Self-powered long-distance high-precision rain gauge based on optical instrument

Abstract

The invention provides a self-powered long-distance high-precision rain gauge based on an optical instrument. The self-powered long-distance high-precision rain gauge comprises a self-powered rainfall sensing module, a long-distance imaging module, a rear-end processing module and a terminal display module. The self-powered rainfall sensing module comprises an energy collection and management unit, a rain gauge and a water level indicator lamp; and the background processing module comprises an image acquisition unit, an image processing unit and a machine learning unit. The long-distance image is observed through the optical instrument in a telescopic mode, the rain gauge can be used for long-distance large-range rainfall measurement, the defects that a traditional rain gauge is small in measurement range, low in measurement precision and the like are overcome, the rain gauge does not depend on a power supply facility and a network communication module during long-distance deployment, and cost is reduced. According to the rain gauge, a machine learning method with high flexibility and high expandability is used for analyzing rainfall, the model can be continuously updated and optimized, and a high-precision long-distance rainfall detection result is obtained.

Description

Technical Field

[0001] The invention relates to the technical field of long-distance rainfall measurement, in particular to a self-powered long-distance high-precision rain gauge based on an optical instrument. Background Art

[0002] Traditional mechanical rain gauges are widely used due to their simple structure and low cost, but their accuracy and resolution are limited, they rely on manual maintenance and data recording, and the data update cycle is long. Electronic rain gauges improve technical performance, but their reliance on batteries increases space requirements and environmental impact, and battery life limits their use. Furthermore, traditional rain gauges can generally only measure rainfall in a nearby area and cannot monitor rainfall conditions over a large area in real time. Therefore, a new type of rain gauge is needed that is self-powered and uses machine learning to achieve high-precision rainfall measurement over long distances to meet the needs of modern meteorological monitoring.

[0003] Currently, traditional rain gauges deployed remotely typically require regular manual inspections to collect data, which is both time-consuming and labor-intensive. Another solution is to integrate a network communication module into the rain gauge. However, this increases reliance on power supply infrastructure, thereby increasing deployment costs, and the range of information transmission depends on network communication methods. Furthermore, this approach has a significant drawback: if the rain sensor module loses power, the rain gauge will completely lose its functionality, resulting in interrupted data collection. Therefore, reducing the cost of information transmission is a pressing need in rain gauge design. Summary of the Invention

[0004] The present invention aims to address some of the problems of the related art mentioned above by proposing a self-powered, long-distance, high-precision rain gauge based on optical instruments. The invention first employs an independent, self-powered rain sensor module. An optical instrument with telescopic capabilities is then used to remotely observe water level indicators, providing operating information from the self-powered rain sensor module to a background processing module. The background processing module uses machine learning techniques to analyze images, calculate high-precision rainfall measurements, and transmit the results to a display terminal for real-time viewing. This results in a self-powered, long-distance, high-precision rain gauge capable of measuring rainfall at remote locations without requiring power.

[0005] The technical implementation scheme of the present invention is as follows: the self-powered long-distance high-precision rain gauge based on optical instruments includes a self-powered rain sensing module, a long-distance imaging module, a back-end processing module and a terminal display module; the self-powered rain sensing module includes an energy collection and management unit, a rain gauge, and a water level indicator light; the back-end processing module includes an image acquisition unit, an image processing unit, and a machine learning unit. This self-powered, long-range, high-precision rain gauge, based on optical instruments, is suitable for long-distance rainfall monitoring. Its optical instrument can observe rainfall conditions from hundreds or even thousands of meters away, ensuring accurate and timely rainfall information even over relatively wide geographic areas. This feature is particularly important for meteorological monitoring networks covering vast areas, as it reduces the number of monitoring points and deployment costs while increasing the effective range of meteorological data. This has applications in a variety of fields, including scientific research, climate change analysis, natural disaster monitoring and early warning, and agricultural irrigation management.

[0006] When rainfall occurs in a large geographical area, the energy collection and management unit converts the mechanical energy and friction energy of falling raindrops into electrical energy and stores it in energy storage devices such as supercapacitors and tantalum capacitors.

[0007] Furthermore, the energy collection and management unit uses electrical energy to illuminate a water level indicator. This indicator uses a high-efficiency, high-brightness light-emitting diode (LED) housed in a rain gauge that meets national standards. As rainfall continues, the water level in the rain gauge rises, and the position of the water level indicator changes. The energy collection and management unit continuously collects energy from the raindrops and supplies it with power.

[0008] Furthermore, the image acquisition unit observes the image of the water level indicator light through the long-range imaging module, captures the signal sent by the water level indicator light, and continuously collects optical image data from the power supply rainfall sensor module.

[0009] Furthermore, the image acquisition unit transmits the data to the image processing unit for image data enhancement processing and feature extraction processing, and transmits the processed data to the machine learning unit.

[0010] Furthermore, the machine learning unit deploys a trained high-precision machine learning rainfall monitoring model and calculates rainfall results using the incoming real-time data.

[0011] Furthermore, the terminal display module provides a user with a visual graphical interface, providing the user with visual rainfall information.

[0012] Furthermore, the self-powered rain sensor module stops working after the rain stops, and the rain gauge enters a low-power dormant state.

