A rain sensor calibration method based on injection method and drainage method and a self-calibration method

Abstract

The purpose of the present application is to provide a rain sensor calibration method and self-calibration method based on injection method and drainage method, which introduces a calibration loss value to improve the accuracy and comprehensiveness of the rain sensor calibration results. In addition, the real error range of each rain sensor can be quickly calibrated, and the system deviation corresponding to different rain intensities can be obtained. The monitoring value of the rain sensor is corrected by using the system deviation, and higher rain monitoring accuracy is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of rain sensor calibration, and specifically relates to a verification method and a self-calibration method for a rain sensor based on an injection method and a drainage method. Background Art

[0002] At present, the conventionally used verification methods for rain sensors are the injection method and the drainage method. The injection method is generally used for on-site verification, while the drainage method is mainly used for factory verification by sensor manufacturers. Although both methods are permitted by relevant specifications, the detection results of the two verification methods are affected by many factors such as wetting error, residual water in the tipping bucket, and injection process control, leading to certain systematic deviations. Moreover, the systematic deviation of each rain sensor varies and is difficult to quantify and calibrate. The existing practice is to uniformly specify the accuracy error range according to the model when the rain sensor leaves the factory, for example and any one of which being less than or equal to 4% is regarded as qualified. In the actual application environment, the verification results are actually inconsistent. Taking a double-tipping-bucket or siphon double-tipping-bucket rain sensor as an example, due to the large change of accumulated water during its detection process, the wetting error is large. For the injection method, due to the existence of wetting error, the measured data of the sensor will be on the small side. For the drainage method, the opposite is true, and the measured data of the sensor will be on the large side. In addition, from the basic principle, the self-drainage method is closer to the real error of the instrument itself. However, due to the structural problem of the tipping bucket rain gauge, if the self-drainage method is to be used for verification of the already installed equipment, the equipment needs to be disassembled from the foundation, but disassembly will change its working state (such as the leveling state), so the injection method is generally used for equipment adjustment on site. For the same rain gauge, when verified within the normal working range, the injection method and the self-drainage method need to conform to a certain rule. Therefore, there is an urgent need for a method that can verify the real error range of the rain sensor at present. Summary of the Invention

[0003] The purpose of the present invention is to propose a verification method and a self-calibration method for a rain sensor based on an injection method and a drainage method, which improve the accuracy and comprehensiveness of the verification result of the rain sensor by introducing a verification loss value; in addition, the method can quickly calibrate the real error range of each rain sensor, obtain the systematic deviation corresponding to different rain intensities, and correct the monitoring value of the rain sensor by using the systematic deviation, so as to achieve higher rain monitoring accuracy.

[0004] To achieve the above technical purpose, the technical solution adopted by the present invention is:

[0005] In a first aspect, the present invention discloses a verification method for a rain sensor based on an injection method and a drainage method, and the method comprises:

[0006] A rain gauge self-calibration platform is constructed, which includes a first electronic balance, a second electronic balance, a third electronic balance, a metering pump, a water supply container, and a drainage container; the water supply container, the drainage container, and the rain gauge to be calibrated are respectively placed on the first electronic balance, the second electronic balance, and the third electronic balance.

[0007] The rainfall intensity is controlled by a metering pump, and water is injected into the rain sensor for a duration of t. The water discharged from the rain sensor is injected into the container below through the collection pipe at its bottom.

[0008] The mass of water in the water supply container is measured in real time using a first electronic balance. The mass of water supplied to the rain gauge sensor to be calibrated is calculated, and the actual rainfall value at time t is obtained. The actual rainfall at time t Water volume measurement with instruments By comparison, the measurement error of the injection method of the rain gauge sensor to be tested is calculated. The mass change of the drainage container before and after calibration was obtained using a second electronic balance. Combined with instrument measurement of water volume The measurement error of the drainage volume of the rain gauge sensor to be tested is calculated. The change in mass of the rain gauge sensor before and after calibration was obtained using a third electronic balance, and the difference in residual water volume of the rain gauge sensor was obtained. This is taken as the wetting error of the rain gauge;

[0009] Among them, the measurement error of the injection method for:

[0010]

[0011]

