Rainfall monitoring method and device based on millimeter wave radar

Abstract

The invention discloses a rainfall monitoring method and device based on millimeter wave radar, and relates to the technical field of meteorological monitoring. The method comprises the following steps: controlling a 24 / 60GHz commercial millimeter wave radar to transmit a millimeter wave signal of an adaptive parameter to a preset area; a raindrop echo signal is received, and clutters are removed through Kalman filtering and wavelet noise reduction; extracting a one-dimensional energy feature, a two-dimensional speed feature and a speed-energy two-dimensional distribution matrix; inputting a low-error model trained by a random forest algorithm, calculating a rainfall energy value and mapping the rainfall energy value into a rainfall level; data is output through a LoRa / ZIGBEE network; the device comprises a millimeter wave radar module, a signal processing module, a calculation output module and a wireless communication module. The problems of single monitoring dimension, high cost and difficult deployment in the prior art are solved, non-contact, low-cost, high-real-time and distributed rainfall monitoring is realized, the error is less than or equal to 5%, the response period is less than or equal to 100ms, and the method is suitable for traffic, municipal administration, emergency and other scenes.

Description

Technical Field

[0001] This invention belongs to the technical field of meteorological monitoring, and relates to a rainfall monitoring method and device based on millimeter-wave radar. Background Technology

[0002] Rainfall monitoring is a core data support for traffic scheduling, municipal infrastructure control, and emergency early warning. Existing technologies are mainly divided into three categories: (1) The first generation of technology was from 2000 to 2015 (contact mechanical structure). This stage of technology is represented by tipping bucket rain gauges, which rely on raindrop impacts to achieve measurement through a mechanical tipping bucket. While it solves the basic need for "quantitative rainfall measurement," it has three major drawbacks: First, the mechanical components are susceptible to damage from strong winds and dust, with annual maintenance costs exceeding 500 yuan per unit; second, the response cycle is as long as 1-5 minutes, making it unable to capture the intensity fluctuations of sudden rainfall such as showers and downpours; and third, it needs to be directly exposed to the outdoor environment, with an equipment wear rate as high as 20% per year, making it difficult to meet the high-density deployment needs of scenarios such as transportation and municipal services. For example, the tipping bucket rain gauge (CN209841939U) relies on raindrop impacts to measure the mechanical structure, is susceptible to damage from strong winds and dust, has high maintenance costs (annual maintenance costs exceeding 500 yuan per unit), and has a response cycle of 1-5 minutes, making it unable to capture the intensity fluctuations of showers.

[0003] (2) The second generation technology is from 2016 to 2022 (non-contact single feature monitoring). To address the "mechanical wear" problem of the first-generation technology, the second-generation technology shifted towards non-contact monitoring, mainly falling into two categories: one is 77GHz millimeter-wave radar (such as CN112327891A), which extracts only the single feature of echo energy, resulting in a monitoring error exceeding 9% in light rain scenarios, and it does not support multi-device networking, making regional synchronous monitoring impossible; the other is dedicated weather radar (such as CN110703671A), which, although highly accurate, costs over 300,000 yuan per unit and requires fixed base station installation (occupying ≥20m²). 2 The large size (over 50cm×50cm) and deployment density of only 0.2 units / 100km² make it unsuitable for distributed installation scenarios such as streetlights and tunnel sidewalls, creating a technical contradiction between "high precision and low cost".

[0004] (3) Existing millimeter-wave radar rainfall monitoring technology For example, CN109884692B uses a 77GHz high-frequency radar (costing over 2000 yuan per unit), lacks power adaptive adjustment, and does not support multi-device networking, thus failing to achieve full regional coverage.

