Gas valve well accumulated water multi-source sensor dynamic calibration pumping drainage system and method

Abstract

The invention relates to the technical field of gas valve well accumulated water detection, in particular to a gas valve well accumulated water multi-source sensor dynamic calibration pumping drainage system and method, and the system comprises a sensing layer module, a verification layer module, an execution layer module and a monitoring layer module. The system collects water level, turbidity and vibration spectrum data through a multi-source sensor, generates an initial confidence coefficient weight in combination with edge calculation, judges the ponding state based on a weighted consistency index, drives pumping and drainage equipment to execute optimization operation, and dynamically adjusts a pumping and drainage mode to prevent dry rotation or overload of a pump at the same time; according to the method, the false alarm rate in a complex underground environment can be remarkably reduced, the invalid pumping and drainage frequency is reduced, the service life of equipment is prolonged, and the system response speed and the energy efficiency performance are improved.

Description

Technical Field

[0001] This invention belongs to the field of gas pipeline safety monitoring and maintenance technology, specifically a dynamic calibration and drainage system and method for multi-source sensors of water accumulation in gas valve wells. Background Technology

[0002] Existing technologies combine a single float-type water level sensor with a fixed threshold triggering mechanism to monitor and pump out water accumulation in gas valve wells, aiming to reduce safety hazards caused by water accumulation. In practical applications, traditional methods rely on sensors to detect the water level in the well in real time to activate the pumping equipment. However, in complex downhole environments, consistent target detection remains challenging. A drawback of existing methods is their insufficient ability to fuse multi-source data. While existing technologies can perform some calibration when processing data from different environmental variables, they are still limited when sediment concentration is >80 NTU or diurnal temperature range is >15°C, affecting the accuracy of water accumulation detection. Sensor reading drift often leads to increased false alarm rates, especially when optical turbidimeters produce ±12% errors due to refractive index variations. Reading drift directly affects the system's decision-making effectiveness; erroneous information may trigger more than 15 invalid pumping initiations per day, thereby reducing system reliability and energy efficiency.

[0003] Under conditions of significant environmental interference, improving detection accuracy and system response speed remains a challenge for existing technologies that use a fixed threshold logic framework and single sensor data for water accumulation detection. Although existing technologies mitigate false triggering by setting a trigger condition of water level >15cm, the false triggering rate is still as high as 41.3% when vehicle vibration causes instantaneous fluctuations in water flow (vibration spectrum dominant frequency >20Hz). This is because the system lacks the ability to dynamically assess sensor confidence or the assessment mechanism is not precise enough, resulting in an inability to effectively distinguish between real water accumulation and noise signals.

[0004] Current flood drainage methods mostly rely on cloud data processing for decision support; however, when transmission latency exceeds 200ms, the system's response speed drops significantly. Even with edge computing or data compression technologies, it remains difficult to achieve ideal drainage results in scenarios where floodwater rises rapidly. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a dynamic calibration and drainage method and system for multi-source sensors for water accumulation in gas valve wells, in order to reduce the high false alarm rate caused by environmental interference.

[0006] The technical solution adopted by this invention to solve the above-mentioned technical problems is as follows: a method for dynamic calibration and drainage of water accumulation in gas valve wells using multi-source sensors, comprising the following steps:

[0007] S1: Taking the detection of water accumulation in gas valve wells as the target, acquire multi-source sensor data in the target well and process it to generate an initial confidence weight matrix. Optimize the sensor confidence weights through a multi-source data consistency threshold verification mechanism.

[0008] S2: Based on the edge computing framework, the system uses a real-time dynamic calibration algorithm to fuse and analyze multi-source sensor data, generating water accumulation status assessment results and triggering conditions;

[0009] S3: By combining a dynamic weighted loss function with a phased optimization strategy, the water accumulation detection model is trained to improve its adaptability in complex environments, and knowledge transfer is achieved through a pseudo-label generation mechanism.

[0010] S4: Determine the water accumulation status in the target well using a trained water accumulation detection model, and ensure the consistency of the water accumulation status by using multi-source data matching and reasoning.

[0011] Optionally, step S1 specifically includes the following steps:

[0012] S11: Acquire raw data from multiple different types of sensors within the target well;

[0013] S12: Preprocess the raw data, including filtering and denoising, normalization and timestamp alignment, to generate a standardized dataset;

[0014] S13: Calculate the initial confidence weight matrix using the standardized dataset, as shown in the following formula:

[0015] ;in, Indicates the first The sensor at the first Confidence weights at each time point Indicates the first The sensor at the first Measurements at each time point Indicates the first The average value of each sensor, Indicates the first The variance of each sensor, To prevent small constants with a denominator of zero;

[0016] S14: Optimize the confidence weight matrix through a multi-source data consistency threshold verification mechanism, as shown in the following formula:

[0017] ;in, Indicates the first The sensor and the first Consistency index among individual sensors Indicates the total number of sensors. This represents the weight assigned to the i-th sensor for the m-th metric or dimension. This represents the weight assigned to the j-th sensor for the same m-th metric or dimension;

[0018] S15: Apply the optimized confidence weight matrix to the subsequent data processing flow, and update the weight matrix through multiple rounds of iteration to improve accuracy.

[0019] Optionally, in step S15, the specific steps for optimizing the confidence weight matrix are as follows:

[0020] Perform a geometric verification on the confidence weight matrix to check whether the weight distribution meets expectations. If it does not, adjust the weight distribution.

[0021] Low-quality data is filtered using a confidence level screening mechanism, as shown in the following formula:

[0022] ;in, Indicates the first Is the data from each sensor retained? This is the confidence threshold.

[0023] Optionally, in step S2, the specific steps for generating the water accumulation status assessment result are as follows:

[0024] Multi-source sensor data are mapped onto a unified time axis using a dynamic calibration algorithm to generate preliminary water accumulation status assessment results;

[0025] The preliminary assessment results are manually checked to verify the accuracy and completeness of the water depth and category labels. If they are not met, they are corrected.

[0026] The checked and corrected assessment results are added to the waterlogging status dataset.

[0027] Optionally, in step S2, the specific steps for generating the triggering condition are as follows:

[0028] The trained dynamic calibration algorithm is used to infer from multi-source sensor data to generate preliminary triggering conditions.

[0029] The initial triggering conditions are mapped to the final triggering conditions through a consistency check mechanism.

[0030] Optionally, step S3 specifically includes the following steps:

[0031] S31: The water accumulation detection model is initialized and trained using the water accumulation state dataset;

[0032] S32: Low-level features of the frozen water accumulation detection model; training is conducted only on high-level features, and optimization is performed using pseudo-labeled data.

[0033] S33: Thaw some low-level features of the water accumulation detection model and continue to fine-tune the model using the water accumulation state dataset; switch the optimizer from Adam to RMSProp, reduce the learning rate to ensure training stability, restore the weights of the loss function to normal, and ensure that training focuses on feature extraction from supervised data.

[0034] S34: The first 10 layers of the frozen water accumulation detection model are fine-tuned using multi-source sensor data through a weighted loss function to transfer knowledge from the model.

[0035] S35: Unfreeze 3 of the first 10 layers of the water accumulation detection model, restore the weights of the loss function, and switch the optimizer to RMSProp; fine-tune the model using the water accumulation state dataset to achieve final convergence on the water accumulation state data.