[0012] Measurement error of the drainage method for:

[0013]

[0014]

[0015] In the formula, This indicates the amount of water measured by the instrument at time t, expressed in grams. This represents the actual rainfall at time t, expressed in grams. The first electronic balance was used to measure the weight before testing. Let t be the weighing value of the first electronic balance at time t; This represents the actual displacement at time t; This represents the weighing value of the second electronic balance at time t. represents the weighing value of the second electronic balance before the test;

[0016] residual water difference of the rain sensor is: ; in the formula, represents the weighing value of the third electronic balance at time t, represents the weighing value of the third electronic balance before the test;

[0017] actual value of the integrated rainfall , the mass change of the drainage container and the residual water difference of the rain sensor , the obtained verification loss value is calculated as:

[0018]

[0019] If the verification loss value is less than the preset error threshold, and the measurement errors and both conform to the corresponding error range of the product, it indicates that the verification is qualified.

[0020] In a second aspect, the present invention discloses a self-calibration method for a rain sensor based on an injection method and a drainage method, the method comprising the following steps:

[0021] S1, acquiring the resolution, structural parameters and tipping bucket material type of the rain sensor, and classifying the rain sensor; the structural parameters include the number of tipping bucket layers, the tipping bucket volume and the tipping bucket shape parameters; the tipping bucket material type includes metal and non-metal;

[0022] S2, for each type of rain sensor, using the aforementioned verification method to perform measurement error detection of the injection method and the drainage method at different rainfall intensities, if the verification is qualified, obtaining the calibrated rainfall intensity threshold corresponding to each type of rain sensor according to the measurement error detection result, to obtain the corresponding calibrated rainfall intensity range and the reliable time threshold corresponding to the calibrated rainfall intensity threshold, wherein the reliable time threshold is less than the minimum reporting time; otherwise, determining that the rain sensor is unqualified in verification, and ending the process;

[0023] S3, setting a first rainfall intensity step size according to the resolution, setting a plurality of rainfall intensity nodes within the calibrated rainfall intensity range according to the first rainfall intensity step size, and dividing the calibrated rainfall intensity range into a plurality of detection sections;

[0024] S4, for each rainfall intensity node, taking the minimum reporting time as the verification duration, performing measurement error detection of the injection method and the drainage method by using the rain sensor self-calibration platform, and calculating to obtain the corresponding system deviation of each rain sensor under different rainfall intensity conditions in combination with the verification loss value , , are respectively the verification loss value of the i-th type of rain sensor Difference between the amount of water and the amount of water remaining The weighting of the deviation is set based on historical experience.

[0025] S5. By combining the systematic deviations of all rainfall intensity nodes, and using the rainfall intensity nodes as independent variables and the corresponding systematic deviations as dependent variables, the calibration function of each rainfall sensor is obtained by fitting.

[0026] S6. The calibration function, calibration rainfall intensity range, and reliable time threshold are all set as factory parameters. If the rainfall intensity reported by the rain sensor exceeds the calibration rainfall intensity range, the measured value within the reliable time threshold range is discarded, and the value outside the reliable time threshold is reported as the rainfall detection value. Otherwise, the calibration function is used to correct the actual rainfall detection value reported by the rain sensor, and the corrected value is used as the rainfall detection value.

[0027] Furthermore, in step S2, the process of obtaining the calibration rainfall intensity threshold corresponding to each type of rain sensor includes the following steps:

[0028] S21, obtain the theoretical rainfall intensity measurement range of the rain sensor, and set the second rainfall intensity step size; the second rainfall intensity step size is greater than the first rainfall intensity step size;

[0029] S22, with the second rainfall intensity step size as the interval, the measurement error detection of injection method and drainage method with different rainfall intensities from high to low is carried out, and the first fitting curve of injection method measurement error relative to detection time and the second fitting curve of drainage method measurement error relative to detection time are generated.

[0030] S23. Judge the two fitted curves generated. Under the same rainfall intensity, is the difference between the two error curves greater than or equal to the difference in residual water volume? If yes, proceed to step S24; otherwise, readjust the rain sensor self-calibration platform and return to step S22.