[0005] In summary, existing technologies suffer from three major drawbacks: "single monitoring dimension, conflict between cost and deployment, and insufficient real-time performance and reliability." Therefore, in order to solve the above-mentioned technical problems, the technical solution of this application is proposed. Summary of the Invention

[0006] The purpose of this invention is to provide a rainfall monitoring method and device based on millimeter-wave radar, achieving the following objectives: 1. Multi-dimensional non-contact rainfall monitoring solves the problem of large errors in single-feature monitoring; 2. Reduce equipment costs to less than one-third of traditional millimeter-wave radar to meet the needs of large-scale deployment; 3. Enables adaptive adjustment of signal parameters and networking of multiple devices to meet monitoring needs in different scenarios; 4. Ensure that the model calculation error is ≤5% and the response period is ≤100ms, thereby improving monitoring accuracy and real-time performance.

[0007] The technical solution adopted in this invention is as follows: A rainfall monitoring method and device based on millimeter-wave radar, using commercial millimeter-wave radar as the core, achieves rainfall monitoring through a five-step process: "adaptive signal transmission - intelligent echo processing - multi-dimensional feature extraction - low-error model calculation - network output," as detailed below: (I) Selection of core monitoring components It adopts 24GHz or 60GHz commercial millimeter-wave radar (single unit cost ≤800 yuan), with a size ≤10cm×10cm×5cm, supports adaptive adjustment of transmit power from 0.1-10dBm, adjustable sampling rate from 0.5-2MHz, IP65 anti-interference level, and is suitable for complex outdoor environments.

[0008] (II) Rainfall Monitoring Process Adaptive signal transmission: Based on the monitoring distance (1-100 meters), control the millimeter-wave radar to transmit continuous wave or pulse millimeter-wave signals in a predetermined direction to the preset area; when the monitoring distance is <20 meters, the transmission power is adjusted to 0.1-3dBm and the sampling rate is 0.5-1MHz; when the monitoring distance is ≥20 meters, the transmission power is adjusted to 3-10dBm and the sampling rate is 1-2MHz.

[0009] Intelligent echo reception: The radar receives the reflected echoes of raindrops and uses a built-in Kalman filter + wavelet noise reduction preprocessing module to remove environmental clutter such as leaf swaying (frequency < 1Hz) and vehicle reflection (speed > 10m / s), while retaining the effective echoes of raindrops (speed 0.5-10m / s).

[0010] Multi-dimensional feature extraction: A hybrid signal processing algorithm (FFT + Doppler frequency shift analysis) is used to extract three types of features: One-dimensional energy characteristics: The energy intensity distribution of the echo signal is analyzed by fast Fourier transform, and the peak echo energy (unit: dBm) per unit area (m²) is calculated. Two-dimensional velocity characteristics: Based on the Doppler effect, the radial velocity of raindrops is extracted, and the standard deviation of the velocity distribution is statistically analyzed (reflecting the uniformity of rainfall intensity). Two-dimensional velocity-energy distribution matrix: The proportion of echo energy in the velocity range of 0.5-10 m / s is statistically analyzed to construct a 10×10 dimension matrix (each row corresponds to a velocity interval of 0.5 m / s, and each column corresponds to a 5% energy proportion interval).

[0011] Low-error model calculation: Model construction: Under scenarios of light rain (0.1-10mm / h), moderate rain (10-25mm / h), heavy rain (25-50mm / h), and torrential rain (>50mm / h), radar features and standard rain gauge (accuracy ±0.1mm) data are collected simultaneously to establish a database of "features-rainfall energy-rainfall level" containing 10,000+ samples; Model training: Random forest algorithm (50 decision trees, 3 feature dimensions) was used to train the samples, and parameters were optimized through 5-fold cross-validation to ensure that the model calculation error was ≤5%. Level determination: Input the features into the model, calculate the rainfall energy value (unit: J / m²), and map it to the rainfall level according to the "Rainfall Level" (GB / T28592-2012).

[0012] Network data output: Through LoRa module (transmission distance 1-5km) or ZIGBEE module (number of network nodes ≤256), the rainfall energy value, rainfall level and monitoring time are transmitted to the monitoring center, supporting synchronous data interaction of multiple devices.

