Fire extinguisher tank bursting pressure prediction method and system

Abstract

The invention provides a fire extinguisher tank bursting pressure prediction method and system. According to the method, a micro-deformation sequential sequence and a pressure gradient value of the surface of the fire extinguisher tank body under high pressure are collected, and a deformation displacement field matrix is generated through optical flow analysis so as to quantify the deformation rate; a laser holographic interferometer is deployed on a pressurization test bench, laser phase parameters are dynamically adjusted based on the deformation rate, matched holographic interference fringes are output, and a deformation evolution topological graph is constructed through fringe phase changes; identifying a deformation rate abnormal fluctuation mode in the topological graph, and associating the pressure gradient value to generate a deformation pressure association tensor; acquiring a deformation propagation path and a stress concentration trend based on the tensor, and simulating a critical fracture pressure value and a potential fracture position; and integrating results to form bursting pressure risk output. According to the invention, millisecond-level accurate prediction of the submillimeter-level fracture position is realized, the burst pressure risk assessment precision is improved, and the test cost is reduced.

Description

Technical Field

[0001] This application relates to the field of pressure prediction technology, and in particular to a method and system for predicting the burst pressure of a fire extinguisher tank. Background Technology

[0002] As a critical pressure-bearing component, the burst pressure prediction of fire extinguisher tanks directly impacts the reliability of fire safety equipment and accident prevention capabilities. In practical applications, challenges arise from multiple sources of uncertainty: fluctuations in tank material properties (e.g., differences in yield strength between low-carbon steel, stainless steel, or aluminum alloys), manufacturing process deviations (residual welding stress, uneven wall thickness, heat treatment defects), and environmental interference (temperature changes causing material creep, humidity accelerating corrosion and thinning). Traditional physical testing methods (such as destructive burst testing) are not only costly and time-consuming, but also cannot dynamically assess the life-cycle degradation effects (e.g., fatigue accumulation after repeated filling, strength degradation due to chemical corrosion). Therefore, a non-destructive prediction method that integrates material properties, process parameters, and service history data is urgently needed to avoid the risk of uncontrolled spraying or explosion due to tank failure.

[0003] Current targeted solutions are physical-experiment coupled prediction models based on finite element simulation. This approach establishes a parametric 3D model of the tank (including weld geometry, wall thickness gradient, and material constitutive relations), inputs measured material property data (such as tensile strength and elongation) and process parameters (such as the range of the weld heat-affected zone), and combines the boundary conditions calibrated by hydrostatic testing to simulate the stress distribution and plastic deformation behavior throughout the entire process from pressurization to burst, outputting the theoretical burst pressure value and potential failure location. However, its core shortcomings lie in its over-reliance on static material constitutive models and idealized geometric assumptions, making it difficult to quantify the abrupt impact of microscopic defects (such as grain boundary inclusions and corrosion microcracks) on macroscopic burst behavior. Furthermore, the computational complexity of multi-field coupled simulations results in prediction timeliness that cannot meet the real-time quality inspection requirements of production lines. Summary of the Invention

[0004] This application provides a method and system for predicting the burst pressure of a fire extinguisher tank, which solves the problems of high destructiveness, inaccurate prediction of rupture location, and uncontrollable cost in the prior art.

[0005] Firstly, this application provides a method for predicting the burst pressure of a fire extinguisher tank, including: In a high-pressure testing environment, the micro-deformation time sequence and pressure gradient value of the surface of the fire extinguisher tank during pressurization are collected. The micro-deformation time sequence is processed by optical flow analysis to generate a deformation displacement field matrix, and the deformation displacement field matrix is ​​used to quantify the deformation rate under different pressure gradient values. A laser holographic interferometer is deployed on a pressure test bench, and the laser phase parameters of the laser holographic interferometer are dynamically adjusted based on the deformation rate, so that the laser holographic interferometer outputs holographic interference fringes that match the deformation rate, and the phase change of the holographic interference fringes is obtained to construct a deformation evolution topology map. Identify abnormal fluctuation patterns of deformation rate in the deformation evolution topology graph and correlate the abnormal fluctuation patterns with pressure gradient values ​​to generate a deformation-pressure correlation tensor. The deformation propagation path and stress concentration trend in the deformation evolution topology are obtained based on the deformation pressure correlation tensor, and the critical rupture pressure value and potential rupture location of the fire extinguisher tank are simulated based on the deformation propagation path and stress concentration trend. The predicted critical rupture pressure value and rupture location are integrated to form a burst pressure risk output.

[0006] Optionally, in a high-pressure testing environment, the micro-deformation time sequence and pressure gradient values ​​of the fire extinguisher tank surface are collected during pressurization. The micro-deformation time sequence is processed by optical flow analysis to generate a deformation displacement field matrix. The deformation displacement field matrix is ​​then used to quantify the deformation rate under different pressure gradient values, including: During the process of applying incremental pressure to the surface of the fire extinguisher tank in a high-pressure test environment, a high-speed camera device is set up to continuously capture images of the tank surface to form a micro-deformation time sequence image set, and the pressure gradient value collected by the set pressure sensor is recorded simultaneously. Obtain the pixel positions of the tank surface images in the micro-deformation time-series image set, and calculate the coordinate offset of the pixel positions in the micro-deformation time-series image set as the deformation displacement field matrix; The time interval between adjacent tank surface images in the micro-deformation time sequence image set is obtained, and the basic value of deformation rate is calculated based on the time interval and the deformation displacement field matrix. The basic value of the deformation rate is matched with the corresponding pressure gradient value to obtain the deformation rate under different pressure gradient values.

[0007] Optionally, the pixel positions of the tank surface images in the micro-deformation time-series image set are obtained, and the coordinate offset of the pixel positions in the micro-deformation time-series image set is calculated as the deformation displacement field matrix, including: The first frame of the micro-deformation time sequence image set is selected as the reference frame, and the initial coordinate positions of all pixels on the surface of the tank in the reference frame are extracted to form a reference coordinate set. In the image frames of the micro-deformation time-series image set, match the pixels at the same positions as the reference coordinate set; Obtain the coordinate position of the pixel in the image frame, calculate the difference between the coordinate position and the corresponding coordinate in the reference coordinate set, and use the horizontal component of the difference as the horizontal offset and the vertical component as the vertical offset. The horizontal and vertical offsets are combined into a two-dimensional offset vector, and the two-dimensional offset vector is arranged in the spatial order of the reference coordinate set. Integrate the two-dimensional offset vectors of all pixels to construct a deformable displacement field matrix containing horizontal and vertical offset components.

[0008] Optionally, a laser holographic interferometer is deployed on a pressure test bench, and the laser phase parameters of the laser holographic interferometer are dynamically adjusted based on the deformation rate, so that the laser holographic interferometer outputs holographic interference fringes that match the deformation rate, and the phase changes of the holographic interference fringes are obtained to construct a deformation evolution topology map, including: The laser emitter and receiver of a laser holographic interferometer are fixed on the surface of the fire extinguisher tank on the pressure test bench to ensure that the laser beam of the laser holographic interferometer covers the surface of the fire extinguisher tank. The deformation rate value is converted into the laser phase adjustment coefficient of the laser holographic interferometer, and the laser phase adjustment coefficient is directly proportional to the deformation rate value. Adjust the wavelength fine-tuning knob at the laser emitter according to the laser phase adjustment coefficient so that the wavelength change of the laser beam of the laser holographic interferometer matches the deformation rate. The interference fringe image output by the laser holographic interferometer is acquired, and the spacing change between adjacent interference fringes is measured simultaneously, and the spacing change is converted into a phase change. The phase change is mapped to a preset spatial coordinate on the surface of the fire extinguisher tank, and the phase change is arranged according to the position of the preset spatial coordinate to form a deformation evolution topology diagram.

[0009] Optionally, identifying anomalous fluctuation patterns of deformation rate in the deformation evolution topology graph and correlating these anomalous fluctuation patterns with pressure gradient values ​​to generate a deformation-pressure correlation tensor includes: Extract the curve data of deformation rate changing with time for each spatial coordinate point in the deformation evolution topology diagram, and calculate the change in deformation rate between adjacent time points in the curve data as the fluctuation intensity value. When the fluctuation intensity value continuously exceeds the preset fluctuation threshold, the spatial coordinate point at the fluctuation intensity value is marked as the location where the abnormal deformation rate fluctuation mode occurs; Obtain the time interval of the location where the abnormal fluctuation pattern occurs, and extract the pressure gradient value of the corresponding time interval; The location of the abnormal fluctuation pattern and the corresponding pressure gradient value are combined into a two-dimensional data unit; The two-dimensional data units are arranged according to the coordinates of a preset spatial grid to form a deformation-pressure correlation tensor.

