Wellhead data acquisition modeling system

Abstract

The invention discloses a wellhead data acquisition modeling system. The wellhead data acquisition modeling system comprises a data acquisition module, a hydraulic characteristic processing module, a well layer coupling modeling module and an interaction control module, the invention belongs to the technical field of well water monitoring management, based on an adaptive time-frequency analysis method, a dynamic window function enhanced by local frequency spectrum entropy is introduced, feature extraction and abnormal early warning are performed on transient hydraulic fluctuation signals, and the time-frequency resolution and change recognition capability of the hydraulic signals are remarkably improved; a dynamic coupling model between a water pumping system and an aquifer is constructed by combining a multi-scale parameter coordination mechanism, and accuracy and dynamic adaptability of well layer response modeling are realized by introducing a gap compression lag function and a joint parameter calibration method; and the interaction control module implements model prediction control according to the hydraulic feature vector, the hydraulic anomaly early warning signal and the well layer coupling prediction parameter combination, and outputs wellhead data acquisition decision support data.

Description

Technical Field

[0001] The present invention belongs to the technical field of well water monitoring and management, and specifically relates to a wellhead data acquisition and modeling system. Background Art

[0002] A wellhead data acquisition and modeling system is used to monitor environmental data such as underground water level, temperature, and water quality in real time. By synchronously collecting data from multiple sensors and performing preprocessing, denoising, and calibration, the accuracy and timeliness of the data are ensured. Through advanced analysis methods and modeling techniques, the system fuses the multi-source data collected, establishes a dynamic monitoring model of groundwater, and can detect anomalies such as water quality changes and water level fluctuations in real time. This system can provide scientific decision-making support for water resource management and water environment protection, timely warn of potential risks such as water quality pollution and changes in groundwater resources, and ensure the sustainable utilization and safe management of water resources.

[0003] However, in the existing wellhead data acquisition and modeling systems, there are technical problems such as the inability to efficiently synchronously collect multi-source wellhead data, an imperfect real-time feature extraction mechanism, and a lack of a response mechanism to hydraulic change trends; in the existing hydraulic feature processing process, there are technical problems such as a fast change speed of transient hydraulic features, fuzzy frequency domain information, difficult positioning of signal energy changes during the pumping process, and easy interference by high-frequency noise; in the existing well layer coupling data modeling process, there are technical problems such as the difficulty in quantifying the interaction relationship between the pumping system and the aquifer, a lag in the response of the coupling model, a lack of dynamic characterization ability for non-linear seepage changes, and a rough model parameter calibration mechanism. Summary of the Invention

[0004] In view of the above situation, to overcome the defects of the prior art, the present invention provides a wellhead data acquisition and modeling system. In the existing wellhead data acquisition and modeling system, there are technical problems such as the inability to efficiently synchronize and collect multi-source wellhead data, an imperfect real-time feature extraction mechanism, and a lack of a response mechanism for hydraulic change trends. This solution creatively adopts a comprehensive optimization method that combines improved extraction and recognition of hydraulic characteristics and optimization of well-layer coupling modeling. Through the improved extraction and recognition of hydraulic characteristics, the accuracy and real-time performance of hydraulic characteristic extraction are solved, enabling the timely capture of hydraulic mode changes and effective early warnings. At the same time, through the optimization of well-layer coupling modeling, the coupling between the pumping system and the aquifer is enhanced, ensuring that the model can dynamically adapt to changes in the downhole environment and improving the accuracy and adaptability of modeling. In the existing hydraulic characteristic processing process, there are technical problems such as the fast change speed of transient hydraulic characteristics, fuzzy frequency domain information, difficult positioning of signal energy changes during the pumping process, and easy interference by high-frequency noise. This solution creatively adopts a method for identifying transient hydraulic fluctuations with enhanced features combined with improved adaptive time-frequency analysis to extract transient hydraulic characteristics. An innovative local spectrum entropy-enhanced dynamic window function is introduced. By integrating local spectrum entropy calculation and combining with the adaptive time-frequency analysis method, the time-frequency resolution of transient hydraulic fluctuation signals is significantly improved. In specific implementation, the calculation of local spectrum entropy parameters can accurately represent the complexity of the signal by analyzing the local frequency distribution of the signal, and dynamically adjust the window width based on this to adapt to changes in instantaneous frequency. In the existing well-layer coupling data modeling process, there are technical problems such as the difficulty in quantifying the interaction relationship between the pumping system and the aquifer, the lag in the response of the coupling model, the lack of dynamic characterization ability for non-linear seepage changes, and the rough model parameter calibration mechanism. This solution creatively adopts a joint modeling method for the pumping system and the aquifer that combines a multi-scale parameter coordination mechanism to perform well-layer coupling data modeling. By constructing a dynamic transmission equation for the pumping system, a two-scale seepage response model for the aquifer, and a coupling boundary setting mechanism, real-time modeling and feedback optimization of the coupling process between the pumping system and the aquifer are achieved. At the same time, a joint parameter calibration method is introduced to dynamically adjust model parameters in combination with sensing data, improving the model's prediction ability for aquifer compression response, water level disturbance conduction, and pumping efficiency changes.

[0005] The technical solution adopted by the present invention is as follows: A wellhead data acquisition and modeling system provided by the present invention includes a data acquisition module, a hydraulic characteristic processing module, a well-layer coupling modeling module, and an interaction control module;

[0006] The data acquisition module is used for data synchronization acquisition and processing. Through data synchronization acquisition and processing, multi-source optimized wellhead data is obtained, and the multi-source optimized wellhead data is sent to the hydraulic characteristic processing module and the well-layer coupling modeling module;

[0007] The hydraulic characteristic processing module is used for transient hydraulic characteristic extraction. Through transient hydraulic characteristic extraction, a hydraulic characteristic vector and hydraulic anomaly warning signal data are obtained, and the hydraulic characteristic vector and hydraulic anomaly warning signal data are sent to the interaction control module;

[0008] The well-layer coupling modeling module is used for well-layer coupling data modeling. Through well-layer coupling data modeling, a well-layer coupling prediction parameter combination is obtained, and the well-layer coupling prediction parameter combination is sent to the interaction control module;

[0009] The interaction control module is used for model predictive control. Through model predictive control, wellhead data acquisition decision support data is obtained.

