Vibrating wire piezometer data processing method and system for reservoir dam

Abstract

The present application provides a vibrating wire piezometer data processing method and system for a reservoir dam. The method respectively obtains the vibrating wire frequency signal and the temperature signal of the area to be monitored of the reservoir dam, determines the temperature feature vector according to the temperature signal, performs multi-resolution spatial decomposition on the vibrating wire frequency signal according to the temperature feature vector to generate the corresponding vibrating wire dynamic characteristic values in each resolution space, generates a vibrating wire dynamic vector, and then determines the current state of the area to be monitored according to the vibrating wire dynamic vector, thereby comprehensively considering the combination of the vibrating wire frequency signal and the temperature signal, and realizing a more accurate assessment of the safety state of the area to be monitored of the reservoir dam by using multi-resolution spatial decomposition and feature extraction.

Description

Technical Field

[0001] The present application relates to data processing technologies, and in particular, to a data processing method and system for vibrating wire piezometers for reservoir dams. Background Art

[0002] In the field of safety monitoring of reservoir dams, accurately and timely obtaining the internal state information of the dam is crucial for ensuring the safe operation of the dam. Traditionally, the monitoring of reservoir dams mainly relies on various sensors and devices, among which vibrating wire piezometers are a commonly used monitoring instrument. A vibrating wire piezometer can sense the change in the seepage pressure inside the dam and convert it into the fluctuation of the vibrating wire frequency. By monitoring these frequency fluctuations, the seepage pressure state inside the dam can be evaluated.

[0003] However, in the actual monitoring process, the change in the vibrating wire frequency signal is not only affected by the seepage pressure, but also interfered by various environmental factors, among which temperature is an important influencing factor. The change in temperature will cause the physical properties of the vibrating wire in the vibrating wire piezometer to change, thereby affecting the stability of the vibrating wire frequency, making it difficult for the simple vibrating wire frequency signal to accurately reflect the true seepage pressure state inside the dam. Summary of the Invention

[0004] The present application provides a data processing method and system for vibrating wire piezometers for reservoir dams, which comprehensively considers the combination of the vibrating wire frequency signal and the temperature signal, and uses multi-resolution spatial decomposition and feature extraction to realize the evaluation of the safety state of the area to be monitored in the reservoir dam.

[0005] In a first aspect, the present application provides a data processing method for vibrating wire piezometers for reservoir dams, including:

[0006] Obtaining the vibrating wire frequency signal and the temperature signal of the area to be monitored in the reservoir dam respectively, where the vibrating wire frequency signal is the data obtained by the vibrating wire piezometer;

[0007] Determining a temperature feature vector according to the temperature signal, and performing multi-resolution spatial decomposition on the vibrating wire frequency signal according to the temperature feature vector to generate corresponding vibrating wire dynamic eigenvalues in each resolution space, so as to generate a vibrating wire dynamic vector, where the temperature feature vector is associated with the decomposition depth of the multi-resolution spatial decomposition;

[0008] Determining the current state of the area to be monitored according to the vibrating wire dynamic vector.

[0009] In the above solution, the vibrating wire frequency signal and the temperature signal of the area to be monitored of the reservoir dam are obtained respectively. The vibrating wire frequency signal comes from a vibrating wire piezometer, which can be used to sense the change of seepage pressure inside the dam and convert it into the fluctuation of the vibrating wire frequency. The temperature signal reflects the temperature change inside or around the dam, and this information is crucial for understanding the background of the change of the vibrating wire frequency signal. Next, the temperature feature vector is determined according to the temperature signal, so as to capture the overall trend and fluctuation of the temperature change. Based on the temperature feature vector, the vibrating wire frequency signal is further decomposed in the multi-resolution space. Among them, the depth of the decomposition is closely related to the temperature feature vector, ensuring the adaptability and pertinence of the signal decomposition. Subsequently, the corresponding vibrating wire dynamic eigenvalues in each resolution space are generated and spliced to form a vibrating wire dynamic vector to capture the dynamic change characteristics of the vibrating wire frequency signal in different resolution spaces. Finally, the current state of the area to be monitored is determined according to the vibrating wire dynamic vector, realizing the evaluation of the safety state of the dam. By comprehensively considering the combination of the vibrating wire frequency signal and the temperature signal and using multi-resolution space decomposition and feature extraction, the evaluation of the safety state of the area to be monitored of the reservoir dam is realized. This process not only improves the accuracy and efficiency of monitoring, but also provides a strong technical guarantee for the safe operation of the reservoir dam.

[0010] Optionally, the determining the temperature feature vector according to the temperature signal includes:

[0011] Performing time series sampling on the temperature signal to determine a temperature sequence, and determining a temperature mean value and a temperature standard deviation according to the temperature sequence;

[0012] Determining the temperature feature vector according to the temperature mean value, the temperature standard deviation and the temperature sequence.

[0013] In the above solution, by performing time series sampling on the temperature signal, a series of discrete temperature data points, i.e., the temperature sequence, can be obtained. Time series sampling not only captures the temperature changes inside or around the dam but also retains the time order information of the temperature changes. Then, based on the obtained temperature sequence, the temperature mean and the temperature standard deviation are calculated. The temperature mean reflects the average level of temperature within a certain time range, while the temperature standard deviation measures the degree of temperature fluctuation. These two statistical quantities together constitute a comprehensive description of the characteristics of temperature changes. By calculating the temperature mean and standard deviation, we can more intuitively understand the overall trend and fluctuation characteristics of temperature changes. After obtaining the temperature mean, temperature standard deviation, and the original temperature sequence, the temperature feature vector is further determined based on this information. The temperature feature vector extracts the key information that can represent the characteristics of temperature changes by comprehensively considering the temperature mean, standard deviation, and each data point in the temperature sequence. It not only retains the main characteristics of temperature changes but also reduces data redundancy, improving the efficiency and accuracy of subsequent data processing. Moreover, the determination of the temperature feature vector has positive technical effects in multiple aspects. On the one hand, it provides a tool for comprehensively and concisely describing temperature changes, enabling us to more accurately understand the influence mechanism of temperature changes on the vibrating wire frequency signal. On the other hand, by combining the temperature feature vector with the vibrating wire frequency signal for multi-resolution spatial decomposition and feature extraction, the safety status of the area to be monitored in the reservoir dam can be more precisely evaluated.

