Sensor Signal Processing Using Volatile Memory Timeslot Arrays
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Solution Overview
Problem
Conventional systems for collecting and processing output signals from sensors in harsh outdoor environments face challenges due to the limitations of mass storage devices, such as hard disks, which are prone to deterioration from temperature and humidity changes, leading to costly and time-consuming maintenance, especially in areas with poor and intermittent network connectivity.
Innovation Solution
A sensor network system that uses function processors to convert and process sensor signals into volatile memory timeslot arrays, allowing for efficient storage and transmission of parameters without relying on mass memory devices, enabling continuous operation even in environments with poor connectivity by storing data in volatile memory and transmitting it when a stable connection is reestablished.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If mass storage devices (hard disks) are used for storing sensor parameters, then large amounts of data can be stored, but the system becomes vulnerable to environmental deterioration and requires costly maintenance
Solution Approach 1:
The patent extracts the mass storage function from the field collection site by using cloud-based storage. The field collection site only performs data aggregation and transmission, while the cloud server handles long-term storage. This eliminates the need for local hard disks at remote sites, solving the reliability issue in harsh environments while maintaining large storage capacity through remote cloud infrastructure.
Solution Approach 2:
The patent introduces a cloud server as an intermediary between the field collection site and the final data storage. The cloud server acts as a mediator that receives data from multiple field sites, performs centralized processing, and provides persistent storage. This intermediary approach allows field sites to operate without reliable local storage while ensuring data persistence through the cloud infrastructure.
2Productivity
If mass storage devices are deployed at remote field collection sites, then data can be stored locally, but maintenance costs and time increase due to environmental factors
Solution Approach 1:
The patent removes mass storage devices from remote field collection sites entirely, extracting the storage function to the cloud. This eliminates the need for on-site maintenance of hard disks in harsh environments, significantly reducing maintenance effort and time while allowing continuous data collection through the cloud-based architecture.
Solution Approach 2:
The patent uses volatile memory (RAM) at field collection sites for temporary data storage during operation, accepting that this memory will be lost when power is interrupted but is quickly replenished from the cloud. This approach trades the cost of maintaining expensive, non-volatile storage devices in harsh environments for the lower cost of volatile memory and cloud storage infrastructure.
3Ease of repair
If volatile memory is used for data storage, then maintenance costs are reduced, but data is lost when power is interrupted
Solution Approach 1:
The patent uses the cloud server as an intermediary that provides persistent storage and data recovery capabilities. When power is interrupted at field collection sites, the cloud server preserves data and enables recovery. The cloud acts as an intermediary layer that decouples the volatile memory at field sites from permanent data storage, allowing maintenance cost reduction while preventing information loss through cloud-based persistence.
Solution Approach 2:
The patent implements preliminary data transmission to the cloud server during operational periods. By proactively sending data to the cloud before power interruptions occur, the system ensures that no data is lost. The cloud server performs preliminary receipt and storage of data, so when power fails at field sites, the data is already safely stored remotely.
4Measurement precision
If parameters are transmitted continuously across network, then real-time monitoring is achieved, but network bandwidth is consumed and connectivity reliability is reduced
Solution Approach 1:
The patent performs preliminary aggregation and processing of sensor data at field collection sites before transmission to the cloud. By pre-processing data locally and transmitting only processed results rather than raw continuous streams, the system reduces network bandwidth consumption while maintaining real-time monitoring capabilities through efficient data preparation in advance.
Solution Approach 2:
The patent transmits only the necessary processed parameters and summary statistics to the cloud rather than all raw sensor data continuously. This partial transmission approach reduces network bandwidth consumption and improves connectivity reliability while still providing sufficient real-time monitoring information through selective data transmission of key metrics.
Data Source
AI summary
Embodiments disclosed herein for processing signals from sensor groups include processor(s) configured to perform operations comprising sampling, at a configurable signal sampling interval, a plurality of signal sample values via the one or more output signal traces from the plurality of remotely located sensors; storing, in a transmission array, an available portion of parameters extracted from a first timeslot array and from a second timeslot array, wherein the available portion of parameters is extracted using an algorithm configured to assign specific parameters to the transmission array, independent of the sampling; and transmitting, to an external communications network, an available portion of the parameters stored in the transmission array, at a transmission interval, wherein the transmission interval is independent of timeslot durations of the first timeslot array and the second timeslot array, and wherein the transmitting operation is performed independently of the storing operation of the available portion of parameters.