A flow box sludge thickness detection method and system

Abstract

The present invention relates to the technical field of ultrasonic signal denoising, and proposes a flow box sludge thickness detection method and system, including: collecting the ultrasonic signal of the flow box and decomposing it into ultrasonic component signals; marking the target ultrasonic component signal, and determining the abnormal fluctuation degree of the target ultrasonic component signal according to the difference between all sampling values ​​in the target ultrasonic component signal and the change trend of the difference between adjacent sampling values; combining the difference between the sampling value of the target ultrasonic component signal and the change characteristics of the target ultrasonic component signal, and the difference between the target ultrasonic component signal and the ultrasonic signal, determining the clutter recognition degree of the target ultrasonic component signal; performing weighted summation on all ultrasonic component signals according to all clutter recognition degrees, obtaining a true ultrasonic echo signal, and determining the sludge thickness of the flow box. The present invention avoids the interference of clutter signals on ultrasonic signals and improves the accuracy of sludge thickness measurement.

Description

Technical Field

[0001] The invention relates to the technical field of ultrasonic signal denoising, and in particular to a flow box sludge thickness detection method and system. Background Art

[0002] The flow meter box is a hydraulic measuring device used in water conservancy and environmental protection projects to detect water flow, flow velocity and other hydrological parameters. During the use of the flow meter box, it is necessary to detect the sludge accumulation in the fluid box to prevent the accumulated sludge in the fluid box from reducing the effective volume of the flow meter box, maintain the flow field characteristics of the flow meter box, and ensure the accuracy of the water body measurement results.

[0003] Generally, ultrasonic echo detection is used to receive echo data from the surface of the accumulated silt in the flow box to estimate the silt thickness. However, during the propagation process, ultrasonic waves may be scattered by suspended matter in the water and interfered by sediments, resulting in inaccurate signal reflection and containing a large amount of clutter signals, which affects the accuracy of silt thickness measurement. Summary of the invention

[0004] The present invention provides a flow box sludge thickness detection method and system to solve the problem that ultrasonic signals are interfered by clutter signals, resulting in insufficient accuracy in sludge thickness measurement. The technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present invention provides a method for detecting sludge thickness in a flow box, the method comprising the following steps:

[0006] Collecting the ultrasonic signal of the flow measuring box, decomposing the ultrasonic signal into a first preset number of ultrasonic component signals, the ultrasonic component signal including a preset number of sampling points;

[0007] Record any ultrasonic component signal as a target ultrasonic component signal, determine the energy mean difference of the target ultrasonic component signal according to the difference between the sampling values ​​of all sampling points in the target ultrasonic component signal, and determine the abnormal fluctuation degree of the target ultrasonic component signal in combination with the change trend of the difference between the sampling values ​​of adjacent sampling points in the target ultrasonic component signal;

[0008] Determine the amplitude difference of the target ultrasonic component signal according to the difference between the sampling value of the sampling point of the target ultrasonic component signal and the change characteristic of the target ultrasonic component signal, determine the amplitude change trend difference of the target ultrasonic component signal according to the difference between the target ultrasonic component signal and the collected ultrasonic signal, and determine the clutter recognition degree of the target ultrasonic component signal according to the abnormal fluctuation degree, amplitude difference and amplitude change trend difference of the target ultrasonic component signal;

[0009] According to the clutter recognition degree of all ultrasonic component signals, the ultrasonic component signals corresponding to the clutter recognition degree are weighted and summed to obtain the real ultrasonic echo signal and determine the sludge thickness of the flow box.

[0010] Further, the method for determining the energy mean difference of the target ultrasonic component signal is:

[0011] The square of the sampling value of any sampling point in the target ultrasonic component signal is recorded as the energy of the any sampling point;

[0012] Divide the energy of all sampling points in the target ultrasonic component signal into high-frequency energy and low-frequency energy;

[0013] The energy mean difference of the target ultrasonic component signal is determined according to the high-frequency energy and the low-frequency energy of all sampling points in the target ultrasonic component signal.

