A high-precision automatic water level monitoring method and system
By constructing a fluctuation stability index to screen effective measurement points and adaptively adjusting the fusion weights, the problem of high-precision water level monitoring of non-contact water level sensors under complex water surface fluctuations was solved, and the accuracy and robustness of high-precision water level measurement were improved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- YOUSHENG JULI (BEIJING) TECH CO LTD
- Filing Date
- 2025-12-22
- Publication Date
- 2026-05-26
AI Technical Summary
Existing non-contact water level sensors struggle to achieve high-precision water level monitoring under complex water surface fluctuations. Fixed weights or empirical setting methods result in insufficient fusion accuracy, failing to meet the requirements for high-precision water level monitoring.
By analyzing the spatiotemporal characteristics of water surface fluctuations, a fluctuation stability index is constructed to screen effective measurement points. An adaptive adjustment of the fusion weights and a weighted average are then performed. Combined with measurement uncertainty correction, high-precision water level measurement results are finally obtained.
It improves the accuracy and robustness of water level measurement, reduces the impact of complex water surface fluctuations on the measurement, and significantly enhances the accuracy and reliability of water level monitoring.
Smart Images
Figure CN121655651B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of liquid level measurement technology, specifically to a high-precision automatic water level monitoring method and system. Background Technology
[0002] Water level monitoring is a key technology in hydrological monitoring, reservoir operation, and river flood control. Accurate water level data is crucial for ensuring the safe operation of water conservancy projects. Currently, water level monitoring mainly employs various sensors such as float-type, pressure-type, ultrasonic, and laser ranging for automatic monitoring. Among these, non-contact ultrasonic and laser ranging methods are widely used because they avoid direct contact with the water body, are unaffected by water pollution, and have low maintenance costs.
[0003] However, while non-contact sensors can avoid contact with water, water surface fluctuations can cause unstable reflection point positions and drastic fluctuations in instantaneous measurements. Therefore, single-point measurements are difficult to accurately reflect the true water level, and measurement errors are relatively large under complex conditions such as wind, waves, rainfall, and ship navigation. Although there are multi-sensor fusion methods that use data from multiple measurement points in existing technologies, the fusion weights are usually fixed values or set based on experience, making it difficult to adaptively adjust according to the state of water surface fluctuations. Furthermore, the impact of the spatiotemporal characteristics of water surface fluctuations on the fusion results is not considered, resulting in insufficient fusion accuracy under complex fluctuation conditions and failing to meet the needs of high-precision water level monitoring. Summary of the Invention
[0004] In view of the above, it is necessary to provide a high-precision automatic water level monitoring method and system to solve the above problems.
[0005] The first aspect of this application provides a high-precision automatic water level monitoring method, the method comprising:
[0006] Measurement points are set up in the monitored water area to analyze the overall distribution characteristics of water level values within a preset time window and determine the fluctuation frequency of each measurement point.
[0007] The fluctuation of the water surface at all measurement points within a preset time window and the fluctuation frequency are analyzed to obtain the fluctuation stability index.
[0008] Based on the fluctuation stability index of the current time window, the measurement points are screened to obtain valid measurement points; the dispersion of the water level value of each valid measurement point in the current time window is analyzed to determine the fusion weight of each valid measurement point;
[0009] The representative water level value of the current time window for each valid measurement point is obtained, and the representative water level value is weighted using the fusion weight to obtain the fused water level value. The fused water level value is then corrected using the measurement uncertainty to obtain the final water level measurement result.
[0010] Preferably, the specific process for determining the fluctuation frequency of each measurement point is as follows:
[0011] Calculate the average water level value for each measurement point in each time window;
[0012] The number of times the water level changed from above the average water level to below the average water level, and the number of times the water level changed from below the average water level to above the average water level, were counted in two consecutive samplings within each time window.
[0013] The ratio of half of the number of times to the corresponding time length of the time window is used as the fluctuation frequency of each measurement point in each time window.
[0014] Preferably, the obtained fluctuation stability index is specifically as follows:
[0015] The standard deviation of the water level value at each measurement point in each time window is used as the fluctuation range.
[0016] The spatial consistency is obtained by acquiring the dispersion of the fluctuation amplitude of all measurement points in each time window and the numerical distribution characteristics.
[0017] Frequency consistency is determined based on the overall distribution of fluctuation frequencies at all measurement points in each time window;
[0018] By positively fusing the spatial consistency and frequency consistency, a fluctuation stability index is obtained.