[0013] (III) Composition of the monitoring device The device includes: Millimeter-wave radar module: used to transmit millimeter-wave signals with adaptive parameters and receive echo signals; Signal processing module: includes Kalman filtering unit, wavelet noise reduction unit, and hybrid signal processing unit, used for clutter removal and feature extraction; Calculation output module: Built-in low-error correspondence model for calculating rainfall information; Wireless communication module: LoRa or Zigbee module, used for data transmission and networking; Power module: Uses DC12V power supply and supports solar complementary power supply (suitable for scenarios without mains power).

[0014] The working principle of this invention is as follows: monitoring is achieved based on the "interaction characteristics between millimeter wave signals and raindrops": the reflection intensity of raindrops on millimeter wave signals is positively correlated with the number and size of raindrops, and the movement speed is positively correlated with the rainfall intensity; by adaptively adjusting the radar signal parameters, the echo signal-to-noise ratio at different distances is ensured to be ≥10dB; after preprocessing to remove clutter, multi-dimensional features can comprehensively reflect the "density-velocity-particle size" synergistic relationship of rainfall; combined with a machine learning model, the features are accurately mapped to actual rainfall parameters, and finally distributed monitoring is achieved through networking.

[0015] This invention employs 4-dimensional feature extraction, reducing the error to 3.8%, which is significantly lower than the error of >9% for single-feature millimeter-wave radar in existing technologies (CN112327891A). This invention also uses 24GHz commercial radar + LoRa networking, reducing the cost to 1 / 384 of that of existing technologies (CN110703671A) and increasing deployment density by 1000 times, thus solving the problem that existing technologies cannot be densely deployed due to the high cost of radar.

[0016] This invention represents the third generation of technology, employing 24GHz radar, multi-dimensional features, and LoRa networking. This invention overcomes the technological shortcomings of the previous two generations. Frequency band selection: 24GHz commercial millimeter-wave radar is adopted, reducing the cost per unit to 780 yuan (only 1 / 2.8 of the cost of the second-generation 77GHz radar and 1 / 384 of the cost of a dedicated weather radar). At the same time, the size is reduced to 10cm×10cm×5cm, which can be integrated into existing facilities such as street light poles and traffic signal poles, thus resolving the contradiction between "cost and deployment flexibility". Feature extraction: Breaking through the limitations of the second-generation "single feature", it proposes four types of multi-dimensional features: "energy + velocity + velocity-energy matrix + spectrum width". Combined with Kalman filtering and wavelet noise reduction preprocessing, the monitoring error of each rainfall level is ≤5% (3.8% error in light rain scene, which is 58% lower than the second-generation 77GHz radar). Networking capabilities: It integrates LoRa / ZIGBEE modules, supports networking of 256 nodes, and increases the deployment density to 2-5 units / km (1000 times higher than the second-generation dedicated weather radar), achieving a leap from "single-point monitoring to full regional coverage" and meeting the distributed monitoring needs of scenarios such as traffic conditions and municipal streetlights.

[0017] From the first generation to the third generation of technology, rainfall monitoring technology has achieved three core breakthroughs: "mechanical contact → non-contact", "single feature → multi-dimensional feature", and "single point monitoring → distributed networking". As the preferred solution of the third generation technology, this invention not only solves the inherent defects of the first two generations of technology, but also builds a technical system of "low cost, high precision and wide coverage".

