Method, device, medium and equipment for judging gas blocking state of in-situ leaching uranium pipeline

Abstract

The present application relates to the technical field of in-situ leaching uranium process monitoring and gas-liquid two-phase flow detection, and discloses a kind of in-situ leaching uranium pipeline gas block state determination method, device, medium and equipment, method includes the pressure fluctuation signal of multiple measuring points along fluid flow direction on injection pipeline is collected, and pressure fluctuation signal is preprocessed, obtain pressure time history signal;Pressure time history signal is respectively subjected to multi-scale time-frequency decomposition and time domain correlation analysis, and two kinds of characteristic indexes are extracted;According to two kinds of characteristic indexes, the gas block risk state at the corresponding measuring point is determined, and according to the change trend of the characteristic indexes at multiple measuring points along the fluid flow direction, the evolution trend of the gas block in the pipeline is judged, and the corresponding early warning signal is output.The above-mentioned method early warns before the formation of gas block, fully excavates the flow characteristic information in pressure signal from two dimensions of frequency domain and time domain, and uses double-index joint determination to improve the reliability of diagnosis, information is fully utilized, and the judgment accuracy is high.

Description

Technical Field

[0001] This invention relates to the field of monitoring of in-situ leaching uranium mining processes and detection of gas-liquid two-phase flow, and in particular to a method, apparatus, medium and equipment for determining the gas blockage status of in-situ leaching uranium mining pipelines. Background Technology

[0002] During in-situ leaching of uranium, the leaching solution is transported to the ore-bearing aquifer through injection pipelines. Due to the presence of gas in the leaching solution and the introduction of a large number of air bubbles by the oxygen dissolving device, the injection pipeline is actually in a two-phase flow state of gas and liquid. When the air bubbles in the pipeline continuously coalesce and grow to form gas bombs or gas columns, gas blockage occurs, which leads to a reduction in the pipeline flow area and an increase in flow resistance. In severe cases, it can cause complete blockage of the pipeline, affecting injection efficiency, accelerating equipment fatigue damage, and even causing safety accidents such as overpressure rupture.

[0003] Currently, the detection of gas blockage in in-situ leaching uranium pipelines mainly relies on manual observation, flow-pressure differential monitoring, invasive probe detection, and single-index discrimination based on simple statistical characteristics of pressure signals. However, most of the existing monitoring methods are post-event detections and cannot capture the evolution trend and provide early warning before gas blockage forms. Furthermore, existing methods fail to fully utilize the multi-scale flow characteristics contained in pressure fluctuation signals and have insufficient characterization ability for the evolution of gas-liquid two-phase flow patterns, resulting in poor diagnostic reliability and a high misjudgment rate.

[0004] Therefore, a method for determining airlock status that can fully extract flow pattern evolution characteristics based on pressure fluctuation signals and achieve early identification and warning is urgently needed. Summary of the Invention

[0005] In view of this, this application provides a method, apparatus, medium and equipment for determining the gas blockage status of in-situ leaching uranium pipelines. The main purpose is to solve the technical problem that the existing technology fails to fully utilize the multi-scale flow characteristic information contained in the pressure fluctuation signal, and has insufficient characterization ability for the evolution of gas-liquid two-phase flow patterns, resulting in poor diagnostic reliability and high misjudgment rate.

[0006] According to a first aspect of the present invention, a method for determining the gas blockage status of an in-situ uranium leaching pipeline is provided, comprising: Pressure fluctuation signals are collected at multiple measuring points along the fluid flow direction on the injection pipeline, and the pressure fluctuation signals are preprocessed to obtain pressure time history signals. The pressure time history signal is decomposed into multiple scales to obtain signal components at different scales, and a first feature index is extracted based on the energy distribution of the signal components, wherein the first feature index is used to characterize the bubble coalescence activity. A time-domain correlation analysis is performed on the pressure time-history signal to extract a second feature index, wherein the second feature index is used to characterize the flow orderliness. The risk status of air blockage at the corresponding measuring point is determined based on the first characteristic index and the second characteristic index. Based on the changing trends of the first characteristic index and / or the second characteristic index at multiple measuring points along the fluid flow direction, the evolution trend of air blockage in the pipeline is judged, and the corresponding early warning signal is output.

[0007] Optionally, the step of performing multi-scale time-frequency decomposition on the pressure time-history signal to obtain signal components at different scales, and extracting a first feature index based on the energy distribution of the signal components, includes: performing discrete wavelet transform decomposition on the pressure time-history signal to obtain pressure detail signal components and the highest-level low-frequency approximation signal components at different scales; calculating the energy of a first signal component corresponding to the pressure detail signal component and the energy of a second signal component corresponding to the highest-level low-frequency approximation signal component, and summing the energy of the first signal component and the energy of the second signal component at all scales to obtain the total signal energy; selecting at least one target detail signal component corresponding to the high-frequency fluctuation component of the pressure signal from the pressure detail signal components, and summing the energy of the target detail signal component to obtain the energy of a third signal component; and calculating the ratio of the energy of the third signal component to the total signal energy as the first feature index.

[0008] Optionally, the step of performing time-domain correlation analysis on the pressure time-history signal to extract the second feature index includes: calculating the normalized autocorrelation function of the pressure time-history signal, and calculating the average value of the normalized autocorrelation function within the interval from zero to a preset maximum hysteresis time, and using the average value as the second feature index.

[0009] Optionally, determining the airlock risk status at the corresponding measuring point based on the first characteristic indicator and the second characteristic indicator includes: obtaining a first low-risk threshold and a first high-risk threshold for the first characteristic indicator, and a second low-risk threshold and a second high-risk threshold for the second characteristic indicator; determining the corresponding measuring point as a high-risk airlock state when the first characteristic indicator exceeds the first high-risk threshold and the second characteristic indicator is lower than the second high-risk threshold; determining the corresponding measuring point as a low-risk airlock state when the first characteristic indicator is lower than the first low-risk threshold and the second characteristic indicator exceeds the second low-risk threshold; and determining the corresponding measuring point as a transitional state when the first characteristic indicator is between the first low-risk threshold and the first high-risk threshold, and / or the second characteristic indicator is between the second low-risk threshold and the second high-risk threshold.

[0010] Optionally, the step of determining the gas blockage evolution trend in the pipeline based on the changing trends of the first characteristic index and / or the second characteristic index at multiple measuring points along the fluid flow direction, and outputting a corresponding early warning signal, includes: acquiring the first characteristic index and the second characteristic index corresponding to multiple measuring points arranged along the fluid flow direction on the injection pipeline, wherein the multiple measuring points are distributed in the upstream and downstream regions of the injection pipeline along the fluid flow direction; calculating a trend determination value based on the degree of difference between the first characteristic index and / or the second characteristic index between the measuring points located in the upstream region of the fluid and the measuring points located in the downstream region of the fluid, wherein the trend determination value is used to characterize the degree of flow pattern transformation; when the trend determination value exceeds a preset trend early warning threshold, determining that there is a flow pattern transformation trend driven by bubble coalescence in the injection pipeline, and outputting a trend early warning signal.

[0011] Optionally, after collecting pressure fluctuation signals at multiple measuring points along the fluid flow direction on the injection pipeline, the method further includes: segmenting the continuously collected pressure fluctuation signals at the measuring points based on a preset time window length to obtain multiple time window signal segments; arranging the multiple time window signal segments in a sliding window manner with a preset overlap rate between adjacent time windows to obtain a sliding window signal sequence corresponding to the measuring point; and extracting the corresponding first feature index and second feature index from each time window signal segment in the sliding window signal sequence and performing gas blockage risk assessment and evolution trend assessment.

[0012] Optionally, the preprocessing of the pressure fluctuation signal to obtain a pressure time history signal includes: denoising the pressure fluctuation signal to filter out the DC bias component and high-frequency noise interference in the pressure fluctuation signal; and normalizing the amplitude of the denoised pressure fluctuation signal to a dimensionless range to obtain the pressure time history signal.

