An online detection method for fire-fighting cylinder burst early warning

Abstract

The present application relates to the technical field of fire safety, and more particularly to an online detection method for fire steel bottle burst early warning. The method comprises the following steps: obtaining fire steel bottle data and performing stress corrosion simulation to obtain stress corrosion simulation data; constructing a steel bottle burst prediction model according to the stress corrosion simulation data and the fire steel bottle data; obtaining bottle pressure data and performing steel bottle burst prediction on the bottle pressure data to obtain steel bottle burst prediction data; detecting steel bottle micro-deformation data through a deformation amount detector and analyzing the data to obtain steel bottle deformation data; sending the steel bottle burst prediction data and the steel bottle deformation data to a steel bottle burst early warning detector to perform an online early warning task; and analyzing the steel bottle deformation data and the steel bottle burst prediction data to obtain optimal discharge values of the steel bottle and sending the values to an active discharge device to perform an automatic pressure relief task. The present application can quickly warn of fire steel bottle burst based on data mining.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of fire safety, and particularly relates to an online detection method for fire steel bottle burst early warning. BACKGROUND

[0002] In the field of fire fighting, steel bottles are common gas storage containers used to store fire extinguishing agents and other important gases. However, due to environmental changes, material aging or operational errors, etc., there is a risk of steel bottle burst, which can lead to fire accidents and casualties. Therefore, it is crucial to provide timely and accurate early warning for steel bottle burst. SUMMARY

[0003] Therefore, it is necessary to provide an online detection method for fire steel bottle burst early warning to solve at least one of the above technical problems.

[0004] To achieve the above-mentioned purpose, an online detection method for fire steel bottle burst early warning is applied to a gas cylinder safety monitoring system, which includes a controller, an active relief device electrically connected to the controller, an electronic pressure sensor, a steel bottle burst early warning detector, and a deformation amount detector. The electronic pressure sensor, the steel bottle burst early warning detector, and the deformation amount detector are all electrically connected to the controller. The online detection method for fire steel bottle burst early warning includes the following steps:

[0005] Step S1: Obtain fire steel bottle data and perform stress corrosion simulation according to the fire steel bottle data to obtain stress corrosion simulation data;

[0006] Step S2: Construct a steel bottle burst prediction model according to the stress corrosion simulation data and the fire steel bottle data;

[0007] Step S3: Obtain bottle pressure data through the electronic pressure sensor and perform steel bottle burst prediction on the bottle pressure data according to the steel bottle burst prediction model to obtain steel bottle burst prediction data;

[0008] Step S4: Obtain steel bottle micro-deformation detection data through the deformation amount detector and perform local deformation analysis on the steel bottle micro-deformation detection data to obtain steel bottle deformation data;

[0009] Step S5: Perform burst probability analysis on the steel bottle burst prediction data according to the steel bottle deformation data to obtain suspected burst steel bottle data and send it to the steel bottle burst early warning detector to perform an online early warning task;

[0010] Step S6: Perform optimal relief analysis on the steel bottle deformation data and the steel bottle burst prediction data to obtain the optimal relief value of the steel bottle and send it to the active relief device to perform an automatic pressure relief task.

[0011] The application can more truly reflect the stress condition of the steel cylinder under different working environments by obtaining the fire-fighting steel cylinder data. The stress corrosion simulation data provides a basis for the subsequent, and through simulation, the stress distribution of the steel cylinder under different corrosion degrees can be understood. The prediction model constructed by using the stress corrosion simulation data can more accurately predict whether the steel cylinder has the risk of bursting. Through continuous updating of the model, real-time monitoring of the state of the steel cylinder can be realized, and the timeliness of early warning is improved. The real-time cylinder pressure data obtained by using the electronic pressure sensor can continuously monitor the actual working state of the steel cylinder. Combined with the prediction model, the abnormality of the cylinder pressure can be identified in time, and the potential risk of bursting can be found in advance. Through the deformation amount detector, the sensitivity to the micro-deformation of the steel cylinder is improved. Local deformation analysis helps to identify the deformation of the specific area of the steel cylinder, and provides more accurate data for the burst probability analysis. Combined with the deformation data, the burst probability analysis can timely issue a warning signal when there is a risk of bursting in the cylinder. Through the analysis of multiple parameters, the accuracy of the prediction is improved, and the false positive rate is reduced. Through optimal discharge analysis, the most suitable discharge value is determined to ensure that the pressure is released in the safest way when the pressure relief occurs. The optimal discharge value is sent to the active discharge device to realize the automatic pressure relief task, reduce the dependence on manual intervention, and improve the operation efficiency.

[0012] Optionally, step S1 is specifically:

[0013] Step S11: Obtain fire-fighting steel cylinder data;

[0014] Step S12: Extract inner wall material data and outer wall material data from the fire-fighting steel cylinder data, thereby obtaining the inner wall material data and the outer wall material data;

[0015] Step S13: Perform inner wall pressure simulation according to the inner wall material data, thereby obtaining inner wall pressure simulation data;

[0016] Step S14: Perform outer wall pressure simulation according to the inner wall pressure simulation data and the outer wall material data, thereby obtaining outer wall pressure simulation data;

[0017] Step S15: Perform stress corrosion simulation on the inner wall pressure simulation data and the outer wall pressure simulation data, thereby obtaining stress corrosion simulation data.

[0018] The data acquisition of the fire-fighting cylinder is the basis of the whole process, which ensures the accuracy of simulation and prediction, because the data is collected from actual operation. By extracting the inner wall and outer wall material data, it helps to understand the structure and material properties of the cylinder. This helps further simulation and analysis, because different materials respond differently to pressure and corrosion. Through the inner wall gas pressure simulation, it can understand the pressure distribution inside the cylinder, which helps to determine whether there is pressure anomaly or potential problem. This helps to find the situation of pressure rise in advance, reducing the risk of explosion. The outer wall gas pressure simulation can help understand the impact of external environment on the cylinder. This helps to evaluate the impact of external environmental factors on the safety of the cylinder, such as temperature change and air pressure change. Stress corrosion simulation combines inner and outer wall gas pressure simulation data to help understand the stress distribution of the cylinder under different working conditions. This helps to evaluate the potential impact of corrosion on the cylinder and whether there is a stress concentration area, so as to find potential explosion risk in advance.

[0019] Optionally, step S13 is specifically:

[0020] Step S131: feature extraction is performed on the fire-fighting cylinder data, so as to obtain cylinder structure data and cylinder working condition data;

[0021] Step S132: material property analysis is performed according to the inner wall material data, so as to obtain the inner wall material property parameters;

[0022] Step S133: a three-dimensional cylinder model is constructed according to the cylinder structure data, and the three-dimensional cylinder model is filled with property parameters by using the inner wall material data, so as to obtain a three-dimensional cylinder inner wall model;

[0023] Step S134: working condition parameter statistical analysis is performed according to the cylinder working condition data, so as to obtain simulation parameters;

[0024] Step S135: inner wall gas pressure simulation is performed on the three-dimensional cylinder inner wall model by using the simulation parameters, so as to obtain inner wall gas pressure simulation data.

[0025] The feature extraction in the present application extracts key information from the original data, including the structural data of the steel cylinder (such as geometry, size, etc.) and the working condition data (such as temperature, pressure, etc.). These data provide an overall understanding of the steel cylinder and lay a foundation for subsequent simulation and analysis. By analyzing the inner wall material data, the characteristic parameters of the material can be determined, such as the elastic modulus, yield strength, etc. This helps to understand the performance of the inner wall material, so that the material response can be accurately considered in the simulation. By constructing a three-dimensional steel cylinder inner wall model, the geometry and internal structure of the steel cylinder can be considered in the calculation simulation, which is very important for accurate simulation. Filling the inner wall material data allows the simulation of material properties in the simulation. By analyzing the working condition data of the steel cylinder, the performance and behavior of the steel cylinder under different conditions can be understood. This helps to determine the working condition parameters that need to be considered in the simulation, such as temperature and pressure. The accuracy of the simulation parameters is crucial for accurate simulation of the behavior of the steel cylinder. Through the inner wall gas pressure simulation, the pressure distribution and response inside the steel cylinder can be understood. This helps to evaluate the performance of the steel cylinder under different working conditions, detect potential problems, such as excessively high pressure or uneven stress distribution.