[0036] Optionally, in step S4, the specific steps for multi-source data matching are as follows:

[0037] The water accumulation status in the target well was detected using a Kalman filter algorithm under different sensor conditions.

[0038] A dynamic calibration algorithm is used to map the water accumulation status of different sensors to a unified time axis;

[0039] The overlapping area and overlap ratio of water accumulation states under different sensors are calculated to construct a matching degree matrix. The matching degree matrix is ​​used to help determine the matching relationship of water accumulation states under multi-source data.

[0040] Optionally, the specific steps for calculating the overlapping area are as follows:

[0041] Convert the water accumulation status of each sensor into a polygon;

[0042] Calculate the area of ​​the intersection region between the water accumulation state polygon of each sensor and the water accumulation state polygon of other sensors in turn to obtain the degree of spatial overlap between the two water accumulation states.

[0043] The specific steps for calculating the overlap ratio are as follows:

[0044] The proportion of the overlapping area of ​​the polygons representing water accumulation states under different sensors to the total area of ​​the water accumulation state is calculated to evaluate the similarity of water accumulation states under different sensors.

[0045] This invention also provides a dynamic calibration and drainage system for multi-source sensors of water accumulation in gas valve wells, characterized by comprising the following modules:

[0046] The multi-source data acquisition submodule is used to acquire multi-source sensor data in the target well and process it to generate an initial confidence weight matrix. The sensor confidence weight is optimized through a multi-source data consistency threshold verification mechanism.

[0047] The dynamic calibration submodule is used to fuse and analyze multi-source sensor data through a real-time dynamic calibration algorithm based on an edge computing framework, and generate water accumulation status assessment results and triggering conditions.

[0048] The model training submodule is used to train the water accumulation detection model by combining a dynamic weighted loss function and a phased optimization strategy to improve its adaptability in complex environments, and to achieve knowledge transfer through a pseudo-label generation mechanism.

[0049] The detection submodule is used to determine the water accumulation status in the target well using a trained water accumulation detection model, and to ensure the consistency of the water accumulation status by using multi-source data matching and reasoning.

[0050] Optionally, the multi-source data acquisition submodule acquires raw data from the target well using a float-type water level sensor, an optical turbidimeter, and a temperature sensor, and performs filtering, noise reduction, normalization, and timestamp alignment on the raw data to generate a standardized dataset.

[0051] The beneficial effects of this invention are as follows:

[0052] The present invention discloses a dynamic calibration and drainage method and system for multi-source sensors for water accumulation in gas valve wells. Based on an edge computing framework, it combines a dynamic weighted loss function and a phased optimization strategy. It utilizes multi-source sensors to generate pseudo-label data, which is then mapped to a unified time axis through a dynamic calibration algorithm. This helps the water accumulation detection model learn more target feature information and solves the problem of high false alarm rate caused by environmental interference.

[0053] This invention employs a multi-source data matching mechanism and a dynamic calibration algorithm. By modifying the training strategy, it better integrates information from different sensors and efficiently integrates multi-source data through phased optimization. A dynamic pseudo-label filtering mechanism is introduced to filter out low-quality or noisy pseudo-labels, ensuring that the water accumulation detection model uses only reliable pseudo-label signals during training, thereby improving the model's learning effect and performance.

[0054] This invention utilizes a multi-source data matching mechanism to optimize the water accumulation state matching process based on overlapping regions and overlap ratios, further reducing misjudgments caused by occlusion. This mechanism, through more accurate matching matrix calculations and leveraging complementary information between multi-source data, effectively reduces erroneous judgments in cases of target occlusion.

[0055] The dynamic calibration algorithm proposed in this invention combines limited labeled and unlabeled data to generate high-quality pseudo-labels through multi-source sensors, reducing reliance on labeled data. Simultaneously, by weightedly combining labeled data loss and pseudo-label loss, it improves the model's generalization ability and detection accuracy under conditions of insufficient labeling. Attached Figure Description

[0056] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0057] Figure 1 This is a flowchart illustrating the dynamic calibration and drainage method for multi-source sensors used in gas valve well water accumulation according to the present invention.

[0058] Figure 2 This is a schematic diagram of the architecture of the gas valve well water accumulation multi-source sensor dynamic calibration and drainage system of the present invention. Detailed Implementation

[0059] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0060] In traditional gas valve well water accumulation monitoring technology, insufficient multi-source sensor data fusion capabilities, lack of dynamic assessment of sensor confidence, and response delays caused by cloud data processing lead to reduced detection accuracy, increased false triggering rates, and slower system response speeds. Specifically, sensor reading drift caused by changes in environmental variables results in incorrect water accumulation status assessments; fixed threshold triggering mechanisms cannot distinguish between real water accumulation and transient noise signals, causing frequent false triggers; and transmission delays in cloud processing affect real-time response. Furthermore, the system lacks dynamic verification capabilities for multi-source data consistency, meaning that in complex downhole environments, abnormal outputs from a single sensor directly impact decision reliability, thereby reducing overall system performance.

[0061] For example, in the actual operation of gas valve wells beneath urban roads, continuous rainfall leads to increased sediment concentration within the wells, and significant diurnal temperature variations cause sensors such as optical turbidimeters to output unstable data due to changes in refractive index. The system cannot dynamically assess the confidence level of each sensor, resulting in frequent misjudgments of water accumulation based on fixed threshold logic from a single sensor. Vibrations from passing vehicles cause instantaneous fluctuations in water flow, further exacerbating the risk of false triggering. Simultaneously, transmission delays in cloud data processing prevent timely issuance of pumping commands, affecting the timeliness of water drainage. Specifically, the lack of consistency verification of sensor data prevents the system from identifying interfered sensors, leading to erroneous decisions.

[0062] If the above problems are not addressed, the system's reliability will decrease. False triggers will lead to unnecessary startup of pumping equipment, increasing energy consumption; inaccurate detection may delay the actual treatment of accumulated water, causing safety hazards; and response delays will be unable to adapt to rapidly rising water levels, threatening facility safety. Consequently, the system's adaptability to complex environments will be weakened, its long-term operational stability will be compromised, and its safety assurance function for gas facilities will be affected.

[0063] Example 1

[0064] like Figure 1 As shown, this invention provides a dynamic calibration and drainage method for multi-source sensors of water accumulation in gas valve wells, comprising the following steps:

[0065] S1: Taking the detection of water accumulation in gas valve wells as the target, acquire multi-source sensor data in the target well and process it to generate an initial confidence weight matrix. Optimize the sensor confidence weights through a multi-source data consistency threshold verification mechanism.

[0066] S2: Based on the edge computing framework, the system uses a real-time dynamic calibration algorithm to fuse and analyze multi-source sensor data, generating water accumulation status assessment results and triggering conditions;

[0067] S3: By combining a dynamic weighted loss function with a phased optimization strategy, the water accumulation detection model is trained to improve its adaptability in complex environments, and knowledge transfer is achieved through a pseudo-label generation mechanism.

[0068] S4: Determine the water accumulation status in the target well using a trained water accumulation detection model, and ensure the consistency of the water accumulation status by using multi-source data matching and reasoning.