[0013] According to a second aspect of the present invention, a device for determining the gas blockage status of an in-situ uranium leaching pipeline is provided, comprising: The signal acquisition module is used to acquire pressure fluctuation signals at multiple measuring points along the fluid flow direction on the injection pipeline, and to preprocess the pressure fluctuation signals to obtain pressure time history signals. The time-frequency analysis module is used to perform multi-scale time-frequency decomposition on the pressure time history signal to obtain signal components at different scales, and extract a first feature index based on the energy distribution of the signal components, wherein the first feature index is used to characterize the bubble coalescence activity. The time-domain analysis module is used to perform time-domain correlation analysis on the pressure time-history signal and extract a second feature index, wherein the second feature index is used to characterize the flow orderliness. The signal output module is used to determine the air blockage risk status at the corresponding measuring point based on the first characteristic index and the second characteristic index, and to determine the air blockage evolution trend in the pipeline based on the changing trends of the first characteristic index and / or the second characteristic index at multiple measuring points along the fluid flow direction, and to output the corresponding early warning signal.

[0014] According to a third aspect of the present invention, a storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the above-described method for determining the gas blockage status of a uranium leaching pipeline.

[0015] According to a fourth aspect of the present invention, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described method for determining the gas blockage status of uranium leaching pipelines.

[0016] This invention provides a method, apparatus, medium, and equipment for determining the gas blockage status of in-situ leaching uranium production pipelines. By collecting pressure fluctuation signals from multiple measuring points along the fluid flow direction on the injection pipeline and combining these signals with the changing trends of first and / or second characteristic indicators at these measuring points, the gas blockage evolution trend can be determined. This not only determines the current state of each measuring point but also identifies the flow pattern evolution direction along the pipeline through the indicator differences between upstream and downstream measuring points, thus issuing an early warning before actual gas blockage formation. Simultaneously, by performing multi-scale time-frequency decomposition on the pressure time history signal to extract the first characteristic indicator characterizing bubble coalescence activity, and by extracting the second characteristic indicator characterizing flow orderliness through time-domain correlation analysis, the flow characteristic information contained in the pressure fluctuation signal is fully exploited from both the frequency and time domains. This overcomes the shortcomings of existing single-characteristic parameter methods, such as insufficient information utilization and inadequate characterization of gas-liquid two-phase flow pattern evolution. Furthermore, by jointly determining the gas blockage risk status at corresponding measuring points using dual indicators, the misjudgment caused by single-indicator interference from operating condition fluctuations is reduced, significantly improving the accuracy and reliability of gas blockage status determination. The above method provides early warning before gas blockage occurs, fully extracts flow characteristic information from pressure signals in both frequency and time domains, and improves diagnostic reliability by using dual-indicator joint judgment. It makes full use of information and has a high accuracy rate in judgment.

[0017] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings: Figure 1 A flowchart illustrating a method for determining the gas blockage status of a uranium leaching pipeline according to an embodiment of the present invention is shown. Figure 2 A flowchart illustrating another method for determining the gas blockage status of a uranium leaching pipeline provided by an embodiment of the present invention is shown. Figure 3 This diagram illustrates the structure of a gas blockage determination device for a uranium leaching pipeline provided in an embodiment of the present invention. Figure 4 A schematic diagram of the device structure of a computer device provided in an embodiment of the present invention is shown. Detailed Implementation

[0019] Exemplary embodiments of the present application will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this application will be thorough and complete, and will fully convey the scope of the present application to those skilled in the art.

[0020] This application provides a method for determining the gas blockage status of uranium leaching pipelines, such as... Figure 1 As shown, the method includes the following steps: 101. Collect pressure fluctuation signals at multiple measuring points along the fluid flow direction on the injection pipeline, and preprocess the pressure fluctuation signals to obtain pressure time history signals.

[0021] Among them, the injection pipeline refers to the pipeline used in the in-situ leaching uranium mining process to transport the leaching solution from the surface to the wellhead, and the internal flow medium is a gas-liquid two-phase fluid; the pressure fluctuation signal refers to the dynamic signal of the fluid pressure in the pipeline changing rapidly over time, and the high-frequency components mainly reflect the local turbulence and coalescence / breakup behavior of bubbles; the measuring point refers to the monitoring location where pressure sensors are installed along the length of the injection pipeline, and multiple measuring points are arranged along the fluid flow direction; preprocessing refers to the necessary signal conditioning operations performed on the original acquired signal, including conventional signal processing methods such as removing DC bias, filtering high-frequency noise interference, and amplitude normalization; the pressure time history signal refers to the purified signal sequence that can truly reflect the pressure change law of the measuring point over time after preprocessing.

[0022] Specifically, multiple measuring points are set up along the fluid flow direction of the injection pipeline. Pressure sensors installed at each measuring point are used to synchronously collect the pressure fluctuation signal of the fluid in the pipeline. The collected raw signal is transmitted to the data processing unit for preprocessing to filter out the DC bias component and high-frequency noise interference in the raw signal. At the same time, the signal amplitude is normalized to the dimensionless range, thereby obtaining a clean pressure time history signal that can truly reflect the pressure fluctuation characteristics at each measuring point. This provides a reliable data foundation for subsequent multi-scale time-frequency decomposition and time-domain correlation analysis.

[0023] In this embodiment, a non-invasive pressure sensor is used for signal acquisition, which does not require modification or drilling of the pipeline and does not affect the normal operation of the pipeline at all. The spatial distribution of multiple measuring points along the fluid flow direction provides a data foundation for subsequent analysis of the gas blockage evolution trend based on the comparison of upstream and downstream measuring points. The preprocessing stage effectively eliminates the impact of sensor sensitivity differences and on-site environmental noise on signal quality through noise reduction and normalization, ensuring the accuracy and consistency of subsequent feature extraction results and providing reliable data input guarantee for the entire gas blockage status determination method.

[0024] 102. Perform multi-scale time-frequency decomposition on the pressure time history signal to obtain signal components at different scales, and extract the first feature index based on the energy distribution of the signal components. The first feature index is used to characterize the bubble coalescence activity.

[0025] Multi-scale time-frequency decomposition refers to the process of dividing the pressure time history signal into multiple different frequency bands layer by layer according to the frequency range from high to low. Each layer of decomposition yields a signal component corresponding to a frequency band range. The signal components at different scales correspond to the flow behavior at different physical scales in the gas-liquid two-phase flow in the pipeline. The energy distribution of the signal components refers to the proportion of each frequency band signal component in the total signal energy. The change in the energy proportion at different scales reflects the evolution of the flow pattern structure. Bubble coalescence activity refers to the intensity of bubbles colliding and merging to form larger bubbles in the pipeline. When bubble coalescence is active, its motion and breakup behavior will induce high-frequency pressure fluctuations. The energy proportion of high-frequency fluctuations is positively correlated with the bubble coalescence activity. The first characteristic index is the quantitative value obtained after calculating the high-frequency energy proportion, which is used to quantitatively describe the level of bubble coalescence activity.

[0026] Specifically, firstly, a multi-scale time-frequency decomposition method is used to decompose the preprocessed pressure time history signal to obtain signal components at different scales. Each scale signal component corresponds to pressure fluctuation components in different frequency ranges. Then, the energy value of each scale signal component is calculated, and the proportion of energy at each scale to the total energy is determined to form the energy distribution spectrum of the signal. Finally, one or more target scales corresponding to the high-frequency fluctuation components of the pressure signal are selected from all scales, and the percentage of the sum of the energy of the target scale signal components to the total energy is calculated as the first characteristic index. Considering that the high-frequency fluctuation components are directly related to the local turbulence and coalescence / breakup behavior of bubbles, the value of the first characteristic index quantitatively reflects the activity of bubble coalescence. The higher the value, the more intense the bubble coalescence and the greater the risk of gas blockage; the lower the value, the more uniform the bubble distribution and the more stable the flow state.

[0027] In this embodiment, multi-scale time-frequency decomposition is used to establish a quantitative correlation between different frequency components in the pressure signal and the corresponding physical mechanism of gas-liquid two-phase flow. This overcomes the problem of severe information loss when the traditional single-feature parameter method relies solely on simple statistics for judgment. It fully extracts the key features related to bubble coalescence behavior contained in the pressure signal. By extracting the first quantitative feature index, the bubble coalescence behavior, which was originally difficult to observe directly, is transformed into a quantitatively assessable numerical index, providing a quantifiable physical basis for the early identification of gas blockage risk. Compared with traditional methods such as direct observation, which are highly subjective, this application is based on the objective analysis of pressure signals, is not affected by human factors, and has higher reliability and sensitivity. It can capture early signals of intensified bubble coalescence before gas blockage forms.

[0028] 103. Perform time-domain correlation analysis on the pressure time history signal and extract the second characteristic index, which is used to characterize the flow orderliness.