[0026] Optionally, step S14 is specifically:

[0027] Step S141: material property analysis according to the outer wall material data, so as to obtain the outer wall material property parameters;

[0028] Step S142: characteristic parameter filling of the three-dimensional steel cylinder inner wall model based on the outer wall material property parameters, so as to obtain the three-dimensional steel cylinder material model;

[0029] Step S143: outer wall internal gas pressure simulation of the inner wall gas pressure simulation data through the three-dimensional steel cylinder material model, so as to obtain the outer wall internal gas pressure simulation data;

[0030] Step S144: environment data extraction of the steel cylinder working condition data, so as to obtain the steel cylinder working environment data;

[0031] Step S145: outer wall external gas pressure simulation of the steel cylinder working environment data through the three-dimensional steel cylinder material model, so as to obtain the outer wall external gas pressure simulation data;

[0032] Step S146: gas pressure interaction simulation of the outer wall internal gas pressure simulation data and the outer wall external gas pressure simulation data using the three-dimensional steel cylinder material model, so as to obtain the outer wall gas pressure simulation data.

[0033] The present application can determine the characteristic parameters of the outer wall material, such as elastic modulus, yield strength, etc. by analyzing the outer wall material data. This helps to understand the performance of the outer wall material, so that the response of the outer wall material can be accurately considered in subsequent simulation. By filling the outer wall material characteristic parameters into the three-dimensional steel bottle inner wall model, a comprehensive three-dimensional steel bottle material model can be created, which includes the inner wall and the outer wall. This helps to more accurately simulate the overall performance of the steel bottle. By using the three-dimensional steel bottle material model, the outer wall internal pressure simulation data is simulated, and the pressure distribution and response of the outer wall internal pressure can be understood. This helps to evaluate the behavior of the steel bottle outer wall under the change of internal pressure. By extracting the working environment data of the steel bottle, the working conditions of the steel bottle under different environmental conditions, such as temperature, humidity, etc. can be understood. These data are very important for simulating the performance of the steel bottle under various working conditions. By using the three-dimensional steel bottle material model, the outer wall external pressure simulation data is simulated, and the behavior of the outer wall under the change of external environmental pressure can be understood. This helps to evaluate the performance of the steel bottle outer wall under different environmental conditions. By interacting the outer wall internal pressure simulation data with the outer wall external pressure simulation data, the influence of the internal and external environment can be considered comprehensively, so as to obtain the comprehensive pressure simulation data of the outer wall. This helps to evaluate the performance and behavior of the steel bottle under different internal and external pressure interaction conditions.

[0034] Optionally, step S15 is specifically:

[0035] Step S151: stress distribution calculation is performed on the inner wall material characteristic parameters and the outer wall material characteristic parameters according to the inner wall pressure simulation data and the outer wall pressure simulation data, so as to obtain the steel bottle stress distribution data;

[0036] Step S152: corrosion simulation is performed based on the steel bottle working condition data and the inner wall material characteristic parameters and the outer wall material characteristic parameters, so as to obtain the corrosion simulation data;

[0037] Step S153: the steel bottle stress distribution data and the corrosion simulation data are calculated by the potential crack score calculation formula, so as to obtain the potential crack score data;

[0038] Step S154: the steel bottle stress distribution data, the corrosion simulation data and the potential crack score data are merged, so as to obtain the stress corrosion simulation data.

[0039] The present application can calculate the stress distribution of the inner and outer walls of the steel cylinder by combining the air pressure simulation data of the inner and outer walls and the corresponding material characteristic parameters. This helps to understand the stress state of the steel cylinder under different internal and external pressure conditions, to evaluate the safety and stability of its structure. By considering the working condition data of the steel cylinder, the material characteristic parameters of the inner and outer walls, and the corrosion simulation, the corrosion condition of the steel cylinder can be estimated. This is very important for evaluating the service life and reliability of the steel cylinder, because corrosion can reduce its strength and performance. Through the potential crack score data calculation, the stress distribution data and the corrosion simulation data can be considered comprehensively to determine whether there is a potential crack problem. This helps to find problems that may cause the failure of the steel cylinder early, so that appropriate maintenance and repair measures can be taken. Combining the stress distribution data, the corrosion simulation data and the potential crack score data into stress corrosion simulation data helps to provide a comprehensive evaluation to determine the performance and reliability of the steel cylinder under different working conditions. These comprehensive data can help make maintenance decisions, including replacing or repairing the steel cylinder.

[0040] Optionally, the potential crack score calculation formula in step S153 is specifically:

[0041]

[0042] In the formula, SG is the potential crack score, σ max is the maximum stress on the steel cylinder, σ min is the minimum stress on the steel cylinder, r is the radius inside the steel cylinder, A co is the surface area in the corrosion simulation data, C co is the corrosion rate constant in the corrosion simulation data, V pr is the pressure inside the steel cylinder, B th is the thickness of the steel cylinder wall, e is the base of natural logarithm, t is time, and z is the stress change amount.

[0043] The present application constructs a potential crack score calculation formula for calculating the stress distribution data and the corrosion simulation data of the steel cylinder. The formula fully considers the maximum stress σ max on the steel cylinder, the minimum stress σ min on the steel cylinder, the radius r inside the steel cylinder, the surface area A co in the corrosion simulation data, the corrosion rate constant C co in the corrosion simulation data, the pressure V pr inside the steel cylinder, the thickness B th of the steel cylinder wall, the base e of natural logarithm, the time t, and the stress change amount z, forming a functional relationship:

[0044]

[0045] wherein, is a Sigmoid function that maps z to a value between 0 and 1, used to limit the potential crack score between 0 and 1. The stress change rate is calculated. It is the derivative of the logarithm of the difference between the maximum stress and the minimum stress divided by the square root of the radius. The corrosion-related, wherein ln represents the natural logarithm. It takes into account the corrosion rate and the corrosion surface area. The cubic root of the ratio of pressure to wall thickness is considered. This part represents the effect of internal pressure on material strength. In the art, the potential crack score is usually calculated by using technical means such as finite element analysis, elastic fracture mechanics, etc. By using the potential crack score calculation formula provided by the present application, the potential crack score can be obtained more accurately.

[0046] Optionally, step S4 is specifically:

[0047] Step S41: obtaining the steel cylinder micro-deformation detection data through the deformation detector;

[0048] Step S42: performing point cloud conversion according to the steel cylinder micro-deformation detection data, thereby obtaining the steel cylinder point cloud data;

[0049] Step S43: performing curvature calculation on the steel cylinder point cloud data, thereby obtaining the steel cylinder point cloud curvature data;

[0050] Step S44: performing local deformation analysis according to the steel cylinder point cloud curvature data, thereby obtaining the steel cylinder deformation data.

