Self-driven humidity detection system based on artificial intelligence

Abstract

The invention provides a self-driven humidity detection system based on artificial intelligence. The self-driven humidity detection system comprises a humidity sensing converter module, an LED bulb, a self-driven module and a background processing module. The humidity sensing converter module comprises a humidity sensing module and an indication module; the self-driving module comprises a water storage device, a TENG, an instillation device and an integrated circuit; and the background processing module comprises a visual monitoring acquisition unit, a data analysis unit, a machine learning unit and a display interface unit. The system can continuously detect the humidity change in the environment, accurately measure the real-time humidity and realize the continuous visualization of the humidity signal. The humidity detection function is based on a pointer suite which deforms along with the change of environment humidity, a pointer of the indication module deforms, and an LED bulb fixed on the pointer of the indication module shifts along with the pointer. In the working state, water drops collected by the water storage device drop on the TENG through the instillation device, friction current is generated through impact, the LED emits light through a connecting circuit of the TENG and the LED, the position and relative displacement of the bulb are recognized and analyzed through the background processing module, and the relation between the position and the environment humidity of the bulb which are input in advance is compared. Therefore, the humidity signal is visualized and the environment humidity is accurately measured. Remote visualization of humidity detection is realized based on image identification and processing of artificial intelligence, sustainability and accuracy of humidity detection are guaranteed, and efficiency and convenience of humidity detection are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of humidity detection, and in particular to a self-driven humidity detection system based on artificial intelligence. Background Art

[0002] Humidity is a physical quantity that measures the water vapor content in the air, usually expressed in the form of relative humidity (RH), absolute humidity or dew point temperature. Humidity is one of the important parameters in meteorology and climate research, HVAC systems, agriculture, construction, healthcare and biomedicine. Humidity has a key impact on human health, biological growth, material preservation, air quality and industrial product quality. Humidity detection can provide accurate air data, help achieve precise control and management of the environment, help maintain a healthy environment, promote agricultural development, ensure food safety, improve production efficiency, and respond to climate change. However, humidity detection technology and equipment still face challenges such as low accuracy, temperature cross-influence and long-term instability, and need to be continuously improved to meet the needs of different application fields.

[0003] Initially, humidity measurement relied on observing weather phenomena such as cloud cover, dew, and fog. Later, dry-bulb and wet-bulb thermometers, electronic hygrometers, and semiconductor hygrometers emerged. In the early 21st century, microelectromechanical system (MEMS) hygrometers emerged, offering advantages such as high precision, low power consumption, and ease of integration. With the advancement of technology and the emergence of new materials, new hygrometers are becoming increasingly diverse, intelligent, and precise. Humidity sensors can effectively detect humidity levels in an environment and play a vital role in fields such as meteorological monitoring, precision agriculture, intelligent greenhouse management, and smart treatment. High-precision humidity measurement equipment and data acquisition systems enable us to better understand and respond to environmental changes and meet the humidity data needs of various industries. The importance of humidity monitoring will continue to grow with technological advancements and climate change.

[0004] The water-droplet triboelectric nanogenerator (TENG) is an innovative ambient energy harvesting technology that utilizes nanotechnology and the triboelectric effect to convert the kinetic energy of water droplets into electricity. With the growing global demand for renewable energy and clean energy technologies, the application of TENG in self-propelled systems demonstrates significant potential for sustainable development. Through a carefully designed mechanical structure and electronic circuitry, TENG effectively captures the impact energy of water droplets and converts it into usable electricity to power low-power devices. Advances in materials science underpin the TENG's performance, while its environmentally adaptable design ensures stable operation under diverse conditions. Furthermore, the integration and application of TENG are continuously expanding, from environmental monitoring to smart agriculture to smart city construction, making this technology a key driver of the energy revolution. With technological advancements and the new challenges posed by climate change, the importance and application prospects of TENG will continue to grow, providing society with cleaner, more efficient, and more intelligent energy solutions. Summary of the Invention

[0005] The purpose of the present invention is to provide a self-driving system for a humidity detection device based on optical architecture and artificial intelligence. By collecting and storing rain and dew produced in nature, when in working state, falling water droplets fall on the TENG, the impact generates friction current, and the LED is illuminated through the connection circuit between the TENG and the LED.