[0031] S24. If the detection time corresponding to the error threshold of any fitted curve is greater than the minimum reporting time, the current test rainfall intensity is determined to be an unstable rainfall intensity. The previous test rainfall intensity is used as the calibration rainfall intensity threshold, the maximum detection time corresponding to the error threshold is used as the reliable time threshold, and the values ​​between the calibration rainfall intensity threshold and the minimum allowable rainfall intensity measurement value are selected as the calibration rainfall intensity range.

[0032] Further, in step S5, the process of fitting the calibration function for each rain gauge sensor by integrating the systematic biases of all rainfall intensity nodes, using the rainfall intensity nodes as independent variables and the corresponding systematic biases as dependent variables, includes the following steps:

[0033] For each type of rain gauge sensor, determine the calibration rainfall intensity range and the corresponding rainfall intensity node sequence, and obtain the system deviation sequence of multiple rain gauge sensors under different rainfall intensity node sequences;

[0034] For each rain gauge, the corresponding system deviation sequence under different rainfall intensity node sequences is analyzed. Based on the system deviation difference between adjacent rainfall intensity nodes, the system deviation sequence is divided into stable error region and unstable error region.

[0035] Multiple calibration function models, including the least squares method, are used to fit the systematic deviation sequences in the stable error region and the unstable error region respectively. The coefficients corresponding to different calibration function models are calculated, and the variance deviation of the fitted calibration function models is calculated respectively. The calibration functions with the smallest variance deviation corresponding to the stable error region and the unstable error region are combined as the final calibration function of the systematic deviation sequence.

[0036] The training dataset is constructed by combining the system bias sequences corresponding to different rainfall intensity sequences from all rainfall sensors and the final calibration function.

[0037] A classification model is built based on support vector machines. The calibration rainfall intensity range and the corresponding rainfall intensity node sequence are determined according to the resolution of the rain sensor, the number of tipping bucket layers and the tipping bucket material type. The rainfall intensity node sequence and the corresponding system bias sequence are used as inputs, and the combination of the final calibration function is used as the output. The classification model is trained using the training dataset.

[0038] For the rain gauge sensor to be calibrated, the trained classification model is used to process the detected systematic deviation sequence and output the final calibration function combination method. Based on the systematic deviation difference between adjacent rainfall intensity nodes, the systematic deviation sequence is divided into stable error region and unstable error region. The combination method of the final calibration function output by the classification model is used to fit the stable error region and the unstable error region respectively. The combined fitting results are used to obtain the calibration function of the rain gauge sensor to be calibrated.

[0039] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0040] First, the rain gauge sensor calibration and self-calibration method based on the injection and drainage methods of the present invention simultaneously employs the injection and drainage methods through a rain gauge sensor self-calibration platform, and introduces calibration loss values ​​in combination with the characteristics of the rain gauge sensor, using three performance parameters to calibrate the rain gauge sensor simultaneously, thereby further improving the accuracy and comprehensiveness of the rain gauge sensor calibration results.

[0041] Secondly, the rain gauge sensor calibration and self-calibration method based on the injection and drainage methods of the present invention can quickly calibrate the true error range of each rain gauge sensor and obtain the system deviation corresponding to different rainfall intensities. The system deviation is used to correct the monitoring values ​​of the rain gauge sensor, thereby achieving higher rainfall monitoring accuracy. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of the structure of the rain sensor self-calibration platform of the present invention.

[0043] Figure 2 This is a flowchart of the verification process of the present invention.

[0044] Figure 3 This is a graph showing the relationship between the drainage volume of a single tipping bucket rain gauge and the rainfall intensity.

[0045] Figure 4 This is a graph showing the relationship between the error of a single-layer tipping bucket rain gauge and rainfall intensity.

[0046] Figure 5 This is a graph showing the relationship between the error of a double-layer tipping bucket rain gauge and rainfall intensity. Detailed Implementation

[0047] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0048] See Figure 1 This invention proposes a self-calibration platform for a rain gauge sensor, comprising an electronic balance 1, an electronic balance 2, an electronic balance 3, a metering pump, a water supply container, and a drainage container. The water supply container, the drainage container, and the rain gauge sensor to be calibrated are respectively placed on electronic balance 1, electronic balance 2, and electronic balance 3.