[0051] The present application can provide real-time and accurate deformation information by obtaining the micro-deformation data of the steel cylinder through the deformation detector, which helps to monitor whether the steel cylinder has been deformed during use, transportation, etc. and take corresponding measures in time. Converting the micro-deformation detection data into point cloud data can provide a more intuitive and visual data form. The point cloud data can be used for subsequent three-dimensional analysis and visualization, which helps to more comprehensively understand the deformation of the steel cylinder. By calculating the curvature of the steel cylinder point cloud data, the curvature information representing the surface morphology of the steel cylinder can be obtained. The curvature data can reflect the degree of fluctuation of the steel cylinder surface, which helps to further analyze the deformation of the steel cylinder. Through local deformation analysis, the deformation information of the steel cylinder can be extracted from the curvature data. This can help to understand the deformation of the steel cylinder in different areas, so as to take corresponding measures to ensure the safety and reliability of the steel cylinder.

[0052] Optionally, step S44 is specifically:

[0053] Step S441: performing statistical analysis on the steel cylinder point cloud curvature data, thereby obtaining abnormal point cloud curvature data;

[0054] Step S442: dividing the area according to the abnormal point cloud curvature data, thereby obtaining abnormal area data;

[0055] Step S443: Perform local deformation calculation on the abnormal point cloud curvature data based on the abnormal area data, to obtain the cylinder deformation data.

[0056] Statistical analysis in the present application can help identify abnormal point cloud curvature data that deviates from normal conditions. These abnormal data may represent protrusions or depressions on the surface of the cylinder, or other unusual features. By identifying abnormal point cloud curvature data, potential deformation areas can be more accurately located. Regional division of abnormal point cloud curvature data helps to locate the problem to a specific area, so that abnormal conditions can be analyzed and processed more accurately. This can provide more detailed information for taking appropriate maintenance or improvement measures. Local deformation calculation can further extract cylinder deformation information within the abnormal area. These data can be used to quantify the degree of abnormality and gain a deeper understanding of the structural condition of the cylinder. This helps to determine whether maintenance, replacement or other corrective measures are needed to ensure the safety of the cylinder.

[0057] Optionally, step S5 is specifically:

[0058] Step S51: Perform cylinder discharge value calculation on the cylinder burst prediction data and cylinder deformation data by a cylinder discharge value calculation formula, to obtain the cylinder discharge value;

[0059] Step S52: Select the optimal discharge value from the cylinder discharge value based on a cylinder burst prediction model, to obtain the optimal discharge value, and send it to the active discharge device to perform automatic pressure relief tasks.

[0060] According to the burst prediction data and deformation data of the cylinder, the present application calculates a cylinder discharge value. This value is calculated based on a physical model or empirical formula, and it reflects the safe range of internal pressure of the cylinder. By calculating this value, it can be assessed whether the cylinder has excessively high internal pressure, thereby helping to prevent explosion or leakage accidents. According to the cylinder burst prediction model, the optimal discharge value is selected to ensure that the internal pressure of the cylinder is always within the safe range. This model may take into account multiple factors such as the material, structure, and usage conditions of the cylinder to determine the most suitable discharge value. Once the optimal discharge value is determined, it will be sent to the active discharge device to perform automatic pressure relief tasks. This helps to maintain the internal pressure of the cylinder at a safe level, avoiding possible dangerous situations.

[0061] Optionally, the cylinder discharge value calculation formula in step S51 is specifically:

[0062]

[0063] where PV is the cylinder discharge value, A is the cross-sectional area of the cylinder, V is the volume of the cylinder, B is the material strength parameter of the cylinder, C is the shape parameter of the cylinder, D is the internal gas pressure parameter of the cylinder, E is the thermal conductivity of the material of the cylinder, F is the density of the stored gas, G is the molar mass of the gas, H is the environmental condition parameter, I is the exposed area of the predicted burst location of the cylinder, J is the predicted burst time, and K is the discharge correction constant.

[0064] The present application provides a cylinder discharge value calculation formula for calculating the cylinder discharge value based on cylinder burst prediction data and cylinder deformation data. The formula takes into account the cross-sectional area A of the cylinder, the volume V of the cylinder, the material strength parameter B of the cylinder, the shape parameter C of the cylinder, the internal gas pressure parameter D of the cylinder, the thermal conductivity E of the material of the cylinder, the density F of the stored gas, the molar mass G of the gas, the environmental condition parameter H, the exposed area I of the predicted burst location of the cylinder, the predicted burst time J, and the discharge correction constant K, forming a functional relationship:

[0065]

[0066] where C is the shape parameter of the cylinder, representing the influence of the geometric shape of the cylinder on the discharge. represents the ratio of the volume of the cylinder to the cross-sectional area, used to consider the distribution of the internal gas in the cylinder. is an exponential term related to material strength and internal gas pressure, used to consider the influence of material strength and internal gas pressure on the discharge. E represents the thermal conductivity of the material of the cylinder, taking into account the heat transfer properties of the material. High thermal conductivity leads to faster discharge. F is the density of the gas stored in the cylinder, and high-density gas has higher kinetic energy during discharge. G is the molar mass of the gas, considering the influence of gas molecular weight on the discharge. Different gases have different molecular weights. H is the environmental condition parameter, reflecting the influence of the environment on the discharge. represents the influence of adverse environmental conditions on the risk of discharge. The square root of the product considers the influence of the exposed area I of the predicted burst location of the cylinder and the predicted burst time J. Large exposed area and short burst time will result in more severe discharge. -K is a correction term used to correct the calculation results to consider other factors or errors. In the field, finite element analysis, computational fluid dynamics, etc. are commonly used to calculate the cylinder discharge value. By using the cylinder discharge value calculation formula provided by the present application, the cylinder discharge value can be more accurately obtained. BRIEF DESCRIPTION OF DRAWINGS

[0067] Other features, objects, and advantages of the present application will become more apparent from the following detailed description of non-limiting embodiments made with reference to the accompanying drawings:

[0068] Fig. 1The step flow schematic diagram of the online detection method for the fire-fighting steel cylinder burst early warning of the present application is shown in the figure.

[0069] Fig. 2 The detailed step flow schematic diagram of step S1 in the present application is shown in the figure.

[0070] The implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0071] The technical method of the present application will be described clearly and completely below in combination with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by the skilled in the art without creative labor fall within the scope of the present application.

[0072] In addition, the accompanying drawings are only schematic illustrations of the present application, and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus repeated description thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities, which do not necessarily have to correspond to physically or logically independent entities. The functional entities can be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0073] It should be understood that although the terms "first", "second" and the like can be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, without departing from the scope of the example embodiments, a first element can be called a second element, and similarly a second element can be called a first element. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0074] To achieve the above-mentioned purpose, please refer to Figs. 1-2 The present application provides an online detection method for fire-fighting steel cylinder burst early warning, which is applied to a gas cylinder safety monitoring system. The gas cylinder safety monitoring system comprises a controller, a positive relief device electrically connected to the controller, an electronic pressure sensor, a steel cylinder burst early warning detector, and a deformation amount detector. The electronic pressure sensor, the steel cylinder burst early warning detector, and the deformation amount detector are all electrically connected to the controller. The online detection method for fire-fighting steel cylinder burst early warning comprises the following steps:

[0075] Step S1: Obtain fire-fighting steel cylinder data, and perform stress corrosion simulation according to the fire-fighting steel cylinder data to obtain stress corrosion simulation data.

[0076] In this embodiment, necessary data is collected from the fire steel cylinder, including information such as the material, thickness, geometry of the cylinder, etc. Then, using these data, stress corrosion simulation is performed. Stress corrosion simulation generally includes establishing a corrosion model, considering the chemical environment and stress situation of the cylinder. Through simulation, detailed data about the corrosion situation of the cylinder can be obtained, including corrosion rate, corrosion location, etc. For example, the extent of corrosion in the cylinder over a certain period of time can be simulated to further predict its life.

