A municipal road waterlogging depth monitoring method and system

Abstract

The application relates to the technical field of water level monitoring, in particular to a municipal road water depth monitoring method and system, which comprises the following steps: acquiring the environmental temperature, rainfall intensity and all echo signals at the current time; calculating the time distribution degree at the current time, calculating the interference evaluation value at the current time; calculating the signal frequency shift degree at the current time, adjusting the wavelet threshold of the wavelet denoising algorithm, and filtering the echo signals; acquiring the historical data set, classifying the samples in the historical data set, screening the clustering clusters representing different environmental conditions, acquiring the matching cluster and the standard cluster at the current time; constructing an error model, calculating the compensation coefficient at the current time, and compensating and correcting the water depth calculated from the filtered echo signals. The application improves the detection accuracy of the water depth of the road under extreme weather conditions.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of accumulated water level monitoring, in particular to a municipal road accumulated water depth monitoring method and system. BACKGROUND

[0002] As the backbone of the urban traffic network, once the municipal road is accumulated with water, not only will it cause traffic congestion and vehicle stalling, but also will endanger personal safety. Especially under extreme weather conditions such as heavy rain, the accumulated water forms quickly and has a wide distribution range, so it is necessary to accurately monitor and warn the accumulated water depth on the road in time.

[0003] When the ultrasonic technology is used to detect the accumulated water depth, due to the influence of complex and extreme weather conditions such as heavy rain, there are a large number of randomly distributed raindrops on the ultrasonic propagation path, which will produce strong reflection, scattering and attenuation effects, resulting in a large amount of clutter mixed in the received echo, interfering with the identification of the effective signal. In addition, heavy rain is often accompanied by drastic changes in environmental conditions, and is affected by the coupling of multiple environmental factors, which will cause the results of ultrasonic detection to have deviations, resulting in inaccurate measurement of the accumulated water depth. SUMMARY

[0004] In order to solve the above technical problems, a municipal road accumulated water depth monitoring method and system are provided to solve the existing problems.

[0005] The technical problem of the application is solved by providing a municipal road accumulated water depth monitoring method and system, which comprises the following steps:

[0006] In the first aspect, the application provides a municipal road accumulated water depth monitoring method, which comprises the following steps:

[0007] Obtain the environmental temperature, rainfall intensity and all echo signals at the current time;

[0008] Analyze the time distribution of the significant change of the signal amplitude in all echo signals, calculate the time distribution degree at the current time, combine the inconsistency of the wave peak occurrence time of different echo signals in the local range, and calculate the interference evaluation value at the current time according to the time periodicity of the significant change of the echo signal;

[0009] Based on the frequency component offset of all echo signals in the frequency domain, calculate the signal frequency shift degree at the current time, combine the interference evaluation value, adjust the wavelet threshold of the wavelet denoising algorithm, and filter the echo signal;

[0010] A plurality of sample data are extracted from a historical rainfall process, including an initial accumulated water level, a water level change amount, and an ambient temperature and a rainfall intensity at all times, to form a historical data set. Based on differences in ambient temperature and temperature change rate between different samples, the samples in the historical data set are classified, and clusters representing different environmental conditions are screened to obtain a matching cluster and a standard cluster at the current time. Based on changes in water level change amount with respect to initial accumulated water level and rainfall intensity of samples in the matching cluster and the standard cluster, error models are constructed. Using accumulated water levels and rainfall intensities at the previous time corresponding to different times within the current time and its local range, a compensation coefficient at the current time is calculated, and the accumulated water depth calculated from the filtered echo signal is compensated and corrected.

[0011] Preferably, the time distribution degree at the current time is calculated, comprising:

[0012] The transmission time and the reception time corresponding to each echo signal are obtained.

[0013] Each time point in each echo signal with a signal amplitude greater than a preset threshold is recorded as a significant time point. The time interval between each significant time point and its corresponding transmission time in each echo signal is obtained. The ratio of the time interval to the reception time corresponding to the first echo signal is taken as the relative time sequence proportion of each significant time point.

[0014] The weighted average of the relative time sequence proportions of all significant time points of all echo signals at the current time is negatively mapped, and the result is taken as the time distribution degree at the current time.

[0015] Preferably, the interference evaluation value at the current time is calculated, comprising:

[0016] The wave peaks of the signal amplitudes at all times in each echo signal are obtained, and the time points corresponding to the wave peaks are recorded as wave peak time points.

[0017] A time window is preset. In each echo signal, all wave peak time points within the time window after the transmission time are taken as a time set. The average of the differences between the time sets corresponding to any two echo signals at the current time is calculated as the random interference degree at the current time.

[0018] Autocorrelation coefficients of a plurality of preset lag orders of all significant time points of all echo signals are obtained, and the maximum autocorrelation coefficient is selected.

[0019] The sum of the time distribution degree and the random interference degree is calculated, and the ratio of the sum to the maximum autocorrelation coefficient is taken as the interference evaluation value at the current time.

