IoT Tips

Greetings, Community Members! PTC has launched ThingWorx 9.6.0 as of June, and it's now ready for your upgrade! Let's explore the key enhancements in this latest version of ThingWorx.   What are the top three areas of updates in ThingWorx 9.6?   Performance, Scalability, Reliability, and Security: There's a significant boost in file transfer performance between connected devices and the ThingWorx platform. A notable enhancement in server startup performance, with some instances showing an 84% reduction in startup time. Numerous logging improvements, including limitations on log verbosity, log filtration, and configurable log storage capabilities, contribute to the stabilization of the ThingWorx system. Additionally, ThingWorx now supports log extraction to third-party software like Sumologic, Datadog, Splunk, Grafana, etc., utilizing the industry-standard OpenTelemetry framework starting from TWX 9.6. Content Security Policy has been implemented, fortifying ThingWorx against script and data injection attacks, man-in-the-middle (MITM) attacks, and clickjacking. Several tech stack updates in 9.6; support now available for Azure B2C and TLS 1.3 (limited)   Developer Productivity: Introducing a new Collection widget with improved performance, enabling a transition away from the legacy Collection and Repeater widgets. Several other new widgets such as KPI dial widget, Tree selector widget, Progress Tracker widget and other critical enhancements for ThingWorx WebComponents are now available Support for viewing mashup configurations such as layouts, bindings, and widget properties in a read-only mode, helping improve user experience by allowing multiple users to review mashup designs simultaneously without making edits.   Solutions updates: Streamlined continuous improvement with the ability to View and Create Actions in One-Click from performance analysis screens for the Digital Performance Management (DPM) solution. Enhanced performance for the Asset Monitoring & Utilization (AMU) solution, with alarm events creation now handled asynchronously. Several fixes and improvements for Connected Work Cell (CWC), Real-Time Production Performance Monitoring (RTPPM), and DPM such as limits evaluation, messages not displayed, incorrectly calculated KPIs and issues with Running Time on the operator display and more, helping customers achieve continuous improvements in their manufacturing operations   View release notes here and be sure to upgrade to 9.6!   Dilanur Bayraktar ThingWorx Product Management