[0018] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. A rainfall monitoring method and device based on millimeter-wave radar with high monitoring accuracy: multi-dimensional feature extraction + low-error model, calculation error ≤5%, which is better than existing millimeter-wave radar methods (error >8%). 2. The invention has low cost: it uses 24 / 60GHz commercial radar, and the cost of a single unit is ≤800 yuan, which is 1 / 100 of that of a dedicated weather radar and 1 / 3 of that of a 77GHz millimeter-wave radar; 3. This invention is flexible in deployment: small in size (≤10cm×10cm), supports installation on street light poles / tunnel sidewalls, and adaptive power adjustment adapts to a monitoring distance of 1-100 meters; 4. This invention has strong real-time performance: the response period is ≤100ms, which is better than the tipping bucket rain gauge (1-5 minutes). 5. This invention has comprehensive coverage: it supports LoRa / ZIGBEE networking, enabling regional distributed monitoring and solving the problem of blind spots in single-point monitoring; 6. The clutter filtering algorithm of this invention is a combination of "Kalman + wavelet" rather than a single filter. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments, experimental examples, and comparative examples will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort, wherein: Figure 1 This is a flowchart of the present invention; Figure 2 This is a schematic diagram of the deployment scenario of the present invention (taking the installation of a street light pole as an example); Figure 3 This is a schematic diagram of the multi-dimensional feature extraction results of the echo signal in this invention (one-dimensional energy curve and two-dimensional velocity-energy distribution matrix). Figure 4 This is a monitoring chart showing the cumulative rainfall of 6.07806 mm according to the present invention; Figure 5 This is a monitoring chart showing the cumulative rainfall of 8.92444 mm according to the present invention; Figure 6 This is a monitoring chart showing the cumulative rainfall of 6.07806 mm according to the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings, embodiments, experimental examples, and comparative examples. It should be understood that the specific embodiments described herein are only for explaining the invention and are not intended to limit the invention; that is, the described embodiments are merely some embodiments of the invention, and not all embodiments. The components of the embodiments of the invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0021] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0022] It should be noted that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0023] The features and performance of the present invention will be further described in detail below with reference to embodiments.

[0024] Example 1 This invention provides a rainfall monitoring method and apparatus based on millimeter-wave radar, such as... Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 As shown, the specific implementation method of this embodiment is as follows: Highway traffic condition monitoring scenario. Equipment deployment: A 24GHz millimeter-wave radar is installed every 500 meters on the streetlight poles (8 meters high) on both sides of the highway. The radar emission direction is at a 30° angle to the road surface, and the monitoring range covers a single lane (4.5 meters wide) and a 50-meter long area. Signal configuration: monitoring distance 50 meters, transmission frequency 24.125 GHz, transmission power 8 dBm, sampling rate 1.5 MHz, preprocessing module filter threshold set to 1 Hz (to remove leaf clutter), speed threshold set to 10 m / s (to remove vehicle clutter). Monitoring and calculation: The radar transmits a signal every 100ms, extracting one-dimensional energy features (peak value -45dBm), two-dimensional velocity features (standard deviation 2.3m / s), and velocity-energy matrix (energy percentage of 85% in the 0.5-5m / s range). The input is used to calculate the rainfall energy value of 80J / m², which is mapped to the "moderate rain" level. Data output: The data is transmitted to the highway management center via the LoRa module. The center triggers a road surface anti-skid warning (speed limit 80km / h) based on the data and pushes it to the car owner's navigation APP simultaneously.

[0025] Example 2 This invention provides a rainfall monitoring method and apparatus based on millimeter-wave radar, such as... Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 As shown, the specific implementation method of this embodiment is as follows: Intelligent control scenario for municipal street lights. Equipment deployment: Install a 60GHz millimeter-wave radar every 300 meters on the lampposts (6 meters high) of the main urban roads, with the transmission direction vertically downward, and the monitoring range covering the road surface (12 meters wide) and 30 meters long. Signal configuration: monitoring distance 30 meters, transmission frequency 60.48 GHz, transmission power 5 dBm, sampling rate 1 MHz, preprocessing module filter threshold set to 0.8 Hz; Monitoring and calculation: Samples are taken every 200ms to extract one-dimensional energy features (peak value -52dBm) and two-dimensional velocity features (standard deviation 1.1m / s), and the rainfall energy value is calculated to be 35J / m², which is mapped to the "light rain" level. Data output: Data is transmitted to the municipal management platform via ZIGBEE module networking. The platform controls the street light brightness to increase by 30% (to meet the needs of reduced visibility in rainy weather), while shutting down the street light sprinkler system (to avoid wasting rainwater).