[0020] Preferably, the signal frequency shift degree at the current time is calculated, comprising:

[0021] All echo signals at the current moment are spliced ​​together in chronological order to form a continuous echo signal. Frequency domain analysis is performed on it to obtain a spectrum. The frequency component with the highest energy in the spectrum is denoted as the fundamental frequency component. The difference between each frequency component in the spectrum and the fundamental frequency component is normalized and denoted as the relative difference.

[0022] The energy of all frequency components in the spectrum is normalized, and the average of the product of the normalized energy of all frequency components in the spectrum and the relative difference is taken as the signal frequency shift at the current moment.

[0023] Preferably, the adjusted wavelet threshold corresponds to time t. The calculation formula is: ,in, Let be the interference coefficient at time t, where the interference coefficient is the normalized result of the product of the interference assessment value and the signal frequency shift. Let t be the wavelet threshold before adjustment at time t.

[0024] Preferably, classifying the samples in the historical dataset includes: for the historical dataset, recording the mean of the ambient temperature at all times for each sample as the average temperature; recording the mean of the rate of change of the ambient temperature at all two adjacent times for each sample as the average rate of change; forming a two-dimensional vector from the average temperature and the average rate of change of each sample, and clustering the two-dimensional vectors of all samples in the historical dataset.

[0025] Preferably, obtaining the matching cluster and standard cluster at the current moment includes:

[0026] Calculate the rate of change of ambient temperature between the current time and the previous time; combine the current ambient temperature and the rate of change into a two-dimensional vector;

[0027] Calculate the average difference between the current two-dimensional vector and the two-dimensional vectors of all samples in each cluster, and select the cluster with the smallest average value as the matching cluster at the current time.

[0028] For all clusters, the cluster with the smallest average rate of change and the average temperature closest to the standard atmospheric temperature corresponding to the cluster center is selected and denoted as the standard cluster.

[0029] Preferably, the construction of the error model includes:

[0030] The mean rainfall intensity at all times for each sample is calculated and denoted as the average rainfall intensity. The initial water level and average rainfall intensity of each sample are used as independent variables, and the water level change is used as the dependent variable. Multivariate nonlinear fitting is performed on all samples in each cluster to obtain the multivariate fitting function of each cluster.

[0031] Error model is: wherein, represents a multi-element fitting function of the matching cluster, and respectively two independent variables, represents a multi-element fitting function of the standard cluster.

[0032] Preferably, the calculation of the compensation coefficient of the current time comprises:

[0033] The actual water depth of the previous time of each time is taken as the initial water level of each time; a plurality of times before the current time are recorded as adjacent times; and the initial water level and the rainfall intensity of the current time and its adjacent times are respectively substituted into the error model, and the output value is taken as the relative error;

[0034] The initial water level and the rainfall intensity of the current time and its adjacent times are respectively taken to form a two-dimensional array; the distance between the two-dimensional array of the current time and each adjacent time thereof is calculated, and after normalization, negative mapping is performed to serve as the distribution weight of each adjacent time; based on the distribution weight, the relative errors of all adjacent times of the current time are weighted and summed; the mean value of the relative error of the current time and the weighted sum is calculated to serve as the compensation coefficient of the current time.

[0035] In a second aspect, the embodiments of the present application further provide a municipal road water depth monitoring system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the steps of the municipal road water depth monitoring method in any one of the above aspects when executing the computer program.

[0036] The present application has at least the following beneficial effects:

[0037] The application has the beneficial effects that the time distribution degree is calculated by analyzing the time distribution characteristics of the echo signal, which reflects the situation that the clutter in the echo signal appears in the early stage; the interference evaluation value at the current moment is calculated, which considers the inconsistency of the wave peak appearing time in different echo signals and the irregularity of the echo signal itself, reflects the randomness of the clutter appearance, and evaluates the influence degree of the clutter on the echo signal; the signal frequency shift degree at the current moment is calculated, which considers the frequency shift degree of the echo signal in the frequency domain, reflects the signal distortion degree caused by the medium change of the ultrasonic wave in the propagation path in the heavy rain, and calculates the interference coefficient, which comprehensively evaluates the severe situation of the ultrasonic detection environment from multiple dimensions of time and frequency, and indicates the severity of the clutter interference in the echo signal; the wavelet threshold of the wavelet denoising algorithm is adjusted, and the echo signal is filtered, which has the beneficial effects that the wavelet threshold of the wavelet denoising algorithm is adjusted according to the influence degree of the clutter on the echo signal, the denoising ability is improved while avoiding the loss of effective information, the clutter in the echo signal is effectively removed, and the detection accuracy of the water depth is improved; the samples in the historical data set are classified, the clustering clusters representing different environmental conditions are screened, the matching cluster and the standard cluster at the current moment are obtained, which has the beneficial effects that the samples in the historical data set are divided into different clustering clusters according to the temperature change in the rainfall process in the historical period, each cluster corresponds to an environmental temperature mode, and the clustering cluster similar to the real-time environmental temperature and the clustering cluster similar to the standard temperature environment are found, so as to analyze the error condition of the water level change amount in the matching cluster and the standard cluster corresponding to the current environmental condition with the rainfall intensity, thereby constructing an error model, which reflects the error degree introduced by the environmental change; the compensation coefficient at the current moment is calculated, and the water depth calculated by the filtered echo signal is compensated and corrected, which has the beneficial effects that the data in the time and space neighborhood are introduced and weighted average is performed, the random fluctuation possibly existing in single calculation is effectively smoothed, the compensation coefficient obtained finally is more stable, the error degree of the water depth measured by the signal under the current environmental condition is reflected through the compensation coefficient, the water depth calculated by the filtered echo signal is compensated, the interference of the extreme environmental condition on the echo signal is overcome, and the detection accuracy and stability of the water depth of the road under the extreme weather condition are effectively improved. BRIEF DESCRIPTION OF DRAWINGS