[0077] Step S2: Construct a cylinder burst prediction model according to the stress corrosion simulation data and fire cylinder data;

[0078] In this embodiment, a cylinder burst prediction model is constructed using stress corrosion simulation data and fire cylinder data. This model can be based on machine learning algorithms or based on physical models. Through this model, the probability of cylinder burst can be predicted, considering corrosion, stress and other factors. For example, the model can consider factors such as the extent of corrosion, temperature, internal pressure, etc. to estimate the risk of burst.

[0079] Step S3: Obtain cylinder internal pressure data through electronic pressure sensor, and perform cylinder burst prediction on cylinder internal pressure data according to cylinder burst prediction model, to obtain cylinder burst prediction data;

[0080] In this embodiment, cylinder internal pressure data is obtained through electronic pressure sensors. These sensors are installed inside the cylinder and can monitor pressure changes in real time. Then, using the cylinder burst prediction model, the cylinder internal pressure data is analyzed and predicted. If the model detects a potential burst risk, cylinder burst prediction data will be generated to warn the operator in advance.

[0081] Step S4: Obtain cylinder micro-deformation detection data through deformation detector, and perform local deformation analysis on cylinder micro-deformation detection data, to obtain cylinder deformation data;

[0082] In this embodiment, the deformation detector is used to monitor the small deformation of the cylinder. These deformations may be caused by internal pressure changes, temperature changes or other factors. After collecting these data, local deformation analysis is performed to identify any abnormal deformation. This helps to understand the actual condition of the cylinder and whether there are any abnormal situations.

[0083] Step S5: Perform burst probability analysis on cylinder burst prediction data according to cylinder deformation data, to obtain suspected burst cylinder data, and send to cylinder burst early warning detector to perform online early warning task;

[0084] In this embodiment, according to the cylinder deformation data and the cylinder burst prediction data, the burst probability analysis is performed. If the model finds potential burst risk, the suspected burst cylinder data will be generated and sent to the cylinder burst early warning detector. This detector can trigger online early warning tasks, such as sending alarms or notifying maintenance personnel.

[0085] Step S6: According to the cylinder deformation data and the cylinder burst prediction data, the optimal relief analysis is performed to obtain the optimal relief value of the cylinder and send it to the active relief device to perform the automatic pressure relief task.

[0086] In this embodiment, according to the cylinder deformation data and the cylinder burst prediction data, the optimal relief analysis is performed. The goal of this step is to determine the best relief strategy to reduce potential risks. Once the optimal relief value is determined, it is sent to the active relief device to perform the automatic pressure relief task, ensuring that the cylinder can safely relieve pressure in dangerous situations.

[0087] The present application can more truly reflect the stress condition of the cylinder under different working environments by obtaining the fire-fighting cylinder data. The stress corrosion simulation data provides a basis for the follow-up, and through simulation, the stress distribution of the cylinder under different corrosion degrees can be understood. The prediction model constructed using the stress corrosion simulation data can more accurately predict whether the cylinder has a burst risk. By continuously updating the model, real-time monitoring of the cylinder state can be realized, and the timeliness of the early warning can be improved. Real-time cylinder pressure data can be obtained using an electronic pressure sensor, which can continuously monitor the actual working state of the cylinder. Combined with the prediction model, the abnormality of the cylinder pressure can be identified in real time, and potential burst risks can be discovered in advance. The small deformation data can be obtained through the deformation detector, which improves the sensitivity to the small deformation of the cylinder. Local deformation analysis helps to identify the deformation of specific areas of the cylinder and provides more accurate data for burst probability analysis. Combined with the deformation data, the burst probability analysis can timely issue an early warning signal when there is a burst risk in the cylinder. By analyzing multiple parameters, the accuracy of the prediction is improved, and the false positive rate is reduced. Through optimal relief analysis, the most suitable relief value is determined to ensure that the pressure is released in the safest way when pressure relief occurs. The optimal relief value is sent to the active relief device to realize the automatic pressure relief task, reduce the dependence on manual intervention, and improve the operation efficiency.

[0088] Optionally, step S1 is specifically:

[0089] Step S11: Obtain fire-fighting cylinder data;

[0090] In this embodiment, data related to the fire extinguishing cylinder is collected. This data can include production information of the cylinder, usage history, results of visual inspection, and previous maintenance and inspection records. For example, the manufacturing date, material specifications, last inspection date, and any past maintenance records of the cylinder can be recorded. These data will provide necessary basic information for the subsequent steps.

[0091] Step S12: Inner wall material data extraction and outer wall material data extraction are performed on the fire extinguishing cylinder data, obtaining inner wall material data and outer wall material data.

[0092] In this embodiment, detailed material data extraction is performed on the inner and outer wall materials of the fire extinguishing cylinder. This can include using material science techniques to analyze the material composition, strength, corrosion resistance, and other characteristics of the inner and outer walls. For example, parameters such as the chemical composition, hardness, thickness, and other parameters of the inner and outer walls can be analyzed to obtain detailed data of the inner and outer wall materials.

[0093] Step S13: Inner wall gas pressure simulation is performed based on the inner wall material data, obtaining inner wall gas pressure simulation data.

[0094] In this embodiment, based on the inner wall material data, inner wall gas pressure simulation can be performed. This means using engineering mechanics principles and data related to the material to simulate the gas pressure on the inner wall when storing liquid or gas inside the cylinder. Through simulation, data such as stress distribution and deformation on the inner wall can be obtained, which helps to understand the performance and stability of the inner wall.

[0095] Step S14: Outer wall gas pressure simulation is performed based on the inner wall gas pressure simulation data and the outer wall material data, obtaining outer wall gas pressure simulation data.

[0096] In this embodiment, using the inner wall gas pressure simulation data and the previously extracted outer wall material data, outer wall gas pressure simulation is performed. This means simulating the stress and deformation of the outer wall under the difference between the inner and outer pressures. The outer wall gas pressure simulation data will help to understand the performance of the outer wall under different working conditions and whether problems may occur.

[0097] Step S15: Stress corrosion simulation is performed on the inner wall gas pressure simulation data and the outer wall gas pressure simulation data, obtaining stress corrosion simulation data.

[0098] In this embodiment, the inner wall gas pressure simulation data and the outer wall gas pressure simulation data are combined to perform stress corrosion simulation. This involves considering factors such as gas pressure, temperature, material properties, etc. to simulate the stress corrosion of the inner and outer walls in actual use. The stress corrosion simulation data will provide key information about the durability and potential corrosion risk of the cylinder, helping to predict the service life and maintenance needs of the cylinder.

[0099] The data acquisition of the fire-fighting cylinder is the basis of the entire process, ensuring the accuracy of simulation and prediction, because these data are collected from actual operation. By extracting the inner wall and outer wall material data, it helps to understand the structure and material properties of the cylinder. This helps further simulation and analysis, because different materials respond differently to pressure and corrosion. Through the inner wall gas pressure simulation, it can understand the pressure distribution inside the cylinder, which helps to determine whether there is pressure anomaly or potential problem. This helps to discover the situation of pressure rise in advance, reducing the risk of explosion. The outer wall gas pressure simulation can help understand the impact of external environment on the cylinder. This helps to evaluate the impact of external environmental factors on the safety of the cylinder, such as temperature change and air pressure change. Stress corrosion simulation combines the inner and outer wall gas pressure simulation data, helping to understand the stress distribution of the cylinder under different working conditions. This helps to evaluate the potential impact of corrosion on the cylinder and whether there is a stress concentration area, so as to discover potential explosion risk in advance.

[0100] Optionally, step S13 is specifically:

[0101] Step S131: feature extraction is performed on the fire-fighting cylinder data, so as to obtain cylinder structure data and cylinder working condition data;

[0102] In this embodiment, feature extraction is performed on the fire-fighting cylinder data to obtain structure data and working condition data about the cylinder. This may include collecting structure-related information such as the size, capacity, wall thickness, external shape, etc. of the cylinder. At the same time, working condition data such as the type of gas stored, temperature, pressure, etc. working condition information are also recorded. The purpose of feature extraction is to provide necessary input data for subsequent analysis.