[0006] The present invention is implemented through the following technical solutions: providing an artificial intelligence-based self-driven humidity detection system, comprising a humidity sensor converter module, an LED bulb, a self-driven module, and a background processing module; the humidity sensor converter module comprises a humidity sensing module and an indication module; the self-driven module comprises a water storage device, a TENG, a dripping device, and an integrated circuit; and the background processing module comprises a visual monitoring acquisition unit, a data parsing unit, a machine learning unit, and a display interface unit. This artificial intelligence-based self-driven humidity detection system can be used to measure humidity outdoors, maintaining long-term stable operation of the system without external power supply, and visualizing humidity information through image processing, thereby improving measurement efficiency and accuracy. The entire system operates stably, has a simple structure, and is low-cost. It expands the method of measuring humidity and solves the problems of energy supply, detection accuracy, and complex structure in traditional humidity detection.

[0007] Among them, the water storage device collects water droplet resources that are commonly found in nature, such as rainwater, dew or industrial water.

[0008] Furthermore, the dripping device drips the stored water resources onto the TENG to generate current. The dripping device can adjust the dripping speed, thereby adjusting the intensity and frequency of the LED light.

[0009] Furthermore, as the ambient humidity changes, the humidity sensing module transmits the humidity signal to the indication module through the deformation of the rear curled iron sheet by tightening or relaxing.

[0010] Furthermore, the pointer of the indicator module is deformed, and the LED bulb fixed on the pointer of the indicator module is displaced accordingly.

[0011] Furthermore, after receiving the power provided by TENG, the LED bulb lights up and starts flashing.

[0012] Furthermore, the visual monitoring acquisition unit can be a high-definition camera, video camera, etc. launched by major manufacturers, which has the ability to capture the light image of the light bulb and distinguish its position and relative displacement.

[0013] Furthermore, the machine learning unit constructs an analysis model of the bulb position and ambient humidity using a machine learning algorithm based on the image of the visual monitoring acquisition unit.

[0014] Furthermore, the data analysis unit uses an analysis model of the bulb position and surrounding humidity to locate the light point and identify relative position changes of the LED bulb position image captured by the visual monitoring acquisition unit, and compares the identified bulb position information with the pre-input relationship between the bulb position and ambient humidity to obtain humidity information.

[0015] Furthermore, the display interface unit displays data changes within a certain period of time on the display.

[0016] The beneficial effects of the present invention are: 1. The present invention is a self-driven humidity detection system based on artificial intelligence. Through modular design thinking, artificial intelligence and humidity detection are combined to achieve the simplification and digitization of the hygrometer. It overcomes the shortcomings of traditional hygrometers such as easy damage, need for regular maintenance, slow response time, and inability to automatically record data. At the same time, it also adds visual processing of humidity information, improves the accuracy and flexibility of the system, and realizes visual humidity detection.

[0017] 2. The present invention provides an artificial intelligence-based self-driven humidity detection system, which can realize ultra-long-distance humidity detection by processing collected images. The background processing module theory is based on artificial intelligence learning and data analysis of collected images. It can detect luminous images at an ultra-long distance from the detection site and convert humidity information in real time, saving human resources while improving the timeliness and convenience of humidity detection.

[0018] 3. In the present invention, the display interface unit of the self-driven humidity detection system based on artificial intelligence can automatically record humidity data and plot the data into a humidity change chart to record humidity changes over a period of time. Automatic data recording helps improve auxiliary forecasting capabilities for weather trends and overall meteorological assessment.

[0019] 4. The present invention provides an artificial intelligence-based self-driven humidity detection system. The self-driven module can collect and store water resources generated in nature, and can control the flow rate of water droplets through a drip device to control the switch of the entire device, thereby achieving the goal of reducing costs and energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 The overall design structure diagram of the self-driven humidity detection system based on artificial intelligence.

[0021] Figure 2 Design structure diagram of the humidity sensor converter module of the self-driven humidity detection system based on artificial intelligence.

[0022] Figure 3 Design structure diagram of the self-driving module of the self-driving humidity detection system based on artificial intelligence.