[0038] The application further discloses a municipal road water depth monitoring method.

[0039] Figure 1 A step flow chart of the municipal road water depth monitoring method provided by the application is shown in the figure.

[0040] Figure 2The step flow chart of the method for obtaining the interference evaluation value of the current time provided by the embodiment of the present application. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical scheme and advantages of the present application more clear, the present application provides a municipal road waterlogging depth monitoring method and system, which will be further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0042] 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 the present application belongs.

[0043] Please refer to Figure 1 which shows the step flow chart of a municipal road waterlogging depth monitoring method provided by an embodiment of the present application, which comprises the following steps:

[0044] Step 1, obtaining the current time of the ambient temperature, rainfall intensity and all echo signals, and historical data set.

[0045] Due to global warming, the frequency and intensity of extreme precipitation events have increased significantly in urban areas, and at the same time, with the accelerating process of urbanization, the impervious area of urban areas has increased, which has increased the risk of urban waterlogging disasters. In order to efficiently carry out urban waterlogging disaster emergency work and reduce the risk of rainstorm waterlogging disasters as much as possible, it is urgent to monitor the municipal road waterlogging in real time to quickly respond to urban waterlogging and reduce losses.

[0046] The air medium type ultrasonic water level meter is a non-contact waterlogging measurement method, mainly composed of an ultrasonic transducer and a signal processing circuit. The ultrasonic transducer is simply called a probe. It uses electrical energy to generate ultrasonic waves of a certain frequency and emits them into the air. When the ultrasonic waves encounter the water surface, they will be reflected. The reflected waves are re-received by the probe and converted into echo electrical signals. The time difference between the emitted wave and the echo is measured, and then multiplied by the propagation speed of the ultrasonic wave in the air to obtain the distance between the emitting surface and the reflecting surface. For water level measurement, the emitting surface is fixed, so the distance between the reflecting surface reflects the change of the water level depth. Therefore, the calculation process of the waterlogging depth is as follows:

[0047]

[0048] Among them, is the waterlogging depth, is the distance between the ultrasonic sensor and the ground, is the propagation speed of the ultrasonic wave in the air, is the time interval from the emission of the ultrasonic wave from the sensor to the reflection back to the sensor, and This indicates the distance between the ultrasonic sensor and the surface of the accumulated water.

[0049] Based on the above analysis, by installing air-medium ultrasonic water level gauges on the lampposts on both sides of the municipal road, wherein the ultrasonic water level gauges are perpendicular to the road surface, a series of ultrasonic pulses are continuously emitted to acquire multiple echo signals, and the emission time and reception time corresponding to each echo signal are recorded.

[0050] It should be noted that each echo signal corresponds to the transmission of a complete ultrasonic pulse and the reception of the echo. In this embodiment, the ultrasonic probe transmits ultrasonic waves at a frequency of 40 kHz with a wave velocity angle of 10°. The sampling frequency of the echo signal is 300 kHz, and the ultrasonic level gauge transmits 10 ultrasonic pulses per minute, acquiring 10 echo signals in real time. As for other implementation methods, the implementer can set them according to the actual situation.

[0051] Ambient temperature and rainfall were collected using a temperature sensor and an optical rain sensor, respectively.

[0052] In this embodiment, the sampling frequency of the temperature sensor is 1 minute and the sampling frequency of the optical rain sensor is 10 seconds. As for other implementation methods, the implementer can set them according to the actual situation.

[0053] Therefore, the average of all rainfall collected per minute is taken as the rainfall intensity; thus, each minute corresponds to 10 echo signals, one ambient temperature and one rainfall intensity.

[0054] Each minute is treated as a moment in time, and all echo signals, ambient temperature, and rainfall intensity at that moment are acquired.

[0055] Secondly, from different rainfall events in historical periods, for a given time period, the water level at the first moment of that time period is recorded as the initial water level; the difference between the water level at the last moment of that time period and the initial water level is recorded as the water level change; and the ambient temperature and rainfall intensity at all moments within that time period are obtained.

[0056] A historical dataset is formed by taking a time period as a sample and selecting the initial water level, water level change, ambient temperature and rainfall intensity of multiple samples.

