Radar water level gauge high-stability data processing and measuring method

Abstract

The invention provides a radar water level gauge high-stability data processing and measuring method, which comprises the following steps that 1, a radar transmits an electromagnetic wave signal to the water surface, and the electromagnetic wave signal generates an echo when encountering the water surface and is received by a radar receiver; an effective signal and an invalid signal are distinguished by detecting the intensity of the echo signal, when the invalid signal is detected, a communication signal abnormity indication is sent out, and the reflected electromagnetic wave signal is set as original measurement data; step 2, processing echoes generated in the step 1 through an echo signal processing module in the radar receiver, obtaining median filtering processing data through median filtering, synchronously removing random noise through median filtering and reserving effective signals, and improving the stability of a measurement result; a median filtering module is arranged in the echo signal processing module; and step 3, processing the median filtering processing data in the step 2 through a median filtering processing module.

Description

Technical Field

[0001] The present invention belongs to the field of measurement data processing, and particularly relates to a high-stability data processing and measurement method for a radar water level gauge. Background Art

[0002] Currently, as a non-contact water level measurement device, the radar water level gauge is widely used in fields such as hydrological monitoring, flood warning, reservoir management, and urban drainage systems due to its advantages of high precision, strong anti-interference ability, and adaptability to complex environments. Its working principle is to emit electromagnetic waves and receive reflected signals, and calculate the propagation time of the electromagnetic waves to determine the water level height. However, although the radar water level gauge shows high reliability in practical applications, there are still many deficiencies in high-stability data processing and measurement accuracy in the prior art, and these drawbacks limit its performance and wide application in complex environments.

[0003] The anti-interference ability of existing radar water level gauges in complex environments is limited. In practical applications, radar water level gauges often face various interference sources, such as rainfall, fog, water surface fluctuations, floating objects, etc. These interferences can cause attenuation of the electromagnetic wave signal or changes in the reflection path, thereby affecting the measurement accuracy. For example, in heavy rainfall or foggy weather, the electromagnetic wave signal may be scattered by water droplets or fog, resulting in a weakening of the received signal intensity and even signal loss. In addition, water surface fluctuations and floating objects will change the reflection path of the electromagnetic wave, causing deviations in the measurement results. The prior art usually uses simple filtering algorithms or fixed thresholds to eliminate interference, but these methods often have poor effects when facing complex and changeable actual environments and cannot effectively distinguish real water level signals from interference signals.

[0004] The data processing algorithms of existing radar water level gauges have poor adaptability in dynamic environments. The water level measurement environment is usually dynamically changing. For example, river water levels can change rapidly due to factors such as rainfall, tides, or reservoir water releases, while the data processing algorithms of the prior art are usually based on static or quasi-static assumptions and are difficult to adapt to the rapid changes in water levels. For example, when the water level rises or falls rapidly, traditional algorithms may not be able to capture the water level change trend in time, resulting in lagged measurement results or increased errors. In addition, existing algorithms perform poorly in processing non-linear signals. For example, in the case of large water surface fluctuations or multi-path effects, the measurement results are prone to fluctuations or instability.

[0005] The measurement accuracy of existing radar water level gauges is limited by hardware performance and calibration methods. The measurement accuracy of radar water level gauges depends on the frequency and power of the transmitted signal, as well as the processing ability of the received signal. However, the hardware performance of existing devices often fails to meet the requirements of high-precision measurement in complex environments. For example, although low-frequency radar signals have strong penetration ability, their resolution is relatively low; high-frequency radar signals have high resolution but are vulnerable to environmental interference. In addition, existing calibration methods are usually based on calibration in a static environment and are difficult to adapt to the real-time calibration requirements in a dynamic environment. For example, when the temperature changes greatly or the installation position of the device shifts slightly, the calibration parameters may become invalid, resulting in an increase in measurement error.

[0006] Existing radar water level gauges have deficiencies in data fusion and multi-sensor collaboration. In practical applications, a single radar water level gauge may not be able to meet the measurement requirements in complex environments. Usually, it is necessary to perform data fusion with other sensors (such as ultrasonic water level gauges, pressure water level gauges, etc.) to improve measurement accuracy and reliability. However, existing technologies lack effective algorithms and strategies for multi-sensor data fusion and are difficult to achieve the efficient integration and collaborative work of data from different sensors. For example, in the data fusion of radar water level gauges and ultrasonic water level gauges, existing algorithms usually use simple weighted averaging or threshold judgment, and cannot make full use of the complementary advantages of multi-sensors, resulting in low accuracy of the fusion results.