[0103] Step S132: material property analysis is performed according to the inner wall material data, so as to obtain the inner wall material property parameters;

[0104] In this embodiment, based on the extracted inner wall material data, material property analysis is performed, which is crucial. This includes analyzing the chemical composition, physical properties, mechanical properties and corrosion resistance of the material. Through experiments and tests, the material property parameters such as elastic modulus, yield strength, creep characteristics and corrosion rate can be obtained. These parameters are very important for subsequent simulation.

[0105] Step S133: a three-dimensional cylinder model is constructed according to the cylinder structure data, and the three-dimensional cylinder model is filled with property parameters using the inner wall material data, so as to obtain a three-dimensional cylinder inner wall model;

[0106] In this embodiment, a three-dimensional cylinder model will be constructed using the structural data of the cylinder. This model includes the external shape, wall thickness, dimensions, and other structural information of the cylinder. Then, the inner wall of the model is filled according to the inner wall material data, including the material property parameters of the inner wall. This creates a complete three-dimensional cylinder inner wall model, providing accurate geometric and material information for subsequent simulation.

[0107] Step S134: statistical analysis of working condition parameters according to cylinder working condition data, so as to obtain simulation parameters;

[0108] In this embodiment, the working condition data is very important for simulation, so the working condition data of the cylinder is statistically analyzed. This may include gas type, temperature, pressure distribution, storage time, and other working condition parameters. Through statistical analysis, the working condition parameters for simulation are obtained to simulate the behavior of the cylinder under different working conditions.

[0109] Step S135: inner wall gas pressure simulation of the three-dimensional cylinder inner wall model by the simulation parameters, so as to obtain inner wall gas pressure simulation data.

[0110] In this embodiment, the three-dimensional cylinder inner wall model is simulated by the simulation parameters. This involves applying engineering mechanics principles and knowledge of material properties and working condition parameters to simulate the gas pressure of the cylinder inner wall in actual use. Through simulation, data such as stress, deformation, and temperature distribution on the inner wall can be obtained, which helps to evaluate the performance and safety of the cylinder.

[0111] In this embodiment, feature extraction extracts key information from raw data, including structural data of the cylinder (such as geometric shape, size, etc.) and working condition data (such as temperature, pressure, etc.). These data provide a comprehensive understanding of the cylinder and provide a basis for subsequent simulation and analysis. By analyzing the inner wall material data, the material property parameters such as elastic modulus and yield strength can be determined. This helps to understand the performance of the inner wall material, so that the material response can be accurately considered in simulation. By constructing a three-dimensional cylinder inner wall model, the geometric shape and internal structure of the cylinder can be considered in computational simulation, which is very important for accurate simulation. Filling the inner wall material data allows the simulation of material properties in simulation. By analyzing the working condition data of the cylinder, the performance and behavior of the cylinder under different conditions can be understood. This helps to determine the working condition parameters that need to be considered in simulation, such as temperature and pressure. The accuracy of the simulation parameters is crucial for accurate simulation of the behavior of the cylinder. Through inner wall gas pressure simulation, the pressure distribution and response inside the cylinder can be understood. This helps to evaluate the performance of the cylinder under different working conditions and detect potential problems, such as excessive pressure or uneven stress distribution.

[0112] Optionally, step S14 is specifically:

[0113] Step S141: Perform material property analysis based on the outer wall material data to obtain outer wall material property parameters;

[0114] In this embodiment, the characteristic parameters of the outer wall material are obtained from the outer wall material data. This includes analyzing the chemical composition, density, elastic modulus, thermal conductivity, expansion coefficient, and other key properties of the outer wall material. For example, experimental tests or existing material databases can be used to obtain these parameters to ensure a deep understanding of the outer wall material.

[0115] Step S142: Perform property parameter filling on the three-dimensional steel cylinder inner wall model based on the outer wall material property parameters to obtain a three-dimensional steel cylinder material model;

[0116] In this embodiment, the obtained outer wall material property parameters are used to fill the outer wall part of the three-dimensional steel cylinder inner wall model. In this way, a complete three-dimensional steel cylinder material model will be obtained, in which the inner wall and outer wall have corresponding material property parameters. This is to more accurately consider the influence of the outer wall material on the inner wall in subsequent simulation.

[0117] Step S143: Perform outer wall internal gas pressure simulation on the three-dimensional steel cylinder material model to obtain outer wall internal gas pressure simulation data;

[0118] In this embodiment, the complete three-dimensional steel cylinder material model, including the inner wall and the outer wall, is used to simulate the gas pressure on the inner wall. This simulation takes into account the distribution of internal gas pressure in the outer wall, which can help understand the interaction between the internal gas pressure and the outer wall, especially under high pressure conditions.

[0119] Step S144: Extract environmental data from the steel cylinder working condition data to obtain steel cylinder working environment data;

[0120] In this embodiment, the working environment data of the steel cylinder is extracted, including external temperature, atmospheric pressure, humidity, and other factors. These environmental data are very important for simulating the behavior of the outer wall, because the outer wall is affected by environmental factors, especially in outdoor use or under harsh weather conditions.

[0121] Step S145: Perform outer wall external gas pressure simulation on the three-dimensional steel cylinder material model based on the steel cylinder working environment data to obtain outer wall external gas pressure simulation data;

[0122] In this embodiment, the obtained steel cylinder working environment data can be used to simulate the external gas pressure of the outer wall. This simulation takes into account the influence of environmental conditions such as external pressure, temperature, and humidity on the outer wall. This helps to understand the stress situation of the outer wall under different working environments.

[0123] Step S146: Perform pressure interaction simulation on the inner wall internal pressure simulation data and the outer wall external pressure simulation data using the three-dimensional steel cylinder material model, to obtain the outer wall pressure simulation data.

[0124] In this embodiment, the inner wall simulation internal pressure data is combined with the outer wall external environment pressure data to simulate the influence of the internal and external pressure difference on the outer wall. This may involve material deformation, stress distribution and other parameters in the simulation process. This allows the consideration of the pressure interaction between the inner wall and the outer wall to obtain simulation data about the internal pressure of the outer wall. These data are crucial for evaluating the performance and safety of the steel cylinder under different environments and working conditions.

[0125] The present application can determine the characteristic parameters of the outer wall material, such as elastic modulus, yield strength, etc. by analyzing the outer wall material data. This helps to understand the performance of the outer wall material, so that the response of the outer wall material can be accurately considered in subsequent simulation. By filling the characteristic parameters of the outer wall material into the three-dimensional steel cylinder inner wall model, a comprehensive three-dimensional steel cylinder material model can be created, which includes the inner wall and the outer wall. This helps to more accurately simulate the overall performance of the steel cylinder. By using the three-dimensional steel cylinder material model, the inner wall pressure simulation data is simulated for the outer wall internal pressure, which can understand the pressure distribution and response inside the outer wall. This helps to evaluate the behavior of the outer wall of the steel cylinder under internal pressure changes. By extracting the working environment data of the steel cylinder, the working conditions of the steel cylinder under different environmental conditions, such as temperature, humidity, etc. can be understood. These data are very important for simulating the performance of the steel cylinder under various working conditions. By using the three-dimensional steel cylinder material model, the outer wall external pressure simulation is performed on the working environment data of the steel cylinder, which can understand the behavior of the outer wall under the change of external environmental pressure. This helps to evaluate the performance of the outer wall of the steel cylinder under different environmental conditions. By interacting the inner wall internal pressure simulation data and the outer wall external pressure simulation data, the influence of the internal and external environment can be considered comprehensively, so as to obtain the comprehensive pressure simulation data of the outer wall. This helps to evaluate the performance and behavior of the steel cylinder under different internal and external pressure interaction conditions.