[0026] Example 3 This invention provides a rainfall monitoring method and apparatus based on millimeter-wave radar, such as... Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 As shown, the specific implementation method of this embodiment is: early warning scenario at the entrance of a highway tunnel. Equipment deployment: Two 24GHz millimeter-wave radars (symmetrical left and right) are installed on the side wall (5 meters high) 50 meters from the tunnel entrance, with the transmission direction facing the tunnel entrance area (covering a width of 10 meters and a length of 20 meters). Signal configuration: monitoring distance 20 meters, transmission frequency 24.075 GHz, transmission power 3 dBm, sampling rate 0.8 MHz, preprocessing module speed threshold set to 8 m / s (adapted to low-speed vehicle scenarios in tunnels). Monitoring and calculation: Samples are taken every 50ms (to improve real-time performance), and after extracting features, the rainfall energy value of 150J / m² is calculated and mapped to the "rainstorm" level; Data output: Data is transmitted to the tunnel control terminal via the LoRa module. The terminal triggers a red light warning at the tunnel entrance, displays "Speed ​​limit 40km / h in heavy rain" on the electronic screen, and simultaneously starts the drainage pump inside the tunnel (to prevent water accumulation).

[0027] Example 4 This invention provides a rainfall monitoring method and apparatus based on millimeter-wave radar, such as... Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 As shown, the specific implementation method of this embodiment is as follows: Urban flood monitoring scenario Equipment deployment: Install a 24GHz millimeter-wave radar every 100 meters on lampposts in low-lying urban areas (such as underpass entrances), with the transmission direction facing the road surface (covering a width of 8 meters and a length of 15 meters). Signal configuration: monitoring distance 15 meters, transmission power 2dBm, sampling rate 0.5MHz, preprocessing module filter threshold set to 0.5Hz; Monitoring and calculation: Samples are taken every 100ms, and the rainfall energy value is calculated to be 220J / m², which is mapped to the "heavy rainstorm" level; Data output: Data is transmitted to the urban flood control center via ZIGBEE networking. The center triggers a flood warning (pushing it to the mobile phones of residents within a 3-kilometer radius) and simultaneously dispatches emergency drainage vehicles to the scene.

[0028] Example 5 This invention provides a rainfall monitoring method and apparatus based on millimeter-wave radar, such as... Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 As shown, the specific implementation method of this embodiment is as follows: simulate a rainy environment for actual monitoring. Table 1 (Test data for Example 5) Experimental Example 1 Performance verification experiments of the method of the present invention (I) Experimental Objective The monitoring accuracy, response speed, and stability of this invention under different rainfall levels were verified, confirming the feasibility and superiority of the technical solution.

[0029] (II) Experimental conditions Experiment location: A municipal traffic meteorological observation station (certified by the national meteorological department and equipped with standard rainfall monitoring equipment); Experiment period: June-August 2024 (rainy season, covering four scenarios: light rain, moderate rain, heavy rain, and rainstorm); Experimental equipment: The device of this invention includes: a 24GHz commercial millimeter-wave radar (cost of 780 yuan per unit, size 10cm×8cm×5cm, IP65 protection), a LoRa communication module, and a standard rain gauge (accuracy ±0.1mm, model: JD-05, used as a true value reference). Auxiliary equipment: anemometer (monitoring wind speed ≤5m / s, eliminating interference from extreme strong winds), thermometer and hygrometer (ambient temperature 15-30℃, humidity 40%-85%). Experimental parameter settings: Monitoring distance: 30 meters (simulating the normal monitoring range of municipal roads / highways); Transmit power: 5dBm, sampling rate: 1MHz; Preprocessing: Kalman filtering + wavelet denoising (filter threshold 1Hz, velocity threshold 10m / s). Model: Random Forest algorithm (50 decision trees, optimized with 5-fold cross-validation).

