In recent years, the poultry breeding industry has been converted into a large-scale, intensive, and intelligent production mode. The Internet of Things (IoT) is under rapid development, which promotes the development of precision livestock farming. In this study, we developed an intelligent service platform for a facility environment based on the IoT structure, utilizing the capabilities of Platform as a Service (PaaS). The platform consists of four layers, including an information perception layer, network layer, management service layer, and application layer. By using the cloud service model with a distributed network architecture, asynchronous data transmission, and a distributed file system, the platform can centrally manage multiple farm’s data. The intelligent service platform includes the following functions: displaying environmental data, water and electricity consumption, data analysis, and managing production data. Over a 500-day trial period in a live poultry house, the platform demonstrated high data integrity (>87%) and resilience against network disruptions and power outages. The data validity of each environmental element exceeded 94%, among which the validity of the temperature, humidity, and carbon dioxide concentration exceeded 99%. The overall accuracy of the dataset remained relatively high, providing a robust data foundation for further research. Key features included audio analysis, environmental monitoring, and production data management. The platform’s operational status was efficiently communicated via data statistics and email alerts, facilitating timely system recovery. The demonstrated modules included sound recognition, psychrometric charts for visual alerts, and financial analysis tools, offering versatile solutions for integrating PLF models and advanced analytics.

Large Language Models (LLMs) remain highly susceptible to jailbreak attacks that bypass safety alignments through sophisticated prompt manipulation. While multi-agent defense systems have emerged as a promising countermeasure, existing frameworks predominantly rely on static agent designs, which struggle to adapt to evolving adversarial strategies. To bridge this gap, we propose an Adversarial-Test-Driven Multi-Agent Defense framework that shifts the focus from model-level fine-tuning to system-level optimization. Our framework introduces a closed-loop evolutionary process consisting of an Attack Design agent that probes vulnerabilities with adaptive adversarial prompts, and an Optimization agent that iteratively refines the defense agents’ system prompts based on feedback. This approach enables the defense system to correct reasoning failures at inference time without requiring gradient-based updates to the underlying LLMs. The experimental results demonstrate that our framework achieves a state-of-the-art Attack Success Rate (ASR). The experiments show that the framework improves jailbreak robustness while making the associated safety–utility trade-off explicit.