Method, system and equipment for detecting internal deposition of ultrasonic flowmeter

Abstract

The invention discloses an ultrasonic flowmeter internal deposition detection method, system and device, and relates to the technical field of ultrasonic detection, and the method comprises the steps: obtaining ultrasonic signals of a plurality of sound channels of an ultrasonic flowmeter in a river channel; analyzing the working state of each sound channel according to the change condition of the ultrasonic signal of each sound channel, identifying the condition of water flow blockage in the working state as an error state, and performing accumulative counting on the error state and all the working states in a specified time period; and comparing the error state count values in a specified time period with all working state count values to obtain error state proportions of all sound channels in a specified time range, comparing the error state proportions with a preset siltation early warning threshold value, and judging the siltation state of each sound channel according to a comparison result. According to the method, the siltation state of each region is represented through the strength of the signal generated by siltation, and the accuracy of siltation detection of each region of the river channel is improved.

Description

Technical Field

[0001] The present invention relates to the field of ultrasonic detection technology, and in particular to a method, system and equipment for detecting internal siltation of an ultrasonic flow meter. Background Art

[0002] As a non-contact flow measurement device, ultrasonic flowmeter is widely used in industrial production and fluid transportation due to its advantages of high precision, wide range ratio, low maintenance cost, etc. It mainly measures the flow velocity indirectly by transmitting and receiving ultrasonic pulses and using the change of the propagation speed of sound waves in the fluid, and then calculates the flow rate.

[0003] However, in practical applications, especially in long-term continuous operation in open fluid systems or fluid systems containing suspended particles, a major challenge faced by ultrasonic flow meters is the accumulation of sludge in the fluid.

[0004] The traditional response method relies on regular manual inspection and cleaning of the accumulated sediment in the ultrasonic flowmeter. The sediment in the ultrasonic flowmeter is often not discovered until the sedimentation has a significant impact, increasing maintenance costs and the risk of production interruption. Although some ultrasonic flowmeters on the market currently have built-in basic signal quality monitoring functions, these functions often focus on obtaining simple signal strength. Simple signals can only detect whether there is sedimentation, and it is difficult to clearly distinguish the signals generated by sedimentation, resulting in insufficient accuracy and sensitivity in sedimentation detection. Summary of the invention

[0005] The purpose of the present invention is to address the deficiencies of the above-mentioned prior art and to provide a method, system, device and storage medium for detecting internal sedimentation in an ultrasonic flowmeter, so as to solve the problem that simple signals in the prior art can only detect the presence or absence of sedimentation, but it is difficult to clearly distinguish interference signals, resulting in insufficient accuracy and sensitivity of sedimentation detection.

[0006] The present invention specifically provides the following technical solutions:

[0007] A method for detecting internal siltation of an ultrasonic flowmeter comprises the following steps:

[0008] Acquire ultrasonic signals of multiple sound channels of ultrasonic flowmeter in a river channel;

[0009] The working state of each channel is analyzed by the change of the ultrasonic signal of each channel, the water flow blockage in the working state is identified as an error state, and the error state and all working states in the specified time period are accumulated and counted;

[0010] By comparing the error state count value in the specified time period with all working state count values, the error state ratio of all channels in the specified time range is obtained, and the error state ratio is compared with the preset congestion warning threshold, and the congestion state of each channel is judged by the comparison result.

[0011] Preferably, the step of obtaining ultrasonic signals of multiple sound channels of an ultrasonic flow meter in a river channel includes:

[0012] An ultrasonic flow meter is constructed by using multiple pairs of ultrasonic transducers, and ultrasonic signals of multiple channels are obtained by using the ultrasonic flow meter; wherein each pair of ultrasonic transducers is one channel;

[0013] The dry state and dormant state in the ultrasonic signal are removed as abnormal values, and the remaining ultrasonic signal is used as the measurement data of the sound channel; the dry state means that there is no water in the river channel, and the dormant state means that the ultrasonic transducer is not working.

[0014] Preferably, before analyzing the working state of each channel through the change of the ultrasonic signal of each channel, the method further includes:

[0015] Conduct data analysis on existing historical ultrasonic signals and mark the ultrasonic signals caused by siltation;

[0016] A multi-layer perceptron structure is used to build an error state recognition model, and the labeled ultrasonic signal data is input into the error state recognition model for training to obtain a trained error state recognition model;

[0017] The ultrasonic signal of each channel is used as the input of the trained error state recognition model, and the working state of each channel is used as the output of the trained error state recognition model.