[0019] Preferably, the step of obtaining spatial consistency is as follows:
[0020] Obtain the range, average, and minimum fluctuation values of all measurement points for each time window; calculate the negative correlation mapping result between the sum of the average and minimum fluctuation values, and positively fuse it with the range values.
[0021] Preferably, determining frequency consistency specifically refers to the sum of the coefficients of variation of all measurement points in each time window and the natural number 1.
[0022] Preferably, the specific formula for filtering measurement points to obtain valid measurement points is as follows: ;in, Indicates the number of valid measurement points; This represents the floor function; Indicates the number of measurement points. This indicates the preset number of reference measurement points. This indicates the stability of volatility.
[0023] Preferably, the determination of the fusion weight for each valid measurement point is specifically the proportion of the reciprocal of the square of the fluctuation amplitude of each valid measurement point among the reciprocals obtained from all valid measurement points.
[0024] Preferably, the representative water level value is specifically the average water level value.
[0025] Preferably, the process of correcting the fused water level value by measuring the uncertainty to obtain the final water level measurement result specifically involves:
[0026] Calculate the sum of squares of the products of the fusion weights of all valid measurement points and the corresponding fluctuation amplitudes, and take the square root of the sum of squares to obtain the uncertainty of the fusion water level value;
[0027] Based on the difference and sum of the fused water level value and uncertainty, the lower and upper limits of the final water level measurement result are determined.
[0028] Secondly, embodiments of this application also provide a high-precision automatic water level monitoring system, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of any of the methods described above.
[0029] This application has at least the following beneficial effects:
[0030] (1) By constructing a wave stability index, the characteristics of water surface waves in both spatial and frequency dimensions can be evaluated, which can accurately quantify the instability of water surface waves and provide a basis for subsequent adaptive sampling. Compared with existing technologies that rely solely on a single wave characteristic, this method improves the comprehensiveness and accuracy of wave assessment.
[0031] (2) By adaptively adjusting the number of effective measurement points and using the inverse variance weighting method, the number of measurement points is reduced when the water surface is stable to reduce computational complexity, and the number of measurement points is increased when the water surface fluctuates violently to improve statistical reliability. At the same time, the weight of measurement points with small fluctuations is automatically increased and the weight of measurement points with large fluctuations is reduced. Compared with the existing technology of fixed weights or weights set according to experience, the adaptive optimization of weights is realized, which improves the accuracy and robustness of the fusion results.
[0032] (3) By weighted fusion of measurement data from multiple measurement points to calculate measurement uncertainty, the influence of abnormal fluctuations can be suppressed, making the fused uncertainty much smaller than the fluctuation amplitude of each individual point. Compared with the existing single-point measurement or simple averaging method, this significantly improves the accuracy of water level measurement. Attached Figure Description
[0033] Figure 1 A flowchart illustrating the steps of a high-precision automatic water level monitoring method provided in one embodiment of this application;
[0034] Figure 2 This is a schematic diagram illustrating the acquisition of the final water level measurement result according to one embodiment of this application. Detailed Implementation
[0035] In the description of the embodiments in this application, the words "exemplary," "or," and "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary," "or," and "for example" is intended to present the relevant concepts in a specific manner.
[0036] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in this application's specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.
[0037] It should also be noted that the terms "first" and "second" in this application and its accompanying drawings are used to distinguish similar objects, rather than to describe a specific order or sequence. The methods disclosed in the embodiments of this application or the methods shown in the flowcharts include one or more steps for implementing the method. Without departing from the scope of protection of this application, the execution order of multiple steps can be interchanged, and some steps can also be deleted.
[0038] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.
[0039] The following description, in conjunction with the accompanying drawings, details the specific scheme of the high-precision automatic water level monitoring method and system provided in this application.
[0040] Please see Figure 1 The diagram illustrates a flowchart of a high-precision automatic water level monitoring method according to an embodiment of this application. The method includes the following steps:
[0041] The first step is to set up measurement points in the monitored water area, analyze the overall distribution characteristics of water level values within a preset time window, and determine the fluctuation frequency of each measurement point.
[0042] The accuracy of water level monitoring is affected by water surface fluctuations. Various factors such as wind, waves, rainfall, and ship movement can cause fluctuations in the water surface at different frequencies and amplitudes, causing instantaneous measurements to deviate from the true water level. To quantify the fluctuation state of the water surface, data needs to be collected from multiple measurement points to analyze the spatiotemporal characteristics of the fluctuations and construct an index that can reflect the stability of the water surface.