[0057] In this embodiment, the length of a time period is 10 minutes and the sample size of the historical dataset is 200. As for other implementation methods, the implementer can set them according to the actual situation.

[0058] The collected data is normalized by using the maximum-minimum value normalization method. The maximum-minimum value normalization method is a known technology, and will not be described here again. As other embodiments, the implementer can use other methods of the prior art, for example, the Z-score standardization method, and the present embodiment does not make special restrictions on this.

[0059] At this point, the ambient temperature, rainfall intensity and all echo signals at the current time are obtained, as well as the historical data set.

[0060] Step 2, analyze the time distribution of the significant change of the signal amplitude in all echo signals, calculate the time distribution degree at the current time, and calculate the interference evaluation value at the current time in combination with the inconsistency of the wave peak occurrence time of different echo signals in the local range and the periodicity of the significant change of the echo signal.

[0061] In the case of facing extreme weather such as heavy rain, a large number of false echoes will be generated when measuring by using ultrasonic waves. At the same time, the extreme weather will cause a large mutation of environmental factors, which will also affect the propagation of ultrasonic waves, resulting in errors in the detection of the depth of accumulated water. First, in the propagation process of ultrasonic waves, a large number of raindrops will cause a large number of reflection, refraction and other phenomena, resulting in signal attenuation, causing a large number of clutter in the echo signal, and thus causing difficulty in identifying the effective echo generated by the accumulated water surface and the ground. Therefore, in order to improve the accuracy of detecting the depth of accumulated water in extreme weather, it is necessary to filter the echo signal to enhance the characteristics of the effective wave.

[0062] Secondly, in the propagation process of ultrasonic waves, the echo signal is mainly affected by the reflection surface caused by the change of medium. Under the influence of no extreme weather, the change of medium in the propagation process of ultrasonic waves is small, and the main change of medium is caused by the accumulated water surface and the ground, so that the echo signal is clear, and the signal attenuation is relatively small, and the echo frequency is close to the center frequency at the time of emission. And because the change of medium in the propagation path is relatively stable, the regularity of signal attenuation is strong. But under the influence of extreme weather, the change of medium in the propagation path will increase, and the distribution of these changes of medium is relatively random, and the main change of medium is before the accumulated water surface, so that a large amount of signal attenuation will exist before the ultrasonic wave propagates to the accumulated water surface. At this time, not only will the echo frequency shift, but also the random distribution of the change of medium will cause the regularity of signal attenuation to be worse.

[0063] Further, the step flow chart of the method for obtaining the interference evaluation value at the current time provided by the embodiment of the present application is shown in Figure 2

[0064] Based on the above analysis, the time distribution degree is calculated through the distribution of the abnormal change time of the signal amplitude of the echo signal, specifically: ​

[0065] The moment when the signal amplitude of each echo signal is greater than a preset threshold is recorded as a significant moment;

[0066] In this embodiment, the preset threshold is the upper quartile of the signal amplitude at all times within each echo signal. The calculation of the upper quartile is a well-known technique and will not be described in detail here.

[0067] Obtain the time interval between each significant moment within each echo signal and its corresponding transmission moment;

[0068] The ratio of the time interval to the reception time corresponding to the first echo signal is used as the relative timing ratio of each significant moment.

[0069] The time distribution degree at the current moment is obtained by negatively mapping the weighted average of the relative temporal proportions of all significant moments of all echo signals at the current moment, with the signal amplitude at each significant moment as the weight;

[0070] In this embodiment, the negative mapping process is as follows: negative mapping is performed using an exponential function, assuming the weighted average is denoted as... ,but The result is used as the degree of time distribution, where, It is an exponential function with the natural constant as the base.

[0071] It should be noted that the calculation process of the weighted average is a well-known technique and will not be elaborated here. The formula for calculating the weighted average is as follows: ,in, For weighted average, For the first The weights assigned to each data point For the i-th data point, The number of all data points; the smaller the relative time ratio, the further away the significant moment is from the reception time of the first echo signal; the greater the time distribution, the more clutter appears in the early echo signal, and the more severe the clutter interference.

[0072] Secondly, the differences in the timing of the peaks of different echo signals are analyzed, and the random interference degree is calculated, specifically:

[0073] Obtain the peak of the signal amplitude at all times within each echo signal, and record the time corresponding to the peak as the peak time;

[0074] In this embodiment, the AMPD (Automatic Multiscale-based Peak Detection) algorithm is used to obtain the peak. The AMPD algorithm is a well-known technology and will not be described in detail here.

[0075] preset a time window; compose a time set by taking all the wave peak time points in the time window after the transmission time as the starting point for each echo signal;

[0076] In this embodiment, the time window has a length of half of the reception time corresponding to the first echo signal.

[0077] Calculate the mean value of the difference of the time sets corresponding to all the arbitrary two echo signals at the current time as the random interference degree at the current time.

[0078] In this embodiment, the mean value of the K-S distance of the time series corresponding to all the arbitrary two echo signals is calculated as the random interference degree, wherein the calculation of the K-S distance is a known technique and will not be described here.

