The motor hour (working time) of an armored vehicle’s engine reflects its technical state to a certain extent. However, even the same type of engine with the same motor hour shows very different technical states in different working environments. At the same time, it is difficult to obtain the full life data or physical failure mechanism required by the traditional life prediction method. In view of the above problems, a model of engine life prediction based on degradation data and neural networks is built in this paper. Firstly, the degradation parameters are selected according to certain principles, and the sample data are standardized. Then, the principal component analysis method is used to simplify multiple parameters to a comprehensive parameter, and the interpolation method is applied to get the parameter’s time series data as the train data of the neural network. Finally, the life prediction model of the engine based on the neural network is established. The validation results indicate that the model runs accurately. It is also practical and worthy of being used abroad.

Warfare is still unavoidable in the contemporary era, and it is essential for a country to estimate its winning probability when making decisions. While most previous studies are concerned with the result of a single battle, this paper focuses on a long-lasting campaign that may consist of many battles. First, this paper considers two parties and a single type of armed force. Moreover, a dynamic approach is proposed to describe the evolution of the campaign. In particular, each party has a certain number of armed forces in the beginning, and the campaign ends if all the armed forces for any party are extinguished. The probability for each party to win and the probability of a draw are analyzed. Numerical examples are presented to illustrate the applications.

Thermoplastic composites are gradually applied to the wind turbine blade because of their advantages of recyclable reuse. In this paper, the mechanical properties of biaxial cloth and triaxial cloth were obtained by establishing the three-dimensional equivalent volume unit model of laminate plates. The layer of a complete thermoplastic composite wind turbine blade was designed by layering respectively from the spar cap to the blade root reinforcement layer. The blade surface pressure and tip wind speed were obtained by using single steady flow solid coupling analysis. The wind speed of the tip was consistent with the theoretical settlement results, indicating that the CFD model was effective. In the end, with reference to the force and moment results of the blade root at GH Blade, the distribution force loading method and multi-point constraint MPC loading method were respectively compared. The results show that the distribution force loading method can better reflect the actual mechanical properties of blades.

Predicting the residual life of long-term storage products with regular inspection and preventive maintenance is of great significance nowadays. In this paper, a model of storage process that takes multi-stage degradation and preventive maintenance into consideration is established. Considering the amount of degradation of the product to follow the Wiener process, we put forward a method to predict the residual life of long-storage products based on the degradation model in a multi-stage storage process. Through a simulation method, five experiments are performed to calculate and compare the residual life in different situations. Finally, we find that the dramatic changes of environmental conditions during the inspection period influence the residual storage life observably. By simulation, this model is effective by making full use of data collected during storage time, including degradation amount and maintenance information.

An oxidation self-heating process of sulfurized rust usually results in a fire or an explosion in crude oil tanks due to the oil’s maximum temperature (T_max) exceeding the critical temperature at which the fire and explosion happens. Some previous studies have shown that T_max is determined by the five main factors including water content, mass of sulfurized rust, operating temperature, air flow rate, and oxygen concentration in the safety valve. In this paper, based on a collected dataset about the five factors and T_max, the Gaussian process regression (GPR) method is adopted to build a nonlinear model describing the relationship between T_max and the five factors, and the new model is then used to predict T_max of other similar processes by inputting the data corresponding to the five factors. The results show that the GPR model can reach the prediction accuracy and the prediction result by the GPR model is more accurate than that by the model of Support Vector Machine (SVM). Thisindicates that the GPR method can be applied to predict T_max of the oxidation self-heating process of sulfurized rust. The prediction of T_max using the GPR model is of great significance to industrial risk control and accident prevention of sulfur-containing oil in production and transportation.

Aiming at enhancing the mission reliability of complex systemscomposed of sequential-parallel parts, a sequential condition deterioration process model was constructed. To better illustrate the effect of varied maintenance behaviors, minimal maintenance, imperfect maintenance, and replacement were taken as optional maintenance actions and modeled. To achieve the maximization of current reliability, a selective maintenance decision-making model and the attached artificial solving measures were brought forward. The case study showed that by integrating the service age reduction model and hazard adjusting model, the imperfect maintenance model can model the actual reliability of the system better. Also, within a given maintenance time, imperfect maintenance was useful for offering more feasible maintenance plans, and the system reliability can be better promoted as a result.