[0014] Optionally, the multi-resolution spatial decomposition of the vibrating wire frequency signal according to the temperature feature vector includes:

[0015] Determine the target decomposition depth according to the temperature feature vector, where the target decomposition depth is used to represent the maximum number of decomposition levels in the preset multi-resolution decomposition framework, and there are preset different wavelet basis functions in the preset multi-resolution decomposition framework;

[0016] Perform wavelet decomposition on the vibrating wire frequency signal according to the target decomposition depth and the preset multi-resolution decomposition framework.

[0017] In the above scheme, the target decomposition depth, as a key parameter in the preset multi-resolution decomposition framework, directly determines the maximum number of layers of wavelet decomposition, which is determined based on the temperature eigenvector. Among them, the temperature eigenvector, as a comprehensive reflection of the temperature change characteristics, contains key information such as the temperature mean and standard deviation, which is crucial for understanding the impact of temperature change on the vibrating string frequency signal. By comprehensively considering the various dimensions of the temperature eigenvector, the target decomposition depth can be determined more accurately, thereby ensuring that the wavelet decomposition can capture the main features of the signal and avoid computational redundancy caused by over-decomposition. Next, the vibrating string frequency signal is wavelet decomposed according to the target decomposition depth and the preset multi-resolution decomposition framework. The preset multi-resolution decomposition framework contains different wavelet basis functions, which have their own unique time-frequency characteristics and can adapt to different types of signal analysis requirements. After determining the target decomposition depth, the corresponding wavelet basis function can be selected according to this depth to decompose the vibrating string frequency signal layer by layer. This process not only realizes the detailed division of the signal at different frequency scales, but also retains the time-frequency characteristics of the signal at different decomposition layers, providing a rich information basis for subsequent feature extraction and state evaluation. By adjusting the target decomposition depth, the appropriate number of decomposition layers can be selected as needed to balance the computational complexity and information extraction capabilities. In addition, wavelet decomposition can capture subtle changes in signals at different frequency scales, which is crucial for identifying abnormal fluctuations in vibrating string frequency signals.

[0018] Optionally, determining the target decomposition depth according to the temperature feature vector includes:

[0019] The target decomposition depth is determined according to the temperature feature vector and a maximum decomposition layer number threshold of a preset multi-resolution decomposition framework.

[0020] In the above scheme, by comprehensively considering the temperature eigenvector and the maximum decomposition layer threshold, the target decomposition depth can be determined, which not only helps to capture the main features in the vibrating string frequency signal, but also avoids computational redundancy and information loss caused by over-decomposition. The introduction of the temperature eigenvector makes it possible to adapt to the signal analysis needs under different temperature change conditions. Whether the temperature changes drastically or gently, the accuracy and efficiency of the analysis can be ensured by adjusting the target decomposition depth. By combining the maximum decomposition layer threshold, unnecessary deep decomposition can be avoided, thereby saving computing resources. This optimization not only improves the efficiency of data processing, but also reduces the dependence on hardware devices.

[0021] Optionally, the preset multi-resolution decomposition framework includes multiple decomposition layers, and a basis function set is configured in the preset multi-resolution decomposition framework. The basis function set includes multiple basis functions, and the basis functions in the basis function set are used to configure wavelet decomposition in the decomposition layer.

[0022] In the above solution, through multi - resolution decomposition and the configuration of basis functions, the detailed features in the vibrating string frequency signal can be captured more accurately. Among them, the introduction of the set of basis functions can adapt to different types of vibrating string frequency signals and analysis requirements. Whether the signal is stationary or non - stationary, linear or non - linear, the accuracy of the analysis can be ensured by selecting appropriate basis functions.

[0023] Optionally, the wavelet decomposition of the vibrating string frequency signal according to the target decomposition depth and the preset multi - resolution decomposition framework includes:

[0024] Select a target basis function from the set of basis functions according to the temperature feature vector, so as to perform wavelet decomposition on the vibrating string frequency signal by using the target basis function.

[0025] In the above solution, by selecting the target basis function according to the temperature feature vector, it can be ensured that the wavelet decomposition is more targeted at the characteristics of the current signal, thereby improving the accuracy and effectiveness of the decomposition. Since the temperature feature vector changes with time and environment, the selection of the basis function will also be adjusted accordingly. This dynamic adjustment ability enables the method to adapt to the signal analysis requirements under different environments and conditions. Then, by using the basis function matching the characteristics of the target signal for wavelet decomposition, the key information in the signal can be extracted more efficiently.

[0026] Optionally, the wavelet decomposition of the vibrating string frequency signal according to the target decomposition depth and the preset multi - resolution decomposition framework includes:

[0027] Configure corresponding target basis functions for each target decomposition layer of the target decomposition depth according to the temperature feature vector, so as to perform wavelet decomposition on the vibrating string frequency signal, where the target basis function is one of the basis functions in the set of basis functions, and different basis functions are configured for each target decomposition layer.

[0028] In the above solution, by configuring basis functions for each target decomposition layer according to the temperature feature vector, it can be ensured that each decomposition layer uses the basis function most suitable for the current signal characteristics for decomposition. This customized decomposition strategy significantly improves the accuracy and effectiveness of the decomposition, enabling the key information in the signal to be captured more accurately.

[0029] In a second aspect, the present application provides a vibrating wire piezometer data processing system for a reservoir dam, including:

[0030] An acquisition module, configured to acquire the vibrating string frequency signal and the temperature signal of the area to be monitored of the reservoir dam respectively, where the vibrating string frequency signal is the data acquired by the vibrating wire piezometer;

[0031] A processing module, configured to determine a temperature feature vector according to the temperature signal, perform multi-resolution spatial decomposition on the vibrating string frequency signal according to the temperature feature vector to generate corresponding vibrating string dynamic eigenvalues in each resolution space, and generate a vibrating string dynamic vector, wherein the temperature feature vector is associated with the decomposition depth of the multi-resolution spatial decomposition;

[0032] The processing module is further configured to determine the current state of the area to be monitored according to the vibrating string dynamic vector.