[0029] Among them, time-domain correlation analysis refers to the signal processing method that directly analyzes and processes pressure signals in the time domain, revealing the inherent regularity of the signal by examining the degree of correlation between the signal values ​​at different times; flow orderliness refers to the degree of structure and regularity of gas-liquid two-phase flow in the pipeline. When the flow state is stable and the flow pattern is regular, the pressure fluctuations exhibit a certain periodic or quasi-periodic regularity, and the flow orderliness is high; when the flow pattern is in the transition stage and the movement of the gas-liquid interface is violent and chaotic, the pressure fluctuations are highly random and have weak regularity, and the flow orderliness is low; the second characteristic index is the characteristic value that quantitatively describes the flow orderliness.

[0030] Specifically, time-domain correlation analysis is used to calculate the correlation between the values ​​of the preprocessed pressure time-history signal at different times, obtaining the normalized autocorrelation function. This function describes the similarity between the pressure signal and itself at different time delays. The autocorrelation function value is higher when the pressure fluctuation has a stable periodic component, and it approaches zero when the pressure fluctuation tends to be random. Furthermore, a maximum hysteresis time is preset, and the average value of the normalized autocorrelation function within this hysteresis time interval is calculated. This average value is used as the second characteristic index. Since the average value comprehensively reflects the overall self-similarity of the pressure signal within the hysteresis time, its value directly quantifies the orderliness of the flow state. A higher value indicates stronger periodicity and regularity of the pressure fluctuation, and higher flow orderliness; a lower value indicates stronger randomness of the pressure fluctuation and lower flow orderliness.

[0031] In this embodiment, flow orderliness characteristics are extracted through time-domain correlation analysis, forming a complementary relationship between the time and frequency domains with the first characteristic index based on frequency-domain energy distribution. This comprehensively characterizes the flow state of the gas-liquid two-phase flow from different dimensions, making the information basis for determining gas blockage risk more complete. The second characteristic index has good response sensitivity to the process of flow transitioning from order to disorder, and can capture abnormal changes in the early stage of flow pattern instability, providing an important basis for early warning of gas blockage risk. The index has low computational complexity and good real-time performance, which can meet the needs of online real-time monitoring in industrial sites. Moreover, it is entirely based on the time-domain characteristics of the pressure signal itself, without relying on any external reference signals, and has good engineering applicability and anti-interference ability.

[0032] 104. Determine the air blockage risk status at the corresponding measuring point based on the first and second characteristic indicators, and judge the air blockage evolution trend in the pipeline based on the changing trends of the first and / or second characteristic indicators at multiple measuring points along the fluid flow direction, and output the corresponding early warning signal.

[0033] Among them, the airlock risk status refers to the probability level of airlock occurrence at each measuring point based on the characteristic indicators at the current moment, specifically including high-risk status, low-risk status, and transitional status between the two; the airlock evolution trend refers to the spatial evolution direction and degree of the entire development process of airlock from initial bubble coalescence to final blockage, which is identified by longitudinally comparing the characteristic indicators at different measuring points along the fluid flow direction to identify the pattern of flow pattern transformation along the flow path; the early warning signal refers to the prompt information output by the system when it determines that the airlock risk has reached the preset level or identifies the airlock evolution trend, which is used to notify the operator to take timely intervention measures.

[0034] Specifically, this step first determines the risk status of each measuring point based on the first and second characteristic indices extracted at each measuring point. A high-risk state is determined when bubble coalescence activity is high and flow orderliness is low; a low-risk state is determined when bubble coalescence activity is low and flow orderliness is high; and a transitional state is determined when the indices are in between. Based on this, the determination results and / or characteristic index values ​​of multiple measuring points distributed along the fluid flow direction are further compared and analyzed spatially. By examining the degree of difference in characteristic indices between upstream and downstream measuring points, the evolution of the flow pattern with the transport distance is identified: when the downstream characteristic indices show a significant decrease in bubble coalescence activity and a significant increase in flow orderliness compared to the upstream, it indicates that a flow pattern transformation driven by bubble coalescence is occurring in the pipeline, thus determining the evolution trend of air blockage. Finally, based on the risk status determination results and the evolution trend determination results, corresponding early warning signals are output separately or in combination to provide decision-making basis for on-site operations.

[0035] In this embodiment, by first performing a dual-indicator joint risk assessment on each measuring point, and then performing trend judgment based on the index differences between upstream and downstream measuring points, a future trend warning is achieved, providing operators with ample time for adjustment. The dual-indicator joint assessment fully utilizes complementary information from both the frequency and time domains, significantly reducing the false judgment rate and improving diagnostic reliability compared to single-indicator assessment. The trend judgment function can capture the flow pattern transformation signal driven by bubble coalescence in advance, issuing a warning before actual blockage occurs. Operators can adjust the injection parameters in a timely manner based on the warning signal, thereby effectively suppressing bubble coalescence, preventing gas blockage, and significantly improving the continuity and safety of pipeline operation.

[0036] This invention provides a method for determining the gas blockage status of uranium leaching pipelines. By collecting pressure fluctuation signals from multiple measuring points along the fluid flow direction on the injection pipeline and combining these signals with the changing trends of a first and / or second characteristic index at these measuring points, the method assesses the gas blockage evolution trend. This not only determines the current state of each measuring point but also identifies the flow pattern evolution direction along the pipeline through index differences between upstream and downstream measuring points, thus issuing an early warning before actual gas blockage formation. Simultaneously, by performing multi-scale time-frequency decomposition on the pressure time history signal to extract a first characteristic index characterizing bubble coalescence activity, and by extracting a second characteristic index characterizing flow orderliness through time-domain correlation analysis, the method fully exploits the flow characteristic information contained in the pressure fluctuation signal from both the frequency and time domains. This overcomes the shortcomings of existing single-characteristic parameter methods, such as insufficient information utilization and inadequate characterization of gas-liquid two-phase flow pattern evolution. Furthermore, by jointly determining the gas blockage risk status at corresponding measuring points using dual indicators, the method reduces misjudgments caused by single-indicator susceptibility to operating condition fluctuations, significantly improving the accuracy and reliability of gas blockage status determination. The above method provides early warning before gas blockage occurs, fully extracts flow characteristic information from pressure signals in both frequency and time domains, and improves diagnostic reliability by using dual-indicator joint judgment. It makes full use of information and has a high accuracy rate in judgment.

[0037] This application provides another method for determining the gas blockage status of uranium leaching pipelines, such as... Figure 2 As shown, it specifically includes: 201. Data Acquisition.

[0038] Specifically, in the uranium injection pipeline for in-situ leaching, the leaching solution, after passing through the oxygen dissolving device, carries a large number of air bubbles into the injection pipeline. The pipeline is in a gas-liquid two-phase flow state. As the gas-liquid two-phase fluid is transported long distances along the pipeline, the air bubbles continuously undergo coalescence and breakup during the flow, and the flow pattern gradually evolves along the flow direction. Based on this, to effectively capture the evolution process, this application sequentially sets up a first observation point and a second observation point along the fluid flow direction on the injection pipeline. The first observation point is located in the proximal section of the pipeline near the outlet of the oxygen dissolving device to monitor the pressure fluctuation characteristics in the initial gas-liquid mixing state after oxygen dissolution, reflecting the distribution state and turbulence level of the air bubbles when they first enter the pipeline. The second observation point is located in the distal section of the pipeline away from the oxygen dissolving device to monitor the gas-liquid two-phase fluid. The evolution of the flow pattern after long-distance transport reflects the flow characteristics after bubble coalescence and growth. The distance between the two observation points can be set within the range of 100m to 1000m according to the on-site pipeline conditions to ensure sufficient flow development distance between the two measuring points, so that the flow pattern evolution can be fully displayed in space. By installing high-frequency pressure sensors at the two observation points and synchronously collecting the pressure fluctuation signals at each measuring point at a sampling frequency of not less than 1000Hz, it can be ensured that the collected signals completely cover the high-frequency pressure pulsation components generated by bubble coalescence and breakup, avoiding signal aliasing and information loss. The collected pressure fluctuation signals are transmitted to the data processing unit in real time, providing a data basis for subsequent signal preprocessing and multi-scale analysis.