Due to aerospace equipment’s need to maintain a certain degree of safety and reduce the system risk of operation, relevant maintenance and inspection strategies should be developed to meet the requirements of risk quantitative indicators. The inertial navigation system commonly used in aerospace products is taken as an example, and a sequential inspection and maintenance model based on quantitative risk constraints and component importance is proposed in this paper. Firstly, based on the quantitative constraints of the system risk and the importance of the components, the reliability constraints of the components in the inertial navigation system are determined by the fault tree analysis method. Secondly, the Wiener process is used to establish a performance degradation model for a key component of the inertial navigation system, and the expression of real-time reliability distribution is obtained with close form by use of the first-hitting time theory. The adaptive estimation method is used to estimate the unknown parameters of the model. Once the new degradation information is available, the parameters should be updated with a Bayesian equation. Thirdly, a sequential inspection model is discussed to determine the optimal intervals to satisfy the requirements for the real-time reliability at a certain time. Finally, an example of the drift data of the gyroscopes in the inertial navigation system is given to illustrate the validity of the proposed method.

Consecutive k-out-of-n: F systems have applications in the fields of telecommunications and oil pumping. A consecutive k-out-of-n: F system fails if at least consecutive k components fail. However, practical systems may fail due to either the failure of a number ofconsecutive components or the failure of a total number of components. In this paper, we consider a system with linearly arranged components, where the system fails if at least a pre-specified number of consecutive components fail or a pre-specified total number of components fail. The system is termed as a combined consecutive k and v-out-of-n system. The iterative relationship between the failure probability of a consecutive k-out-of-n: F system has been proposed. This paper focuses on the iterative relationship between the success probabilities and proposes the detailed calculation process for illustrative purposes.

Speech recognition is becoming the key technology of man-machine interfaces in information technology. The application of voice technology has become a competitive and new high-tech industry. However, due to the big volume of vocabulary, continuous voice, and personalized accents, it is hard to make speech recognition completely accurate. In this paper, a reliability model is proposed to measure the performance of speech recognition. In particular, two types of task failures are suggested and an iterative approach is adopted. Numerical examples are proposed for illustrative purposes.

Considering a large number of fuzzy uncertain factors in the evaluation of rescue capability of rescue equipment, a quantitative evaluation method is proposed by using an improved fuzzy comprehensive evaluation method. The AHP-fuzzy comprehensive evaluation model, through a comprehensive analysis of the key factors affecting the rescue capability, constructed a three-level index system for evaluation of the rescue ability of the equipment. AHP is used to determine the weight of the index, and then the evaluation algorithm is proposed based on the fuzzy comprehensive evaluation method. Finally, an example is given to verify the effectiveness. The uncertainty inherent in normal evaluation method can be lowered, and the conclusion can provide help for more rational selection and improvement of rescue equipment.

Many research works have been conducted in the preventive maintenance area since maintenance strategies have become more and more significant in industry and supply chain services. However, previous studies are mainly based on age maintenance policies and other multi-criteria approaches instead of Multi-Attribute Utility Theory (MAUT). There are some studies proposed by using MAUT, but they assume that the maintenances are perfect. This paper presents an imperfect maintenance model of a one-unit system to obtain an optimal inspection interval based on MAUT. The proposed model is designed to identify the systems’ status by making a trade-off between cost attribute and reliability attribute. By taking decision makers’ preferences into account, with the assumption of imperfect maintenance, the model receives a result of an optimal inspection interval. This model is applicable for equipment or systems that suffer graded failures and can be repaired at any time during the operation. A numerical application is given to illustrate that with consideration of decision-makers’ priorities, the model can provide new solutions for imperfect maintenance interval optimization, which is a suggestion for future research.

The availability of support stations directly affects the departure ability of the fleet. New faulty aircraft will perturb the existing security plan of support stations, so a new security plan needs to be developed to ensure that the effect of disturbance is minimal. The traditional first-come-first-serve (FCFS) algorithm and the priority-weighted scheduling algorithm focus on local adjustments or do not take into full account the global coverage time. Some other optimization algorithms and models, such as heuristic algorithms and mesh flow models, have the disadvantages of high computational costs, over complexity, or low availability. In view of the above problems, this paper proposes to deal with the time relationship of fault aircrafts with the Allen relationship, obtain a set of dominant maintenance programs, and then achieve the best maintenance support program as well as the minimization of the disturbance influence.