[0079] It should be noted that the greater the random interference degree, the greater the difference in the time of the wave peak in different echo signals, reflecting that the transmission path of the ultrasonic wave is not only severely disturbed by the initial clutter but also has completely different and unpredictable interference modes each time.

[0080] Further, the periodic variation of the echo signal is evaluated, and the interference evaluation value is determined in combination with the time distribution degree and the random interference degree, specifically:

[0081] Obtain the autocorrelation coefficients of a plurality of preset lag orders of all the significant time points of all the echo signals, and select the maximum autocorrelation coefficient.

[0082] In this embodiment, the autocorrelation function is used to calculate the autocorrelation coefficient, and the preset lag order is an integer from 1 to 30, wherein the autocorrelation function is a known technique and will not be described here.

[0083] Calculate the sum of the time distribution degree and the random interference degree, and take the ratio of the sum to the maximum autocorrelation coefficient as the interference evaluation value at the current time.

[0084] It should be noted that the greater the sum, the more chaotic and disordered the echo signal is in time and space, the smaller the maximum autocorrelation coefficient, the worse the regularity of the echo signal itself, and the more chaotic the waveform; the greater the obtained interference evaluation value, the more seriously the echo signal is affected by the clutter at the time.

[0085] Thus, the interference evaluation value at the current time is obtained.

[0086] Step 3, based on the frequency component offset of all the echo signals in the frequency domain, calculate the signal frequency shift degree at the current time, adjust the wavelet threshold of the wavelet denoising algorithm in combination with the interference evaluation value, and filter the echo signal.

[0087] Further, the shift of the echo signal in the frequency domain is analyzed, and a signal frequency shift degree is calculated, specifically:

[0088] All echo signals at the current time are spliced into a continuous echo signal in time sequence, and frequency domain analysis is performed thereon to obtain a spectrum diagram;

[0089] In this embodiment, fast Fourier transform is used for frequency domain analysis, wherein the fast Fourier transform is a known technology and will not be described here.

[0090] The frequency component with the maximum energy in the spectrum diagram is recorded as a fundamental frequency component; and the difference between each frequency component and the fundamental frequency component in the spectrum diagram is normalized and recorded as a relative difference.

[0091] In this embodiment, the absolute value of the difference between each frequency component and the fundamental frequency component in the spectrum diagram is normalized and recorded as a relative difference.

[0092] The energy of all frequency components in the spectrum diagram is normalized, and the average of the product of the normalized energy of all frequency components in the spectrum diagram and the relative difference is taken as the signal frequency shift degree at the current time.

[0093] In this embodiment, maximum-minimum value normalization is used for normalization, wherein the maximum-minimum value normalization is a known technology and will not be described here.

[0094] It should be noted that the greater the relative difference, the more significant the deviation of the frequency component, reflecting a larger change in the medium on the signal propagation path, and the greater the energy, the greater the signal strength of the frequency component, the greater the obtained signal frequency shift degree, indicating that the signal frequency shift is more significant, reflecting that the echo signal has serious distortion relative to the original transmitted signal, indicating that the medium change of the ultrasonic wave on the propagation path is larger.

[0095] Further, based on the interference evaluation value and the signal frequency shift degree, an interference coefficient is determined, specifically:

[0096] The normalized result of the product of the interference evaluation value and the signal frequency shift degree is taken as the interference coefficient at the current time.

[0097] In this embodiment, the inverse tangent function is used for normalization, wherein the inverse tangent function is a known technology and will not be described here.

[0098] It should be noted that the greater the interference coefficient, the worse the ultrasonic detection environment, the more serious the clutter interference in the signal, and thus the more unreliable the measurement result of the road water depth.

[0099] To reduce the interference of clutter in the echo signal, the echo signal is filtered by a wavelet denoising algorithm, wherein the setting of the wavelet threshold has a significant impact on the filtering effect. If the wavelet threshold is set too large, many valid information will be misjudged as noise, thus losing important information in the signal and causing signal distortion. If the wavelet threshold is set too small, the denoising effect of the signal is not complete, and a large amount of clutter remains in the signal, making it difficult to identify valid echoes. Therefore, based on the interference coefficient, the wavelet threshold in the wavelet denoising algorithm is adjusted, specifically as follows:

[0100]

[0101] wherein, is the adjusted wavelet threshold corresponding to the t th moment, is the interference coefficient at the t th moment, is the wavelet threshold before adjustment at the t th moment;

[0102] In this embodiment, the wavelet threshold before adjustment adopts the general threshold of the wavelet denoising algorithm, so wherein, is the noise standard deviation, is the length of the echo signal.

[0103] Based on the adjusted wavelet threshold, the echo signal is filtered by the wavelet denoising algorithm to obtain the filtered echo signal.

[0104] It should be noted that the wavelet denoising algorithm is a known technology and will not be described here. In addition, in extreme weather conditions, there are too many clutters in the echo signal, which can easily cause errors in the judgment of the depth of accumulated water. At this time, the ability to filter out clutter needs to be enhanced. Therefore, the larger the interference coefficient is, the larger the wavelet threshold should be, and the higher the denoising degree should be.

