Gas leakage detection method and device for laser cutting air compressor

Abstract

The invention provides a gas leakage detection method and device for a laser cutting air compressor, and relates to the technical field of laser cutting air compressor test.The gas leakage detection method comprises the steps that the gas transmission flow direction of the laser cutting air compressor is recognized, a system key pipeline and an equipment circulation node are analyzed, an ultrasonic sensor is configured, and an ultrasonic monitoring signal is obtained; collecting gas state data; abnormal point identification is carried out, and abnormal positioning analysis is carried out to determine abnormal transfer nodes; positioning node circulation influence parameters and leakage risk, and configuring ultrasonic monitoring sensitive parameters; adjusting parameters of the ultrasonic sensor; and carrying out combined leakage analysis to obtain a gas leakage detection result. The technical problem that gas leakage is difficult to find and position in time due to the fact that gas flow abnormity in a system is difficult to dynamically monitor in real time in the prior art can be solved, the technical target of dynamically monitoring the gas flow abnormity in real time is achieved, and the technical effect of efficiently and accurately identifying and positioning potential leakage points is achieved.

Description

Technical Field

[0001] This application relates to the field of laser cutting air compressor testing technology, and in particular to a method and apparatus for detecting gas leaks in laser cutting air compressors. Background Technology

[0002] The working principle of a laser cutting air compressor is to provide the required air source for the laser cutting machine by compressing air. Due to its complex structure, the gas may encounter various resistances and abnormal situations during the flow process, which may affect the overall efficiency and stability of the equipment.

[0003] Traditional technologies for analyzing gas flow paths typically focus only on the performance and maintenance of static equipment, neglecting the real-time monitoring of dynamic airflow characteristics. This leads to the failure to promptly detect and locate gas flow anomalies. Furthermore, current monitoring systems lack intelligent adjustment mechanisms, unable to dynamically adjust sensor sensitivity based on real-time data. For example, in areas with high leakage risk, failure to promptly increase the sensitivity of monitoring parameters may result in some potential gas leaks going undetected, impacting production safety and product quality. Therefore, under this traditional monitoring approach, system anomalies often rely solely on manual judgment or post-event analysis, failing to reflect real-time dynamic changes in the system. This leads to long maintenance cycles, low production efficiency, and potentially even more serious safety hazards.

[0004] In summary, existing technologies suffer from several problems. Due to the difficulty in real-time dynamic monitoring of abnormal gas flow in the system, most lack intelligent and adaptive adjustment mechanisms, making it difficult to detect and locate gas leaks in a timely manner. This further affects the stable operation and cutting quality of laser cutting air compressors, increases the risk of equipment failure, prolongs maintenance time, and may even lead to safety hazards and production downtime, thereby affecting production efficiency and product quality. Summary of the Invention

[0005] The purpose of this application is to provide a gas leak detection method and device for laser cutting air compressors, in order to solve the technical problems in the prior art where it is difficult to monitor abnormal gas flow in the system in real time, and most of them lack intelligent and adaptive adjustment mechanisms, which makes it difficult to detect and locate gas leaks in a timely manner, further affecting the stable operation and cutting quality of laser cutting air compressors, increasing the risk of equipment failure, prolonging maintenance time, and even bringing safety hazards and production downtime, thereby affecting production efficiency and product quality.

[0006] In view of the above problems, this application provides a gas leakage detection method and apparatus for laser cutting air compressors.

[0007] In a first aspect, this application provides a gas leak detection method for a laser-cutting air compressor, implemented using a gas leak detection device for the laser-cutting air compressor. The method includes: identifying the gas transmission flow direction of the laser-cutting air compressor, analyzing key pipelines and equipment flow nodes in the system, configuring an ultrasonic sensor, and obtaining ultrasonic monitoring signals; collecting gas state data of the laser-cutting air compressor system, including temperature, humidity, and pressure; identifying anomalies based on the ultrasonic monitoring signals and the gas state data, obtaining abnormal data, and performing anomaly location analysis based on the abnormal data to determine abnormal flow nodes; locating the flow influence parameters and leakage risk of the abnormal flow nodes, configuring ultrasonic monitoring sensitive parameters using the abnormal data and the flow influence parameters and leakage risk; adjusting the ultrasonic sensor parameters based on the ultrasonic monitoring sensitive parameters to obtain enhanced monitoring data; and performing joint leak analysis using the enhanced monitoring data and the abnormal data to obtain gas leak detection results.

[0008] Secondly, this application also provides a gas leak detection device for a laser-cutting air compressor, used to perform the gas leak detection method for a laser-cutting air compressor as described in the first aspect, comprising: an ultrasonic monitoring signal acquisition module, which is used to identify the gas transmission flow direction of the laser-cutting air compressor, analyze key pipelines and equipment flow nodes of the system, configure ultrasonic sensors, and obtain ultrasonic monitoring signals; a gas state data acquisition module, which is used to acquire gas state data of the laser-cutting air compressor system, including temperature, humidity, and pressure; and an abnormal flow node determination module, which is used to identify abnormal points based on the ultrasonic monitoring signals and the gas state data, and obtain abnormal flow node determination results. The system comprises: a normal data set and an abnormal data set; an abnormal data set and ...