[0010] Furthermore, the data synchronous acquisition and processing is used to provide synchronous and efficient wellhead data. Specifically, data synchronous acquisition is carried out to obtain wellhead raw data, and through preprocessing operations, wellhead multi-source optimized data is obtained;

[0011] The wellhead raw data includes water level data, instantaneous flow rate data, water temperature data, conductivity data, wellhead vibration signals, and well pipe displacement data;

[0012] The wellhead multi-source optimized data specifically refers to the wellhead raw data after time synchronization, denoising filtering, and basic error calibration.

[0013] Furthermore, the transient hydraulic characteristic extraction is used to extract hydraulic characteristics and identify abnormal hydraulic fluctuation states. Specifically, based on the wellhead multi-source optimized data, a feature-enhanced transient hydraulic fluctuation identification method combined with adaptive time-frequency analysis is adopted to carry out transient hydraulic characteristic extraction to obtain a hydraulic characteristic vector and hydraulic anomaly warning signal data, including the following steps: adaptive time-frequency analysis improvement, transient feature extraction, dynamic feature enhancement, adaptive hydraulic mode classification, hydraulic characteristic vector construction, and hydraulic mode anomaly detection and warning;

[0014] The adaptive time-frequency analysis improvement is used to improve the time-frequency resolution of transient hydraulic fluctuation signals. Specifically, by constructing a dynamic window function with enhanced integrated local spectral entropy and adopting a time-varying sampling strategy, the wellhead sampling step is adjusted according to the change of instantaneous frequency, and through adaptive time-frequency distribution calculation, hydrodynamic signal time-frequency distribution data is obtained;

[0015] The dynamic window function with enhanced integrated local spectral entropy adjusts the transient signal according to the instantaneous frequency in the wellhead multi-source optimized data, and through constructing local spectral entropy parameters, the window width adjustment and improvement of the dynamic window function are carried out to obtain a dynamic window adjustment calculation function. The calculation formula is:

[0016] ;

[0017] In the formula, is the dynamic window adjustment calculation function, t is the time index, is the local time difference variable, is the dynamic window width, which is used to adjust the time-frequency resolution over time. The calculation formula is , where a is the base window width, b is the adjustment coefficient, and H f (t) is the local spectral entropy parameter;

[0018] The local spectral entropy parameter is obtained by calculating the local frequency distribution of the signal at each sampling moment and representing the complexity of the signal spectrum based on the local spectral distribution;

[0019] The calculation formula of the local spectral distribution is:

[0020] ;

[0021] In the formula, P(f,t) is the local spectral distribution function, X(f,t) is the complex spectrum coefficient after time-frequency transformation. The complex spectrum coefficient X(f,t) after time-frequency transformation represents the complex frequency-domain response of the original signal at time t and instantaneous frequency f, and is specifically calculated by the short-time Fourier transform. |X(f,t)| is the amplitude of the original signal at time t and instantaneous frequency f, f is the instantaneous frequency, and F is the set of instantaneous frequency bands of the hydraulic characteristics;

[0022] The specific calculation formula of the local spectral entropy parameter is:

[0023] ;

[0024] In the formula, H f (t) is the local spectral entropy parameter, P(f,t) is the local spectral distribution function, and the local spectral distribution function P(f,t) is used to represent the normalized frequency distribution as a whole, is the frequency component information amount calculation term, and specifically uses the natural logarithm base;

[0025] The time-varying sampling strategy is used to adaptively adjust the data sampling period according to the instantaneous frequency change caused by the pumping behavior. Specifically, the sampling step size is calculated based on the maximum instantaneous frequency in the wellhead multi-source optimized data to obtain the time-varying sampling step size;

[0026] The adaptive time-frequency distribution calculation is specifically to calculate the time-frequency distribution based on the dynamic window adjustment calculation function and the time-varying sampling step size by maximizing the time-frequency aggregation index to obtain the time-frequency distribution data of the hydrodynamic signal;

[0027] The transient feature extraction is used to extract transient change features from the hydraulic mode signal. Specifically, by calculating multi-scale gradient features and extracting transient energy ratios based on the time-frequency distribution data of the hydrodynamic signal, transient gradient feature data and transient energy ratio data are obtained. And by constructing a transient feature matrix based on the transient gradient feature data and the transient energy ratio data, transient feature data is obtained;

[0028] The multi-scale gradient feature calculation is used to extract the local transient change rate from the time-frequency distribution of the hydraulic signal and reflect the energy mutation characteristics caused by pumping disturbances, water level mutations, aquifer responses or equipment abnormalities in the hydraulic system, so as to obtain transient gradient feature data;

[0029] The transient energy ratio extraction is used to detect mutations, surges, pumping fluctuations and pump interference in the hydraulic signal, so as to obtain transient energy ratio data;

[0030] The transient feature matrix construction combines the transient gradient feature data and the transient energy ratio data to construct transient feature data;

[0031] The dynamic feature enhancement specifically adopts a statistics-based dynamic feature weighting method to perform weighted calculation on the transient feature data according to the variance and change rate of the features, and through a variance screening method, feature selection is carried out to obtain screened feature set data;

[0032] The adaptive hydraulic mode classification is used to perform real-time classification of hydraulic modes and identify hydraulic mode changes based on the screened features. Specifically, by constructing a basic classification model and based on real-time data, the category centers of dynamic hydraulic mode data are designed to optimize the adaptability of hydraulic mode classification, so as to obtain the category center feature data of hydraulic mode data. According to the category center feature data of hydraulic mode data, by calculating the similarity between the features in the screened feature set data and the category center feature data of hydraulic mode data, and through a Gaussian model, hydraulic mode classification is carried out to obtain hydraulic mode classification label data.

[0033] The construction of the hydraulic feature vector specifically constructs a hydraulic feature vector based on the hydraulic mode classification label data, the feature mean value, the feature standard deviation value and the feature change rate value in the screened feature set data;

[0034] The hydraulic mode anomaly detection and early warning specifically adopts a dynamic threshold mechanism based on the feature change rate value in the hydraulic feature vector and the hydraulic mode classification label data to realize the real-time identification and early warning of pumping mutations and unstable behaviors of the hydraulic system, so as to obtain hydraulic anomaly early warning signal data.