[0018] Preferably, the cumulative counting of the error status and all working statuses in the specified time period is specifically as follows:

[0019] Get the cumulative count value Err of the error status within the specified time period t sum , and obtain the total number of measurements N of all working states within the specified time period t num , where the total number of measurements is N num The specific expression is:

[0020] N num =t×3600×f;

[0021] Wherein, t is the specified time period; f is the measurement frequency of the ultrasonic flowmeter, in Hz; and the updating calculation method is adopted in the cumulative calculation process, and the error state value of the moment before the t period is subtracted for each new moment added.

[0022] Preferably, the error state proportion of all channels within the specified time range is obtained by comparing the error state count value in the specified time period with all working state count values, specifically:

[0023] For all channels, calculate the proportion of error states contained in the ultrasonic signal within the specified time period t; the specific expression is:

[0024] θ=Err sum / N num ;

[0025] Wherein, θ is the error state ratio in the specified time period t.

[0026] Preferably, the error state ratio is compared with a preset siltation warning threshold, and the siltation state of each channel is determined by the comparison result, including:

[0027] Determine the relationship between the error state ratio and the siltation warning threshold; wherein the siltation warning threshold includes a first threshold T1 and a second threshold T2, and the first threshold T1 is less than the second threshold T2;

[0028] For multiple channels, they are arranged from the bottom of the water to the surface of the water according to their installation positions, and the channels are recorded as C1, C2, ...C j , …, C n ;

[0029] Starting from C1, if the error state ratio of C1 is greater than the second threshold value T2, the sound channel is determined to be congested; if the error state ratio exceeds the first threshold value T1 but is less than the second threshold value T2, the sound channel is determined to be in a potential congestion state; if the error state ratio is less than the first threshold value T1, it is determined that the sound channel does not have congestion and is not congested as a whole;

[0030] For C j , if C j-1 There is no siltation, no C j and other channels to make a decision; if C j-1 There is siltation, and C j If the error state ratio is also greater than the second threshold T2, then C1 to C j As a sedimentation area; if C j-1 There is siltation, and C j If the error state ratio exceeds the first threshold T1 but is less than the second threshold T2, C j Considered as a potential siltation area; if C j-1 is a potential siltation state, and C j If the error state ratio exceeds the first threshold value T1 but is less than the second threshold value T2 or is greater than the second threshold value T2, then C j-1 and C jIt is determined as a potential siltation area; the value range of j is [2,n], and n is the number of installed ultrasonic transducers;

[0031] When the determination is completed, the siltation status, siltation area and potential siltation area are output, and when all channels exceed the first threshold value T1, an alarm is triggered and the equipment self-check is started.

[0032] Preferably, the method further comprises: for a case where there is only one independent sound channel, only determining the siltation and potential siltation, and not determining the siltation range.

[0033] The present invention provides an ultrasonic flowmeter internal siltation detection system, comprising:

[0034] A data collection module, used for acquiring ultrasonic signals of multiple sound channels of an ultrasonic flow meter in a river channel;

[0035] An error state recognition module is used to analyze the working state of each channel through the change of the ultrasonic signal of each channel, identify the water flow blockage in the working state as an error state, and accumulate and count the error states and all working states in a specified time period;

[0036] The siltation detection module is used to obtain the error state ratio of all channels within the specified time range by comparing the error state count value in the specified time period with all working state count values, and compare the error state ratio with the preset siltation warning threshold, and judge the siltation state of each channel through the comparison result.

[0037] The present invention provides a computer device, including a memory and a processor, wherein a program is stored in the memory, and when the program is executed by the processor, the processor executes the steps of the above-mentioned method for detecting internal siltation of an ultrasonic flow meter.

[0038] Compared with the prior art, the present invention has the following significant advantages:

[0039] The present invention adopts a multi-channel ultrasonic flowmeter to collect multi-channel ultrasonic signals, analyzes the working state of each channel through the multi-channel ultrasonic signals, so as to clearly determine whether there is a signal generated by siltation in each area of ​​the ultrasonic flowmeter, and uses the water flow blockage as an error state for identification and counting, determines the strength of the signal generated by siltation through the relationship between the real-time error state ratio and the siltation warning threshold, and characterizes the siltation state of each area through the strength of the signal generated by siltation, thereby improving the accuracy of siltation detection in the area covered by each channel of the ultrasonic flowmeter. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 The present invention is an overall flow chart of a method for detecting internal siltation in an ultrasonic flow meter. DETAILED DESCRIPTION

[0041] The following is a clear and complete description of the technical solutions of the embodiments of the present invention in conjunction with the drawings in the present invention. Obviously, the described embodiments are 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 should fall within the scope of protection of the present invention.

