TENG-based high-precision self-powered thermometer

Abstract

The invention provides a high-precision self-powered thermometer based on TENG. The high-precision self-powered thermometer comprises a self-powered module, an image acquisition module and a data processing module. The self-powered module comprises an energy conversion unit and an LED display unit. And the data processing module comprises an image analysis unit, a deep learning unit and a temperature display unit. On the basis of an original thermometer, a self-energy-supply technology is added, mechanical energy in the environment is collected through the friction nanometer generator to supply power to the LED display unit, and debattery treatment is carried out on the thermometer. And since a traditional power supply is not used, the use and maintenance cost of temperature monitoring in smart agriculture is reduced. The thermometer can work in specific agricultural environments such as high temperature, high humidity and low temperature, and has good adaptability and stability. In addition, the temperature is indicated by adopting an optical image of the LED lamp, so that the thermometer is more sensitive in response, higher in efficiency and better in real-time performance; and meanwhile, the deep learning technology is utilized to construct a training model to identify and process the image, so that the accuracy of the thermometer is greatly improved.

Description

Technical Field

[0001] The present invention relates to the field of temperature measurement technology in smart agriculture, and in particular to a high-precision self-powered thermometer based on TENG. Background Art

[0002] Smart agriculture is a key trend in modern agricultural development. Leveraging advanced information technologies such as the Internet of Things, big data, and artificial intelligence (AI) can improve agricultural production efficiency and sustainability. Environmental monitoring is a key component of smart agriculture, with temperature being one of the key factors influencing crop growth and development. Traditional agricultural temperature monitoring typically uses wired or wireless sensor networks. These systems require regular maintenance or battery replacement, are costly and complex, and lack the accuracy and stability to meet the stringent environmental monitoring requirements of modern smart agriculture.

[0003] The rapid rise of deep learning and self-powered technologies in recent years has provided new approaches for temperature monitoring in smart agriculture. Self-powered technologies can reduce thermometers' reliance on external power sources, lower maintenance costs, and improve their stability. By combining triboelectric nanogenerator (TENG) technology with a temperature sensor, a highly accurate self-powered thermometer can be developed. Furthermore, deep learning techniques are used to process optical images of LED lights, enabling automatic data recognition and judgment. This thermometer boasts high accuracy, high efficiency, self-powered operation, and excellent stability. Summary of the Invention

[0004] The purpose of the present invention is to provide a high-precision self-powered thermometer based on TENG to address the problems existing in the above-mentioned prior art. TENG is used to collect energy from rainwater and dew in the environment and convert it into electrical energy to power LED lamps. Three LED lamps present different optical images at different temperatures. By identifying the optical images of LED lamps and processing them through deep learning technology, specific temperature information is obtained, thereby realizing self-powered, efficient, high-precision, and long-distance temperature monitoring in smart agriculture.

[0005] The present invention is achieved through the following technical solutions: providing a high-precision self-powered thermometer based on TENG, including a self-powered module, an image acquisition module, and a data processing module; the self-powered module includes an energy conversion unit and an LED display unit; the data processing module includes an image analysis unit, a deep learning unit, and a temperature display unit. The high-precision self-powered thermometer based on TENG can be used for temperature monitoring in the field of smart agriculture. It does not rely on an external power supply, has high stability, and high monitoring accuracy. It also uses LED optical images to represent temperature, which facilitates data reception and processing, thereby improving monitoring efficiency and accuracy. The entire thermometer has a simple and clear architecture, runs smoothly, and is low-cost. It expands the energy supply method of agricultural thermometers and solves the problems of traditional thermometers relying on external power supplies, insufficient stability, low monitoring efficiency, and low accuracy.

[0006] Among them, the energy conversion unit uses a friction nanogenerator to collect mechanical energy from rainwater, dew, etc. in the environment and convert it into electrical energy to power the LED bulb.

[0007] Furthermore, the LED display unit includes three commercial LED bulbs, which flash under the power of the friction nanogenerator. When the temperature changes, the optical image of the flashing position of the LED bulb changes accordingly.