[0007] Existing radar water level gauges have problems in long-term stability and maintenance costs. Radar water level gauges usually need to operate continuously for a long time, but in complex environments, the long-term stability of the device is difficult to guarantee. For example, the device may experience performance degradation or failure due to environmental temperature changes, humidity erosion, or mechanical vibration. In addition, the maintenance cost of existing devices is relatively high. For example, it is necessary to calibrate regularly, clean the antenna, or replace components, which increases the operating cost and management difficulty.

[0008] Although radar water level gauges have important application values in the field of water level measurement, existing technologies still have significant deficiencies in high-stability data processing and measurement accuracy, including limited anti-interference ability, poor adaptability to dynamic environments, limited hardware performance and calibration methods, deficiencies in data fusion and multi-sensor collaboration, as well as high long-term stability and maintenance costs. These disadvantages limit the performance and wide application of radar water level gauges in complex environments. Therefore, developing a high-stability radar water level gauge data processing and measurement method to overcome the deficiencies of existing technologies has become an important research direction in the current hydrological monitoring field. By introducing advanced data processing algorithms, multi-sensor fusion technologies, and adaptive calibration methods, it is expected to achieve higher-precision, more stable, and more reliable radar water level gauges in the future, thus providing more powerful technical support for hydrological monitoring and flood warning. Summary of the Invention

[0009] The present invention provides a high-stability data processing and measurement method for a radar water level gauge. This method solves the problems of unstable data measurement, large influence of noise interference, and difficulty in processing abnormal data of traditional radar water level gauges in harsh environments, and improves the accuracy and reliability of measurement results through multi-level data processing techniques.

[0010] The technical solution of the present invention is realized as follows: A high-stability data processing and measurement method for a radar water level gauge, the method comprising the following steps

[0011] Step 1: The radar emits an electromagnetic wave signal towards the water surface. The electromagnetic wave signal generates an echo when it encounters the water surface and is received by the radar receiver. By detecting the intensity of the echo signal, valid signals and invalid signals are distinguished. When an invalid signal is detected, a communication signal abnormality indication is sent, and the reflected electromagnetic wave signal is set as the original measurement data.

[0012] Step 2: The echo generated in Step 1 is processed by an echo signal processing module in the radar receiver. Median filtering processing data is obtained through median filtering. At the same time, random noise is removed through median filtering to retain valid signals, improving the stability of the measurement results. The echo signal processing module is internally provided with a median filtering module.

[0013] Step 3: The median filtering processing data in Step 2 is processed by the median filtering processing module. By setting a jump threshold, abnormal data is detected and processed to avoid the influence of mutant data on the measurement results. Data exceeding the threshold is considered abnormal data and is specially processed or ignored.

[0014] Step 4: The original measurement data is smoothed by mean filtering through a sliding window in Step 3, reducing data fluctuations and improving the stability of the measurement results.

[0015] Step 5: The smoothed filtering processing data in Step 4 is processed by a water level threshold jump damping algorithm processing module to obtain the final measurement data. For the detected water level mutation, a damping algorithm is used for processing, and the measurement results are gradually adjusted, thus completing a process of processing measurement data.

[0016] When traditional radar water level gauges perform data measurement, they often face the problem of being affected by environmental noise, resulting in large fluctuations in measurement results and insufficient accuracy. Many existing technologies rely on simple signal processing methods and fail to effectively filter out random noise and abnormal data, thus affecting the final water level measurement results. However, this method divides the entire data processing process into multiple steps, gradually optimizing the signal quality and improving the reliability of the final measurement results.

[0017] In Step 1, the radar emits electromagnetic wave signals and receives echo signals. In this process, the intensity of the echo signals is detected to distinguish effective signals from invalid signals, and an indication of abnormal communication signals is sent in a timely manner. In traditional technologies, there is usually a lack of an effective signal quality detection mechanism, resulting in invalid signals interfering with the final result. However, this method ensures the quality of the subsequent processed data by distinguishing between effective and invalid signals, laying a foundation for subsequent filtering and processing.

