Optimal measuring point detection system for floor radiation heating tail end

Abstract

The utility model provides an optimal measuring point detection system for a floor radiation heating terminal. Mobile measuring equipment comprises a Raspberry Pi master control module, a positioning module, an offline processing module, a sensor module and a display operation module. The mobile measurement equipment and the positioning base station equipment collect position information through positioning modules of the mobile measurement equipment and the positioning base station equipment, and the main positioning base station equipment collects the position information of the slave positioning base station equipment and the mobile measurement equipment and sends the position information to the Raspberry Pi main control module or the offline processing module according to the online or offline state of the system; the Raspberry Pi master control module obtains coordinate position information of the test point through the position information, and transmits the environment information and the coordinate position information of the test point to the display operation module for display. According to the utility model, by analyzing the heat distribution mode in the floor radiation heating system and through temperature collection and data comparison, the most ideal measuring point position is determined, so that the accuracy and representativeness of data collection are ensured, and the regulation and management of the whole heating system are optimized.

Description

TECHNICAL FIELD

[0001] The utility model relates to indoor thermal environment detection technical field, concretely is a floor radiant heating terminal optimal measuring point detection system. BACKGROUND

[0002] The planning of the optimal measuring point of the floor radiant heating terminal involves measuring various temperature indicators of the indoor space, including dry-bulb temperature, black-bulb temperature, etc. These measurements help accurately assess the quality of the floor radiant heating and are the basis for evaluating the performance and effects of the heating, ventilation, and air conditioning system, and are crucial for improving indoor comfort and increasing living and working efficiency.

[0003] Due to the complex structure of the building space and the uneven heat distribution caused by the floor radiant heating system, the temperature parameters of the indoor environment, such as dry-bulb temperature and black-bulb temperature, are extremely unevenly distributed in different areas. This phenomenon causes the data obtained from a single measuring point to often fail to accurately reflect the true thermal environment state of the entire space, so it is particularly crucial to accurately measure the thermal environment parameters at multiple locations inside the building. The traditional indoor environment monitoring equipment on the market can mostly only monitor fixed and specific areas, and this equipment has obvious deficiencies in flexibility and accurate and rapid collection of data, making it difficult to comprehensively capture the thermal environment details of the entire indoor space, and these limitations highlight the importance of developing an advanced detection system that can dynamically and extensively monitor indoor environment data. SUMMARY

[0004] In view of the above-mentioned shortcomings and deficiencies, the utility model provides a floor radiant heating terminal optimal measuring point detection system, which can accurately measure the dry-bulb temperature and black-bulb temperature parameters at different positions in the room, thereby more truly reflecting the condition of the overall indoor thermal environment and the position of the optimal measuring point.

[0005] A floor radiant heating terminal optimal measuring point detection system, comprising a mobile measuring device and a positioning base station device, the mobile measuring device comprising a Raspberry Pi master control module, a positioning module, an offline processing module, a sensor module, and a display operation module;

[0006] The positioning base station device comprises a master control module and a positioning module. The positioning base station device can have one or more groups, and each group of positioning base station devices comprises a master positioning base station device and two slave positioning base station devices.

[0007] The Raspberry Pi master control module collects the dry-bulb temperature and black-bulb temperature environment information of the indoor thermal environment test point through the sensor module;

[0008] The mobile measurement device and the positioning base station device both collect position information through their positioning modules, and the master positioning base station device collects the position information from the positioning base station device and the mobile measurement device and sends it to the Raspberry Pi master control module or the offline processing module according to the online or offline state of the system.

[0009] The Raspberry Pi master control module obtains the coordinate position information of the test point through the position information, and transmits the environmental information and the coordinate position information of the test point to the display operation module and displays it.

[0010] The sensor module is a black ball thermometer, and the Raspberry Pi master control module is connected to the sensor module through an ADC digital-analog converter.

[0011] When the system is in an online working state, the master positioning base station device receives the position information and the current timestamp sent by the two slave positioning base station devices and the mobile measurement device, determines the optimal position point, and directly transmits the position information of the optimal position point to the Raspberry Pi master control module, which is displayed in real time through the display operation module.

[0012] When the system is in an offline working state, the master positioning base station device receives the position information and the current timestamp sent by the two slave positioning base station devices and the mobile measurement device, determines the optimal position point, and directly transmits the position information of the optimal position point to the offline processing module. When the system state changes to an online state, the offline processing module transmits the optimal point position information of the two slave positioning base station devices to the Raspberry Pi master control module, which is displayed through the display operation module.

[0013] The Raspberry Pi obtains the distance between the mobile measurement device and each positioning base station device through the position information of the optimal position point of each positioning base station device received, and obtains the coordinate position information of the test point by comparing the different distances between the three master positioning base stations and their corresponding position relationships through the three-point positioning method.

