Knowledge-Graph Service Scheduling for Overload-Aware Allocation
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Solution Overview
Problem
Existing smart home systems struggle with suboptimal service scheduling, leading to inconsistent and low-quality user experiences due to reliance on fixed rules, which fail to adapt when services are unavailable or overloaded.
Innovation Solution
A service scheduling method that utilizes a service scheduling knowledge graph to determine optimal service allocation by calculating a scheduling value based on importance degree, utilization rate, and occupancy, and then notifies devices to access the appropriate services in a target sequence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If constant rules are used for service scheduling, then the scheduling process is simple, but the service allocation quality deteriorates and cannot adapt to service unavailability or overload
Solution Approach 1:
The patent implements dynamic service scheduling by transitioning from static constant rules to a dynamic selection mechanism. The system dynamically selects services based on real-time status information (availability, load conditions) and calculates scheduling values that change with system state, enabling the scheduling process to adapt to varying service conditions while maintaining reasonable complexity through structured evaluation criteria.
Solution Approach 2:
The patent incorporates feedback mechanisms by continuously monitoring service status and using this information to adjust scheduling decisions. The system obtains service status information, calculates scheduling values based on current conditions, and selects services accordingly, creating a closed-loop feedback system that improves adaptability without requiring overly complex scheduling logic.
2Ease of operation
If multiple services are scheduled with constant rules, then the scheduling implementation is straightforward, but service quality deteriorates when services are down or at maximum visits
Solution Approach 1:
The system implements feedback by monitoring service status (availability, visit counts) and using this information to make informed scheduling decisions. The scheduling value calculation incorporates service status, ensuring that services that are down or at maximum visits are not selected, thereby maintaining reliability while keeping the implementation straightforward through structured status checking.
Solution Approach 2:
The patent changes the scheduling parameter from fixed constant rules to dynamic scheduling values that vary based on service status. By calculating scheduling values that reflect current service conditions (availability, load), the system maintains easy implementation through a consistent calculation framework while improving reliability by adapting to actual service states.
3Adaptability or versatility
If dynamic service scheduling is implemented, then service allocation quality improves, but the scheduling complexity increases
Solution Approach 1:
The patent segments the scheduling system into distinct functional modules: status information acquisition, scheduling value calculation, and service selection. This segmentation allows the complex dynamic scheduling process to be managed through separate, well-defined components, reducing overall system complexity while maintaining high adaptability through the coordinated operation of these modules.
Solution Approach 2:
The system manages complexity by changing from fixed rules to parameter-based dynamic scheduling. The scheduling value calculation uses standardized parameters (service status, importance weights) that can be systematically adjusted without redesigning the entire system, enabling adaptability while maintaining structured, manageable complexity through consistent parameter handling.
4Ease of manufacture
If service scheduling uses fixed allocation rules, then the system is easy to implement, but user experience quality deteriorates
Solution Approach 1:
The patent implements dynamic service scheduling that adapts to user needs and service conditions, improving user experience quality. The system dynamically calculates scheduling values based on current service status and selects appropriate services, moving from static fixed allocation to dynamic adaptation while maintaining ease of implementation through structured calculation methods and clear decision logic.
Data Source
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AI summary
Embodiments of the present disclosure provide a method and an apparatus for scheduling a service, an electronic device, and a storage medium. The method includes: a service scheduling task is acquired, the service scheduling task is composed of a first identifier corresponding to a service request, and a second identifier corresponding to a device receiving the service request; a third identifier corresponding to a service associated with the service scheduling task is searched in a preset service scheduling knowledge graph; at least one service scheduling sequence is generated on the basis of the service scheduling task and the third identifier corresponding to the service; a target service scheduling sequence is determined from the at least one service scheduling sequence; and the device is notified to access a service corresponding to the third identifier in the target service scheduling sequence. By determining a target service scheduling sequence from the at least one service scheduling sequence, and notifying the device to access the service corresponding to the third identifier in the target service scheduling sequence, the optimization of service allocation can be realized, and the high-quality service and experience can be provided for users.