[0035] Further, the well-layer coupling data modeling is used to establish a dynamic response model between the pumping system and the aquifer and realize real-time quantitative modeling and optimization of the coupling relationship. Specifically, based on the multi-source optimized data at the wellhead, a combined modeling method for the pumping system aquifer that combines a multi-scale parameter coordination mechanism is adopted to perform well-layer coupling data modeling, and a well-layer coupling prediction parameter combination is obtained, including the following steps: pumping system modeling, aquifer modeling, coupling boundary setting, and joint parameter calibration;

[0036] The pumping system modeling is used to describe the water flow behavior inside the water pump and the well pipe. Specifically, a dynamic transfer equation between the pumping flow rate, the well pipe water level, and the system load is established, and a state residual feedback mechanism is introduced to improve the adaptability of the model to disturbance changes;

[0037] The aquifer modeling is used to characterize the seepage response behavior of the aquifer during pumping. Specifically, a dual-scale seepage response model is adopted to jointly represent the macroscopic and microscopic aquifer characteristics; the dual-scale seepage response model includes a macroscopic layer model and a microscopic layer model. The macroscopic layer model is constructed based on the unsteady groundwater flow control equation; the microscopic layer model introduces a void compression hysteresis function to calculate the aquifer response hysteresis;

[0038] The coupling boundary setting is specifically to establish a coupling boundary condition between the pumping system model and the aquifer model, and by establishing the coupling boundary condition, calculate the flow continuity at the wellbore boundary, and realize real-time parameter adjustment by introducing a multi-source data weight coefficient mechanism to obtain a joint initial model of the well layer;

[0039] The joint parameter calibration is used to optimize and adjust the parameters of the pumping system model and the aquifer model. Specifically, based on the water level, flow rate, and conductivity sensor data collected in actuality, through a parameter calibration method that combines the error minimum criterion, parameter update and iterative model training are carried out to obtain a well-layer coupling comprehensive model, and by using the well-layer coupling comprehensive model, a well-layer coupling prediction parameter combination is obtained;

[0040] The well-layer coupling prediction parameter combination specifically includes a real-time hydraulic characteristic vector, a pumping system water level parameter, an aquifer response flow rate parameter, and an aquifer dynamic water storage parameter.

[0041] Further, the model predictive control is used to dynamically optimize the well-layer coupling prediction model according to the real-time monitoring data during the operation of the groundwater well, and guide the adjustment and optimization of the wellhead data acquisition scheme. Specifically, based on the hydraulic characteristic vector, the hydraulic anomaly warning signal data, and the well-layer coupling prediction parameter combination, model predictive control is carried out to obtain wellhead data acquisition decision support data;

[0042] The wellhead data acquisition decision support data specifically includes wellhead data acquisition priority decision data, data acquisition time attribute data, and wellhead data quality adjustment and optimization suggestions;

[0043] The wellhead data acquisition priority decision data is used to determine the data types to be preferentially acquired and their corresponding data acquisition frequencies, including preferentially acquired parameter types and data acquisition frequencies;

[0044] The data acquisition time attribute data is used to optimize the acquisition scheduling strategy, including the acquisition window size and acquisition duration data;

[0045] The wellhead data quality adjustment and optimization suggestions are used to provide the quality assessment results of the currently acquired data, specifically referring to the quality assessment results of real-time data.

[0046] The beneficial effects achieved by the present invention using the above solution are as follows:

[0047] (1) Aiming at the technical problems in the existing wellhead data acquisition modeling system, such as the inability to efficiently synchronize and acquire multi-source wellhead data, the imperfect real-time feature extraction mechanism, and the lack of a response mechanism to hydraulic change trends, this solution creatively adopts a comprehensive optimization method that combines the improvement of hydraulic feature extraction and recognition and the optimization of well-layer coupling modeling. Through the improvement of hydraulic feature extraction and recognition, the accuracy and real-time performance of hydraulic feature extraction are solved, enabling the timely capture and effective early warning of hydraulic mode changes; at the same time, through the optimization of well-layer coupling modeling, the coupling between the pumping system and the aquifer is enhanced, ensuring that the model can dynamically adapt to the changes in the underground environment and improving the accuracy and adaptability of the modeling;

[0048] the changes in the environment, improving the accuracy and adaptability of the modeling;

[0049] (2) Aiming at the technical problems in the existing hydraulic feature processing process, such as the fast change speed of transient hydraulic features, the fuzzy frequency domain information, the difficult positioning of signal energy changes during the pumping process, and the easy interference by high-frequency noise, this solution creatively adopts a feature-enhanced transient hydraulic fluctuation recognition method that combines the improvement of adaptive time-frequency analysis for transient hydraulic feature extraction. An innovative local spectrum entropy-enhanced dynamic window function is introduced. By integrating the calculation of local spectrum entropy and combining the adaptive time-frequency analysis method, the time-frequency resolution of the transient hydraulic fluctuation signal is significantly improved; in the specific implementation, the calculation of the local spectrum entropy parameter can accurately represent the complexity of the signal by analyzing the local frequency distribution of the signal, and on this basis, the window width is dynamically adjusted to adapt to the change of the instantaneous frequency;

[0050] (3) Aiming at the technical problems existing in the existing well-layer coupling data modeling process, such as the difficulty in quantifying the interaction relationship between the pumping system and the aquifer, the lag of the coupling model response, the lack of the ability to dynamically characterize the non-linear seepage change, and the rough model parameter calibration mechanism, this solution creatively adopts a combined modeling method of the pumping system and the aquifer with a multi-scale parameter coordination mechanism to carry out well-layer coupling data modeling. By constructing a dynamic transmission equation of the pumping system, a two-scale seepage response model of the aquifer, and a coupling boundary setting mechanism, the real-time modeling and feedback optimization of the coupling process between the pumping system and the aquifer are realized. At the same time, a joint parameter calibration method is introduced to dynamically adjust the model parameters in combination with sensing data, improving the prediction ability of the model for aquifer compression response, water level disturbance conduction, and pumping efficiency change. Brief Description of the Drawings

[0051] Figure 1 It is a schematic structural diagram of a wellhead data acquisition and modeling system provided by the present invention;

[0052] Figure 2 It is a schematic flow diagram of the steps executed by the system provided by the present invention;

[0053] Figure 3 It is a schematic flow diagram of the steps executed by the hydraulic characteristic processing module;

[0054] Figure 4 It is a schematic flow diagram of the steps executed by the well-layer coupling modeling module.