[0018] In Step 2, a median filtering processing module is introduced, which can effectively remove random noise and retain effective signals. Median filtering is a non-linear filtering technique that can maintain the edge information of signals in the presence of noise. Traditional methods often use mean filtering, which is vulnerable to extreme values and cannot achieve effective noise suppression. However, this method improves the stability of the processed data through median filtering technology, avoiding measurement errors caused by noise interference. In Step 3, a jump threshold is set to detect and process abnormal data, which is a major innovation of this method. Traditional systems often lack an effective processing mechanism for mutant data, resulting in abnormal data having a significant impact on the measurement results. However, this method sets a threshold to clarify the standard of abnormal data and takes special processing or ignoring measures to avoid the impact of mutant data on the final result, enhancing the accuracy of the measurement. In Step 4, the original measurement data is smoothed by mean filtering through a sliding window, further reducing data fluctuations. Traditional methods usually rely on simple filtering, while the sliding window mean processing of this method can more effectively smooth data fluctuations and improve the stability of the measurement results. This design is particularly important in a dynamically changing water level environment and can effectively cope with the challenges brought by water level fluctuations.

[0019] In Step 5, a water level threshold jump damping algorithm processing module is adopted to process the detected water level mutations, gradually adjusting the measurement results to ensure that the data is more in line with the actual situation. Traditional systems often fail to effectively handle mutation situations, resulting in inaccurate data. However, the damping algorithm design of this method can achieve a smooth transition, making the final result more reliable and continuous and reducing data fluctuations caused by mutations.

[0020] In summary, this high-stability data processing and measurement method for a radar water level gauge overcomes the deficiencies of traditional technologies in data accuracy, noise suppression, and abnormal processing through multi-step data processing and optimized design, demonstrating higher performance and adaptability.

[0021] As a preferred implementation manner, when the radar emits electromagnetic wave signals to the water surface, multi-band radar technology is adopted. By simultaneously emitting multiple radar waves with different frequencies and combining multi-band signal processing algorithms, the interference of environmental noise is reduced. When the radar receiver detects the intensity of the echo signals, the phase difference of the echo signals is detected simultaneously through phase difference detection to improve the signal recognition accuracy.

[0022] As a preferred embodiment, when continuously detecting invalid signals within a set time interval, the communication signal anomaly indication continuously issues a communication signal anomaly indication and keeps the current water level measurement result unchanged until the signal returns to normal.

[0023] As a preferred embodiment, when performing median filtering, the radar water level gauge obtains a time series of the echo signal intensities of several echoes, and uses the median filtering algorithm to filter out outliers and noise in the echo signal intensity data. After the time series of the echo signal intensity data passes through the median filtering algorithm, several filtered echo signal intensity data are obtained.

[0024] As a preferred embodiment, the step - by - step process of performing jump threshold processing on the original measurement data includes: obtaining the echo signal intensity sequence, recording the echo signal intensity data within the current preset time window of the radar water level gauge and storing it in the buffer, obtaining the maximum and minimum values of the echo signal intensity data, determining whether the difference between the maximum and minimum values is greater than or equal to the water level jump threshold, obtaining the water level state detection index, and determining the water level change by comparing the water level state detection index with the water level jump threshold. When it is less than the water level jump threshold, the water level is judged to be stable; when it is greater than the water level jump threshold, the water level is judged to have changed, thereby improving the accuracy and stability of the measurement.

[0025] As a preferred embodiment, the mean filtering and smoothing process includes obtaining N consecutive measured water levels, and using the exponentially weighted moving average method to perform smoothing filtering on the measured water levels to filter out noise and outliers, thereby reducing false alarms in water level change detection.

[0026] As a preferred embodiment, the damping algorithm obtains a water level detection index by judging whether the maximum and minimum values of the echo signals appearing within the same time period are the same; obtains a water level change detection index by judging whether there is a sudden drop in the water level within the same time period; through damping factor smoothing and filtering, judges the water level state within the same time window based on the maximum and minimum values appearing within the same time period or whether there is a sudden drop in the water level within the same time period; the time window of the same time period is dynamically adjusted.

