Heat pump equipment management system based on Internet of Things

Abstract

The invention relates to a heat pump equipment management system based on the Internet of Things. The heat pump equipment management system is characterized by comprising an equipment control acquisition end, a network transmission end, an application service and data analysis end and a display and control end, the first automatic management process comprises the following steps: equipment controls an acquisition end, a network transmission end, an application service, a data analysis end and a display and control end; a second manual management process: the display and control end application service and data analysis end network transmission end equipment controls the acquisition end; the system has the advantages that remote intelligent monitoring and fine management of the heat pump system are achieved, the operation and maintenance cost is reduced, the stability and reliability of the system are enhanced, through intelligent scheduling and optimization algorithms, system operation parameters are adjusted, the energy efficiency ratio is maximized, energy consumption is reduced, a user is assisted to make a more scientific energy use decision, energy conservation and emission reduction are promoted, and the system is suitable for popularization and application. The user experience is enhanced, the user can conveniently participate in energy management, and the satisfaction degree and the use stickiness are improved.

Description

Technical Field

[0001] The invention relates to a heat pump equipment management system based on the Internet of Things. Background Art

[0002] At present, with the intensification of the global energy crisis and the improvement of environmental protection awareness, energy-efficient heat pump systems have become the preferred alternative to traditional heating and cooling methods. However, most heat pump systems on the market face problems such as unfriendly operation interfaces, slow system response, low degree of automation, high maintenance costs, and lack of precise energy management strategies, which not only affects the user experience, but also limits its potential in energy conservation and emission reduction.

[0003] Existing heat pump control systems mostly rely on local operations, making it difficult to achieve remote monitoring and intelligent adjustment, resulting in delayed fault detection and difficulty in optimizing energy efficiency in real time. In addition, the lack of data collection and analysis capabilities makes it impossible for the system to automatically adjust operating parameters according to environmental changes and user habits, further reducing efficiency. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a heat pump equipment management system based on the Internet of Things, which realizes remote intelligent monitoring and refined management of the heat pump system, reduces operation and maintenance costs, and enhances the stability and reliability of the system. Through intelligent scheduling and optimization algorithms, it automatically adjusts the system operating parameters, maximizes the energy efficiency ratio, reduces energy consumption, and provides detailed energy consumption analysis reports to help users make more scientific energy use decisions, promote energy conservation and emission reduction, enhance user experience, and enable users to easily participate in energy management, thereby improving satisfaction and usage stickiness.

[0005] In summary, the heat pump Internet of Things software system of the present invention provides an effective way to achieve the deep integration of smart home and energy management. The technical solution is helpful to promote the realization of green building and sustainable development goals. In order to achieve the above-mentioned purpose, the present invention is implemented as follows: it is a heat pump equipment management system based on the Internet of Things, including an equipment control and collection terminal, a network transmission terminal, an application service and data analysis terminal, and a display and control terminal; Its first automatic management process is; Equipment control and collection end ⇔ network transmission end ⇔ application service and data analysis end ⇒ display and control end The steps are as follows: Step 1: The device control acquisition end collects relevant data of the heat pump through the temperature sensor, pressure sensor, humidity sensor, and liquid level sensor, and then transmits the signal to the network transmission end to enter step 2; Step 2: The network transmission end converts the signal of the device control acquisition end into the signal content of the Modbus protocol, and sends it to the MQTT client to enter step 3; Step 3: The MQTT client creates an MQTT template message based on the information content of the Modbus protocol and sends it to the MQTT server to proceed to step 4; Step 4: The MQTT server sends the Modbus protocol in the MQTT template message to the KAFKA message server and configures the KAFKA rules before proceeding to step 5. Step 5: The KAFKA message server transmits the information to the application service and data analysis end; Step 6: The application service and data analysis end decode the Modbus protocol information transmitted by the KAFKA message service, convert it into JSON format data, and then match the object model for data analysis and proceed to step 7 and step 8 respectively; Step 7: The matching object model performs data analysis and transmits the data after algorithm analysis to the display and control end, and the signal is displayed to the user through the screen, thus ending the above process; Step 8: The data after the matching model and algorithm analysis is used to create Modbus protocol information according to the rules configured by the Liteflow rule engine, and then proceed to step 9; Step 9: The application service and data analysis end sends the Modbus protocol to the KAFKA message server and configures the KAFKA rules, and then proceeds to step 10; Step 10: The KAFKA message server passes the information to the MQTT server, and the MQTT server forwards the information as MQTT rule configuration, and then proceeds to step 11; Step 11: The MQTT server sends the MQTT template message that complies with the Modbus protocol to the MQTT client, and then proceeds to step 12. Step 12: The MQTT client transmits the collected information to the device control and collection end, and the device control and collection end controls the heat pump to make adjustments; The second manual management process is: Display and control end ⇒ application service and data analysis end ⇒ network transmission end ⇒ device control and collection end; The steps are as follows: Step 1: The user triggers the command through the display and control terminal to transmit it to the application service and data analysis terminal, and then proceeds to step 2; Step 2: The application service and data analysis end converts the received control end trigger command into Modbus protocol information and sends it to the KAFKA message service end of the network transmission end, and then proceeds to step 3; Step 3: The KAFKA message server configures the KAFKA rules for the received Modbus protocol information and enters the step; Step 4: The KAFKA message server passes the information to the MQTT server, and the MQTT server forwards the information as MQTT rule configuration, and proceeds to step 5; Step 5: The MQTT server sends the Modbus protocol in the MQTT template message to the MQTT client, and then proceeds to step 6; Step 6: The MQTT client transmits the collected information to the device control acquisition terminal, and the device control acquisition terminal controls the heat pump for adjustment.