[0049] Considering cost, the electronic balance used in this invention cannot accurately reflect the real-time weight change curve of the measured object; it can only detect the mass change of the measured object after a certain time interval or before and after the calibration process. This invention first proposes a method for calibrating a rain gauge sensor that combines both injection and drainage measurements. This rain gauge sensor calibration method includes:

[0050] A metering pump is used to control the set rainfall intensity, and water is injected into the rain gauge sensor. An electronic balance 1 is used to weigh the water in the supply container in real time, calculating the mass of water supplied to the rain gauge sensor to be calibrated, thus obtaining the actual rainfall value (actual injected water volume). The actual rainfall value is compared with the theoretical value to calculate the injection volume measurement error of the rain gauge sensor to be calibrated. Simultaneously, the discharged water is injected into the lower container through the collection tube at the bottom of the rain gauge sensor. An electronic balance 2 acquires the mass change before and after calibration, calculating the drainage volume measurement error of the rain gauge sensor to be calibrated. Simultaneously, an electronic balance 3 acquires the mass change of the rain gauge sensor before and after calibration, obtaining the difference in residual water volume in the rain gauge sensor. Comparing the mass change before and after immersion in the rain gauge sensor yields the immersion error. By comprehensively considering the water volumes from the three electronic balances, precise control and traceability of water volume are achieved throughout the entire rain gauge sensor calibration process. This facilitates more accurate assessment and analysis of the errors and precision of different rain gauge sensors, laying an experimental foundation for further research and upgrading of rain gauge sensors.

[0051] Specifically, the measurement error of the injection method is first calculated using electronic balance 1 and electronic balance 2 respectively. Measurement error of the drainage method Assuming the calibration time is t, the measurement error of the injection method... for:

[0052]

[0053]

[0054] Measurement error of the drainage method for:

[0055]

[0056]

[0057] In the formula, This indicates the amount of water measured by the instrument at time t, expressed in grams. This represents the actual rainfall at time t, expressed in grams. To test the weight of the electronic balance 1, Let be the weight of electronic balance 1 at time t; This represents the actual displacement at time t; This indicates the weight of the electronic balance at time t. This indicates the weight of the electronic balance 2 before the test.

[0058] By introducing the measurement value of electronic balance 3 as an intermediate process quantity, the mass change value of the rain sensor at time t is calculated: The calculated verification loss value is:

[0059]

[0060] If the verification loss value S is less than a preset error threshold (for example, 0.5 g), and the measurement error and both meet the error range corresponding to the product, it indicates that the verification is qualified. This verification method is particularly suitable for non-single tipping bucket rain sensors, because the single tipping bucket rain sensor has less loss from wetting variation in the intermediate process, while the double tipping bucket or siphon double tipping bucket rain sensor has relatively large variation in stored water during the intermediate process.

[0061] Taking the double tipping bucket rain sensor as an example, if the verification loss value S is less than the preset error threshold, it indicates that the error value between the injection method and the drainage method is mainly caused by the water stored in the rain sensor. At this time, the measurement error and are authentic and credible. If both of them meet the error range corresponding to the product, the verification is passed.

[0062] In practical applications, rainfall changes dynamically. Under different rainfall intensity conditions, rain sensors are affected to varying degrees by multiple aspects such as wetting error, residual water in the tipping bucket, and injection process control, resulting in different measured system deviations. Facing the increasingly high demand for accuracy, the present invention proposes a self-calibration method for rain sensors. During the verification process, the corresponding system deviation of each rain sensor under different rainfall intensity conditions is acquired, the system deviation is taken as one of the factory parameters, and the monitoring value of the rain sensor is corrected in the actual application process, so as to achieve higher rainfall monitoring accuracy.

[0063] The self-calibration method for rain sensors of the present invention includes the following steps:

[0064] S1: Acquire the resolution, structural parameters and material parameters of the rain sensor, and classify the rain sensor. For ordinary rain sensors, the structural parameters that have a great influence on the error include the number of tipping bucket layers, the volume of the tipping bucket, the shape parameters of the tipping bucket, etc., and the material parameter is mainly the tipping bucket material, such as metallic and non-metallic materials.