[0069] This application relates to a dynamic calibration and drainage method for multi-source sensors of water accumulation in gas valve wells. The initial confidence weight matrix refers to a set of values ​​used to quantify the reliability of each sensor at a specific point in time. It can be implemented by assigning weights based on the accuracy index of the sensor's historical calibration records. For example, the initial weight value can be set according to the error range specified by the sensor at the factory, or the weight matrix can be generated by statistically analyzing the deviation distribution of repeated measurement data under a standard laboratory environment. The main purpose is to achieve a basic quantitative assessment of the reliability of multi-source sensor data.

[0070] Furthermore, the multi-source data consistency threshold verification mechanism refers to the verification process used to optimize sensor confidence weights. This can be achieved by comparing Euclidean distances between sensor readings, such as calculating the distance between different sensor measurements in the feature space and setting a threshold range, or by judging data consistency through majority voting rules. Its main purpose is to dynamically adjust sensor weights to eliminate interference from abnormal data. The real-time dynamic calibration algorithm refers to the computational process of fusing and analyzing multi-source sensor data. This can be achieved by using a sliding window averaging technique to perform real-time weighted processing of the data, such as fusing multi-source inputs using exponential smoothing based on time series, or dynamically updating state predictions using a Bayesian estimation framework. Its main purpose is to generate waterlogging state assessment results and suppress noise. Therefore, the dynamic weighted loss function refers to the mathematical expression that adjusts the contribution of different samples or features during model training. This can be achieved using an adaptive weight allocation mechanism based on sample difficulty, such as dynamically adjusting the weight of the loss term according to the magnitude of the prediction error during training, or setting a piecewise weight function based on changes in environmental parameters. Its main purpose is to achieve robust optimization of the model in complex environments.

[0071] Specifically, the phased optimization strategy refers to the method of adjusting the model training parameters step by step. It can be achieved by gradually unfreezing the training order of the model layers. For example, first freeze the deep network and train only the output layer, and then gradually unfreeze the intermediate layers for fine-tuning. Alternatively, it can improve the model's generalization ability by increasing the complexity of the training data in stages. Its main purpose is to achieve a gradual enhancement of the model's feature extraction capability.

[0072] As a preferred implementation, the pseudo-label generation mechanism refers to a technique that expands the training set using unlabeled data. This can be achieved using a model prediction confidence threshold filtering method, such as retaining only high-confidence prediction results as pseudo-labels, or generating pseudo-labels by grouping unlabeled data using clustering algorithms. Its main purpose is to achieve knowledge transfer and improve the model's ability to identify abnormal data. Multi-source data matching inference refers to the verification process that ensures the consistency of water accumulation status judgment. This can be achieved using timestamp alignment and statistical consistency checks, such as cross-validating the data distribution of different sensors within the same time window, or using a rule engine to match the consistency of key feature points. Its main purpose is to ensure the reliability of water accumulation status judgment. In practical applications, this embodiment, through the systematic integration of the above mechanisms, effectively addresses the problem of inaccurate detection caused by insufficient multi-source data fusion capabilities, lack of dynamic assessment of sensor confidence, and cloud processing response delays in complex downhole environments. It avoids the limitations of single-sensor dependence and fixed threshold mechanisms, thereby maintaining the accuracy and response efficiency of water accumulation monitoring under conditions of sediment concentration or temperature variation.

[0073] In the scenario of monitoring water accumulation in gas valve wells, the dynamic calibration and extraction method of multi-source sensor data achieves accurate identification and efficient response to water accumulation by systematically integrating data preprocessing, real-time analysis, model training, and state judgment. Specifically, this method first targets water accumulation detection in gas valve wells, acquires multi-source sensor data from the target well, and processes it to generate an initial confidence weight matrix. The initial confidence weight matrix is ​​quantified based on the variance and mean of the sensor measurements, thereby dynamically evaluating the reliability of each sensor.

[0074] Furthermore, a multi-source data consistency threshold verification mechanism optimizes sensor confidence weights. This mechanism calculates a consistency index based on the weight differences between sensors, automatically identifying and reducing the weights of sensors affected by environmental interference, such as suppressing drift signal interference during sudden changes in sediment concentration or drastic temperature variations. Building upon this, a real-time dynamic calibration algorithm, based on an edge computing framework, fuses and analyzes multi-source sensor data, mapping data from different sensors onto a unified time axis to generate water accumulation status assessment results. Simultaneously, a consistency verification mechanism processes initial triggering conditions, ensuring that decisions are based on a comprehensive judgment of multi-source data rather than a single signal. This effectively distinguishes between actual water accumulation and instantaneous noise (such as water flow fluctuations caused by vehicle vibrations), avoiding false triggering problems caused by fixed threshold mechanisms.

[0075] Furthermore, the water accumulation detection model is trained by combining a dynamic weighted loss function with a phased optimization strategy. This process prioritizes learning environmental robustness features to adapt to complex scene changes by freezing the low-level features of the model and gradually unfreezing and fine-tuning them. The pseudo-label generation mechanism utilizes unlabeled data to achieve knowledge transfer, expands the model's ability to identify abnormal data, and enhances its generalization performance under data-scarce conditions.

[0076] Finally, the water accumulation status in the target well is determined by the trained water accumulation detection model, and the consistency of the water accumulation status is ensured by multi-source data matching inference. The multi-source data matching inference detects the water accumulation status through the Kalman filter algorithm and calculates the overlapping area and ratio of the water accumulation status polygons under different sensors to construct a matching degree matrix. The measurement results are cross-validated to avoid decision bias caused by the failure of a single sensor.

[0077] In a specific embodiment, a float-type water level sensor and an optical turbidimeter are deployed in the target well as multi-source sensors to acquire raw data from the well. The raw data is filtered, denoised, normalized, and timestamped to generate a standardized dataset, which is then used to calculate an initial confidence weight matrix. The weight distribution is optimized through a multi-source data consistency threshold verification mechanism. For example, when the optical turbidimeter experiences measurement fluctuations due to changes in refractive index, its weight is automatically reduced to minimize the impact of errors. Subsequently, the edge computing device runs a dynamic calibration algorithm in real time to fuse and analyze the data from the water level sensor and turbidimeter, generating water depth assessment results and pumping trigger conditions. The reliability of the trigger conditions is ensured through consistency verification. The water accumulation detection model adopts a phased optimization strategy during the training phase. First, low-level features are frozen and pseudo-labeled data is used for initial training. Then, some low-level features are gradually unfrozen and the optimizer is switched for fine-tuning. When the final model is used to determine the water accumulation status, Kalman filtering is used to perform matching inference on the multi-source data, and the overlap ratio of the water accumulation status polygon is calculated to verify the consistency of the status.

[0078] Therefore, this method effectively solves the problems of inaccurate detection, high false trigger rate, and slow system response caused by insufficient multi-source sensor data fusion capability, lack of dynamic assessment of sensor confidence, and response delay caused by cloud processing in gas valve well water accumulation monitoring. By optimizing sensor weights through a dynamic calibration mechanism, the data fusion accuracy under complex environments (such as high sediment concentration or large temperature difference conditions) is significantly improved. Real-time processing based on edge computing avoids cloud transmission delays, ensuring timely system response in scenarios with rapidly rising water levels. At the same time, phased model training and multi-source data matching inference enhance the system's robustness to environmental interference and reduce the risk of false triggers caused by noise signals, thereby improving the overall reliability and energy efficiency of water accumulation monitoring.

