The investigation on the rumor propagation model is of great significance in developing rumor-dispelling strategy and maintaining social stability. Considering the rumor propagation of college students on college network social platforms, the IG2D2 rumor model with six states is proposed, and then the rumor propagation model based on average field theory is established. Furthermore, taking the factors such as individual differences, conformity effect and trust degree into consideration, a novel non-consistent dynamical rumor propagation model is proposed. The simulation results show that the influence of the degree of the network on the peak value of rumor propagation decreases with the increase of conformity effect, and the retransmission degree of the rumor itself plays an important role in the propagation of rumor.

To address the challenge of achieving low-power consumption and rapid online measurement of chemical oxygen demand (COD) in small-scale wastewater treatment plants, an online self-organizing neural network (OSNN) prediction method based on radial basis function (RBF) is proposed. This method realizes accurate prediction of COD by dynamically controlling the number of neurons and their update rate. By leveraging the excellent continuous function approximation capability of RBF, combined with the flexibility and adaptability of self-organization, the accuracy and adaptability of the measurement model are improved. The proposed method for controlling the update rate of neuron number can maintain the compactness of the neural network and reduce the extension of training time caused by frequent and substantial changes in neuron number. Experimental results demonstrate that the RBF neural network with self-organizing capability can reliably predict the COD parameter values.

In order to address issues of low simulation accuracy, poor universality and poor visualization effect, a universal high-fidelity air-to-air missile simulation system based on Unity3D and CADAC is designed. On the one hand, the designed simulation system uses a high fidelity CADAC software package, which comprehensively considers the missile kinematics and aerodynamics, different guidance laws and target maneuvering modes, fuel consumption and center of gravity changes, and other factors, thus improving the fidelity of the simulation system. On the other hand, the designed system can be applied to different category of missiles such as air-to-air missiles, air-to-ground missiles, etc., which improves the universality and adaptability. Finally, Unity3D is used to conduct visual simulation, and visually display the whole process from launching, searching, guidance and hitting the target. Typical simulation cases and performances analysis is given to verify the effectiveness of the simulation system.

For the lot-sizing scheduling problem of uncorrelated parallel machines considering the adjustment time of sequence-dependent machines, a mathematical model of the problem is established with the optimization objectives of minimizing the maximum completion time (Makespan) and the total number of machine switching times, and an improved multi-objective biogeography optimizer (IMOBBO) is designed to solve the problem. In order to meet the needs of lot-sizing scheduling, a partitioned matrix coding method is designed in the first stage, and the initial population is generated by the fusion strategy of random strategy, Logistic mapping and reverse learning mechanism, and wandering mechanism of wolves are introduced into the process of species migration, adaptive catastrophe operator and two-stage neighborhood search strategy are used in the catastrophe process; in order to minimize the adjustment time of each machine, the second stage adopts machine-based matrix sequence coding to optimize the processing sequence of each batch, and finally, the individual evaluation method based on hypervolume is introduced to Pareto non-dominated rank ordering. The effectiveness and superiority of the algorithm proposed are proved through simulation experiments of different scales and examples and comparison with related algorithms.

To improve the accuracy of unmanned ship pose estimation under complex sea conditions, a unmanned ship pose estimation algorithm based on non-Gaussian feature recognition and Gaussian sum cubature particle filter (GSCPF) is proposed. Firstly, based on the idea of normality test, the largest vertical difference between the sample distribution function and the standard Gaussian cumulative distribution function, and the sample skewness-kurtosis are combined to analyze the distribution feature of the data from several angles. Then, if the data shows non-Gaussian distribution feature, an unmanned ship pose estimation method based on Gaussian sum filtering and particle cubature particle filtering is adopted, otherwise, the cubature Kalman filter (CKF) is directly adopted to estimate the unmanned ship pose. Finally, the results of two kinds of simulation experiments show that the proposed algorithm can significantly improve the accuracy of parameters such as position, velocity and course of unmanned ship.