[0065] The system bias of rain gauges is mainly caused by wetting error, system structural error, and environmental factors. Regarding environmental factors, both the injection method and the self-drainage method currently rely on the weighing of an electronic balance. The accuracy of the electronic balance is affected by temperature and gravitational acceleration. Gravitational acceleration is closely related to altitude. Since the mass method converts mass into precipitation, it is also affected by water density. Therefore, environmental factors include at least temperature, altitude, and water density. Regarding system structural errors, the tipping friction (the greater the tipping friction, the longer the tipping time and the more water) and the structural characteristics of the tipping bucket itself (the tipping angle, the amount of water remaining inside the bucket, etc.) all affect the system measurement results. Rainfall intensity is also an important influencing factor. When the rainfall intensity is low, the impact of wetting error is relatively small. As the rainfall intensity gradually increases, the rainwater entering the rain gauge will tip over more quickly, failing to effectively wet the sensor. If system structural errors and environmental factors are not considered, only the initially unstable measurement values ​​need to be discarded. Correspondingly, environmental factors and system structural errors are positively correlated with rainfall intensity. Therefore, to achieve high-precision calibration, the self-calibration process needs to consider wetting error, system structural error, and environmental factors simultaneously. It requires classifying each type of rain gauge sensor according to its specific parameters and analyzing the calibration rainfall intensity range for that type of sensor, rather than simply selecting a weak rainfall intensity for calibration. Similarly, when evaluating system deviation, the deviation values ​​caused by the aforementioned three factors also need to be considered. In this embodiment, the rain gauge sensor self-calibration platform is used to classify these deviation values ​​into accurately detectable verification loss values. Difference between the amount of water and the amount of water remaining This consists of two parts, thus providing a relevant data foundation for subsequent self-calibration methods.

[0066] S2, for each type of rain gauge sensor, the measurement error of the injection and drainage methods under different rainfall intensities is detected using the previously described calibration method. If the calibration is successful, the calibration rainfall intensity threshold corresponding to each type of rain gauge sensor is obtained based on the measurement error detection results, resulting in the corresponding calibration rainfall intensity range and the reliable time threshold corresponding to the calibration rainfall intensity threshold. The reliable time threshold is less than the minimum reporting time. Preferably, the acquisition process includes:

[0067] S21, Obtain the theoretical rainfall intensity measurement range of the rain gauge sensor and set the second rainfall intensity step size; the second rainfall intensity step size is larger than the first rainfall intensity step size. The purpose of setting the second rainfall intensity step size here is only to obtain the theoretical rainfall intensity measurement range, so a larger step size can be selected, and more calibration space can be left, which can further improve the measurement accuracy of the rain gauge sensor.

[0068] S22, with the second rainfall intensity step size as the interval, the measurement error of the injection method and the drainage method with different rainfall intensities from high to low is detected, and the first fitting curve of the measurement error of the injection method relative to the detection time and the second fitting curve of the measurement error of the drainage method relative to the detection time are generated.

[0069] S23. Judge the two fitted curves generated. First, judge whether the difference between the two error curves under the same rainfall intensity is greater than or equal to the difference in residual water volume. If not, it means that the calibration data is unreasonable and the rain sensor self-calibration platform needs to be readjusted. If it is in compliance, proceed to step S24 to continue the judgment.

[0070] S24. If the detection time corresponding to the error threshold of any fitted curve is greater than the minimum reporting time, the current test rainfall intensity is determined to be an unstable rainfall intensity. The previous test rainfall intensity is used as the calibration rainfall intensity threshold, the maximum detection time corresponding to the error threshold is used as the reliable time threshold, and the values ​​between the calibration rainfall intensity threshold and the minimum allowable rainfall intensity measurement value are selected as the calibration rainfall intensity range.

[0071] S3. Set a first rainfall intensity step size based on the resolution, and set several rainfall intensity nodes within the calibration rainfall intensity range based on the first rainfall intensity step size, dividing the calibration rainfall intensity range into several detection segments. Preferably, the following formula is used to set the first rainfall intensity step size based on the resolution. :

[0072]

[0073] In the formula, c represents the resolution of the rain gauge sensor. Indicates rainfall intensity. First rainfall intensity step size. The appropriate value can be selected within the range related to the resolution and rainfall intensity of the rain gauge sensor. Regarding rainfall intensity, please refer to the following document: GB / T 21978.2-2014 "Precipitation Observation Instruments Part 2: Tipping Bucket Rain Gauge Sensors", specifically the provisions on rainfall intensity in section "6.2.5 Resolution, Rainfall Intensity Measurement Range and Measurement Accuracy".