[0027] After adopting the above - mentioned technical solutions, the beneficial effects of the present invention are as follows: By introducing a multi - level data processing mechanism, the accuracy and reliability of water level measurement are ensured. The distinction between effective signals and invalid signals and the application of median filtering technology greatly reduce the fluctuations in the final measurement results, providing a more stable data output. This stability is crucial in practical applications, especially in scenarios that require precise monitoring of water level changes, such as reservoir management and river monitoring, and can provide more reliable data support for decision - making.

[0028] The innovative design of abnormal data processing significantly improves the intelligence level of the system. By setting jump thresholds to detect and process mutation data, it avoids incorrect measurements caused by extreme value interference. This intelligent data processing ability enables users to maintain efficient and accurate data acquisition in complex and changing environments, reduces the need for manual intervention, and enhances the automation level of the system.

[0029] The introduction of moving window mean filtering and damping algorithms enhances the flexibility and adaptability of data processing. The application of these technologies not only improves the smoothness and readability of data but also ensures that the system can quickly adjust and respond in the face of emergencies, enhancing the overall operation efficiency and safety. The economic benefits of this method are also very significant. By improving the accuracy and stability of water level measurement, it reduces economic losses and resource waste caused by data errors. Especially in the fields of water resource management and flood prevention warning, accurate water level monitoring can effectively reduce potential risks brought by water level changes, reduce disaster losses, and protect people's lives and property safety.

[0030] The efficient operation and reliability of the system also provide users with a good usage experience, promoting the popularization and application of related technologies. Users can obtain better data feedback and support during use, thereby improving work efficiency and enhancing the scientificity and rationality of management decisions. This high-stability data processing and measurement method for radar water level gauges provides strong technical support for the water level monitoring field by improving data accuracy, enhancing system intelligence, and increasing economic benefits, and promotes the digital and intelligent development of related industries. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0032] Figure 1 It is a flow chart of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0033] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0034] Embodiment:

[0035] As Figure 1 shown, the high-stability data processing measurement method of the radar water level gauge described in this application document aims to improve the accuracy and reliability of water level measurement, especially in application scenarios under dynamic environments, such as real-time water level monitoring of reservoirs, rivers, or lakes. In this implementation scenario, the working principle of the system and each step cooperate with each other to form a complete measurement and data processing process.

[0036] First, in step one, the radar water level gauge emits electromagnetic wave signals towards the water surface. These electromagnetic wave signals generate echoes when they encounter the water surface, and the radar receiver is responsible for receiving these echo signals. By detecting the intensity of the echo signals, the system can distinguish between valid signals and invalid signals. When an invalid signal is detected, the system issues an indication of abnormal communication signals, prompting the user that there may be a problem. In this process, the reflected electromagnetic wave signals are set as the original measurement data, serving as the basic data for subsequent processing. The function of the radar receiver is crucial. It not only receives the signals but also ensures the signal quality so that the subsequent processing module can obtain a reliable data source.

[0037] In step two, the generated echoes are processed by the echo signal processing module in the radar receiver. This module is built-in with a median filtering module, and by processing the echo signals through median filtering technology, it can effectively remove random noise and retain valid signals. This process greatly improves the stability of the measurement results. Especially in the case of large environmental interference, median filtering can effectively reduce the impact of noise on the measurement results, making the output results of the system more real and reliable.

[0038] Next, in step three, a set jump threshold is used to detect and process abnormal data. The data processed by median filtering will undergo further analysis through the echo signal processing module. The system can identify the data that exceeds the threshold and mark it as abnormal data. Special processing or direct ignoring of these abnormal data avoids the negative impact of mutant data on the measurement results. The design of this step ensures that the system can maintain the accuracy of the measurement results and avoid false alarms or missed alarms when facing sudden events.

[0039] In step four, the original measurement data undergoes mean filtering smoothing processing through a sliding window, reducing data fluctuations and improving the stability of the measurement results. The sliding window technology can effectively smooth the data curve and eliminate small fluctuations by averaging the data within a certain range, ensuring that the output data is more coherent and usable.

[0040] Finally, in step five, the data processed by the smoothing filter enters the water level threshold jump damping algorithm processing module. The main task of this module is to process the detected sudden change in water level and gradually adjust the measurement result using the damping algorithm. This process enables the system to smoothly transition when the water level changes rapidly, avoiding misjudgment caused by sudden change data. Finally, the measurement result output by the system is not only accurate but also can reflect the real change of the water level.