[0042] The purpose of the present invention is to provide a method, system and equipment for detecting internal siltation of an ultrasonic flow meter.

[0043] like Figure 1 As shown, the present invention proposes a method for detecting internal siltation of an ultrasonic flow meter, comprising the following steps:

[0044] Step S1: Acquire ultrasonic signals of multiple sound channels of an ultrasonic flow meter in a river channel.

[0045] Specifically:

[0046] According to the characteristics of the river section, multiple pairs of ultrasonic transducers are installed in different water depth layers, and an ultrasonic flow meter is constructed by using the multiple pairs of ultrasonic transducers. Ultrasonic signals of multiple channels are obtained by the ultrasonic flow meter. Among them, each pair of ultrasonic transducers is a channel, and each pair of ultrasonic transducers is divided into an upstream transducer and a downstream transducer. When the ultrasonic signal propagates from the upstream transducer to the downstream transducer, it is called forward propagation, and when the ultrasonic signal propagates from the downstream transducer to the upstream transducer, it is called reverse propagation.

[0047] The dry state and dormant state in the ultrasonic signal are removed as abnormal values, and the remaining ultrasonic signals are used as the measurement data of the sound channel; the dry state means that there is no water in the river channel, and the dormant state means that the ultrasonic transducer is not working. The measurement data of each sound channel includes water temperature, forward propagation signal amplitude, reverse propagation signal amplitude, forward propagation signal gain, reverse propagation signal gain, forward propagation time and reverse propagation time.

[0048] Step S2: Analyze the working state of each channel through the change of the ultrasonic signal of each channel, identify the water flow blockage in the working state as an error state, and accumulate and count the error state and all working states in a specified time period.

[0049] Before analyzing the working status of each channel through the change of the ultrasonic signal of each channel, it also includes:

[0050] Build an error state judgment network model and train the model.

[0051] Training data preparation: Data analysis was performed on the existing historical ultrasonic measurement data sets, and the data labels of erroneous measurements caused by siltation were marked as 1, and other cases were marked as 0. All attributes were normalized before entering the network. In order to make the training model accurate, when selecting historical data for training data annotation, try to cover various working conditions, including river sections of different widths, different water depths, different flow rates, different seasons, etc.

[0052] An error state recognition model is constructed using a multi-layer perceptron structure. The error state recognition model includes a forward propagation feature extraction submodule and a reverse propagation feature extraction submodule, as well as a feature fusion and prediction submodule.

[0053] The labeled ultrasonic signal data is input into the error state recognition model, and the water temperature, forward propagation signal amplitude, forward propagation signal gain and forward propagation time are input into the forward propagation feature extraction submodule, and the water temperature, reverse propagation signal amplitude, reverse propagation signal gain and reverse propagation time are input into the reverse propagation feature extraction submodule.

[0054] The output of the forward propagation feature extraction submodule and the output of the reverse propagation feature extraction submodule are spliced, and the splicing result is input into the feature fusion and prediction submodule for feature fusion and result prediction. The feature fusion and result prediction are processed in a loop, and the error state recognition model is optimized with the loop processing result to obtain the trained error state recognition model. The ultrasonic signal of each channel is used as the input of the trained error state recognition model, and the working state of each channel is used as the output of the trained error state recognition model.

[0055] Error state recognition model construction: Each ultrasonic flow meter measurement data input is a one-dimensional sequence S containing 7 attributes i =[S1 i ,S2 i ,S3 i ,S4 i ,S5 i ,S6 i ,S7 i ], and binary label data L i , i is the index of the labeled sample, the value range of i is [1, N], and N is the total number of labeled samples. i is water temperature, S2 i 、S3 i and S4 i are the forward propagation signal amplitude, forward propagation signal gain, and forward propagation time respectively; S5 i 、S6 i and S7 i are the reverse propagation signal amplitude, reverse propagation signal gain, and reverse propagation time respectively.