[0105] At this point, all the filtered echo signals at the current moment are obtained.

[0106] Step 4: Based on the differences in environmental temperature and its temperature change rate between different samples, the samples in the historical data set are classified, the clustering clusters representing different environmental conditions are screened, and the matching cluster and the standard cluster at the current moment are obtained. Based on the change of water level variation with respect to the initial accumulated water level and rainfall intensity in the samples in the matching cluster and the standard cluster, an error model is constructed.

[0107] Further, the extreme weather not only causes a large amount of clutter, but also influences the propagation process of the ultrasonic wave, so that the echo time deviates, and the measurement accuracy of the water depth is affected. For example, the propagation speed of the ultrasonic wave is closely related to the temperature. The higher the temperature, the more intense the thermal motion of air molecules, and the faster the propagation speed of the ultrasonic wave. Conversely, the lower the temperature, the slower the propagation speed of the ultrasonic wave. Therefore, in the case of extreme weather, the temperature will decrease significantly in a short time. The rapid change of the temperature will cause the propagation speed of the ultrasonic wave to change significantly, thereby affecting the measurement of the echo time. Therefore, it is necessary to compensate for the water depth measured by the echo signal according to the temperature change at the current time and the rainfall intensity.

[0108] Firstly, all samples in the historical data set are classified according to the change of the average temperature in the historical data set, specifically:

[0109] For the historical data set, the average of the environmental temperature at all times under each sample is denoted as the average temperature;

[0110] The average of the change rate of the environmental temperature of all adjacent two times under each sample is denoted as the average change rate;

[0111] It should be noted that the calculation of the change rate is a known technology, which will not be repeated here.

[0112] The average temperature and the average change rate of each sample form a two-dimensional vector;

[0113] The two-dimensional vectors of all samples in the historical data set are clustered to obtain a plurality of clustering clusters;

[0114] In this embodiment, the DBSCAN clustering algorithm is used for clustering, wherein the DBSCAN clustering algorithm is a known technology, which will not be repeated here. As other embodiments, the implementer can use other methods of prior art, for example, K-means clustering algorithm, and the present embodiment does not specially limit this.

[0115] It should be noted that each clustering cluster represents an environmental temperature condition.

[0116] Secondly, the difference between the change of the environmental temperature and the rainfall intensity at the current time and the clustering cluster is analyzed, and a matching cluster is selected, specifically:

[0117] The change rate of the environmental temperature between the current time and the previous time is calculated; the environmental temperature at the current time and the change rate form a two-dimensional vector;

[0118] It should be noted that the calculation of the change rate is a known technology, which will not be repeated here.

[0119] Calculate the average of the differences between the two-dimensional vector of the current time and the two-dimensional vectors of all samples in each cluster, and select the cluster with the smallest average, which is recorded as the matching cluster of the current time.

[0120] In this embodiment, the average of the Euclidean distances between the two-dimensional vector of the current time and the two-dimensional vectors of all samples in each cluster is calculated, wherein the calculation of the Euclidean distance is a known technology and will not be described here.

[0121] For all cluster centers, select the cluster with the smallest average change rate and the average temperature closest to the standard atmospheric temperature, which is recorded as the standard cluster.

[0122] In this embodiment, the standard atmospheric temperature is 20℃.

[0123] Secondly, analyze the differences in water level change with initial accumulated water level and rainfall intensity under different environmental conditions represented by the matching cluster and the standard cluster, and construct an error model, which is specifically:

[0124] Calculate the average of all rainfall intensities at each sample, which is recorded as the average rainfall intensity.

[0125] Take the initial accumulated water level and the average rainfall intensity of each sample as independent variables, and take the water level change as the dependent variable, and perform multivariate nonlinear fitting on all samples in each cluster to obtain a multivariate fitting function for each cluster.

[0126] It should be noted that multivariate nonlinear fitting is a known technology and will not be described here.

[0127] The error model is:

[0128]

[0129] wherein, represents the error model, represents the multivariate fitting function of the matching cluster, and are two independent variables, represents the multivariate fitting function of the standard cluster.

[0130] It should be noted that the error model reflects the degree of error introduced by environmental changes.

[0131] At this point, the error model corresponding to the current time is obtained.

[0132] Step 5, using the accumulated water level and rainfall intensity of the previous time corresponding to the current time and different times within its local range, calculate the compensation coefficient of the current time, and compensate and correct the accumulated water depth calculated from the filtered echo signal.

[0133] Further, based on the error model, the compensation coefficient is calculated by using the accumulated water depth of the previous time and the rainfall intensity at different times, specifically:

[0134] The actual accumulated water depth of the previous time at each time is taken as the initial accumulated water level at each time;

[0135] It should be noted that the actual accumulated water depth is the accumulated water depth after compensation.

[0136] The multiple times before the current time are denoted as each adjacent time;

[0137] In the embodiment, the 10 times before the current time are denoted as adjacent times, and as an alternative, the implementer can set it according to the actual situation.