For the problems such as large prediction errors and few data features of traditional models in the study of aquaculture water quality, a pH prediction model of aquaculture water quality based on feature con-struction is proposed in this paper. The main part of the model is mixed double convolution layer gated circula-tion unit neural network (MDconv-GRU). Firstly, the original 3 effective features are increased to 6 effective features by correlation calculation and feature construction. Then, the data after the feature construction is input into the MDconv-GRU model for training prediction. The results showed that the prediction accuracy of the model is 92.26%, the root-mean-square error is 0.083 8, the average absolute error is 0.063 5, and mean abso-lute percentage error is 0.830 2. The evaluation criteria are better than other models. This model can accurately predict the pH value of water quality of Stichopus japonicus aquaculture, and lay a foundation for realizing pH warning and increasing the yield of Stichopus Japonicus.

Currently, indoor mobile robots are used in industries such as intelligent storage and security systems, in which high-precision positioning technology plays an important role. To this end, a vision servo-based indoor 3D localization technique is designed in order to achieve high accuracy of localization error while getting rid of the limitations of camera placement and field of view in visual image processing localization techniques. Firstly, the framework and application scenarios of the positioning algorithm are introduced. Then, the 3D positioning algorithm based on geometric relations is designed and its least-squares problem is solved, and the tracking and aiming problem of the visual pan tilt is solved by using image processing and feedback control techniques. Finally, the experimental results of positioning the target robot show that the proposed indoor 3D positioning algorithm has high accuracy and stability with a positioning accuracy within 4 cm in the laboratory scenario.

For the four-motor synchronous drive system with backlash, a high-performance Partial loss of effectiveness (PLOE) fault-tolerance control technique is proposed. Firstly, the nonlinear conditions on the motor side and load side are approximated separately using the fuzzy logic system to compensate during the control design process to reduce the tracking error and ensure the tracking performance. In the designed control method, only one adaptive parameter needs to be estimated, reducing the design difficulty. Secondly, a current observer is used to monitor the motor current in real time and estimate the failure factor to obtain the current system parameter information. Finally, a four-motor synchronization architecture based on cross-coupled synchronization control is designed to synchronize the control signals and ensure synchronization performance among the four motors. To verify the effectiveness of the method, the proposed method is compared with dynamic surface fault-tolerant control through simulation. The results show that the proposed method can effectively reduce the tracking error of the servo system.

A model-free control framework based on iterative learning is proposed to realize the synchronous and coordinated control of a double-lift overhead cranes system for the problems of inaccurate modeling, system parameter variation and uncertainty perturbation are common in double-lift overhead cranes system. Firstly, a time-varying sliding mode surface using Sigmoid-like functions is proposed to improve the convergence speed of the system state. Then, an iterative learning law based on the time-varying sliding mode surface is introduced to compensate for the inclusion of unknown system dynamics and external disturbances, etc., to achieve model-free control. At the same time, a dynamic learning rate is designed instead of the fixed-value learning rate to improve the convergence speed of the error of the double-lift overhead cranes system as well as the steady-state performance. Secondly, an improved adaptive convergence law is proposed to reduce unnecessary chattering, improve the robustness of the double-lift overhead cranes system, and achieve finite time convergence. Finally, the stability of the controlled system is demonstrated using Lyapunov stability theory. The simulation experiments verify the effectiveness of the designed synchronous control scheme.

A fuzzy dynamic-output-feedback control method is presented based on adaptive event-triggered mechanism for vehicle nonlinear active suspension systems. Firstly, an interval type-2 fuzzy model of vehicle nonlinear suspension system is established to accurately characterize the nonlinear characteristics of spring and damper. Secondly, an adaptive event-triggered strategy is adopted to reduce network load. Compared with the sampling communication mechanism, it can effectively save communication resources. Thirdly, the closed-loop system model of adaptive event-triggered control of nonlinear suspension is constructed based on the description method of time-delay. On the basis, a co-design method of the event generator and the dynamic-output-feedback controller is obtained. Finally, the bench test under different road disturbances is carried out. The experimental results show that the proposed approach is more effective to simultaneously improve the performance of the ride comfort and saving communication resource.