[0006] Compared with the prior art, the advantages of the present invention are as follows: it realizes remote intelligent monitoring and refined management of the heat pump system, reduces operation and maintenance costs, enhances the stability and reliability of the system, automatically adjusts system operating parameters through intelligent scheduling and optimization algorithms, maximizes energy efficiency, reduces energy consumption, and provides detailed energy consumption analysis reports, helping users make more scientific energy use decisions, promoting energy conservation and emission reduction, enhancing user experience, allowing users to easily participate in energy management, and improving satisfaction and usage stickiness. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0008] The specific embodiments of the present invention are further described below in conjunction with the accompanying drawings. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0009] In the description of the present invention, the terms "upper", "lower", "top" and "bottom" indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings. They are only for the convenience of describing the present invention and do not require that the present invention must be constructed and operated in a specific orientation. Therefore, they should not be understood as limitations on the present invention.

[0010] In the present invention, unless otherwise clearly specified and limited, the terms "set", "connected", "located" and the like should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or a detachable arrangement or integration; it can be a mechanical connection, a direct connection, or an indirect connection through an intermediate medium, which can enable the internal connectivity of two elements or the interaction relationship between the two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances.

[0011] It is a heat pump equipment management system based on the Internet of Things, which is characterized by including an equipment control acquisition terminal, a network transmission terminal, an application service and data analysis terminal, and a display and control terminal; Its first automatic management process is; Equipment control and collection end ⇔ network transmission end ⇔ application service and data analysis end ⇒ display and control end The steps are as follows: Step 1: The device control acquisition end collects relevant data of the heat pump through the temperature sensor, pressure sensor, humidity sensor, and liquid level sensor, and then transmits the signal to the network transmission end to enter step 2; Step 2: The network transmission end converts the signal of the device control acquisition end into the signal content of the Modbus protocol, and sends it to the MQTT client to enter step 3; Step 3: The MQTT client creates an MQTT template message based on the information content of the Modbus protocol and sends it to the MQTT server to proceed to step 4; Step 4: The MQTT server sends the Modbus protocol in the MQTT template message to the KAFKA message server and configures the KAFKA rules before proceeding to step 5. Step 5: The KAFKA message server transmits the information to the application service and data analysis end; Step 6: The application service and data analysis end decode the Modbus protocol information transmitted by the KAFKA message service, convert it into JSON format data, and then match the object model for data analysis and proceed to step 7 and step 8 respectively; Step 7: The matching object model performs data analysis and transmits the data after algorithm analysis to the display and control end, and the signal is displayed to the user through the screen, thus ending the above process; Step 8: The data after the matching model and algorithm analysis is used to create Modbus protocol information according to the rules configured by the Liteflow rule engine, and then proceed to step 9; Step 9: The application service and data analysis end sends the Modbus protocol to the KAFKA message server and configures the KAFKA rules, and then proceeds to step 10; Step 10: The KAFKA message server passes the information to the MQTT server, and the MQTT server forwards the information as MQTT rule configuration, and then proceeds to step 11; Step 11: The MQTT server sends the MQTT template message that complies with the Modbus protocol to the MQTT client, and then proceeds to step 12. Step 12: The MQTT client transmits the collected information to the device control and collection end, and the device control and collection end controls the heat pump to make adjustments; The second manual management process is: Display and control end ⇒ application service and data analysis end ⇒ network transmission end ⇒ device control and collection end; The steps are as follows: Step 1: The user triggers the command through the display and control terminal to transmit it to the application service and data analysis terminal, and then proceeds to step 2; Step 2: The application service and data analysis end converts the received control end trigger command into Modbus protocol information and sends it to the KAFKA message service end of the network transmission end, and then proceeds to step 3; Step 3: The KAFKA message server configures the KAFKA rules for the received Modbus protocol information and enters the step; Step 4: The KAFKA message server passes the information to the MQTT server, and the MQTT server forwards the information as MQTT rule configuration, and proceeds to step 5; Step 5: The MQTT server sends the Modbus protocol in the MQTT template message to the MQTT client, and then proceeds to step 6; Step 6: The MQTT client transmits the collected information to the device control acquisition terminal, and the device control acquisition terminal controls the heat pump for adjustment.