[0013] The beneficial effects of the present invention are: 1. This invention is a self-powered, long-distance, high-precision rain gauge based on an optical instrument. The rain sensor module is self-powered. When deployed remotely, the module can operate without requiring additional power supply facilities. This reduces deployment costs and is energy-efficient and environmentally friendly. 2. The self-powered, long-distance, high-precision rain gauge based on an optical instrument in this invention is suitable for long-distance rainfall monitoring. Leveraging the telescopic observation capabilities of the optical instrument, a background processing module can obtain distant rainfall information. Therefore, multiple rainfall sensing modules can be deployed over a relatively wide geographic area, spanning hundreds or even thousands of meters, enabling large-scale rainfall monitoring. 3. This invention presents a self-powered, long-range, high-precision rain gauge based on optical instruments. It uses machine learning techniques to achieve high-precision measurements with minimal data. Machine learning algorithms are more robust when processing small or noisy datasets, making them ideal for rainfall monitoring applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 The overall design structure diagram of a self-powered long-distance high-precision rain gauge based on optical instruments.

[0015] Figure 2 Design structure block diagram of the self-powered rainfall sensing module of the self-powered long-distance high-precision rain gauge based on optical instruments.

[0016] Figure 3 The structural block diagram of the back-end processing module design of a self-powered long-distance high-precision rain gauge based on optical instruments. Specific implementation cases

[0017] In order to enable relevant personnel in this field 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 drawings of the present invention. Based on the embodiments in this application, other similar embodiments made by those skilled in the art without making creative work should all fall within the scope of protection of this application. Nowadays, with the increasing demand for meteorological monitoring, traditional rain gauges can only measure rainfall in local areas and cannot monitor rainfall conditions at long distances in real time. Moreover, they rely on power supply facilities and network communication modules when deployed at long distances, which limits their deployment in remote areas and application in large-scale monitoring. In addition, the real-time acquisition of rain gauge data and the high-precision calculation of rainfall results are also challenges. To this end, we propose a self-powered long-distance high-precision rain gauge based on optical instruments, which can achieve self-powered and long-distance high-precision rainfall measurement.

[0018] like Figure 1 As shown in the figure, the overall design structure of the rain gauge is divided into a self-powered rain sensor module, a remote imaging module, a back-end processing module and a terminal display module; Figure 2 The self-powered rain sensor module shown in the figure includes an energy collection and management unit, a rain gauge, and a water level indicator light; Figure 3 The background processing module includes an image acquisition unit, an image processing unit, and a machine learning unit.

[0019] Figure 2 When rainfall occurs, the remotely deployed self-powered rain gauge sensor module begins operation. The energy collection and management unit converts the mechanical and frictional energy of falling raindrops into electrical energy, which is stored in an energy storage device. This unit utilizes a triboelectric nanogenerator as an energy converter, coupled with circuits that provide voltage stabilization, rectification, and storage. This converts multiple instantaneous pulses of energy into a constant output of electrical energy, which is then supplied to the water level indicator in the rain gauge. Therefore, the module only operates when rainfall occurs, achieving a self-powered operation.

[0020] During rainfall, the water level in the rain gauge continues to rise, and the water level indicator light floats on the liquid surface of the rain gauge, changes its position with the height of the liquid level, and flashes continuously under a continuous and stable power supply.

[0021] The telescopic imaging module can observe the remotely deployed self-powered rainfall sensor module. The image acquisition unit in the background processing module collects the observed real-time image data. The telescopic imaging module uses an optical instrument with telescopic capabilities, such as a periscope, camera, or other device that can capture distant images.

[0022] The image processing unit in the background processing module first optimizes the collected real-time image data, including image enhancement and noise reduction, to ensure that the data quality meets the requirements of the machine learning unit. Next, the machine learning unit uses transfer learning technology to analyze the processed images using a pre-trained model to calculate high-precision rainfall results. These results are then transmitted to the terminal display module, which can be a monitor, mobile phone, or other device to provide users with visual rainfall information. The machine learning unit is flexible and scalable, capable of deploying models using different algorithms and improving prediction accuracy by updating the model. In addition, the machine learning unit can use the collected real-time data as a new training dataset to retrain and optimize the model to further improve its performance and efficiency. The image processing unit and machine learning unit can be implemented on different computing platforms, including host computers, laptops, or embedded systems, to adapt to different application environments and performance requirements.

[0023] When the rainfall ends, the energy collection and management unit can no longer obtain energy, and the water level indicator stops flashing. The self-powered rainfall sensor module and background processing module enter a low-power standby state until the next rainfall occurs.

[0024] 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.

[0025] The above technical solution only reflects one technical solution of this technical solution. Some changes that may be made to certain parts thereof by technical personnel in this technical field all reflect the principles of this design system and should also fall within the scope of protection of this patent.

Claims

1. A self-powered long-distance high-precision rain gauge based on an optical instrument, characterized in that: It includes a self-powered rain sensor module, a long-range imaging module, a back-end processing module and a terminal display module; the self-powered rain sensor module includes an energy collection and management unit, a rain gauge, and a water level indicator light; the energy collection and management unit converts the mechanical energy and friction energy of falling raindrops into electrical energy, stores it in an energy storage device through a voltage stabilization and rectification circuit, and then supplies the electrical energy to the water level indicator light. The background processing module includes an image acquisition unit, an image processing unit, and a machine learning unit; the image acquisition unit is responsible for acquiring images of the water level indicator light and transmitting the images to the image processing unit. The image processing unit first optimizes the acquired real-time image data, including image enhancement and noise reduction processing, and then transmits it to the machine learning unit for transfer learning, that is, using the trained model to analyze the processed images.

2. A self-powered long-distance high-precision rain gauge based on an optical instrument as claimed in claim 1, characterized in that: A long-range imaging module has been added, which uses optical instruments to telescope and observe self-powered rainfall sensor modules deployed at a distance of hundreds to thousands of meters, so that the image acquisition unit in the background processing module can collect long-range images in real time.

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