[0030] (III) Experimental Procedure Data Collection: Under light rain (0.1-10 mm / h), moderate rain (10-25 mm / h), heavy rain (25-50 mm / h), and torrential rain (>50 mm / h) scenarios, the following data were collected simultaneously: The device of this invention outputs rainfall energy value and rainfall level (recorded once every 100ms, each scene lasts for 2 hours, and each scene is repeated 3 times). Rainfall measured by a standard rain gauge (recorded once every minute, as the true value); Environmental interference data (leaf swaying frequency, passing vehicle speed, to verify the clutter filtering effect); Data processing: Calculate the monitoring error of the device of the present invention under different scenarios ((device-calculated rainfall - standard measured rainfall) / standard measured rainfall × 100%) and the response period (time from raindrops entering the monitoring area to outputting results); Stability verification: Run continuously for 30 days and record the device failure rate (number of days without failure / total number of days × 100%).

[0031] Table 2 (Experimental results of Experiment Example 1) (iv) Experimental Conclusions The present invention has a monitoring error of ≤5% under all rainfall levels, which is far superior to the existing technology (see comparative example), and it still maintains low error under rainstorm scenarios, which verifies the effectiveness of multi-dimensional feature extraction and low error model; The response period is ≤100ms, which meets the real-time early warning requirements for sudden rainfall (such as showers); The noise filtering success rate is ≥98%, effectively eliminating interference from leaves and vehicles, and ensuring data reliability. It operated without failure for 30 days, demonstrating strong equipment stability and solving the problem of easy damage to contact-type equipment.

[0032] Comparative Example 1 Tipping bucket rain gauge (contact type, model: JD-03, corresponding to the first generation technology in the background technology) Experimental conditions: Same as the experimental example (same location, same rainfall scenario, same environmental parameters); Comparison metrics: monitoring error, response time, cost per unit, average annual maintenance cost, and deployment density.

[0033] Table 3 (Experimental results of Comparative Example 1) Conclusion: Tipping bucket rain gauges have long response cycles (minutes), large errors in light rain scenarios, and high maintenance costs, making them unsuitable for distributed and dense deployment. This invention is significantly superior in terms of real-time performance and reliability.

[0034] Comparative Example 2 Existing millimeter-wave radar rainfall monitoring methods (CN112327891A, single energy characteristic, corresponding to the second-generation technology in the background technology) Experimental conditions: Same as the experimental example, the equipment is the 77GHz millimeter-wave radar disclosed in the patent (cost of 2200 yuan per unit). Comparison metrics: monitoring error, response cycle, unit cost, and networking capability.

[0035] Table 4 (Experimental results of Comparative Example 2) Conclusion: The existing technology relies on only a single energy feature, has a monitoring error of >8% (over 12% in rainstorm scenarios), a long response cycle (150ms), and costs 2.8 times that of the invention. Furthermore, it does not support networking and cannot achieve regional distributed monitoring. The present invention comprehensively surpasses its performance through multi-dimensional features and networking design.

[0036] Comparative Example 3 Dedicated weather radar (CN110703671A, corresponding to the second-generation technology in the background technology) Experimental conditions: Same as the experimental example, the equipment is the S-band weather radar disclosed in the patent (cost of 300,000 yuan per unit). Comparison metrics: monitoring error, unit cost, deployment flexibility (installation carrier requirements), and coverage.

[0037] Table 5 (Experimental results of Comparative Example 3) Conclusion: Although dedicated weather radar has high accuracy, the cost per unit is 384 times that of this invention. It requires fixed base station installation (and cannot be adapted to street light poles / tunnel sidewalls). Although the coverage area is large, there are "monitoring blind spots" (such as the inability to capture local differences in rainfall on roads). This invention has an absolute advantage in terms of cost and deployment flexibility, and the monitoring accuracy meets the needs of traffic, municipal and other scenarios (error ≤ 5%).

[0038] Table 6 (Comparison conclusions of Comparative Examples 1-3) In summary, this invention overcomes the triple defects of contact-type equipment ("easily damaged, poor real-time performance"), existing millimeter-wave radar ("large error, no networking"), and dedicated meteorological radar ("high cost, difficult deployment") through the design of "multi-dimensional feature extraction + adaptive signal adjustment + low-cost networking".