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages: By identifying the gas flow direction of a laser-cutting air compressor, analyzing key pipelines and equipment flow nodes in the system, configuring ultrasonic sensors, and obtaining ultrasonic monitoring signals, the system collects gas state data, including temperature, humidity, and pressure. Anomalies are identified based on the ultrasonic monitoring signals and the gas state data, and abnormal flow nodes are determined through anomaly location analysis. Based on these abnormal flow nodes, the system locates the flow impact parameters and leakage risk, and configures ultrasonic monitoring sensitive parameters using the abnormal data and these parameters. The ultrasonic sensors are then adjusted based on these sensitive parameters to obtain enhanced monitoring data. Finally, a joint leak analysis is performed using the enhanced monitoring data and the abnormal data to obtain gas leak detection results. In other words, by achieving the technical goals of real-time dynamic monitoring of abnormal gas flow and intelligent adjustment of sensor sensitivity, the system efficiently and accurately identifies and locates potential leak points, thereby ensuring the stable operation of the laser-cutting air compressor, improving system safety, reducing equipment failures and maintenance costs, and enhancing production efficiency and cutting quality.

[0010] The above description is merely an overview of the technical solution of this application. To better understand the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0012] Figure 1 This is a schematic flowchart of the gas leak detection method for laser-cut air compressors used in this application; Figure 2 This is a schematic diagram of the gas leak detection device for laser-cut air compressors used in this application.

[0013] Explanation of reference numerals in the attached figures: The module includes an ultrasonic monitoring signal acquisition module 11, a gas state data acquisition module 12, an abnormal flow node determination module 13, an ultrasonic monitoring sensitive parameter configuration module 14, an enhanced monitoring data acquisition module 15, and a gas leak detection result acquisition module 16. Detailed Implementation

[0014] This application provides a gas leak detection method and apparatus for laser cutting air compressors, solving the technical problems in existing technologies where real-time dynamic monitoring of abnormal gas flow in the system is difficult, and most lack intelligent and adaptive adjustment mechanisms. This results in gas leaks being difficult to detect and locate in a timely manner, further affecting the stable operation and cutting quality of the laser cutting air compressor, increasing equipment failure risks, extending maintenance time, and even causing safety hazards and production downtime, thus impacting production efficiency and product quality. The method achieves the technical goal of real-time dynamic monitoring of abnormal gas flow and intelligent adjustment of sensor sensitivity, enabling efficient and accurate identification and location of potential leak points. This ensures the stable operation of the laser cutting air compressor, improves system safety, reduces equipment failure and maintenance costs, and enhances production efficiency and cutting quality.

[0015] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0016] Example 1, please refer to the appendix. Figure 1 This application provides a gas leak detection method for a laser-cutting air compressor, which is applied to a gas leak detection device for a laser-cutting air compressor, and specifically includes the following steps: Step 1: Identify the gas transmission flow direction of the laser-cut air compressor, analyze the key pipelines and equipment flow nodes of the system, configure ultrasonic sensors, and obtain ultrasonic monitoring signals.

[0017] Specifically, gas is input through the air compressor inlet, passing through an air filter and a refrigerated dryer, etc., to identify the gas flow direction within the laser-cut air compressor and obtain the gas transmission direction. Next, key pipelines and equipment flow nodes, such as air filters and refrigerated dryers, are analyzed, with customized settings by those skilled in the art based on actual conditions. Ultrasonic sensors detect leaks by monitoring ultrasonic signals generated from changes in gas flow velocity. To ensure accuracy and reliability, ultrasonic sensors are configured to detect sound fluctuations in gas flow. By monitoring ultrasonic signals, minute leaks in pipelines or equipment can be detected promptly, allowing for effective prevention and repair. If the ultrasonic sensor detects a flow velocity change exceeding five meters per second, it may indicate a leak at a pipeline connection, requiring immediate inspection and repair.

[0018] Step 2: Collect gas state data of the laser cutting air compressor system, including temperature, humidity, and pressure.

[0019] Specifically, since temperature, humidity, and pressure have a significant impact on the flow characteristics of gas and the normal operation of equipment, affecting gas density and flow rate, and thus affecting system performance and detection accuracy, key gas state data, including temperature, humidity, and pressure, are collected during the monitoring of the operation of the laser cutting air compressor system.

[0020] Step 3: Identify anomalies based on the ultrasonic monitoring signal and the gas state data, obtain abnormal data, and perform anomaly location analysis based on the abnormal data to determine abnormal flow nodes.

[0021] Specifically, ultrasonic monitoring signals and gas state data (such as temperature, humidity, and pressure) are combined to identify potential anomalies. When abnormal fluctuations occur in the ultrasonic signal, a leak or blockage in a certain area can be suspected. By comparing this abnormal signal with the gas state data at the time, the anomaly is further confirmed. Next, location analysis is performed based on the anomaly data to trace the gas flow path and identify abnormal flow nodes. The specific location of the anomaly source is determined, thus locking that location as the abnormal flow node, thereby accurately identifying and locating potential fault areas in the system.

[0022] Step 4: Based on the abnormal flow nodes, locate the flow impact parameters and leakage risk of the nodes, and configure the ultrasonic monitoring sensitive parameters using the abnormal data and the flow impact parameters and leakage risk of the nodes.

[0023] Specifically, by identifying abnormal flow nodes, the impact parameters on system operation are determined, i.e., node flow impact parameters. The degree of impact of node anomalies on the overall system is determined by these parameters; for example, a high leakage risk might directly lead to a decrease in the cutting effect of the laser cutting machine. Then, the abnormal data, node flow impact parameters, and leakage risk are combined to configure the sensitivity parameters of the ultrasonic sensor. For example, the higher the risk, the higher the sensor sensitivity might be set to ensure faster and more accurate detection of even minor leaks.