[0074] S4. For each rainfall intensity node, using the minimum reporting time as the calibration period, the measurement error of the injection and drainage methods is detected using a rainfall sensor self-calibration platform. Combined with the calibration loss value, the system deviation of each rainfall sensor under different rainfall intensities is calculated. , and The weights of the calibration loss value and the residual water volume difference of the i-th type of rain gauge sensor in the deviation are respectively set based on historical experience values.

[0075] S5, combining the systematic biases of all rainfall intensity nodes, uses the rainfall intensity nodes as independent variables and the corresponding systematic biases as dependent variables to fit a calibration function for each rainfall sensor. Specifically, this includes:

[0076] For each type of rain gauge sensor, determine the calibration rainfall intensity range and the corresponding rainfall intensity node sequence, and obtain the system deviation sequence of multiple rain gauge sensors under different rainfall intensity node sequences;

[0077] The discharge rate of a single tipping bucket rain gauge will change regularly with rainfall intensity (e.g.) Figure 3 Tests showed that its error exhibited a reverse oblique pattern within the rainfall intensity range (0.4 mm / min - 4 mm / min) (e.g. Figure 4 When the rainfall is less than 0.4 mm / min, the error does not change with the rainfall intensity and remains linear because the water is in droplet form. The different colors in the figure represent test results from the same rain gauge on different days to verify data reproducibility.

[0078] Correspondingly, in double-tipping-bucket rain gauges, because different rainfall intensities act on the upper tipping bucket, while the lower metering tipping bucket is mainly affected by the amount of water discharged from the upper tipping bucket, the error does not change significantly with rainfall intensity (e.g., Figure 5 At this point, regardless of the rainfall intensity, it remains nearly constant for the lower tipping bucket. However, once the rainfall intensity exceeds the reversal limit of the upper tipping bucket (for example, for some double-tip rain gauges, when the rainfall intensity exceeds 6 mm / min, the reversal speed of the upper tipping bucket is too fast), the rainfall intensity and error revert to a typical linear relationship (reverse slope). These characteristics are closely related to the tipping bucket structure and resolution, but the systematic bias relative to the rainfall intensity sequence can be divided into stable and unstable regions. Therefore, this invention proposes the following calibration function fitting method:

[0079] For each rain gauge, the corresponding system deviation sequence under different rainfall intensity node sequences is analyzed. Based on the system deviation difference between adjacent rainfall intensity nodes, the system deviation sequence is divided into stable error region and unstable error region.

[0080] Multiple calibration function models, including the least squares method, are used to fit the systematic deviation sequences in the stable error region and the unstable error region respectively. The coefficients corresponding to different calibration function models are calculated, and the variance deviation of the fitted calibration function models is calculated respectively. The calibration functions with the smallest variance deviation corresponding to the stable error region and the unstable error region are combined as the final calibration function of the systematic deviation sequence.

[0081] A training dataset is constructed by combining the system bias sequences corresponding to different rainfall intensity sequences from all rain gauges and the final calibration function.

[0082] A classification model is built based on support vector machines. The calibration rainfall intensity range and the corresponding rainfall intensity node sequence are determined according to the resolution of the rain sensor, the number of tipping bucket layers and the tipping bucket material type. The rainfall intensity node sequence and the corresponding system bias sequence are used as inputs, and the combination of the final calibration function is used as the output. The classification model is trained using the training dataset.

[0083] For the rain gauge sensor to be calibrated, the trained classification model is used to process the detected systematic deviation sequence and output the final calibration function combination method. Based on the systematic deviation difference between adjacent rainfall intensity nodes, the systematic deviation sequence is divided into stable error region and unstable error region. The combination method of the final calibration function output by the classification model is used to fit the stable error region and the unstable error region respectively. The combined fitting results are used to obtain the calibration function of the rain gauge sensor to be calibrated.