[0014] The mobile measurement device is connected to the web and the mobile phone through the Wi-Fi unit, and the Raspberry Pi master control module generates a building thermal environment view and the optimal measurement position coordinate on the web and the mobile phone based on the environmental information and the coordinate position information of the test point.

[0015] The mobile measurement device and the positioning base station device are both provided with a battery module, which includes a rechargeable battery and a Type-c interface, facilitating device charging.

[0016] The Raspberry Pi master control module includes a processor, a Wi-Fi unit, and a memory; the positioning module includes a radio frequency transceiver, a baseband processor, and a clock synchronization unit; and the display operation module includes a TFT touch display screen, an operation panel, and an HDMI interface, providing an intuitive user interaction interface.

[0017] The utility model has the following beneficial effects and advantages:

[0018] 1. The system's online and offline dual mode operation function greatly enhances its applicability and flexibility; in the online mode, the system can send data to Raspberry Pi in real time, so that the user can receive and analyze the environmental measurement results in time; in the offline mode, the system stores the data in the local memory, and automatically uploads after the network connection is restored, ensuring that the data will not be lost due to connection problems; in addition, the system uses efficient three-point positioning technology to determine the accurate coordinates of the test point with high precision, enhancing the accuracy of measurement; the structural design of the system considers energy saving and cost effectiveness, adopts low-power technology and economically effective components, so that the overall operation and maintenance cost is greatly reduced, while maintaining the long-term stable operation of the equipment;

[0019] 2. By using high-precision sensors and data analysis technology, the heat distribution mode in the floor radiant heating system is analyzed, and the measurement points that can accurately reflect the thermal comfort of the entire house type are selected, and the influence of the uneven heat distribution inside the house type and external factors is considered, this method systematically evaluates multiple measurement points, collects temperature and compares data to determine the most ideal measurement point position, to ensure the accuracy and representativeness of data collection, thereby optimizing the adjustment and management of the entire heating system;

[0020] 3. Real-time display and data upload function: the system can display the dry-bulb temperature and black-bulb temperature environmental parameters in real time, and the display operation module of the mobile measurement equipment can display the data, at the same time, the Raspberry Pi master module can process and calculate the collected data, and generate building thermal environment view and optimal measurement position coordinates on the Web and mobile terminal, to provide more intuitive and convenient indoor thermal environment reflection of floor radiant heating. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 It is the house type equipment layout of the utility model embodiment;

[0022] Figure 2 It is the mobile measurement equipment connection block diagram of the utility model;

[0023] Figure 3 It is the Raspberry Pi master module and sensor module connection diagram of the utility model;

[0024] Figure 4 It is the online state data transmission flow chart of the utility model;

[0025] Figure 5 It is the offline state data transmission flow chart of the utility model;

[0026] Figure 6 It is the circuit connection diagram of the mobile measurement equipment Raspberry Pi master module, sensor module and positioning module;

[0027] Figure 7 This is a circuit connection diagram of the main control module and the positioning module of the positioning base station equipment;

[0028] Wherein, T represents the mobile measuring device; T01 is the Raspberry Pi main control module; T02 is the mobile measuring device positioning module; T03 is the sensor module; T04 is the display and operation module; T05 is the mobile measuring device battery module; and T06 is the offline processing module. Detailed Implementation

[0029] The present invention will be further described below with reference to the accompanying drawings. The present invention provides an optimal measuring point detection system for floor radiant heating terminals. This embodiment uses, for example... Figure 1 Taking the apartment layout shown as an example, a mobile measurement device T and nine positioning base station devices are set up. One positioning base station device in each room serves as the main positioning base station device, and the other two serve as the slave positioning base station devices. Specifically, the main positioning base station A, the first slave positioning base station B, and the second slave positioning base station C form one group; the main positioning base station A1, the first slave positioning base station B1, and the second slave positioning base station C1 form one group; and the main positioning base station A2, the first slave positioning base station B2, and the second slave positioning base station C2 form one group.

[0030] like Figure 2 As shown, the mobile measurement device includes a Raspberry Pi main control module T01, a positioning module T02, an offline processing module T06, a sensor module T03, and a display and operation module T04;

[0031] Positioning base station equipment includes a main control module and a positioning module; such as Figure 7 As shown, the main control module and positioning module of the positioning base station equipment are interconnected. Through data interaction and analysis, they determine the optimal location of the positioning base station within the apartment layout. When the main control module determines that the location meets the requirements based on a specific algorithm and preset standards, it considers the location suitable. Simultaneously, when placing a mobile measuring device, the positioning module of the mobile measuring device is responsible for transmitting distance and location information signals to the main positioning base station equipment. This ensures that the mobile measuring device can effectively acquire distance information without the signal penetrating the apartment walls.