[0079] Specifically, in some embodiments of this application, step S1 is proposed to obtain multi-source sensor data and optimize confidence weights. However, in its implementation, the sensors are easily interfered with in complex downhole environments (such as high sediment concentration or large temperature difference), resulting in high noise and severe drift of the original data, which leads to inaccurate calculation of the initial confidence weights, thereby affecting the reliability of water accumulation status assessment and causing false triggering and invalid pumping.

[0080] In this regard, this application further proposes that, in the above-mentioned dynamic calibration and drainage method for multi-source sensors of water accumulation in gas valve wells, the specific steps in step S1 are as follows:

[0081] S11: Acquire raw data from multiple different types of sensors within the target well;

[0082] S12: Preprocess the raw data, including filtering and denoising, normalization and timestamp alignment, to generate a standardized dataset;

[0083] S13: Calculate the initial confidence weight matrix using the standardized dataset, as shown in the following formula:

[0084] ;in, Indicates the first The sensor at the first Confidence weights at each time point Indicates the first The sensor at the first Measurements at each time point Indicates the first The average value of each sensor, Indicates the first The variance of each sensor, To prevent small constants with a denominator of zero;

[0085] S14: Optimize the confidence weight matrix through a multi-source data consistency threshold verification mechanism, as shown in the following formula:

[0086] ;in, Indicates the first The sensor and the first Consistency index among individual sensors Indicates the total number of sensors. This represents the weight assigned to the i-th sensor for the m-th metric or dimension. This represents the weight assigned to the j-th sensor for the same m-th metric or dimension;

[0087] S15: Apply the optimized confidence weight matrix to the subsequent data processing flow, and update the weight matrix through multiple rounds of iteration to improve accuracy.

[0088] Among these, multiple different types of sensors refer to a combination of sensors that can provide complementary data. This can be achieved using liquid level sensors, optical sensors, and thermal sensors, etc., with the aim of reducing the impact of single sensor failure or environmental interference through multi-source data redundancy. Filtering and denoising refers to eliminating random noise in the data, which can be achieved using digital filters such as Kalman filtering or wavelet transform, with the aim of suppressing instantaneous interference such as vehicle vibration. Normalization refers to converting data with different dimensions to a unified scale, which can be achieved using min-max normalization or Z-score normalization, with the aim of ensuring the comparability of multi-source data. Timestamp alignment refers to synchronizing the time base of multi-source data, which can be achieved through interpolation algorithms or time... The synchronization protocol is used to ensure data consistency over time. The initial confidence weight matrix is ​​a dynamically assigned weight based on the stability of sensor data, which can be implemented using the inverse of variance as the weight basis. Its purpose is to objectively quantify the reliability of sensors at a specific point in time. The multi-source data consistency threshold verification mechanism adjusts the weights by evaluating the consistency of data between sensors. It can be implemented by using an exponential decay function to smooth the weight differences. Its purpose is to achieve mutual verification and anomaly filtering of multi-sensor data. Multi-round iterative updates refer to dynamically adjusting the weight distribution according to environmental changes. This can be implemented through a cyclic optimization algorithm. Its purpose is to continuously improve the accuracy of the weight matrix to adapt to complex environments.

[0089] Specifically, the proposed solution first acquires raw data from multiple different types of sensors, leveraging the complementary nature of multi-source data to provide a comprehensive and redundant input foundation. Second, the raw data undergoes filtering, denoising, normalization, and timestamp alignment to eliminate noise interference and ensure data consistency across dimensions and time, generating a standardized dataset. Then, an initial confidence weight matrix is ​​calculated based on the standardized dataset, dynamically quantifying the reliability of each sensor using variance and mean to avoid the limitations of subjective weighting. Next, the confidence weight matrix is ​​optimized through a multi-source data consistency threshold verification mechanism, using a consistency index to assess weight differences between sensors and performing exponential decay smoothing to automatically filter out abnormal data. Finally, the optimized weight matrix is ​​applied to subsequent processes and iteratively updated multiple times, enabling the system to dynamically adjust the weight distribution based on changes in sediment concentration or temperature difference. These steps form a closed-loop optimization process, ensuring that the accuracy of the confidence weights is unaffected by sensor drift in harsh downhole environments, thus providing a reliable basis for water accumulation status assessment.

[0090] As a preferred embodiment, the solution of this application is implemented as follows: A float-type water level sensor, an optical turbidimeter, and a temperature sensor are installed in the target well. The raw data is transmitted to the edge computing node via a wired communication interface. In the preprocessing stage, a low-pass filter is used to eliminate high-frequency noise caused by vehicle vibration, the data is normalized using the Z-score method, and timestamp alignment is achieved through a network time protocol. In the initial confidence weight calculation, sensors with smaller variance are assigned higher weights; after consistency verification, the weight matrix is ​​optimized, and the weights of sensors with low consistency are automatically reduced. This process is executed iteratively until the weight distribution converges.

[0091] Through the above solution, this application effectively solves the problem of sensor data reliability in complex downhole environments, improves the accuracy of confidence weights, thereby reducing the risk of false triggering of water accumulation status assessment and improving the reliability and energy efficiency of the system.

[0092] In practical applications, some of the embodiments described above in this application propose to optimize the confidence weight matrix to improve the fusion accuracy of multi-source sensor data and the reliability of water accumulation status assessment. However, in its implementation, the weight distribution may not meet expectations and there is a lack of an effective filtering mechanism for low-quality data, which leads to increased sensor data drift in complex environments such as high sediment concentration or large temperature difference, thereby causing an increase in the false alarm rate of water accumulation detection and inaccurate system decision-making.

[0093] In this regard, this application further proposes the following specific steps for optimizing the confidence weight matrix in step S15:

[0094] Perform a geometric verification on the confidence weight matrix to check whether the weight distribution meets expectations. If it does not, adjust the weight distribution.

[0095] Low-quality data is filtered using a confidence level screening mechanism, as shown in the following formula:

[0096] ;in, Indicates the first Is the data from each sensor retained? This is the confidence threshold.

[0097] Geometric verification refers to the statistical analysis of the distribution of the confidence weight matrix, which can be achieved using skewness testing or distribution fit testing, with the aim of identifying abnormal deviations in the weight distribution. Checking whether the weight distribution meets expectations can be understood as verifying whether the weight values ​​are within a reasonable range set based on the sensor's historical behavior. Specifically, the expected distribution model can be dynamically adjusted according to environmental parameters, with the aim of ensuring that the weights reflect the sensor's true reliability under current conditions. Adjusting the weight distribution specifically refers to correcting the weight values ​​through a reweighting algorithm, which can be achieved using exponential smoothing or linear transformation techniques, with the aim of making the distribution more closely match the sensor's response characteristics to changes in sediment concentration or temperature fluctuations. The confidence screening mechanism refers to a data filtering mechanism based on dynamic confidence weights, which can be implemented by a software threshold judgment module or a hardware comparator circuit, with the aim of eliminating low-quality data affected by environmental interference.

[0098] Specifically, the solution in this application performs a geometric verification of the confidence weight matrix to detect whether the weight distribution deviates from the expected model established based on the sensor's physical characteristics and historical data; if an anomaly is detected, the weight distribution is dynamically adjusted to match the sensor's actual response in complex environments; simultaneously, a confidence screening mechanism is used based on... and The comparison results automatically filter out data with confidence levels below the threshold, thereby preventing drift signals from participating in the water accumulation status assessment. The close cooperation between geometric verification and confidence screening mechanisms ensures that the optimized weight matrix can accurately reflect the real-time reliability of each sensor, providing high-quality input for subsequent data fusion.