[0055] The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention. Detailed Embodiments

[0056] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0057] Embodiment 1, refer to Figure 1 , the technical solution adopted by the present invention is as follows: A wellhead data acquisition and modeling system provided by the present invention includes a data acquisition module, a hydraulic characteristic processing module, a well-layer coupling modeling module, and an interaction control module;

[0058] The data acquisition module is used for data synchronous acquisition and processing. Through data synchronous acquisition and processing, wellhead multi-source optimized data is obtained, and the wellhead multi-source optimized data is sent to the hydraulic characteristic processing module and the well-layer coupling modeling module;

[0059] The hydraulic characteristic processing module is used for transient hydraulic characteristic extraction. Through transient hydraulic characteristic extraction, a hydraulic characteristic vector and hydraulic anomaly warning signal data are obtained, and the hydraulic characteristic vector and hydraulic anomaly warning signal data are sent to the interaction control module;

[0060] The well-layer coupling modeling module is used for well-layer coupling data modeling. Through well-layer coupling data modeling, a well-layer coupling prediction parameter combination is obtained, and the well-layer coupling prediction parameter combination is sent to the interaction control module;

[0061] The interaction control module is used for model predictive control. Through model predictive control, wellhead data acquisition decision support data is obtained.

[0062] By performing the above operations, aiming at the technical problems existing in the existing wellhead data acquisition and modeling system, such as the inability to efficiently synchronously collect multi-source wellhead data, the imperfect real-time feature extraction mechanism, and the lack of a response mechanism for hydraulic change trends, this solution creatively adopts a comprehensive optimization method combining improved extraction and recognition of hydraulic characteristics and optimization of well-layer coupling modeling. Through improved extraction and recognition of hydraulic characteristics, the accuracy and real-time performance of hydraulic characteristic extraction are solved, enabling timely capture and effective warning of hydraulic mode changes; at the same time, through optimization of well-layer coupling modeling, the coupling between the pumping system and the aquifer is enhanced, ensuring that the model can dynamically adapt to changes in the downhole environment, and improving the accuracy and adaptability of modeling.

[0063] Embodiment 2. This embodiment is based on the above embodiment, referring to Figure 1 、 Figure 2 The data synchronization acquisition and processing is used to provide synchronous and efficient wellhead data, specifically for performing data synchronization acquisition to obtain wellhead raw data, and through preprocessing operations, to obtain multi-source optimized wellhead data;

[0064] The wellhead raw data includes water level data, instantaneous flow rate data, water temperature data, conductivity data, wellhead vibration signals, and well pipe displacement data;

[0065] The multi-source optimized wellhead data specifically refers to the wellhead raw data after time synchronization, denoising filtering, and basic error calibration.

[0066] Embodiment 3. This embodiment is based on the above embodiment, referring to Figure 1 、 Figure 2 and Figure 3, the transient hydraulic feature extraction is used to extract hydraulic features and identify abnormal hydraulic fluctuation states. Specifically, based on the multi-source optimized wellhead data, a feature-enhanced transient hydraulic fluctuation identification method improved by combining adaptive time-frequency analysis is adopted to perform transient hydraulic feature extraction, obtaining a hydraulic feature vector and hydraulic anomaly warning signal data, including the following steps: adaptive time-frequency analysis improvement, transient feature extraction, dynamic feature enhancement, adaptive hydraulic mode classification, hydraulic feature vector construction, and hydraulic mode anomaly detection and warning;

[0067] The adaptive time-frequency analysis improvement is used to improve the time-frequency resolution of transient hydraulic fluctuation signals. Specifically, by constructing a dynamic window function enhanced by integrated local spectral entropy and adopting a time-varying sampling strategy, the wellhead sampling step is adjusted according to the change of instantaneous frequency. Through adaptive time-frequency distribution calculation, the time-frequency distribution data of hydrodynamic signals is obtained;

[0068] The dynamic window function enhanced by integrated local spectral entropy adjusts the transient signal according to the instantaneous frequency in the multi-source optimized wellhead data, and improves the window width adjustment of the dynamic window function by constructing local spectral entropy parameters, obtaining a dynamic window adjustment calculation function. The calculation formula is:

[0069] ;

[0070] In the formula, is the dynamic window adjustment calculation function, t is the time index, is the local time difference variable, is the dynamic window width, used to adjust the time-frequency resolution over time. The calculation formula is , where a is the basic window width, with a default value of 0.1, b is the adjustment coefficient, with a default value of 0.3, and H f (t) is the local spectral entropy parameter;

[0071] The local spectral entropy parameter is constructed by calculating the local frequency distribution of the signal at each sampling moment and representing the complexity of the signal spectrum based on the local spectral distribution;

[0072] The calculation formula for the local spectral distribution is:

[0073] ;

[0074] Wherein, P(f,t) is the local spectrum distribution function, X(f,t) is the complex spectrum coefficient after time-frequency transformation, and the complex spectrum coefficient X(f,t) after time-frequency transformation represents the complex frequency-domain response of the original signal at time t and instantaneous frequency f, which is specifically calculated by short-time Fourier transform. |X(f,t)| is the amplitude of the original signal at time t and instantaneous frequency f, f is the instantaneous frequency, and F is the set of instantaneous frequency bands of hydraulic characteristics;

[0075] The specific calculation formula of the local spectrum entropy parameter is:

[0076] ;

[0077] Wherein, H f (t) is the local spectrum entropy parameter, P(f,t) is the local spectrum distribution function, and the local spectrum distribution function P(f,t) is used to represent the normalized frequency distribution as a whole. is the frequency component information quantity calculation term, and specifically uses the natural logarithm base;

[0078] The time-varying sampling strategy is used to adaptively adjust the data sampling period according to the instantaneous frequency change caused by the pumping behavior. Specifically, based on the maximum instantaneous frequency in the multi-source optimized data at the wellhead, the sampling step size is calculated to obtain the time-varying sampling step size. The calculation formula is:

[0079] ;

[0080] Wherein, is the time-varying sampling step size, where k is the sampling time index, and f max (t k ) is the maximum instantaneous frequency corresponding to the kth sampling time;