[0032] The Raspberry Pi main control module T01 collects indoor thermal environment test point dry-bulb temperature and black-bulb temperature information through sensor module T03. In this embodiment, the sensor module is a black-bulb thermometer, and the environmental information includes highly unevenly distributed dry-bulb and black-bulb temperatures, covering temperature ranges of -40℃ to +85℃ and -40℃ to +100℃, respectively. Figure 3 As shown, the sensor module T03 connects to the black ball thermometer PT100 and the Raspberry Pi via an ADC digital-to-analog converter (MAX31865) to ensure accurate data transmission.

[0033] The sensor module T03 calculates the average of the dry-bulb temperature and the black-bulb temperature as the apparent temperature, calculates the real temperature of the entire house, and quantitatively analyzes the relationship between the dry-bulb temperature and the real temperature: first, the average and the maximum of the dry-bulb temperature and the real temperature of each coordinate are calculated; then, a measurement value is defined for judgment, if the average of the dry-bulb temperature and the real temperature is greater than 0.25℃, the measurement value is the sum of the average and the maximum, otherwise the measurement value is 0; finally, the ratio of the measurement value to the maximum is the standardized measurement value, the value ranges from 0 to 1, and the position with the minimum value is the optimal position for measuring the dry-bulb temperature at this moment, at this time the dry-bulb temperature corresponding to the position can be uploaded to the Raspberry Pi as a control parameter.

[0034] Both the mobile measurement device and the positioning base station device collect position information through their positioning modules, according to the online or offline state of the system, the master positioning base station device collects the position information from the positioning base station devices and the mobile measurement device and sends it to the Raspberry Pi master control module T01 or the offline processing module T06;

[0035] The circuit connection diagram of the Raspberry Pi master control module T01, the sensor module T03 and the positioning module T02 is shown in Figure 6 .

[0036] Further, as shown in Figure 4 , when the system is in an online working state, the master positioning base station device receives the position information and the current timestamp sent by the two slave positioning base station devices and the mobile measurement device, determines the optimal position point, and directly transmits the position information of the optimal position point to the Raspberry Pi master control module T01, and displays it in real time through the display operation module.

[0037] Further, as shown in Figure 5 , when the system is in an offline working state, the master positioning base station device receives the position information and the current timestamp sent by the two slave positioning base station devices and the mobile measurement device, determines the optimal position point, and directly transmits the position information of the optimal position point to the offline processing module, when the system state changes to an online state, the offline processing module transmits the optimal point position information of the two slave positioning base station devices to the Raspberry Pi master control module T01, and displays it through the display operation module T04.

[0038] The Raspberry Pi master control module T01 obtains the distance between the mobile measurement device and each positioning base station device through the position information of the optimal position point of each positioning base station device received, and obtains the coordinate position information of the test point by comparing the different distances between the three master positioning base stations A, A1 and A2 and their corresponding position relationships through the three-point positioning method.

[0039] The Raspberry Pi master module T01 obtains the coordinate position information of the test point through the position information, and transmits the environmental information and the coordinate position information of the test point to the display operation module T04 and displays.

[0040] The Raspberry Pi master module T01 adopts a high-performance Broadcom BCM2711 chip, which is a quad-core processor based on the ARM Cortex-A72 architecture, with powerful computing performance and efficient energy management. The chip supports 64-bit computing, with a maximum frequency of 1.5 GHz, capable of handling complex tasks and multi-task processing requirements, suitable for a wide range of scenarios from daily computing to high-performance applications. In addition to high-performance computing, BCM2711 also has strong graphics processing capabilities, supporting high-definition output and video decoding, suitable for use as a multimedia center and embedded system. The chip also has an efficient 802.11b / g / n Wi-Fi SoC module, providing stable wireless network connection, suitable for Internet of Things (IoT) applications and wireless data transmission. In addition, the Raspberry Pi is equipped with a Bluetooth 5.0 module, allowing the device to efficiently connect with various Bluetooth devices, enhancing its wireless communication flexibility and expandability. The BCM2711 processor of the Raspberry Pi has strong graphics computing power and efficient multi-core performance, capable of supporting multiple display outputs simultaneously, meeting the needs of modern users for high-resolution, multi-task processing and high-performance computing. At the same time, the system design of the Raspberry Pi ensures high performance while focusing on low-power operation, making it an ideal choice for development, education and embedded applications. Through support for various peripheral interfaces (such as USB, HDMI, GPIO, I2C, SPI, etc.), the Raspberry Pi can easily connect with external hardware devices.