[0014] Further, the energy mean difference of the target ultrasonic component signal is determined according to the high-frequency energy and low-frequency energy of all sampling points in the target ultrasonic component signal, and the specific method includes:

[0015] The difference between the mean value of high-frequency energy and the mean value of low-frequency energy of all sampling points in the target ultrasonic component signal is recorded as the energy mean difference of the target ultrasonic component signal.

[0016] Further, the abnormal fluctuation degree of the target ultrasonic component signal is determined by combining the variation trend of the difference between the sampling values ​​of adjacent sampling points in the target ultrasonic component signal, and the specific method includes:

[0017] Performing a first-order difference on the target ultrasonic component signal to obtain the first-order difference signal of the target ultrasonic component, arranging all the values ​​contained in the first-order difference signal of the target ultrasonic component in ascending order to obtain a fluctuation sequence of the target ultrasonic component signal;

[0018] Perform straight line fitting on all values ​​contained in the fluctuation sequence, obtain the slope of the fitting straight line, and record the slope of the fitting straight line as the fluctuation degree of the target ultrasonic component signal;

[0019] The abnormal fluctuation degree of the target ultrasonic component signal is determined according to the energy mean difference and fluctuation degree of the target ultrasonic component signal.

[0020] Further, the abnormal fluctuation degree of the target ultrasonic component signal is determined according to the energy mean difference and fluctuation degree of the target ultrasonic component signal, and the specific method includes:

[0021] The product of the energy mean difference and the fluctuation of the target ultrasonic component signal is recorded as the first product of the target ultrasonic component signal, and the ratio of the number 1 to the first product of the target ultrasonic component signal is recorded as the abnormal fluctuation of the target ultrasonic component signal.

[0022] Further, the amplitude difference of the target ultrasonic component signal is determined by:

[0023] Performing curve fitting on the target ultrasonic component signal to obtain a fitting curve of the target ultrasonic component signal, and determining a fitting value of each sampling point of the target ultrasonic component signal according to the fitting curve of the target ultrasonic component signal;

[0024] The cumulative sum of the absolute values ​​of the differences between the fitting values ​​and the sampling values ​​of all sampling points of the target ultrasonic component signal is recorded as the amplitude difference of the target ultrasonic component signal.

[0025] Further, the method for determining the difference in amplitude variation trend of the target ultrasonic component signal is:

[0026] The DTW distance between the target ultrasonic component signal and the collected ultrasonic signal is recorded as the difference in amplitude change trend of the target ultrasonic component signal.

[0027] Further, the clutter recognition degree of the target ultrasonic component signal is determined according to the abnormal fluctuation degree, amplitude difference and amplitude change trend difference of the target ultrasonic component signal, including the specific method of:

[0028] The product of the abnormal fluctuation degree and the amplitude difference of the target ultrasonic component signal is recorded as the second product of the target ultrasonic component signal, and the ratio of the second product of the target ultrasonic component signal to the amplitude change trend difference is recorded as the clutter recognition degree of the target ultrasonic component signal.

[0029] Further, the method of performing weighted summation on the ultrasonic component signals corresponding to the clutter recognition degrees according to the clutter recognition degrees of all ultrasonic component signals, obtaining the real ultrasonic echo signal, and determining the sludge thickness of the flow box includes the following specific methods:

[0030] The linear normalized value of the ratio of the number 1 to the clutter recognition degree of the target ultrasonic component signal is recorded as the correction coefficient of the target ultrasonic component signal, and the product of the correction coefficient of the target ultrasonic component signal and each sampling value in the target ultrasonic component signal is arranged in sequence to obtain the corrected ultrasonic component signal corresponding to the target ultrasonic component signal; the corrected ultrasonic component signals corresponding to all ultrasonic component signals are combined by direct addition to obtain a true ultrasonic echo signal;

[0031] The envelope peak detection method is used to process the real ultrasonic echo signal to determine the sludge thickness in the flow box.

[0032] In a second aspect, an embodiment of the present invention further provides a flow box sludge thickness detection system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above methods when executing the computer program.