Simultaneous localization and mapping (SLAM) is a key problem in the research and application of mobile robots, which is used to realize autonomous and accurate localization of mobile robots in complex environments. The system composition, key technologies and applications of SLAM are briefly introduced. Focusing on five aspects: feature point method, filtering method, graph optimization method, multi-sensor fusion and dynamic scene, the key technologies, domestic and foreign research status and symbolic application progress of SLAM system are reviewed. Combined with representative systems, the advantages and disadvantages of different methods are compared and analyzed, and the multi-sensor fusion SLAM systems are elaborated in detail, and the SLAM technology in complex scenes is prospected.

In order to solve the problems of external disturbance and uncertain system model parameters in the formation control of multiple mobile robots, a disturbance observer based finite-time prescribed performance formation control strategy is proposed. Firstly, an adaptive fast terminal sliding mode disturbance observer is designed to estimate the sum disturbance of the system. Secondly, a new finite-time prescribed performance function is introduced to make the tracking error converge to the steady-state within the setting time. Then, the formation tracking controller is constructed via the backstepping method and the tracking error can satisfy the prescribed performance requirements by using the ln-type obstacle function. In addition, the state boundedness of closed-loop systems is also discussed. Finally, the effectiveness of the control method is verified by simulating the formation of five mobile robots.

For the actuator fault of CRH380A type EMU during braking, an adaptive fault-tolerant control strategy based on multiple-model is proposed. Firstly, the dynamic model of EMU braking system with unknown system parameters and actuator faults is derived. Then, all possible fault modes are analyzed to obtain a set of fault modes, and an adaptive fault-tolerant controller is designed for each fault mode in the set. Finally, a performance loss function based on the state estimation error is designed, and the controller corresponding to the minimum performance loss function value is selected as the current controller of the system. The simulation results show that the designed control system can achieve rapid and accurate compensation for unknown faults, so that the EMU can still track the target braking curve asymptotically after the occurrence of faults, and ensure the safe and smooth braking of the EMU.

The caustic ratio of the dissolved slurry is the operating indicator characterizing the quality, efficiency and consumption in the alumina dissolution process. Due to frequent fluctuations in the composition of the dissolved slurry, the detection accuracy of the foreign caustic ratio detection instruments currently used is low, only manual off-line assay of caustic ratios can be used. However, the severe lag of the assay results leads to the inability to achieve the automatic control of the caustic ratio, which affects the quality of alumina products. Based on the analysis of the dynamic characteristics of alumina dissolution, this paper establishes a caustic ratio forecasting model described by a linear model and an unknown nonlinear dynamic system, and combining parameter identification with adaptive deep learning, an intelligent forecasting method is proposed for caustic ratio of the alumina dissolution process based on the end-edge-cloud collaboration. The application verification of the proposed method is performed on actual production data from an alumina manufacturer, and the results show that the intelligent forecasting method proposed can accurately forecast caustic ratio of the dissolution process in real time, providing conditions for achieving the closed-loop optimal control of the caustic ratio.

Plate crown is the most important quality index to evaluate the cross-section profile of hot-rolled plate, and good plate crown is the guarantee of normal production, so accurate prediction of bad crown is of crucial significance to ensure production. In the actual production of hot rolling, the number of qualified crown samples is much higher than the undesirable crown, and a large number of nonlinear parameters and strong coupling between parameters make plate crown prediction a very complex imbalanced classification problem. According to the complex data characteristics of hot-rolled plate crown, considering the powerful nonlinear fitting ability of deep learning, combined with cost-sensitive learning to improve the misclassification cost of bad crown, a cost-sensitive deep belief network (CS-DBN) model is proposed. Evaluation indicators such as Macro-F1, Micro-F1, G-Mean and Aacc are used as evaluation indicators of the model. By adjusting the model hyperparameters and optimizer to determine the optimal cost-sensitive deep belief network, and comparing and analyzing it with traditional machine learning algorithms ANN, SVC, KNN, DBN, LR, the results show that CS-DBN is better than traditional machine learning models in all evaluation indicators, and the plate crown prediction results are good.