[0056] The input of the forward propagation feature extraction submodule is S1 i , S2 i ,S3 i ,S4 i , using a multi-layer perceptron structure, in the present invention, the input layer nodes are 4, the hidden layer nodes are 128, and the output layer nodes are 16, but it is not limited to this; the input of the back propagation feature extraction submodule is S1 i ,S5 i , S6 i ,S7 i , using the same multi-layer perceptron structure as the forward propagation feature extraction submodule; the feature fusion and prediction submodule splices the output of the forward propagation feature extraction submodule and the output of the reverse propagation feature extraction submodule as input, that is, the number of input nodes of the feature fusion and prediction submodule is 32, and the multi-layer perceptron structure is adopted. In the present invention, a two-layer hidden layer structure is adopted, the number of hidden layer nodes is 512 and 64 respectively, and the number of output nodes is 2. Except for the last layer of the feature fusion and prediction submodule, which uses Softmax as the activation function, the activation functions of other layers in the network use LeakyRelu.

[0057] Error state recognition model training: The above error state recognition model is trained using the training data, where the loss function adopts binary cross entropy loss, the training rounds are set to 200, and the batch size is set to 128; during the training process, AdamW is used as the optimizer.

[0058] Accumulate the error status and all working status in the specified time period, specifically:

[0059] The trained error state recognition model is used to determine the error state of the ultrasonic signal in each channel at each moment, 1 represents an error state and 0 represents a normal state.

[0060] Get the cumulative count value Err of the error status within the specified time period t sum , and obtain the total number of measurements N of all working states within the specified time period t num , where the total number of measurements is N num The specific expression is:

[0061] N num =t×3600×f;

[0062] Wherein, t is the specified time period; f is the measurement frequency of the ultrasonic flowmeter, in Hz; and the updating calculation method is adopted in the cumulative calculation process, and the error state value of the moment before the t period is subtracted for each new moment added.

[0063] The specified time period t is set by the user. In the present invention, T is set to 12, in hours. The measurement frequency of the ultrasonic flowmeter is recorded as f, in Hz, that is, how many times per second. f is determined by the operating frequency of the transducer and the user setting. The user can set f to a value that is not higher than the operating frequency of the transducer according to actual needs.

[0064] Step S3: By comparing the error state count value in the specified time period with all working state count values, the error state ratio of all channels in the specified time range is obtained, and the error state ratio is compared with the preset congestion warning threshold, and the congestion state of each channel is determined by the comparison result.

[0065] For all channels, calculate the proportion of error states contained in the channel signal in the specified time period t, the calculation formula is θ = Errsum / Tnum, and the value range of θ is [0,1]. Among them, θ is the proportion of error states in the specified time period t.

[0066] The error state ratio is compared with the preset congestion warning threshold, and the congestion state of each channel is determined based on the comparison results, including:

[0067] Determine the relationship between the error state ratio and the siltation warning threshold; wherein the siltation warning threshold includes a first threshold T1 and a second threshold T2, and the first threshold T1 is less than the second threshold T2. The first threshold T1 and the second threshold T2 can be set by human experience or obtained by a statistical model.

[0068] For multiple channels, they are arranged from the bottom of the water to the surface of the water according to their installation positions, and the channels are recorded as C1, C2, ...C j , …, C n .

[0069] Starting from C1, if the error state ratio of C1 is greater than the second threshold T2, the sound channel is judged to be congested; if the error state ratio exceeds the first threshold T1 but is less than the second threshold T2, the sound channel is judged to be in a potential congestion state; if the error state ratio is less than the first threshold T1, it is judged that there is no congestion in the sound channel and there is no congestion as a whole.

[0070] For C j , if C j-1 There is no siltation, no C j and other channels to make a decision; if C j-1 There is siltation, and C j If the error state ratio is also greater than the second threshold T2, then C1 to C j As a sedimentation area; if C j-1 There is siltation, and C j If the error state ratio exceeds the first threshold T1 but is less than the second threshold T2, C jConsidered as a potential siltation area; if C j-1 is a potential siltation state, and C j If the error state ratio exceeds the first threshold value T1 but is less than the second threshold value T2 or is greater than the second threshold value T2, then C j-1 and C j It is determined to be a potential siltation area; the value range of j is [2,n], and n is the number of installed ultrasonic transducers.

[0071] The judgment of other channels is similar. When the previous channel is not congested, no subsequent judgment is made. When the judgment is completed, the congestion status, congestion area and potential congestion area are output, and when all channels exceed the first threshold T1, an alarm is triggered and the device self-check is started.