[0033] Optionally, the processing module is specifically configured to:

[0034] Perform time series sampling on the temperature signal to determine a temperature sequence, and determine a temperature mean value and a temperature standard deviation according to the temperature sequence;

[0035] Determine the temperature feature vector according to the temperature mean value, the temperature standard deviation, and the temperature sequence.

[0036] Optionally, the processing module is specifically configured to:

[0037] Determine a target decomposition depth according to the temperature feature vector, where the target decomposition depth is used to represent the maximum number of decomposition layers in a preset multi-resolution decomposition framework, and different wavelet basis functions are preset in the preset multi-resolution decomposition framework;

[0038] Perform wavelet decomposition on the vibrating string frequency signal according to the target decomposition depth and the preset multi-resolution decomposition framework.

[0039] Optionally, the processing module is specifically configured to:

[0040] Determine the target decomposition depth according to the temperature feature vector and a maximum decomposition layer threshold of the preset multi-resolution decomposition framework.

[0041] Optionally, the preset multi-resolution decomposition framework includes multiple decomposition layers, a set of basis functions is configured in the preset multi-resolution decomposition framework, the set of basis functions includes multiple basis functions, and the basis functions in the set of basis functions are used to configure the wavelet decomposition in the decomposition layer.

[0042] Optionally, the processing module is specifically configured to:

[0043] Select a target basis function from the set of basis functions according to the temperature feature vector, and perform wavelet decomposition on the vibrating string frequency signal by using the target basis function.

[0044] Optionally, the processing module is specifically configured to:

[0045] Configure corresponding target basis functions for each target decomposition layer of the target decomposition depth according to the temperature feature vector, so as to perform wavelet decomposition on the vibrating wire frequency signal, where the target basis function is one of the basis functions in the basis function set, and different basis functions are configured for each target decomposition layer.

[0046] In a third aspect, the present application provides an electronic device, including:

[0047] a processor; and,

[0048] a memory for storing executable instructions of the processor;

[0049] wherein, the processor is configured to execute any possible method described in the first aspect by executing the executable instructions.

[0050] In a fourth aspect, the present application provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, they are used to implement any possible method described in the first aspect.

[0051] The vibrating wire piezometer data processing method and system for a reservoir dam provided by the present application respectively obtain the vibrating wire frequency signal and the temperature signal of the area to be monitored of the reservoir dam, determine the temperature feature vector according to the temperature signal, perform multi-resolution spatial decomposition on the vibrating wire frequency signal according to the temperature feature vector to generate corresponding vibrating wire dynamic characteristic values in each resolution space, generate a vibrating wire dynamic vector, and then determine the current state of the area to be monitored according to the vibrating wire dynamic vector, thereby comprehensively considering the combination of the vibrating wire frequency signal and the temperature signal, and using multi-resolution spatial decomposition and feature extraction to realize a more accurate assessment of the safety state of the area to be monitored of the reservoir dam. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The accompanying drawings herein are incorporated into the specification and constitute a part of this specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.

[0053] Figure 1 is a schematic flowchart of a vibrating wire piezometer data processing method for a reservoir dam shown according to an exemplary embodiment of the present application;

[0054] Figure 2 is a schematic flowchart of a vibrating wire piezometer data processing method for a reservoir dam shown according to another exemplary embodiment of the present application;

[0055] Figure 3 is a schematic structural diagram of a vibrating wire piezometer data processing system for a reservoir dam shown according to an exemplary embodiment of the present application;

[0056] Figure 4 It is a schematic structural diagram of an electronic device shown according to an exemplary embodiment of the present application.

[0057] Through the above-mentioned drawings, specific embodiments of the present application have been shown, and there will be more detailed descriptions hereinafter. These drawings and textual descriptions are not intended to limit the scope of the concept of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. Specific Embodiments

[0058] Here, exemplary embodiments will be described in detail, and examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numerals in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0059] Figure 1 It is a schematic flowchart of a vibrating wire piezometer data processing method for a reservoir dam shown according to an exemplary embodiment of the present application. As Figure 1 shown, the method provided in this embodiment includes:

[0060] S101. Obtain the vibrating wire frequency signal and temperature signal of the area to be monitored in the reservoir dam respectively.

[0061] Specifically, install a vibrating wire piezometer in the area to be monitored in the reservoir dam. This piezometer can sense the change in seepage pressure inside the dam and convert it into fluctuations in the vibrating wire frequency. Regularly or real-time collect the vibrating wire frequency signal output by the vibrating wire piezometer through a data acquisition system to ensure the continuity and accuracy of the signal. Optionally, the collected vibrating wire frequency signal can be preprocessed, such as denoising, filtering, etc., to improve the quality of the signal.

[0062] In addition, install a temperature sensor in or near the area to be monitored in the reservoir dam to monitor the temperature change inside or around the dam. Synchronously collect the output signal of the temperature sensor with the vibrating wire frequency signal to ensure the temporal consistency between the temperature signal and the vibrating wire frequency signal. Similarly, preprocess the collected temperature signal, such as calibration, denoising, etc., to improve the measurement accuracy of the temperature.

[0063] S102. Determine the temperature feature vector according to the temperature signal.

[0064] Specifically, feature extraction can be performed on the preprocessed temperature signal, such as calculating the average value, fluctuation range, change trend, etc. of the temperature, to form a temperature feature vector. The temperature feature vector should be able to comprehensively reflect the overall trend and fluctuation characteristics of the temperature change, providing a basis for subsequent multi-resolution spatial decomposition.

[0065] In a possible implementation, time series sampling can be performed on the temperature signal to determine the temperature sequence , where is the sampling frequency within a preset sampling period;

[0066] Using formula 1 and based on the temperature sequence determine the temperature mean , where formula 1 is:

[0067] ;

[0068] Using formula 2 and based on the temperature sequence determine the temperature standard deviation , where formula 2 is:

[0069] ;

[0070] Using formula 3 and based on the temperature mean and the temperature standard deviation determine the temperature feature vector , where formula 3 is:

[0071] ;

[0072] where is the th temperature feature element in the temperature feature vector .

[0073] It should be noted that for the temperature feature vector obtained through formula 3 processing, the element values not only contain information on the degree of deviation of the temperature value but also reflect the relative importance of this deviation in the temperature distribution. Such a feature vector is more easily utilized by subsequent multi-resolution spatial decomposition algorithms, thereby more accurately capturing the correlation between the vibrating string frequency signal and the temperature signal. The introduction of the Gaussian function dynamically adjusts the weights of the feature elements according to the degree of deviation of the temperature value from the mean, making the temperature values close to the mean occupy a more important position in the feature vector, thus more accurately reflecting the main characteristics of the temperature distribution.