Short-term power load forecasting is very important to the stable operation of power system, in order to further improve the accuracy of load prediction, a combined short-term power load prediction model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the improved northern goshawk optimization (INGO) algorithm is proposed to optimize bidirectional long short-term memory neural network (BiLSTM). Firstly, the original load sequence is decomposed by CEEMDAN to obtain more stable load data. Then, through Arnold chaotic reverse learning initialization, adaptive Cauchy-Gaussian mixture mutation strategy and nonlinear convergence factor, the problems in the northern goshawk optimization algorithm were improved, and its optimization ability and convergence speed were significantly improved, so as to optimize the BiLSTM related hyperparameters. Finally, the CEEMDAN-INGO-BiLSTM power load prediction model is obtained by integrating and reconstructing each subsequence. The simulation results show that, compared with the comparison algorithm, the model effectively improves prediction accuracy

For the problem of intelligent online monitoring of typical equipment operation condition of thermal power units, an intelligent condition monitoring system for typical equipment of thermal power units based on improved seagull optimization algorithm and temporal convolutional network (ISOA-TCN) is proposed. Firstly, the historical operation data of field units are collected, and the Spearman correlation coefficient analysis method is used to screen out feature parameters with high correlation coefficients with Monitoring parameter and the dimension of the data is reduced. Secondly, TCN hyperparameter is optimized through ISOA, and an intelligent monitoring model of equipment operation status is established, which is driven by real-time data. Finally, the distance between the model output and the actual value is measured based on JS divergence to complete the health degree evaluation of the equipment operation condition. Taking primary air fan and coal mill as examples, the development process of monitoring model is described in detail. The system is applied to a power plant and the results show that it can realize the online intelligent monitoring of the typical equipment operation condition of thermal power units.

To achieve accurate and reasonable investment prediction for power grid enterprises and improve the comprehensive investment benefits, an investment benefit optimization model is studied and proposed. The investment factors of power enterprises are determined by the grey relational degree method, and the firefly algorithm is used to improve the support vector machine to calculate the demand scale. Based on the theory of cash flow balance, an investment capacity measurement model is designed. The research optimizes the investment portfolio of electrical projects through the fuzzy comprehensive evaluation method and the simulated annealing genetic algorithm. The results show that during the period from 2017 to 2022, the proposed optimization model has a higher accuracy in predicting electrical investment than similar models, with a relative error of only 2.31%. The number of investment projects and the investment amount decreased by 11 and 102 914 yuan respectively, while the comprehensive benefits of the projects increased by 10 930 yuan. This optimization model can accurately predict the grid investment capacity, and its optimization results have significant theoretical value for achieving the maximum investment efficiency.
Key words: Power grid enterprise; investment prediction; firefly algorithm; annealing

For the problem of positioning and anti-swing of underactuated bridge crane system, a complementary sliding mode control PD control strategy is proposed. On the basis of the linear sliding mode surface composed of displacement, swing angle and respective first-order differential state errors as variables in the system, the error integral of displacement and swing angle is introduced to form an integrated sliding mode surface, and a supplementary sliding mode surface is designed correspondingly, so that the variable space of the system is limited to the intersection of the sliding mode surfaces, and the distance between the variable and the sliding mode surface is shortened. The speed of the system is improved. At the same time, the introduction of PD compensation control of load swing angle enhances the ability to suppress the swing angle and makes the car run more smoothly. The stability of the system is proved by constructing the Lyapunov function. Simulation and experimental results verify the effectiveness of the control strategy for positioning and anti-swing.

In the face of fierce market competition at home and abroad, the traditional mineral processing plant relies on operators to realize the mode of equipment control is not in line with the development trend of the industry. For newly built extra-large mineral processing plants, adopting a self-aware and self-regulating “unmanned” construction solution is crucial for reducing costs and enhancing competitiveness. For the construction of large magnetite “unmanned” processing plant target, the study investigates the core components and key technologies for building its automation system. From four aspects— comprehensive perception of the production process, network interconnection, intelligent control, and industrial cybersecurity protection—it elaborates on the construction content and required key technologies regarding online detection instrument selection, third-party equipment integration, intelligent control systems, and industrial cybersecurity protection for the automation system of a magnetite concentrator. This is intended to achieve the goal of building an “unmanned” concentrator.