[0126] Optionally, step S15 is specifically:

[0127] Step S151: Calculate the stress distribution of the inner wall material characteristic parameters and the outer wall material characteristic parameters according to the inner wall pressure simulation data and the outer wall pressure simulation data, to obtain the steel cylinder stress distribution data;

[0128] In this embodiment, finite element analysis or other numerical simulation techniques are used to combine the pressure data with material parameters to calculate the stress distribution inside the cylinder. This can help understand the stress state of the cylinder under different operating conditions, especially when the pressure changes. Using finite element analysis software, these data can be used to calculate the stress distribution inside the cylinder. For example, using the inner wall, a finite element model can be used to input the geometry, boundary conditions, material properties, and other parameters of the inner wall into the model to simulate the stress distribution inside the cylinder under different pressures. This will generate a series of stress distribution data to address different pressure working conditions.

[0129] Step S152: corrosion simulation based on cylinder working condition data and inner wall material characteristic parameters and outer wall material characteristic parameters, to obtain corrosion simulation data;

[0130] In this embodiment, corrosion simulation software is used to input these working condition data, and then consider the material characteristics of the inner and outer walls, such as corrosion rate, corrosion resistance, etc. This will simulate the corrosion of the cylinder under the action of chemicals, including the time evolution of corrosion.

[0131] Step S153: calculate the potential crack score data by calculating the stress distribution data of the cylinder and the corrosion simulation data of the potential crack score calculation formula;

[0132] In this embodiment, the potential crack score calculation formula is selected according to the specific engineering requirements and standards. A practical example is to use API 579-1 / ASME FFS-1 standard, which provides a method for crack assessment. The stress distribution data calculated in step S151 and the corrosion condition data simulated in step S152 are input into the selected potential crack score calculation formula. This formula can consider the effects of stress and corrosion to calculate the potential crack score. For example, the formula can calculate the score according to the stress level, corrosion depth and crack size.

[0133] Step S154: data merging of cylinder stress distribution data, corrosion simulation data and potential crack score data to obtain stress corrosion simulation data.

[0134] In this embodiment, a database or data processing tool is used to integrate the cylinder stress distribution data, corrosion simulation data and potential crack score data. The data such as stress distribution, corrosion degree and potential crack score are associated together to create a comprehensive data set.

[0135] The present application can calculate the stress distribution of the inner and outer walls of the steel cylinder by combining the air pressure simulation data of the inner and outer walls and the corresponding material characteristic parameters. This helps to understand the stress state of the steel cylinder under different internal and external pressure conditions, to evaluate the safety and stability of its structure. By considering the working condition data of the steel cylinder, the material characteristic parameters of the inner and outer walls, and the corrosion simulation, the corrosion condition of the steel cylinder can be estimated. This is very important for evaluating the service life and reliability of the steel cylinder, because corrosion can reduce its strength and performance. Through the potential crack score data calculation, the stress distribution data and the corrosion simulation data can be considered comprehensively to determine whether there is a potential crack problem. This helps to find problems that may cause the failure of the steel cylinder early, so that appropriate maintenance and repair measures can be taken. Combining the stress distribution data, the corrosion simulation data and the potential crack score data into stress corrosion simulation data helps to provide a comprehensive evaluation to determine the performance and reliability of the steel cylinder under different working conditions. These comprehensive data can help make maintenance decisions, including replacing or repairing the steel cylinder.

[0136] Optionally, the potential crack score calculation formula in step S153 is specifically:

[0137]

[0138] In the formula, SG is the potential crack score, σ max is the maximum stress on the steel cylinder, σ min is the minimum stress on the steel cylinder, r is the radius inside the steel cylinder, A co is the surface area in the corrosion simulation data, C co is the corrosion rate constant in the corrosion simulation data, V pr is the pressure inside the steel cylinder, B th is the thickness of the steel cylinder wall, e is the base of natural logarithm, t is time, and z is the stress change amount.

[0139] The present application constructs a potential crack score calculation formula for calculating the stress distribution data and the corrosion simulation data of the steel cylinder. The formula fully considers the maximum stress σ max on the steel cylinder, the minimum stress σ min on the steel cylinder, the radius r inside the steel cylinder, the surface area A co in the corrosion simulation data, the corrosion rate constant C co in the corrosion simulation data, the pressure V pr inside the steel cylinder, the thickness B th of the steel cylinder wall, the base e of natural logarithm, the time t, and the stress change amount z, forming a functional relationship:

[0140]

[0141] wherein, is a Sigmoid function that maps z to a value between 0 and 1, used to limit the potential crack score between 0 and 1. The stress rate of change is calculated. It is the derivative of the difference between the maximum stress and the minimum stress divided by the square root of the radius. The corrosion-related, where ln denotes the natural logarithm. It takes into account the corrosion rate and the corroded surface area. The cubic root of the ratio of pressure to wall thickness is considered. This part represents the effect of internal pressure on material strength. In the art, the potential crack score is usually calculated by using technical means such as finite element analysis, elastic fracture mechanics, etc. By using the potential crack score calculation formula provided by the present application, the potential crack score can be obtained more accurately.

[0142] Optionally, step S4 is specifically:

[0143] Step S41: Obtain the steel cylinder micro-deformation detection data through the deformation amount detector;

[0144] In this embodiment, a special deformation amount detector is used to monitor the micro-deformation of the steel cylinder surface. This can cover a variety of sensor technologies, such as strain gauges, piezoelectric sensors or optical measurement devices. Taking strain gauges as an example, these sensors will be installed on the surface of the steel cylinder to monitor any slight stretching or compression. These sensors will regularly collect deformation data, usually with high frequency sampling. This can provide detailed information about the deformation of the steel cylinder surface, including stress and strain distribution.

[0145] Step S42: According to the steel cylinder micro-deformation detection data, point cloud conversion is carried out to obtain the steel cylinder point cloud data;

[0146] In this embodiment, the micro-deformation data obtained from the deformation amount detector is converted into point cloud data of the steel cylinder. This can be achieved through mathematical modeling and computer vision technology. By calculating the position changes of different measuring points, a point cloud can be created, where each point represents a discrete point on the surface of the steel cylinder. This will form a three-dimensional point cloud model, where each point has its coordinates in three-dimensional space.

[0147] Step S43: Curvature calculation is performed on the steel cylinder point cloud data to obtain the steel cylinder point cloud curvature data;

[0148] In this embodiment, the steel cylinder point cloud data is analyzed to calculate the curvature of the surface. Curvature refers to the degree of bending or curvilinearity of the surface at a certain point. Mathematical methods such as curvature formulas in differential geometry can be used to calculate the curvature of each point. This will produce a curvature field, where each point is associated with its local curvature value. Curvature data is very useful for analyzing the deformation and damage of the steel cylinder surface.

[0149] Step S44: Perform local deformation analysis based on the cylinder point cloud curvature data to obtain cylinder deformation data.

[0150] In this embodiment, the cylinder point cloud curvature data is used for local deformation analysis. This step aims to detect and quantify any minor deformations or shape abnormalities on the cylinder surface. By comparing the actual curvature data with the baseline curvature data in the normal state, potential local deformations can be identified and measured. This helps monitor the integrity of the cylinder, detect any possible damage or cracks, and take necessary repair or replacement measures to ensure the safety and reliable use of the cylinder. These deformation data can be used for preventive maintenance and safety evaluation.