[0033] The beneficial effects of the present invention are:

[0034] According to the characteristics that the ultrasonic signal after the clutter signal is removed has a relatively concentrated distribution in the time domain, while the clutter signal is relatively dispersed in the time domain and has a long duration, the present application evaluates the degree of energy dispersion presented by the target ultrasonic component signal and the significance of sudden changes in sampling values ​​at different sampling points, and obtains the abnormal fluctuation degree of the target ultrasonic component signal; according to the characteristics that the ultrasonic signal after the clutter signal is removed has a specific amplitude variation characteristic, and the amplitude variation characteristic of the clutter signal is irregular, the amplitude difference of the target ultrasonic component signal is determined; according to the characteristics that there is a high similarity between the variation trends of the ultrasonic signal and the clutter signal, and a large difference between the variation trends of the ultrasonic signal and the ultrasonic signal after the clutter signal is removed, the present application determines the abnormal fluctuation degree of the target ultrasonic component signal; The difference in amplitude change trend of the target ultrasonic component signal is determined, and then the clutter recognition degree of the target ultrasonic component signal is determined. The greater the clutter recognition degree of the target ultrasonic component signal, the smaller the difference between the change trend of the target ultrasonic component signal and the collected ultrasonic signal, the more obvious the irregular amplitude change characteristics of the target ultrasonic component signal, the greater the possibility that the target ultrasonic component signal corresponds to the clutter signal, and the more serious the interference by the clutter signal; finally, according to the clutter recognition degrees of all ultrasonic component signals, the ultrasonic component signals corresponding to the clutter recognition degrees are weighted and summed to obtain the real ultrasonic echo signal, and the sludge thickness of the flow box is determined, so as to solve the problem that the ultrasonic signal is interfered by the clutter signal, resulting in insufficient accuracy of sludge thickness measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0036] Figure 1 A schematic flow chart of a flow box sludge thickness detection method provided by an embodiment of the present invention;

[0037] Figure 2 A flowchart for obtaining abnormal fluctuations provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] See also Figure 1 , which shows a flow chart of a method for detecting sludge thickness in a flow box provided by an embodiment of the present invention, the method comprising the following steps:

[0040] Step S001, collecting the ultrasonic signal of the flow measuring box, and decomposing the ultrasonic signal into a first preset number of ultrasonic component signals, wherein the ultrasonic component signal includes a preset number of sampling points.

[0041] An ultrasonic sensor is set in the flow measuring box, and the ultrasonic sensor can transmit and receive ultrasonic signals. The ultrasonic sensor is used to collect ultrasonic signals.

[0042] Preferably, in one embodiment of the present application, when collecting ultrasonic signals, the sampling frequency of the sampling points of the ultrasonic signals in this embodiment is 50KHz, and the sampling period is 60min. In actual application, as other implementation methods, the implementer can determine the sampling frequency and sampling period of the sampling points of the ultrasonic signals according to actual conditions, and this application does not impose any special restrictions.

[0043] The Wiener filter algorithm is used to preprocess the ultrasonic signal to remove the noise generated by the environment and the instrument.

[0044] Suspended matter and bubbles in water are scatterers of ultrasonic waves. Therefore, when ultrasonic waves propagate in water, they will be affected by the interaction between sound waves and suspended matter, causing part of the ultrasonic energy to be scattered in all directions to form scattered waves. The ultrasonic waves reflected back by the accumulated sludge in the flow meter box will be superimposed on the scattered waves, causing the collected ultrasonic signals to contain a large number of clutter signals. At the same time, suspended matter and bubbles in water will absorb part of the ultrasonic energy, accelerate the attenuation of ultrasonic energy, and further reduce the ultrasonic energy reflected back by the sludge, causing the collected ultrasonic signals to be submerged by clutter signals. Therefore, it is necessary to remove the clutter signals of the collected ultrasonic signals.

[0045] The ultrasonic signal is decomposed by using an empirical wavelet transform algorithm, and a segmentation boundary is determined by using an adaptive segmentation method to obtain a first preset number of ultrasonic component signals.

[0046] In this embodiment, the value of the first preset number is 7; the empirical wavelet transform algorithm is a well-known algorithm and will not be described in detail.

[0047] At this point, a first preset number of ultrasonic component signals are acquired.

[0048] Step S002, record any ultrasonic component signal as the target ultrasonic component signal, determine the energy mean difference of the target ultrasonic component signal based on the difference between the sampling values ​​of all sampling points in the target ultrasonic component signal, and determine the abnormal fluctuation degree of the target ultrasonic component signal in combination with the change trend of the difference between the sampling values ​​of adjacent sampling points in the target ultrasonic component signal.