[0074] S103. Perform multi-resolution spatial decomposition on the vibrating string frequency signal according to the temperature feature vector to generate corresponding vibrating string dynamic characteristic values in each resolution space, so as to generate a vibrating string dynamic vector.

[0075] Specifically, a suitable wavelet basis function and decomposition level are selected, and the vibrating wire frequency signal is decomposed in the multi-resolution space according to the temperature feature vector. The decomposition depth is closely related to the temperature feature vector. For example, when the temperature changes greatly, a deeper decomposition level can be selected to capture more detailed information; when the temperature changes slightly, a shallower decomposition level can be selected to improve efficiency. Through multi-resolution space decomposition, the vibrating wire frequency signal is decomposed into sub-signals in different resolution spaces, and each sub-signal corresponds to a specific frequency range and detail level.

[0076] Then, feature extraction is performed on the sub-signals in each resolution space, such as calculating the energy, entropy, peak value, etc. of the sub-signals, to generate the corresponding vibrating wire dynamic characteristic values in each resolution space. The vibrating wire dynamic characteristic values can accurately reflect the dynamic change characteristics of the vibrating wire frequency signal in different resolution spaces. Then, the vibrating wire dynamic characteristic values in each resolution space are spliced in a certain order to form a vibrating wire dynamic vector.

[0077] In a possible implementation, the target decomposition depth can be determined according to the temperature feature vector, where the target decomposition depth is used to represent the maximum decomposition level in the preset multi-resolution decomposition framework, and there are different wavelet basis functions in the preset multi-resolution decomposition framework. According to the target decomposition depth and the preset multi-resolution decomposition framework, wavelet decomposition is performed on the vibrating wire frequency signal.

[0078] It should be noted that a threshold or rule related to temperature fluctuation can be set according to the statistical analysis results of the temperature feature vector. For example, when the temperature fluctuation exceeds a certain specific range, it is considered that the vibrating wire frequency signal needs to be decomposed more deeply to capture the frequency change details caused by temperature changes. According to this threshold or rule, the target decomposition depth is determined. The target decomposition depth is an integer, representing the maximum number of wavelet decomposition layers required in the preset multi-resolution decomposition framework.

[0079] S104. Determine the current state of the area to be monitored according to the vibrating wire dynamic vector.

[0080] Specifically, a state evaluation model can be established, which can input the vibrating wire dynamic vector and output the current state of the area to be monitored. The state evaluation model can adopt advanced artificial intelligence technologies such as machine learning algorithms and neural network algorithms to improve the accuracy and efficiency of evaluation. The generated vibrating wire dynamic vector is input into the state evaluation model for state evaluation. According to the output of the state evaluation model, the current state of the area to be monitored is determined, such as normal, abnormal, warning, etc. The evaluation results can also be visually displayed or alarmed to promptly discover and handle potential safety hazards.

[0081] In this embodiment, by separately obtaining the vibrating wire frequency signal and the temperature signal of the area to be monitored of the reservoir dam, determining the temperature feature vector according to the temperature signal, performing multi-resolution spatial decomposition on the vibrating wire frequency signal based on the temperature feature vector to generate the corresponding vibrating wire dynamic eigenvalues in each resolution space, generating the vibrating wire dynamic vector, and then determining the current state of the area to be monitored according to the vibrating wire dynamic vector, thus comprehensively considering the combination of the vibrating wire frequency signal and the temperature signal, and using multi-resolution spatial decomposition and feature extraction, a more accurate assessment of the safety state of the area to be monitored of the reservoir dam is realized.

[0082] Figure 2 FIG. is a schematic flowchart of a vibrating wire piezometer data processing method for a reservoir dam according to another exemplary embodiment of the present application. As Figure 2 shown, the vibrating wire piezometer data processing method for a reservoir dam provided in this embodiment includes:

[0083] S201. Separately obtain the vibrating wire frequency signal and the temperature signal of the area to be monitored of the reservoir dam.

[0084] Specifically, install a vibrating wire piezometer in the area to be monitored of the reservoir dam. This piezometer can sense the change in seepage pressure inside the dam and convert it into the fluctuation of the vibrating wire frequency. Regularly or real-time collect the vibrating wire frequency signal output by the vibrating wire piezometer through a data acquisition system to ensure the continuity and accuracy of the signal. Optionally, the collected vibrating wire frequency signal can be preprocessed, such as denoising, filtering, etc., to improve the quality of the signal.

[0085] In addition, install a temperature sensor in or near the area to be monitored of the reservoir dam to monitor the temperature change inside or around the dam. Synchronously collect the output signal of the temperature sensor with the vibrating wire frequency signal to ensure the temporal consistency between the temperature signal and the vibrating wire frequency signal. Similarly, preprocess the collected temperature signal, such as calibration, denoising, etc., to improve the measurement accuracy of the temperature.

[0086] S202. Determine the temperature feature vector according to the temperature signal.

[0087] Specifically, feature extraction can be performed on the preprocessed temperature signal, such as calculating the average value, fluctuation range, change trend, etc. of the temperature to form the temperature feature vector. The temperature feature vector should be able to comprehensively reflect the overall trend and fluctuation characteristics of the temperature change, providing a basis for subsequent multi-resolution spatial decomposition.

[0088] S203. Determine the target decomposition depth according to the temperature feature vector.

[0089] Optionally, formula 4 can be used and according to the temperature feature vector and the maximum decomposition layer threshold of the preset multi-resolution decomposition framework Determine the target decomposition depth , where Formula 4 is as follows:

[0090] ;

[0091] Where is the maximum decomposition layer threshold of the preset multi - resolution decomposition framework.

