Our technical team is continuously researching and developing new tools and automations to further build on the user experience within the True View Ecosystem.
True View Reckon is the “hub” of a True View sensor ecosystem. Reckon is an Amazon Web Services hosted Server that connects you, our True View customers, to us (as well as providing data hosting services). It manages a number of bookkeeping tasks behind the scenes such as allocating True View Points for subscription systems and managing “pay as you go” use of Trimble/Applanix Smart Base and PP-RTK positioning services.
On the technical side, we have always managed sensor calibration information within Reckon. During post-processing in True View EVO, your account in Reckon is checked to ensure you are using the proper calibration for the sensor in use and the date on which the project being processed was collected. This automated management of calibration removes a complex and oft error-prone task from your workflow concerns. The interface for this is shown in Figure 1.
In November of 2020, we phased in a new automated sensor health check monitoring service.
True View 3DIS® systems record an extensive log file ( the “Cycle Log”) during flight. This file contains environmental, processing and health information about the flight and the sensor. The Cycle Log is one of the Layers formed when you import a flight into True View EVO.
A graphical display is provided with geospatially correct one second “heartbeat” points that can be clicked to read log data relevant to that time slice – very cool and useful! However, subtle things can happen in a sensor that do not damage the output but may be indicators of an impending issue.
These Cycle Logs are pushed up to Reckon as a background operation while you are post-processing in True View EVO. Algorithms in Reckon analyze the log and, if something is amiss, automatically send an email to our support staff, along with the log file. Our support staff then have a look at the log and will inform you if some action needs to be taken.
I am very excited about this new proactive monitoring. Reliability is critically important and these are smart ways to approach improvements. This is an area where we will begin to apply “machine learning” (really, “inferencing”) to determine if something is amiss.
For example, an overtemperature trend requires more than just examining the temperate of the Core Compute Unit (CCU) of the sensor. Is a 40 C core temperature five minutes into a flight an anomaly? If the ambient temperature is 32 C then probably not. However, if the ambient is 10 C then something may be amiss.
This service is included in both subscription and purchased systems at no additional cost. It is yet another example of purchasing an entire solution as opposed to just a sensor.