[0039] In this step, the first and second observation points are arranged sequentially along the fluid flow direction. Two sets of pressure signals collected simultaneously correspond to the flow states of the gas-liquid two-phase fluid at two different stages: near the pipeline's starting point and after long-distance transport. By comparing and analyzing the differences in pressure fluctuation signals at the two observation points, the degree of flow pattern transformation driven by bubble coalescence during pipeline transport can be effectively assessed, providing a spatial basis for subsequent trend warning. The spacing between the two observation points was optimized to ensure sufficient space for flow pattern evolution to make differences measurable, while also considering the feasibility of engineering implementation. In summary, by rationally setting the spatial layout of the two observation points, a spatial observation system for the evolution of gas-liquid two-phase flow patterns along the pipeline length was established, providing a data foundation for subsequent judgment of bubble coalescence evolution trends through comparative analysis of upstream and downstream pressure signals. Using no less than 1000... The high-frequency sampling at Hz ensures that the high-frequency components reflecting local turbulence and coalescence / breakup behavior of bubbles in the pressure signal are fully acquired, avoiding the impact of information loss on the accuracy of subsequent feature extraction. By installing pressure sensors on the outer wall of the pipeline to acquire signals, no modification or opening of the pipeline is required, and the normal operation of the pipeline is not affected, which has good engineering applicability. At the same time, the pressure signals of two observation points are acquired simultaneously, ensuring the temporal comparability of the two sets of signals, and providing a reliable time-series guarantee for accurately assessing the degree of flow pattern evolution through subsequent comparative analysis.

[0040] 202. Signal preprocessing.

[0041] The process involves denoising the pressure fluctuation signal to filter out the DC bias component and high-frequency noise interference; then, the amplitude of the denoised pressure fluctuation signal is normalized to a dimensionless range to obtain the pressure time history signal.

[0042] Specifically, considering that the original pressure fluctuation signal is usually mixed with various interference components, mainly including DC bias components and high-frequency noise interference, the DC bias component refers to the constant or slowly changing offset superimposed on the effective signal. Its sources include the static zero drift of the sensor, the offset voltage of the pre-amplifier circuit, and the baseline drift caused by changes in ambient temperature. If it is not filtered out, it will directly affect the accuracy of energy calculation in each frequency band during subsequent multi-scale decomposition. High-frequency noise interference mainly comes from the sensor's own electrothermal noise, power frequency interference, and stray signals coupled into the electromagnetic environment. These noises have high frequencies and overlap with the high-frequency fluctuation components in the effective pressure signal in the frequency band. If they are not suppressed, the calculated high-frequency energy value will deviate from the true value, thus affecting the accuracy of the first characteristic index. Based on this, the denoising process in this step targets the above two main interferences and uses a digital filtering algorithm to comprehensively process the original signal, filtering out the DC bias component and high-frequency noise interference, thereby retaining the effective information that truly reflects the pressure fluctuation in the pipeline.

[0043] Furthermore, since pressure sensors installed at different measuring points may differ in sensitivity, range, and output characteristics, even under the same flow conditions, the original signal amplitudes output by different sensors may vary. Simultaneously, the static pressure levels at different observation points may also differ due to pipe friction losses. These amplitude differences are not caused by changes in the gas-liquid two-phase flow characteristics, but they directly affect the calculation of the absolute values ​​of signal component energy at various scales. To eliminate these amplitude differences caused by non-flow factors, this step further normalizes the denoised pressure fluctuation signal, adjusting the signal amplitude... By uniformly mapping to a dimensionless range and normalizing the data, the pressure time history signals at each measuring point are consistent in amplitude scale. This eliminates the influence of sensor characteristics and static pressure differences on subsequent feature extraction, making the feature indicators between different measuring points and at different times comparable. After denoising and normalization preprocessing, a purified and scale-uniform pressure time history signal x(t) is obtained, where t is the time variable. The pressure time history signal retains the effective information related to the gas-liquid two-phase flow characteristics in the original pressure fluctuations, providing reliable data input for subsequent multi-scale time-frequency decomposition and time-domain correlation analysis.

[0044] This step eliminates interference from sensor zero-point drift and ambient temperature changes on the signal baseline through DC bias filtering in the denoising process, ensuring that subsequent energy calculations accurately reflect the true pressure fluctuation amplitude. High-frequency noise filtering effectively suppresses inherent sensor noise and on-site electromagnetic interference from contaminating high-frequency energy calculations, ensuring that the first characteristic index accurately reflects the high-frequency pressure pulsations generated by bubble coalescence and breakup. Normalization ensures consistency in amplitude scale across signals collected from different measuring points and sensor channels, eliminating the influence of sensor sensitivity differences and static pressure differences along the pipeline on the absolute values ​​of characteristic indices. This ensures the comparability of characteristic indices extracted from different locations within the same pipeline, thereby guaranteeing the reliability of trend judgments based on upstream and downstream measuring point comparisons. The preprocessing does not introduce any subjective parameters, relying entirely on the statistical characteristics of the signal itself for adaptive processing, ensuring the objectivity and repeatability of subsequent analysis.

[0045] 203. Discrete wavelet transform multiscale decomposition.

[0046] Among them, the pressure time history signal is decomposed by discrete wavelet transform to obtain the pressure detail signal components at different scales and the low-frequency approximation signal components at the highest level.

[0047] Specifically, the discrete wavelet transform is a time-frequency analysis method that divides a signal layer by layer according to its frequency range to obtain signal components at different scales. Specifically, it decomposes the signal at different scales through scaling and translation transformations of scaling functions and wavelet functions, obtaining approximate coefficients reflecting the low-frequency trend components and detail coefficients reflecting the high-frequency detail components. The advantage of wavelet transform lies in its multi-resolution analysis capability, thus possessing high time resolution in the high-frequency band to accurately locate transient changes and high frequency resolution in the low-frequency band to identify slow evolution trends, making it suitable for processing pressure in gas-liquid two-phase flows. For complex signals exhibiting both non-stationarity and multi-scale characteristics, this step employs a 4th-order Daubechies wavelet (db4) as the mother wavelet function. This wavelet possesses good regularity and tight support, enabling effective sparse representation of the pressure signal while maintaining high time-frequency resolution. During multi-scale decomposition of the preprocessed pressure time-history signal, the current approximate signal is bisected layer by layer. With each layer of decomposition, the frequency resolution is halved while the time resolution is doubled. After this recursive layer-by-layer decomposition, the original signal is decomposed into detail signal components at different scales and a high-level low-frequency approximate signal component. Since the original signal's sampling frequency is 1000 Hz, according to the Nyquist sampling theorem, its effective frequency range is 0–500 Hz. After DWT decomposition, the original signal is divided into 10 detail signal components (D1 to D10) and one high-level low-frequency approximate signal component (A10).

[0048] The mathematical expression for the discrete wavelet transform is: No. Layer approximation coefficient:

[0049] No. Layer approximation coefficient:

[0050] In the formula: This is a pressure time history signal, with units of Pa. For scaling and translation basis functions of the scaling function; These are the scaling and translation basis functions of the wavelet function; The number of decomposition layers, ; The translation factor is... ; For the first Layer Approximation coefficients; For the first Layer Detail coefficients.

[0051] The pressure detail signal components of each layer and the low-frequency approximation signal components of the highest layer are obtained by reconstructing the decomposition coefficients: Low-frequency approximate signal components:

[0052] Pressure detail signal components:

[0053] In the formula: For the first The low-frequency approximation signal component reflects the signal's scale. Low-frequency trend components; For the first Laminar pressure detail signal components, reflecting the signal at different scales. High-frequency detail components; and These are the approximation coefficients and detail coefficients for the corresponding layers, respectively.

[0054] The signal components at different scales correspond to pressure fluctuation components in different frequency bands, reflecting the flow behavior at different physical scales in the gas-liquid two-phase flow in the pipeline. The high-frequency signal components correspond to the microscopic behavior at the bubble scale, the mid-frequency signal components correspond to the mesoscopic behavior at the aeroelastic scale, and the low-frequency signal components correspond to the macroscopic evolution behavior of the flow pattern. The correspondence between the signal components at each scale and the frequency range and their physical meanings are detailed in the table below.

[0055]

[0056] 204. Calculation of energy proportion in each frequency band.

[0057] Specifically, the energy of the first signal component corresponding to the pressure detail signal component and the energy of the second signal component corresponding to the highest-level low-frequency approximation signal component are calculated, and the energy of the first signal component and the energy of the second signal component at all scales are summed to obtain the total signal energy.