[0081] The adaptive time-frequency distribution calculation is specifically based on the dynamic window adjustment calculation function and the time-varying sampling step size. By maximizing the time-frequency aggregation index, the time-frequency distribution calculation is performed to obtain the time-frequency distribution data of the hydrodynamic signal. The calculation formula is:

[0082] ;

[0083] Wherein, TFR[t k ,f] as a whole is the output of the maximum time-frequency aggregation calculation function, which is specifically implemented in the form of an improved short-time Fourier transform and is used to represent the time-frequency distribution data of the hydrodynamic signal. Among them, f is the instantaneous frequency, is the original transient signal corresponding to the local time difference variable and is used to represent the original sampling signal. Among them, n is the discretized time index, is the dynamic window adjustment calculation function centered on the time-varying sampling step size ​ Overall, it is a Fourier basis function for performing frequency transformation on local signals;

[0084] The transient feature extraction is used to extract transient change features from the hydraulic mode signal. Specifically, by calculating the multi-scale gradient features and extracting the transient energy ratio based on the time-frequency distribution data of the hydrodynamic signal, transient gradient feature data and transient energy ratio data are obtained, and by constructing a transient feature matrix based on the transient gradient feature data and the transient energy ratio data, transient feature data is obtained;

[0085] The multi-scale gradient feature calculation is used to extract the local transient change rate from the time-frequency distribution of the hydraulic signal and reflect the energy mutation characteristics caused by pumping disturbance, water level mutation, aquifer response or equipment abnormality in the hydraulic system, so as to obtain transient gradient feature data. The calculation formula is:

[0086] ;

[0087] In the formula, G(t, s) is the transient gradient feature data, where t is the time index, s is the multi-scale parameter, which is specifically adjusted by adjusting the window width of the dynamic window adjustment calculation function, and TFR(t, f) is the time-frequency distribution data of the hydrodynamic signal corresponding to time t and instantaneous frequency f;

[0088] The transient energy ratio extraction is used to detect mutations, surges, pumping fluctuations and pump interference in the hydraulic signal, so as to obtain transient energy ratio data. The calculation formula is:

[0089] ;

[0090] In the formula, TER(t) is the transient energy ratio data, f is the instantaneous frequency, F is the set of instantaneous frequency bands of the hydraulic characteristics, T is the total integration duration, and TFR(t, f) is the time-frequency distribution data of the hydrodynamic signal corresponding to time t and instantaneous frequency f;

[0091] The transient feature matrix construction combines the transient gradient feature data and the transient energy ratio data to construct transient feature data. The calculation formula is:

[0092] ;

[0093] In the formula, F trans is the transient feature data, G(t, s max ) is the transient gradient feature data at the optimal scale, s max is the optimal scale index, TER(t) is the transient energy ratio data, Overall, it is the transient energy ratio change rate parameter;

[0094] The dynamic feature enhancement specifically adopts a statistical-based dynamic feature weighting method, calculates the weighted value of the transient feature data based on the variance and change rate of the features, and selects features through a variance screening method to obtain the screened feature set data;

[0095] The adaptive hydraulic mode classification is used to classify the hydraulic mode in real time according to the screened features and identify the change of the hydraulic mode. Specifically, by constructing a basic classification model and based on the real-time data, the dynamic hydraulic mode data category center is designed to optimize the adaptability of the hydraulic mode classification, and the hydraulic mode data category center feature data is obtained. According to the hydraulic mode data category center feature data, the similarity between the features in the screened feature set data and the hydraulic mode data category center feature data is calculated, and the hydraulic mode classification is carried out through the Gaussian model to obtain the hydraulic mode classification label data;

[0096] The basic classification model specifically adopts a Gaussian mixture model, receives the transient feature vector as the input, calculates the response probability of the input under each Gaussian component, and adopts a soft assignment strategy to assign the input sample to each category. Finally, the category corresponding to the maximum probability is used as the classification result output;

[0097] The design of the dynamic hydraulic mode data category center adjusts the category center in real time by introducing an attenuation factor, and the calculation formula is:

[0098] ;

[0099] In the formula, C c (t + 1) is the clustering center of the c-th category of dynamic hydraulic mode data at the (t + 1)-th moment, is the attenuation factor, and the default value is 0.9. C c (t) is the clustering center of the c-th category of dynamic hydraulic mode data at the t-th moment. X c is the set of data samples classified as the c-th category in the current time step, is the transient feature data of the set of data samples belonging to the c-th category;

[0100] For the hydraulic mode classification, the standard Gaussian model is used to output the category probability, and the hydraulic mode type with the highest classification probability is selected as the hydraulic mode classification output to obtain the hydraulic mode classification label data;

[0101] The specific hydraulic mode types of the hydraulic mode classification label data include the continuous pumping state, the pulsed pumping state, the sudden interference state, and the intermittent recharge state;

[0102] The construction of the hydraulic feature vector is specifically carried out according to the feature mean value, feature standard deviation value and feature change rate value in the hydraulic mode classification label data and the screened feature set data to construct the hydraulic feature vector. The calculation formula is as follows:

[0103] ;

[0104] In the formula, V(t) is the hydraulic feature vector, t is the time index, is the hydraulic mode classification label data, is the feature mean value in the screened feature set data, is the feature standard deviation, is the feature change rate value;

[0105] The detection and early warning of hydraulic mode anomalies are specifically based on the feature change rate value in the hydraulic feature vector and the hydraulic mode classification label data, and a dynamic threshold mechanism is used to realize the real-time identification and early warning of pumping mutations and unstable behaviors of the hydraulic system, and obtain hydraulic anomaly early warning signal data.

[0106] By performing the above operations, in view of the technical problems existing in the existing hydraulic feature processing process, such as the fast change speed of transient hydraulic features, fuzzy frequency domain information, difficult positioning of signal energy changes during the pumping process and easy interference by high-frequency noise, this solution creatively adopts a feature-enhanced transient hydraulic fluctuation identification method improved by combining adaptive time-frequency analysis to extract transient hydraulic features, and innovatively introduces a dynamic window function enhanced by local spectral entropy. By integrating the calculation of local spectral entropy and combining the adaptive time-frequency analysis method, the time-frequency resolution of transient hydraulic fluctuation signals is significantly improved; in the specific implementation, the calculation of local spectral entropy parameters can accurately represent the complexity of the signal by analyzing the local frequency distribution of the signal, and on this basis, the window width is dynamically adjusted to adapt to the change of instantaneous frequency.