[0024] Step 5: Adjust the parameters of the ultrasonic sensor based on the ultrasonic monitoring sensitive parameters to obtain enhanced monitoring data.

[0025] Specifically, the settings of the ultrasonic sensor are adjusted according to the ultrasonic monitoring sensitivity parameters to better suit the current leakage risk level. These sensitivity parameters determine the sensor's detection sensitivity and response speed. Parameter adjustments include regulating sensitivity and sampling frequency. For example, in high-risk conditions, higher sensitivity and a higher sampling frequency result in more sensitive detection of leakage signals. Adjusting the ultrasonic sensor allows for the acquisition of more accurate and clear monitoring data, facilitating timely detection and analysis of gas leaks.

[0026] Step Six: Perform joint leak analysis using the enhanced monitoring data and the abnormal data to obtain gas leak detection results.

[0027] Specifically, by analyzing enhanced monitoring data and combining it with adjusted sensitivity and sampling frequency of ultrasonic sensors, more accurate gas flow information and potential leak signals can be obtained. For example, a higher sensor sensitivity allows for more sensitive detection of abnormal fluctuations in gas velocity or pressure. Furthermore, comparing and combining the enhanced data with previously identified anomaly data allows for the determination of the specific location of the gas leak. Through joint analysis, the location and severity of gas leaks can be accurately identified, thereby generating reliable gas leak detection results.

[0028] The gas leak detection method for laser cutting air compressors is applied to a gas leak detection device for laser cutting air compressors. It can achieve the technical goals of real-time dynamic monitoring of abnormal gas flow and intelligent adjustment of sensor sensitivity, thereby efficiently and accurately identifying and locating potential leak points. This ensures the stable operation of the laser cutting air compressor, improves system safety, reduces equipment failure and maintenance costs, and enhances production efficiency and cutting quality.

[0029] Furthermore, this application also includes: Based on the ultrasonic monitoring signal, ultrasonic features, including frequency, intensity, and time of arrival, are extracted; based on the gas state data, propagation influence features and propagation influence coefficients are analyzed; using the propagation influence features and propagation influence coefficients, the ultrasonic features are corrected to obtain corrected ultrasonic features; based on the gas transport flow path, the corrected ultrasonic features are aligned and compared to obtain the abnormal data.

[0030] Specifically, when an ultrasonic sensor detects gas flow, it captures the sound wave signal during transmission, i.e., the ultrasonic monitoring signal. Different characteristics of the ultrasonic monitoring signal are extracted, including frequency, intensity, and time of arrival. Frequency represents the vibration velocity of the sound wave, intensity represents the energy of the sound wave, and time of arrival is the time interval between the sound wave's transmission and reception. These ultrasonic characteristics help analyze the gas flow state and identify any anomalies. For example, if the frequency and intensity of the ultrasound are significantly abnormal, it may indicate a gas leak or other problems.

[0031] Next, gas state data, including parameters such as gas pressure, temperature, and density, affects the propagation characteristics of sound waves in the gas. Therefore, the influence characteristics of sound wave propagation are analyzed based on the actual gas state. For example, high temperature or high pressure gases may cause changes in the speed of sound wave propagation, thus affecting the signal strength and arrival time. By analyzing the gas state data, the degree of influence on sound wave propagation is calculated, and the propagation influence coefficient is obtained.

[0032] Then, since different gas states affect sound wave propagation, the characteristics of the ultrasonic signal are modified based on the influence coefficient. For example, if the gas temperature is high, the frequency and intensity are adjusted according to the propagation influence coefficient to ensure that the ultrasonic signal can accurately reflect the gas flow state, thereby more accurately determining whether a leak exists.

[0033] Finally, the gas transmission path is fixed in the air compressor system. The ultrasonic signal in a specific area is determined by observing the gas flow path. Specifically, the corrected ultrasonic characteristics are compared with the expected signal pattern. If a significant difference occurs, it indicates a potential anomaly, such as a leak or pipe rupture, resulting in abnormal data.

[0034] By extracting the acoustic characteristics of gas flow using ultrasonic sensors and correcting these characteristics by combining gas state data and propagation influence coefficients, abnormalities in the system can be detected more accurately. This can effectively identify potential leaks or other problems, thereby ensuring the stable operation of the laser cutting air compressor system.

[0035] Furthermore, this application also includes: Based on the key pipelines and equipment flow nodes of the system, the geometric layout of the pipelines and equipment is identified, including pipeline layout and equipment location, as well as pipeline length, diameter, bends, and joint locations; based on historical anomaly cases, potential leak locations are located; based on the gas transmission and flow path, a gas flow model is constructed, and the geometric layout of the pipelines and equipment and the potential leak locations are fitted into the gas flow model to establish a spatiotemporal correlation model. The spatiotemporal correlation model is used to describe the gas flow path in the pipelines and equipment and the spatiotemporal characteristics of each node on the flow path; the corrected ultrasonic features are fitted into the spatiotemporal correlation model to identify gas flow signal deviations and obtain the abnormal data.

[0036] Specifically, based on the key pipelines and equipment flow nodes of the system, the geometric layout of pipelines and equipment is identified through measurement and other methods, including pipeline layout and equipment location, as well as pipeline length, diameter, bends, and joint locations, to obtain the pipeline direction, the location of each piece of equipment, and detailed dimensional parameters, so as to subsequently analyze the flow characteristics of gas in different regions.