[0010] Optionally, the deformation propagation path and stress concentration trend in the deformation evolution topology are obtained based on the deformation-pressure correlation tensor, and the predicted results of the critical rupture pressure value and potential rupture location of the fire extinguisher tank are simulated based on the deformation propagation path and stress concentration trend, including: The pressure value and deformation rate at each spatial location point in the deformation-pressure correlation tensor are obtained to calculate the pressure value increment and deformation rate increment, and the ratio of the pressure value increment to the deformation rate increment is used as the stress sensitivity coefficient. Connect adjacent spatial locations whose stress sensitivity coefficient exceeds a preset threshold in the deformation evolution topology diagram to form a sequence of spatial locations for the deformation propagation path; The clustering density of stress sensitivity coefficient spatial locations in the deformation-pressure correlation tensor is statistically analyzed, and regions where the clustering density exceeds a preset critical value are marked as stress concentration trend regions. The peak point of the stress sensitivity coefficient is selected within the stress concentration trend region, and the pressure value of the peak point is obtained as a reference to calculate the critical rupture pressure value. The spatial location sequence of the deformation propagation path is integrated with the spatial coordinates of the stress concentration trend area to output a prediction result that includes the critical rupture pressure value and the potential rupture location.

[0011] Optionally, the predicted results of the critical rupture pressure value and rupture location are integrated to form a burst pressure risk output, including: Read the specific numerical calculation results of the critical rupture pressure value and simultaneously obtain the three-dimensional spatial coordinate data of the potential rupture location; Obtain the rated pressure value of the fire extinguisher tank design, and calculate the safety margin ratio coefficient as the benchmark value for risk level classification based on the rated pressure value and the critical rupture pressure value. Based on the aforementioned risk level classification benchmark value, determine the category of the blasting risk level identifier; The spatial coordinates of potential rupture locations are mapped onto the surface of a pre-defined 3D model of the fire extinguisher tank to generate spatial markers for the rupture locations. The critical rupture pressure value, rupture risk level identifier, and rupture location spatial marker are integrated to generate a rupture pressure risk report.

[0012] Secondly, this application provides a fire extinguisher tank burst pressure prediction system, comprising: The acquisition module is used to acquire the micro-deformation time sequence and pressure gradient value of the surface of the fire extinguisher tank during pressurization in a high-pressure test environment. The micro-deformation time sequence is processed by optical flow analysis to generate a deformation displacement field matrix, and the deformation displacement field matrix is ​​used to quantify the deformation rate under different pressure gradient values. An adjustment module is used to deploy a laser holographic interferometer on a pressure test bench and dynamically adjust the laser phase parameters of the laser holographic interferometer based on the deformation rate, so that the laser holographic interferometer outputs holographic interference fringes that match the deformation rate, and obtains the phase change of the holographic interference fringes to construct a deformation evolution topology map. The correlation module is used to identify abnormal fluctuation patterns of deformation rate in the deformation evolution topology graph and correlate the abnormal fluctuation patterns with pressure gradient values ​​to generate a deformation-pressure correlation tensor. The simulation module is used to obtain the deformation propagation path and stress concentration trend in the deformation evolution topology based on the deformation pressure correlation tensor, and to simulate the critical rupture pressure value and potential rupture location prediction results of the fire extinguisher tank based on the deformation propagation path and stress concentration trend. An integration module is used to integrate the predicted results of the critical rupture pressure value and the rupture location to form a burst pressure risk output.

[0013] Thirdly, this application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are to be invoked and executed by the processing component to implement a method for predicting the burst pressure of a fire extinguisher tank as described in the first aspect above.

[0014] Fourthly, this application provides a computer storage medium storing a computer program, which, when executed by a computer, implements a method for predicting the burst pressure of a fire extinguisher tank as described in the first aspect.

[0015] This application's technical solution achieves accurate prediction of fire extinguisher tank explosion risk through multimodal sensing and intelligent deformation analysis. Specifically, the micro-deformation displacement field matrix based on optical flow analysis significantly improves the quantification accuracy of the deformation rate; adaptive phase adjustment of the laser holographic interferometer ensures high-resolution capture of the deformation evolution process; and intelligent analysis of the deformation-pressure correlation tensor effectively identifies potential rupture characteristics. This method overcomes the limitations of traditional destructive testing, achieving dynamic evaluation of the entire process from microscopic deformation to macroscopic rupture. It provides a non-contact, high-precision intelligent early warning method for pressure vessel safety performance testing, significantly improving the safety and reliability assessment level of industrial equipment.

[0016] Furthermore, the deformation characteristics of fire extinguisher tanks are accurately quantified through the collaborative acquisition of high-speed images and pressure sensors. Specifically, the construction of a deformation displacement field matrix based on pixel-level coordinate offset analysis significantly improves the accuracy of micro-deformation detection; strict synchronization of time-series images and pressure data ensures the dynamic reliability of deformation rate calculation; and a pressure gradient matching mechanism enables multi-condition evaluation of material mechanical properties. This method overcomes the limitations of traditional contact-based measurements, forming a non-contact, full-field deformation monitoring system. It provides high spatiotemporal resolution deformation characteristic data for pressure vessel safety assessment, significantly improving the accuracy of blast risk prediction and equipment safety early warning capabilities.

[0017] These or other aspects of this application will become more apparent in the following description of the embodiments. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of 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 some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart of a method for predicting the burst pressure of a fire extinguisher tank provided in this application is shown; Figure 2 This application provides a schematic diagram of the structure of a fire extinguisher tank burst pressure prediction system. Figure 3 A schematic diagram of the structure of a computing device provided in this application is shown. Detailed Implementation

[0020] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0021] In some of the processes described in the specification, claims, and accompanying drawings of this application, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not themselves represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a chronological order, nor do they limit "first" and "second" to different types.

[0022] The technology for predicting the burst pressure of fire extinguisher tanks faces a fundamental bottleneck: while traditional finite element simulation-based methods can simulate macroscopic failure behavior, their lack of response to microscopic defects and insensitivity to dynamic evolution limits their predictive effectiveness. Specifically, the abrupt changes in local stress caused by subsurface defects such as grain boundary inclusions and corrosion microcracks are difficult to quantify in idealized models; furthermore, static constitutive assumptions cannot capture the nonlinear transitions in deformation rates during pressurization, leading to distorted assessments of the full life-cycle degradation effect. This contradiction stems from the decoupling blind spot of the material's actual deformation trajectory and the inherent lag of offline simulation mechanisms, necessitating the construction of a closed-loop prediction architecture that integrates dynamic deformation tracking and real-time risk warning.

[0023] To address the aforementioned challenges, this invention proposes a method for predicting the burst pressure of fire extinguisher tanks. Its innovation lies in overcoming the limitations of static modeling through the synergistic feedback of optical flow analysis and tunable laser interferometry. Specifically: during pressurization, the micro-deformation sequence and pressure gradient values ​​on the tank surface are simultaneously acquired. These are then processed by optical flow to generate a dynamic displacement field matrix, quantifying the real-time response characteristics of the deformation rate to pressure changes. By dynamically tuning the phase parameters using a laser holographic interferometer, the phase changes of interference fringes matching the deformation rate are captured, constructing a high-resolution deformation evolution topology map. Abnormal fluctuation patterns in the deformation rate within the topology map are identified, and a deformation-pressure correlation tensor is generated by combining the pressure gradient. Based on the tensor analysis of the deformation propagation path and stress concentration trend, the critical burst pressure and potential failure location are accurately predicted. This method overturns the traditional simulation paradigm: the optical flow-holographic collaborative mechanism achieves millisecond-level dynamic visualization of the stress response of microscopic defects in materials for the first time; the deformation pressure tensor maintains predictive robustness under complex service conditions by integrating the spatiotemporal evolution characteristics of multi-physics fields; the closed-loop early warning mechanism forms a real-time decision chain of "deformation perception-interference feedback-tensor deduction-risk output", providing pressure-bearing equipment with an essential safety prediction capability from subsurface defects to macroscopic explosions.

[0024] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0025] Figure 1 This application provides a flowchart of a method for predicting the burst pressure of a fire extinguisher tank, as shown in the embodiments. Figure 1 As shown, the method includes: 101. In a high-pressure test environment, the micro-deformation time sequence and pressure gradient value of the surface of the fire extinguisher tank are collected during the pressurization process. The micro-deformation time sequence is processed by optical flow analysis to generate a deformation displacement field matrix, and the deformation displacement field matrix is ​​used to quantify the deformation rate under different pressure gradient values.

[0026] Optionally, step 101 may specifically include the following steps: 1011. During the process of applying increasing pressure to the surface of the fire extinguisher tank in a high-pressure test environment, a high-speed camera device is set up to continuously capture images of the tank surface to form a micro-deformation time sequence image set, and the pressure gradient value collected by the set pressure sensor is recorded simultaneously.