[0049] The ultrasonic signal after removing the clutter signal is generated by the reflection of the silt. Since the shape and position of the silt are relatively fixed, the distribution of the ultrasonic signal in the time domain after removing the clutter signal is relatively concentrated, that is, the energy distribution of the ultrasonic signal after removing the clutter signal is concentrated. The clutter signal is generated by the reflection of suspended matter and bubbles in the water body. The position of suspended matter and bubbles in the water body is not fixed, so the distribution of the clutter signal in the time domain is relatively dispersed and the duration is longer, that is, the energy distribution of the clutter signal is dispersed.

[0050] An arbitrary ultrasonic component signal is recorded as a target ultrasonic component signal.

[0051] The square of the sampling value of any sampling point in the target ultrasonic component signal is recorded as the energy of the any sampling point. The energy of all sampling points in the target ultrasonic component signal is divided using the maximum inter-class variance method to obtain a division threshold, and the energy greater than or equal to the division threshold is recorded as high-frequency energy, and the energy less than the division threshold is recorded as low-frequency energy. The difference between the mean of the high-frequency energy and the mean of the low-frequency energy of all sampling points in the target ultrasonic component signal is recorded as the energy mean difference of the target ultrasonic component signal.

[0052] Using the maximum inter-class variance method to divide high-frequency energy and low-frequency energy is a well-known technology and will not be described in detail. In actual application, on the basis of achieving the purpose of dividing high-frequency energy and low-frequency energy, the implementer can use other existing methods such as K-means, regional growing method, etc. to divide high-frequency energy and low-frequency energy according to the energy of the sampling point, and this application does not make special restrictions.

[0053] When the difference between the sampling values ​​of different sampling points in the target ultrasonic component signal is smaller, the significance of the dispersed clutter signal presented by the target ultrasonic component signal is greater, and the possibility that the target ultrasonic component signal corresponds to the clutter signal is greater. At this time, the energy mean difference of the target ultrasonic component signal is smaller.

[0054] Perform a first-order difference on the target ultrasonic component signal to obtain the first-order difference signal of the target ultrasonic component, arrange all the values ​​contained in the first-order difference signal of the target ultrasonic component in ascending order, obtain the fluctuation sequence of the target ultrasonic component signal, use the least squares method to perform a straight line fit on all the values ​​contained in the fluctuation sequence, obtain the slope of the fitted straight line, and record the slope of the fitted straight line as the fluctuation degree of the target ultrasonic component signal.

[0055] Among them, using the least square method to fit the line is a well-known technology and will not be described in detail. In the actual application process, as other implementation methods, on the basis of achieving the purpose of fitting the line, the implementer can use other existing methods such as polynomial fitting to fit the line, and this application does not make special restrictions.

[0056] The fluctuation of the target ultrasonic component signal is used to evaluate the significance of sudden changes in the sampling values ​​of different sampling points contained in the target ultrasonic component signal. When the amplitude of the sudden change in the sampling values ​​contained in the target ultrasonic component signal is larger, the possibility that the target ultrasonic component signal is an ultrasonic signal after the clutter signal is removed is greater. At this time, the fluctuation of the target ultrasonic component signal is greater.

[0057] The abnormal fluctuation degree of the target ultrasonic component signal is determined according to the energy mean difference and fluctuation degree of the target ultrasonic component signal.

[0058] Preferably, as an embodiment of the present application, the product of the energy mean difference and the fluctuation of the target ultrasonic component signal is recorded as the first product of the target ultrasonic component signal, and the ratio of the number 1 to the first product of the target ultrasonic component signal is recorded as the abnormal fluctuation of the target ultrasonic component signal.

[0059] In the process of calculating the ratio, in order to avoid the situation where the denominator is zero, a preset value needs to be added to the denominator, and an embodiment of the preset value is 0.01.

[0060] The smaller the energy mean difference and fluctuation of the target ultrasonic component signal, the greater the abnormal fluctuation of the target ultrasonic component signal, the more obvious the more dispersed distribution of the clutter signal in the time domain, and the greater the possibility that the target ultrasonic component signal is a clutter signal.