[0099] As a specific implementation method, geometric verification may include calculating the kurtosis value of the weight distribution and comparing it with a preset range; if it exceeds the range, a nonlinear transformation is applied to adjust the weights; in the confidence screening mechanism, The determination is dynamically based on the sensor type and current environmental conditions; for example, when an optical turbidimeter is affected by refractive index... The Tw value is automatically lowered to allow more data to pass through, while it remains high to strictly filter noise when the float-type water level sensor is working stably. In scenarios where vehicle vibration causes instantaneous fluctuations in water flow, this mechanism effectively identifies and filters low-confidence data points.

[0100] The above scheme significantly improves the accuracy of confidence weight matrix optimization, effectively suppresses the impact of sensor data drift on water accumulation detection, thereby reducing the false alarm rate and enhancing the decision reliability of the system in complex downhole environments.

[0101] Specifically, in some of the embodiments described above in this application, a method for generating water accumulation status assessment results through a real-time dynamic calibration algorithm is proposed. However, during its implementation, the assessment results may be distorted due to sensor noise or environmental interference. There is a lack of a verification mechanism for the accuracy and completeness of water accumulation depth and category labels, which leads to errors or omissions in the assessment results in complex downhole environments with high sediment concentration or large temperature differences. This can result in erroneous triggering of pumping operations and a decrease in system reliability.

[0102] In this regard, this application further proposes the following specific steps in step S2 for generating the water accumulation status assessment results:

[0103] Multi-source sensor data are mapped onto a unified time axis using a dynamic calibration algorithm to generate preliminary water accumulation status assessment results;

[0104] The preliminary assessment results are manually checked to verify the accuracy and completeness of the water depth and category labels. If they are not met, they are corrected.

[0105] The checked and corrected assessment results are added to the waterlogging status dataset.

[0106] Among them, the dynamic calibration algorithm refers to an algorithm used to correct the time deviation of multi-source sensor data. It can be implemented using techniques such as Kalman filtering or sliding window averaging, with the aim of ensuring the consistency of data from different sensors in the time dimension. Manual inspection can be understood as the process by which operators review the automatically generated evaluation results. Specifically, this can be done by reviewing the water depth and category labels through a visual interface. Its purpose is to use human experience to identify errors that the automatic algorithm may have missed. Correction operation refers to adjusting or supplementing the erroneous data found during inspection. This can be done using methods such as data interpolation or re-collection, with the aim of ensuring the completeness and accuracy of the water status information. Adding the inspection and correction evaluation results to the water status dataset refers to storing the validated data in the training database. This can be achieved using database write operations, with the aim of providing high-quality samples for model training.

[0107] Specifically, the proposed solution first maps multi-source sensor data onto a unified time axis using a dynamic calibration algorithm to address evaluation bias caused by inconsistent sensor timestamps. Then, the generated preliminary water accumulation status assessment results are manually reviewed to verify the accuracy and completeness of water depth and category labels, and corrective actions are taken for assessment results that do not meet the requirements. Finally, the reviewed and corrected assessment results are added to the water accumulation status dataset, thus forming a closed-loop data quality control process. This process, through manual intervention, compensates for the shortcomings of automated algorithms, effectively improving the reliability of water accumulation status assessment.

[0108] As a specific implementation method, the solution of this application is implemented as follows: At the gas valve well monitoring site, the operator views the water depth trend map and category label generated by the dynamic calibration algorithm through the graphical user interface of the monitoring terminal. When it is found that the water depth displayed by the optical turbidimeter is inconsistent with that of the float-type water level sensor when the sediment concentration is high, the operator judges based on experience and manually adjusts the depth value. After confirming the correctness of the category label, the corrected evaluation result is submitted to the water status dataset.

[0109] Through the above technical solution, this application effectively solves the reliability problem of water accumulation status assessment results in complex environments, avoids assessment distortion caused by sensor noise or environmental interference, reduces the occurrence of accidental triggering of pumping operations, and thus improves the overall reliability and energy efficiency of the system.

[0110] Specifically, in some of the embodiments described above in this application, triggering conditions are proposed to achieve automatic decision-making for water pumping. However, in the process of implementation, due to the lack of consistency verification of multi-source sensor data, the triggering conditions are easily affected by sensor noise and environmental interference in complex environments such as high sediment concentration or large temperature difference, resulting in a high false triggering rate.

[0111] In this regard, this application further proposes specific steps for generating triggering conditions, including:

[0112] The trained dynamic calibration algorithm is used to infer from multi-source sensor data to generate preliminary triggering conditions.

[0113] The initial triggering conditions are mapped to the final triggering conditions through a consistency check mechanism.

[0114] In practical applications, a well-trained dynamic calibration algorithm refers to an algorithm trained on a dataset of water accumulation conditions. It can be implemented using a neural network model or a Kalman filter algorithm. Its purpose is to adapt to sensor drift and environmental changes in real time, thereby suppressing abnormal fluctuations of a single sensor during the data fusion stage. The consistency verification mechanism can be understood as a mechanism to verify the logical consistency of multi-source data. It can be implemented using a threshold comparison method or a statistical consistency test. Its purpose is to confirm the final output only when different sensor data support the same trigger state, effectively filtering out false trigger signals caused by transient interference or local anomalies.

[0115] Specifically, the solution in this application first uses a trained dynamic calibration algorithm to infer from multi-source sensor data to generate preliminary triggering conditions. This process fully utilizes the complementary characteristics of multi-source data, and the dynamic calibration algorithm can adapt to sensor drift and environmental changes in real time, thereby suppressing the direct conversion of noise signals during the data fusion stage. Subsequently, the preliminary triggering conditions are mapped to the final triggering conditions through a consistency verification mechanism. This mapping process verifies the logical consistency between multi-source data, and only confirms the final output when different sensor data support the same triggering state, ensuring that the triggering decision is based on the reliability of multi-source evidence, thus forming a complete triggering condition generation chain.

[0116] As a specific implementation method, the dynamic calibration algorithm can be implemented as a long short-term memory network for time-series reasoning of multi-source sensor data; the consistency verification mechanism can specifically adopt a threshold comparison method based on the consistency index. When the consistency index exceeds the preset threshold, the initial triggering condition is mapped to the final triggering condition. This implementation method effectively distinguishes between real water accumulation and noise signals through the cross-validation mechanism of multi-source data.

[0117] Through the above solution, this application effectively reduces the impact of sensor noise and environmental interference on triggering conditions in complex environments, and significantly improves the accuracy of automatic decision-making for water pumping and the stability of the system under interference scenarios such as vehicle vibration or turbidity changes.

[0118] In some of the embodiments described above in this application, trigger conditions are proposed to initiate the sampling operation. However, in the process of implementation, due to the inconsistency of multi-source sensor data under complex environmental interference, the directly generated trigger conditions are easily affected by noise or instantaneous fluctuations of a single sensor, resulting in an increased false trigger rate and affecting system reliability and energy efficiency.

[0119] In this regard, this application further proposes that the specific steps for generating the triggering conditions in step S2 are as follows: using a trained dynamic calibration algorithm to infer multi-source sensor data to generate preliminary triggering conditions; and mapping the preliminary triggering conditions to the final triggering conditions through a consistency verification mechanism.

