Food Processing Traceability Monitoring for Unit-Level Settings
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Solution Overview
Problem
Current food processing systems lack granular monitoring and data integration to provide detailed information on food production processes, environmental impact, and resource utilization, limiting opportunities for further efficiency improvements and sustainability enhancements.
Innovation Solution
A computer-implemented method and system for monitoring food processing systems, which involves receiving process traceability data, process event data, and settings data to determine the specific settings used for each food production unit during processing. This data is then used to calculate environmental impact measures, such as carbon footprint, and optimize resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If granular monitoring of food production processes is implemented, then information completeness and transparency are improved, but system complexity and data processing requirements increase
Solution Approach 1:
The patent segments the food production system into discrete processing units (mixers, reactors, separators, etc.), each with unique identifiers and specific process parameters. This segmentation allows granular tracking of information at each stage without requiring a monolithic complex system, as each unit can be monitored and data collected independently through standardized interfaces.
Solution Approach 2:
The patent introduces a data processing system that acts as an intermediary between the physical food production units and the monitoring/analysis functions. This intermediary collects data from various sources, standardizes it, and processes it to generate comprehensive production information, thereby reducing the complexity burden on individual production units while maintaining complete traceability.
2Object-generated harmful factors
If resource usage optimization is implemented through detailed monitoring, then environmental impact is reduced, but measurement and data collection complexity increase
Solution Approach 1:
The patent implements universal measurement standards and data collection protocols that can be applied across different food production units and resource types (energy, water, materials). This multi-functional approach allows the same monitoring framework to track diverse resources, reducing the complexity of implementing separate measurement systems for each resource type while enabling comprehensive environmental impact assessment.
Solution Approach 2:
The patent establishes feedback loops where measured resource consumption data is continuously analyzed and used to optimize production processes. By implementing real-time or near-real-time feedback mechanisms, the system can automatically adjust operations to minimize environmental impact, reducing the need for complex manual measurement and intervention while achieving sustained resource optimization.
3Use of energy by moving object
If production scheduling is optimized to increase green electricity usage, then sustainability is improved, but scheduling complexity and computational requirements increase
Solution Approach 1:
The patent implements preliminary scheduling actions by predicting green electricity availability and pre-planning production tasks to align with renewable energy supply. By performing advance scheduling based on forecasted green energy availability, the system can optimize energy usage without requiring complex real-time decision-making, thereby reducing computational complexity while maintaining high sustainability performance.
Solution Approach 2:
The patent introduces dynamic scheduling capabilities that can adapt to changing green electricity availability and production requirements. The scheduling system dynamically adjusts task timing and resource allocation based on real-time conditions, allowing flexible optimization of green energy usage without requiring overly complex fixed schedules, thus balancing sustainability goals with manageable system complexity.
Data Source
AI summary
A computer-implemented method (500) for monitoring a food processing system (100), arranged to produce a food product (102), using a data processing apparatus (112) is provided. The method comprisesreceiving (502) process traceability data (200) related to the food product (102), wherein the process traceability data (200) comprises information about which food processing units (104a-e) that were involved in processing the food product (102) as well as during which time slots the food processing units were involved for processing the food product (102),receiving (504) process event data (202) related to the food processing units of the food production system (100), wherein the process event data (202) comprises information about state changes of the food production units that occurred during processing of the food product (102) as well as points of time when the state changes occurred,receiving (506) settings (204) of the processing units (104a-e) associated with different states,determining (508) the settings used for the food production units (104a-e) during different time slots by combining the process event data (202) and the settings of the processing units (204) associated with the different states, anddetermining (510) the settings of the food production units (104a-e) used for processing the food product (102) by combining the process traceability data (200) and the settings used for the food production units during the different time slots, andproviding (512) the settings of the food production units (104a-e) used for processing the food product (102).