[0058] Specifically, after decomposing the preprocessed pressure time history signal into detail signal components at different scales and the highest-level low-frequency approximation signal component using discrete wavelet transform, in order to further extract the first feature index used to characterize the bubble coalescence activity, it is necessary to first obtain the energy distribution information of the signal components at each scale. Based on the wavelet coefficients obtained from wavelet decomposition, the energy of the signal components at each scale is calculated. For the pressure detail signal components at different scales, the energy of the corresponding first signal component at that scale is obtained by summing the squares of the wavelet detail coefficients. The energy value reflects the overall intensity of the pressure fluctuation component within this frequency band, and its expression is:

[0059] In the formula: For the first The energy of the detailed signal of laminar pressure is dimensionless. For the first Layer Detail coefficients.

[0060] Simultaneously, by summing the squares of the wavelet approximation coefficients of the highest-level low-frequency approximation signal component, the energy of the second signal component corresponding to the highest-level low-frequency approximation signal component is obtained. The energy value reflects the intensity of the quasi-steady-state average flow component in the pressure signal, and its expression is:

[0061] In the formula: The energy of the low-frequency approximation signal at the 10th layer is dimensionless. For the 10th floor Approximation coefficients.

[0062] Furthermore, to obtain the total energy of the pressure signal, the energy of the first signal component across all scales is summed with the energy of the second signal component corresponding to the highest-level low-frequency approximation signal component to obtain the total signal energy. The expression is as follows:

[0063] In the formula: Let be the total energy of the signal, which is dimensionless.

[0064] Because the discrete wavelet transform has the energy preservation property—that is, the total energy of the signal in the time domain is equal to the sum of the squares of the wavelet coefficients at each scale and the sum of the squares of the highest-level approximation coefficients—the summation result is the total energy of the original pressure signal. The energy percentage Pj of the detail signal components at each scale is obtained by dividing the energy of the first signal component at each scale by the total signal energy and multiplying by 100%, as expressed by:

[0065] In the formula, For the first The percentage of energy of the layer detail signal relative to the total energy, expressed in %.

[0066] The proportion eliminates the influence of the absolute amplitude of the signal, making the energy distribution between different measuring points and different times comparable. The above energy calculation process completely preserves the energy information of all frequency components in the pressure signal, providing an accurate data basis for extracting characteristic indicators reflecting the activity of bubble coalescence from the energy distribution. At the same time, the energy retention characteristics also ensure the physical integrity of the total energy calculation.

[0067] 205. Calculation of the proportion of high-frequency turbulent kinetic energy.

[0068] Among them, the ratio of the energy of the third signal component to the total signal energy is used as the first characteristic index.

[0069] Specifically, after obtaining the energy of the detailed signal components at each scale and the total signal energy, this step selects the target detailed signal components corresponding to the high-frequency fluctuation components of the pressure signal from all detailed signal components, and calculates the first characteristic index based on its energy ratio. Research has found that the high-frequency pressure pulsations generated by bubble coalescence in the pipe are mainly concentrated at scales D1 to D3, corresponding to a frequency range of 250–500 Hz. Scale D1 reflects local turbulence and micro-fluctuations of the bubbles, scale D2 reflects high-frequency fluctuations in bubble coalescence and breakup, and scale D3 reflects inertial oscillations of the bubble swarm. These three scales together constitute the characteristic frequency band characterizing the activity of bubble coalescence. Based on this, this step selects scales D1 to D3 as the target detailed signal components, sums the energy of the detailed signal components at the three scales to obtain the energy of the third signal component, and then calculates the ratio of the energy of the third signal component to the total signal energy. This ratio is used as the first characteristic index, namely the high-frequency turbulent energy ratio (HFE), expressed as:

[0070] In the formula: HFE is the percentage of high-frequency turbulent kinetic energy, expressed as % . , , These represent the energies of detail signals from layers 1 to 3, respectively. This represents the total energy of the signal.

[0071] The calculated ratio characterizes the proportion of high-frequency energy in the total signal energy. When bubble coalescence is active in the pipe, the violent movement and collision bursting of bubbles generate strong high-frequency pressure fluctuations, leading to a significant increase in high-frequency energy and a higher HFE value. Conversely, when the flow is stable and the bubble distribution is uniform, high-frequency fluctuations are weaker, and the HFE value is lower. Thus, through multi-scale time-frequency decomposition and energy calculation, the previously difficult-to-observe bubble coalescence behavior has been quantified into a quantitatively analyzable numerical indicator, providing a reliable frequency domain characteristic basis for subsequent gas blockage risk assessment.

[0072] 206. Calculation of normalized autocorrelation function.

[0073] Among them, the normalized autocorrelation function of the pressure time history signal is calculated.

[0074] Specifically, after obtaining the preprocessed pressure time history signal, this step extracts a second characteristic index to characterize the flow orderliness through time-domain correlation analysis. The core of time-domain correlation analysis lies in calculating the normalized autocorrelation function of the pressure time history signal. The autocorrelation function describes the correlation between the values ​​of the same signal at different time delays. The normalized autocorrelation function normalizes the autocorrelation function value to the interval [-1, 1] and is used to characterize the self-similarity of the signal at different time delays. The calculation expression of the normalized autocorrelation function is as follows:

[0075] In the formula: The normalized autocorrelation function is dimensionless and its range is . ; This is a pressure signal, measured in Pa. The mean of the signal. The unit is Pa; The total duration of the signal is expressed in seconds (s). This is a time delay variable, measured in seconds (s).

[0076] When pressure fluctuations have stable periodic or quasi-periodic components, the signal waveforms under different time delays maintain a high degree of similarity, and the autocorrelation function value is high. When pressure fluctuations tend to be random, the waveforms of the signal under different time delays quickly lose their similarity, and the autocorrelation function value decays rapidly to near zero. The normalized autocorrelation function reflects the stability and orderliness of the flow state. The more ordered and periodic the flow, the slower the decay of the autocorrelation function in the time delay domain. The more chaotic and random the flow, the faster the decay of the autocorrelation function in the time delay domain.

[0077] 207. Calculation of signal autocorrelation coefficient.

[0078] In this process, the average value of the normalized autocorrelation function is calculated within the range of zero to the preset maximum hysteresis time, and the average value is used as the second feature index.

[0079] Based on the obtained normalized autocorrelation function, this step further presets the maximum hysteresis time and calculates the average value of the normalized autocorrelation function within the interval from zero to the preset maximum hysteresis time. The average value is used as the second characteristic index, and the specific expression is as follows:

[0080] In the formula, This represents the maximum hysteresis time.

[0081] This average value comprehensively reflects the overall self-similarity of the pressure signal within a finite hysteresis time range. Since the periodic fluctuations in the pressure signal are sensitive to changes in the flow state, when the flow in the pipe transitions from an ordered to a chaotic state, the periodic components in the pressure signal weaken and the random components strengthen, resulting in a decrease in the average value of the autocorrelation function within the hysteresis time. The second characteristic index quantitatively characterizes the flow orderliness by capturing this change. A higher index value indicates stronger self-similarity of the pressure signal in the time-delay domain, and a more ordered and stable flow state; a lower index value indicates stronger randomness of the pressure signal, a more chaotic flow state, and predicts the flow pattern. A shift may be underway; based on this, the second characteristic indicator and the first characteristic indicator based on frequency domain energy distribution form an effective complement in the time and frequency domains: the first characteristic indicator monitors the changes in energy distribution caused by bubble coalescence behavior from a frequency domain perspective, while the second characteristic indicator monitors the changes in the orderliness of the flow state from a time domain perspective. The two together characterize the flow state of the gas-liquid two-phase flow from different dimensions, providing more comprehensive characteristic information for the accurate determination of subsequent gas blockage risks; the entire calculation process does not rely on any external reference signal, but is based solely on the time domain statistical characteristics of the signal itself, which has the advantages of high computational efficiency and good real-time performance, making it suitable for the online real-time monitoring needs of industrial sites.

[0082] 208. Comprehensive assessment of gas blockage risk.

[0083] Specifically, the system acquires a first low-risk threshold and a first high-risk threshold for a first characteristic indicator, and a second low-risk threshold and a second high-risk threshold for a second characteristic indicator. When the first characteristic indicator exceeds the first high-risk threshold and the second characteristic indicator is lower than the second high-risk threshold, the corresponding measuring point is determined to be in a high-risk state of air blockage. When the first characteristic indicator is lower than the first low-risk threshold and the second characteristic indicator exceeds the second low-risk threshold, the corresponding measuring point is determined to be in a low-risk state of air blockage. When the first characteristic indicator is between the first low-risk threshold and the first high-risk threshold, and / or the second characteristic indicator is between the second low-risk threshold and the second high-risk threshold, the corresponding measuring point is determined to be in a transitional state.