[0043] Evenly distributed above the monitored water area An array of ultrasonic sensors forms a measurement point array. Each sensor collects multiple instantaneous water level values at a fixed frequency within a preset time window. Specifically:
[0044] Evenly distributed above the monitored water area One ultrasonic sensor ( The ultrasonic sensors, spaced more than 10 meters apart, form a measurement point array. They employ a non-contact measurement principle, emitting ultrasonic pulses towards the water surface and receiving the echoes reflected back. The distance from the sensor to the water surface is calculated based on the time difference between transmission and reception. In this embodiment, each sensor collects water level values at a frequency of 10Hz, and the length of the time window... The time is 5 seconds, which can be adjusted by the implementer according to the actual situation; the i-th measurement point's... The instantaneous water level value is recorded as ( ), where m represents the number of water level values collected for each measurement within each time window.
[0045] Calculate the average water level and standard deviation at each measurement point. Use the zero-crossing method to count the number of times the water level crosses the average value. Calculate the fluctuation frequency at each measurement point. Specifically:
[0046] For the i-th measurement point, first calculate the average water level value within the current time window. Then calculate the fluctuation range. In this embodiment, the standard deviation of the water level value is used to represent the fluctuation amplitude, reflecting the degree of vertical fluctuation of the water surface at the measurement point. When the water surface fluctuates violently, the instantaneous measurement value deviates more from the average value, and the standard deviation increases; when the water surface is calm, the standard deviation decreases.
[0047] Calculating the fluctuation frequency using the zero-crossing method Specifically, this refers to the statistical time window. Internal water level value crosses the average water level value Number of times In this embodiment, the method for statistically analyzing water level crossing the average water level is as follows: The number of times the water level changes from above the average water level to below the average water level in two adjacent sampling periods, and the number of times it changes from below the average water level to above the average water level; the specific formula for the fluctuation frequency is: It should be noted that the division by 2 in the formula is because a complete fluctuation cycle contains two zero crossovers, one upward and one downward.
[0048] Frequency reflects the speed of water surface fluctuations. Higher frequencies indicate that the water surface is more likely to be affected by rapid disturbances such as wind, waves, and ships, resulting in frequent and unstable fluctuations. Lower frequencies indicate that the water surface is more likely to be affected by slow swells or water level changes, resulting in relative stability. Combining frequency and amplitude characteristics provides a more comprehensive reflection of the fluctuation state. This is because large-amplitude, high-frequency fluctuations (such as strong winds and waves) and small-amplitude, low-frequency fluctuations (such as calm water) have vastly different effects on measurements, and amplitude alone cannot distinguish between them.
[0049] The second step is to analyze the water surface fluctuations and fluctuation frequencies at all measurement points within a preset time window to obtain the fluctuation stability index.
[0050] To comprehensively assess the stability of water surface fluctuations, both spatial and frequency dimensions need to be considered. First, the statistics of fluctuation amplitudes at all measurement points are calculated: the maximum fluctuation amplitude. Minimum fluctuation range Average fluctuation range and the average value of the fluctuation frequency. and standard deviation Constructing a volatility stability index The specific formula is as follows: To avoid the risk of division by zero caused by extremely calm water surfaces, the minimum fluctuation amplitude... When it is zero, the value is 0.01 mm. This represents a preset parameter used to prevent the denominator from being 0; in this embodiment, the value is 0.01Hz.
[0051] It should be understood that, It indicates spatial consistency, when the amplitude of fluctuations is consistent throughout the water surface (such as uniform swells). and The smaller the molecule, the closer this term is to 0; when there is a severe local disturbance (such as floating objects or local wind blowing near a certain measurement point). Much larger As the numerator increases, this term increases. The denominator acts as a normalization factor, ensuring that the indicator is not affected by absolute fluctuations. The larger the value of this term, the more uneven and unstable the water surface condition.
[0052] This indicates frequency consistency; the closer the fluctuation frequencies at each measurement point are, the better. The smaller the value, the closer the term is to 1; when the difference in fluctuation frequency at different locations is greater, The larger the value, the greater the value of this term, indicating a more complex and irregular fluctuation pattern. Adding 1 ensures that this term is always greater than 1, preventing the multiplier factor from being zero when the frequencies are completely uniform, thus avoiding the overall indicator from failing. A larger value indicates a more complex and unstable fluctuation pattern.
[0053] When the water surface is calm and all points are uniform Approaching 0; when the water surface fluctuations are violent and complex, Significantly increased. This indicator, combining spatial statistics and frequency domain analysis, can effectively distinguish different fluctuation states.
[0054] The third step is to screen the measurement points to obtain valid measurement points based on the fluctuation stability index of the current time window; analyze the dispersion of the water level value of each valid measurement point in the current time window, and determine the fusion weight of each valid measurement point.
