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%.

The measurement of position accuracy of industrial robots has high complexity, and its uncertainty assessment is one of the important research topics. This study analyses the principle of measuring the position accuracy of industrial robots by laser tracker, the evaluation model of the measurement uncertainty of the position accuracy of industrial robots is established, and various factors affecting the measurement results are analysed, including industrial robot itself, the measurement instrument, the measurement personnel, the measurement strategy and the data processing method. There is a very small sample problem in the uncertainty evaluation process of the position accuracy of industrial robots. The literature and experiments show that the reliability of The Guide to the Expression of Uncertainty in Measurement is low under the condition of very small sample. In this study, an improved grey method is proposed to evaluate the uncertainty. The principle of the improved grey method is discussed in detail, and the general steps of the improved grey method are summarized. The experimental data prove that the standard deviation of very small sample calculated by the improved grey method has higher accuracy. Taking the position accuracy of an industrial robot measured by a laser tracker as an example, the improved grey method is successfully applied to the measurement uncertainty evaluation of the position accuracy of industrial robots.

Currently, there are various types of microscales and the conventional line detection system usually has only one detection method, which is difficult to adapt to the diverse calibration needs of microscales. This article investigates the high-precision measurement method of a microscale based on optoelectronics and the image integration method to solve the diversified calibration needs of microscales. The automatic measurement and processing system integrates two methods: the photoelectric signal measurement method and the photoelectric image measurement method. This article studies the smooth motion method based on ordinary linear guides, investigates the method of reducing the cosine error of a small-range interference length measurement, proposes an image-based line positioning method, and studies the edge and center recognition algorithms of the line. According to the experimental data, the system’s measurement accuracy was analyzed using the photoelectric signal measurement method to measure the 1 mm microscale, the maximum difference from the reference value was 0.105 μm, the standard uncertainty was 0.068 μm, and the absolute value of normalized error was less than 1. The accuracy of the image measurement method to measure the 1 mm microscale was consistent with that of the photoelectric signal method. The results show good consistency in the measurement results between the two methods of the integrated measurement system. The photoelectric signal method has the technical characteristics of high measurement efficiency and high accuracy, while the pixel-based measurement of the image method has two-dimensional measurement characteristics, which can realize measurements that cannot be realized by the photoelectric signal method; therefore, the measurement system of optoelectronics and image integration is characterized by high precision and a wide range of measurement adaptations.

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.