[0072] In the case of only one independent sound channel, only the siltation and potential siltation are determined, and the siltation range is not determined.

[0073] For the sound channel that is dry or dormant during the T period, no congestion judgment and detection is performed.

[0074] Real-time channel status intelligent monitoring and trend prediction solution:

[0075] Taking full advantage of the fact that ultrasonic transducers are installed in layers according to water depth, it is possible to continuously track and analyze the real-time working status of all sound channels, including but not limited to water temperature, forward propagation signal amplitude, reverse propagation signal amplitude, forward propagation signal gain, reverse propagation signal gain, forward propagation time, reverse propagation time, etc. By introducing time series analysis and pattern recognition technology, trend features can be extracted from these dynamic data, and possible water flow obstruction (such as siltation accumulation) events can be predicted, providing early warning information for maintenance.

[0076] By integrating big data processing and machine learning technologies, the system simultaneously analyzes multiple physical parameters such as amplitude, frequency, and waveform, constructs a multi-dimensional feature space, and uses multi-parameter fusion technology to conduct deep learning on historical measurement data. It can automatically learn signal characteristics under different conditions, improve the accuracy and response speed of anomaly detection, and ensure the timeliness and reliability of the early warning system.

[0077] Complex algorithms are used to identify abnormal patterns in the space, greatly enhancing the ability to identify various complex situations and achieving 24 / 7 uninterrupted monitoring of all transducers and channels. Once an abnormality is detected, the system can immediately trigger an early warning mechanism, notify relevant personnel through SMS, email, etc., and provide a detailed abnormal analysis report to ensure a quick response.

[0078] Based on the above method, the present invention provides an ultrasonic flow meter internal siltation detection system, including: a data collection module, an error state recognition module, and a siltation detection module.

[0079] Among them, the data collection module is used to obtain ultrasonic signals of multiple channels of the ultrasonic flowmeter in the river channel; the error state identification module is used to analyze the working state of each channel through the changes in the ultrasonic signals of each channel, identify the water blockage in the working state as an error state, and accumulate the error state and all working states in the specified time period; the siltation detection module is used to obtain the error state ratio of all channels within the specified time range by comparing the error state count value of the specified time period with the count value of all working states, and compare the error state ratio with the preset siltation warning threshold, and judge the siltation state of each channel through the comparison result.

[0080] The present invention also provides a computer device, including a memory and a processor. The memory stores a program. When the program is executed by the processor, the processor executes the steps of a method for detecting internal siltation of an ultrasonic flow meter.

[0081] In accordance with the disclosed embodiments, a computing device may communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth communications, etc.), or with any device (e.g., routers, modems, etc.) that enables a computing device to communicate with one or more other computing devices.

[0082] The present invention also provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, a method for detecting internal siltation of an ultrasonic flow meter is implemented.

[0083] According to the disclosed embodiments, the storage medium may be a non-volatile computer-readable storage medium, such as but not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, the storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0084] The various embodiments of the present invention are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to in detail.

[0085] The present invention uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the method and core ideas of the present invention. At the same time, for those skilled in the art, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for detecting internal siltation of an ultrasonic flowmeter, characterized in that: include: Acquire ultrasonic signals of multiple sound channels of ultrasonic flowmeter in a river channel; The working state of each channel is analyzed by the change of the ultrasonic signal of each channel, the water flow blockage in the working state is identified as an error state, and the error state and all working states in the specified time period are accumulated and counted; By comparing the error state count value in the specified time period with all working state count values, the error state ratio of all channels in the specified time range is obtained, and the error state ratio is compared with the preset congestion warning threshold, and the congestion state of each channel is judged by the comparison result.

2. A method for detecting internal siltation of an ultrasonic flowmeter as claimed in claim 1, characterized in that: The method of obtaining ultrasonic signals of multiple sound channels of an ultrasonic flow meter in a river channel comprises: An ultrasonic flow meter is constructed by using multiple pairs of ultrasonic transducers, and ultrasonic signals of multiple channels are obtained by using the ultrasonic flow meter; wherein each pair of ultrasonic transducers is one channel; The dry state and dormant state in the ultrasonic signal are removed as abnormal values, and the remaining ultrasonic signal is used as the measurement data of the sound channel; the dry state means that there is no water in the river channel, and the dormant state means that the ultrasonic transducer is not working.