[0138] The initial accumulated water level and the rainfall intensity at the current time and each adjacent time are respectively substituted into the error model, and the output value is taken as the relative error;

[0139] It should be noted that the greater the relative error, the greater the difference between the environmental conditions at the time and the standard conditions, resulting in a larger water level error measured by the ultrasonic water level meter.

[0140] The initial accumulated water level and the rainfall intensity at each time are combined to form a two-dimensional array;

[0141] The distance of the two-dimensional array of the current time and each adjacent time is calculated, and the distance is normalized and negatively mapped to serve as the allocation weight of each adjacent time;

[0142] In the embodiment, the normalization process is: calculating the cumulative sum of the distances of all adjacent times, and taking the ratio of the distance of each adjacent time to the cumulative sum as the normalized result; the negative mapping process is: taking the difference between the value 1 and the normalized result as the allocation weight of each adjacent time.

[0143] Based on the allocation weight, the relative errors of all adjacent times are weighted and summed;

[0144] The mean value of the relative error of the current time and the weighted sum is calculated as the compensation coefficient of the current time;

[0145] It should be noted that if the current time corresponds to the environment condition is relatively large difference from the standard environment condition, the water depth calculated by the echo signal has a large error, the water level should be compensated, the temperature of the current time is lower than the temperature of the standard environment condition, the propagation speed of the ultrasonic wave will decrease, resulting in the measurement time of the echo signal being longer, using the propagation speed of the standard environment condition to calculate the water level, the calculation result will be larger, so the calculation result needs to be reduced; and the compensation coefficient reflects the error between the water level change under the current environment condition and the water level change under the standard environment condition, when the compensation coefficient is greater than 1, it means that the water level change measured under the current environment condition is larger than that under the standard environment condition, reflecting the degree of the water depth measured by the current environment condition signal being too high, so the error needs to be reduced, when the compensation coefficient is less than 1, reflecting the degree of the water depth measured by the current environment condition signal being too low.

[0146] Further, based on the compensation coefficient, the water depth calculated based on the filtered echo signal is compensated and corrected, specifically:

[0147] Based on the filtered echo signal, the water depth at the current time is calculated, and the ratio of the water depth at the current time to the compensation coefficient is taken as the water depth after compensation corresponding to the current time, which is taken as the final measured water depth, and the water depth on the municipal road is measured in real time.

[0148] It should be noted that the process of calculating the water level by the echo signal is a known technology, which will not be described here.

[0149] Based on the same inventive concept as the above method, the embodiments of the present application also provide a municipal road water depth monitoring system, which comprises a memory, a processor and a computer program stored in the memory and running on the processor, and the processor implements the steps of any one of the above municipal road water depth monitoring methods when executing the computer program.

[0150] It should be understood that, although Figure 1 The steps in the flowchart are displayed in sequence according to the direction of the arrow, but these steps are not necessarily executed in sequence according to the direction of the arrow. Unless otherwise stated herein, the execution of these steps has no strict order limitation, and these steps can be executed in other order. Moreover, Figure 1 At least part of the steps in the above method can include a plurality of sub-steps or a plurality of stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least part of other steps or sub-steps or stages of other steps.

[0151] Any combination of the technical features in the above embodiments can be made, and for the sake of brevity, not all possible combinations are described above. However, any combination of the technical features is deemed to be within the scope of the present disclosure as long as such a combination does not result in an inconsistency.

[0152] The above embodiments only express several implementation manners of the present application, and the description is relatively specific and detailed, but should not be understood as a limitation on the present application. It should be pointed out that, for ordinary skilled persons in the art, several modifications and improvements can be made without departing from the concept of the present application, and therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present application, without departing from the technical solution of the present application, shall be deemed to be within the protection scope of the technical solution of the present application.

Claims

1. A method of monitoring the depth of municipal road water accumulation, characterized by, The method comprises the following steps: Obtain the ambient temperature, rainfall intensity and all echo signals at the current time; Analyze the time distribution of the significant changes in signal amplitude in all echo signals, calculate the time distribution degree at the current time, and combine the inconsistency of the wave peak occurrence time of different echo signals in the local range and the periodicity of the significant changes in echo signals to calculate the interference evaluation value at the current time; Based on the frequency component offset of all echo signals in the frequency domain, calculate the signal frequency shift degree at the current time, adjust the wavelet threshold of the wavelet denoising algorithm in combination with the interference evaluation value, and filter the echo signal; Extract the data of multiple samples from the historical environmental rainfall process, including the initial water level, water level change and ambient temperature and rainfall intensity at all times, form a historical data set, classify the samples in the historical data set based on the differences in ambient temperature and its temperature change rate between different samples, select the clustering clusters representing different environmental conditions, obtain the matching cluster and the standard cluster at the current time; based on the change of water level change with respect to initial water level and rainfall intensity in the matching cluster and the standard cluster, construct an error model, and calculate the compensation coefficient at the current time using the corresponding water level and rainfall intensity at the previous time at the current time and other time points in the local range, and compensate and correct the water depth calculated by the filtered echo signal.