[0027] 1012. Obtain the pixel positions of the tank surface images in the micro-deformation time-series image set, and calculate the coordinate offset of the pixel positions in the micro-deformation time-series image set as the deformation displacement field matrix.

[0028] Step 1012 may specifically include the following processes: selecting the first frame image in the micro-deformation time-series image set as the reference frame, and extracting the initial coordinate positions of all pixels on the surface of the tank in the reference frame to form a reference coordinate set; matching pixels in the image frames of the micro-deformation time-series image set that are at the same position as the reference coordinate set; obtaining the coordinate position of the pixel in the image frame, and calculating the difference between the coordinate position and the corresponding coordinate in the reference coordinate set, and taking the horizontal component of the difference as the horizontal offset and the vertical component as the vertical offset; combining the horizontal and vertical offsets into a two-dimensional offset vector, and arranging the two-dimensional offset vector according to the spatial order of the reference coordinate set; integrating the two-dimensional offset vectors of all pixels to construct a deformation displacement field matrix containing the horizontal and vertical offset components.

[0029] 1013. Obtain the time interval between adjacent tank surface images in the micro-deformation time sequence image set, and calculate the basic value of deformation rate based on the time interval and the deformation displacement field matrix.

[0030] 1014. Match the basic value of the deformation rate with the corresponding pressure gradient value to obtain the deformation rate under different pressure gradient values.

[0031] In the above scheme, the high-pressure test environment refers to the device used for high-pressure testing. The fire extinguisher tank refers to the container part of a fire extinguisher. The micro-deformation time series refers to the time series data of minute deformations. The pressure gradient value refers to the rate of pressure change. Optical flow analysis refers to the method of analyzing the motion of objects in an image. The deformation displacement field matrix refers to the matrix reflecting deformation displacement. The deformation rate refers to the rate of change of deformation over time. The high-speed camera device refers to the equipment that captures images at high speed. The pressure sensor refers to the sensor that measures pressure. The pixel position refers to the coordinate position of a pixel in an image. The coordinate offset refers to the change in pixel position. The reference frame refers to the image frame used as a reference. The reference coordinate set refers to the set of coordinates in the reference frame. The horizontal offset refers to the displacement in the horizontal direction. The vertical offset refers to the displacement in the vertical direction. The two-dimensional offset vector refers to a vector containing both horizontal and vertical displacements. The horizontal offset component refers to the displacement component in the horizontal direction. The vertical offset component refers to the displacement component in the vertical direction. The deformation rate baseline value refers to the baseline value used to calculate the deformation rate. The time interval refers to the time difference between adjacent images.

[0032] In this embodiment, firstly, during the application of incremental pressure to the surface of the fire extinguisher tank in a high-pressure testing environment, the system continuously captures images of the tank surface using a high-speed camera. This device captures the surface morphology of the tank under pressure changes at a fixed frame rate, generating a time-series image set of micro-deformation sequences arranged chronologically. Simultaneously, a pressure sensor is activated to collect pressure change data in real time, recording the pressure gradient value (i.e., the pressure change per unit time) corresponding to each image frame, ensuring strict alignment between the timestamps of the image sequence and the pressure data. This process achieves precise matching between optical recording and physical measurement through spatiotemporal synchronization technology, providing a raw dataset for subsequent deformation analysis.

[0033] Subsequently, the system processes the micro-deformation time-series image set to generate a deformation displacement field matrix. The image processing engine selects the first frame of the sequence as the reference frame and extracts the initial coordinate positions of all its pixels to form a reference coordinate set. Using a pixel matching algorithm (such as feature point tracking), pixels in subsequent image frames that match the reference coordinate set are located, and the difference between the current coordinate position of each pixel and the reference position is calculated: the difference in the horizontal direction is the horizontal offset, and the difference in the vertical direction is the vertical offset. The vector synthesis module combines the horizontal and vertical offsets of each pixel into a two-dimensional offset vector, then arranges all vectors in pixel spatial order, finally integrating them into a deformation displacement field matrix containing full-field displacement data. This process is based on the principle of optical flow analysis, transforming visual displacement into a structured mathematical expression.

[0034] Next, the system calculates the baseline deformation rate. The time sequence parser reads the inter-frame time interval (i.e., the time difference between adjacent image captures) of the micro-deformation time sequence image set. The rate calculation module divides the displacement recorded in the deformation displacement field matrix by the corresponding time interval to obtain the displacement change rate of each pixel per unit time, i.e., the baseline deformation rate. This process uses a differential approximation method, quantifying the instantaneous deformation rate of the tank surface by the ratio of displacement increment to time increment.

[0035] Finally, the system correlates deformation rate and pressure data. The data matching engine aligns the baseline deformation rate values ​​with the pressure gradient values ​​collected by the pressure sensors according to the timestamp, generating a pressure-deformation rate correspondence table. The rate mapping module fits deformation rate distribution curves under different pressure gradient values ​​based on this table, thereby establishing a quantitative relationship between pressure increment and the deformation rate of the tank surface. This process reveals the response characteristics of material deformation under pressure load through dynamic calibration technology, providing core indicators for structural safety assessment.

[0036] In practical applications, under high-pressure testing environments, increasing pressure is first applied to the surface of the fire extinguisher tank. A high-speed camera continuously captures images of the tank surface, forming a time-series image set of micro-deformation. Simultaneously, pressure gradient values ​​are recorded by a pressure sensor, fulfilling the requirements of step 1011. Then, the pixel positions of the tank surface images in the time-series image set are obtained: the first frame is selected as the reference frame, and the initial coordinate positions of all pixels on the tank surface in the reference frame are extracted to form a reference coordinate set. In subsequent image frames, pixels at the same positions as those in the reference coordinate set are matched, and the coordinate positions of the pixels in the image frames are obtained. The difference between the pixel position and the corresponding coordinate in the reference coordinate set is calculated. The horizontal component of the difference is used as the horizontal offset, and the vertical component as the vertical offset. The horizontal and vertical offsets are combined into a two-dimensional offset vector. All two-dimensional offset vectors are arranged in spatial order according to the reference coordinate set, and finally, a deformation displacement field matrix containing the horizontal and vertical offset components is constructed (e.g., each vector in the matrix represents the displacement direction and amplitude of the corresponding pixel under pressure), satisfying the complete process of step 1012. Next, the time intervals between adjacent tank surface images in the micro-deformation time-series image set are obtained. Based on the time intervals and the deformation displacement field matrix, the basic deformation rate value is calculated (e.g., the instantaneous deformation rate of each pixel is obtained by dividing the displacement vector by the time interval), achieving the objective of step 1013. Finally, the basic deformation rate value is matched with the corresponding pressure gradient value to obtain the deformation rate under different pressure gradient values, completing the closed-loop operation of step 1014. This process lays the foundation for burst pressure prediction: the deformation displacement field matrix reveals the non-uniform deformation mode of the tank surface (e.g., the displacement vector in the weld area is significantly larger than in other areas), and the deformation rate under different pressure gradient values ​​highlights the critical failure point (e.g., the deformation rate increases sharply when the pressure gradient value reaches a certain threshold), driving engineers to optimize the tank material thickness distribution or welding process.

[0037] The scheme described in step 101 above achieves high-precision dynamic monitoring and quantitative analysis of the micro-deformation process of the fire extinguisher tank. Through multi-frame image acquisition using a high-speed camera and optical flow analysis algorithm, a two-dimensional vector field reflecting the displacement changes on the tank surface is innovatively constructed. This technology employs a reference coordinate matching and displacement component decomposition method to transform pixel-level deformation information into computable matrix data, achieving a leap from visual observation to numerical analysis. By establishing a dual correlation between time interval and pressure gradient, a dynamic response model of deformation rate as a function of pressure is established, providing precise mechanical parameter basis for structural safety assessment.

[0038] 102. Deploy a laser holographic interferometer on a pressure test bench, and dynamically adjust the laser phase parameters of the laser holographic interferometer based on the deformation rate, so that the laser holographic interferometer outputs holographic interference fringes that match the deformation rate, and obtain the phase change of the holographic interference fringes to construct a deformation evolution topology map.

[0039] Optionally, step 102 may specifically include the following steps: 1021. Fix the laser emitter and receiver of the laser holographic interferometer on the surface of the fire extinguisher tank on the pressure test bench to ensure that the laser beam of the laser holographic interferometer covers the surface of the fire extinguisher tank.

[0040] 1022. Convert the deformation rate value into the laser phase adjustment coefficient of the laser holographic interferometer, wherein the laser phase adjustment coefficient is directly proportional to the deformation rate value.

[0041] 1023. Adjust the wavelength fine-tuning knob at the laser emitting end according to the laser phase adjustment coefficient so that the wavelength change of the laser beam of the laser holographic interferometer matches the deformation rate.