[0037] By analyzing past leakage cases in the system, the areas most likely to leak can be inferred, helping to identify the most problematic parts of the system. For example, if historical data indicates that elbows or joints are more prone to leakage, then these locations become key inspection areas to improve inspection efficiency and accuracy.

[0038] By utilizing the geometry of pipelines and equipment, as well as potential leak locations, a gas flow model is constructed. The geometry of the pipelines and equipment, and the potential leak locations are then parameterized and assigned to the gas flow model, generating a spatiotemporal correlation model. This model not only describes the overall path of gas flow but also reflects the flow characteristics of gas at specific locations and times, facilitating the prediction and monitoring of abnormal signals. For example, the spatiotemporal correlation model may show a decrease in gas velocity at bends or an increase in velocity at long straight pipes.

[0039] Applying modified ultrasonic features to spatiotemporal correlation models allows for precise analysis of deviations in gas flow signals, thereby identifying the presence of abnormal data. If the signal characteristics at certain nodes deviate significantly from the standard flow characteristics predicted by the model, it indicates a potential leak or equipment malfunction at that location.

[0040] By identifying the pipeline geometry of the system, locating high-risk areas by combining historical leakage cases, establishing a gas flow and spatiotemporal correlation model, and comparing it with modified ultrasonic signals, abnormal signals in the gas flow can be accurately identified, enabling effective prediction and detection of system leak locations.

[0041] Furthermore, this application also includes: All deployed ultrasonic sensors are aligned according to their acquisition time to determine the arrival time of abnormal data; the arrival time difference is obtained based on the arrival time of all ultrasonic sensors; abnormal distance positioning analysis is performed based on the arrival time difference to obtain the first abnormal positioning; based on the abnormal data, reverse engineering analysis is performed through the spatiotemporal correlation model to perform spatial abnormal positioning analysis to obtain the second abnormal positioning; the first and second abnormal positioning are used for overlapping positioning to obtain the abnormal flow node.

[0042] Specifically, the data collected by all ultrasonic sensors installed in the system are aligned chronologically to ensure that the timing information of the signals is synchronized, so as to more clearly determine the arrival time of abnormal data. For example, if a sensor collects a signal with abnormal intensity within two seconds, while other sensors do not show any abnormality within the same time period, it may indicate that the abnormal signal first appeared in the vicinity of that sensor.

[0043] Then, the arrival times of the abnormal data recorded by each sensor are compared to calculate the time difference between the different sensors. The time difference reflects the propagation speed and path of the abnormal signal at different locations.

[0044] By calculating the time difference of arrival, the distance of the abnormal signal can be calculated. Combined with the signal propagation speed, a location analysis can be performed to preliminarily determine the location of the abnormal area and obtain the approximate range of the abnormality. For example, if it is determined through calculation that the abnormal signal may appear two meters away from sensor A, the first abnormality location can be formed.

[0045] By utilizing a spatiotemporal correlation model, the spatial location of anomalous data can be further analyzed by reverse engineering based on the path of the anomalous data. This reverse engineering approach can more accurately pinpoint the source of anomalous signals; for example, reverse analysis might reveal that an anomalous signal may be located at a specific bend in a pipeline, thus obtaining a secondary anomaly location.

[0046] Finally, the first and second anomaly locations are overlaid and analyzed to determine their intersection point as the final anomaly flow node, thereby accurately identifying the specific location of the leak or fault. For example, if both location results point to the same node, then that node can be identified as the anomaly flow node.

[0047] By aligning the data from ultrasonic sensors with time, calculating the time difference of arrival, using the time difference of arrival for preliminary anomaly localization, and then performing reverse analysis through a spatiotemporal correlation model, the two localization results are finally superimposed. This can accurately locate abnormal nodes in the gas flow of the system, improve the detection accuracy of gas leaks or fault locations, and help to take timely measures.

[0048] Furthermore, this application also includes: The influence relationship between temperature, humidity, and pressure on ultrasonic signal propagation is fitted using experimental data to obtain the influence relationship interval and the corresponding influence coefficient. Based on the influence coefficient, the influence relationship interval is clustered to obtain the influence thresholds of temperature, humidity, and pressure. The gas state data is then matched using the influence thresholds of temperature, humidity, and pressure to obtain the propagation influence features. The propagation influence features are gas state data that meet the influence thresholds, including one or more of temperature, humidity, and pressure. The propagation influence features are then mapped and matched with the influence coefficient to obtain the propagation influence coefficient.

[0049] Specifically, by fitting experimental data to the effects of temperature, humidity, and pressure on ultrasonic signal propagation, the influence of environmental factors such as temperature, humidity, and pressure on ultrasonic signal propagation is determined, obtaining the influence range and corresponding influence coefficients. The influence range indicates that within this range, temperature, humidity, and pressure significantly affect ultrasonic signal propagation. For example, when the temperature exceeds 30 degrees Celsius, the propagation speed of the ultrasonic signal increases, thus affecting the signal's arrival time and intensity. Furthermore, each factor among temperature, humidity, and pressure has a corresponding influence coefficient, indicating the specific degree of influence of that factor on ultrasonic signal propagation.