[0084] S6. The calibration function, calibration rainfall intensity range, and reliable time threshold are all set as factory parameters. If the rainfall intensity reported by the rain sensor exceeds the calibration rainfall intensity range, the measured value within the reliable time threshold range is discarded, and the value outside the reliable time threshold is reported as the rainfall detection value. Otherwise, the calibration function is used to correct the actual rainfall detection value reported by the rain sensor, and the corrected value is used as the rainfall detection value.

[0085] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0086] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for calibrating a rain gauge sensor based on injection and drainage methods, characterized in that, The method includes: A rain gauge self-calibration platform is constructed, which includes a first electronic balance, a second electronic balance, a third electronic balance, a metering pump, a water supply container, and a drainage container; the water supply container, the drainage container, and the rain gauge to be calibrated are respectively placed on the first electronic balance, the second electronic balance, and the third electronic balance. The rainfall intensity is controlled by a metering pump, and water is injected into the rain sensor for a duration of t. The water discharged from the rain sensor is injected into the container below through the collection pipe at its bottom. The mass of water in the water supply container is measured in real time using a first electronic balance. The mass of water supplied to the rain gauge sensor to be calibrated is calculated, and the actual rainfall value at time t is obtained. The actual rainfall at time t Water volume measurement with instruments By comparison, the measurement error of the injection method of the rain gauge sensor to be tested is calculated. The mass change of the drainage container before and after calibration was obtained using a second electronic balance. Combined with instrument measurement of water volume The measurement error of the drainage volume of the rain gauge sensor to be tested is calculated. The change in mass of the rain gauge sensor before and after calibration was obtained using a third electronic balance, and the difference in residual water volume of the rain gauge sensor was obtained. This is taken as the wetting error of the rain gauge; Among them, the measurement error of the injection method for: Measurement error of the drainage method for: In the formula, This indicates the amount of water measured by the instrument at time t, expressed in grams. This represents the actual rainfall at time t, expressed in grams. The first electronic balance was used to measure the weight before testing. Let t be the weighing value of the first electronic balance at time t; This represents the actual displacement at time t; This represents the weighing value of the second electronic balance at time t. This indicates the weighing value of the second electronic balance before the test; Rain gauge residual water difference for: In the formula, This represents the weighing value of the third electronic balance at time t. This indicates the weighing value of the third electronic balance before the test; Actual value of comprehensive rainfall Changes in the mass of the drainage container and the difference in residual water volume from the rain gauge. The calculated verification loss value is: If the test loss value Less than the preset error threshold, and the measurement error and If all the results meet the error range requirements corresponding to the product, then the calibration is considered qualified.

2. A self-calibration method for a rain gauge sensor based on the injection and drainage methods according to the calibration method of claim 1, characterized in that, The method includes the following steps: S1. Obtain the resolution, structural parameters, and tipping bucket material type of the rain gauge sensor to classify the rain gauge sensor; structural parameters include the number of tipping bucket layers, tipping bucket volume, and tipping bucket shape parameters; tipping bucket material type includes metal and non-metal. S2, for each type of rain gauge sensor, the measurement error detection of injection and drainage methods with different rainfall intensities is carried out using the verification method described in claim 1. If the verification is qualified, the calibration rainfall intensity threshold corresponding to each type of rain gauge sensor is obtained according to the measurement error detection result, and the corresponding calibration rainfall intensity range and the reliable time threshold corresponding to the calibration rainfall intensity threshold are obtained. The reliable time threshold is less than the minimum reporting time; otherwise, the rain gauge sensor is determined to be unqualified and the process ends. S3, set the first rainfall intensity step size according to the resolution, set several rainfall intensity nodes within the calibration rainfall intensity range according to the first rainfall intensity step size, and divide the calibration rainfall intensity range into several detection segments; S4. For each rainfall intensity node, using the minimum reporting time as the calibration period, the measurement error of the injection and drainage methods is detected using a rainfall sensor self-calibration platform. Combined with the calibration loss value, the system deviation of each rainfall sensor under different rainfall intensities is calculated. , , The respective calibration loss values ​​of the i-th type of rain gauge sensor Difference between the amount of water and the amount of water remaining The weighting of the deviation is set based on historical experience. S5. By combining the systematic deviations of all rainfall intensity nodes, and using the rainfall intensity nodes as independent variables and the corresponding systematic deviations as dependent variables, the calibration function of each rainfall sensor is obtained by fitting. S6. The calibration function, calibration rainfall intensity range, and reliable time threshold are all set as factory parameters. If the rainfall intensity reported by the rain sensor exceeds the calibration rainfall intensity range, the measured value within the reliable time threshold range is discarded, and the value outside the reliable time threshold is reported as the rainfall detection value. Otherwise, the calibration function is used to correct the actual rainfall detection value reported by the rain sensor, and the corrected value is used as the rainfall detection value.