[0151] The present application can provide real-time and accurate deformation information by using the deformation detector to obtain the minor deformation data of the cylinder, which helps to monitor whether the cylinder has been deformed during use, transportation, etc., and take corresponding measures in time. Converting the micro-deformation detection data into point cloud data can provide more intuitive and visual data forms. Point cloud data can be used for subsequent three-dimensional analysis and visualization, which helps to better understand the deformation of the cylinder. By calculating the curvature of the cylinder point cloud data, the curvature information representing the surface shape of the cylinder can be obtained. The curvature data can reflect the degree of fluctuation of the cylinder surface, which helps to further analyze the deformation of the cylinder. Through local deformation analysis, the deformation information of the cylinder can be extracted from the curvature data. This can help understand the deformation of the cylinder in different areas, so as to take corresponding measures to ensure the safety and reliability of the cylinder.

[0152] Optionally, step S44 is specifically:

[0153] Step S441: Perform statistical analysis on the cylinder point cloud curvature data to obtain abnormal point cloud curvature data;

[0154] In this embodiment, the abnormal point cloud data is detected by performing statistical analysis on the cylinder point cloud curvature data. Various statistical methods can be used, such as mean, variance, standard deviation, distribution analysis, etc. First, calculate the statistical indicators of the curvature data of the entire point cloud data set. Then, by setting a threshold or using an anomaly detection algorithm (such as Z-score or Isolation Forest), identify those point cloud data points that deviate greatly from the normal state, which are considered abnormal. This will generate abnormal point cloud curvature data, which includes specific abnormal points and their curvature values.

[0155] Step S442: Perform regional division based on the abnormal point cloud curvature data to obtain abnormal region data;

[0156] In this embodiment, the abnormal point cloud curvature data is used to divide the steel cylinder surface into regions to obtain abnormal region data. This can be achieved through clustering algorithms such as K-means clustering or region growing methods. Abnormal points usually gather together to form abnormal regions. By grouping abnormal points according to their adjacency, different abnormal regions can be identified. These regions correspond to local problems on the steel cylinder surface, such as damage, corrosion or cracks.

[0157] Step S443: Calculate the local deformation of the abnormal point cloud curvature data according to the abnormal region data, and obtain the steel cylinder deformation data.

[0158] In this embodiment, the abnormal region data is used to calculate the local deformation of the abnormal point cloud curvature data to obtain the deformation data of the steel cylinder. For each abnormal region, its local deformation can be calculated, which usually involves analyzing the point cloud around the abnormal point to determine the degree and direction of deformation. Regions with a local deformation within 0.6% are marked as repairable regions; regions with a local deformation exceeding 0.6% are marked as irreversible development regions; regions with a local deformation exceeding 10% are marked as fracture regions.

[0159] In this embodiment, statistical analysis can help identify abnormal point cloud curvature data that deviates from normal conditions. These abnormal data may represent protrusions or depressions on the surface of the steel cylinder, or other unusual features. By identifying abnormal point cloud curvature data, potential deformation regions can be more accurately located. Regional division of abnormal point cloud curvature data helps to locate the problem to a specific region for more accurate analysis and processing of abnormal conditions. This can provide more detailed information for appropriate maintenance or improvement measures. Local deformation calculation can further extract steel cylinder deformation information within the abnormal region. These data can be used to quantify the degree of abnormality and gain a deeper understanding of the structural condition of the steel cylinder. This helps to determine whether repair, replacement or other corrective measures are needed to ensure the safety of the steel cylinder.

[0160] Optionally, step S5 is specifically:

[0161] Step S51: Calculate the steel cylinder burst prediction data and the steel cylinder deformation data by the steel cylinder burst prediction formula to obtain the steel cylinder burst prediction data;

[0162] In this embodiment, the steel cylinder burst prediction formula is used to input the collected data into the formula. This formula may be a complex mathematical expression that takes into account various factors such as material properties, wall thickness, pressure and temperature relationships, etc. The calculation formula is used to calculate the data and obtain the burst value. This value represents the degree of burst that the steel cylinder may have under current conditions. This can be a specific numerical value, usually expressed in standard units (such as psi or bar).

[0163] Step S52: Select the optimal relief value of the cylinder relief value according to the cylinder burst prediction model, so as to obtain the optimal relief value, and send it to the active relief device to perform the automatic pressure relief task.

[0164] In this embodiment, the cylinder burst prediction model is constructed in advance, which is based on historical data, material properties, environmental conditions, etc. The calculated relief value is analyzed. The prediction model will recommend an optimal relief value according to the current situation and parameters. This value is the best relief amount predicted by the model to ensure that the cylinder does not burst or cause safety problems during pressure relief. Once the optimal relief value is obtained, the value is sent to the active relief device, which may be a valve or other control equipment. The device will perform the automatic pressure relief task according to the received value, ensuring that the pressure inside the cylinder is maintained at a safe level.

[0165] According to the cylinder burst prediction data and deformation data, the present application calculates a cylinder relief value. This value is calculated according to a physical model or empirical formula, which reflects the safe range of internal pressure of the cylinder. By calculating this value, it can be evaluated whether the cylinder has too high internal pressure, thereby helping to prevent explosion or leakage accidents. According to the cylinder burst prediction model, the optimal relief value is selected to ensure that the pressure inside the cylinder is always within the safe range. This model may consider multiple factors such as the material, structure, and use conditions of the cylinder to determine the most suitable relief value. Once the optimal relief value is determined, it will be sent to the active relief device to perform the automatic pressure relief task. This helps to maintain the internal pressure of the cylinder at a safe level and avoid possible dangerous situations.

[0166] Alternatively, the cylinder relief value calculation formula in step S51 is specifically:

[0167]

[0168] In the formula, PV is the cylinder relief value, A is the cross-sectional area of the cylinder, V is the volume of the cylinder, B is the material strength parameter of the cylinder, C is the shape parameter of the cylinder, D is the internal gas pressure parameter of the cylinder, E is the thermal conductivity of the cylinder material, F is the density of the stored gas, G is the molar mass of the gas, H is the environmental condition parameter, I is the exposed area of the predicted burst location of the cylinder, J is the predicted burst time, and K is the relief correction constant.

[0169] The present application constructs a steel cylinder discharge value calculation formula, which is used for steel cylinder discharge value calculation of steel cylinder burst prediction data and steel cylinder deformation data. The formula fully considers the cross-sectional area A of the steel cylinder, the volume V of the steel cylinder, the steel cylinder material strength parameter B, the steel cylinder shape parameter C, the steel cylinder internal gas pressure parameter D, the heat conductivity E of the steel cylinder material, the density F of the stored gas, the molar mass G of the gas, the environmental condition parameter H, the exposure area I of the predicted burst position of the steel cylinder, the predicted burst time J, the discharge correction constant K, and forms a functional relationship:

[0170]

[0171] In the formula, C is the steel cylinder shape parameter, which represents the influence of the geometric shape of the steel cylinder on the discharge. represents the ratio of the volume of the steel cylinder to the cross-sectional area, which is used to consider the distribution of the internal gas of the steel cylinder. is an exponential term related to material strength and internal gas pressure, which is used to consider the influence of material strength and internal gas pressure on the discharge. E represents the heat conductivity of the steel cylinder material, which considers the heat transfer property of the material. High heat transfer rate leads to faster discharge. F is the density of the gas stored in the steel cylinder, and the gas with high density has higher kinetic energy during discharge. G is the molar mass of the gas, which considers the influence of the molecular weight of the gas on the discharge. Different gases have different molecular weights. H is the environmental condition parameter, which reflects the influence of the environment on the discharge. represents the influence of the dangerous environment on the danger of the discharge. The square root of the product considers the influence of the exposure area I of the predicted burst position of the steel cylinder and the predicted burst time J. Large exposure area and short burst time will lead to more serious discharge. -K is a correction term, which is used to correct the calculation result to consider other factors or errors. In the field, finite element analysis, computational fluid dynamics and other technical means are usually used to calculate the steel cylinder discharge value. By using the steel cylinder discharge value calculation formula provided by the present application, the steel cylinder discharge value can be obtained more accurately.

