This paper proposes a design and optimization scheme for an Internet of Things (IoT)-based cloud platform aimed at enhancing the communication efficiency and operational performance of autonomous agricultural machinery. The platform integrates the dual communication capabilities of Narrowband Internet of Things (NB-IoT) and 5G, where NB-IoT is utilized for low-power, reliable data transmission from environmental sensors, such as soil information and weather monitoring, while 5G supports high-bandwidth, low-latency tasks like task scheduling and path tracking to effectively address the diverse communication requirements of modern complex agricultural scenarios. The cloud platform improves operational efficiency and resource utilization through real-time task scheduling, dynamic optimization, and seamless coordination between devices. To accommodate the diverse operational demands of agricultural environments, the system incorporates a real-time data feedback mechanism leveraging sensor data for path tracking and adjustment, enhancing adaptability and stability. Furthermore, a multi-machine collaborative scheduling strategy combining Dijkstra’s algorithm and an improved Harris hawk optimization (IHHO) algorithm, along with a multi-objective optimized path tracking method, is introduced to further improve scheduling efficiency and resource utilization while improving path tracking accuracy and smoothness and reducing external interferences, including environmental fluctuations and sensor inaccuracies. Experimental results demonstrate that the IoT-based cloud platform excels in data transmission reliability, path tracking accuracy, and resource optimization, validating its feasibility in smart agriculture and providing an efficient and scalable solution for large-scale agricultural operations.

The Use of Artificial Intelligence to Increase the Functional Stability of UAV Systems

The article is devoted to the development of a system for biometric identification of a person by voice. The article discusses algorithms for the analysis of audio recordings for biometric identification of a person by voice. The technique of experimental research is considered, the process of processing the results of identification is described. Used algorithms MFCC and PLP for digital processing and analysis of audio recordings. An algorithm based on multi-criteria optimization has been developed for acoustic speech analysis. Various algorithms are used, such as hidden Markov models (CMM or HMM), as well as a model of a mixture of Gaussian distributions (GMM or GMM); in recent years, Wave Net neural networks have been actively used. The result of determining the tone of speech and the content of speech for the purposes of identification by voice is obtained.In the work, to compare the recorded voice with the saved voice for the purpose of personal identification, an unlimited text independent recognition system was applied using the Gaussian mixture model. The recorded voices were processed and stored during the registration phase, and the probing voices were used for comparison during the verification/ recognition phase of the system. For biometric identification of a person by voice, the MFCC and PLP algorithms were used for digital processing and analysis of audio recordings. The result obtained makes it possible to determine the fundamental harmonics of speech for the purposes of identification by voice. The “Multiparameter automated system of biometric identification of a person” was developed on the Visual FoxPro DBMS. Today, speech identification and authentication is used in a wide range of applications ranging from smartphone applications to access control systems. Additional confirmation of the relevance of this area is the many research centers.

The article deals with the problem of analyzing the controllability of an unmanned aerial vehicle based on interval analysis, computer algebra. On the basis of interval mathematics, a criterion for the controllability of the system under study was obtained. When developing the software, the library of interval mathematics was used. The developed software that implements the introduced interval arithmetic operations can be used in the study of the dynamic properties of automatic control systems, energy, economic and other nonlinear systems.

The article is devoted to the development of a mathematical model for preventing a breakthrough of a dam and predict its possible consequences. In this work, the task of developing a single integrated approach to ensuring the safe operation of hydraulic structures, based on the notification of interested bodies in real time, was solved. A mathematical model of the state of the reservoir is developed, on the basis of which a hardware-software complex for operational notification of interested organizations (akimats) and local emergency departments is implemented. A mathematical model of predicting the consequences of a dam break is proposed. An algorithm for calculating the maximum level of the breakout wave has been formulated, taking into account many parameters of the hydraulic structures. The convergence of the developed algorithm in the form of a theorem has been proved. This method has a large practical focus, compared with existing formulas. The Java language implements a hardware-software complex (PAC) for predicting the effects of a dam break, consisting of the following modules: 1) a module for receiving and transmitting current information about the water level, humidity and temperature on the crest of the dam; 2) a module for processing constant and operational information about the threat of dam breakthrough (server); 3) a module for predicting the effects of a dam break. Based on the solution of the model problem, the effectiveness of the developed hardware-software complex is shown. The practical basis for the model task was the events that took place in Kyzylagash village of Almaty region of the Republic of Kazakhstan.