A hydraulic skirt pile gripper (the gripper or the pile gripper) is widely used in the installation and construction of offshore platforms, and it mainly plays the role of clamping pipe piles during the construction of the offshore platform for ensuring the subsequent grouting. The number of holes in the body of the pile gripper has a great influence on the force of the body. Finite element software was used to simulate the force of the body with different numbers of holes. Then, a suitable number of holes was selected according to the stress and strain of the body. At the same time, with a smaller diameter of the pile and a larger diameter of the hole, the diameter and the height of the gripper body had a significant effect on the stress-strain of the body. In this paper, the ANSYS software was used to establish a design model and optimization was conducted. The fuzzy matter-element method was used to select the optimal design. After optimization, the maximum equivalent stress of the body was reduced by 14.75% and the volume of the body was reduced by 4.8%. The optimization method in this paper is effective.

In order to improve the reliability of cantilevered low-pressure die casting machines, the reliability of the cantilever beam, which is a key component of low pressure cantilever casting machines, is analyzed based on the ANSYS/PDS module and combined with a hybrid stochastic finite element method in this paper. The hybrid stochastic finite element method combines the Monte Carlo method and response surface method to analyze the finite element of cantilever beam. The results show that the width of the cantilever beam and the length of the free end are the design variables that influence the cantilever beam, and they can be used to further optimize the structure size.

TBM main bearing is the key part of the main driving system of tunnel boring machines. Special working conditions determine its relatively complex structure, and at the same time, high reliability requirements are put forward. Based on the stress-strength interference model, the study of the residual strength time history of roller-raceway contact takes the nonlinear strength degradation and the dispersion of material into account, and the reliability of main bearing structure is calculated. Combined with engineering practice, taking the main bearing of rock tunnel boring machines in the water diversion project of Dahuofang Reservoir in Liaoning as an object, the remaining strength and reliability of each main structure of a three-stage time varying load are calculated, and the reliability of main bearing can be predicted based on this main bearing system model.

As an effective model for processing time series data, the recurrent neural network has been widely used in the problem of sequence tagging tasks. In order to solve the typical sequence tagging task of Chinese word segmentation, in this paper we propose an improved bidirectional gated recurrent unit conditional random field (BI-GRU-CRF) model based on the gated recurrent unit (GRU) neural network. This network is more easily trained than the LSTM neural network. This method can not only effectively utilize text information in two directions through bidirectional gated recurrent units, but also obtain the globally optimal tagging sequence as a result by considering the correlation between neighbor tags through the conditional random field. In this paper, experiments are carried out on the common evaluation set (PKU, MSRA, CTB) with the four-tag-set and six-tag-set respectively. The results show that the BI-GRU-CRF model has high performance in Chinese word segmentation, and the six-tag-set can improve the performance of the network.

A monitoring and rescheduling system for steelmaking and continuous casting (SCC) production is studied with the consideration of three functions as simulation, monitoring, and rescheduling. To classify the knowledge and enhance knowledge reusability, modularity knowledge management is used in this system. In this system, the monitoring and rescheduling knowledge of the SCC production are organized into a two-level tree construction, and a combination of object-oriented and rule-based representations is applied. Moreover, based on the characteristics of monitored disturbances, we present two rescheduling methods named time adjustment and machine reassignment. The former is designed for disturbances with weak influence by utilizing reserved times and flexible factors in the SCC production. The latter reassigns machines based on unused capacities of machines to eliminate serious disturbances. Simulation results demonstrate the proposed rescheduling algorithms are feasible and effective, and the system has well real-time capability to maintain the stability and continuity of SCC production.

Nowadays, aiming at the problem of long testing duration of software reliability demonstration testing, this paper presents a method that can greatly shorten testing duration. First, the idea of a monotone decreasing function is integrated into the Bayesian theory of software reliability demonstration testing, and then a software reliability demonstration testing scheme of a Bayesian method based on monotone decreasing function is proposed. Secondly, according to the real-time prior information in the testing phase, priori information is dynamically integrated. Two kinds of priori dynamic integration methods suitable for two different conditions are proposed. Finally, through experimental analysis, it is proven that the testing scheme can significantly reduce testing duration under the same confidence condition, and the two kinds of prior dynamic integration methods are feasible.