[0092] In the above Formula 4, first, all elements in the temperature feature vector are accumulated, and this accumulated value reflects the overall intensity or trend of the temperature feature. When the element values in the temperature feature vector are large (i.e., the temperature fluctuation is obvious), the accumulated value will also increase accordingly. Then, the accumulated value is input into the sigmoid function. The sigmoid function has the characteristic that when the input value is small, the output value is close to 0; when the input value is large, the output value is close to 1. Therefore, through the mapping of the sigmoid function, the accumulated value is converted into a value between 0 and 1, representing the ratio of the temperature feature intensity or trend to the maximum value. Next, the output value of the sigmoid function is multiplied by the maximum decomposition layer threshold of the preset multi - resolution decomposition framework. The purpose of this step is to determine the target decomposition depth according to the ratio of the temperature feature intensity or trend. When the temperature feature is strong, the output value of the sigmoid function is close to 1, and the target decomposition depth will also increase accordingly; when the temperature feature is weak, the output value of the sigmoid function is close to 0, and the target decomposition depth will also decrease accordingly. Finally, a floor operation is performed on the product result to ensure that the target decomposition depth is an integer.

[0093] S204. Perform wavelet decomposition on the vibrating wire frequency signal according to the target decomposition depth and the preset multi - resolution decomposition framework.

[0094] Optionally, the above - mentioned preset multi - resolution decomposition framework includes at least a first decomposition layer, a second decomposition layer, and a third decomposition layer. The preset multi - resolution decomposition framework is configured with a set of basis functions, and the set of basis functions includes multiple basis functions, including a first basis function, a second basis function, and a third basis function.

[0095] Among them, the set of basis functions in the preset multi - resolution decomposition framework. This set includes multiple basis functions with different frequency resolutions, time - domain characteristics, and shape characteristics, such as Haar wavelet, Daubechies wavelet, Symlet wavelet, Coiflet wavelet, etc. Each basis function has its unique applicable scenario and advantages. Selecting the appropriate basis function is crucial for accurately capturing the characteristics in the vibrating wire frequency signal.

[0096] Based on the analysis results of the temperature feature vector and combining the characteristics of each basis function in the basis function set, select the target basis function that is most suitable for the current ambient temperature conditions. For example, if the temperature feature vector shows that the ambient temperature changes violently and contains many high-frequency components, basis functions with high frequency resolution, such as Haar wavelet or Daubechies wavelet, can be selected to better capture these high-frequency features.

[0097] If the ambient temperature changes relatively smoothly and the low-frequency trend is mainly concerned, basis functions with low frequency resolution, such as Morlet wavelet or Mexican Hat wavelet, can be selected.

[0098] In another possible implementation, corresponding target basis functions are sequentially configured for each target decomposition layer with the target decomposition depth according to the temperature feature vector to perform wavelet decomposition on the vibrating wire frequency signal, where the target basis function is one of the basis functions in the basis function set, and different basis functions are configured for each target decomposition layer.

[0099] The first decomposition layer: As the most basic decomposition layer, it is mainly responsible for capturing the high-frequency components in the signal, such as noise and short-term fluctuations. This layer uses a basis function with relatively high frequency resolution to accurately identify and separate these components.

[0100] The second decomposition layer: Based on the first decomposition layer, it further decomposes the intermediate-frequency components in the signal. These components may include seepage pressure changes caused by reservoir water level changes, temperature changes, etc. The basis function used in the second decomposition layer has a moderate frequency resolution, which can capture intermediate-frequency changes without over-decomposing high-frequency or low-frequency components.

[0101] The third decomposition layer: As the topmost decomposition layer, it is mainly responsible for capturing the low-frequency components in the signal, such as long-term trends and seasonal changes. This layer uses a basis function with relatively low frequency resolution to accurately reflect these long-term changes.

[0102] A hierarchical progressive relationship is formed among the decomposition layers. Each layer further decomposes the signal on the basis of the previous layer, thereby realizing multi-scale and multi-level analysis of the signal. Through this hierarchical progressive method, different components in the signal can be more effectively separated, providing a more accurate basis for subsequent data analysis and processing.

[0103] And the preset multi-resolution decomposition framework is configured with a basis function set, which includes multiple basis functions for decomposing the signal on different decomposition layers. The basis function set includes at least a first basis function, a second basis function, and a third basis function, corresponding to the first decomposition layer, the second decomposition layer, and the third decomposition layer respectively.

[0104] First basis function: Select wavelet functions with high frequency resolution, such as Haar wavelet or Daubechies wavelet, etc. These wavelet functions have compact support and orthogonality, and are suitable for capturing high-frequency components in the signal.

[0105] Second basis function: Select wavelet functions with medium frequency resolution, such as Symlet wavelet or Coiflet wavelet, etc. These wavelet functions have good localization performance in both the frequency domain and the time domain, and are suitable for capturing intermediate-frequency components in the signal.

[0106] Third basis function: Select wavelet functions with low frequency resolution, such as Morlet wavelet or Mexican Hat wavelet, etc. These wavelet functions have a relatively wide bandwidth in the frequency domain, and are suitable for capturing low-frequency components in the signal.

[0107] In a possible implementation, the target basis function can be selected from the set of basis functions according to the temperature feature vector, so as to perform wavelet decomposition on the vibrating string frequency signal using the target basis function.

[0108] S205. Perform wavelet decomposition on the vibrating string frequency signal according to each decomposition layer in the multi-resolution space, so as to obtain the set of target characteristic waves corresponding to each decomposition layer.

[0109] Specifically, perform wavelet decomposition on the vibrating string frequency signal according to each decomposition layer in the multi-resolution space, so as to obtain the set of target characteristic waves corresponding to each decomposition layer. The set of target characteristic waves includes an approximation wave set and a detail wave set.

[0110] It should be noted that the wavelet decomposition of the vibrating string frequency signal can be performed using the basis functions in each decomposition layer to determine the characteristic wavelets. Among them, the approximation wave set in the set of target characteristic waves is the low-frequency component of the characteristic wavelet with a frequency lower than the preset frequency threshold, and the detail wave set in the set of target characteristic waves is the high-frequency component of the characteristic wavelet with a frequency higher than the preset frequency threshold.

[0111] In the above solution, multi-resolution analysis allows the signal to be decomposed and reconstructed at different scales (or resolutions). Through wavelet decomposition in the multi-resolution space, the vibrating string frequency signal can be decomposed into components in multiple different frequency bands, and these components represent the characteristics of the signal at different scales.