[0055] After obtaining the fluctuation stability index and the fluctuation amplitude at each measurement point, the measurement points participating in the final fusion need to be dynamically adjusted according to the water surface fluctuation state. When the water surface is stable, a small number of measurement points can obtain reliable results; when the water surface fluctuates violently, more measurement points are needed to smooth out the fluctuation effects. At the same time, due to differences in location, different measurement points are affected by local interference to varying degrees; data from points with smaller fluctuation amplitudes are more reliable and should be given higher weight.
[0056] The number of valid measurement points is calculated based on the fluctuation stability index, using the following formula: .in, Indicates the number of valid measurement points, with a value range of [value range missing]. ; This represents the floor function; Indicates the number of measurement points. This indicates the preset number of reference measurement points. .
[0057] It should be understood that when the water surface is completely calm, stable results can be obtained using only reference measurement points, thereby reducing computational complexity. In one embodiment, the number of reference measurement points is 5. When water surface fluctuations increase, Increase Monotonically increasing (this function is a saturation function, and its range is...) ), As the fluctuations gradually increase, more measurement points are needed to smooth out the effects of the fluctuations; when the fluctuations are extremely severe, all measurement points need to be used to reduce the impact of the fluctuations.
[0058] Sort all measurement points by fluctuation amplitude from smallest to largest, select the top few points with the smallest fluctuation amplitude as the set of valid measurement points, and calculate the fusion weight for each valid measurement point, specifically as follows:
[0059] Sort all measurement points by fluctuation amplitude from smallest to largest, and select the top... The point with the smallest fluctuation amplitude is selected as the set of valid measurement points. This is because the reliability of data is inversely proportional to its uncertainty. Measurement points with small fluctuations indicate that the measured values at that point change little within the time window, have good repeatability, are less affected by local disturbances, and the data is closer to the true average water level at that location, thus having high reliability. Conversely, points with large fluctuations indicate that they are subject to stronger disturbances, resulting in larger data deviations and lower reliability.
[0060] For each valid measurement point The fusion weights are calculated using the following formula: .in, Indicates measurement point The fusion weights, and satisfy ; Indicates measurement point The fluctuation range, similarly, when the minimum fluctuation range When it is zero, the value is 0.01mm.
[0061] In measurement, the fluctuation range (standard deviation) It characterizes the uncertainty and variance of a measurement. This indicates the degree of dispersion of the measured values. When multiple independent measurements are linearly fused, the weight of the measurement that minimizes the variance of the fused result is inversely proportional to the variance. Specifically, measurements with high precision (small variance) should receive a larger weight, while measurements with low precision (large variance) should receive a correspondingly smaller weight. This maximizes the use of high-quality data and suppresses the influence of low-quality data.
[0062] The fourth step is to obtain the representative water level value of each valid measurement point in the current time window, and to use the fusion weight to weight the representative water level value to obtain the fused water level value. The fused water level value is then corrected by the measurement uncertainty to obtain the final water level measurement result.
[0063] The average water level value of each valid measurement point within the time window is extracted as a representative value. Then, a weighted average of the representative water level values is calculated according to the fusion weight of each valid measurement point to obtain the fused water level value. Specifically:
[0064] For each valid measurement point The average water level calculated using the current time window. This serves as the representative water level value for that point. The average water level value is smoothed through multiple samples within a time window, making it more stable than a single instantaneous value and reducing the impact of random fluctuations. The merged water level value is calculated using a weighted average formula. .
[0065] Finally, based on the law of measurement uncertainty propagation, the uncertainty of the fusion result is calculated using the weights of each point and the fluctuation amplitude, and the final water level measurement result and its uncertainty are output, as follows:
[0066] To further quantify and integrate water level values The measurement accuracy is determined by calculating the uncertainty according to the law of propagation of measurement uncertainty, using the formula: .in, This indicates the uncertainty of the merged water level value, used to represent the expected error range of the measurement result; Indicates measurement point The fusion weight; Indicates measurement point The fluctuation range.
[0067] It should be understood that this formula is based on the law of measurement uncertainty propagation (error propagation formula). When multiple independent measurements... (Each uncertainty is) When performing weighted fusion, based on the additivity of variance (the variance of a linear combination of independent random variables is equal to the weighted sum of the variances of each component), the variance of the combined result is... ,in Therefore, the uncertainty (i.e., the standard deviation) is .
[0068] Due to the inverse variance weighting, the weights are... Tilt towards points with smaller fluctuations ( minor (large), and in the uncertainty formula The value for high-precision points (small) The uncertainty after fusion will be further compressed. Typically much smaller than the fluctuation amplitude of each individual point. This demonstrates the improved accuracy of the fusion method.