[0042] 1024. Acquire the interference fringe image output by the laser holographic interferometer, simultaneously measure the change in spacing between adjacent interference fringes, and convert the change in spacing into a phase change.

[0043] 1025. Map the phase change amount to preset spatial coordinates on the surface of the fire extinguisher tank, and arrange the phase change amount according to the position of the preset spatial coordinates to form a deformation evolution topology diagram.

[0044] In the above scheme, the pressure test bench refers to the equipment used for pressure testing. The laser holographic interferometer refers to a measuring device based on the principle of laser interference. The laser phase parameter refers to the phase characteristic parameter of the laser. Holographic interference fringes refer to the fringe pattern formed by interference. The deformation evolution topology diagram refers to the topological graph reflecting the deformation evolution. The laser emitting end refers to the component that emits the laser. The laser receiving end refers to the component that receives the laser. The laser beam refers to the laser beam. The laser phase adjustment coefficient refers to the proportional parameter for adjusting the phase. The wavelength fine-tuning knob refers to the knob for adjusting the wavelength. The interference fringe image refers to the fringe image formed by interference. The spacing change refers to the change in the fringe spacing. The phase change refers to the change in the phase. The preset spatial coordinates refer to the pre-set coordinate positions.

[0045] In this embodiment, firstly, the laser transmitter and receiver of a laser holographic interferometer are fixed on the surface of the fire extinguisher tank on a pressure test bench. The equipment positioning module installs the transmitter on a bracket directly in front of the tank and adjusts the beam direction using a laser collimator to ensure that the laser beam completely covers the surface area of ​​the tank under test. The receiver calibrator synchronously adjusts the position and tilt angle of the receiver to align it with the reflected light path, and the coverage uniformity is fed back in real time by a spot monitoring sensor, forming an optical monitoring network without blind spots. This process is based on spatial optical positioning technology to ensure that the geometric relationship between the laser beam and the tank surface meets the imaging requirements of holographic interferometry.

[0046] Subsequently, the system converts the deformation rate value into the laser phase adjustment coefficient of the laser holographic interferometer. The parameter conversion engine receives the deformation rate data stream output from step 101 and establishes a direct proportional relationship between the deformation rate and the phase adjustment coefficient through a linear mapping algorithm (such as a scaling factor model): for every unit increase in the deformation rate, the phase adjustment coefficient increases synchronously by a fixed proportion. The coefficient generator outputs the dynamically adjusted laser phase adjustment coefficient to the interferometer control unit, providing a quantitative basis for wavelength adjustment. This process employs dynamic parameter coupling technology to achieve real-time correlation between material deformation and optical parameters.

[0047] Next, the system adjusts the wavelength fine-tuning knob at the laser emitter based on the laser phase adjustment coefficient. The knob controller interprets the value of the phase adjustment coefficient and drives the precision stepper motor to rotate the wavelength fine-tuning knob (physical adjustment mechanism) on the interferometer. The wavelength feedback module monitors the real-time wavelength of the laser beam through a built-in spectrometer and compares it with the target change: if the actual wavelength offset does not meet expectations, the knob is fine-tuned until it matches the wavelength change required by the deformation rate. This process relies on closed-loop wavelength control technology to ensure that the laser wavelength dynamically responds to the deformation state of the tank.

[0048] Then, the system acquires the interference fringe image output by the laser holographic interferometer and converts the phase change. The image acquisition unit uses a high-speed camera to capture the interference fringe sequence, and the fringe analysis algorithm identifies the bright and dark boundaries of adjacent fringes and calculates their spacing changes (such as pixel distance differences). The phase converter substitutes the spacing changes into the optical path difference formula and calculates the corresponding phase change in combination with the current laser wavelength. This process is based on interference fringe analysis technology, converting geometric spacing into phase information.

[0049] Finally, the system maps the phase changes onto the surface of the fire extinguisher tank to form a deformation evolution topology map. The spatial mapping engine, based on a pre-defined spatial coordinate grid on the tank surface (such as coordinate points divided in a 3D CAD model), associates the phase change corresponding to each coordinate point with the appropriate location using a coordinate binding algorithm. The topology synthesizer arranges all phase changes in coordinate order, generating a 2D / 3D deformation evolution topology map where color depth represents deformation intensity. This process employs phase-deformation visualization technology to visually present the global deformation gradient distribution of the tank.

[0050] In practical applications, during the water pressure burst test of a fire extinguisher tank, on a pressurized test bench, the laser transmitter and receiver of a laser holographic interferometer are first fixed to the surface of the fire extinguisher tank (e.g., the transmitter is mounted on a bracket on top of the tank, and the receiver is placed in a side observation window) to ensure that the laser beam of the laser holographic interferometer covers the surface of the fire extinguisher tank (step 1021). Then, based on the deformation rate value obtained in step 101 (e.g., the change in the deformation rate of the tank surface as the pressure gradient increases), the deformation rate value is converted into the laser phase adjustment coefficient of the laser holographic interferometer (this coefficient is directly proportional to the deformation rate value and is used to dynamically match the optical response) (step 1022). Next, the wavelength fine-tuning knob of the laser transmitter is adjusted according to the laser phase adjustment coefficient (e.g., by fine-tuning the laser cavity mirror spacing using a piezoelectric ceramic driver) to match the wavelength change of the laser beam of the laser holographic interferometer with the deformation rate (step 1023). During pressurization, the system acquires interference fringe images output by a laser holographic interferometer (e.g., real-time capture of the interferogram on the tank surface), and simultaneously measures the change in spacing between adjacent interference fringes (e.g., calculating the pixel displacement of the fringe spacing using image processing algorithms), converting the spacing change into a phase change (step 1024). Finally, the phase change is mapped to preset spatial coordinates on the surface of the fire extinguisher tank (e.g., dividing the tank surface into a grid coordinate system), and the phase changes are arranged according to their preset spatial coordinates to form a deformation evolution topology (step 1025). This topology dynamically presents the failure mechanism: the deformation evolution topology displays the plastic deformation process of the tank surface in the form of spatial gradients (e.g., dense distribution of phase changes in the weld area indicates stress concentration), and the mapping relationship between the spacing change of the interference fringes and the phase change accurately quantifies the degree of local deformation, providing a visual basis for predicting the critical burst pressure.

[0051] The scheme described in step 102 above achieves holographic visualization and dynamic control of the deformation evolution process. Through an intelligent matching mechanism between a laser holographic interferometer and the deformation rate, an innovative interference fringe pattern reflecting the deformation characteristics of the tank surface is constructed. This technology employs a dynamic phase parameter adjustment algorithm to ensure that the interference fringes always clearly reflect the current deformation state, overcoming the limitations of traditional static interferometry. The innovative fringe spacing-phase conversion method achieves accurate conversion of optical signals into deformation data, and the generated deformation evolution topology map intuitively presents the three-dimensional deformation distribution law of the tank surface.

[0052] 103. Identify the abnormal fluctuation patterns of deformation rate in the deformation evolution topology graph, and associate the abnormal fluctuation patterns with the pressure gradient values ​​to generate a deformation-pressure correlation tensor.

[0053] Optionally, step 103 may specifically include the following steps: 1031. Extract the curve data of the deformation rate of each spatial coordinate point in the deformation evolution topology map as a function of time, and calculate the change in deformation rate at adjacent time points in the curve data as the fluctuation intensity value.

[0054] 1032. When the fluctuation intensity value continuously exceeds the preset fluctuation threshold, mark the spatial coordinate point at the fluctuation intensity value as the location where the abnormal deformation rate fluctuation mode occurs.

[0055] 1033. Obtain the time interval of the location where the abnormal fluctuation pattern occurs, and extract the pressure gradient value of the corresponding time interval.

[0056] 1034. Combine the location of the abnormal fluctuation pattern with the corresponding pressure gradient value into a two-dimensional data unit.

[0057] 1035. Arrange the two-dimensional data units according to the coordinates of a preset spatial grid to form a deformation-pressure correlation tensor.

[0058] In the above scheme, the abnormal deformation rate fluctuation pattern refers to the pattern of abnormal changes in the deformation rate. The deformation-pressure correlation tensor is a tensor reflecting the relationship between deformation and pressure. Curve data refers to deformation rate data that changes over time. The fluctuation intensity value refers to the intensity of the change in the deformation rate. The preset fluctuation threshold is the critical value for judging abnormal fluctuations. The occurrence location refers to the location of the abnormal fluctuation. The time interval refers to the time range of the abnormal fluctuation. A two-dimensional data unit refers to a data unit containing two dimensions. The preset spatial grid refers to a pre-divided spatial grid.