[0008] Furthermore, the image acquisition module is generally a high-definition camera, video camera, etc. from major manufacturers, which can capture the image of the flashing position of the LED bulb in real time and transmit the image to the image analysis unit.

[0009] Furthermore, the image analysis unit is used to extract the optical image of the LED bulb flickering position captured by the image acquisition module, and construct an analysis model of the bulb flickering position and real-time temperature through a deep learning algorithm.

[0010] Furthermore, the deep learning unit uses a trained model for analyzing optical images of light bulb flickering locations to locate flickering points and identify the distance between the three light bulbs. Three LED bulbs are fixed in a triangle configuration to the tail and tip of a pointer needle on a dial. The line connecting the tail bulb and one of the tip bulbs is parallel to the ground. The other tip bulb serves as an indicator. When the temperature changes, the indicator bulb will slightly shift, causing its distance from the line connecting the two bulbs to change. This information is then compared with the pre-trained analysis model to obtain temperature information.

[0011] Furthermore, the temperature display unit can display the current temperature and the temperature curve within a certain period of time in real time.

[0012] The beneficial effects of the present invention are: 1. The high-precision self-powered thermometer based on TENG in this invention adopts modular design thinking. Based on existing technologies, it realizes the self-powered battery-free processing of the thermometer, reduces the maintenance cost and energy consumption of temperature monitoring in agriculture, improves the efficiency and stability of temperature monitoring, and realizes the use of mechanical energy in the environment to monitor temperature.

[0013] 2. The present invention combines self-powered technology with temperature monitoring to create a miniaturized, highly efficient thermometer based on a TENG. This overcomes the shortcomings of traditional agricultural thermometers, which rely solely on wired or wireless sensor networks, resulting in limited accuracy and stability. Furthermore, considering the unique characteristics of the agricultural environment, the present invention's thermometer exhibits excellent corrosion resistance and resistance to temperature and humidity fluctuations, ensuring stable operation in various climates.

[0014] 3. A high-precision self-powered thermometer based on TENG in the present invention combines temperature monitoring with LED optical images, so that the real-time temperature corresponds to the different flashing positions of three LED bulbs. Equipped with a high-definition camera, the optical image is easy to monitor and has a sensitive response. The data can be transmitted to the back-end processing system in real time, facilitating remote monitoring and management of smart agriculture, effectively improving the level of agricultural intelligence.

[0015] 4. This invention combines temperature monitoring with deep learning technology, using a high-precision, self-powered thermometer based on a TENG. This technology builds a highly accurate training model for the flashing position and real-time temperature of an LED bulb, accurately measuring and displaying the real-time temperature. Furthermore, an image acquisition unit can remotely capture and analyze images, allowing experimenters to remotely monitor and adjust the temperature to create an optimal temperature environment for crop growth. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 The overall design structure diagram of a high-precision self-powered thermometer based on TENG.

[0017] Figure 2 Design structure diagram of the self-powered module of a high-precision self-powered thermometer based on TENG.

[0018] Figure 3 Design structure diagram of the data processing module of a high-precision self-powered thermometer based on TENG. Specific implementation cases

[0019] In order to enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention is clearly and completely described below in conjunction with the accompanying drawings of the present invention. Based on the embodiments in this application, other similar embodiments obtained by ordinary technicians in this field without making creative work should all fall within the scope of protection of this application. As modern agriculture gradually develops in the direction of intelligence, real-time monitoring and precise control of the temperature of the crop growth environment are crucial to the yield and quality of crops. Smart agriculture has put forward higher requirements on the accuracy, efficiency, cost, stability and adaptability of thermometers to specific agricultural environments. To this end, we propose a high-precision self-powered thermometer based on TENG, which can realize wireless sensing and real-time monitoring of the temperature of the crop growth environment under no power supply conditions.

[0020] like Figure 1 As shown in Figure 2, the entire system is divided into three parts: self-power module, image acquisition module, and data processing module. Figure 2 As shown in the figure, the self-powered module is divided into an energy conversion unit and an LED display unit; Figure 3 As shown, the data processing module includes an image analysis unit, a deep learning unit, and a temperature display unit.