[0050] The study analyzes the changes in ultrasonic signal propagation under different environmental conditions. By clustering data based on different values ​​of temperature, humidity, and pressure, i.e., clustering data based on the influence intervals, the influence thresholds of temperature, humidity, and pressure are obtained, and the intervals that have a significant impact on signal propagation and the intervals that have a relatively small impact on signal propagation are identified.

[0051] Data matching of gas state data is performed using the influence thresholds of temperature, humidity, and pressure. Specifically, the actual state data of the gas system is compared with the influence thresholds to filter out gas state data that will affect the propagation of ultrasonic signals within a specific range, thus obtaining propagation influence characteristics. These propagation influence characteristics are gas state data that meet the influence thresholds, including one or more of temperature, humidity, and pressure.

[0052] By combining propagation influence characteristics and influence coefficients, a modified propagation influence coefficient is assigned to each specific gas state data point using a mapping matching method. Based on the mapping relationship, the influence coefficient under this combined condition is calculated, thereby correcting the propagation characteristics of the ultrasonic signal.

[0053] By fitting the influence relationship between temperature, humidity, and pressure on ultrasonic signal propagation using experimental data and obtaining the influence coefficient, environmental conditions that significantly affect signal propagation are identified through data clustering. Influence thresholds are then used to match actual gas state data, and features that meet the influence criteria are selected. Based on the features and influence coefficients, a mapping is performed to finally obtain the corrected propagation influence coefficient. This allows for the adjustment and optimization of ultrasonic signal propagation prediction, thereby improving the accuracy of gas leak detection.

[0054] Furthermore, this application also includes: Based on the abnormal flow nodes, identify the co-influencing nodes; based on the abnormal flow nodes, conduct impact analysis through historical abnormal cases to locate the flow impact parameters and leakage risks of the nodes. The flow impact parameters are the operating parameters of the laser cutting air compressor affected by gas leakage at the abnormal flow nodes, and the leakage risk describes the degree of influence of the flow impact parameters of the nodes on the laser cutting air compressor's ability to achieve the cutting target; based on the co-influencing nodes, perform cumulative gas flow impact analysis to obtain the co-leaking risk; based on the leakage risk and the co-leaking risk, obtain the leakage risk trend; based on the leakage risk and the leakage risk trend, configure the ultrasonic monitoring sensitive parameters, which are positively correlated with the leakage risk and the leakage risk trend.

[0055] Specifically, after identifying the abnormal flow node, the nodes affected by it are further identified; these are called co-influence nodes. Co-influence nodes are associated with the abnormal flow node due to connected pipes, joints, or equipment, thus indirectly affecting gas flow or system operation. For example, if an abnormal flow occurs at a pipe joint, nodes near that joint may also be affected.

[0056] By utilizing historical anomaly cases, a detailed analysis of the impact of identified abnormal flow nodes is conducted. This analysis assesses the specific effects of gas leakage at these nodes on the operating parameters of the air compressor, such as the reduction in system gas pressure or flow rate, thereby affecting cutting performance. Leakage risk measures the direct impact of these parameter changes on the cutting target; for example, a 50% decrease in gas flow rate may lead to insufficient or uneven cutting depth.

[0057] Subsequently, a cumulative analysis of gas flow at all synergistically affected nodes was performed to calculate the cumulative risk caused by leaks at multiple nodes, thus obtaining the synergistic leakage risk. The synergistic leakage risk reflects the combined impact of multiple related nodes. For example, if the cumulative leakage of multiple adjacent nodes causes a velocity reduction of more than 100 kPa, it may significantly affect the quality and accuracy of the entire cutting process.

[0058] Dynamic trend analysis of leakage risk and associated leakage risk can reveal the risk change trend over a future period, thus identifying the leakage risk trend. For example, a continuous increase in risk may indicate an escalation of system leakage problems, requiring timely intervention.

[0059] Finally, the sensitivity parameters of the ultrasonic monitoring are adjusted based on the leakage risk and its trend. The sensitivity of the parameter settings should be positively correlated with the leakage risk. Specifically, the greater the risk trend, the higher the sensitivity; conversely, the lower the risk, the lower the sensitivity. However, when the risk is high and the risk trend is decreasing, the sensitivity is low. For example, in a high-risk trend, the sensitivity of the ultrasonic sensor can be increased to detect even minor leaks earlier.

[0060] Based on the risk analysis results, the changing trend of leakage risk was further derived, and the ultrasonic monitoring parameters were configured accordingly to detect system risks in a timely manner and ensure the stable operation of the laser cutting air compressor.

[0061] Furthermore, this application also includes: Obtain the acquisition sensitive parameters and adjustment range of the ultrasonic sensor; establish the configuration relationship between the ultrasonic monitoring sensitive parameters and the sensitive parameters of data acquisition timeliness and accuracy; using the adjustment range as a constraint, set an adaptive fuzzy list according to the sensitive parameter configuration relationship, including leakage risk, leakage risk trend and corresponding ultrasonic monitoring sensitive parameters; using the leakage risk and leakage risk trend as input quantities, perform adaptive matching through the adaptive fuzzy list to obtain the ultrasonic monitoring sensitive parameters.

[0062] Specifically, this involves acquiring the sensitive parameters and adjustable range of the ultrasonic sensor. Sensitive parameters include the sensor's signal strength and acquisition frequency, which directly affect the detection accuracy and response speed. The adjustment range refers to the range within which these sensitive parameters can be adjusted. For example, if a sensor's signal strength ranges from one to ten units and its acquisition frequency is five to ten times per second, the adjustment range would be the upper and lower limits of these parameters.

