Comprehensive Backend Development for Heat Pump Monitoring and Control System

Project Overview:

We are seeking a skilled freelancer or team to undertake the initial phase of developing a robust backend system for a heat pump monitoring and control application. This phase focuses on conducting an in-depth study and creating detailed documentation that will serve as a blueprint for the subsequent development stages.

Heat Pump Models:

1. LG Therma V R32
2. LG Therma V R290
3. Nibe S2125

Hardware Integration:

Each heat pump will be equipped with a Waveshare RS485 TO WIFI/ETH module to facilitate data communication. This module supports Modbus RTU over RS485 and can bridge to Modbus TCP over Ethernet or WiFi. It also offers MQTT capabilities for seamless integration with cloud services.

Rapid On-Site Configuration & Connectivity Resilience
To ensure efficient deployment, we aim to establish a streamlined process for configuring the Waveshare RS485 TO WIFI/ETH modules on-site within 1–2 minutes. This involves setting up WiFi credentials and a few essential parameters swiftly, minimizing installation time and reducing the need for specialized technical expertise. Additionally, we seek to implement robust fallback mechanisms to maintain connectivity and eliminate the necessity for return visits in case of server disruptions/change. The deliverables should include a comprehensive, user-friendly configuration guide or tool to facilitate this rapid setup process.

Objectives:

1. Modbus Protocol Analysis:
- Identify and document the Modbus RTU/TCP communication protocols for each specified heat pump model.
- Detail the Modbus addresses, data registers, and command sets for each model.

2. Data Flow and System Architecture:
- Design comprehensive data flow diagrams illustrating the interaction between heat pumps, Waveshare modules, AWS cloud services, and the Bubble-based client application.
- Propose an AWS-based architecture leveraging services such as AWS IoT Core, AWS Lambda, Amazon Timestream, and Amazon QuickSight for data storage, processing, and visualization.

3. AI Integration Planning:
- Outline strategies for incorporating AI and machine learning capabilities to analyze collected data for predictive maintenance and anomaly detection.
- Suggest suitable AWS services like Amazon SageMaker for implementing these features.

4. Comprehensive Documentation:
- Compile all findings into a detailed project guideline document, including:
- Modbus communication protocols and configurations for each heat pump model.
- Data flow diagrams and system architecture schematics.
- AWS service configurations and integration plans.
- AI integration strategies and proposed models.

Deliverables:

- A detailed report encompassing all aspects mentioned above.
- Visual diagrams illustrating data flows and system architecture.
- A roadmap for the next development phases based on the study's findings.

Ideal Candidate:

- Proven experience with Modbus protocol and industrial IoT systems.
- Familiarity with AWS services, particularly those related to IoT and data analytics.
- Background in integrating AI/ML solutions for predictive maintenance.
- Experience with Bubble.io or similar no-code platforms.
- Strong technical writing skills for comprehensive documentation.

Project Timeline:

The study and documentation phase is expected to be completed within 1 week from the project commencement date.

Potential for Ongoing Collaboration
Should this initial study and planning phase be executed successfully, we are open to extending our collaboration by entrusting the selected freelancer or team with the project management responsibilities for the full development cycle.This would encompass overseeing the implementation of the backend system, coordinating with various stakeholders, and ensuring that the project milestones are met efficiently and effectively.

We look forward to collaborating with professionals who can lay a solid foundation for our heat pump monitoring and control system.