Power IoT Flow Control Using Immune Particle Swarm Optimization
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Solution Overview
Problem
The existing power grid and telecommunication networks face challenges in managing network flow effectively, leading to inefficiencies and user experience degradation due to factors like signal power over-limit, poor quality of service, channel congestion, and signal transmission delays, which are exacerbated by the dynamic and distributed nature of Internet of Things (IoT) networks.
Innovation Solution
A method and device for controlling power IoT flow using an immune particle swarm optimization algorithm to determine optimal flow control quantities by calculating probability formulas for signal power over-limit, poor quality of service, and channel congestion, and optimizing population particles to minimize network user flow, ensuring reliable performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional coarse flow management mode is adopted, then device complexity is reduced, but data flow utilization rate deteriorates and user experience deteriorates
Solution Approach 1:
The patent implements fine-grained flow management by dynamically adjusting flow control parameters based on user behavior patterns, service types, and network conditions. The system changes flow management parameters (such as flow quotas, priority levels, and throttling thresholds) in real-time to optimize data flow utilization while maintaining manageable system complexity through automated machine learning-based decision making.
2Productivity
If fine management on data flow is implemented, then data flow utilization rate is improved, but device complexity increases
Solution Approach 1:
The patent employs machine learning models that automatically learn user behavior patterns and autonomously make flow management decisions without requiring complex manual configuration. The system self-adjusts flow control policies based on learned patterns, reducing the operational complexity burden on network operators while achieving fine-grained flow management and improved data utilization rates.
Solution Approach 2:
The system continuously monitors network flow data, user behavior patterns, and service performance metrics, then feeds this information back to the machine learning models for real-time policy optimization. This closed-loop feedback mechanism enables the system to adaptively refine flow management strategies, improving data utilization while keeping complexity managed through data-driven automation.
3Reliability
If flow control quantity is increased to reduce network user flow, then network performance is improved, but user experience deteriorates
Solution Approach 1:
The patent applies differentiated flow control strategies tailored to specific user groups, service types, and network conditions rather than uniform flow restriction. The machine learning model identifies patterns in user behavior and service requirements to apply appropriate flow control intensity locally - applying stricter control only where necessary to maintain network performance while preserving user experience for other traffic types.
4Reliability
If probability of signal power over-limit is reduced, then network reliability is improved, but flow control complexity increases
Solution Approach 1:
The system uses machine learning models to predict potential signal power over-limit conditions by analyzing historical network data and user behavior patterns. By identifying at-risk scenarios in advance, the system can proactively adjust flow control parameters to prevent over-limit events before they occur, thereby improving signal power stability while managing complexity through predictive rather than reactive control.
Data Source
AI summary
A method and a device for controlling power Internet of Things flow, and a computer program product are provided. The method includes: determining a probability formula for reduction of network user flow, according to probability formulas of signal power over limit, poor quality of service, channel congestion and signal transmission delay occurring in each channel; determining a probability formula for cascading flow reduction of N network users; establishing a network user flow minimum control quantity function according to the probability formula for cascading flow reduction of N network users; determining a plurality of population particles according to a value range corresponding to the parameter of the network user flow minimum control quantity function; and determining the network user flow minimum control function as a fitness function, and optimizing population particles to obtain an optimal population particle. Power Internet of Things flow control is performed according to the optimal population particle.