[0107] Example 4, this example is based on the above example, refer to Figure 1 、 Figure 2 and Figure 4 The well-layer coupling data modeling is used to establish a dynamic response model between the pumping system and the aquifer and realize the real-time quantitative modeling and optimization of the coupling relationship. Specifically, according to the wellhead multi-source optimization data, a combined pumping system-aquifer joint modeling method with a multi-scale parameter coordination mechanism is adopted to perform well-layer coupling data modeling to obtain a well-layer coupling prediction parameter combination, including the following steps: pumping system modeling, aquifer modeling, coupling boundary setting and joint parameter calibration;

[0108] The pumping system modeling is used to describe the water flow behavior inside the water pump and the well pipe. Specifically, a dynamic transfer equation between the pumping flow rate, the well pipe water level, and the system load is established, and a state residual feedback mechanism is introduced to improve the adaptability of the model to disturbance changes;

[0109] The calculation formula of the dynamic transfer equation is:

[0110] ;

[0111] In the formula, h w (t) is the well water level parameter, Q pump (t) is the pump pumping flow rate parameter, Q loss (t) is the hydraulic system loss, which is specifically used to represent the pipeline leakage parameter, A w is the well pipe cross-sectional area parameter, is the residual correction coefficient, and its specific calculation formula is , where is the residual adjustment parameter, with a default value of 0.4, h meas (t) is the actual water level parameter, h pred (t) is the predicted water level parameter;

[0112] The aquifer modeling is used to characterize the seepage response behavior of the aquifer during the pumping process. Specifically, a dual-scale seepage response model is adopted to jointly represent the macroscopic and microscopic aquifer characteristics; the dual-scale seepage response model includes a macroscopic layer model and a microscopic layer model. The macroscopic layer model is constructed based on the unsteady groundwater flow control equation; the microscopic layer model introduces a void compression hysteresis function to calculate the aquifer response hysteresis;

[0113] The calculation formula of the dual-scale seepage response model is:

[0114] ;

[0115] In the formula, is the aquifer dynamic water storage parameter, and its specific calculation formula is , where S s is the aquifer static water production parameter, is the void compression weight, as a whole is the derivative term of the void compression hysteresis function, which is used for the microscopic layer model modeling, is the pore compression hysteresis term, which is specifically calculated by fitting an empirical function based on the water level change. h is the equivalent water level height parameter in the aquifer, K is the permeability coefficient, is the equivalent water level height permeability gradient term, which is used to represent the groundwater flow modeling, Q well (t) is the pumping source parameter per unit volume;

[0116] The coupling boundary setting specifically involves establishing the coupling boundary conditions between the pumping system model and the aquifer model. By establishing these coupling boundary conditions, the flow continuity at the wellbore boundary is calculated. Through the introduction of a multi-source data weight coefficient mechanism, real-time parameter adjustment is achieved, and the initial well-layer combined model is obtained.

[0117] The calculation formula for the coupling boundary conditions is as follows:

[0118] ;

[0119] In the formula, Q pump (t) is the pump pumping flow rate parameter. In the calculation formula of the coupling boundary conditions, the pump pumping flow rate parameter is used to represent the flow conservation relationship at the coupling boundary between the pumping system and the aquifer model. K is the permeability coefficient, and A b is the total wellbore area, as a whole is the normal aquifer water level gradient, r w is the well radius, r is the radial coordinate index, is the boundary normal direction parameter;

[0120] The calculation formula for the multi-source data weight coefficient mechanism is as follows:

[0121] ;

[0122] In the formula, is the weighted model parameter, which is used to represent the model parameters after real-time adjustment of the initial well-layer combined model, is the weight of the initial model parameters, with a default value of 0.4, is the initial set parameter value of the pumping system model and the aquifer model, is the weight of the real-time parameters, with a default value of 0.6, is the optimal parameter corrected according to real-time data observation;

[0123] The joint parameter calibration is used to optimize and adjust the parameters of the pumping system model and the aquifer model. Specifically, based on the water level, flow rate, and conductivity sensor data collected in actuality, through the parameter calibration method combined with the error minimum criterion, parameter update and iterative model training are carried out to obtain the well-layer coupling comprehensive model. By using the well-layer coupling comprehensive model, the well-layer coupling prediction parameter combination is obtained.

[0124] The calculation formula for the parameter calibration method combined with the error minimum criterion is as follows:

[0125] ;

[0126] In the formula, is the well-layer coupling model parameter, which is used to represent the internal parameter set of the well-layer coupling comprehensive model, is the well-layer coupling model parameter of the previous iteration during the iterative model training process, is the update step size, with a default value of 0.2, is the optimal model parameter value estimate obtained by inverse estimation based on actual water level, flow rate, and conductivity parameters, is the model parameter after real-time adjustment of the initial well-layer combined model;

[0127] The well-layer coupling prediction parameter combination specifically includes a real-time hydraulic characteristic vector, a pumping system water level parameter, an aquifer response flow rate parameter, and an aquifer dynamic water storage parameter.

[0128] By performing the above operations, in view of the technical problems existing in the existing well-layer coupling data modeling process, such as the difficult quantification of the interaction relationship between the pumping system and the aquifer, the lag of the coupling model response, the lack of dynamic characterization ability for non-linear seepage changes, and the rough model parameter calibration mechanism, this solution creatively adopts a combined pumping system-aquifer joint modeling method with a multi-scale parameter coordination mechanism to perform well-layer coupling data modeling. By constructing a dynamic transmission equation of the pumping system, a two-scale seepage response model of the aquifer, and a coupling boundary setting mechanism, the real-time modeling and feedback optimization of the coupling process between the pumping system and the aquifer are realized; at the same time, a joint parameter calibration method is introduced to dynamically adjust the model parameters in combination with sensing data, improving the model's prediction ability for aquifer compression response, water level disturbance conduction, and pumping efficiency changes.