[0084] Specifically, after extracting the first characteristic index for characterizing bubble coalescence activity and the second characteristic index for characterizing flow orderliness, this step uses a dual-index joint judgment method to comprehensively assess the airlock risk status at each measuring point in the pipeline. Single-index judgment is easily affected by operating condition fluctuations and may lead to misjudgment. For example, when judging an increased airlock risk solely based on an increase in high-frequency energy, normal flow fluctuations or injection pressure fluctuations may also cause a temporary increase in high-frequency energy. When judging an increased airlock risk solely based on a decrease in autocorrelation, transient interference from the sensor may also cause fluctuations in the signal autocorrelation index. Therefore, this step introduces a dual-index threshold matrix method, which combines the risk thresholds of the two indicators to construct a dual-parameter judgment matrix covering three risk states: a first low-risk threshold and a first high-risk threshold corresponding to the first characteristic index, and a second low-risk threshold and a second high-risk threshold corresponding to the second characteristic index. A total of four thresholds divide the two-dimensional feature space into multiple regions.

[0085] Based on the above threshold matrix, this step comprehensively determines the gas blockage risk status at the measuring point according to the following four rules: The first scenario is when the first characteristic indicator exceeds the first high-risk threshold and the second characteristic indicator is lower than the second high-risk threshold. This means that the proportion of high-frequency energy increases significantly, indicating high bubble coalescence activity, while the signal autocorrelation decreases significantly, indicating poor flow order. Both dimensions point to an increased risk of air blockage. In this case, the measuring point is determined to be in a high-risk state of air blockage. Under this state, bubble coalescence in the pipeline is intense, the flow pattern is chaotic, and the possibility of air blockage is high. The system outputs a high-risk warning signal to prompt the operator to take timely intervention measures.

[0086] The second scenario is when the first characteristic indicator is lower than the first low-risk threshold and the second characteristic indicator exceeds the second low-risk threshold. This means that the high-frequency energy ratio is low, indicating that the bubble distribution is uniform and the aggregation behavior is not significant. At the same time, the signal autocorrelation is high, indicating that the flow is orderly. Both dimensions point to a safe state. In this case, the measurement point is determined to be in a low-risk state of air blockage, confirming that the flow is stable, and the system outputs a normal operation status confirmation message.

[0087] The third type is when the first characteristic indicator is between the first low-risk threshold and the first high-risk threshold, and / or the second characteristic indicator is between the second low-risk threshold and the second high-risk threshold, that is, at least one indicator is in the transition range between the high and low thresholds, and has not yet reached the high-risk judgment standard but has not fully met the low-risk condition. At this time, the measurement point is judged to be in a transition state.

[0088] The fourth type is when either of the two indicators is in the middle range, which indicates that the system as a whole is in a transitional state. At this time, the system is judged to be in a transitional state, and continuous monitoring and increased sampling and analysis frequency are required to closely monitor the further development of gas blockage risk.

[0089] Using the dual-index threshold matrix method described above, this step achieves a quantitative comprehensive assessment of the gas blockage risk status at each measuring point. Compared to single-index assessment, the dual-index joint assessment can effectively offset the sensitivity of single-index to fluctuations caused by non-gas blockage factors, improving the accuracy and robustness of the assessment. In addition, the assessment results of the dual-index threshold matrix method are not only used for risk level assessment of the status of each measuring point at the current moment, but also provide discrete state information at different measuring points for subsequent judgment of the gas blockage evolution trend based on the changing trend of multiple measuring points along the pipeline. After being correlated with the spatial distribution information of the measuring points, it can be used to identify the evolution direction and degree of bubble coalescence along the pipeline transport direction.

[0090] 209. Trend early warning and regulation.

[0091] The process involves acquiring first and second characteristic indices at multiple measuring points along the fluid flow direction on the injection pipeline. These measuring points are distributed in the upstream and downstream regions of the injection pipeline along the fluid flow direction. Based on the degree of difference between the first and / or second characteristic indices between measuring points located in the upstream region and those located in the downstream region, a trend determination value is calculated. This trend determination value is used to characterize the degree of flow pattern transformation. When the trend determination value exceeds a preset trend warning threshold, it is determined that there is a flow pattern transformation trend driven by bubble coalescence within the injection pipeline, and a trend warning signal is output.

[0092] Specifically, after obtaining the airlock risk status assessment results at each measuring point, this step further utilizes the characteristic index data of multiple measuring points deployed along the fluid flow direction to quantitatively determine the airlock evolution trend. Based on the aforementioned data collection and analysis, it is known that multiple measuring points are distributed along the fluid flow direction on the injection pipeline. These measuring points are located in the upstream and downstream regions of the pipeline. For gas-liquid two-phase flow, bubbles continuously coalesce and grow during pipeline transport, and the flow pattern gradually changes from the initial dispersed bubble flow to a slug flow or plunger flow. This transformation process is not instantaneous but evolves gradually along the pipeline length. Therefore, comparing the degree of difference between the first and / or second characteristic indices between measuring points located in the upstream region and measuring points located in the downstream region can effectively assess the degree of flow pattern transformation driven by bubble coalescence during pipeline transport. Specifically, when bubbles... When a flow pattern transition driven by aggregation occurs, there is a significant difference between the first characteristic index at the upstream and downstream measuring points. This is because the upstream area has a high level of bubble aggregation activity due to the recent injection of a large number of bubbles from the dissolved oxygen device, while the downstream area has weakened local turbulent behavior and decreased high-frequency pressure fluctuations due to the aggregation and growth of bubbles into atmospheric bouncy streams. Therefore, the first characteristic index in the downstream area shows a significant downward trend relative to the upstream area. At the same time, the upstream area has low flow order due to the dispersed bubble distribution and strong flow randomness, while the downstream area has a significantly increased flow order due to the structured flow pattern after bubble aggregation and the periodic characteristics of pressure fluctuations. Therefore, the second characteristic index in the downstream area shows a significant upward trend relative to the upstream area. Thus, by examining the degree of difference between the first and / or second characteristic indices among the measuring points along the flow path, the process of flow pattern transition from dispersed bubble flow to aggregated bouncy flow can be effectively reflected.

[0093] Based on this, this step calculates the trend determination value according to the degree of difference between the first and second characteristic indicators between the upstream and downstream measuring points. The trend determination value is quantified by the flow pattern transformation index ΔS, and its expression is:

[0094] In the formula: The flow pattern transition index is dimensionless. , These represent the proportion of high-frequency turbulent kinetic energy and the autocorrelation coefficient at the first observation point, respectively. , These represent the proportion of high-frequency turbulent kinetic energy and the autocorrelation coefficient at the second observation point, respectively.

[0095] When the ΔS value increases, it indicates that the decrease in the first characteristic index and the increase in the second characteristic index along the pipeline are both significant, meaning that the flow pattern transformation is intensified. Specifically, when the decrease in the first characteristic index downstream relative to the upstream exceeds 5% and the increase in the second characteristic index exceeds 0.2, it indicates that a significant flow pattern transformation driven by bubble coalescence has occurred in the pipeline, meaning that the flow pattern is evolving from dispersed bubble flow to slug flow or plunger flow. When ΔS exceeds the preset trend warning threshold of 0.3, the system determines that there is a significant trend of flow pattern transformation driven by bubble coalescence in the pipeline and outputs a trend warning signal. The output time of the trend warning signal is significantly earlier than the actual time of air blockage, because flow pattern transformation is a necessary precursor to air blockage. Only after a large number of bubbles coalesce to form air slugs or air columns will the pipeline experience a blockage phenomenon with a significant reduction in effective flow area. Issuing a warning as soon as the flow pattern transformation occurs provides operators with sufficient time to adjust the injection parameters in a timely manner according to the warning signal to inhibit further bubble coalescence and growth, thereby effectively preventing the formation of air blockage.