[0012] Intelligent scheduling and optimization algorithm: Using machine learning technology, it automatically generates the most optimized operation strategy based on historical operation data, ambient temperature, user preferences and other factors, and dynamically adjusts the working mode and parameters of the heat pump to achieve the best energy efficiency ratio and comfort.

[0013] Kafka message queue integration: Introducing Kafka's high-throughput message queue system ensures the real-time and reliability of data transmission between systems, effectively responds to the concurrent data processing requirements when large-scale equipment is connected, and provides a solid foundation for remote monitoring and immediate fault warning.

[0014] Liteflow rule engine application: By configuring the flexible Liteflow rule engine, predefined rule matching and automatic response for various operating scenarios can be achieved, simplifying the system configuration and maintenance process, and improving fault handling speed and system adaptability.

[0015] Application services and data analysis: Build a cloud computing-based data analysis platform to conduct in-depth mining of the massive amount of operating data collected, provide energy consumption analysis reports, help users and managers identify energy-saving space, and formulate more reasonable energy management strategies.

[0016] Display and control end: Design intuitive and easy-to-use mobile and web user interfaces so that users can easily view system status, adjust settings, receive warning notifications, and participate in the decision-making process of energy efficiency optimization, enhancing user experience and sense of participation.

[0017] Introducing advanced rule engines to achieve adaptive regulation of heat pump operation and introducing high-throughput message queues to ensure Ensure the real-time and reliability of data transmission between systems.

[0018] Design a highly scalable system architecture that is easy to integrate with other smart home systems.

[0019] The optimized data processing process greatly improves monitoring accuracy and response speed.

[0020] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited to the described embodiments. For those skilled in the art, various changes, modifications, substitutions and deformations of these embodiments without departing from the principles and purposes of the present invention still fall within the scope of protection of the present invention.

Claims

1. A heat pump equipment management system based on the Internet of Things, characterized in that Including equipment control and collection end, network transmission end, application service and data analysis end, and display and control end; The first automatic management process is: Equipment control and collection end ⇔ network transmission end ⇔ application service and data analysis end ⇒ display and control end The steps are as follows: Step 1: The device control acquisition end collects relevant data of the heat pump through the temperature sensor, pressure sensor, humidity sensor, and liquid level sensor, and then transmits the signal to the network transmission end to enter step 2; Step 2: The network transmission end converts the signal of the device control acquisition end into the signal content of the Modbus protocol, and sends it to the MQTT client to enter step 3; Step 3: The MQTT client creates an MQTT template message based on the information content of the Modbus protocol and sends it to the MQTT server to proceed to step 4; Step 4: The MQTT server sends the Modbus protocol in the MQTT template message to the KAFKA message server and configures the KAFKA rules before proceeding to step 5. Step 5: The KAFKA message server transmits the information to the application service and data analysis end; Step 6: The application service and data analysis end decode the Modbus protocol information transmitted by the KAFKA message service, convert it into JSON format data, and then match the object model for data analysis and proceed to step 7 and step 8 respectively; Step 7: The matching object model performs data analysis and transmits the data after algorithm analysis to the display and control end, and the signal is displayed to the user through the screen, thus ending the above process; Step 8: The data after the matching model and algorithm analysis is used to create Modbus protocol information according to the rules configured by the Liteflow rule engine, and then proceed to step 9; Step 9: The application service and data analysis end sends the Modbus protocol to the KAFKA message server and configures the KAFKA rules, and then proceeds to step 10; Step 10: The KAFKA message server passes the information to the MQTT server, and the MQTT server forwards the information as MQTT rule configuration, and then proceeds to step 11; Step 11: The MQTT server sends the MQTT template message that complies with the Modbus protocol to the MQTT client, and then proceeds to step 12. Step 12: The MQTT client transmits the collected information to the device control and collection end, and the device control and collection end controls the heat pump to make adjustments; The second manual management process is: Display and control end ⇒ application service and data analysis end ⇒ network transmission end ⇒ device control and collection end; The steps are as follows: Step 1: The user triggers the command through the display and control terminal to transmit it to the application service and data analysis terminal, and then proceeds to step 2; Step 2: The application service and data analysis end converts the received control end trigger command into Modbus protocol information and sends it to the KAFKA message service end of the network transmission end, and then proceeds to step 3; Step 3: The KAFKA message server configures the KAFKA rules for the received Modbus protocol information and enters the step; Step 4: The KAFKA message server passes the information to the MQTT server, and the MQTT server forwards the information as MQTT rule configuration, and then proceeds to step 5; Step 5: The MQTT server sends the Modbus protocol in the MQTT template message to the MQTT client, and then proceeds to step 6; Step 6: The MQTT client transmits the collected information to the device control acquisition terminal, and the device control acquisition terminal controls the heat pump for adjustment.

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