[0129] Example Five, this example is based on the above example, referring to Figure 1 and Figure 2 The model predictive control is used to dynamically optimize the well-layer coupling prediction model according to the real-time monitoring data during the operation of the groundwater well, and guide the adjustment and optimization of the wellhead data acquisition scheme. Specifically, based on the hydraulic characteristic vector, the hydraulic anomaly warning signal data, and the well-layer coupling prediction parameter combination, model predictive control is performed to obtain wellhead data acquisition decision support data;

[0130] The wellhead data acquisition decision support data specifically includes wellhead data acquisition priority decision data, data acquisition time attribute data, and wellhead data quality adjustment and optimization suggestions;

[0131] The wellhead data acquisition priority decision data is used to determine the data types to be preferentially collected and their corresponding data acquisition frequencies, including preferentially collected parameter types and data acquisition frequencies;

[0132] The data acquisition time attribute data is used to optimize the acquisition scheduling strategy, including the acquisition window size and acquisition duration data;

[0133] The wellhead data quality adjustment and optimization suggestions are used to provide the quality assessment results of the currently collected data, specifically referring to the quality assessment results of real-time data.

[0134] It should be noted that in this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process and method comprising a series of elements not only includes those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such a process and method.

[0135] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention.

[0136] The above describes the present invention and its implementation manners. Such a description is not restrictive. What is shown in the drawings is only one of the implementation manners of the present invention, and the actual structure is not limited thereto. Generally speaking, if those of ordinary skill in the art are inspired by it and design similar structural manners and embodiments without creative efforts without departing from the purpose of the present invention, they shall fall within the protection scope of the present invention.

Claims

1. A wellhead data acquisition and modeling system, characterized in that: It includes a data acquisition module, a hydraulic characteristic processing module, a well-layer coupling modeling module, and an interactive control module; The data acquisition module is used for synchronous data acquisition and processing. Through synchronous data acquisition and processing, it obtains multi-source optimized wellhead data and sends the multi-source optimized wellhead data to the hydraulic characteristic processing module and the well-layer coupling modeling module; The hydraulic characteristic processing module is used for transient hydraulic characteristic extraction. It adopts a feature-enhanced transient hydraulic fluctuation identification method combined with improved adaptive time-frequency analysis to obtain hydraulic characteristic vectors and hydraulic anomaly warning signal data, including the following steps: improved adaptive time-frequency analysis, transient feature extraction, dynamic feature enhancement, adaptive hydraulic mode classification, construction of hydraulic characteristic vectors, and detection and warning of hydraulic mode anomalies; The improved adaptive time-frequency analysis is used to improve the time-frequency resolution of transient hydraulic fluctuation signals. By constructing a dynamic window function with enhanced integrated local spectral entropy and adopting a time-varying sampling strategy, the wellhead sampling step is adjusted according to the change of instantaneous frequency, and the adaptive time-frequency distribution is calculated through this; The local spectral entropy parameter is obtained by calculating the local frequency distribution of the signal at each sampling moment and representing the complexity of the signal spectrum based on the local spectral distribution, thus constructing the local spectral entropy parameter; The well-layer coupling modeling module is used for well-layer coupling data modeling. It adopts a combined pumping system aquifer joint modeling method with a multi-scale parameter coordination mechanism to obtain a well-layer coupling prediction parameter combination, including the following steps: pumping system modeling, aquifer modeling, coupling boundary setting, and joint parameter calibration; The interactive control module is used for model predictive control. Through model predictive control, it obtains decision-making support data for wellhead data acquisition.

2. The wellhead data acquisition and modeling system according to claim 1, wherein: The synchronous data acquisition and processing is used to provide synchronous and efficient wellhead data. Specifically, it conducts synchronous data acquisition to obtain wellhead raw data, and through preprocessing operations, it obtains multi-source optimized wellhead data; The wellhead raw data includes water level data, instantaneous flow rate data, water temperature data, conductivity data, wellhead vibration signals, and well pipe displacement data; The multi-source optimized wellhead data specifically refers to the wellhead raw data after time synchronization, denoising filtering, and basic error calibration.

3. The wellhead data acquisition and modeling system according to claim 2, wherein: The transient hydraulic characteristic extraction is used to extract hydraulic characteristics and identify abnormal hydraulic fluctuation states. Specifically, based on the multi-source optimized wellhead data, it adopts a feature-enhanced transient hydraulic fluctuation identification method combined with improved adaptive time-frequency analysis to conduct transient hydraulic characteristic extraction, obtaining hydraulic characteristic vectors and hydraulic anomaly warning signal data, including the following steps: improved adaptive time-frequency analysis, transient feature extraction, dynamic feature enhancement, adaptive hydraulic mode classification, construction of hydraulic characteristic vectors, and detection and warning of hydraulic mode anomalies; The improved adaptive time-frequency analysis is used to improve the time-frequency resolution of transient hydraulic fluctuation signals. Specifically, by constructing a dynamic window function with enhanced integrated local spectral entropy and adopting a time-varying sampling strategy, the wellhead sampling step is adjusted according to the change of instantaneous frequency, and through adaptive time-frequency distribution calculation, it obtains time-frequency distribution data of hydrodynamic signals; The integrated local spectral entropy enhanced dynamic window function adjusts transient signals according to the instantaneous frequency in the wellhead multi-source optimized data, and improves the window width adjustment of the dynamic window function by constructing local spectral entropy parameters, obtaining a dynamic window adjustment calculation function. The calculation formula is as follows: ； In the formula, is the dynamic window adjustment calculation function, t is the time index, is the local time difference variable, is the dynamic window width, used to adjust the time-frequency resolution over time, and the calculation formula is , where a is the base window width, b is the adjustment coefficient, and H f (t) is the local spectral entropy parameter; The time-varying sampling strategy is used to adaptively adjust the data sampling period according to the instantaneous frequency change caused by the pumping behavior. Specifically, the sampling step size is calculated based on the maximum instantaneous frequency in the wellhead multi-source optimized data to obtain a time-varying sampling step size. The adaptive time-frequency distribution calculation is specifically carried out by maximizing the time-frequency concentration index according to the dynamic window adjustment calculation function and the time-varying sampling step size to obtain the time-frequency distribution data of the hydrodynamic signal.