[0041] The positioning module includes a radio frequency transceiver, a baseband processor and a clock synchronization unit. In order to realize high-precision positioning, the positioning base station uses ESPN8266 chip combined with DWM1000 module, supporting frequency range of 3.5GHz to 6GHz, and with maximum transmission rate of 6.8Mbps, capable of ensuring fast and stable data transmission. The mobile tag uses Raspberry Pi master chip combined with DWM1000 module, controls DWM1000 through the SPI interface of Raspberry Pi, realizes high-precision positioning and data exchange.

[0042] The display operation module T04 integrates a 7-inch TFT touch display screen, with a resolution of 1024x600, which can display important thermal environment parameters inside the building in real time, including dry-bulb temperature and black-bulb temperature, as well as the coordinates of the optimal test point position. With sensitive response speed and intuitive interface design, the touch screen greatly simplifies the user's operation process, providing a convenient way for viewing and analyzing environmental data. In addition, the module is also equipped with a multifunctional operation panel and various external interfaces (such as HDMI and USB), significantly enhancing the device's expandability and data interaction capabilities. Users can connect larger display devices through HDMI or easily export data using the USB interface for in-depth analysis or backup. These features make the device not only suitable for daily monitoring, but also capable of complex environmental assessment tasks, providing users with an efficient operation experience and data management method.

[0043] The above is only a preferred embodiment of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A floor radiant heating terminal optimum measurement point detection system, characterized by, The mobile measurement device comprises a Raspberry Pi master module, a positioning module, an offline processing module, a sensor module and a display operation module. The positioning base station device comprises a master module and a positioning module; the positioning base station device can have one or more groups, and each group of positioning base station devices comprises one master positioning base station device and two slave positioning base station devices. The Raspberry Pi master module collects the dry-bulb temperature and the black-bulb temperature environment information of the indoor thermal environment test point through the sensor module. The mobile measurement device and the positioning base station device both collect position information through the positioning module; according to the online or offline state of the system, the master positioning base station device collects the position information of the slave positioning base station device and the mobile measurement device and sends the position information to the Raspberry Pi master module or the offline processing module. The Raspberry Pi master module obtains the coordinate position information of the test point through the position information and transmits the environment information and the coordinate position information of the test point to the display operation module for display.

2. The optimal measurement point detection system for a radiant floor heating terminal according to claim 1, wherein The sensor module is a black-bulb thermometer, and the Raspberry Pi master module is connected to the sensor module through an ADC digital-analog converter.

3. The optimal measurement point detection system for a radiant floor heating terminal according to claim 1, wherein When the system is in an online working state, the master positioning base station device receives the position information and the current timestamp sent by the two slave positioning base station devices and the mobile measurement device, determines the optimal position point, and directly transmits the position information of the optimal position point to the Raspberry Pi master module and displays the position information in real time through the display operation module.

4. The optimal measurement point detection system for a radiant floor heating terminal according to claim 1, wherein When the system is in an offline working state, the master positioning base station device receives the position information and the current timestamp sent by the two slave positioning base station devices and the mobile measurement device, determines the optimal position point, and directly transmits the position information of the optimal position point to the offline processing module; when the system state is switched to an online state, the offline processing module transmits the position information of the optimal point of the two slave positioning base station devices to the Raspberry Pi master module and displays the position information through the display operation module.

5. The optimal measurement point detection system for a radiant floor heating terminal according to claim 3 or 4, characterized in that, The Raspberry Pi obtains the distance between the mobile measurement device and each positioning base station device through the position information of the optimal position point of each positioning base station device, compares the different distances between the three master positioning base stations and the corresponding position relationship through the three-point positioning method, and obtains the coordinate position information of the test point.

6. The optimal measurement point detection system for a radiant floor heating terminal according to claim 5, wherein The mobile measurement device is connected to the web end and the mobile phone end through a Wi-Fi unit, and the Raspberry Pi master module generates the building thermal environment view and the optimal measurement position coordinate of the test point on the web end and the mobile phone end.

7. The optimal measurement point detection system for a radiant floor heating terminal according to claim 1, wherein The mobile measurement device and the positioning base station device are both provided with a battery module comprising a rechargeable battery and a Type-c interface, which facilitates charging of the devices.

8. The optimal measurement point detection system for a radiant floor heating terminal according to claim 1, wherein The Raspberry Pi master module comprises a processor, a Wi-Fi unit and a memory; the positioning module comprises a radio frequency transceiver, a baseband processor and a clock synchronization unit; the display operation module comprises a TFT touch display screen, an operation panel and an HDMI interface, and provides an intuitive user interaction interface.

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