Hello everyone,   After a recent presales experience, I wanted to make my knowledge available to you. The core of this article is to demonstrate how you can format a Flux request in Thingworx and post it to InfluxDB, with the aim of reporting the need for performance in calculations to InfluxDB. The following context is renewable energy. There is no detail about Kepware and how to connect to InfluxDB. As a prerequisite, you may like to read this article: Using Influx to store Value Stream properties from... - PTC Community     Introduction   The following InfluxDB usage has been developped for an electricity energy provider We deal with Wind farm, Solar farm and Battery Storage.   Technical Context Kepware is used as a source of data. A simulation for Wind assets based on excel file is configured, delivering sata in realtime. SQL Database also gather the same data than the simulation in Kepware. It is used to load historical data into InfluxDB, addressing cases of temporary data loss. Once back online, SQL help to records the lost data in InfluxDB and computes the KPIs. InfluxDB is used to store data overtime as well as calculated KPIs. Invoicing third party system is simulated to get electricity price according time of the day.   Orchestration between TWX and InfluxDB Thingworx v9.4.4 Set the numeric property to log Maintain control over execution logic Format Flux request with dynamic inputs to send to Influx DB  InfluxDB Cloud v2 Store logged property Enable quick data read Execute calculation Note: Free InfluxDB version is slower in write and read, and only 30 days data retention max.     Thingworx model and services   Thingworx context Due to the fact relevant numeric properties are logged overtime, new KPIs are calculated based on the logged data. In the following example, each Wind asset triggered each minute a calculation to get the monetary gain based on current power produced and current electricity price. The request is formated in Thingworx, pushed and executed in InfluxDB. Thus, Thingworx server memory is not used for this calculation.   Services breakdown CalculateMonetaryKPIs Entry point service to calculate monetary KPIs. Use the two following services: Trigger the FormatFlux service then inject it in Post service. Inputs: No input Output: NOTHING FormatFlux_CalculateMonetaryKPI Format the request in Flux format for monetary KPI calculation. Respect the Flux synthax used by InfluxDB. Inputs: bucketName (STRING) thingName (STRING) Output: TEXT PostTextToInflux Generic service to post the request to InfluxDB, whatever the request is Inputs: FluxQuery (TEXT) influxToken (STRING) influxUrl (STRING) influxOrgName (STRING) influxBucket (STRING) thingName (STRING) Output: INFOTABLE   Highlights - CalculateMonetaryKPIs Find in attachments the full script in "CalculateMonetaryKPIs script.docx". Url, token, organization and bucket are configured in the Persitence Provider used by the ValueStream. We dynamically get it from the ValueStream attached to this thing. From here, we can reuse it to set the inputs of two other services using “MyConfig”.   Highlights - FormatFlux_CalculateMonetaryKPI Find in attachments the full script in "FormatFlux_CalculateMonetaryKPI script.docx". The major part of this script is a text, in Flux synthax, where we inject dynamic values. The service get the last values of ElectricityPrice, Power and Capacity to calculate ImmediateMonetaryGain, PotentialMaxMonetaryGain and PotentialMonetaryLoss.   Flux logic might not be easy for beginners, so let's break down the intermediate variables created on the fly in the Flux request. Let’s take the example of the existing data in the bucket (with only two minutes of values): _time _measurement _field _value 2024-07-03T14:00:00Z WindAsset1 ElectricityPrice 0.12 2024-07-03T14:00:00Z WindAsset1 Power 100 2024-07-03T14:00:00Z WindAsset1 Capacity 150 2024-07-03T15:00:00Z WindAsset1 ElectricityPrice 0.15 2024-07-03T15:00:00Z WindAsset1 Power 120 2024-07-03T15:00:00Z WindAsset1 Capacity 160   The request articulates with the following steps: Get source value Get last price, store it in priceData _time ElectricityPrice 2024-07-03T15:00:00Z 0,15 Get last power, store it in powerData _time Power 2024-07-03T15:00:00Z 120 Get last capacity, store it in capacityData _time Capacity 2024-07-03T15:00:00Z 160 Join the three tables *Data on the same time. Last values of price, power and capacity maybe not set at the same time, so final joinedData may be empty. _time ElectricityPrice Power Capacity 2024-07-03T14:00:00Z 0,15 120 160 Perform calculations gainData store the result: ElectricityPrice * Power _time _measurement _field _value 2024-07-03T15:00:00Z WindAsset1 ImmediateMonetaryGain 18 maxGainData store the result: ElectricityPrice * Capacity lossData store the result: ElectricityPrice * (Capacity – Power) Add the result to original bucket   Highlights - PostTextToInflux Find in attachments the full script in "PostTextToInflux script.docx". Pretty straightforward script, the idea is to have a generic script to post a request. The header is quite original with the vnd.flux content type Url needs to be formatted according InfluxDB API     Well done!   Thanks to these steps, calculated values are stored in InfluxDB. Other services can be created to retrieve relevant InfluxDB data and visualize it in a mashup.     Last comment It was the first time I was in touch with Flux script, so I wasn't comfortable, and I am still far to be proficient. After spending more than a week browsing through InfluxDB documentation and running multiple tests, I achieved limited success but nothing substantial for a final outcome. As a last resort, I turned to ChatGPT. Through a few interactions, I quickly obtained convincing results. Within a day, I had a satisfactory outcome, which I fine-tuned for relevant use.   Here is two examples of two consecutive ChatGPT prompts and answers. It might need to be fine-tuned after first answer.   Right after, I asked to convert it to a Thingworx script format:   In this last picture, the script won’t work. The fluxQuery is not well formatted for TWX. Please, refer to the provided script "FormatFlux_CalculateMonetaryKPI script.docx" to see how to format the Flux query and insert variables inside. Despite mistakes, ChatGPT still mainly provides relevant code structure for beginners in Flux and is an undeniable boost for writing code.  

As of May 24, 2023, ThingWorx 9.4.0 is available for download!  Here are some of the highlights from the recent ThingWorx release.   What’s new in ThingWorx 9.4? Composer and Mashup Builder  New Combo chart and Pie chart based on modern Web Components architecture along with several other widget enhancements such as Grid toolbar improvements for custom actions, highlighting newly added rows for Grid widget and others Ability to style the focus on each widget when they are selected to get specific styling for different components Absolute positioning option with a beta flag to use by default is available now Several other Mashup improvements, such as Export function support is made available; improved migration dialogue (backported to 9.3 as well) to allow customers to move to new widgets; and legacy widgets language changes   Foundation  Heavily subscribed events could now be distributed across the ThingWorx HA cluster nodes to be scaled horizontally for better performance and processing of ThingWorx subscriptions. Azure Database for PostgreSQL Flex Server GA support with ThingWorx 9.4.1 for customers deploying ThingWorx Foundation on their own Azure Cloud infrastructure Improved ThingWorx APIs for InfluxDB to avoid Data loss at a high scale with additional monitoring metrics in place for guardrails Several security fixes and key third-party stack updates   Remote Access and Control (RAC) Remote Service Edge Extension (RSEE) for C-SDK, currently under Preview and planned to be Generally Available (GA) by Sept 2023, would allow ThingWorx admins to use Remote Access and Control with the C-SDK based connected devices to use Global Access Server (GAS) for a stable, secure, and large number of remote sessions With 9.4, a self-signed certificate or certificate generated by the trusted certificate authority can be used for RAC with the ThingWorx platform Ability to use parameter substitution to update Auto Launch commands dynamically based on data from the ThingWorx platform is now available   Software Content Management  Support for 100k+ assets with redesigned and improved performance on Asset Search Better control over package deployment with redesigned package tracking, filtering, and management Improved overall SCM stability, performance, and scalability and a more user-friendly and intuitive experience   Analytics  Improvements to the scalability of property transforms to enable stream processing of the larger number of properties within a ThingWorx installation Refactoring of Analytics Builder UI to use updated widgets and align with the PTC design system Updates to underlying libraries to enable the creation and scoring of predictive models in the latest version of PMML (v4.4) End of Support for Analytics Manager .NET Agent SDK     View release notes here and be sure to upgrade to 9.4!     Cheers, Ayush Tiwari Director, Product Management, ThingWorx      