[0112] In the process of wavelet decomposition, the basis functions in each decomposition layer play a key role. The basis function is the basic unit in wavelet analysis. They can be sine functions, cosine functions, Gaussian functions, etc., but more commonly wavelet functions with localization characteristics. These basis functions can form a set of basis function systems that can cover the entire frequency range through dilation and translation operations. Using these basis functions to decompose the vibrating string frequency signal can extract the characteristic wavelets of the signal in different frequency bands.

[0113] Through wavelet decomposition, a series of characteristic wavelets can be obtained, which represent the components of the vibrating string frequency signal in different frequency bands. These characteristic wavelets not only contain the frequency information of the signal but also the time localization information of the signal, enabling us to more precisely analyze the variation characteristics of the signal at different times and frequencies.

[0114] After obtaining the characteristic wavelets, they need to be divided into a set of target characteristic waves. According to the preset frequency threshold, the components with frequencies lower than the threshold in the characteristic wavelets are classified into the approximate wave set, while the components with frequencies higher than the threshold are classified into the detail wave set.

[0115] The approximate wave set represents the low-frequency components in the vibrating string frequency signal. In the monitoring of water conservancy projects, the low-frequency components are usually related to the overall structural response and long-term change trends of the reservoir dam. By analyzing the approximate wave set, important information such as the overall stability and long-term deformation trend of the dam structure can be obtained, providing strong support for engineering safety assessment.

[0116] The detail wave set represents the high-frequency components in the vibrating string frequency signal. These high-frequency components are usually related to the short-term dynamic responses such as local deformation and crack propagation of the reservoir dam. By analyzing the detail wave set, potential problems in the dam structure can be detected in a timely manner, providing important references for engineering maintenance and repair.

[0117] Through the above steps, using wavelet decomposition technology to decompose the vibrating string frequency signal into an approximate wave set and a detail wave set not only improves the accuracy of data processing but also significantly enhances the processing efficiency, and can effectively extract the key feature information in the signal.

[0118] In addition, the process of wavelet decomposition can also involve selecting appropriate basis functions (such as Haar wavelet, Daubechies wavelet, etc.) and setting the decomposition levels according to the signal characteristics. Through discrete wavelet transform (DWT) or continuous wavelet transform (CWT), the vibrating string frequency signal is decomposed into signal components in different frequency bands, forming the approximate wave set and the detail wave set corresponding to each decomposition level. In each decomposition level, the approximate wave set and the detail wave set are respectively extracted. The approximate wave set contains the low-frequency components of the signal, reflecting the overall trend of the signal; while the detail wave set contains the high-frequency components of the signal, reflecting the local details and fluctuations of the signal.

[0119] S206. Determine the corresponding approximate fluctuation eigenvalue according to the approximate wave set corresponding to each decomposition level.

[0120] Specifically, determine the corresponding approximate fluctuation eigenvalue according to the approximate wave set corresponding to each decomposition level. The approximate fluctuation eigenvalue is the standard deviation of each wave peak in the approximate wave set.

[0121] Among them, peak detection can be performed on the approximate wave sets in each decomposition layer. Peak detection algorithms can adopt methods such as the local maximum method, zero-crossing method, etc., to accurately identify the positions and amplitudes of the peaks.

[0122] Calculate the standard deviation of each peak in the approximate wave set. The standard deviation is an important indicator to measure the degree of data dispersion, and here it is used to quantify the degree of fluctuation of the peaks in the approximate wave set. By calculating the standard deviation, the corresponding approximate fluctuation eigenvalue for each decomposition layer can be obtained, and this eigenvalue reflects the overall fluctuation characteristics of the signal at this decomposition layer.

[0123] S207. Determine the corresponding vibrating string energy eigenvalue according to the detail wave sets corresponding to each decomposition layer.

[0124] Specifically, determine the corresponding vibrating string energy eigenvalue according to the detail wave sets corresponding to each decomposition layer. The vibrating string energy eigenvalue is the integral of each detail wave in the detail wave set along the time direction.

[0125] Among them, perform integration along the time direction on the detail wave sets in each decomposition layer. The integration operation can capture the energy distribution and change trend of each detail wave in the detail wave set. Through the integration operation, the corresponding vibrating string energy eigenvalue for each decomposition layer is obtained. This eigenvalue reflects the local energy distribution and detail characteristics of the signal at this decomposition layer.

[0126] S208. Generate a vibrating string dynamic eigenvalue according to the approximate fluctuation eigenvalue and the vibrating string energy eigenvalue of each decomposition layer.

[0127] In this step, generate a vibrating string dynamic eigenvalue according to the approximate fluctuation eigenvalue and the vibrating string energy eigenvalue of each decomposition layer, so as to splice the vibrating string dynamic eigenvalues of each decomposition layer to form a vibrating string dynamic vector.

[0128] Specifically, splice the approximate fluctuation eigenvalue and the vibrating string energy eigenvalue corresponding to each decomposition layer to form the vibrating string dynamic eigenvalue of this decomposition layer. Splice the vibrating string dynamic eigenvalues of each decomposition layer in the order of the decomposition levels to form the final vibrating string dynamic vector. The vibrating string dynamic vector is a high-dimensional feature vector that contains the dynamic characteristic information of the vibrating string frequency signal in different resolution spaces.

[0129] S209. Determine the current state of the area to be monitored according to the vibrating string dynamic vector.

[0130] Specifically, a state evaluation model can be established, which can output the current state of the area to be monitored based on the input of the vibrating wire dynamic vector. The state evaluation model can adopt advanced artificial intelligence technologies such as machine learning algorithms and neural network algorithms to improve the accuracy and efficiency of evaluation. The generated vibrating wire dynamic vector is input into the state evaluation model for state evaluation. According to the output of the state evaluation model, the current state of the area to be monitored is determined, such as normal, abnormal, warning, etc. The evaluation results can also be visually displayed or alarmed to timely discover and handle potential safety hazards.

[0131] In a possible implementation, it can be to normalize the vibrating wire dynamic vector to generate a normalized vibrating wire dynamic vector. The normalized vibrating wire dynamic vector is input to a support vector machine for classification to determine the current state of the area to be monitored, where the current state includes a safe state, a warning state, and a dangerous state.