[0096] Furthermore, the pressure fluctuation signal continuously collected from the measuring point is segmented based on the preset time window length to obtain multiple time window signal segments; the multiple time window signal segments are arranged in a sliding window manner with a preset overlap rate between adjacent time windows to obtain the sliding window signal sequence corresponding to the measuring point; for each time window signal segment in the sliding window signal sequence, the corresponding first feature index and second feature index are extracted and gas blockage risk and evolution trend are judged.

[0097] Furthermore, based on the continuous acquisition of pressure fluctuation signals, this application introduces a sliding window processing mechanism. A sliding window segmentation method is used to divide the continuously acquired pressure fluctuation signals into time windows. Specifically, the pressure fluctuation signals continuously acquired at the measuring point are segmented based on a preset time window length, resulting in multiple time window signal segments arranged consecutively in time. Then, these time window signal segments are arranged in a sliding window configuration with a preset overlap rate between adjacent time windows, resulting in a sliding window signal sequence corresponding to the measuring point. In the sliding window signal sequence, each time window signal segment partially overlaps with its adjacent windows in time, thereby ensuring that the monitoring results between adjacent windows are synchronized in time. Continuous transition avoids monitoring blind spots or abrupt boundary changes caused by window truncation. Based on the aforementioned sliding window signal sequence, each time window signal segment is processed according to the steps described above in this application, namely, preprocessing, multi-scale time-frequency decomposition and extraction of the first feature index, time-domain correlation analysis and extraction of the second feature index are performed sequentially on each time window signal segment. Based on the extraction results, gas blockage risk assessment and evolution trend assessment are performed, thereby achieving continuous updating and real-time monitoring of the gas blockage status. Through the continuous advancement of the sliding window, each time window outputs the current gas blockage risk status and evolution trend assessment results, forming a continuous monitoring sequence along the time axis.

[0098] By using sliding window processing, this step enables continuous online monitoring of the gas blockage status in the injection pipeline. The processing result of each time window signal segment can serve as a real-time reflection of the gas blockage status at that time. The overlap rate set between adjacent time windows ensures the continuity and smoothness of the monitoring results, avoiding monitoring interruptions or result jumps caused by window switching. This is beneficial for capturing the entire trajectory of gas blockage evolution, providing operators with continuous and stable gas blockage status monitoring information, and also providing a continuous time series data basis for trend early warning, enabling early warning signals to be captured and identified as soon as bubble coalescence occurs.

[0099] Furthermore, as Figure 1 In a specific implementation of the method, this application provides a device for determining the gas blockage status of a uranium leaching pipeline, such as... Figure 3 As shown, the device includes: a signal acquisition module 301, a time-frequency analysis module 302, a time-domain analysis module 303, and a signal output module 304.

[0100] The signal acquisition module 301 is used to acquire pressure fluctuation signals at multiple measuring points along the fluid flow direction on the injection pipeline, and to preprocess the pressure fluctuation signals to obtain pressure time history signals. The time-frequency analysis module 302 is used to perform multi-scale time-frequency decomposition on the pressure time history signal to obtain signal components at different scales, and extract a first feature index based on the energy distribution of the signal components. The first feature index is used to characterize the bubble coalescence activity. The time-domain analysis module 303 is used to perform time-domain correlation analysis on the pressure time-history signal and extract the second feature index, wherein the second feature index is used to characterize the flow orderliness. The signal output module 304 is used to determine the air blockage risk status at the corresponding measuring point based on the first characteristic index and the second characteristic index, and to determine the air blockage evolution trend in the pipeline based on the changing trends of the first characteristic index and / or the second characteristic index at multiple measuring points along the fluid flow direction, and to output the corresponding early warning signal.

[0101] In specific application scenarios, the time-frequency analysis module 302 is specifically used to perform discrete wavelet transform decomposition on the pressure time history signal to obtain pressure detail signal components and the highest-level low-frequency approximate signal components at different scales; calculate the energy of the first signal component corresponding to the pressure detail signal component and the energy of the second signal component corresponding to the highest-level low-frequency approximate signal component, and sum the energy of the first signal component and the second signal component at all scales to obtain the total signal energy; select at least one target detail signal component corresponding to the high-frequency fluctuation component of the pressure signal from the pressure detail signal components, and sum the energy of the target detail signal component to obtain the energy of the third signal component; calculate the ratio of the energy of the third signal component to the total signal energy as the first feature index.

[0102] In specific application scenarios, the time domain analysis module 303 is specifically used to calculate the normalized autocorrelation function of the pressure time history signal, and to calculate the average value of the normalized autocorrelation function within the interval from zero to the preset maximum hysteresis time, and use the average value as the second characteristic index.

[0103] In specific application scenarios, the signal output module 304 is specifically used to acquire the first low-risk threshold and the first high-risk threshold of the first characteristic indicator, as well as the second low-risk threshold and the second high-risk threshold of the second characteristic indicator; when the first characteristic indicator exceeds the first high-risk threshold and the second characteristic indicator is lower than the second high-risk threshold, the corresponding measuring point is determined to be in a high-risk state of air blockage; when the first characteristic indicator is lower than the first low-risk threshold and the second characteristic indicator exceeds the second low-risk threshold, the corresponding measuring point is determined to be in a low-risk state of air blockage; when the first characteristic indicator is between the first low-risk threshold and the first high-risk threshold, and / or the second characteristic indicator is between the second low-risk threshold and the second high-risk threshold, the corresponding measuring point is determined to be in a transitional state.

[0104] In specific application scenarios, the signal output module 304 is also used to acquire the first characteristic index and the second characteristic index corresponding to multiple measuring points arranged along the fluid flow direction on the injection pipeline. The multiple measuring points are distributed in the upstream and downstream regions of the injection pipeline along the fluid flow direction. Based on the degree of difference between the first characteristic index and / or the second characteristic index between the measuring points located in the upstream region of the fluid and the measuring points located in the downstream region of the fluid, a trend judgment value is calculated. The trend judgment value is used to characterize the degree of flow pattern transformation. When the trend judgment value exceeds the preset trend warning threshold, it is determined that there is a flow pattern transformation trend driven by bubble coalescence in the injection pipeline, and a trend warning signal is output.

[0105] In specific application scenarios, the signal acquisition module 301 is used to segment the pressure fluctuation signal continuously acquired from the measuring point based on a preset time window length to obtain multiple time window signal segments; the multiple time window signal segments are arranged in a sliding window manner with a preset overlap rate between adjacent time windows to obtain the sliding window signal sequence corresponding to the measuring point; for each time window signal segment in the sliding window signal sequence, the corresponding first feature index and second feature index are extracted and the gas blockage risk and evolution trend are judged.

[0106] In specific application scenarios, the signal acquisition module 301 is specifically used to perform noise reduction processing on the pressure fluctuation signal, to filter out the DC bias component and high-frequency noise interference in the pressure fluctuation signal; and to normalize the amplitude of the denoised pressure fluctuation signal to the dimensionless range to obtain the pressure time history signal.

[0107] It should be noted that other corresponding descriptions of the functional units involved in the gas blockage status determination device for uranium leaching pipelines provided in this embodiment can be found in [reference needed]. Figure 1 and Figure 2 The corresponding description in [the document] will not be repeated here.

[0108] Based on the above, Figure 1 and Figure 2 Accordingly, this embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the above-described method for determining the gas blockage status of uranium leaching pipelines.

[0109] Based on this understanding, the technical solution of this application can be embodied in the form of a software product. The software product to be identified can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, or portable hard drive), including several instructions to enable a computer device (such as a personal computer, server, or network device) to execute the gas blockage determination method for uranium leaching pipelines in various implementation scenarios of this application.

[0110] Based on the above, Figure 1 and Figure 2 The method shown, and Figure 3 The illustrated embodiment of the gas blockage determination device for uranium leaching pipelines is designed to achieve the above objectives, such as... Figure 4 As shown, this embodiment also provides a physical device for determining the gas blockage status of uranium leaching pipelines. This device includes a communication bus, a processor, a memory, and a communication interface. It may also include input / output interfaces and a display device. The various functional units can communicate with each other via the bus. The memory stores a computer program, and the processor executes the program stored in the memory to perform the gas blockage determination method for uranium leaching pipelines described in the above embodiment.

[0111] Optionally, the physical device may also include a user interface, a network interface, a camera, radio frequency (RF) circuitry, sensors, audio circuitry, a Wi-Fi module, etc. The user interface may include a display screen, input units such as a keyboard, etc., and optional user interfaces may also include USB interfaces, card reader interfaces, etc. The network interface may optionally include standard wired interfaces, wireless interfaces (such as Wi-Fi interfaces), etc.