[0059] In this embodiment, the system first extracts the deformation rate curve data of each spatial coordinate point in the deformation evolution topology map as a function of time: the data parsing engine reads the deformation evolution topology map (containing the spatiotemporal distribution of deformation rate at various points on the tank surface) generated in step 102, and extracts the deformation rate numerical sequence of each spatial coordinate point (e.g., three-dimensional grid coordinates [x, y, z]) in chronological order to generate curve data (i.e., time-deformation rate relationship curve). The fluctuation calculation module traverses this curve data and calculates adjacent time points (e.g., t). n With t n The absolute value of the difference in deformation rate ₊1) is used as the fluctuation intensity value (quantifying the degree of instantaneous deformation abrupt change). This process is based on the time-series difference algorithm, which transforms the continuous deformation rate into a discrete fluctuation index, providing basic data for anomaly detection.

[0060] Subsequently, the system detects locations where the fluctuation intensity value continuously exceeds a preset fluctuation threshold and marks them as locations where abnormal fluctuation patterns occur: the threshold comparator compares the fluctuation intensity value sequence of each spatial coordinate point with the preset fluctuation threshold (an empirically set critical value for deformation abrupt change). The pattern recognizer monitors the number of fluctuations that continuously exceed the threshold: if the fluctuation intensity value of a coordinate point exceeds the limit three times consecutively (configurable), then that point is marked as the location of an abnormal deformation rate fluctuation pattern (such as the initiation point of a local crack). This process uses a sliding window detection technique to ensure that brief, drastic deformations are not misjudged, and only persistent anomalies are captured.

[0061] Next, the system obtains the time interval of the location where the abnormal fluctuation pattern occurred and associates it with the corresponding pressure gradient value: the time positioning module traces back the time interval from when the fluctuation intensity value first exceeded the limit to when it returned to normal, based on the marked location. The pressure synchronizer extracts pressure data that completely overlaps with this time interval from the pressure gradient value time series database recorded in step 101, and obtains the pressure gradient value of the corresponding time interval. This process relies on precise timestamp matching technology to ensure the causal relationship between deformation anomalies and pressure loads.

[0062] Then, the system combines the location of the abnormal fluctuation pattern with the pressure gradient value into a two-dimensional data unit: the data encapsulator binds the spatial coordinates (which can be [x, y, z]) of each location with its corresponding pressure gradient value sequence (which can be {p1, p2, p3}), generating a two-dimensional data unit (structure example: [x, y, z | p1, p2, p3]). The unit validator verifies the uniqueness of the mapping between spatial coordinates and pressure data, avoiding duplicate bindings. This process achieves precise coupling between spatial entities and physical quantities through key-value pair storage technology.

[0063] Finally, the system arranges two-dimensional data cells according to preset spatial grid coordinates to form a deformation-pressure correlation tensor: the grid mapper assigns each two-dimensional data cell to its corresponding grid according to its coordinates based on the preset spatial grid on the tank surface. The tensor synthesizer arranges all cells in the order of grid rows and columns to construct a three-dimensional deformation-pressure correlation tensor (dimension example: spatial grid X × spatial grid Y × pressure gradient sequence). This process is based on sparse tensor compression technology, which efficiently stores global correlation data and forms a three-dimensional mapping relationship of "location-deformation anomaly-pressure load".

[0064] In practical applications, in the scenario of strength testing of the circumferential weld of a fire extinguisher tank, after obtaining the deformation evolution topology map generated in step 102 (such as showing the spatial distribution of phase change on the tank surface), the curve data of the deformation rate changing with time at each spatial coordinate point in the deformation evolution topology map is first extracted (for example, the deformation rate change curve of coordinate point C in the circumferential weld area of ​​the tank during the pressurization process). The change in deformation rate at adjacent time points in the curve data is calculated as the fluctuation intensity value. When the fluctuation intensity value of a certain coordinate point continuously exceeds the preset fluctuation threshold (such as the fluctuation intensity value continuously exceeding the standard at three consecutive time points), the fluctuation intensity value is further tested. The system marks the spatial coordinates of the fluctuation intensity value as the location of the abnormal deformation rate fluctuation mode (e.g., coordinate point D at the junction of the circumferential weld and the cylinder). Then, it acquires the time interval of the abnormal fluctuation mode's location and extracts the pressure gradient value for that time interval. Next, it combines the location of the abnormal fluctuation mode with the corresponding pressure gradient value into a two-dimensional data unit (e.g., coordinate point D + specific pressure gradient value). Finally, it arranges the two-dimensional data units according to a preset spatial grid (e.g., dividing the tank surface into a polar coordinate grid with circumferential angle and axial distance as coordinate axes) to form a deformation-pressure correlation tensor. This tensor maps failure risk through spatial structure: the deformation-pressure correlation tensor presents the coupling relationship between the abnormal fluctuation mode and the pressure gradient in the circumferential weld region in matrix form. Continuous exceedances of the fluctuation intensity value directly expose material fatigue or welding defects (e.g., incomplete weld fusion leading to a sudden change in local deformation rate), providing a quantitative basis for directional reinforcement processes.

[0065] The scheme described in step 103 above achieves intelligent correlation analysis between deformation anomaly modes and pressure loads. Through continuous detection and threshold determination of spatiotemporal fluctuation characteristics, abnormal fluctuation regions in the deformation evolution process are accurately identified. This innovative two-dimensional data unit construction method organically integrates spatial location information and pressure parameters, forming a deformation-pressure correlation tensor with clear physical meaning. This multi-parameter fusion data structure provides a complete data foundation encompassing spatiotemporal characteristics and mechanical responses for subsequent deformation propagation analysis.

[0066] 104. Obtain the deformation propagation path and stress concentration trend in the deformation evolution topology diagram based on the deformation pressure correlation tensor, and simulate the predicted results of the critical rupture pressure value and potential rupture location of the fire extinguisher tank based on the deformation propagation path and stress concentration trend.

[0067] Optionally, step 104 may specifically include the following steps: 1041. Obtain the pressure value and deformation rate at each spatial location point in the deformation-pressure correlation tensor, calculate the pressure value increment and deformation rate increment, and use the ratio of the pressure value increment to the deformation rate increment as the stress sensitivity coefficient.

[0068] 1042. Connect adjacent spatial locations where the stress sensitivity coefficient exceeds a preset threshold in the deformation evolution topology diagram to form a spatial location point sequence of the deformation propagation path.

[0069] 1043. Calculate the cluster density of the spatial location points of the stress sensitivity coefficient in the deformation-pressure correlation tensor, and mark the region where the cluster density exceeds a preset critical value as the stress concentration trend region.

[0070] 1044. Select the peak point of the stress sensitivity coefficient within the stress concentration trend region, and use the pressure value of the peak point as a benchmark to calculate the critical rupture pressure value.

[0071] 1045. Integrate the spatial location sequence of the deformation propagation path with the spatial coordinates of the stress concentration trend area to output a prediction result that includes the critical rupture pressure value and the potential rupture location.

[0072] In the above scheme, the deformation propagation path refers to the path of deformation propagation in space. Stress concentration trend refers to the tendency of stress to accumulate. Critical rupture pressure value refers to the critical pressure that leads to rupture. Potential rupture location refers to the location where rupture may occur. Pressure increment refers to the change in pressure. Deformation rate increment refers to the change in deformation rate. Stress sensitivity coefficient refers to the degree of sensitivity of stress to deformation rate. Preset threshold refers to the critical value for judging stress sensitivity. Spatial location point sequence refers to location points arranged in spatial order. Concentration density refers to the density of stress-sensitive points. Preset critical value refers to the critical value for judging stress concentration. Peak point refers to the highest point of the stress sensitivity coefficient. Spatial coordinates refer to the spatial identifier of the location. Prediction result refers to the prediction of the rupture situation.

[0073] In this embodiment, the system first acquires the pressure value and deformation rate at each spatial location point in the deformation-pressure correlation tensor: the data extraction module traverses the deformation-pressure correlation tensor (containing correlation data of spatial coordinate points, pressure gradient values, and deformation rates) generated in step 103, and reads the pressure gradient value sequence and the corresponding deformation rate sequence for each spatial location point. The incremental calculator calculates the difference between adjacent pressure gradient values ​​as the pressure value increment using a difference algorithm, and simultaneously calculates the difference between adjacent deformation rates as the deformation rate increment. The coefficient generator uses the ratio of the pressure value increment to the deformation rate increment as the stress sensitivity coefficient (quantifying the amount of pressure change required per unit deformation rate change), which reflects the local sensitivity of the material to pressure changes.

