Against the backdrop of precision agriculture and the development of intelligent agricultural machinery, current domestic monitoring systems for wheat combine harvesters are plagued by limited functionality, low intelligence, significant errors in parameter monitoring, and yield estimation results prone to inaccuracies. Specifically, they lag behind mature international systems in terms of fault warning accuracy, data transmission efficiency, and yield visualization capabilities. This study seeks to realize comprehensive and precise monitoring, reliable fault early warning, and intelligent yield prediction for wheat combine harvesters across all operating conditions. To this end, it innovatively adopts CAN bus integration technology and impulse-type grain flow sensors to develop a comprehensive system for monitoring the operational status and warning faults of wheat combine harvesters, which covers the entire operational process. By integrating GPS positioning, multi-sensor parameter acquisition, and intelligent analysis modules through CAN bus integration, the system enables unified monitoring of geographic information, operational data, cleaning loss, and fault status. Additionally, it incorporates a yield measurement module based on an impulse-type grain flow sensor to generate the real-time yield distribution maps. Field experiments demonstrate that the system achieves an alarm accuracy of 97.3%, controls the fuel consumption measurement error within 5%, and limits the relative error of yield measurement accuracy to no more than 4%. Notably, the impulse-type grain flow sensor exhibits stable static detection accuracy and rapid, precise dynamic measurement performance—laying a solid foundation for the automation and intelligent advancement of combine harvester technologies.

The core objective of this study is to address critical challenges in the operational monitoring and fault early warning of wheat combine harvesters. To this end, this study designed a field-oriented multi-parameter detection system for wheat combine harvesters, which utilizes the CAN bus and virtual instrumentation. Key challenges in this field include three aspects: first, manual inspection is inefficient and lacks automated detection methods, making it difficult to meet the real-time requirements of large-scale operations; second, fault early warning accuracy is low, as single-parameter evaluation is prone to false positives and false negatives; third, monitoring parameters function in isolation, leading to significant data inconsistencies that hinder the early detection of potential faults. To address these issues, this study focuses on three key tasks: establishing a multi-parameter collaborative monitoring framework, optimizing hardware and communication protocols, and developing data processing methods for fault detection and warning. Specifically, sensors for fuel consumption, Hall-effect rotational speed, and strain-gauge torque are deployed at critical components of the harvester. The system then efficiently transmits operational status data via the CAN bus to a processing module, enabling remote real-time monitoring of the harvester’s comprehensive operational conditions. For the designed fault warning algorithm, it dynamically adjusts warning thresholds by comparing characteristic parameters with historical data, thereby achieving accurate fault identification and timely warning responses. This study innovatively transmitted multi-source sensor data through the high-anti-interference CAN bus and developed a fault warning algorithm incorporating feature recognition and dynamic thresholds. In simulated experiments, the measurement errors of both instantaneous and cumulative fuel consumption were ≤5%, while the system achieved a warning accuracy of 97.3% and a response time of ≤180 ms. This represents a 15.3-percentage-point improvement in accuracy compared to traditional single-parameter warning systems. Overall, this study addresses the challenge of multi-parameter integrated monitoring for wheat combine harvesters and provides a scalable technical solution for hardware integration and comprehensive data analysis. It also offers a reference for the intelligent upgrading of Chinese harvesters, which is expected to accelerate the transformation of agricultural mechanization toward precision and informatization.

In order to improve the screening performance and cleaning effect of the jujube harvesting machinery cleaning device, a vibrating curved screen device was designed in this study. By analyzing the structure mechanism of the curved sieve body, it was obtained that the arc-shaped mesh hole spacing S was 15-25 mm and the curved mesh hole curvature U was 90°-150°. By exploring the movement state and stress of jujube and impurities on the curved sieve body, it was determined that the horizontal spacing L of the curved layer sieve was 30 mm and the vertical spacing H was 45-65 mm. Taking the vertical spacing H of the curved layer sieve, the curvature U of the curved mesh hole, and the spacing S of the curved mesh hole as the experimental factors, considering the screening efficiency α and the impurity content β of the jujube as the response values, the three-factor three-level quadratic regression orthogonal experiment was designed, establishing the regression mathematical model of each factor and response value, and the multiple target optimization algorithm of Design-expert software was used to optimize various factors. The results showed that the influence factors on the screening efficiency were in the descending order as: the arc screen spacing, the vertical spacing of the curved layer screen, and the curved screen hole curvature; The significant factors affecting the impurity content of jujube were in the descending order as: the arc screen spacing, the curved screen hole curvature, and the vertical spacing of the curved layer screen. The experimental results were verified by the optimized combination of parameters: when the vertical spacing H of the curved layer screen was 65 mm, the curved screen hole curvature U was 110°, and the arc screen spacing S was 23 mm, the average screening efficiency α in the test was 91.09%. The relative error between the experimental verification value and the theoretical optimization value was 1.36%, which was less than 5%. The impurity content of jujube β in the test was 1.02%. The relative error between the experimental verification value and the theoretical optimization value was 2.00%, which was also less than 5%. The test results can provide a reference for the research and optimization of the subsequent air-suction-type jujube harvester cleaning device.