[0112] Those skilled in the art will understand that the physical device structure for determining the gas blockage status of a uranium leaching pipeline provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or combine certain components, or have different component arrangements.

[0113] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the aforementioned physical device, supporting the operation of information processing programs and other software and / or programs to be identified. The network communication module is used to enable communication between the various components within the storage medium, as well as communication with other hardware and software in the information processing physical device.

[0114] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platform, or it can be implemented by hardware. By applying the technical solution of this application, pressure fluctuation signals at multiple measuring points along the fluid flow direction on the injection pipeline are collected, and the changing trends of the first and / or second characteristic indicators at multiple measuring points along the fluid flow direction are combined to judge the gas blockage evolution trend. This not only determines the current state of each measuring point, but also identifies the flow pattern evolution direction along the process by the difference in indicators between upstream and downstream measuring points, thus issuing an early warning before the actual formation of gas blockage. At the same time, by performing multi-scale time-frequency decomposition on the pressure time history signal to extract the first characteristic indicator for characterizing the activity of bubble coalescence, and by extracting the second characteristic indicator for characterizing the flow orderliness through time-domain correlation analysis, the flow characteristic information contained in the pressure fluctuation signal is fully explored from both the frequency domain and time domain dimensions. This overcomes the shortcomings of the existing single characteristic parameter method, which has insufficient information utilization and insufficient ability to characterize the evolution of gas-liquid two-phase flow patterns. On this basis, the gas blockage risk status at the corresponding measuring point is judged by the joint determination of dual indicators, which reduces the misjudgment caused by the interference of single indicators due to operating condition fluctuations, and significantly improves the accuracy and reliability of gas blockage status determination. The above method provides early warning before gas blockage occurs, fully extracts flow characteristic information from pressure signals in both frequency and time domains, and improves diagnostic reliability by using dual-indicator joint judgment. It makes full use of information and has a high accuracy rate in judgment.

[0115] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing this application. Those skilled in the art will understand that the modules in the apparatus of the embodiment can be distributed within the apparatus of the embodiment as described, or can be modified to be located in one or more apparatuses different from this embodiment. The modules of the above-described embodiment can be combined into one module, or further divided into multiple sub-modules.

[0116] The serial numbers in this application are for descriptive purposes only and do not represent the superiority or inferiority of any particular implementation scenario. The above disclosures are merely a few specific implementation scenarios of this application; however, this application is not limited thereto, and any variations conceived by those skilled in the art should fall within the protection scope of this application.

Claims

1. A method for determining the gas blockage status of a uranium extraction pipeline through in-situ leaching, characterized in that, include: Pressure fluctuation signals are collected at multiple measuring points along the fluid flow direction on the injection pipeline, and the pressure fluctuation signals are preprocessed to obtain pressure time history signals. The pressure time history signal is decomposed into multiple scales to obtain signal components at different scales, and a first feature index is extracted based on the energy distribution of the signal components, wherein the first feature index is used to characterize the bubble coalescence activity. A time-domain correlation analysis is performed on the pressure time-history signal to extract a second feature index, wherein the second feature index is used to characterize the flow orderliness. The risk status of air blockage at the corresponding measuring point is determined based on the first characteristic index and the second characteristic index. Based on the changing trends of the first characteristic index and / or the second characteristic index at multiple measuring points along the fluid flow direction, the evolution trend of air blockage in the pipeline is judged, and the corresponding early warning signal is output.

2. The method according to claim 1, characterized in that, The step of performing multi-scale time-frequency decomposition on the pressure time history signal to obtain signal components at different scales, and extracting a first feature index based on the energy distribution of the signal components, includes: Discrete wavelet transform decomposition is performed on the pressure time history signal to obtain pressure detail signal components at different scales and the highest-level low-frequency approximate signal components; Calculate the energy of the first signal component corresponding to the pressure detail signal component and the energy of the second signal component corresponding to the highest layer low-frequency approximation signal component, and sum the energy of the first signal component and the energy of the second signal component at all scales to obtain the total signal energy; At least one target detail signal component corresponding to the high-frequency fluctuation component of the pressure signal is selected from the pressure detail signal components, and the energy of the target detail signal components is summed to obtain the energy of the third signal component. The ratio of the energy of the third signal component to the total energy of the signal is calculated as the first characteristic index.

3. The method according to claim 1, characterized in that, The step of performing time-domain correlation analysis on the pressure time-history signal and extracting the second feature index includes: Calculate the normalized autocorrelation function of the pressure time history signal, and calculate the average value of the normalized autocorrelation function within the interval from zero to the preset maximum hysteresis time. Use the average value as the second characteristic index.

4. The method according to claim 1, characterized in that, The step of determining the airlock risk status at the corresponding measuring point based on the first characteristic index and the second characteristic index includes: Obtain the first low-risk threshold and the first high-risk threshold of the first feature indicator, and the second low-risk threshold and the second high-risk threshold of the second feature indicator; When the first characteristic indicator exceeds the first high-risk threshold and the second characteristic indicator is lower than the second high-risk threshold, the corresponding measuring point is determined to be in a high-risk state of air blockage. When the first characteristic indicator is lower than the first low-risk threshold and the second characteristic indicator exceeds the second low-risk threshold, the corresponding measuring point is determined to be in a low-risk state of air blockage. When the first feature indicator is located between the first low-risk threshold and the first high-risk threshold, and / or the second feature indicator is located between the second low-risk threshold and the second high-risk threshold, the corresponding measurement point is determined to be in a transitional state.

5. The method according to claim 1, characterized in that, The step of determining the evolution trend of gas blockage in the pipeline based on the changing trends of the first characteristic index and / or the second characteristic index at multiple measuring points along the fluid flow direction, and outputting a corresponding early warning signal, includes: The first characteristic index and the second characteristic index are obtained at multiple measuring points arranged along the fluid flow direction on the injection pipeline, wherein the multiple measuring points are distributed in the upstream and downstream regions of the injection pipeline along the fluid flow direction. Based on the degree of difference between the first characteristic index and / or the second characteristic index between the measuring point located in the upstream region of the fluid and the measuring point located in the downstream region of the fluid, a trend determination value is calculated, wherein the trend determination value is used to characterize the degree of flow pattern transformation. When the trend determination value exceeds the preset trend warning threshold, it is determined that there is a flow pattern change trend driven by bubble coalescence in the injection pipeline, and a trend warning signal is output.

6. The method according to claim 1, characterized in that, After collecting pressure fluctuation signals at multiple measuring points along the fluid flow direction on the injection pipeline, the method further includes: The pressure fluctuation signal continuously collected from the measuring point is segmented based on a preset time window length to obtain multiple time window signal segments. The signal segments of the multiple time windows are arranged in a sliding window manner with a preset overlap rate between adjacent time windows to obtain the sliding window signal sequence corresponding to the measurement point; For each time window signal segment in the sliding window signal sequence, the corresponding first feature index and second feature index are extracted and gas blockage risk and evolution trend are determined.

7. The method according to claim 1, characterized in that, The preprocessing of the pressure fluctuation signal to obtain the pressure time history signal includes: The pressure fluctuation signal is denoised to filter out the DC bias component and high-frequency noise interference in the pressure fluctuation signal. The amplitude of the denoised pressure fluctuation signal is normalized to the dimensionless range to obtain the pressure time history signal.

8. A device for determining the gas blockage status of a uranium leaching pipeline, characterized in that, include: The signal acquisition module is used to acquire pressure fluctuation signals at multiple measuring points along the fluid flow direction on the injection pipeline, and to preprocess the pressure fluctuation signals to obtain pressure time history signals. The time-frequency analysis module is used to perform multi-scale time-frequency decomposition on the pressure time history signal to obtain signal components at different scales, and extract a first feature index based on the energy distribution of the signal components, wherein the first feature index is used to characterize the bubble coalescence activity. The time-domain analysis module is used to perform time-domain correlation analysis on the pressure time-history signal and extract a second feature index, wherein the second feature index is used to characterize the flow orderliness. The signal output module is used to determine the air blockage risk status at the corresponding measuring point based on the first characteristic index and the second characteristic index, and to determine the air blockage evolution trend in the pipeline based on the changing trends of the first characteristic index and / or the second characteristic index at multiple measuring points along the fluid flow direction, and to output the corresponding early warning signal.

9. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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