In order to reduce the cost of wind turbines and boost their operation reliability, the aerodynamic load of wind turbine blade is analyzed by taking the key parameters of wind turbine blade airfoil and operation as design variables. According to the actual wind force and wind shear theory, the Planck loss factor is introduced based on the existing algorithm. The axial induction factor and tangential induction factor were modified, and the optimization algorithm of aerodynamic load of wind turbine blade was deduced. The feasibility of the algorithm is verified by simulation.

To ensure the efficient and stable operation of a whole production line, the reliability evaluation of assembly line is studied. In this paper, the LEC method is used to evaluate the human factor reliability of the assembly line production efficiency and its stability. To reduce the subjectivity of the LEC method and make the evaluation more objective, this paper used the objective reliability and error analysis (CREAM) to calculate objectively the rate of human error, while carrying out the evaluation of the possibility L of work suspension accidents. At the same time, the two indexes of E and C were modified and improved. The proposed evaluation model is applied and verified to the automobile assembly line. The result shows that it has certain feasibility and reference value.

In order to solve the problem of serious ablation of weapon tubes, a method is presented to reduce ablation of high temperature gas on the barrel bore surface by application of magnetron plasma. The turbulent dissipation model of high temperature conducting gas in a cylinder structure is constructed by using the magnetic fluid description method. Numerical simulation of the flow and heat transfer characteristics of conductive gas in a cylinder are studied, along with the effects of different magnetic field directions on the wall temperature of the cavity. The effect of a vertical magnetic field on the heat transfer characteristics of the conductive gas is tested by infrared thermal imaging technology. The results show a that magnetic field can effectively reduce the turbulent kinetic energy of conductive gas, and its distribution has the characteristics of anisotropy. Turbulent kinetic energy along the magnetic field direction is significantly lower than that in the direction perpendicular to the magnetic field. The magnetic field perpendicular to the flow direction of the conductive gas can weaken its heat transfer capacity.

This paper discusses a multi-objective portfolio in the case where the distributions of security returns are subject to reputable experts' evaluations instead of historical data. Based on the assumption that the security returns of risk assets are uncertain variables, we put forward a new risk-free protection index (RFPI) model for multi-objective uncertain portfolio selections with entropy and variance constraints by using expected value as a measurement of portfolio return and take both variance and RFPI as measurements of a portfolio's risk measurements. To determine a suitable confidence level and criticality value of FRPI, we regard FRPI maximization as the second goal under the premise of maximum expected return rather than relying on the investors' risk preference and tolerance. To avoid excessive dispersion or concentration of investment, the proportion entropy and variance constraints are added to the FRPI model. The Delphi method is used to solve our model, and its algorithm is also shown. In the ending example, a comparative analysis is illustrated to show our multi-objective uncertain portfolio model with FRPI maximization added as the second objective function performs better than the traditional mean-variance model (MVM) and the risk-free protection index-entropy model (RFPIEM).

The pantograph-catenary system is one of the most important ways to supply power for running trains. Electromechanical phenomena affect its economic operation. Because former research was mostly experimental, this paper presents a preliminary model for electromechanical interactions. In this model, electrical contact resistance and contact force are calculated in a defined contact interspace as finite cuboid elastic elements. On the assumption that the heights of all asperities follow the Gaussian distribution, a simulation is carried out and the following conclusions can be drawn: 1) the real contact area of the strip and contact wire can increase with the nominal contact area, but when the nominal contact area exceeds a value, the real contact area will no longer increase; 2) the growing contact force can enlarge the real contact area and reduce electrical contact resistance with its randomness; 3) pantograph sliding can be equivalent to contact elements disconnecting and connecting, which can explain why arc occurs more frequently in two conditions: when the contact force decreases and when the contact force is low.

The automatic image segmentation of liver and liver tumors is important in the diagnosis and treatment of hepatocellular carcinoma. A novel triplanar fully convolutional neural network (FCN) composed of three 2D convolutional neural networks (CNNs) is proposed to handle the issue. It performs segmentation through the transverse plane, coronal plane, and sagittal plane and can effectively use multi-dimensional features for 3D segmentation. Then, a cascaded structure is used to balance the positive and negative samples for segmentation of the tumor. The experimental results are obtained through data analysis and tested on the 3DIRCADb. They show that our method outperforms the existing methods and achieves a volume overlap error of 6.7%and 3.6% on the liver and tumors respectively.