[0063] Next, the timeliness of data acquisition reflects the real-time nature of data acquisition, while the accuracy of data acquisition indicates the precision of the acquired data. The configuration relationships between sensitive parameters of ultrasonic monitoring and the timeliness and accuracy of data acquisition are established, illustrating the impact of sensitive parameters on data acquisition performance under different configuration conditions. For example, when the signal strength is set to eight units and the acquisition frequency is ten times per second, the timeliness of the data may reach 0.1 seconds, and the accuracy may reach 95%.

[0064] Then, using the adjustment range as a constraint, an adaptive fuzzy list is generated based on the sensitive parameter configuration relationship. The adaptive fuzzy list includes leakage risk, leakage risk trend, and corresponding ultrasonic monitoring sensitive parameters. For example, when the leakage risk is high and the trend is increasing, the adaptive fuzzy list recommends a high-sensitivity monitoring parameter configuration.

[0065] Finally, the leakage risk and risk trend are used as inputs, and an adaptive fuzzy list is used for matching to obtain the ultrasonic monitoring sensitivity parameters suitable for the current risk situation. For example, when the leakage risk reaches 80 and the risk trend increases, a high-frequency, high-intensity acquisition configuration is automatically matched to ensure rapid and accurate detection of the leakage.

[0066] By acquiring the sensitive parameters and adjustment range of the ultrasonic sensor and establishing the relationship between the parameters and the timeliness and accuracy of data acquisition, an adaptive fuzzy list is generated based on this. Then, by using leakage risk and trend as inputs for adaptive matching, the automatic adjustment of the sensitive parameters of the ultrasonic sensor is finally realized, thereby adapting to the detection needs under different risk conditions.

[0067] In summary, the gas leak detection method for laser-cut air compressors provided in this application has the following technical advantages: By identifying the gas flow direction of a laser-cutting air compressor, analyzing key pipelines and equipment flow nodes in the system, configuring ultrasonic sensors, and obtaining ultrasonic monitoring signals, the system collects gas state data, including temperature, humidity, and pressure. Anomalies are identified based on the ultrasonic monitoring signals and the gas state data, and abnormal flow nodes are determined through anomaly location analysis. Based on these abnormal flow nodes, the system locates the flow impact parameters and leakage risk, and configures ultrasonic monitoring sensitive parameters using the abnormal data and these parameters. The ultrasonic sensors are then adjusted based on these sensitive parameters to obtain enhanced monitoring data. Finally, a joint leak analysis is performed using the enhanced monitoring data and the abnormal data to obtain gas leak detection results. In other words, by achieving the technical goals of real-time dynamic monitoring of abnormal gas flow and intelligent adjustment of sensor sensitivity, the system efficiently and accurately identifies and locates potential leak points, thereby ensuring the stable operation of the laser-cutting air compressor, improving system safety, reducing equipment failures and maintenance costs, and enhancing production efficiency and cutting quality.

[0068] Example 2: Based on the same inventive concept as the gas leak detection method for laser-cutting air compressors described in the previous examples, this application also provides a gas leak detection device for laser-cutting air compressors. Please refer to the appendix. Figure 2 ,include: The system includes: an ultrasonic monitoring signal acquisition module 11, used to identify the gas transmission flow direction of the laser-cutting air compressor, analyze key pipelines and equipment flow nodes of the system, configure ultrasonic sensors, and acquire ultrasonic monitoring signals; a gas state data acquisition module 12, used to acquire gas state data of the laser-cutting air compressor system, including temperature, humidity, and pressure; and an abnormal flow node determination module 13, used to identify abnormal points based on the ultrasonic monitoring signals and the gas state data, obtain abnormal data, and perform abnormal location analysis based on the abnormal data to determine abnormal flow nodes; and ultrasonic monitoring... The ultrasonic monitoring sensitive parameter configuration module 14 is used to locate the node flow influence parameters and leakage risk based on the abnormal flow node, and configure the ultrasonic monitoring sensitive parameters using the abnormal data and the node flow influence parameters and leakage risk; the enhanced monitoring data acquisition module 15 is used to adjust the parameters of the ultrasonic sensor based on the ultrasonic monitoring sensitive parameters to obtain enhanced monitoring data; the gas leak detection result acquisition module 16 is used to perform joint leak analysis using the enhanced monitoring data and the abnormal data to obtain gas leak detection results.

[0069] Furthermore, the gas leak detection device for laser-cut air compressors is also used for: Based on the ultrasonic monitoring signal, ultrasonic features, including frequency, intensity, and time of arrival, are extracted; based on the gas state data, propagation influence features and propagation influence coefficients are analyzed; using the propagation influence features and propagation influence coefficients, the ultrasonic features are corrected to obtain corrected ultrasonic features; based on the gas transport flow path, the corrected ultrasonic features are aligned and compared to obtain the abnormal data.

[0070] Furthermore, the gas leak detection device for laser-cut air compressors is also used for: Based on the key pipelines and equipment flow nodes of the system, the geometric layout of the pipelines and equipment is identified, including pipeline layout and equipment location, as well as pipeline length, diameter, bends, and joint locations; based on historical anomaly cases, potential leak locations are located; based on the gas transmission and flow path, a gas flow model is constructed, and the geometric layout of the pipelines and equipment and the potential leak locations are fitted into the gas flow model to establish a spatiotemporal correlation model. The spatiotemporal correlation model is used to describe the gas flow path in the pipelines and equipment and the spatiotemporal characteristics of each node on the flow path; the corrected ultrasonic features are fitted into the spatiotemporal correlation model to identify gas flow signal deviations and obtain the abnormal data.