3. The method for detecting internal siltation of an ultrasonic flowmeter according to claim 1, characterized in that: Before analyzing the working state of each channel through the change of the ultrasonic signal of each channel, the method further includes: Conduct data analysis on existing historical ultrasonic signals and mark the ultrasonic signals caused by siltation; A multi-layer perceptron structure is used to build an error state recognition model, and the labeled ultrasonic signal data is input into the error state recognition model for training to obtain a trained error state recognition model; The ultrasonic signal of each channel is used as the input of the trained error state recognition model, and the working state of each channel is used as the output of the trained error state recognition model.

4. A method for detecting internal siltation of an ultrasonic flowmeter as claimed in claim 1, characterized in that: The cumulative counting of error states and all working states in a specified time period is specifically as follows: Get the cumulative count value Err of the error status within the specified time period t sum , and obtain the total number of measurements N of all working states within the specified time period t num , where the total number of measurements is N num The specific expression is: N num =t×3600×f; Wherein, t is the specified time period; f is the measurement frequency of the ultrasonic flowmeter, in Hz; and the updating calculation method is adopted in the cumulative calculation process, and the error state value of the moment before the t period is subtracted for each new moment added.

5. A method for detecting internal siltation of an ultrasonic flowmeter as claimed in claim 4, characterized in that: The error state proportion of all channels within the specified time range is obtained by comparing the error state count value in the specified time period with all working state count values, specifically: For all channels, calculate the proportion of error states contained in the ultrasonic signal within the specified time period t; the specific expression is: θ=Err sum / N num ; Wherein, θ is the error state ratio in the specified time period t.

6. A method for detecting internal siltation of an ultrasonic flowmeter as claimed in claim 2, characterized in that: The error state ratio is compared with a preset siltation warning threshold, and the siltation state of each channel is determined by the comparison result, including: Determine the relationship between the error state ratio and the siltation warning threshold; wherein the siltation warning threshold includes a first threshold T1 and a second threshold T2, and the first threshold T1 is less than the second threshold T2; For multiple channels, they are arranged from the bottom of the water to the surface of the water according to their installation positions, and the channels are recorded as C1, C2, ...C j , …, C n ; Starting from C1, if the error state ratio of C1 is greater than the second threshold value T2, the sound channel is determined to be congested; if the error state ratio exceeds the first threshold value T1 but is less than the second threshold value T2, the sound channel is determined to be in a potential congestion state; if the error state ratio is less than the first threshold value T1, it is determined that the sound channel does not have congestion and is not congested as a whole; For C j , if C j-1 There is no siltation, no C j and other channels to make a decision; if C j-1 There is siltation, and C j If the error state ratio is also greater than the second threshold T2, then C1 to C j As a sedimentation area; if C j-1 There is siltation, and C j If the error state ratio exceeds the first threshold T1 but is less than the second threshold T2, C j Considered as a potential siltation area; if C j-1 is a potential siltation state, and C j If the error state ratio exceeds the first threshold value T1 but is less than the second threshold value T2 or is greater than the second threshold value T2, then C j-1 and C j It is determined as a potential siltation area; the value range of j is [2,n], and n is the number of installed ultrasonic transducers; When the determination is completed, the siltation status, siltation area and potential siltation area are output, and when all channels exceed the first threshold value T1, an alarm is triggered and the equipment self-check is started.

7. A method for detecting internal siltation of an ultrasonic flowmeter as claimed in claim 6, characterized in that: The method also includes: for a situation where there is only one independent sound channel, only the siltation and potential siltation determination is performed, and no determination of the siltation range is performed.

8. An ultrasonic flow meter internal siltation detection system, characterized in that: include: A data collection module, used for acquiring ultrasonic signals of multiple sound channels of an ultrasonic flow meter in a river channel; An error state recognition module is used to analyze the working state of each channel through the change of the ultrasonic signal of each channel, identify the water flow blockage in the working state as an error state, and accumulate and count the error states and all working states in a specified time period; The siltation detection module is used to obtain the error state ratio of all channels within the specified time range by comparing the error state count value in the specified time period with all working state count values, and compare the error state ratio with the preset siltation warning threshold, and judge the siltation state of each channel through the comparison result.

9. A computer device, characterized in that: It comprises a memory and a processor, wherein a program is stored in the memory, and when the program is executed by the processor, the processor executes the steps of a method for detecting internal siltation of an ultrasonic flow meter as claimed in any one of claims 1 to 7.

Images