[0132] Specifically, normalizing the vibrating wire dynamic vector is a key step to ensure the accuracy of subsequent classification. The vibrating wire dynamic vector contains various data features collected by the vibrating wire piezometer, such as frequency, amplitude, etc. The dimensions and numerical ranges of these features may vary. Normalization can convert these feature values into the same numerical range, usually [0, 1] or [-1, 1], thereby eliminating the influence of dimension and numerical range differences on the classification results. The normalized vibrating wire dynamic vector, that is, the normalized vibrating wire dynamic vector, provides a more balanced and comparable data basis for subsequent classification tasks.

[0133] When inputting the normalized vibrating wire dynamic vector into the support vector machine for classification, first, a support vector machine model needs to be trained. During the training process, the vibrating wire dynamic vectors with known states (such as safe state, warning state, dangerous state) are used as training samples, and the classification performance is optimized by adjusting the model parameters. After training, the new normalized vibrating wire dynamic vector can be input into the model for classification to determine the current state of the area to be monitored.

[0134] Through the classification of the support vector machine, the state of the area to be monitored can be accurately divided into a safe state, a warning state, and a dangerous state. Such classification results not only provide an intuitive judgment basis for the safety monitoring of the reservoir dam but also provide strong support for subsequent engineering decisions and maintenance. In the safe state, the existing monitoring frequency and engineering measures can be maintained; in the warning state, monitoring and inspection need to be strengthened and necessary engineering measures are prepared to be taken; in the dangerous state, immediate emergency measures need to be taken to ensure the safety of the reservoir dam.

[0135] It should be noted that the embodiments provided in this application are intended to process the data of vibrating wire piezometers for reservoir dams, so as to improve the adaptability between the vibrating wire dynamic vectors input into the support vector machine and the actual temperature. Specifically, this adaptability is manifested in the ability to meet the signal analysis requirements under different temperature change conditions. Whether the temperature changes violently or gently, the accuracy and efficiency of the analysis can be ensured by adjusting the target decomposition depth. By combining the maximum decomposition layer threshold, unnecessary deep decomposition can be avoided, thus saving computing resources. This optimization not only improves the efficiency of data processing but also reduces the dependence on hardware devices. The training process of the support vector machine is not specifically limited in this embodiment.

[0136] In a possible implementation, historical data from vibrating wire piezometers is collected, and these data reflect the vibrating wire responses of the dam under different states. For each vibrating wire dynamic vector data, there needs to be a clear label indicating the state of the dam at that time (safe, warning, dangerous). These labels are usually based on expert judgments or records of historical events.

[0137] Then, the vibrating wire dynamic vectors are normalized to eliminate the dimensional differences between data of different dimensions, improving the convergence speed and accuracy of the model. Then, outliers, missing values, etc. are removed or corrected to ensure data quality.

[0138] Features useful for the classification task are extracted from the normalized vibrating wire dynamic vectors. This may include the change amounts or combinations of parameters such as frequency, amplitude, and phase. If the feature dimension is too high, it may lead to overfitting of the model or low computing efficiency. Methods such as principal component analysis can be considered for feature dimensionality reduction.

[0139] Then, a kernel function is selected, for example, a linear kernel, a polynomial kernel, a radial basis function (RBF) kernel, etc. According to the characteristics of the data and the requirements of the classification task, a suitable kernel function is selected. For example, in this embodiment, the RBF kernel can be selected.

[0140] The preprocessed data is trained using the selected kernel function and tuned parameters to obtain a support vector machine classification model.

[0141] The trained support vector machine classification model is then deployed to the actual reservoir dam monitoring system for real-time or periodic data processing and state classification. Subsequently, the system performance can also be monitored, and the data can be updated regularly and the model can be retrained to adapt to the changes in the dam state.

[0142] Figure 3 It is a schematic structural diagram of a vibrating wire piezometer data processing system for a reservoir dam shown according to an exemplary embodiment of the present application. As Figure 3As shown in the figure, the vibrating wire piezometer data processing system 300 provided in this embodiment includes:

[0143] An acquisition module 310, configured to acquire the vibrating wire frequency signal and the temperature signal of the area to be monitored in the reservoir dam respectively, where the vibrating wire frequency signal is the data acquired by the vibrating wire piezometer;

[0144] A processing module 320, configured to determine a temperature feature vector according to the temperature signal, perform multi-resolution spatial decomposition on the vibrating wire frequency signal according to the temperature feature vector to generate corresponding vibrating wire dynamic eigenvalues in each resolution space, and generate a vibrating wire dynamic vector, where the temperature feature vector is associated with the decomposition depth of the multi-resolution spatial decomposition;

[0145] The processing module 320 is further configured to determine the current state of the area to be monitored according to the vibrating wire dynamic vector.

[0146] Optionally, the processing module 320 is specifically configured to:

[0147] Perform time series sampling on the temperature signal to determine a temperature sequence, and determine the temperature mean and the temperature standard deviation according to the temperature sequence;

[0148] Determine the temperature feature vector according to the temperature mean, the temperature standard deviation, and the temperature sequence.

[0149] Optionally, the processing module 320 is specifically configured to:

[0150] Determine a target decomposition depth according to the temperature feature vector, where the target decomposition depth is used to represent the maximum decomposition layer number in a preset multi-resolution decomposition framework, and different wavelet basis functions are preset in the preset multi-resolution decomposition framework;

[0151] Perform wavelet decomposition on the vibrating wire frequency signal according to the target decomposition depth and the preset multi-resolution decomposition framework.

[0152] Optionally, the processing module 320 is specifically configured to:

[0153] Determine the target decomposition depth according to the temperature feature vector and the maximum decomposition layer number threshold of the preset multi-resolution decomposition framework.

[0154] Optionally, the preset multi-resolution decomposition framework includes multiple decomposition layers, a basis function set is configured in the preset multi-resolution decomposition framework, the basis function set includes multiple basis functions, and the basis functions in the basis function set are used to configure the wavelet decomposition in the decomposition layer.

[0155] Optionally, the processing module 320 is specifically configured to:

[0156] Select a target basis function from the set of basis functions according to the temperature feature vector, so as to perform wavelet decomposition on the vibrating string frequency signal by using the target basis function.

[0157] Optionally, the processing module 320 is specifically configured to:

[0158] Configure corresponding target basis functions for each target decomposition layer of the target decomposition depth in sequence according to the temperature feature vector, so as to perform wavelet decomposition on the vibrating string frequency signal, where the target basis function is one of the basis functions in the set of basis functions, and different basis functions are configured for each target decomposition layer.