Science technology innovation (sci-tech innovation) is significant for economy growth under innovation-driven development, and financial capital is an essential factor to provide cash for innovation. Thus, it is necessary to measure the combination of sci-tech innovation and financial capital. To solve this problem, this paper provided a structure vector auto-regressive model for measuring the effects of sci-tech innovation and financial capital. The five variation components considered were the amounts of direct financing, indirect financing, technical turnovers, sales revenue of products that are adopted new technology, and high-tech product exports. The results indicated that the scale of direct and indirect financing and sci-tech innovation are positively related; both have a significant role in promoting sci-tech innovation; the two complementary financing modes supporting sci-tech innovation are stronger than the alternative; both direct and indirect financing have a positive effect in the long run, and they mainly impact the technology diffusion in the short term; innovation is characterized by time lag; and the response for indirect financing is greater than that for direct financing. Meanwhile, indirect financing has more explanatory power on output growth error.

Malicious webpage attacks occur frequently, and most of the JavaScript attack code is implemented through obfuscation. In order to further confront malicious webpage attacks, detecting JavaScript obfuscation scripts has become one of the most urgent issues to be addressed. This paper proposes a method for detecting JavaScript code obfuscation based on Convolutional Neural Networks (CNNs). Firstly, the character matrix feature method of Bigram is used to extract features of JavaScript code. Secondly, a CNN model is applied to the JavaScript code obfuscation detection, which overcomes the high requirement of the machine code learning and the low accuracy of the obfuscation feature extraction of JavaScript code. Finally, the simulation results show that this method can not only reduce the requirements for the features, but also effectively improve the accuracy of the detection of JavaScript code obfuscation.

Affinity Propagation (AP) clustering is an algorithm based on message passing between data points, which mainly achieves clustering through the similarity between data. Compared with traditional clustering methods, the AP clustering algorithm can implement clustering without giving a predetermined number of clusters. Therefore, it has the advantages of fast and high efficiency. However, it has certain limitations in dealing with high-dimensional complex datasets. In order to improve the efficiency and accuracy of the AP clustering algorithm, a coarse-grained parallel AP clustering algorithm based on intra-class and inter-class distances is proposed: IOCAP. Firstly, the idea of granularity is introduced to divide the initial dataset into multiple subsets. Secondly, the similarity matrix is improved by combining the intra-class and inter-class distances for each subset. Finally, the improved parallel AP clustering is implemented based on the MapReduce model. Experiments on the Iris dataset, the Diabetes dataset, and the MNIST dataset show that the IOCAP algorithm has good adaptability on large datasets and can effectively improve the accuracy of the algorithm while maintaining the AP clustering effect.

Lithium-ion batteries have been widely used as energy storage for electric vehicles (EV) due to their high power density and long lifetime. The high capacity and large quantity of battery cells in EV as well as the high standards of vehicle safety and reliability call for the agile and adaptive battery management system (BMS). BMS is one of the key technologies for electric vehicle development, which contributes to the overall performance of lithium-ion batteries in operations. Through a comprehensive literature review, this paper presents a review of lithium-ion battery management systems, including the main measurement parameters within a BMS, state estimation methods, cell equalization issues, thermal management strategies and research trends and progresses. The paper discusses and highlights the key elements and challenges with recommendations in terms of the development of next generation BMS technologies.

As a kind of repeated extra-static fixed point supporting structure, the irregular-plate bridge is extremely sensitive about the settlement of various pier columns. The construction risk of the subway tunnel structure crossing the irregular-plate bridge is very high, and it is necessary to research a mature design analysis method and reliable construction control measures. This paper takes Beijing Subway crossing Xinxing Irregular-plate Bridge as a case study. Firstly, it analyzes the displacement changing rules of the subway passing through the irregular-plate bridge through comparing the deformation characteristics of the irregular-plate bridge under different working conditions. Secondly, it studies the judgment standard of pile foundation underpinning and key construction control points. Finally, it combines with the control of the construction process, and analyze the underpinning and stratum reinforcement construction data. It can be concluded that only the stratum reinforcement measures cannot control the settlement of the piers located at the center of the subsidence area within a reasonable range. After adopting the scheme of the underpinning pile foundation and then excavating the tunnel, the risks are more controllable. Then, the paper puts forward key points of the pile underpinning. Finally, the improvement measures is discussed.

