As a key parameter in the sintering process, the ferrous oxide content of sinter can reflect the working condition, energy consumption level, and quality level of the final sintered products in the sintering process. It has become a key problem to realize the prediction of ferrous oxide content in sinter and feedback control of sinter quality accordingly. The two commonly used methods for detecting ferrous oxide content in industrial production currently do not meet real-time requirements and cannot provide timely feedback for production regulation. Therefore, research on real-time prediction technology of ferrous oxide content in sinter was carried out, and an optimized back propagation neural network model was established to realize the mapping between characteristic parameters and the FeO content in sinter. The characteristic parameters include image parameters and process parameters. Through the research on the brightness change trend of the machine tail cross-section image, the best cross-section image acquisition method based on brightness difference is realized, and image parameters are obtained by image processing technology. The process parameters were selected using correlation analysis. Through data processing techniques such as data cleaning, normalization, and feature fusion, feature parameters were obtained as input vectors for the neural network. To improve prediction accuracy and system stability, an adaptive learning rate and genetic algorithm were used to optimize the traditional BP neural network. The average test error of the optimized prediction model was 0.32%. Taking actual data production as an example, test data on the FeO content of sinter were extracted from the laboratory. Compared with the FeO content predicted by the system, the prediction time of the system was about 2 h earlier than the test time. In terms of prediction accuracy, the average absolute error was 0.25%, and the absolute prediction error was not more than ±1%.

Accurately measuring the verticality of a single pile is of crucial importance for ensuring the safe operation of offshore wind power projects. However, mainstream methods have disadvantages such as high dependence on manual labor, low real-time performance, and susceptibility to construction site conditions and marine environmental impacts. The method of measuring the verticality of a single offshore pile based on binocular vision is one of the emerging measurement methods, but there is currently a lack of research on measurement models. In order to clarify the principle of the method for measuring the verticality of a single pile at sea based on binocular vision, this paper starts from the imaging principle of the camera and studies and derives the measurement model of the verticality of a single pile in the global coordinate system and the error model of the measurement system. To verify the correctness of the model and method, a testing experimental platform was built to simulate the measurement of the ship under static and dynamic conditions, and the measurement results were compared with those of the total station. The experimental results show that in the static simulation experiment, the maximum absolute error of the verticality of a single pile is 0.2°, the maximum absolute error of the roll angle is 0.3°, and the maximum absolute error of the pitch angle is 0.3°. In the dynamic simulation experiment, the maximum absolute error of the verticality of a single pile is 0.4°, the maximum absolute error of the roll angle is 0.3°, and the maximum absolute error of the pitch angle is 0.3°. This paper verified the correctness of the model and provided model support for measuring the verticality of single piles at sea.