the algorithm and ensure the optimization efficiency of the algorithm,the concept of the second wolf is introduced to improve the calling and siege behavior of the wolf pack, and the update mechanism of the wolf pack algorithm is optimized. The simulation results show that the proposed method can quickly and accurately find the optimal objective function value, and to a certain extent improves the situation that the traditional wolf pack algorithm is easy to fall into the local optimum.

The current method for digital video tampering, no denoising, results in forensic error, high missing value and serious video distortion. Based on artificial immunity digital video tampering method, preliminary filter through the median filter, and similar blocks, a third order tensor is established according to the matching results, according to the nature of the tensor and noise to eliminate the Lagrange multiplier noise. Artificial immunity algorithm is used to build the autodynamic evolution model and the antigen dynamic evolution model,based on the digital video tampering forensics model to realize the forensics of digital video tampering in the denoised digital video input model. The experimental results show that the proposed method has small forensic error, low video missing value and small video deformation coefficient.

Edge detection is a basic problem in digital image processing. Its purpose is to detect the pixels whose gray level changes obviously in the neighborhood. The Canny edge detector is currently the most popular edge detection tool. The specific implementation of Canny detector in the quantum computing paradigm is studied. For Gaussian smoothing filtering and Sobel sharpening operators, we have designed a new method called Translation, Stacking and Weighted Summation, which can make full use of the parallelism of quantum computing to accelerate its classical counterpart and avoid convolution operation. For the gradient and angle calculations required in edge detection, we design new operators such as addition,multiplication and division of signed number by introducing the binary complement description of gray-scale value. For the non-maximum suppression and double threshold processing required in edge detection, we have separately designed the quantum circuits that implement these tasks by introducing quantum complement comparators. Complexity analysis shows that the quantum Canny edge detector has exponential speedup compared to its classical counterpart. The simulation results on the classical computer verify the effectiveness of the proposed method, and reveal that the research idea of integrating quantum computing and image processing is feasible.

Aiming at the problem of rock physical facies identification, a identification method based on the interpretable one-dimensional convolutional neural network is proposed. By introducing the global average pooling layer, the dynamic variation part of the logging waveform is highlighted. The interpretability of the method is enhanced by the classification activation mapping. By introducing dilated convolution and batch normalization, the performance degradation caused by the global average pooling layer is compensated. According to the experimental results, the average F1 score of the four petrophysical facies in the test set is 0. 97, which is about 0. 15 higher than that of other similar methods. The results show that the proposed method can be used to identify petrophysical facies and improve the interpretability in the classification process, providing a new deep learning method for the prediction of high quality tight sandstone reservoirs.

In view of the employment problem of college students, the hierarchical clustering strategy is used as technical support to construct a short-term forecast of employment system. According to the amount of data to be mined, the distance metric between clusters is selected. A hierarchical clustering tree composed of clusters and sub-clusters is constructed. The clustering or splitting of the target data is completed. The relevant algorithm operation process in the hierarchical clustering algorithm module is constructed. And combining with the prediction system roles and responsibilities of users and administrators, the multiple system database entities is designed. A mapping model of the relationship between entities is established. In the experimental stage, based on the collected information about the previous graduates of a certain university, a hierarchical structure model of the employment destination composed of the target layer, the criterion layer and the program layer is obtained. By comparing the predicted results of the employment destination of the students of the school with the actual results, the accuracy rate and recall are combined. It can be seen from the numerical value of the rate index that the system has high prediction accuracy, and it can meet the application needs of short-term prediction of student employment.

Aiming at the problems of redundancy elimination efficiency and low recall rate in traditional cloud storage, a hierarchical redundancy elimination optimization method based on Pearson correlation algorithm is proposed. According to the attribute distribution similarity measure value of redundant information in the
hierarchical structure, the distance matrix of redundant information is constructed to classify the hierarchical redundant information by calculating the similarity between redundant information. By analyzing the structure of different types of redundant information, using data dimension reduction constraints and central limit principle,the objective function of redundant information feature space compression is constructed, and the hierarchical redundant information features are extracted based on the redundancy optimization hyperplane, the distance between redundant information sample points and positive and negative hyperplanes is calculated. The fuzzy factor is defined by Pearson correlation algorithm, and then the feature effectiveness in the hierarchical structure of cloud storage is defined. The effectiveness function of redundant information features is constructed, and the redundancy optimization of cloud storage hierarchy is realized. Experimental results show that the design method improves the redundancy removal efficiency and has good performance in recall rate.

In order to solve the problem that the commonly used artificial intelligence water-flood prediction fails to consider the correlation of data in time, by selecting an improved LSTM ( Long Short-Term Memory Neural Network) based on RNN (Recurrent Neural Network) the oilfield water injection prediction model is built. The model can take into account the relationship between the historical water injection volume and the influencing factors, and take into account the trend and correlation of water injection volume with time. Taking the water injection prediction of a complex fault-block reservoir in China for example, the LSTM water injection prediction model is established to predict the water injection volume of a single well in a period of time, and compared with the prediction model established by traditional RNN. The experimental results show that the model has more ideal prediction effect and higher prediction accuracy, which can effectively improve the accuracy of oil field water injection prediction.