[0069] The final water level measurement result is expressed as follows According to the normal distribution theory in statistics, this means that under normal circumstances, there is approximately a 68% probability that the actual water level will be below the specified level. Within the interval (corresponding to a 1 standard deviation confidence interval), or with approximately a 95% probability, it lies within... Within the interval (corresponding to a 2-standard-deviation confidence interval).
[0070] The schematic diagram for obtaining the final water level measurement results is shown below. Figure 2 As shown.
[0071] Based on the same inventive concept as the above method, this application embodiment also provides a high-precision automatic water level monitoring system, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described high-precision automatic water level monitoring methods.
[0072] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description; sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.
[0073] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from its essential characteristics. Therefore, the embodiments described above should be considered exemplary and non-limiting in all respects; modifications to the technical solutions described in the foregoing embodiments, or equivalent substitutions of some technical features, without causing the essence of the corresponding technical solutions to deviate from the scope of the technical solutions in the embodiments of this application, should all be included within the protection scope of this application.
Claims
1. A high-precision automatic water level monitoring method, characterized in that, The method includes the following steps: Measurement points are set up in the monitored water area to analyze the overall distribution characteristics of water level values within a preset time window and determine the fluctuation frequency of each measurement point. The fluctuation of the water surface at all measurement points within a preset time window and the fluctuation frequency are analyzed to obtain the fluctuation stability index. Based on the fluctuation stability index of the current time window, the measurement points are screened to obtain valid measurement points; the dispersion of the water level value of each valid measurement point in the current time window is analyzed to determine the fusion weight of each valid measurement point; The representative water level value of the current time window for each valid measurement point is obtained, and the representative water level value is weighted using the fusion weight to obtain the fused water level value. The fused water level value is then corrected using the measurement uncertainty to obtain the final water level measurement result. The obtained volatility stability index is specifically as follows: The standard deviation of the water level value at each measurement point in each time window is used as the fluctuation range. The spatial consistency is obtained by acquiring the dispersion of the fluctuation amplitude of all measurement points in each time window and the numerical distribution characteristics. Frequency consistency is determined based on the overall distribution of fluctuation frequencies at all measurement points in each time window; By positively fusing the spatial consistency and frequency consistency, a fluctuation stability index is obtained.
2. The high-precision automatic water level monitoring method as described in claim 1, characterized in that, The specific process for determining the fluctuation frequency at each measurement point is as follows: Calculate the average water level value for each measurement point in each time window; The number of times the water level changed from above the average water level to below the average water level, and the number of times the water level changed from below the average water level to above the average water level, were counted between two consecutive samples within each time window. The ratio of half of the number of times to the corresponding time length of the time window is used as the fluctuation frequency of each measurement point in each time window.
3. The high-precision automatic water level monitoring method as described in claim 1, characterized in that, The steps to achieve spatial consistency are as follows: Obtain the range, average, and minimum fluctuation values of all measurement points for each time window; calculate the negative correlation mapping result between the sum of the average and minimum fluctuation values, and positively fuse it with the range values.
4. The high-precision automatic water level monitoring method as described in claim 1, characterized in that, The determination of frequency consistency is specifically the sum of the coefficient of variation of all measurement points in each time window and the natural number 1.
5. The high-precision automatic water level monitoring method as described in claim 1, characterized in that, The specific formula for selecting valid measurement points is as follows: ;in, Indicates the number of valid measurement points; This represents the floor function; Indicates the number of measurement points. This indicates the preset number of reference measurement points. This indicates the stability of volatility.
6. The high-precision automatic water level monitoring method as described in claim 1, characterized in that, The specific method for determining the fusion weight of each valid measurement point is the proportion of the square of the fluctuation amplitude of each valid measurement point to the reciprocal of the reciprocals obtained from all valid measurement points.
7. The high-precision automatic water level monitoring method as described in claim 2, characterized in that, The representative water level value is specifically the average water level value.
8. The high-precision automatic water level monitoring method as described in claim 1, characterized in that, The process of correcting the fused water level value by measuring uncertainty to obtain the final water level measurement result is as follows: Calculate the sum of squares of the products of the fusion weights of all valid measurement points and the corresponding fluctuation amplitudes, and take the square root of the sum of squares to obtain the uncertainty of the fusion water level value; Based on the difference and sum of the fused water level value and uncertainty, the lower and upper limits of the final water level measurement result are determined.
9. A high-precision automatic water level monitoring system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1-8.