Learn how to use the DBConnection building block to create your own DB tables in ThingWorx.

This video is Module 11: ThingWorx Analytics Mashup Exercise of the ThingWorx Analytics Training videos. It shows you how to create a ThingWorx project and populate it with entities that collectively comprise a functioning application. 

This video is Module 10: ThingWorx Foundation & Analytics Integration of the ThingWorx Analytics Training videos. It gives a brief review of core ThingWorx Platform functionality, and how the Analytics server works on top of the platform. It also describes the process of creating a simple application, complete with a mashup to display the information from a predictive model.

This video concludes Module 9: Anomaly Detection of the ThingWorx Analytics Training videos. It gives an overview of the "Statistical Process Control (SPC) Accelerator"

This video continues Module 9: Anomaly Detection of the ThingWorx Analytics Training videos. It begins with a ThingWatcher exercise, and concludes by describing Statistical Process Control (SPC). The "SPC Accelerator" will be covered in Module 9 Part 3.

This video begins Module 9: Anomaly Detection of the ThingWorx Analytics Training videos. It describes how Thingwatcher can be set up to monitor values streaming from connected assets, and send an alert if its behavior deviates from its 'normal' behavior.

This video concludes Module 8: Time Series Modeling of the ThingWorx Analytics Training videos. 

This video continues Module 8: Time Series Modeling of the ThingWorx Analytics Training videos. It continues to show how ThingWorx Analytics automatically transforms time series datasets into ones that are ready for machine learning. It also describes the concept of virtual sensors. It finishes by describing the time series dataset that will be used in the following modules.

This video begins Module 8: Time Series Modeling of the ThingWorx Analytics Training videos. It describes the differences between time series and cross-sectional datasets. It begins to show how ThingWorx Analytics automatically transforms time series datasets into ones that are ready for machine learning. 

This video concludes Module 7: Predictive & Prescriptive Scoring of the ThingWorx Analytics Training videos. It describes how ThingWorx Analytics automatically evaluates a range of values for chosen fields to produce prescriptive scores. 

This video begins Module 7: Predictive & Prescriptive Scoring of the ThingWorx Analytics Training videos. It describes how a trained machine learning model takes inputs and makes predictions of different kinds, depending on the use case. It shows how scoring works in production, taking inputs from various sources and producing a score to help users make informed decisions. It also covers the concept of field importance in an individual score.

This video concludes Module 6: Predictive Models & Model Validation of the ThingWorx Analytics Training videos. 

This video continues Module 6: Predictive Models & Model Validation of the ThingWorx Analytics Training videos. It covers some modeling techniques to help build better predictive models. It discusses the dangers of models that overfit data, and how to avoid overfitting. 

This video continues Module 6: Predictive Models & Model Validation of the ThingWorx Analytics Training videos. It then begins to describe some of the performance metrics used to evaluate predictive models. 

This video continues Module 6: Predictive Models & Model Validation of the ThingWorx Analytics Training videos. It describes the remaining machine learning algorithms used by ThingWorx Analytics to build predictive models that weren't covered in Part 1. In addition, this video describes the different kinds of ensembles you can build that utilize multiple algorithms. 

This video begins Module 6: Predictive Models & Model Validation of the ThingWorx Analytics Training videos. It gives examples of different types of goal variables. It also discusses data considerations in predictive modeling, It begins describing the machine learning algorithms used by ThingWorx Analytics to build predictive models. 

This video concludes Module 5: Descriptive Analytics of the ThingWorx Analytics Training videos. It covers signals, profiles, and clusters, and how these forms of descriptive analytics provide crucial insight into your data.

This video begins Module 5: Descriptive Analytics of the ThingWorx Analytics Training videos. It covers signals, profiles, and clusters, and how these forms of descriptive analytics provide crucial insight into your data.