[0071] Furthermore, the gas leak detection device for laser-cut air compressors is also used for: All deployed ultrasonic sensors are aligned according to their acquisition time to determine the arrival time of abnormal data; the arrival time difference is obtained based on the arrival time of all ultrasonic sensors; abnormal distance positioning analysis is performed based on the arrival time difference to obtain the first abnormal positioning; based on the abnormal data, reverse engineering analysis is performed through the spatiotemporal correlation model to perform spatial abnormal positioning analysis to obtain the second abnormal positioning; the first and second abnormal positioning are used for overlapping positioning to obtain the abnormal flow node.

[0072] Furthermore, the gas leak detection device for laser-cut air compressors is also used for: The influence relationship between temperature, humidity, and pressure on ultrasonic signal propagation is fitted using experimental data to obtain the influence relationship interval and the corresponding influence coefficient. Based on the influence coefficient, the influence relationship interval is clustered to obtain the influence thresholds of temperature, humidity, and pressure. The gas state data is then matched using the influence thresholds of temperature, humidity, and pressure to obtain the propagation influence features. The propagation influence features are gas state data that meet the influence thresholds, including one or more of temperature, humidity, and pressure. The propagation influence features are then mapped and matched with the influence coefficient to obtain the propagation influence coefficient.

[0073] Furthermore, the gas leak detection device for laser-cut air compressors is also used for: Based on the abnormal flow nodes, identify the co-influencing nodes; based on the abnormal flow nodes, conduct impact analysis through historical abnormal cases to locate the flow impact parameters and leakage risks of the nodes. The flow impact parameters are the operating parameters of the laser cutting air compressor affected by gas leakage at the abnormal flow nodes, and the leakage risk describes the degree of influence of the flow impact parameters of the nodes on the laser cutting air compressor's ability to achieve the cutting target; based on the co-influencing nodes, perform cumulative gas flow impact analysis to obtain the co-leaking risk; based on the leakage risk and the co-leaking risk, obtain the leakage risk trend; based on the leakage risk and the leakage risk trend, configure the ultrasonic monitoring sensitive parameters, which are positively correlated with the leakage risk and the leakage risk trend.

[0074] Furthermore, the gas leak detection device for laser-cut air compressors is also used for: Obtain the acquisition sensitive parameters and adjustment range of the ultrasonic sensor; establish the configuration relationship between the ultrasonic monitoring sensitive parameters and the sensitive parameters of data acquisition timeliness and accuracy; using the adjustment range as a constraint, set an adaptive fuzzy list according to the sensitive parameter configuration relationship, including leakage risk, leakage risk trend and corresponding ultrasonic monitoring sensitive parameters; using the leakage risk and leakage risk trend as input quantities, perform adaptive matching through the adaptive fuzzy list to obtain the ultrasonic monitoring sensitive parameters.

[0075] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The gas leak detection method and specific examples for laser-cutting air compressors in the foregoing embodiment 1 are also applicable to the gas leak detection device for laser-cutting air compressors in this embodiment. Through the foregoing detailed description of the gas leak detection method for laser-cutting air compressors, those skilled in the art can clearly understand the gas leak detection device for laser-cutting air compressors in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0076] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0077] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for detecting gas leaks in a laser-cut air compressor, characterized in that, include: Identify the gas transmission flow direction of the laser-cut air compressor, analyze the key pipelines and equipment flow nodes of the system, configure ultrasonic sensors, and obtain ultrasonic monitoring signals; Collect gas state data from the laser cutting air compressor system, including temperature, humidity, and pressure; Anomaly points are identified based on the ultrasonic monitoring signals and the gas state data to obtain abnormal data, and anomaly location analysis is performed based on the abnormal data to determine abnormal flow nodes. Based on the abnormal flow nodes, locate the flow impact parameters and leakage risk of the nodes, and use the abnormal data and the flow impact parameters and leakage risk of the nodes to configure ultrasonic monitoring sensitive parameters; Based on the ultrasonic monitoring sensitive parameters, the ultrasonic sensor is adjusted to obtain enhanced monitoring data; By combining the enhanced monitoring data with the abnormal data for joint leak analysis, gas leak detection results are obtained.

2. The gas leak detection method for a laser-cut air compressor as described in claim 1, characterized in that, Anomaly point identification is performed based on the ultrasonic monitoring signal and the gas state data to obtain abnormal data, including: Based on the ultrasonic monitoring signal, ultrasonic features are extracted, including frequency, intensity, and time of arrival. Based on the gas state data, analyze the propagation influence characteristics and propagation influence coefficient; The propagation influence characteristics and propagation influence coefficient are used to correct the ultrasonic wave characteristics to obtain corrected ultrasonic wave characteristics; Based on the gas transport flow path, the modified ultrasonic features are aligned and compared to obtain the abnormal data.