[0061] At this point, the abnormal fluctuation degree of the target ultrasonic component signal is obtained. The abnormal fluctuation degree acquisition flow chart is as follows: Figure 2 shown.

[0062] Step S003, determining the amplitude difference of the target ultrasonic component signal based on the difference between the sampling value of the sampling point of the target ultrasonic component signal and the change characteristic of the target ultrasonic component signal, determining the amplitude change trend difference of the target ultrasonic component signal based on the difference between the target ultrasonic component signal and the collected ultrasonic signal, and determining the clutter recognition degree of the target ultrasonic component signal based on the abnormal fluctuation degree, amplitude difference and amplitude change trend difference of the target ultrasonic component signal.

[0063] The ultrasonic signal after removing the clutter signal has a specific amplitude variation characteristic. The difference between the sampling value of the ultrasonic signal and the fitting value of the amplitude variation characteristic is small. However, the distribution of suspended matter and bubbles in the water body is uneven and the position is not fixed, so the amplitude variation characteristic of the clutter signal is irregular, and the difference between the sampling value of the ultrasonic signal and the fitting value of the amplitude variation characteristic is large.

[0064] The target ultrasonic component signal is subjected to curve fitting using the least squares method to obtain a fitting curve of the target ultrasonic component signal. The fitting value of each sampling point of the target ultrasonic component signal is determined based on the fitting curve of the target ultrasonic component signal. The sum of the absolute values ​​of the differences between the fitting values ​​and the sampling values ​​of all sampling points of the target ultrasonic component signal is recorded as the amplitude difference of the target ultrasonic component signal.

[0065] Among them, using the least square method to perform curve fitting is a well-known technology and will not be described in detail. In the actual application process, as other implementation methods, on the basis of achieving the purpose of curve fitting, the implementer can use other existing methods such as polynomial fitting to fit the curve, and this application does not make special restrictions.

[0066] When the difference between the fitting value and the sampling value of the sampling point of the target ultrasonic component signal is greater, the amplitude difference of the target ultrasonic component signal is greater, the irregular amplitude change characteristics of the target ultrasonic component signal are more obvious, and the possibility that the target ultrasonic component signal corresponds to a clutter signal is greater.

[0067] Since the collected ultrasonic signal is interfered by the clutter signal, there is a high similarity between the change trends of the collected ultrasonic signal and the clutter signal. On the contrary, the ultrasonic signal after the clutter signal is removed has a specific amplitude change characteristic, so there is a large difference between the change trends of the collected ultrasonic signal and the ultrasonic signal after the clutter signal is removed.

[0068] The DTW distance between the target ultrasonic component signal and the collected ultrasonic signal is recorded as the difference in amplitude change trend of the target ultrasonic component signal.

[0069] The calculation of the DTW distance between the target ultrasonic component signal and the collected ultrasonic signal is a well-known technique and will not be described in detail.

[0070] When the difference between the change trends of the target ultrasonic component signal and the collected ultrasonic signal is large, the DTW distance between the target ultrasonic component signal and the collected ultrasonic signal is larger, and at this time, the possibility that the target ultrasonic component signal corresponds to the ultrasonic signal after the clutter signal is removed is greater.

[0071] The clutter recognition degree of the target ultrasonic component signal is determined according to the abnormal fluctuation degree, amplitude difference and amplitude change trend difference of the target ultrasonic component signal.

[0072] Preferably, as an embodiment of the present application, the product of the abnormal fluctuation degree and the amplitude difference of the target ultrasonic component signal is recorded as the second product of the target ultrasonic component signal, and the ratio of the second product of the target ultrasonic component signal to the amplitude change trend difference is recorded as the clutter recognition degree of the target ultrasonic component signal.

[0073] The greater the abnormal fluctuation and amplitude difference of the target ultrasonic component signal, and the smaller the difference in the amplitude change trend of the target ultrasonic component signal, the greater the clutter recognition degree of the target ultrasonic component signal. At this time, the difference between the change trend of the target ultrasonic component signal and the collected ultrasonic signal is smaller, the more obvious the irregular amplitude change characteristics of the target ultrasonic component signal are, the greater the possibility that the target ultrasonic component signal corresponds to the clutter signal, and the more serious the interference from the clutter signal is.