[0021] When the TENG in the energy conversion unit collects mechanical energy from the environment, it can use the triboelectric effect to convert the mechanical energy into electrical energy, thereby powering the LED display unit.

[0022] When the LED bulbs are powered by the TENG, they light up and flash. The optical image of their flashing positions changes with temperature, which can be captured and transmitted by the image acquisition module. The flashing positions of the three LED bulbs change slightly with changes in ambient temperature, and the corresponding optical image can reflect the real-time temperature.

[0023] The image acquisition module is a module responsible for collecting LED optical images. The high-definition camera can capture clear images of the flashing position of the LED bulb in real time, and connect with the back-end data processing module to transmit the image data to the image analysis unit.

[0024] The image analysis unit receives the LED optical image transmitted by the image acquisition module and uses the analysis model constructed by the deep learning unit to obtain an accurate temperature value.

[0025] The deep learning unit is the core unit of the data processing module. Using Python as a platform, it repeatedly trains and calibrates the collected training set image data to ensure that the optical image of the LED can be accurately identified and matched with the temperature, thereby building a highly accurate analysis model for real-time analysis of the LED position image.

[0026] The temperature display unit displays the specific temperature value obtained by the image analysis unit on the computer screen in real time, and can simultaneously display the temperature change curve within half an hour, which makes it easy for experimenters to analyze whether the environmental temperature of the crops is normal and make adjustments to ensure a favorable environment for crop growth.

[0027] All features disclosed in this specification, or steps in all methods or processes disclosed, except for mutually exclusive features and / or steps, may be combined in any manner. Any feature disclosed in this specification (including any appended claims and abstract), unless otherwise stated, may be replaced by an equivalent or similar alternative feature. That is, unless otherwise stated, each feature is merely an example of a set of equivalent or similar features.

[0028] The present invention is not limited to the aforementioned specific embodiments, but extends to any new features or any new combination disclosed in this specification, as well as any new method or process steps or any new combination disclosed.

Claims

1. A high-precision self-powered thermometer based on TENG, characterized by: It includes a self-powered module, an image acquisition module, and a data processing module; the self-powered module includes an energy conversion unit and an LED display unit. The energy conversion unit collects mechanical energy from dripping rainwater, dew, etc. in the environment through a friction nanogenerator, and uses the triboelectric effect to convert the mechanical energy into electrical energy to power the LED display unit; the three LED bulbs of the LED display unit light up and flash under the power supply of the energy conversion unit, and the optical image of the position information of the three bulbs will change with the change of ambient temperature. By capturing its optical image, real-time temperature monitoring can be achieved. The image acquisition module is a high-definition camera connected to the data processing module, which can capture the optical image of the LED display unit in time and transmit the data to the data processing module. The data processing module includes an image analysis unit, a deep learning unit, and a temperature display unit. The image analysis unit receives the LED optical image transmitted by the image acquisition module and compares the optical image constructed by the deep learning unit with the real-time temperature analysis model to obtain ambient temperature information. The deep learning unit uses deep learning technology to repeatedly train and verify the collected LED training set images to obtain a high-accuracy LED optical image and real-time temperature analysis model, ensuring that the image analysis unit can accurately output temperature information. The temperature display unit is connected to the image analysis unit to record and display the ambient temperature information obtained by the image analysis unit in real time and provide a temperature change curve within a certain time period.

2. A high-precision self-powered thermometer based on TENG according to claim 1, characterized in that: The energy conversion unit is connected to the LED display unit and is used to convert the mechanical energy of rainwater and dewdrops falling in the environment into electrical energy.

3. The high-precision self-powered thermometer based on TENG according to claim 1, characterized in that: The LED display unit is powered by an energy conversion unit, and its optical image is captured by an image acquisition module.

4. The high-precision self-powered thermometer based on TENG according to claim 1, characterized in that: The deep learning unit uses Python as a platform and performs multiple training and verifications on the training set LED optical images captured by the image acquisition module, ultimately obtaining a highly accurate analysis model of LED position information images and environmental real-time temperature for temperature monitoring.

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