[0074] Subsequently, the system connects adjacent spatial points in the deformation evolution topology map where the stress sensitivity coefficient exceeds a preset threshold: a threshold filter compares the stress sensitivity coefficient of each spatial point with the preset threshold (an empirically set sensitivity critical value) and marks all points exceeding the limit. The path construction algorithm, based on the spatial grid relationship of the deformation evolution topology map, searches for adjacent marked points (such as adjacent points in the up, down, left, right, front, and back directions), and connects these points in spatial order using graph theory connectivity analysis to generate a sequence of spatial location points for the deformation propagation path (such as a path chain along the tank surface from point A to point B). This process uses neighborhood traversal technology to ensure the spatial continuity of the path.

[0075] Next, the system statistically analyzes the clustering density of spatial locations of stress sensitivity coefficients in the deformation-pressure correlation tensor: the density analyzer divides the tank surface into grid cells, calculates the ratio of the number of stress sensitivity coefficient exceeding the limit points to the cell area within each cell, and generates a clustering density distribution map. The region labeling module merges grid cells with clustering densities exceeding a preset critical value (e.g., the number of exceeding the limit points ≥ 5 per unit area) into continuous blocks, marking them as stress concentration trend regions (e.g., the annular high-density area at the waist of the tank). This process uses a spatial clustering algorithm to identify high-risk areas for material failure.

[0076] Then, the system selects the peak point of the stress sensitivity coefficient within the stress concentration trend region: the peak locator scans the stress sensitivity coefficient of all points within each stress concentration trend region and identifies the local maximum point as the peak point (e.g., the coefficient value is highest at the center of a certain region). The pressure reference reads the real-time pressure value corresponding to this peak point as the reference. The fracture calculation module, based on the linear elastic fracture mechanics theory, converts the pressure value at this point into a critical load threshold through the critical stress intensity factor model (combined with material fracture toughness parameters), and outputs the critical fracture pressure value (e.g., peak point pressure × safety factor). This process uses the brittle fracture criterion to quantify the pressure safety boundary.

[0077] Finally, the system integrates the spatial coordinates of the deformation propagation path and the stress concentration trend area: the data fusion engine overlays the spatial location point sequence of the deformation propagation path (output of step 1042) and the spatial boundary coordinates of the stress concentration trend area (output of step 1043) onto the same three-dimensional coordinate system. The prediction generator associates the critical rupture pressure value (output of step 1044) with its corresponding spatial location to generate prediction results containing two types of information: one is a distribution map of the critical rupture pressure value marked with contour lines, and the other is a thermal point marker of potential rupture locations (such as path intersections or the center of high-density areas). This process uses spatial data fusion technology to output a visualized diagnostic report.

[0078] In practical applications, in the scenario of assessing the structural strength of the circumferential weld seam of a fire extinguisher cylinder, based on the deformation-pressure correlation tensor generated in step 103 (such as containing the pressure gradient values ​​and deformation rates of each spatial location point in the circumferential weld seam region), the pressure value and deformation rate of each spatial location point in the deformation-pressure correlation tensor are first obtained (e.g., the pressure value sequence and corresponding deformation rate curve of coordinate point E at the connection between the cylinder and the head). The pressure value increment and deformation rate increment (i.e., the change in deformation rate under adjacent pressure gradients) are calculated, and the ratio of the pressure value increment to the deformation rate increment is used as the stress sensitivity coefficient (step 1041). Subsequently, adjacent spatial location points with stress sensitivity coefficients exceeding a preset threshold are connected in the deformation evolution topology diagram to form a spatial location point sequence of the deformation propagation path (step 1042). Simultaneously, the cluster density of spatial location points of stress sensitivity coefficient in the deformation-pressure correlation tensor is statistically analyzed (e.g., the number of outliers per square centimeter is calculated using a polar coordinate grid). Regions with cluster density exceeding a preset critical value are marked as stress concentration trend regions (e.g., the annular band at the end of the longitudinal weld of the cylinder) (step 1043). Then, the peak point of the stress sensitivity coefficient is selected within the stress concentration trend region, and the pressure value of the peak point is used as a reference to calculate the critical rupture pressure value (step 1044). Finally, the spatial location point sequence of the deformation propagation path is integrated with the spatial coordinates of the stress concentration trend region (e.g., the vector direction of the deformation propagation path is superimposed with the contour distribution of the stress concentration region), and the prediction results containing the critical rupture pressure value and potential rupture location are output (step 1045). The prediction results accurately pinpoint failure risks: the deformation propagation path reveals the direction of plastic deformation expansion in the heat-affected zone of the weld (such as the path sequence extending from the center of the circumferential weld to the base material), the stress concentration trend area exposes the aggregation effect of micro-defects in the material (such as local high-density anomalies caused by incomplete welding fusion), the critical rupture pressure value provides a quantitative boundary for engineering safety margin design (such as triggering process optimization when the predicted value is lower than the standard minimum burst pressure), and the potential rupture location directly guides the key detection areas of non-destructive testing (such as the high-risk grid around the focus coordinate point F).

[0079] The solution described in step 104 above achieves accurate prediction and location of the risk of fire extinguisher tank rupture. Based on a spatial analysis algorithm using the stress sensitivity coefficient, it innovatively reveals the intrinsic correlation between deformation propagation paths and stress concentration trends. This technology scientifically predicts the theoretical rupture pressure threshold of the tank through peak pressure extraction and critical value calculation. An innovative spatial coordinate integration method achieves precise three-dimensional location of potential rupture sites, providing clear technical guidance for product structure optimization. This technical approach, combining mechanical analysis with spatial positioning, significantly improves the accuracy and practicality of safety assessments.

[0080] 105. Integrate the predicted results of the critical rupture pressure value and rupture location to form a burst pressure risk output.

[0081] Optionally, step 105 may specifically include the following steps: 1051. Read the specific numerical calculation results of the critical rupture pressure value and simultaneously obtain the three-dimensional spatial coordinate data of the potential rupture location.

[0082] 1052. Obtain the rated pressure value of the fire extinguisher tank design, and calculate the safety margin ratio coefficient as the benchmark value for risk level classification based on the rated pressure value and the critical rupture pressure value.

[0083] 1053. Determine the category of the blasting risk level identifier based on the aforementioned risk level classification benchmark value.

[0084] 1054. Map the spatial coordinates of the potential rupture location to the surface of the preset three-dimensional model of the fire extinguisher tank to generate spatial markers for the rupture location.

[0085] 1055. Integrate the critical rupture pressure value, rupture risk level identifier, and rupture location spatial marker to generate a rupture pressure risk report.

[0086] In the above scheme, blast pressure risk output refers to the result of assessing blast risk. Rated pressure value refers to the standard design pressure value. Safety margin ratio coefficient refers to the proportion of safety margin. Risk level classification benchmark value refers to the benchmark for classifying risk levels. Blasting risk level identifier refers to the symbol that identifies the risk level. Three-dimensional spatial coordinate data refers to coordinate data in three-dimensional space. Pre-set three-dimensional model refers to a pre-established three-dimensional model. Fracture location spatial marker point refers to the point that identifies the fracture location. Blasting pressure risk report refers to a report containing risk information.

[0087] In this embodiment, the system first reads the specific numerical calculation result of the critical rupture pressure value: the data extraction module obtains the critical rupture pressure value (i.e., the theoretical pressure threshold for the failure of the fire extinguisher tank material) generated by the rupture calculation module from the prediction result output in step 104, and simultaneously calls the spatial coordinate resolver to extract the three-dimensional spatial coordinate data (such as [x,y,z] values ​​in the Cartesian coordinate system) of the potential rupture location in the same prediction result. This process achieves automatic access to the prediction result database through structured data interface technology, ensuring the spatiotemporal consistency of pressure values ​​and location data.

[0088] Subsequently, the system obtains the rated pressure value of the fire extinguisher tank design and calculates the safety margin ratio coefficient: the parameter call interface extracts the preset rated pressure value (the manufacturer-guaranteed upper limit of safe operating pressure) from the fire extinguisher design specifications. The margin calculation engine inputs the rated pressure value and the critical rupture pressure value into the ratio formula, and the calculation result serves as the benchmark value for risk level classification, quantifying the deviation between the actual pressure-bearing capacity of the tank and the design standard. This process is based on the engineering safety margin model, converting the absolute pressure difference into a relative risk indicator in percentage form.

[0089] Next, the system determines the category of the blasting risk level identifier based on the risk level classification benchmark value: the risk classifier compares the calculated risk level classification benchmark value with a preset threshold range: if the benchmark value ≥ the first preset threshold, a low-risk identifier (such as a green label) is output; if the second preset value ≤ the benchmark value < the first preset value, a medium-risk identifier (such as a yellow label) is output; if the benchmark value < the second preset value, a high-risk identifier (such as a red label) is output. This process uses a threshold range matching algorithm to map continuous values ​​to discrete risk level categories.