3. The gas leak detection method for a laser-cut air compressor as described in claim 2, characterized in that, The method of performing feature alignment and comparison on the modified ultrasonic features based on the gas transport flow path to obtain the abnormal data includes: Based on the key pipeline and equipment flow nodes of the system, identify the geometric layout of the pipelines and equipment, including pipeline layout and equipment location, as well as pipeline length, diameter, bends, and joint locations; Based on historical anomaly cases, locate potential leak locations; Based on the gas transmission and flow path, a gas flow model is constructed, and the geometric layout of the pipeline and equipment and the potential location of the leak are fitted into the gas flow model to establish a spatiotemporal correlation model. The spatiotemporal correlation model is used to describe the gas flow path in the pipeline and equipment and the spatiotemporal characteristics of each node on the flow path. The modified ultrasonic features are fitted into the spatiotemporal correlation model to identify gas flow signal deviations and obtain the abnormal data.

4. The gas leak detection method for a laser-cut air compressor as described in claim 3, characterized in that, Based on the aforementioned abnormal data, anomaly location analysis is performed to determine abnormal flow nodes, including: Align all deployed ultrasonic sensors according to their acquisition time to determine the arrival time of abnormal data; The time difference of arrival is obtained based on the arrival times of all ultrasonic sensors; Anomaly location analysis is performed based on the arrival time difference to obtain the first anomaly location. Based on the abnormal data, reverse engineering analysis is performed using the spatiotemporal correlation model to conduct spatial anomaly localization analysis and obtain the second anomaly location. The abnormal flow node is obtained by overlapping positioning using the first abnormal positioning and the second abnormal positioning.

5. The gas leak detection method for a laser-cut air compressor as described in claim 2, characterized in that, Based on the gas state data, the propagation influence characteristics and propagation influence coefficients are analyzed, including: By fitting experimental data to the influence of temperature, humidity, and pressure on ultrasonic signal propagation, the range of influence and the corresponding influence coefficients were obtained. Based on the influence coefficient, data clustering is performed on the influence relationship interval to obtain the influence thresholds of temperature, humidity, and pressure, respectively. The gas state data is matched using the influence thresholds of temperature, humidity, and pressure to obtain the propagation influence features. The propagation influence features are gas state data that meet the influence thresholds, including one or more of temperature, humidity, and pressure. The propagation impact coefficient is obtained by mapping and matching the propagation impact characteristics with the impact coefficient.

6. The gas leak detection method for a laser-cut air compressor as described in claim 1, characterized in that, Based on the abnormal flow nodes, the flow impact parameters and leakage risk of the nodes are located. Using the abnormal data and the flow impact parameters and leakage risk of the nodes, ultrasonic monitoring sensitive parameters are configured, including: Based on the abnormal flow nodes, identify the nodes that are affected by collaboration; Based on the abnormal flow nodes, an impact analysis is conducted using historical abnormal cases to locate the flow impact parameters and leakage risk of the nodes. The flow impact parameters are the operating parameters of the laser cutting air compressor affected by gas leakage at the abnormal flow nodes, and the leakage risk describes the degree of influence of the flow impact parameters of the nodes on the laser cutting air compressor's ability to achieve the cutting target. Based on the aforementioned synergistic impact nodes, a cumulative impact analysis of gas flow is performed to obtain the synergistic leakage risk. Based on the described leakage risk and the described collaborative leakage risk, a leakage risk trend is obtained; Based on the leakage risk and the leakage risk trend, the ultrasonic monitoring sensitive parameters are configured, and the ultrasonic monitoring sensitive parameters are positively correlated with the leakage risk and the leakage risk trend.

7. The gas leak detection method for a laser-cut air compressor as described in claim 6, characterized in that, Based on the leakage risk and the leakage risk trend, configure the ultrasonic monitoring sensitive parameters, including: Obtain the acquisition sensitivity parameters and adjustment range of the ultrasonic sensor; Establish the relationship between sensitive parameters for ultrasound monitoring and the configuration of sensitive parameters for the timeliness and accuracy of acquired data; Using the adjustment range as a constraint, an adaptive fuzzy list is set according to the sensitive parameter configuration relationship, which includes leakage risk, leakage risk trend and corresponding ultrasonic monitoring sensitive parameters; Using the leakage risk and the leakage risk trend as inputs, the ultrasonic monitoring sensitive parameters are obtained through adaptive matching using the adaptive fuzzy list.

8. A gas leak detection device for laser-cut air compressors, characterized in that, The steps for implementing the gas leak detection method for a laser-cut air compressor according to any one of claims 1 to 7 include: An ultrasonic monitoring signal acquisition module is used to identify the gas transmission flow direction of the laser cutting air compressor, analyze the key pipelines and equipment flow nodes of the system, configure ultrasonic sensors, and acquire ultrasonic monitoring signals. A gas state data acquisition module is used to acquire gas state data of the laser cutting air compressor system, including temperature, humidity, and pressure. An abnormal flow node determination module is used to identify abnormal points based on the ultrasonic monitoring signal and the gas state data, obtain abnormal data, and determine abnormal flow nodes based on the abnormal data through abnormal location analysis. An ultrasonic monitoring sensitive parameter configuration module is used to locate the node flow impact parameters and leakage risk based on the abnormal flow node, and to configure ultrasonic monitoring sensitive parameters using the abnormal data and the node flow impact parameters and leakage risk. An enhanced monitoring data acquisition module is used to adjust the parameters of the ultrasonic sensor based on the ultrasonic monitoring sensitive parameters to obtain enhanced monitoring data. A gas leak detection result acquisition module is used to perform joint leak analysis using the enhanced monitoring data and the abnormal data to obtain gas leak detection results.

Images