[0074] At this point, the clutter recognition degree of the target ultrasonic component signal is obtained.

[0075] The clutter recognition degree of any ultrasonic component signal can be obtained in the same way, that is, for each ultrasonic component signal, there is a corresponding clutter recognition degree.

[0076] Step S004: weighted summing is performed on the ultrasonic component signals corresponding to the clutter recognition degrees according to the clutter recognition degrees of all ultrasonic component signals, so as to obtain a true ultrasonic echo signal and determine the sludge thickness of the flow box.

[0077] The linear normalized value of the ratio of the number 1 to the clutter recognition degree of the target ultrasonic component signal is recorded as the correction coefficient of the target ultrasonic component signal, and the product of the correction coefficient of the target ultrasonic component signal and each sampling value in the target ultrasonic component signal is arranged in sequence to obtain the corrected ultrasonic component signal corresponding to the target ultrasonic component signal.

[0078] The corrected ultrasonic component signal corresponding to any ultrasonic component signal can be obtained in the same way, that is, for each ultrasonic component signal, there is a corresponding corrected ultrasonic component signal.

[0079] In the process of calculating the correction coefficient of the target ultrasonic component signal, it is necessary to calculate a normalized value for the ratio of the number 1 to the clutter recognition degree of the target ultrasonic component signal. The role of normalization is to ensure that the correction coefficient of the target ultrasonic component signal is more evenly distributed within the range of greater than or equal to 0 and less than or equal to 1. It should be noted that this embodiment uses the Z-Score standard normalization method to calculate the normalized value. In actual application, the implementer may use other methods of the prior art such as the maximum and minimum value normalization method, the sigmoid function, etc. to calculate the normalized value, which is not limited here.

[0080] The corrected ultrasonic component signals corresponding to all ultrasonic component signals are combined by direct addition to obtain a true ultrasonic echo signal.

[0081] The real ultrasonic echo signal suppresses the interference of clutter signals to a great extent and can reflect the real echo signal of the silt interface.

[0082] The envelope peak detection method is used to process the real ultrasonic echo signal to determine the sludge thickness in the flow box.

[0083] Among them, using the envelope peak detection method to process the real ultrasonic echo signal is a well-known technology and will not be described in detail.

[0084] At this point, the sludge thickness detection of the flow box is realized.

[0085] Based on the same inventive concept as the above method, an embodiment of the present invention also provides a flow box sludge thickness detection system, including a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, it implements the steps of any one of the above-mentioned flow box sludge thickness detection methods.

[0086] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A flow box sludge thickness detection method, characterized in that: The method comprises the following steps: Collecting the ultrasonic signal of the flow measuring box, decomposing the ultrasonic signal into a first preset number of ultrasonic component signals, the ultrasonic component signal including a preset number of sampling points; Record any ultrasonic component signal as a target ultrasonic component signal, determine the energy mean difference of the target ultrasonic component signal according to the difference between the sampling values ​​of all sampling points in the target ultrasonic component signal, and determine the abnormal fluctuation degree of the target ultrasonic component signal in combination with the change trend of the difference between the sampling values ​​of adjacent sampling points in the target ultrasonic component signal; According to the difference between the sampling value of the sampling point of the target ultrasonic component signal and the change characteristic of the target ultrasonic component signal, the amplitude difference of the target ultrasonic component signal is determined; according to the difference between the target ultrasonic component signal and the collected ultrasonic signal, the amplitude change trend difference of the target ultrasonic component signal is determined; the product of the abnormal fluctuation degree of the target ultrasonic component signal and the amplitude difference is recorded as the second product of the target ultrasonic component signal; the ratio of the second product of the target ultrasonic component signal to the amplitude change trend difference is recorded as the clutter recognition degree of the target ultrasonic component signal; According to the clutter recognition degree of all ultrasonic component signals, the ultrasonic component signals corresponding to the clutter recognition degree are weighted and summed to obtain the real ultrasonic echo signal and determine the sludge thickness of the flow box.