[0041] In summary, the high-stability data processing and measurement method of this radar water level gauge ensures the accuracy and reliability of water level measurement in a changing environment through the precise cooperation of multiple steps, combined with advanced signal processing technologies and algorithms. Each component plays an indispensable role in the system, forming a complete water level monitoring chain from signal transmission, reception, processing to final output. Through this efficient measurement method, it can provide strong data support for fields such as water resource management and environmental monitoring, improving the overall management level.

[0042] In this technical solution, the radar uses multi-band radar technology to transmit electromagnetic wave signals to the water surface and simultaneously emits multiple radar waves with different frequencies, combined with multi-band signal processing algorithms to reduce the interference of environmental noise. The difference between this design and the existing technology is that traditional radar systems usually use signals with a single frequency and are easily affected by environmental noise and multi-path propagation, resulting in a decrease in the recognition accuracy of signals. However, through the application of multi-band technology in this solution, the recognition accuracy of signals can be effectively improved, enhancing the detection ability of the water surface echo signal. In specific working scenarios, this technology is particularly suitable for fields such as water level monitoring and flood warning, and can provide more reliable data support under complex environmental conditions. Through phase difference detection, the system further improves the processing accuracy of the echo signal, enabling accurate identification of water level changes even in harsh environments and ensuring the accuracy and real-time nature of the data.

[0043] In terms of communication signal anomaly indication, this solution stipulates that when invalid signals are continuously detected within a set time interval, the system will continuously issue an anomaly indication and keep the currently measured water level result unchanged until the signal returns to normal. The difference between this design and the existing technology is that traditional systems often automatically reset or stop measurement when the signal is abnormal, resulting in data interruption and potential information loss. However, through maintaining the current water level measurement result in this solution, it ensures that reference data is still provided when the signal is unstable, thereby reducing false alarms and misjudgments caused by communication problems. This design can significantly improve the stability and availability of data in actual working scenarios, especially in applications with high requirements for the continuity and reliability of water level monitoring, ensuring accurate monitoring at critical moments.

[0044] In the implementation of median filtering, the radar water level gauge acquires the time series of the intensities of multiple echo signals and uses the median filtering algorithm to filter out outliers and noise. Compared with the prior art, this process demonstrates higher data processing accuracy. Traditional signal processing methods may rely on simple average calculations and are unable to effectively eliminate extreme values or noise, resulting in data distortion. However, through median filtering, this solution can remove noise while retaining valid data, improving the accuracy of water level measurement. This processing method is particularly suitable for scenarios with large environmental changes or numerous interference factors, ensuring the reliability of water level monitoring and the authenticity of data.

[0045] Regarding the processing of the jump threshold of the original measurement data, this solution obtains the echo signal intensity sequence, records the intensity data within a preset time window, and then determines whether the difference between the maximum value and the minimum value is greater than or equal to the water level jump threshold. The main difference between this method and the prior art is that traditional methods often lack dynamic monitoring of water level changes, which may lead to a lag in response to water level fluctuations. However, by setting the jump threshold, this solution can determine in real time whether the water level is in a stable state, and thus quickly respond when the water level changes. This dynamic monitoring mechanism can effectively improve the response speed of water level monitoring in actual working scenarios, ensuring timely countermeasures in case of emergencies.

[0046] In terms of mean filtering smoothing processing, this solution uses the exponentially weighted moving average method to perform smoothing filtering on N consecutive measured water levels. The difference between this design and the prior art is that traditional smoothing filtering methods may lack flexibility and are unable to effectively adapt to the characteristics of water level fluctuations. However, through the weighted average method, this solution can dynamically adjust the filtering weights according to the real-time changes of the data, thereby better filtering out noise and outliers and reducing the occurrence of false alarms. This smoothing processing method can effectively improve the stability and reliability of data in the actual application of water level monitoring, ensuring the accuracy of the monitoring results.

