Journal of Control Science and Engineering

In order to solve the problem that it is difficult to effectively enhance the details of the compressed domain and maintain the overall brightness and clarity of the image when improving the image contrast in the current image enhancement method in the compressed domain, a multimedia semantic extraction method is applied in fast image enhancement control. It has been proposed that thealgorithm that synthesizes training samples according to the Retinex model converts the original low-light image from RGB (red-green-blue) space to HSI (hue saturation intensity) color space, keeps the chrominance and saturation components unchanged, and uses DCNN to enhance the luminance component; finally, it converts the HSI color space to RGB space to get the final enhanced image. The experimental results show that the performance of the model will increase with the increase of the number of convolution kernels, but the increase of the number of convolution kernels will undoubtedly increase the amount of calculation; it can also be found that when the number of network layers is 7, the PSNR of the image output by the model increases. The highest value, increasing the number of network layers, does not necessarily improve the performance of the model; with or without BN, his training method converges more easily than direct RGB image enhancement, with higher average PSNR and SSIM values. The experimental results show that, compared with the traditional Retinex enhancement algorithm and the DCT compression domain enhancement algorithm, the algorithm has better detail enhancement and color preservation effects and can better suppress the block effect.

In order to solve the problem of highly extensible vibration test data acquisition and analysis, the author proposes a method for software development data analysis and processing under the Internet of Things monitoring system. The software platform is mainly designed through the design of software architecture based on multitask operation, active window design, reserved API interface and hardware universal design; it ensures the strong expansibility of the software platform, so as to realize the universality of the software platform. High-level vibration data analysis software designed based on this platform, such as modal parameter identification and dynamic load identification software, can be easily redeveloped by using the existing functions and software architecture of the platform, expand software functions, realize more complex vibration data analysis and processing, reduce repetitive labor, and speed up the software development process. The results showed that: the amplitude error is less than 4%. Conclusions. The feasibility and availability of software development data under the IoT monitoring system are verified.

In order to meet the informatization requirements of coal mine safety monitoring, the author proposes a method for the application of smart sensors in the acquisition of mine electrical equipment. The system uses a variety of sensor fusion methods, with the help of Zigbee wireless network nodes, and passes the data collected by the sensor to the MCU core processor; thus, the collected data are processed, and then, the RS-485 communication protocol is used to upload the data to the upper station; finally, the monitoring of coal mine safety is realized through the background monitoring interface. Experimental results show that, among the five randomly selected nodes, most of the errors between the actual measured results and the collected results are concentrated within the 2% error range. Conclusion. The effect of the abovementioned acquisition scheme in coal mine application is verified, so as to realize the scientific monitoring of coal mine safety.

In order to solve the problems of high consumption cost and long transportation and distribution cycle of cross-border logistics, a control optimization design method of radio frequency identification technology in the express logistics distribution system of the Internet of Things is proposed. The details of the method are RFID positioning technology, laser ranging technology, RFID and laser fusion positioning feasibility analysis, and moving target state estimation. The experimental results show that when the phase shift threshold φ and the included angle of the antenna are 90°, the error can be reduced to 0.36 m, and the recognition rate can be increased to 94.8%. The simulation results show that, on the premise of meeting the customer’s expectation of timeliness, the actual logistics distribution cost is significantly lower than the customer’s expected logistics distribution cost, which verifies the effectiveness of the proposed method.

In order to solve the problem that the traditional computer-aided teaching system is affected by communication technology, which leads to the inability to interact between teachers and students, the author proposes a research on a computer teaching system based on the Internet of Things and machine learning. The hardware structure is designed according to the functions of each module of the system, in which the student learning module is composed of a teaching coordination agent and a number of other agents, responsible for the presentation of specific teaching materials, problem solving, knowledge sharing through a collaborative mechanism, and providing personalized teaching basis for the system. The teacher’s teaching module mainly provides students with corresponding teaching strategies according to the learning requirements and uses its own reasoning mechanism to provide intelligent guidance to the problems encountered in the teaching process; the assessment module uses assessment rules to analyze student responses, comprehensive assessment of students’ learning behaviors, attitudes, effects, and abilities. The software function is designed with SQL Server 2000 as the database server, in the case of determining the data attributes, the data online evaluation is carried out, and the distance teaching is completed by combining the network technology. The experimental results show that when the time is 20s, the teaching efficiency of the traditional system is 61%, and the teaching efficiency of the artificial intelligence system is 91%. As a result, the teaching efficiency of the system based on the Internet of Things and machine learning is high, and it can provide equipment support for students’ learning.

In order to solve the problem of low development efficiency of agricultural mechanization information network system, a design and discussion method of agricultural machinery equipment information management and control system is proposed. This paper introduces the technologies required to realize the agricultural machinery operation management control system: Android development technology required to develop mobile phone positioning software, Internet map API for displaying location data, and construction of agricultural machinery resource allocation model for the realization of the scheduling module. Different objective functions are set, and the model results in different situations are obtained through experimental data. The experimental data are in the case of schedulable agricultural machinery, if the target is short-distance short-distance operation, it can save time by 0.16 h, and the existing M1 model is in Changying. Village work, the work end time is about 2.5 hours; M4 and M2 models work in the Xuzhuang Village, and the work end time is about 0.5 h; M3 model works in the Jiangtang Village, and the calculation results for different goals are as follows: M1 model first dispatches short-distance operations; the M3 type is dispatched first for short-distance operations; the M4 type operating in Xuzhuang Village is given priority to dispatch short-distance and short-distance operations. According to the analysis of system requirements, each submodule of the system is designed, including the function description and realization method of the module. The history track query module, mobile phone positioning module, and agricultural machinery scheduling module are introduced in detail. Using the Android development technology required by mobile phone positioning software, the information management of user interface, server system, and database is realized, which provides conditions for the collection of agricultural machinery and equipment information and the generation of information management control.