[0039] Comparative Example 4 Millimeter-wave solutions in the same frequency band: Search for patents on 24GHz radar used for rainfall monitoring and compare the differences of this invention in feature dimensions (single vs. multi-dimensional) and networking capabilities (no vs. LoRa).

[0040] Table 7 (Example data from Comparative Example 4) Comparative Example 5 (Simplified dual-frequency radar solution): For existing Ku / Ka dual-frequency radars, the cost-accuracy trade-off data between "single 24GHz + algorithm compensation" and "dual-frequency hardware".

[0041] Table 8 (Example data from Comparative Example 5) The multi-dimensional features are specifically composed of four types of features: raindrop echo energy, radial velocity standard deviation, spectral width, and polarization ratio. Adaptive adjustment logic: This section describes the specific algorithm formulas for "dynamically adjusting the filter threshold based on wind speed (threshold drops to 0.5Hz when wind speed > 3m / s)" and "correcting the energy coefficient based on humidity." Examples are provided. Corrected rainfall = original calculated value × (1 - 0.01 × (humidity - 60%)) (60% humidity is the baseline value).

[0042] Parameter optimization basis: Explain the selection process of "50 decision trees" and "1Hz filter threshold".

[0043] Table 9 (Experimental data for parameter optimization) Extreme environment verification experiment.

[0044] Table 10 (Validation of data from three types of extreme scenarios) Add a control group for comparative experiments: set up a control group with "no multi-feature extraction" and "no LoRa networking".

[0045] Table 11 (Technological Contributions for Verifying Core Innovations) The above description is only a preferred embodiment, experimental example, and comparative example of the present invention, and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A rainfall monitoring method based on millimeter-wave radar, characterized in that, Includes the following steps: (1) Adaptive signal transmission: control the commercial millimeter-wave radar to transmit millimeter-wave signals in a predetermined monitoring area. The frequency band of the millimeter-wave radar is 24GHz or 60GHz. The transmission power is adaptively adjusted in the range of 0.1-10dBm according to the monitoring distance, and the sampling rate is adaptively adjusted in the range of 0.5-2MHz according to the monitoring distance. (2) Intelligent echo reception: The millimeter-wave radar receives the echo signal formed by the reflection of raindrops in the monitoring area, and uses a preprocessing method combining Kalman filtering and wavelet noise reduction to remove environmental clutter; (3) Multidimensional feature extraction: A hybrid signal processing algorithm combining fast Fourier transform and Doppler frequency shift analysis is used to extract three types of feature parameters related to rainfall, namely one-dimensional energy features, two-dimensional velocity features, and velocity-energy two-dimensional distribution matrix. (4) Low-error model calculation: Input the three types of characteristic parameters into the preset correspondence model to calculate the rainfall energy value of the monitoring area, and map the rainfall energy value to the rainfall level according to the national standard "Rainfall Level" GB / T28592-2012; (5) Network data output: The rainfall energy value and rainfall level are transmitted to the monitoring center through the wireless communication module to realize synchronous data interaction of multiple devices.

2. The rainfall monitoring method based on millimeter-wave radar according to claim 1, characterized in that, In step (1), when the monitoring distance is less than 20 meters, the transmission power is adjusted to 0.1-3dBm and the sampling rate is adjusted to 0.5-1MHz; when the monitoring distance is greater than or equal to 20 meters, the transmission power is adjusted to 3-10dBm and the sampling rate is adjusted to 1-2MHz.

3. The rainfall monitoring method based on millimeter-wave radar according to claim 1, characterized in that, In step (2), the environmental clutter includes leaf swaying clutter with a frequency of less than 1 Hz and vehicle reflection clutter with a speed of greater than 10 m / s. The preprocessing method retains raindrop echo signals with a speed in the range of 0.5-10 m / s.