[0047] In the use of the damping algorithm, this solution determines whether the maximum value and the minimum value of the echo signals in the same time period are the same, obtains the water level detection index, and determines whether there is a sudden drop in the water level. The main difference between this design and the prior art is that traditional methods usually only focus on single measurement data and lack in-depth analysis of the data change trend. However, by dynamically adjusting the time window and integrating multiple indicators, this solution can more comprehensively evaluate the water level change situation, improving the accuracy and sensitivity of water level monitoring. This method is particularly suitable for water level monitoring and flood warning in actual working scenarios and can effectively

[0048] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A high-stability data processing and measurement method for a radar water level gauge, characterized in that: The method comprises the following steps Step 1: The radar transmits an electromagnetic wave signal to the water surface. The electromagnetic wave signal generates an echo when encountering the water surface and is received by the radar receiver; and by detecting the strength of the echo signal, the valid signal and the invalid signal are distinguished. When an invalid signal is detected, a communication signal abnormality indication is issued, and the reflected electromagnetic wave signal is set as the original measurement data; Step 2: The echo generated in step 1 is processed by the echo signal processing module in the radar receiver, and the median filtering is used to obtain the median filtering processing data, and the random noise is removed by the median filtering to retain the effective signal, so as to improve the stability of the measurement result; the echo signal processing module has a built-in median filtering module; Step 3: The median filter processing data in step 2 is processed by the median filter processing module. By setting the jump threshold, abnormal data is detected and processed to avoid the influence of mutation data on the measurement results. Data exceeding the threshold is considered to be abnormal data and is specially processed or ignored. Step 4: In step 3, the original measurement data is smoothed by a sliding window to reduce data fluctuations and improve the stability of the measurement results; Step 5: The data processed by smoothing filter in step 4 is processed by the water level threshold jump damping algorithm processing module to obtain the final measurement data. For the detected water level mutation, the damping algorithm is used to process it and gradually adjust the measurement results, thereby completing a measurement data processing process.

2. A radar water level meter high stability data processing and measurement method as claimed in claim 1, characterized in that: When the radar transmits electromagnetic wave signals to the water surface, multi-band radar technology is adopted. By simultaneously transmitting multiple radar waves of different frequencies and combining a multi-band signal processing algorithm, the interference of environmental noise is reduced. When detecting the strength of the echo signal, the radar receiver detects the phase difference of the echo signal through phase difference detection and simultaneously detects the phase difference of the echo signal to improve the signal recognition accuracy.

3. A radar water level meter high stability data processing and measurement method as claimed in claim 1, characterized in that: The communication signal abnormality indication continues to issue a communication signal abnormality indication when an invalid signal is continuously detected within a set time interval, and keeps the current water level measurement result unchanged until the signal returns to normal.

4. A radar water level meter high stability data processing and measurement method as claimed in claim 1, characterized in that: When performing median filtering, the radar water level meter obtains several time series of echo signal strengths, and uses the median filtering algorithm to filter out abnormal values ​​and noise in the echo signal strength data. After the time series of the echo signal strength data passes through the median filtering algorithm, several filtered echo signal strength data are obtained.

5. A radar water level meter high stability data processing and measurement method as claimed in claim 1, characterized in that: The jump threshold processing of the original measurement data includes the following steps: obtaining an echo signal strength sequence, recording the echo signal strength data within the current preset time window of the radar water level meter and saving it in a cache, obtaining the maximum and minimum values ​​of the echo signal strength data, determining whether the difference between the maximum and minimum values ​​is greater than or equal to the water level jump threshold, obtaining a water level state detection index, and determining the water level change by comparing the water level state detection index with the water level jump threshold. When the water level is less than the water level jump threshold, the water level is determined to be stable, and when the water level is greater than the water level jump threshold, the water level is determined to have changed, thereby improving the accuracy and stability of the measurement.

6. A radar water level meter high stability data processing and measurement method as claimed in claim 1, characterized in that: The mean filtering and smoothing process includes obtaining N consecutive measured water levels, using an exponentially weighted moving average method to perform smoothing filtering on the measured water levels, filtering out noise and abnormal points, thereby reducing false alarms in water level change detection.

7. A radar water level meter high stability data processing and measurement method as claimed in claim 1, characterized in that: The damping algorithm obtains the water level detection index by judging whether the maximum and minimum values ​​of the echo signal appearing in the same time period are consistent; obtains the water level change detection index by judging whether there is a sudden drop in water level in the same time period; obtains the water level status in the same time window by smoothing and filtering the damping factor, judging by the maximum and minimum values ​​appearing in the same time period or whether there is a sudden drop in water level in the same time period; the time window of the same time period is dynamically adjusted.

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