[0120] In practical applications, a well-trained dynamic calibration algorithm refers to an optimized algorithm model, which can be implemented using deep learning networks or adaptive filtering algorithms. Its purpose is to dynamically integrate sensor confidence weights and improve the reliability of data fusion. The consistency verification mechanism can be understood as a logical unit that verifies the consistency of multi-source data. It can be implemented using statistical consistency index calculation or threshold comparison methods. Its purpose is to filter noise signals and ensure the accuracy of triggering decisions.

[0121] Specifically, the solution in this application first uses a trained dynamic calibration algorithm to infer multi-source sensor data. Based on the previous training and optimization process, this algorithm can dynamically integrate sensor confidence weights and perform fusion analysis on multi-source data, thereby avoiding reliance on fixed threshold judgments from a single sensor in complex environments with high sediment concentration or large temperature variations. Subsequently, the generated preliminary triggering conditions are input into a consistency verification mechanism. This mechanism verifies the consistency index of multi-source data and only maps the data to the final triggering conditions when the data meets a preset threshold. This effectively filters noise signals caused by instantaneous fluctuations such as vehicle vibration, ensuring that the triggering decision is based on the overall trend of multi-source data rather than local anomalies.

[0122] As a specific implementation method, the solution of this application is implemented as follows: the trained dynamic calibration algorithm can be specifically implemented as a lightweight neural network model deployed on an edge computing device, which is trained using a historical water accumulation state dataset; the consistency verification mechanism can specifically adopt a consistency index method for calculating the weight differences between sensors, for example, by comparing the weight distribution of different sensors on key dimensions to evaluate data reliability.

[0123] Through the above technical solution, this application effectively reduces the risk of false triggering caused by inconsistency of multi-source data, improves the decision-making accuracy and operational stability of the system in dynamic environments, and thus ensures the reliability and energy efficiency of the gas valve well water pumping operation.

[0124] Specifically, in some of the embodiments described above in this application, multi-source data matching is proposed to ensure the consistency of water accumulation status. However, in this process, due to the asynchronous acquisition of data from different sensors, reading fluctuations caused by environmental noise, and sensor drift, it is impossible to accurately quantify the correlation between multi-source data in the spatial and temporal dimensions. As a result, it is difficult to reliably distinguish between real water accumulation signals and interference noise, which affects the accuracy of water accumulation status judgment.

[0125] In this regard, this application further proposes the following specific steps for multi-source data matching in step S4:

[0126] The water accumulation status in the target well was detected using a Kalman filter algorithm under different sensor conditions.

[0127] A dynamic calibration algorithm is used to map the water accumulation status of different sensors to a unified time axis;

[0128] The overlapping area and overlap ratio of water accumulation states under different sensors are calculated to construct a matching degree matrix. The matching degree matrix is ​​used to help determine the matching relationship of water accumulation states under multi-source data.

[0129] Among them, the Kalman filter algorithm refers to a recursive estimation algorithm based on a state-space model, which can be implemented using an extended Kalman filter or an unscented Kalman filter. Its purpose is to dynamically correct the current reading based on the sensor's historical observation data, effectively suppressing environmental noise and short-term fluctuation interference. The dynamic calibration algorithm can be understood as a time series alignment mechanism, which can be implemented using timestamp interpolation based on a sliding window or adaptive time delay compensation technology. Its purpose is to eliminate the sampling time differences between different sensors and ensure that the data are compared on a unified time reference. The overlap area and overlap ratio are objective indicators for quantifying spatial similarity. They can be implemented using overlap measures based on statistical distribution or non-geometric similarity calculation methods. Their purpose is to evaluate the degree of matching of data from different sensors in spatial distribution. The matching degree matrix can be understood as a data structure that characterizes the correlation strength of multi-source data. It can be implemented using a weighted graph model or a probabilistic correlation matrix. Its purpose is to assist the system in accurately identifying the matching relationship between multi-source data.

[0130] Specifically, the proposed solution first uses a Kalman filter algorithm to suppress noise in the raw data from each sensor, generating stable water accumulation state detection results and avoiding misjudgments caused by instantaneous vibrations or drift. Then, a dynamic calibration algorithm maps the water accumulation states from different sensors to a unified time axis, eliminating sampling time differences based on the temporal characteristics of the sensor data and ensuring that all state information is compared on the same time reference. On this basis, a matching degree matrix is ​​constructed by calculating the overlapping area and overlap ratio of water accumulation states under different sensors, forming an objective metric by quantifying spatial distribution similarity. Finally, the matching degree matrix assists the system in determining the matching relationship of water accumulation states under multi-source data, achieving a reliable judgment of water accumulation state consistency. This overall operational logic effectively solves the inconsistency problem of multi-source data in complex environments through the systematic integration of noise suppression, time alignment, and spatial matching.

[0131] As a specific implementation method, the solution of this application is implemented as follows: a float-type water level sensor and an optical turbidimeter are deployed in the gas valve well. The Kalman filter algorithm can be specifically implemented as a recursive filter module to perform real-time filtering processing on the sensor data. The dynamic calibration algorithm can use a linear interpolation method to align the water accumulation status data of different sensors to a unified time series. The water accumulation status can be converted into a statistical distribution model. By calculating the intersection area of ​​the distribution, an overlap ratio index is constructed, and then a matching degree matrix is ​​generated to help determine whether the water accumulation status is consistent.

[0132] Through the above scheme, this application can accurately quantify the correlation of multi-source data in spatial and temporal dimensions, effectively distinguish between real water accumulation signals and interference noise, improve the accuracy of water accumulation status judgment, and ensure the reliability of water accumulation detection, especially in complex downhole environments with high sediment concentration or large temperature difference.

[0133] Specifically, in some of the embodiments described above in this application, specific steps are proposed to calculate the overlapping area and the overlapping ratio to construct the matching degree matrix. However, in this process, due to the lack of an accurate calculation method for the overlapping area and the ratio, the spatial overlap of the water accumulation state cannot be accurately quantified under sensor data drift or environmental interference (such as high sediment concentration or large temperature difference), which affects the reliability of multi-source data matching and makes the water accumulation state similarity assessment susceptible to noise interference and distortion.

[0134] In this regard, this application further proposes the following specific steps for calculating the overlapping area:

[0135] Convert the water accumulation status of each sensor into a polygon;

[0136] Calculate the area of ​​the intersection region between the water accumulation state polygon of each sensor and the water accumulation state polygon of other sensors in turn to obtain the degree of spatial overlap between the two water accumulation states.

[0137] The specific steps for calculating the overlap ratio are as follows:

[0138] The proportion of the overlapping area of ​​the polygons representing water accumulation states under different sensors to the total area of ​​the water accumulation state is calculated to evaluate the similarity of water accumulation states under different sensors.

[0139] The process of converting the water accumulation status of each sensor into a polygon refers to mapping the water level data collected by the sensors into a geometric shape. This can be achieved using a polygon generation algorithm based on a coordinate point sequence. The purpose is to transform the abstract water level value into a calculable spatial coverage area, avoiding misjudgments caused by local sensor drift. Calculating the area of ​​the intersection region between the water accumulation status polygons of each sensor and those of other sensors can be understood as determining the overlapping area through polygon clipping. This can be achieved using the Weiler-Atherton algorithm or the scanline algorithm. The purpose is to quantify the spatial consistency of the detection results from different sensors and effectively filter out instantaneous noise interference. Calculating the ratio of the overlapping area of ​​the water accumulation status polygons under different sensors to the total area of ​​that water accumulation status refers to using the ratio of the overlapping area to the total area as a similarity index. This can be achieved using normalized floating-point calculations. The purpose is to eliminate the influence of differences in water accumulation scale on similarity assessment, making the judgment more objectively reflect the inherent correlation between the data of the sensors.