2. The method of claim 1, wherein, The calculation of the time distribution degree at the current time comprises: Obtain the transmission time and reception time corresponding to each echo signal; Mark the time when the signal amplitude in each echo signal is greater than a preset threshold as a significant time, obtain the time interval between each significant time and its corresponding transmission time in each echo signal, and take the ratio of the time interval to the reception time corresponding to the first echo signal as the relative time sequence proportion of each significant time; Take the weighted average of the relative time sequence proportions of all significant times of all echo signals at the current time as the time distribution degree at the current time.

3. The method of claim 2, wherein the method further comprises: The calculation of the interference evaluation value at the current time comprises: Obtain the wave peak of the signal amplitude at all times in each echo signal, and mark the time corresponding to the wave peak as the wave peak time; Pre-set a time window; in each echo signal, take the transmission time as the starting point, and form a time set with all wave peak times in the time window; calculate the average of the difference between the time sets corresponding to any two echo signals at the current time as the random interference degree at the current time; Obtain the autocorrelation coefficients of multiple preset lag orders of all significant times of all echo signals, and select the maximum autocorrelation coefficient; Calculate the sum of the time distribution degree and the random interference degree, and take the ratio of the sum to the maximum autocorrelation coefficient as the interference evaluation value at the current time.

4. The method of claim 1, wherein the method comprises: The calculation of the signal frequency shift degree at the current time comprises: Splicing all echo signals at the current moment in time sequence into a continuous echo signal, performing frequency domain analysis on the continuous echo signal to obtain a spectrum graph; recording a frequency component with maximum energy in the spectrum graph as a fundamental frequency component; and performing normalization processing on differences between each frequency component and the fundamental frequency component in the spectrum graph, and recording the differences as relative differences; Normalizing energies of all frequency components in the spectrum graph, and taking a mean value of products of the normalized energies of all frequency components in the spectrum graph and the relative differences as a signal frequency shift degree at the current moment.

5. The method of claim 1, wherein the method comprises: The wavelet threshold after adjustment corresponding to the tth moment The calculation formula is: Wherein, The interference coefficient of the tth moment, which is the normalized result of the product of the interference evaluation value and the signal frequency shift degree, The wavelet threshold before adjustment at the tth moment.

6. The method of claim 1, wherein, The classifying the samples in the historical data set comprises: recording a mean value of the ambient temperatures at all moments for each sample as an average temperature; recording a mean value of variation rates of the ambient temperatures of two adjacent moments for each sample as an average variation rate; and grouping the average temperature and the average variation rate of each sample into a two-dimensional vector, and clustering the two-dimensional vectors of all samples in the historical data set.

7. The method of claim 6, wherein the method further comprises: The obtaining the matching cluster and the standard cluster at the current moment comprises: calculating a variation rate of the ambient temperature at the current moment and a moment before the current moment; and grouping the ambient temperature at the current moment and the variation rate into a two-dimensional vector; calculating a mean value of differences between the two-dimensional vector at the current moment and two-dimensional vectors of all samples in each clustering cluster; and selecting a clustering cluster corresponding to the minimum mean value as a matching cluster at the current moment; for all clustering clusters, selecting a clustering cluster with the minimum average variation rate corresponding to a cluster center and the closest average temperature to a standard atmospheric temperature as a standard cluster.

8. The municipal road water depth monitoring method of claim 1, wherein, The constructing the error model comprises: calculating a mean value of the rainfall intensities at all moments for each sample as an average rainfall intensity; and performing multivariate nonlinear fitting on all samples in each clustering cluster by taking the initial accumulated water level and the average rainfall intensity of each sample as independent variables and taking the water level variation as a dependent variable to obtain a multivariate fitting function of each clustering cluster. Error model is: where, represents a multivariate fitting function of the matching cluster, and are two independent variables, represents a multivariate fitting function of the standard cluster.

9. The municipal road water depth monitoring method of claim 1, wherein, The calculating the compensation coefficient at the current moment comprises: taking an actual accumulated water depth at a moment before each moment as an initial accumulated water level at the moment; taking a plurality of moments before the current moment as adjacent moments; and respectively inputting the initial accumulated water levels and the rainfall intensities at the current moment and the adjacent moments into the error model to take output values of the error model as relative errors; respectively grouping the initial accumulated water levels and the rainfall intensities at the current moment and the adjacent moments into two-dimensional arrays; calculating distances of the two-dimensional arrays at the current moment and the adjacent moments; performing negative mapping on the distances after normalization to obtain distribution weights of the adjacent moments; performing weighted summation on the relative errors of all adjacent moments of the current moment based on the distribution weights; and calculating a mean value of the relative error at the current moment and the weighted summation as a compensation coefficient at the current moment. 10.A municipal road ponding depth monitoring system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, The processor executes the computer program to implement the steps of the municipal road accumulated water depth monitoring method in any one of claims 1-9.

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