[0172] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting, the scope of the present application is defined by the appended claims rather than the above description, and therefore all changes falling within the meaning and scope of the equivalent elements of the application file are intended to be included in the present application.

[0173] The foregoing is considered as illustrative only of the principles of the application. Numerous modifications and changes will readily occur to those skilled in the art, and it is intended to embrace all such modifications and changes that fall within the scope of the application. Accordingly, the application is not to be restricted in scope to the specific embodiments disclosed herein but is to be accorded the full scope that the principles and novel features request appropriately granted.

Claims

1. An online detection method for early warning of fire extinguishing cylinder burst, characterized in that, The application is applied to a gas cylinder safety monitoring system, and the gas cylinder safety monitoring system comprises a controller, a positive relief device, an electronic pressure sensor, a steel cylinder burst early warning detector and a deformation amount detector electrically connected with the controller. The electronic pressure sensor, the steel cylinder burst early warning detector and the deformation amount detector are electrically connected with the controller. Step S1: obtaining fire-fighting cylinder data, and performing stress corrosion simulation according to the fire-fighting cylinder data to obtain stress corrosion simulation data; step S1 specifically comprises: Step S11: obtaining fire-fighting cylinder data; Step S12: extracting inner wall material data and outer wall material data from the fire-fighting cylinder data to obtain the inner wall material data and the outer wall material data; Step S13: performing inner wall gas pressure simulation according to the inner wall material data to obtain inner wall gas pressure simulation data; Step S14: performing outer wall gas pressure simulation according to the inner wall gas pressure simulation data and the outer wall material data to obtain outer wall gas pressure simulation data; Step S15: performing stress corrosion simulation on the inner wall gas pressure simulation data and the outer wall gas pressure simulation data to obtain stress corrosion simulation data; step S15 specifically comprises: Step S151: calculating stress distribution of the inner wall material characteristic parameters and the outer wall material characteristic parameters according to the inner wall gas pressure simulation data and the outer wall gas pressure simulation data to obtain steel cylinder stress distribution data; Step S152: performing corrosion simulation based on the steel cylinder working condition data and the inner wall material characteristic parameters and the outer wall material characteristic parameters to obtain corrosion simulation data; Step S153: calculating the steel cylinder stress distribution data and the corrosion simulation data by a potential crack score calculation formula to obtain potential crack score data; the potential crack score calculation formula in step S153 is specifically: ； wherein is the potential cracking score, is the maximum stress on the cylinder, is the minimum stress on the cylinder, is the radius of the interior of the cylinder, is the surface area in the corrosion simulation data, is the corrosion rate constant in the corrosion simulation data, is the pressure inside the cylinder, is the thickness of the cylinder wall, is the base of the natural logarithm, is the time, is the stress change amount; Step S154: merging the steel cylinder stress distribution data, the corrosion simulation data and the potential crack score data to obtain stress corrosion simulation data; Step S2: constructing a steel cylinder burst prediction model according to the stress corrosion simulation data and the fire-fighting cylinder data; Step S3: obtaining bottle inner pressure data by the electronic pressure sensor, and performing steel cylinder burst prediction on the bottle inner pressure data according to the steel cylinder burst prediction model to obtain steel cylinder burst prediction data; Step S4: obtaining steel cylinder deformation data by performing local deformation analysis on steel cylinder micro-deformation detection data obtained by the deformation amount detector; Step S5: performing burst probability analysis on the steel cylinder burst prediction data according to the steel cylinder deformation data to obtain suspected burst cylinder data, and sending the suspected burst cylinder data to the steel cylinder burst early warning detector to perform an online early warning task; Step S6: performing optimal relief analysis according to the steel cylinder deformation data and the steel cylinder burst prediction data to obtain a steel cylinder optimal relief value, and sending the steel cylinder optimal relief value to the positive relief device to perform an automatic pressure relief task.

2. The method of claim 1, wherein, Step S13 specifically comprises: Step S131: extracting features from the fire-fighting cylinder data to obtain steel cylinder structure data and steel cylinder working condition data; Step S132: material property analysis is performed according to the inner wall material data, so as to obtain the inner wall material property parameters; Step S133: a three-dimensional steel cylinder model is constructed according to the steel cylinder structure data, and the three-dimensional steel cylinder model is filled with the inner wall material data, so as to obtain a three-dimensional steel cylinder inner wall model; Step S134: working condition parameter statistical analysis is performed according to the steel cylinder working condition data, so as to obtain simulation parameters; Step S135: the three-dimensional steel cylinder inner wall model is simulated by the simulation parameters, so as to obtain inner wall gas pressure simulation data.

3. The method of claim 1, wherein, Step S14 specifically comprises: Step S141: material property analysis is performed according to the outer wall material data, so as to obtain the outer wall material property parameters; Step S142: the three-dimensional steel cylinder inner wall model is filled with the outer wall material property parameters, so as to obtain a three-dimensional steel cylinder material model; Step S143: the inner wall gas pressure simulation data is simulated by the three-dimensional steel cylinder material model, so as to obtain outer wall internal gas pressure simulation data; Step S144: environment data is extracted from the steel cylinder working condition data, so as to obtain steel cylinder working environment data; Step S145: the steel cylinder working environment data is simulated by the three-dimensional steel cylinder material model, so as to obtain outer wall external gas pressure simulation data; Step S146: the outer wall internal gas pressure simulation data and the outer wall external gas pressure simulation data are simulated by the three-dimensional steel cylinder material model, so as to obtain outer wall gas pressure simulation data.

4. The method of claim 1, wherein, Step S4 specifically comprises: Step S41: steel cylinder micro-deformation detection data is obtained by a deformation detector; Step S42: point cloud data is obtained by point cloud conversion according to the steel cylinder micro-deformation detection data; Step S43: curvature calculation is performed on the steel cylinder point cloud data, so as to obtain steel cylinder point cloud curvature data; Step S44: local deformation analysis is performed according to the steel cylinder point cloud curvature data, so as to obtain steel cylinder deformation data.

5. The method of claim 4, wherein, Step S44 specifically comprises: Step S441: statistical analysis is performed on the steel cylinder point cloud curvature data, so as to obtain abnormal point cloud curvature data; Step S442: region division is performed according to the abnormal point cloud curvature data, so as to obtain abnormal region data; Step S443: local deformation calculation is performed on the abnormal point cloud curvature data according to the abnormal region data, so as to obtain steel cylinder deformation data.

6. The method of claim 1, wherein, Step S5 specifically comprises: Step S51: steel cylinder relief value calculation is performed on the steel cylinder burst prediction data and the steel cylinder deformation data by a steel cylinder relief value calculation formula, so as to obtain a steel cylinder relief value; Step S52: optimal relief value selection is performed on the steel cylinder relief value according to the steel cylinder burst prediction model, so as to obtain an optimal relief value, which is sent to the active relief device to perform an automatic pressure relief task.

7. The method of claim 6, wherein, The steel cylinder relief value calculation formula in step S51 specifically comprises: ； wherein, is the cylinder relief value, is the cross-sectional area of the cylinder, is the volume of the cylinder, is the cylinder material strength parameter, is the cylinder shape parameter, is the cylinder internal gas pressure parameter, is the thermal conductivity of the cylinder material, is the density of the stored gas, is the molar mass of the gas, is the ambient condition parameter, is the exposed area of the predicted burst location of the cylinder, is the predicted burst time, is the relief correction constant.

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