[0023] Figure 4 Design structure diagram of the background processing module of the self-driven humidity detection system based on artificial intelligence. Specific implementation cases

[0024] 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. With the requirements of development and the iteration of products, humidity detection plays an important role in more and more fields. Humidity detection has become indispensable in industries such as meteorology, human health, industrial production, indoor and outdoor climate control, food processing and storage, and medical equipment manufacturing. With the increase in the demand for humidity measurement, higher requirements are put forward for detection accuracy, stability, energy drive, etc. during actual use. To this end, we propose a self-driven humidity automatic detection system based on artificial intelligence, which can realize self-driven measurement of humidity in the field and visualization of humidity information, and improve humidity detection accuracy.

[0025] The entire system consists of four parts: a humidity sensor transducer module, an LED bulb, a self-driving module, and a background processing module. The humidity sensor transducer module includes a humidity sensing module and an indicator module. The self-driving module includes a water storage device, a TENG (Telescopic Enginurgical Engineer), a dripping device, and an integrated circuit. The background processing module includes a visual monitoring acquisition unit, a data analysis unit, a machine learning unit, and a display interface unit. The humidity sensor module spontaneously senses changes in ambient humidity and transmits the humidity signal to the indicator module through a mechanical structure. The indicator module's pointer deforms, causing the LED bulb attached to it to move accordingly. The LED bulb begins to flash after receiving current from the TENG in the self-driving module. The visual monitoring acquisition unit captures an image of the LED bulb's position and sends it to the data analysis unit for analysis and processing. The analyzed data is then sent to the display interface unit, which displays the data changes over time on a monitor.

[0026] The humidity sensing module can spontaneously receive humidity signals and drive the indication module to work through a mechanical structure.

[0027] The LED bulb receives electrical signals to emit light, and its intensity and frequency can be adjusted through the drip device. The position of the bulb changes with the change of the surrounding humidity, and the visual monitoring acquisition unit captures and collects its position information.

[0028] The background processing module is a key link, responsible for information collection, processing, and display. The visual monitoring acquisition unit is specifically a camera, which can obtain clear, high-frame-rate images of the flashing LED bulb. The camera can continuously collect images of the LED bulb and promptly transmit the images to the machine learning unit. By recognizing and processing the training set images, it constructs an analysis model of the relationship between the bulb position and the ambient humidity. The data analysis unit uses this model to analyze the bulb image captured by the visual monitoring acquisition unit in a specific environment, and derives information about the ambient humidity by comparing the bulb position in the image with the model. The image analysis unit can be a laptop, desktop host, or other terminal with computing and monitoring capabilities. It is required to accurately identify the position, quantity, and relative displacement of the light through the image.

[0029] The data analysis unit processes and obtains the accurate value of humidity, outputs the final humidity value result through the display interface unit, and draws a humidity change chart based on the recorded data.

[0030] 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.

[0031] 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 self-driven humidity detection system based on artificial intelligence, characterized in that: The device includes a humidity sensor transducer module, an LED bulb, a self-driving module, and a background processing module. The humidity sensor transducer module includes a humidity sensing module and an indicator module. The humidity sensing module spontaneously senses changes in ambient humidity and transmits the humidity signal to the indicator module through a mechanical structure. The indicator module's pointer deforms, causing the LED bulb attached to the pointer to move accordingly. The LED bulb then lights up and begins flashing after receiving power from an external power source. The self-driving module includes a water storage device, a TENG, a dripping device, and an integrated circuit; the water storage device collects and stores raindrops produced in nature. When in working state, the collected water droplets drip onto the TENG through the dripping device, and the impact generates friction current, which causes the LED to emit light through the connection circuit between the TENG and the LED. The background processing module includes a visual monitoring acquisition unit, a data analysis unit, a machine learning unit, and a display interface unit; the visual monitoring acquisition unit captures the position image of the LED bulb and sends it to the data analysis unit for analyzing and processing its relative position, and sends the analysis data to the display interface unit, which displays the data changes within a certain period of time on the display.

2. The self-driven humidity detection system based on artificial intelligence according to claim 1, characterized in that: The self-driving module supplies power to the LED, thereby realizing low-cost and low-energy-consumption energy supply.

3. The self-driven humidity detection system based on artificial intelligence according to claim 1, characterized in that: The TENG needs to undergo multiple adjustments such as material screening, mechanical design, and circuit integration to ensure the normal operation of the self-driving module.

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