In the process of network operation, it is easy to be invaded by external abnormal data or virus, which leads to network collapse or even failure to operate normally. Therefore, a network security operation platform based on multi-source heterogeneous sensors is designed. Firstly, the DS / AHP ( Dempster-Shafer/ Analytic Hierarchy Process) method is improved by combining rules of DS(Dempster/ Shafer)evidence demonstration, to fuse the security data submitted by multi-source heterogeneous sensors. Finally, through centralized log management, threat analysis, control analysis of security assets, asset identification management, attack traceability analysis, function analysis of threat intelligence and work order, intelligence driven security operation analysis, intrusion alarm can be realized and the source of intrusion can be traced to complete the design. The experimental results show that the platform can identify the intrusion data, trace the source of the intrusion successfully, and effectively ensure the security of the platform operation.

In view of the problems of traditional geographic information collection, such as low real-time, poor data authenticity and unclear description of detail area, a design scheme of geographic information system based on remote sensing technology is proposed. It is mainly composed of information acquisition module, imaging processing module, data storage and management module, scene control module and data transmission module.Remote sensing technology is used to collect the reflected and radiated electromagnetic waves in the target area.Transformation is used to enhance the image clarity, and weighted fusion algorithm is used to process the image details. According to the respective storage and management, the attribute self association mechanism is established. The SketchUp software is used to render the ground objects in the picture to achieve the actual control of the virtual objects. Finally, the RS422 protocol is used to transmit information in the wireless office network realizing the multi-purpose effect of one machine and enhancing the anti-interference ability of the system. The experimental results show that the proposed system is excellent in practice, the presented geographic information image is clear, and the edge details can be clearly visible, providing users with effective geographic information data.

Tax risk management is the core for building a scientific and rigorous tax collection and management system. In order to achieve better tax risk management, improve the scientific and applicability of tax risks and perfect the foundation of tax risk management, a tax risk management platform supporting risk ranking, risk query and risk indicator management that combines tax database and tax risk management is proposed. It can solve the problem that tax departments can not handle a large amount of data in the existing tax risk analysis platform, and is conducive to tax departments to find out various types of risks. It is conducive to the taxation authorities to identify various types of risks that may arise in the taxation process from a large amount of data, so that such risks can be avoided or reduced according to the risk tips.

Electric power enterprises are in the market of short supply. In order to prevent the production as the guiding concept of power marketing enterprises, a design scheme of power marketing audit business monitoring management system is proposed based on data mining, which can meet the requirements of legality, equality and comprehensiveness and high efficiency. Firstly, line loss management, customer service, power supply and consumption contract, measurement management, electricity consumption inspection, electricity price and electricity fee management and business expansion report and installation are taken as the audit contents and objects. Based on the minimum support and minimum confidence degree of monitoring data calculation, the probability of interest degree is introduced to dig out the credible information and remove the non representative data to improve the operation efficiency of the excavation system layer, business layer and management layer build and visual panoramic monitoring and management system. Experiments show that the designed audit system has high diagnostic accuracy of electricity price, which can provide certain audit basis for power marketing workers.

In order to solve the problems of efficient storage, processing and analysis of medical data, a medical big data platform is designed and developed. HDFS (Hadoop distributed file system) is built and deployed, and a website platform based on Tomcat server is designed. The web server is combined with distributed file system by writing Hadoop web API, and a Python script program with high data processing efficiency is designed to read and analyze medical data. The test results show that medical big data platform achieves the expected functions of data storage, sharing and visualization.

In order to reveal the overall situation in the field of international computing technology, the research status and hotspots of the main participating countries and regions, the main funding institutions, the main host institutions and the dominant disciplines, the statistics and analysis of the main direction of China's computing research. Taking the scientific research achievements in the field of computing technology in the global scientific research project database as the research object, the data of related scientific research projects established from 2011 to 2020 are statistically analyzed by bibliometric method. The results show that in the past 10 years, the number of mathematical research projects has increased in a wave-like way, and the United States and China have invested the most in scientific research, with information science as the dominant direction.

A laser communication system based on pulse dialing modulation is designed to improve the time jitter performance in free space optical communication. Different from the OOK( On-Off Keying) and PPM( Pulse Position Modulation) methods, this system only needs to distinguish whether there is a pulse or not, which can avoid the influence of time jitter. The system implements the pulse dialing modulation at the transmitter, the signal decision and error control at the receiver through STM32 MCU(Microcontroller Unit) programming. The receiver of this system adopts a method of multi-channel reception. It enlarges the receiving area to reduce the aiming error. The experiment results show that this system is quick-response, reliable and easy to realize, and its communication distance can reach 10 meters.

To make AGV ( Automated Guided Vehicle ) work efficiently in various environments, it is necessary to select a suitable path planning algorithm according to the actual terrain. We use the A* and DWA ( Dynamic Window Approach ) hybrid path planning algorithm and build four typical terrains,U-shaped, S-shaped, L-shaped and narrow passage in the simulation environment to conduct pathfinding experiments. Furthermore, we improve the weight recursive formula of Gmapping, remove the dependence on the previous moment data and improve the efficiency of the algorithm. The results show that the hybrid path planning algorithm has faster pathfinding speed and better obstacle avoidance ability than the single algorithm. It has the fastest pathfinding speed in L-shaped terrain and was relatively slow in U-shaped terrain and S-shaped terrain.