RPA Platform Adapters Using Hash-Based Change Filtering
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Solution Overview
Problem
The complexity of monitoring, controlling, and managing multiple robotic process automation (RPA) platforms and bots becomes resource-intensive as the number of platforms and bots increases, leading to inefficiencies in data processing and resource consumption.
Innovation Solution
A unified automation platform (UAP) is introduced, which uses a platform-specific adapter to receive data records from multiple RPA platforms, tags records based on content, and employs a data change tracking mechanism to compare hash values, reducing the burden on technical resources by directly sending tagged records to the data processing layer and filtering out unchanged records.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data records from multiple RPA platforms are monitored and processed in detail, then monitoring completeness and control accuracy are improved, but memory consumption and processor consumption increase significantly
Solution Approach 1:
The patent extracts only the essential monitoring elements by comparing hash values of data records against previously stored hash values. Instead of processing complete data records, the system extracts and compares only the hash values, which are significantly smaller in size. This extraction principle reduces the amount of data that needs to be processed and stored in memory, thereby reducing processor consumption and memory usage while maintaining monitoring completeness.
Solution Approach 2:
The patent transforms data records into a different parameter representation by calculating hash values. This parameter change converts large, complex data records into compact numerical representations that are much more efficient to store and process. The hash value transformation maintains the essential information needed for monitoring while dramatically reducing the computational resources required for processing and the memory needed to store monitoring data.
2Loss of information
If all data records from multiple RPA platforms are processed and stored, then data completeness is improved, but memory consumption increases by approximately 96%
Solution Approach 1:
The patent extracts only the critical identifying feature of each data record - its hash value - for storage and comparison purposes. By extracting this essential identifier rather than storing complete data records, the system maintains the ability to detect changes and ensure data completeness while using minimal memory. The hash value serves as a compact representation that preserves the unique identity of each record without requiring storage of the entire record content.
Solution Approach 2:
The patent creates a simplified copy of the data record in the form of a hash value. This copy contains the essential information needed for monitoring and change detection while occupying minimal storage space. The hash value copy allows the system to track data completeness and detect changes without storing the full original records, thereby maintaining data integrity information while dramatically reducing memory consumption.
3Manufacturing precision
If detailed processing of all data records is performed, then processing accuracy is improved, but the system becomes resource-intensive and complex to manage
Solution Approach 1:
The patent replaces the mechanical approach of comparing complete data records with a computational substitution using hash value comparison. Instead of performing complex, resource-intensive comparisons of entire data records, the system uses mathematical hash functions to generate compact representations that can be compared efficiently. This substitution maintains processing accuracy for change detection while dramatically reducing computational complexity and resource requirements.
4Use of energy by moving object
If hash value comparison is used to filter unchanged records, then processor consumption is reduced by about 30%, but additional computational steps are required
Solution Approach 1:
The patent performs preliminary action by calculating hash values of data records before the main processing step. This preliminary hash calculation allows the system to quickly identify and filter out unchanged records without performing the more expensive operation of comparing complete data records. The preliminary hash value computation enables efficient filtering that reduces overall processor consumption, as the hash comparison step is much less resource-intensive than full record processing.
Data Source
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AI summary
Implementations directed to reducing data flow between an AP, and a RPA platform include receiving, by a platform-specific adapter, a set of data records, providing a data change tuple including an identifier, and a hash value, providing a sub-set of data records from the second set of data records based on comparing hash values of data change tuples to a set of stored hash values, the sub-set of data records including fewer data records than the second set of data records, and each data record in the sub-set of data records including a change indicator, determining, by the platform-specific adapter, a tag for each data record in the sub-set of data records based on a respective change indicator, and transmitting a set of messages to the AP, the set of messages communicating tagged data records of the first set of data records, and the sub-set of data records.