The development of cloud computing has brought about rapid changes in the data application environment. The contradiction between the availability of data fusion and privacy protection is increasing. Traditional information security focuses on hiding attributes, while big data privacy protection pays more attention to access control and data using methods. As a semi-structured representation model, XML data is an ideal carrier for heterogeneous data exchange and unstructured data storage. This paper deeply studies several problems involved in user's individual access process for XML data privacy preserving, and proposes prior knowledge as dynamic context. Based on the innovative privacy bipartite graph and the XML document semantic encoding, we propose the XML data privacy preserving model DCPPM and the algorithm KCQ to resist reasoning attack. The scheme to preventive response of the real-time inference attack in the actual operation process is realized. Finally, the example verification and experimental data show that the proposed model can effectively protect the safe use of private data and sensitive data in real time, which has certain theoretical and practical significance.

For the fuzziness and randomness of risk assessment of aerospace embedded Linux, a comprehensive assessment method of aerospace embedded Linux based on cloud model theory is proposed. Cloud model was used to replace the traditional membership function. Seven factors of embedded Linux were selected as the risk assessment set, and the risk level was established. By using test data and expert scoring cameras, the weight coefficient matrix and comprehensive evaluation matrix are constructed by the reverse cloud generator, and the comprehensive evaluation grade was obtained by the forward cloud generator. Taking Linux based on PREEMPT-RT patch as an example, the risk assessment method of space embedded Linux based on cloud model was validated. The results show that the assessment method is effective and feasible.

The most common way to improve spectral efficiency of the orthogonal frequency-division multiplexing (OFDM) communication system is to increase the modulation orders. However, studies have shown that this method will increase the bit error rate and symbol error rate simultaneously. In order to improve the spectral efficiency of OFDM system without reducing its reliability, this paper proposes a rotated four leaves clover (R-FLC) constellation. In this scheme, each symbol can carry additional bits through alternately employing two rotated constellations with different phases. A general rotated constellation aided OFDM system for wireless communication is also proposed. The bit error rate and effective spectral efficiency performances of this system under additional white Gaussian noise and multipath Rayleigh fading channel are evaluated in detail. Compared with the traditional Quadrature Amplitude Modulation (QAM) aided OFDM system, simulation results show that the proposed system is capable of improving the spectral efficiency without sacrificing signal-to-noise ratio under additional white Gaussian noise channel and multipath Rayleigh fading channel. The proposed system can effectively overcome the Inter Symbol Interference (ISI). The system architecture has a certain reference and practical value for the development of OFDM modulation technology.

To investigate the propagation regularity of multiple-hole fracture grouting, a numerical model was used to simulate the simultaneous multiple-hole fracture grouting. The numerical method considered the coupling of mesh element damage and the stress distribution. The propagation process of single-hole and multiple-hole simultaneous fracture grouting can be numerically simulated. Simulation results showed that when multiple holes were injected simultaneously, fractures among grouting holes might change their propagation direction and go towards each other. Grouting holes spacing had significant influence on the deflection direction of grout veins. Furthermore, when multiple holes were grouted simultaneously,the uplift displacement was larger than the sum of uplift displacements when each grouting hole separately grouted. Results indicated that multiple-hole simultaneous grouting induced grouting veins to attract each other, which was beneficial to the formation of grouting curtains and better uplift effect.

In the process of single-point thermal incremental forming, the phenomenon of bulging on the bottom of the forming part is called convex. It is an important factor that affects the forming precision. This article took AZ31B magnesium alloy sheet metal as the research object and carried out the numerical simulation of single-point thermal incremental forming process. With the simulated data, this article analyzed the causes of convex, studied the influencing factors, achieved the influencing laws of each factor and verified the conclusion. The results showed that changes of forming temperature influenced the forming performance of the material, thus affecting the size of the convex. Single factors, such as the distance between the bottom side length and the layer of the forming part, have certain effects on the convex. During the forming process, these laws can be used to reduce the convex on the bottom surface of the forming part by adjusting the process parameters, so as to improve the machining precision of the forming part.

The problem of poor real-time performance of standard Linux greatly limits its application in the aerospace field. Dealing with the characteristics of multi-tasks and high concurrency of the payload during the space station mission of our country, this paper analyzes Linux optimization solution based on three patches: RT patch, Xenomai, and RTAI. This paper tests the switching between tasks and preemption time to ensure the effectiveness of switching between tasks and preemption time and to avoid the problem of priority inversion. The test results show that RT-patch is suitable for units that emphasize data processing which rely heavily on IPC; Xenomai or RTAI is suitable for units that emphasize controlling processing which require better stability of real-time performance.