[0140] Specifically, the proposed solution first converts the water accumulation state of each sensor into a polygon. Based on the spatial distribution characteristics of the water accumulation state, the abstract water level data is transformed into a geometric shape, enabling subsequent calculations to capture the actual coverage area of ​​the water accumulation rather than a single numerical point. Second, by calculating the area of ​​the intersection region between the water accumulation state polygons of each sensor and the water accumulation state polygons of other sensors, the degree of spatial overlap is quantified based on the geometric characteristics of the polygon intersection. By comparing the detection results of different sensors pairwise, the influence of environmental interference such as water flow fluctuations is effectively filtered out. Finally, the proportion of the overlapping area to the total area is calculated, and the similarity is evaluated based on the relative area, so that the similarity judgment is not affected by the difference in the scale of water accumulation, thereby forming a complete spatial quantification chain and improving the robustness of the matching matrix.

[0141] As a preferred embodiment, the solution of this application is implemented as follows: In the gas valve well, the multi-source sensors include a float-type water level sensor and an optical turbidimeter, and the water level data collected by them is converted into polygons; the processing unit uses an embedded microcontroller to perform polygon intersection calculation, and uses the ratio of the overlapping area to the total area as a similarity index; this index is input to the matching degree matrix construction module to help determine the matching relationship of the water accumulation state under multi-source data.

[0142] Through the above scheme, this application can accurately quantify the spatial overlap of water accumulation states, reduce the impact of sensor data drift caused by changes in sediment concentration or temperature fluctuations, improve the reliability of multi-source data matching, and ensure the stability of water accumulation state similarity assessment in complex environments.

[0143] like Figure 2 As shown, in another embodiment, this application also discloses a dynamic calibration and drainage system for multi-source sensors of water accumulation in gas valve wells, including the following modules:

[0144] The multi-source data acquisition submodule is used to acquire multi-source sensor data in the target well and process it to generate an initial confidence weight matrix. The sensor confidence weight is optimized through a multi-source data consistency threshold verification mechanism.

[0145] The dynamic calibration submodule is used to fuse and analyze multi-source sensor data through a real-time dynamic calibration algorithm based on an edge computing framework, and generate water accumulation status assessment results and triggering conditions.

[0146] The model training submodule is used to train the water accumulation detection model by combining a dynamic weighted loss function and a phased optimization strategy to improve its adaptability in complex environments, and to achieve knowledge transfer through a pseudo-label generation mechanism.

[0147] The detection submodule is used to determine the water accumulation status in the target well using a trained water accumulation detection model, and to ensure the consistency of the water accumulation status by using multi-source data matching and reasoning.

[0148] The core innovation of this embodiment lies in the systematic integration of a multi-source data consistency threshold verification mechanism and a real-time dynamic calibration algorithm based on an edge computing framework. It also introduces a dynamic weighted loss function, a phased optimization strategy, and a pseudo-label generation mechanism. This effectively solves the problems of high false alarm rates and low pumping efficiency in gas valve well water accumulation monitoring caused by insufficient multi-source data fusion capabilities, lack of dynamic sensor confidence assessment, and system response delays. This achieves the effects of improving detection accuracy, reducing the risk of false triggering, and enhancing the system's real-time response capabilities. Specifically, this scheme avoids false judgments caused by single sensor drift when sediment concentration changes abruptly or temperature fluctuates drastically by dynamically optimizing sensor confidence weights. Real-time data fusion analysis based on edge computing eliminates the impact of cloud transmission delays, ensuring timely generation of reliable triggering conditions in scenarios of rapidly rising water levels. Simultaneously, the phased optimization and knowledge transfer mechanisms during model training significantly improve the system's adaptability to complex environments, effectively suppressing false triggering problems caused by instantaneous noise such as vehicle vibration.

[0149] Specifically, in some of the embodiments described above in this application, a multi-source data acquisition submodule is proposed to acquire multi-source sensor data. However, in its implementation, due to the lack of clear definition of sensor types and the absence of data preprocessing procedures, sensor data is easily affected by environmental interference in complex downhole environments with high sediment concentration or drastic temperature differences, resulting in a decline in the quality of raw data. This leads to inaccurate calculation of confidence weights, ultimately causing misjudgment of water accumulation status and frequent false triggering of the pumping system.

[0150] In response, this application further proposes a multi-source data acquisition submodule that acquires raw data from the target well using a float-type water level sensor, an optical turbidimeter, and a temperature sensor, and performs filtering, noise reduction, normalization, and timestamp alignment on the raw data to generate a standardized dataset.

[0151] In practical applications, filtering and denoising refers to eliminating noise interference in the original data through signal processing techniques. This can be achieved using methods such as moving average filtering, Kalman filtering, or wavelet transform. The aim is to suppress instantaneous noise signals caused by external factors such as vehicle vibration, preventing false alarms triggered by brief fluctuations. Normalization can be understood as a standardization process that converts sensor data with different dimensions to a unified numerical range. Specifically, methods such as min-max normalization or Z-score normalization can be used. The goal is to ensure the comparability of water level values ​​from float-type water level sensors, turbidity values ​​from optical turbidimeters, and temperature values ​​from temperature sensors during fusion analysis, reducing weighting imbalances caused by differences in measurement ranges. Specifically, timestamp alignment refers to the forced synchronization of time references for multi-source data. This can be achieved using synchronization mechanisms based on Network Time Protocol (NTP) or GPS timing. The aim is to ensure that the measurements from float-type water level sensors, optical turbidimeters, and temperature sensors precisely correspond to the same moment, avoiding logical inconsistencies in water accumulation status caused by misaligned data acquisition sequences.

[0152] Specifically, the solution in this application integrates a float-type water level sensor, an optical turbidimeter, and a temperature sensor through a multi-source data acquisition submodule to achieve comprehensive monitoring of environmental parameters within the target well. The float-type water level sensor directly acquires water level information, the optical turbidimeter identifies water turbidity to distinguish between actual water accumulation and sediment interference, and the temperature sensor monitors changes in ambient temperature. The data from these three sensors mutually compensate for the inherent limitations of a single sensor. The raw data undergoes filtering and denoising to eliminate instantaneous noise, normalization to unify data dimensions, and timestamp alignment to ensure data temporal consistency, ultimately generating a standardized dataset. This standardized dataset provides high-quality input for subsequent confidence weight matrix calculations, effectively improving data reliability in environments with high sediment concentrations or drastic temperature differences, and avoiding misjudgments of water accumulation status due to environmental interference.

[0153] As a preferred embodiment, the solution of this application is implemented as follows: the float-type water level sensor in the multi-source data acquisition submodule can be a stainless steel float assembly; the optical turbidimeter can be a transmission-type turbidimeter with an infrared light source; and the temperature sensor can be a thermistor-type temperature sensor. Filtering and noise reduction processing can employ a second-order Butterworth low-pass filter; normalization processing uses a minimum-maximum normalization method to convert the data to a uniform numerical range; and timestamp alignment is achieved through a precise time protocol. The generated standardized dataset is stored in a structured format, containing timestamps and measurements from each sensor.