[0159] Figure 4 It is a schematic structural diagram of an electronic device shown according to an exemplary embodiment of the present application. As Figure 4 shown, an electronic device 400 provided in this embodiment includes: a processor 401 and a memory 402; wherein:

[0160] The memory 402 is used to store a computer program, and this memory can also be a flash (flash memory).

[0161] The processor 401 is configured to execute the execution instructions stored in the memory to implement each step in the above method. For specific reference, please refer to the relevant descriptions in the foregoing method embodiments.

[0162] Optionally, the memory 402 can be either independent or integrated with the processor 401.

[0163] When the memory 402 is a device independent of the processor 401, the electronic device 400 may further include:

[0164] A bus 403 for connecting the memory 402 and the processor 401.

[0165] This embodiment also provides a readable storage medium, in which a computer program is stored. When at least one processor of the electronic device executes this computer program, the electronic device executes the methods provided by the above various embodiments.

[0166] This embodiment also provides a program product, which includes a computer program, and this computer program is stored in a readable storage medium. At least one processor of the electronic device can read this computer program from the readable storage medium, and at least one processor executes this computer program so that the electronic device implements the methods provided by the above various embodiments.

[0167] Other embodiments of the present application will be readily apparent to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. The present application is intended to cover any variations, uses, or adaptations of the present application, which follow the general principles of the present application and include known common general knowledge or conventional technical means in the technical field not disclosed in the present application. The specification and examples are only to be considered as exemplary, and the true scope and spirit of the present application are pointed out by the claims.

[0168] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present application is only limited by the appended claims.

Claims

1. A vibrating wire piezometer data processing method for a reservoir dam, characterized in that: include: Respectively obtain a vibrating wire frequency signal and a temperature signal of the area to be monitored in the reservoir dam, wherein the vibrating wire frequency signal is data obtained by a vibrating wire osmometer; Determine a temperature characteristic vector according to the temperature signal, perform multi-resolution spatial decomposition on the vibrating string frequency signal according to the temperature characteristic vector, so as to generate corresponding vibrating string dynamic characteristic values ​​under each resolution space, so as to generate a vibrating string dynamic vector, wherein the temperature characteristic vector is associated with a decomposition depth of the multi-resolution spatial decomposition; Determining the current state of the area to be monitored according to the vibrating wire dynamic vector; The performing multi-resolution spatial decomposition on the vibrating-wire frequency signal according to the temperature characteristic vector comprises: Determining a target decomposition depth according to the temperature feature vector, wherein the target decomposition depth is used to characterize a maximum number of decomposition layers in a preset multi-resolution decomposition framework, wherein different wavelet basis functions are preset in the preset multi-resolution decomposition framework; The vibrating string frequency signal is subjected to wavelet decomposition according to the target decomposition depth and the preset multi-resolution decomposition framework.

2. The vibrating wire osmometer data processing method for a reservoir dam according to claim 1, characterized in that: The step of determining a temperature characteristic vector according to the temperature signal comprises: Performing time series sampling on the temperature signal to determine a temperature sequence, and determining a temperature mean and a temperature standard deviation according to the temperature sequence; The temperature feature vector is determined according to the temperature mean, the temperature standard deviation, and the temperature sequence.

3. The vibrating wire osmometer data processing method for a reservoir dam according to claim 1, characterized in that: The step of determining the target decomposition depth according to the temperature characteristic vector comprises: The target decomposition depth is determined according to the temperature feature vector and a maximum decomposition layer number threshold of a preset multi-resolution decomposition framework.

4. The vibrating wire osmometer data processing method for a reservoir dam according to claim 1 or 3, characterized in that: The preset multi-resolution decomposition framework includes multiple decomposition layers. A basis function set is configured in the preset multi-resolution decomposition framework. The basis function set includes multiple basis functions. The basis functions in the basis function set are used to configure wavelet decomposition in the decomposition layer.

5. The vibrating wire osmometer data processing method for a reservoir dam according to claim 4, characterized in that: The step of performing wavelet decomposition on the vibrating string frequency signal according to the target decomposition depth and the preset multi-resolution decomposition framework includes: A target basis function is selected from the basis function set according to the temperature characteristic vector, so as to perform wavelet decomposition on the vibrating wire frequency signal by using the target basis function.

6. The vibrating wire osmometer data processing method for a reservoir dam according to claim 4, characterized in that: The step of performing wavelet decomposition on the vibrating string frequency signal according to the target decomposition depth and the preset multi-resolution decomposition framework includes: According to the temperature feature vector, corresponding target basis functions are sequentially configured for each target decomposition layer of the target decomposition depth to perform wavelet decomposition on the vibrating string frequency signal, wherein the target basis function is one of the basis functions in the basis function set, and each target decomposition layer is configured with a different basis function.

7. A vibrating wire piezometer data processing system for a reservoir dam, characterized in that: include: An acquisition module, used to respectively acquire a vibrating string frequency signal and a temperature signal of a reservoir dam to be monitored area, wherein the vibrating string frequency signal is data acquired by a vibrating string osmometer; A processing module, configured to determine a temperature characteristic vector according to the temperature signal, to perform a multi-resolution spatial decomposition on the vibrating string frequency signal according to the temperature characteristic vector, to generate a corresponding vibrating string dynamic characteristic value under each resolution space, to generate a vibrating string dynamic vector, wherein the temperature characteristic vector is associated with a decomposition depth of the multi-resolution spatial decomposition; The processing module is further used to determine the current state of the area to be monitored according to the vibrating string dynamic vector; The processing module is specifically used for: Determining a target decomposition depth according to the temperature feature vector, wherein the target decomposition depth is used to characterize a maximum number of decomposition layers in a preset multi-resolution decomposition framework, wherein different wavelet basis functions are preset in the preset multi-resolution decomposition framework; The vibrating string frequency signal is subjected to wavelet decomposition according to the target decomposition depth and the preset multi-resolution decomposition framework.

8. An electronic device, characterized in that: include: processor; as well as, A memory, configured to store executable instructions of the processor; The processor is configured to perform the method of any one of claims 1 to 6 by executing the executable instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 6 when executed by a processor.

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