3. The rain gauge self-calibration method based on injection and drainage methods according to claim 2, characterized in that, Step S2, the process of obtaining the calibration rainfall intensity threshold corresponding to each type of rain sensor includes the following steps: S21, obtain the theoretical rainfall intensity measurement range of the rain sensor, and set the second rainfall intensity step size; the second rainfall intensity step size is greater than the first rainfall intensity step size; S22, with the second rainfall intensity step size as the interval, the measurement error detection of injection method and drainage method with different rainfall intensities from high to low is carried out, and the first fitting curve of injection method measurement error relative to detection time and the second fitting curve of drainage method measurement error relative to detection time are generated. S23. Judge the two fitted curves generated. Under the same rainfall intensity, is the difference between the two error curves greater than or equal to the difference in residual water volume? If yes, proceed to step S24; otherwise, readjust the rain sensor self-calibration platform and return to step S22. S24. If the detection time corresponding to the error threshold of any fitted curve is greater than the minimum reporting time, the current test rainfall intensity is determined to be an unstable rainfall intensity. The previous test rainfall intensity is used as the calibration rainfall intensity threshold, the maximum detection time corresponding to the error threshold is used as the reliable time threshold, and the values ​​between the calibration rainfall intensity threshold and the minimum allowable rainfall intensity measurement value are selected as the calibration rainfall intensity range.

4. The rain gauge self-calibration method based on injection and drainage methods according to claim 2, characterized in that, In step S5, the process of fitting the calibration function for each rain gauge sensor by combining the systematic biases of all rainfall intensity nodes, using the rainfall intensity nodes as independent variables and the corresponding systematic biases as dependent variables, includes the following steps: For each type of rain gauge sensor, determine the calibration rainfall intensity range and the corresponding rainfall intensity node sequence, and obtain the system deviation sequence of multiple rain gauge sensors under different rainfall intensity node sequences; For each rain gauge, the corresponding system deviation sequence under different rainfall intensity node sequences is analyzed. Based on the system deviation difference between adjacent rainfall intensity nodes, the system deviation sequence is divided into stable error region and unstable error region. Multiple calibration function models, including the least squares method, are used to fit the systematic deviation sequences in the stable error region and the unstable error region respectively. The coefficients corresponding to different calibration function models are calculated, and the variance deviation of the fitted calibration function models is calculated respectively. The calibration functions with the smallest variance deviation corresponding to the stable error region and the unstable error region are combined as the final calibration function of the systematic deviation sequence. The training dataset is constructed by combining the system bias sequences corresponding to different rainfall intensity sequences from all rainfall sensors and the final calibration function. A classification model is built based on support vector machines. The calibration rainfall intensity range and the corresponding rainfall intensity node sequence are determined according to the resolution of the rain sensor, the number of tipping bucket layers and the tipping bucket material type. The rainfall intensity node sequence and the corresponding system bias sequence are used as inputs, and the combination of the final calibration function is used as the output. The classification model is trained using the training dataset. For the rain gauge sensor to be calibrated, the trained classification model is used to process the detected systematic deviation sequence and output the final calibration function combination method. Based on the systematic deviation difference between adjacent rainfall intensity nodes, the systematic deviation sequence is divided into stable error region and unstable error region. The combination method of the final calibration function output by the classification model is used to fit the stable error region and the unstable error region respectively. The combined fitting results are used to obtain the calibration function of the rain gauge sensor to be calibrated.

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