[0154] Through the above-mentioned scheme, this application effectively improves the reliability and consistency of the original data. In complex downhole environments with high sediment concentration or drastic temperature differences, it reduces the impact of environmental interference on sensor data, ensures the accuracy of confidence weight calculation, and thus reduces the probability of misjudgment of water accumulation status and frequent false triggering of the pumping system.

[0155] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for dynamic calibration and drainage of water accumulation in gas valve wells using multi-source sensors, characterized in that: Includes the following steps: S1: Taking the detection of water accumulation in gas valve wells as the target, acquire multi-source sensor data in the target well and process it to generate an initial confidence weight matrix. Optimize the sensor confidence weights through a multi-source data consistency threshold verification mechanism. S2: Based on the edge computing framework, the system uses a real-time dynamic calibration algorithm to fuse and analyze multi-source sensor data, generating water accumulation status assessment results and triggering conditions; S3: By combining a dynamic weighted loss function with a phased optimization strategy, the water accumulation detection model is trained to improve its adaptability in complex environments, and knowledge transfer is achieved through a pseudo-label generation mechanism. S4: Determine the water accumulation status in the target well using a trained water accumulation detection model, and ensure the consistency of the water accumulation status by using multi-source data matching and reasoning.

2. The method for dynamic calibration and drainage of water accumulation in gas valve wells using multi-source sensors according to claim 1, characterized in that: The specific steps in step S1 are as follows: S11: Acquire raw data from multiple different types of sensors within the target well; S12: Preprocess the raw data, including filtering and denoising, normalization and timestamp alignment, to generate a standardized dataset; S13: Calculate the initial confidence weight matrix using the standardized dataset, as shown in the following formula: ;in, Indicates the first The sensor at the first Confidence weights at each time point Indicates the first The sensor at the first Measurements at each time point Indicates the first The average value of each sensor, Indicates the first The variance of each sensor, To prevent small constants with a denominator of zero; S14: Optimize the confidence weight matrix through a multi-source data consistency threshold verification mechanism, as shown in the following formula: ;in, Indicates the first The sensor and the first Consistency index among individual sensors Indicates the total number of sensors. This represents the weight assigned to the i-th sensor for the m-th metric or dimension. This represents the weight assigned to the j-th sensor for the same m-th metric or dimension; S15: Apply the optimized confidence weight matrix to the subsequent data processing flow, and update the weight matrix through multiple rounds of iteration to improve accuracy.

3. The dynamic calibration and drainage method for multi-source sensors of water accumulation in gas valve wells according to claim 2, characterized in that: In step S15, the specific steps for optimizing the confidence weight matrix are as follows: Perform a geometric verification on the confidence weight matrix to check whether the weight distribution meets expectations. If it does not, adjust the weight distribution. Low-quality data is filtered using a confidence level screening mechanism, as shown in the following formula: ;in, Indicates the first Is the data from each sensor retained? This is the confidence threshold.

4. The method for dynamic calibration and drainage of water accumulation in gas valve wells using multi-source sensors according to claim 1, characterized in that: In step S2, the specific steps for generating the water accumulation status assessment result are as follows: Multi-source sensor data are mapped onto a unified time axis using a dynamic calibration algorithm to generate preliminary water accumulation status assessment results; The preliminary assessment results are manually checked to verify the accuracy and completeness of the water depth and category labels. If they are not met, they are corrected. The checked and corrected assessment results are added to the waterlogging status dataset.

5. The method for dynamic calibration and drainage of water accumulation in gas valve wells using multi-source sensors according to claim 1, characterized in that: In step S2, the specific steps for generating the triggering condition are as follows: The trained dynamic calibration algorithm is used to infer from multi-source sensor data to generate preliminary triggering conditions. The initial triggering conditions are mapped to the final triggering conditions through a consistency check mechanism.

6. The method for dynamic calibration and drainage of water accumulation in gas valve wells using multi-source sensors according to claim 1, characterized in that: The specific steps in step S3 are as follows: S31: The water accumulation detection model is initialized and trained using the water accumulation state dataset; S32: Low-level features of the frozen water accumulation detection model; training is conducted only on high-level features, and optimization is performed using pseudo-labeled data. S33: Thaw some low-level features of the water accumulation detection model and continue to fine-tune the model using the water accumulation state dataset; The optimizer was switched from Adam to RMSProp, the learning rate was reduced to ensure training stability, the weights of the loss function were restored to normal, and the training was focused on feature extraction from the supervised data. S34: The first 10 layers of the frozen water accumulation detection model are fine-tuned using multi-source sensor data through a weighted loss function to transfer knowledge from the model. S35: Unfreeze 3 of the first 10 layers of the water accumulation detection model, restore the weights of the loss function, and switch the optimizer to RMSProp; fine-tune the model using the water accumulation state dataset to achieve final convergence on the water accumulation state data.

7. The dynamic calibration and drainage method for multi-source sensors of water accumulation in gas valve wells according to claim 1, characterized in that: In step S4, the specific steps of multi-source data matching are as follows: The water accumulation status in the target well was detected using a Kalman filter algorithm under different sensor conditions. A dynamic calibration algorithm is used to map the water accumulation status of different sensors to a unified time axis; The overlapping area and overlap ratio of water accumulation states under different sensors are calculated to construct a matching degree matrix. The matching degree matrix is ​​used to help determine the matching relationship of water accumulation states under multi-source data.

8. The dynamic calibration and drainage method for multi-source sensors of water accumulation in gas valve wells according to claim 7, characterized in that: The specific steps for calculating the overlapping area are as follows: Convert the water accumulation status of each sensor into a polygon; Calculate the area of ​​the intersection region between the water accumulation state polygon of each sensor and the water accumulation state polygon of other sensors in turn to obtain the degree of spatial overlap between the two water accumulation states. The specific steps for calculating the overlap ratio are as follows: The proportion of the overlapping area of ​​the polygons representing water accumulation states under different sensors to the total area of ​​the water accumulation state is calculated to evaluate the similarity of water accumulation states under different sensors.

9. A dynamic calibration and drainage system for multi-source sensors of water accumulation in gas valve wells, characterized in that: Includes the following modules: The multi-source data acquisition submodule is used to acquire multi-source sensor data in the target well and process it to generate an initial confidence weight matrix. The sensor confidence weight is optimized through a multi-source data consistency threshold verification mechanism. The dynamic calibration submodule is used to fuse and analyze multi-source sensor data through a real-time dynamic calibration algorithm based on an edge computing framework, and generate water accumulation status assessment results and triggering conditions. The model training submodule is used to train the water accumulation detection model by combining a dynamic weighted loss function and a phased optimization strategy to improve its adaptability in complex environments, and to achieve knowledge transfer through a pseudo-label generation mechanism. The detection submodule is used to determine the water accumulation status in the target well using a trained water accumulation detection model, and to ensure the consistency of the water accumulation status by using multi-source data matching and reasoning.

10. The gas valve well water accumulation multi-source sensor dynamic calibration and drainage system according to claim 9, characterized in that, The multi-source data acquisition submodule acquires raw data from the target well through a float-type water level sensor, an optical turbidimeter, and a temperature sensor. It then performs filtering, noise reduction, normalization, and timestamp alignment on the raw data to generate a standardized dataset.

Images