[0090] Then, the system maps the spatial coordinates of the potential rupture location onto the surface of a pre-defined 3D model of the fire extinguisher tank: the coordinate converter aligns the 3D spatial coordinate data (based on the laboratory coordinate system) obtained in step 1051 to the global coordinate system of the pre-defined 3D model of the tank using an affine transformation algorithm. The marker generator adds highlighted symbols (such as flashing triangle icons) at the corresponding positions on the model surface, forming spatial marker points for the rupture location, which are then visualized in real time by the 3D rendering engine. This process utilizes computer-aided design (CAD) integration technology to achieve precise overlay of physical coordinates and digital models.

[0091] Finally, the system integrates the critical rupture pressure value, risk level identifier, and rupture location markers to generate a rupture pressure risk report: The report synthesis engine embeds the critical rupture pressure value, rupture risk level identifier, and spatial markers of the rupture location output from the previous steps into a standardized template. The data binding module associates the markers with the pressure value data table to generate a rupture pressure risk report containing three core parts: Pressure Safety Summary: A list displaying the critical rupture pressure value and its corresponding risk level identifier; 3D Risk Map: An embedded tank model with spatial markers; Maintenance Recommendations: Recommended inspection cycles or reinforcement measures based on the risk level (e.g., high-risk markers require immediate discontinuation). This process outputs a structured document based on an automated report generation framework, supporting PDF or interactive interface display.

[0092] In practical applications, in the scenario of detecting defects in the circumferential weld of a fire extinguisher cylinder, based on the critical rupture pressure value and potential rupture location (such as the predicted value of coordinate point G of the heat-affected zone of the circumferential weld) output in step 104, the specific numerical calculation result of the critical rupture pressure value is first read (such as the failure threshold simulated by deformation propagation path and stress concentration trend), and the three-dimensional spatial coordinate data of the potential rupture location is simultaneously acquired (step 1051); then, the rated pressure value of the fire extinguisher cylinder design (such as the maximum working pressure specified at the factory) is acquired, and the safety margin ratio coefficient is calculated based on the rated pressure value and the critical rupture pressure value as the benchmark value for risk level classification (this coefficient reflects the actual pressure bearing capacity). Step 1052: The ratio of the difference between the force and the design threshold. Then, the category of the rupture risk level identifier is determined according to the risk level classification benchmark value (e.g., the benchmark value is divided into three levels: low risk, medium risk, and high risk, corresponding to green, yellow, and red identifiers, respectively) (Step 1053). At the same time, the spatial coordinates of the potential rupture location are mapped to the surface of the preset three-dimensional model of the fire extinguisher tank to generate spatial markers of the rupture location (Step 1054). Finally, the critical rupture pressure value, the rupture risk level identifier, and the spatial markers of the rupture location (e.g., the red high-risk identifier is associated with the circumferential weld marker) are integrated to generate a rupture pressure risk report (Step 1055). The report achieves a closed-loop risk visualization: the safety margin ratio coefficient quantifies the actual safety boundary of the tank (e.g., a low coefficient value triggers a process optimization warning), the explosion risk level identifier intuitively distinguishes the degree of risk through color coding (e.g., a red identifier marks a defect area that needs urgent treatment), the spatial markers of the rupture location accurately locate the source of failure in the 3D model (e.g., a cluster of markers exposes a weld incomplete fusion defect zone), and the explosion pressure risk report is simultaneously output to the quality management system and the production line interception module, forming an engineering prevention and control chain of "prediction-location-disposal".

[0093] The solution described in step 105 above enables a multi-dimensional comprehensive assessment of blasting risks and the generation of a visualized report. Through the scientific calculation of safety margin coefficients, an objective risk level classification system is established. The innovative three-dimensional model mapping algorithm transforms abstract prediction results into intuitive spatial markers. The generated blasting pressure risk report integrates numerical analysis, level assessment, and spatial positioning information, forming a complete risk assessment system covering both quantitative calculation and qualitative analysis, providing comprehensive decision support for product quality control and safe production.

[0094] The following are specific examples of steps 101 to 105: In the scenario of strength testing of the circumferential weld of a carbon steel fire extinguisher cylinder, during the process of applying increasing pressure to the surface of the fire extinguisher cylinder in a high-pressure test environment, a high-speed camera device is set up to continuously capture images of the cylinder surface to form a micro-deformation time sequence image set, and the pressure gradient value collected by the pressure sensor is recorded simultaneously; based on the micro-deformation time sequence image set, the first frame image is selected as the reference frame and the initial coordinate positions of all pixels are extracted to form a reference coordinate set. The horizontal and vertical offsets of pixels at the same position in subsequent image frames relative to the reference coordinates are calculated, combined into a two-dimensional offset vector, and integrated into a deformation displacement field matrix; the basic value of the deformation rate is calculated according to the time interval between adjacent image frames and the deformation displacement field matrix, and this value is matched with the corresponding pressure gradient value to obtain the deformation rate under different pressure gradients. A laser holographic interferometer is deployed on a pressure test bench, with its laser emitter and receiver fixed to the surface of the cylinder. The deformation rate value is converted into a laser phase adjustment coefficient (which is directly proportional to the deformation rate value). Based on this, the wavelength fine-tuning knob of the laser emitter is adjusted to match the laser wavelength change with the deformation rate. The output interference fringe image is acquired, and the change in the spacing between adjacent fringes is measured and converted into a phase change. This change is then mapped to the preset spatial coordinates on the surface of the tank and arranged to form a deformation evolution topology map.

[0095] The deformation rate curve data of each spatial coordinate point in the deformation evolution topology map is extracted as a function of time. The change in deformation rate at adjacent time points is calculated as the fluctuation intensity value. When the fluctuation intensity value continuously exceeds a preset fluctuation threshold, the spatial coordinate point at that location is marked as the occurrence location of the abnormal deformation rate fluctuation mode. The time interval of this occurrence location is obtained, and the pressure gradient value of the corresponding interval is extracted. The occurrence location and pressure gradient value are combined into a two-dimensional data unit, and the coordinates are arranged according to a preset spatial grid to generate a deformation-pressure correlation tensor. Based on this tensor, the pressure value and deformation rate of each spatial location point are obtained, and the ratio of the pressure value increment to the deformation rate increment is calculated as the stress sensitivity coefficient. Adjacent spatial location points with stress sensitivity coefficients exceeding the preset threshold are connected in the deformation evolution topology map to form a spatial location point sequence of the deformation propagation path. The aggregation density of stress sensitivity coefficient points is statistically analyzed, and areas with density exceeding a preset critical value are marked as stress concentration trend areas. The peak point of the stress sensitivity coefficient in this area is selected, and the critical rupture pressure value is calculated based on its pressure value. The spatial location point sequence of the deformation propagation path and the spatial coordinates of the stress concentration trend area are integrated to output the prediction results containing the critical rupture pressure value and the potential rupture location.

[0096] The system reads the specific numerical calculation results of the critical rupture pressure value and simultaneously acquires the three-dimensional spatial coordinate data of the potential rupture location. It obtains the rated pressure value designed for the fire extinguisher tank and calculates a safety margin ratio coefficient based on this value and the critical rupture pressure value as a benchmark for risk level classification. Based on this, it determines the category of the blast risk level identifier (e.g., low / medium / high risk). The spatial coordinates of the potential rupture location are mapped onto the surface of a pre-set three-dimensional model of the tank to generate spatial markers for the rupture location. Finally, it integrates the critical rupture pressure value, the blast risk level identifier, and the spatial markers for the rupture location to generate a blast pressure risk report. This report reveals the direction of plastic deformation extension in the weld heat-affected zone through deformation propagation paths (e.g., the path sequence extending from the center of the circumferential weld to the base material), exposes the aggregation effect of weld non-fusion defects in stress concentration trend areas, precisely locates high-risk grids using spatial markers for the rupture location, and uses the safety margin ratio coefficient to drive the production line to automatically intercept tanks with low safety margins, forming a closed-loop quality control system of "deformation monitoring - risk quantification - defect location".

[0097] Figure 2 This application provides a schematic diagram of the structure of a fire extinguisher tank burst pressure prediction system, as shown in the embodiment. Figure 2 As shown, the system includes: The acquisition module 21 is used to acquire the micro-deformation time sequence and pressure gradient value of the surface of the fire extinguisher tank during pressurization in a high-pressure test environment, process the micro-deformation time sequence through optical flow analysis to generate a deformation displacement field matrix, and use the deformation displacement field matrix to quantify the deformation rate under different pressure gradient values. The adjustment module 22 is used to deploy a laser holographic interferometer on the pressure test bench and dynamically adjust the laser phase parameters of the laser holographic interferometer based on the deformation rate, so that the laser holographic interferometer outputs holographic interference fringes that match the deformation rate, and obtains the phase change of the holographic interference fringes to construct a deformation evolution topology map. The association module 23 is used to identify abnormal fluctuation patterns of deformation rate in the deformation evolution topology graph and associate the abnormal fluctuation patterns with pressure gradient values ​​to generate a deformation-pressure correlation tensor. The simulation module 24 is used to obtain the deformation propagation path and stress concentration trend in the deformation evolution topology diagram based on the deformation pressure correlation tensor, and to simulate the critical rupture pressure value and potential rupture location prediction results of the fire extinguisher tank based on the deformation propagation path and stress concentration trend. Integration module 25 is used to integrate the predicted results of the critical rupture pressure value and rupture location to form a burst pressure risk output.