2. A flow box sludge thickness detection method according to claim 1, characterized in that: The method for determining the energy mean difference of the target ultrasonic component signal is: The square of the sampling value of any sampling point in the target ultrasonic component signal is recorded as the energy of the any sampling point; Divide the energy of all sampling points in the target ultrasonic component signal into high-frequency energy and low-frequency energy; The energy mean difference of the target ultrasonic component signal is determined according to the high-frequency energy and the low-frequency energy of all sampling points in the target ultrasonic component signal.

3. A flow box sludge thickness detection method according to claim 2, characterized in that: The specific method of determining the energy mean difference of the target ultrasonic component signal according to the high-frequency energy and low-frequency energy of all sampling points in the target ultrasonic component signal includes: The difference between the mean value of high-frequency energy and the mean value of low-frequency energy of all sampling points in the target ultrasonic component signal is recorded as the energy mean difference of the target ultrasonic component signal.

4. A flow box sludge thickness detection method according to claim 1, characterized in that: The specific method of determining the abnormal fluctuation degree of the target ultrasonic component signal by combining the variation trend of the difference between the sampling values ​​of adjacent sampling points in the target ultrasonic component signal includes: Performing a first-order difference on the target ultrasonic component signal to obtain the first-order difference signal of the target ultrasonic component, arranging all the values ​​contained in the first-order difference signal of the target ultrasonic component in ascending order to obtain a fluctuation sequence of the target ultrasonic component signal; Perform straight line fitting on all values ​​contained in the fluctuation sequence, obtain the slope of the fitting straight line, and record the slope of the fitting straight line as the fluctuation degree of the target ultrasonic component signal; The abnormal fluctuation degree of the target ultrasonic component signal is determined according to the energy mean difference and fluctuation degree of the target ultrasonic component signal.

5. A flow box sludge thickness detection method according to claim 4, characterized in that: The specific method of determining the abnormal fluctuation degree of the target ultrasonic component signal according to the energy mean difference and fluctuation degree of the target ultrasonic component signal includes: The product of the energy mean difference and the fluctuation of the target ultrasonic component signal is recorded as the first product of the target ultrasonic component signal, and the ratio of the number 1 to the first product of the target ultrasonic component signal is recorded as the abnormal fluctuation of the target ultrasonic component signal.

6. A flow box sludge thickness detection method according to claim 1, characterized in that: The method for determining the amplitude difference of the target ultrasonic component signal is: Performing curve fitting on the target ultrasonic component signal to obtain a fitting curve of the target ultrasonic component signal, and determining a fitting value of each sampling point of the target ultrasonic component signal according to the fitting curve of the target ultrasonic component signal; The cumulative sum of the absolute values ​​of the differences between the fitting values ​​and the sampling values ​​of all sampling points of the target ultrasonic component signal is recorded as the amplitude difference of the target ultrasonic component signal.

7. A flow box sludge thickness detection method according to claim 1, characterized in that: The method for determining the difference in amplitude variation trend of the target ultrasonic component signal is as follows: The DTW distance between the target ultrasonic component signal and the collected ultrasonic signal is recorded as the difference in amplitude change trend of the target ultrasonic component signal.

8. A flow box sludge thickness detection method according to claim 1, characterized in that: The method of performing weighted summation on the ultrasonic component signals corresponding to the clutter recognition degrees according to the clutter recognition degrees of all ultrasonic component signals, obtaining the real ultrasonic echo signal, and determining the sludge thickness of the flow box includes the following specific methods: The linear normalized value of the ratio of the number 1 to the clutter recognition degree of the target ultrasonic component signal is recorded as the correction coefficient of the target ultrasonic component signal, and the product of the correction coefficient of the target ultrasonic component signal and each sampling value in the target ultrasonic component signal is arranged in sequence to obtain the corrected ultrasonic component signal corresponding to the target ultrasonic component signal; the corrected ultrasonic component signals corresponding to all ultrasonic component signals are combined by direct addition to obtain a true ultrasonic echo signal; The envelope peak detection method is used to process the real ultrasonic echo signal to determine the sludge thickness in the flow box.

9. A flow box silt thickness detection 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, the steps of the method according to any one of claims 1 to 8 are implemented.

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