4. The wellhead data acquisition and modeling system according to claim 3, wherein: The local spectral entropy parameter is constructed by calculating the local frequency distribution of the signal at each sampling moment and representing the complexity of the signal spectrum based on the local spectral distribution. The calculation formula of the local spectral distribution is as follows: ； In the formula, P(f,t) is the local spectral distribution function, X(f,t) is the complex spectral coefficient after time-frequency transformation. The complex spectral coefficient X(f,t) after time-frequency transformation represents the complex frequency domain response of the original signal at time t and instantaneous frequency f, and is specifically calculated by short-time Fourier transform. |X(f,t)| is the amplitude of the original signal at time t and instantaneous frequency f, f is the instantaneous frequency, and F is the set of instantaneous frequency bands of hydraulic characteristics. The specific calculation formula of the local spectral entropy parameter is as follows: ； where H f (t) is the local spectral entropy parameter, P(f,t) is the local spectral distribution function, and the local spectral distribution function P(f,t) is used to represent the normalized frequency distribution as a whole. is the frequency component information quantity calculation term, and specifically uses the natural logarithm base.

5. The wellhead data acquisition and modeling system according to claim 4, wherein: The transient feature extraction is used to extract transient change features from the hydraulic mode signal. Specifically, by calculating the multi-scale gradient features and extracting the transient energy ratio based on the time-frequency distribution data of the hydrodynamic signal, transient gradient feature data and transient energy ratio data are obtained. And by constructing a transient feature matrix based on the transient gradient feature data and the transient energy ratio data, transient feature data is obtained. The multi-scale gradient feature calculation is used to extract the local transient change rate from the time-frequency distribution of the hydraulic signal and reflect the energy mutation characteristics caused by pumping disturbance, water level mutation, aquifer response or equipment abnormality in the hydraulic system, obtaining transient gradient feature data. The transient energy ratio extraction is used to detect mutations, surges, pumping fluctuations and pump interference in the hydraulic signal, obtaining transient energy ratio data. The transient feature matrix construction combines the transient gradient feature data and the transient energy ratio data to construct transient feature data. The dynamic feature enhancement is specifically carried out by using a statistics-based dynamic feature weighting method to weight the transient feature data according to the variance and change rate of the features, and through a variance screening method to perform feature selection, obtaining screened feature set data. The adaptive hydraulic mode classification is used to classify hydraulic modes in real time based on screening features and identify changes in hydraulic modes. Specifically, by constructing a basic classification model and based on real-time data, a dynamic hydraulic mode data category center is designed to optimize the adaptability of hydraulic mode classification, obtaining hydraulic mode data category center feature data. Based on the hydraulic mode data category center feature data, by calculating the similarity between the features in the screening feature set data and the hydraulic mode data category center feature data, and through a Gaussian model, hydraulic mode classification is performed to obtain hydraulic mode classification label data.

6. The wellhead data acquisition and modeling system according to claim 5, characterized in that: The construction of the hydraulic feature vector is specifically to construct a hydraulic feature vector based on the hydraulic mode classification label data, the feature mean value, the feature standard deviation value, and the feature change rate value in the screening feature set data, obtaining the hydraulic feature vector. The hydraulic mode anomaly detection and early warning specifically uses the feature change rate value in the hydraulic feature vector and the hydraulic mode classification label data, and adopts a dynamic threshold mechanism to achieve real-time identification and early warning of pumping mutations and unstable behaviors of the hydraulic system, obtaining hydraulic anomaly early warning signal data.

7. The wellhead data acquisition and modeling system according to claim 6, wherein: The well-layer coupling data modeling is used to establish a dynamic response model between the pumping system and the aquifer and realize real-time quantitative modeling and optimization of the coupling relationship. Specifically, based on the wellhead multi-source optimization data, using a combined pumping system-aquifer joint modeling method with a multi-scale parameter coordination mechanism, well-layer coupling data modeling is performed to obtain a well-layer coupling prediction parameter combination, including the following steps: pumping system modeling, aquifer modeling, coupling boundary setting, and joint parameter calibration. The pumping system modeling is used to describe the water flow behavior inside the water pump and the well pipe. Specifically, a dynamic transfer equation between pumping flow rate, well pipe water level, and system load is established, and a state residual feedback mechanism is introduced to improve the adaptability of the model to disturbance changes. The aquifer modeling is used to characterize the seepage response behavior of the aquifer during pumping. Specifically, a dual-scale seepage response model is adopted to jointly represent the macroscopic and microscopic aquifer characteristics. The dual-scale seepage response model includes a macroscopic layer model and a microscopic layer model. The macroscopic layer model is constructed based on the unsteady groundwater flow control equation. The microscopic layer model introduces a void compression hysteresis function to calculate the aquifer response hysteresis. The coupling boundary setting is specifically to establish a coupling boundary condition between the pumping system model and the aquifer model, and by establishing the coupling boundary condition, calculate the flow continuity at the wellbore boundary, and through the introduction of a multi-source data weight coefficient mechanism, realize real-time parameter adjustment, obtaining a well-layer joint initial model. The joint parameter calibration is used to optimize and adjust the parameters of the pumping system model and the aquifer model. Specifically, based on the actually collected water level, flow rate, and conductivity sensor data, through a parameter calibration method combined with the error minimum criterion, parameter update and iterative model training are performed to obtain a well-layer coupling comprehensive model, and by using the well-layer coupling comprehensive model, a well-layer coupling prediction parameter combination is obtained. The well-layer coupling prediction parameter combination specifically includes real-time hydraulic characteristic vectors, pumping system water level parameters, aquifer response flow parameters, and aquifer dynamic water storage parameters.

8. The wellhead data acquisition and modeling system according to claim 7, characterized in that: The model predictive control is used to dynamically optimize the well-layer coupling prediction model based on real-time monitoring data during the operation of the groundwater well, and to guide the adjustment and optimization of the wellhead data acquisition plan. Specifically, based on the hydraulic characteristic vectors, the hydraulic anomaly warning signal data, and the well-layer coupling prediction parameter combination, model predictive control is performed to obtain decision support data for wellhead data acquisition. The decision support data for wellhead data acquisition specifically includes decision data on the priority of wellhead data acquisition, data acquisition time attribute data, and suggestions for adjusting and optimizing the quality of wellhead data. The decision data on the priority of wellhead data acquisition is used to determine the data types to be preferentially acquired and their corresponding data acquisition frequencies, including the types of parameters to be preferentially acquired and the data acquisition frequencies. The data acquisition time attribute data is used to optimize the acquisition scheduling strategy, including the size of the acquisition window and the data acquisition duration data. The suggestions for adjusting and optimizing the quality of wellhead data are used to provide the quality assessment results of the currently acquired data, specifically referring to the quality assessment results of real-time data.

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