4. The rainfall monitoring method based on millimeter-wave radar according to claim 1, characterized in that, In step (3), the one-dimensional energy feature is the peak echo energy per unit area obtained by fast Fourier transform analysis, with the unit being dBm; the two-dimensional velocity feature is the standard deviation of the distribution of the radial motion velocity of raindrops extracted based on the Doppler effect; and the velocity-energy two-dimensional distribution matrix is ​​a 10×10 dimension matrix constructed by statistically analyzing the proportion of echo energy in the velocity range of 0.5-10 m / s.

5. The rainfall monitoring method based on millimeter-wave radar according to claim 1, characterized in that, In step (4), the correspondence model is constructed in the following way: (a) Under four rainfall scenarios—light rain, moderate rain, heavy rain, and torrential rain—the three types of characteristic parameters of the millimeter-wave radar and the measured data of the standard rain gauge are collected simultaneously to establish a sample database; (b) The sample database is trained using a random forest algorithm with 50 decision trees and a feature dimension of 3. The model parameters are optimized by 5-fold cross-validation to make the model calculation error less than or equal to 5%.

6. The rainfall monitoring method based on millimeter-wave radar according to claim 5, characterized in that, The rainfall intensity of light rain is 0.1-10 mm / h, that of moderate rain is 10-25 mm / h, that of heavy rain is 25-50 mm / h, and that of torrential rain is greater than 50 mm / h; the measurement accuracy of the standard rain gauge is ±0.1 mm.

7. The rainfall monitoring method based on millimeter-wave radar according to claim 1, characterized in that, In step (5), the wireless communication module is a LoRa module or a ZIGBEE module; the transmission distance of the LoRa module is 1-5km, and the maximum number of networking nodes of the ZIGBEE module is 256.

8. A rainfall monitoring device for implementing the method of any one of claims 1-7, characterized in that, It includes a millimeter-wave radar module, a signal processing module, a computing output module, a wireless communication module, and a power supply module; The millimeter-wave radar module is used to perform the adaptive signal transmission in step (1) of claim 1 and the echo signal reception in step (2). The millimeter-wave radar module is a 24GHz commercial millimeter-wave radar. The transmission power can be adaptively adjusted in the range of 0.1-10dBm, the sampling rate can be adaptively adjusted in the range of 0.5-2MHz, the size is less than or equal to 10cm×10cm×5cm, and the anti-interference level is IP65. The signal processing module is used to perform the preprocessing in step (2) of claim 1 and the multi-dimensional feature extraction in step (3), including a Kalman filter unit, a wavelet noise reduction unit and a hybrid signal processing unit; The calculation output module has a built-in correspondence model of step (4) of claim 1, which is used to calculate the rainfall energy value and rainfall level; The wireless communication module is used to perform the network data output in step (5) of claim 1; The power module is powered by DC12V and supports solar-powered complementary power supply.

9. A rainfall monitoring device according to claim 8, characterized in that, The one-dimensional energy characteristic is the peak echo energy per unit area obtained by analyzing the echo signal using Fast Fourier Transform, with the unit being dBm. The two-dimensional velocity characteristic is the standard deviation of the distribution of raindrop radial velocity extracted based on the Doppler effect. The velocity-energy two-dimensional distribution matrix is ​​a 10×10 dimension matrix constructed by statistically analyzing the proportion of echo energy in the velocity range of 0.5-10 m / s, where each row corresponds to a velocity interval of 0.5 m / s and each column corresponds to an energy percentage interval of 5%. The multi-dimensional features also include spectral width features: the frequency distribution span obtained through echo signal spectral analysis, in MHz, used to distinguish the spectral differences between raindrops and environmental clutter.

10. A rainfall monitoring device according to claim 8, characterized in that, The device can be mounted on a street light pole, a traffic signal pole, or a tunnel sidewall; when mounted on a street light pole, the angle between the transmission direction of the millimeter-wave radar module and the road surface is 15°-45°.

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