[0098] Figure 2 The aforementioned fire extinguisher tank burst pressure prediction system can perform... Figure 1 The implementation principle and technical effects of the fire extinguisher tank burst pressure prediction method described in the illustrated embodiment will not be repeated here. The specific operation methods of each module and unit in the fire extinguisher tank burst pressure prediction system described in the above embodiments have been described in detail in the embodiments related to this method, and will not be elaborated upon here.

[0099] In one possible design, Figure 2 The fire extinguisher tank burst pressure prediction system of the embodiment shown can be implemented as a computing device, such as... Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32; The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 32.

[0100] The processing component 32 is used for the above Figure 1 The method for predicting the burst pressure of a fire extinguisher tank as described in this embodiment.

[0101] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above-described method. Alternatively, the processing component may be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0102] Storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0103] Of course, computing devices may also include other components, such as input / output interfaces, display components, communication components, etc.

[0104] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices, input devices, etc.

[0105] The communication components are configured to facilitate wired or wireless communication between computing devices and other devices.

[0106] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server, and the aforementioned processing components, storage components, etc., can be basic server resources rented or purchased from the cloud computing platform.

[0107] This application also provides a computer storage medium storing a computer program, which, when executed by a computer, can perform the above-described functions. Figure 1 The embodiment shown illustrates a method for predicting the burst pressure of a fire extinguisher tank.

[0108] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0109] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0110] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0111] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for predicting the burst pressure of a fire extinguisher tank, characterized in that, include: In a high-pressure testing environment, the micro-deformation time sequence and pressure gradient value of the surface of the fire extinguisher tank during pressurization are collected. The micro-deformation time sequence is processed by optical flow analysis to generate a deformation displacement field matrix, and the deformation displacement field matrix is ​​used to quantify the deformation rate under different pressure gradient values. A laser holographic interferometer is deployed on a pressure test bench, and the laser phase parameters of the laser holographic interferometer are dynamically adjusted based on the deformation rate, so that the laser holographic interferometer outputs holographic interference fringes that match the deformation rate, and the phase change of the holographic interference fringes is obtained to construct a deformation evolution topology map. Identify abnormal fluctuation patterns of deformation rate in the deformation evolution topology graph and correlate the abnormal fluctuation patterns with pressure gradient values ​​to generate a deformation-pressure correlation tensor. The deformation propagation path and stress concentration trend in the deformation evolution topology are obtained based on the deformation pressure correlation tensor, and the critical rupture pressure value and potential rupture location of the fire extinguisher tank are simulated based on the deformation propagation path and stress concentration trend. The predicted critical rupture pressure value and rupture location are integrated to form a burst pressure risk output.

2. The method according to claim 1, characterized in that, In a high-pressure testing environment, the micro-deformation time sequence and pressure gradient values ​​of the fire extinguisher tank surface are collected during pressurization. The micro-deformation time sequence is processed by optical flow analysis to generate a deformation displacement field matrix. This matrix is ​​then used to quantify the deformation rate under different pressure gradient values, including: During the process of applying incremental pressure to the surface of the fire extinguisher tank in a high-pressure test environment, a high-speed camera device is set up to continuously capture images of the tank surface to form a micro-deformation time sequence image set, and the pressure gradient value collected by the set pressure sensor is recorded simultaneously. Obtain the pixel positions of the tank surface images in the micro-deformation time-series image set, and calculate the coordinate offset of the pixel positions in the micro-deformation time-series image set as the deformation displacement field matrix; The time interval between adjacent tank surface images in the micro-deformation time sequence image set is obtained, and the basic value of deformation rate is calculated based on the time interval and the deformation displacement field matrix. The basic value of the deformation rate is matched with the corresponding pressure gradient value to obtain the deformation rate under different pressure gradient values.

3. The method according to claim 2, characterized in that, Obtain the pixel positions of the tank surface images in the micro-deformation time-series image set, and calculate the coordinate offset of the pixel positions in the micro-deformation time-series image set as the deformation displacement field matrix, including: The first frame of the micro-deformation time sequence image set is selected as the reference frame, and the initial coordinate positions of all pixels on the surface of the tank in the reference frame are extracted to form a reference coordinate set. In the image frames of the micro-deformation time-series image set, match the pixels at the same positions as the reference coordinate set; Obtain the coordinate position of the pixel in the image frame, calculate the difference between the coordinate position and the corresponding coordinate in the reference coordinate set, and use the horizontal component of the difference as the horizontal offset and the vertical component as the vertical offset. The horizontal and vertical offsets are combined into a two-dimensional offset vector, and the two-dimensional offset vector is arranged in the spatial order of the reference coordinate set. Integrate the two-dimensional offset vectors of all pixels to construct a deformable displacement field matrix containing horizontal and vertical offset components.

4. The method according to claim 1, characterized in that, A laser holographic interferometer is deployed on a pressure test bench, and the laser phase parameters of the laser holographic interferometer are dynamically adjusted based on the deformation rate, so that the laser holographic interferometer outputs holographic interference fringes that match the deformation rate. The phase changes of the holographic interference fringes are then acquired to construct a deformation evolution topology map, including: The laser emitter and receiver of a laser holographic interferometer are fixed on the surface of the fire extinguisher tank on the pressure test bench to ensure that the laser beam of the laser holographic interferometer covers the surface of the fire extinguisher tank. The deformation rate value is converted into the laser phase adjustment coefficient of the laser holographic interferometer, and the laser phase adjustment coefficient is directly proportional to the deformation rate value. Adjust the wavelength fine-tuning knob at the laser emitter according to the laser phase adjustment coefficient so that the wavelength change of the laser beam of the laser holographic interferometer matches the deformation rate. The interference fringe image output by the laser holographic interferometer is acquired, and the spacing change between adjacent interference fringes is measured simultaneously, and the spacing change is converted into a phase change. The phase change is mapped to a preset spatial coordinate on the surface of the fire extinguisher tank, and the phase change is arranged according to the position of the preset spatial coordinate to form a deformation evolution topology diagram.

5. The method according to claim 1, characterized in that, Identifying anomalous fluctuation patterns of deformation rate in the deformation evolution topology graph and correlating these anomalous fluctuation patterns with pressure gradient values ​​to generate a deformation-pressure correlation tensor includes: Extract the curve data of deformation rate changing with time for each spatial coordinate point in the deformation evolution topology diagram, and calculate the change in deformation rate between adjacent time points in the curve data as the fluctuation intensity value. When the fluctuation intensity value continuously exceeds the preset fluctuation threshold, the spatial coordinate point at the fluctuation intensity value is marked as the location where the abnormal deformation rate fluctuation mode occurs; Obtain the time interval of the location where the abnormal fluctuation pattern occurs, and extract the pressure gradient value of the corresponding time interval; The location of the abnormal fluctuation pattern and the corresponding pressure gradient value are combined into a two-dimensional data unit; The two-dimensional data units are arranged according to the coordinates of a preset spatial grid to form a deformation-pressure correlation tensor.

6. The method according to claim 1, characterized in that, Based on the deformation-pressure correlation tensor, the deformation propagation path and stress concentration trend in the deformation evolution topology are obtained. Based on the deformation propagation path and stress concentration trend, the predicted results of the critical rupture pressure value and potential rupture location of the fire extinguisher tank are simulated, including: The pressure value and deformation rate at each spatial location point in the deformation-pressure correlation tensor are obtained to calculate the pressure value increment and deformation rate increment, and the ratio of the pressure value increment to the deformation rate increment is used as the stress sensitivity coefficient. Connect adjacent spatial locations whose stress sensitivity coefficient exceeds a preset threshold in the deformation evolution topology diagram to form a sequence of spatial locations for the deformation propagation path; The clustering density of stress sensitivity coefficient spatial locations in the deformation-pressure correlation tensor is statistically analyzed, and regions where the clustering density exceeds a preset critical value are marked as stress concentration trend regions. The peak point of the stress sensitivity coefficient is selected within the stress concentration trend region, and the pressure value of the peak point is obtained as a reference to calculate the critical rupture pressure value. The spatial location sequence of the deformation propagation path is integrated with the spatial coordinates of the stress concentration trend area to output a prediction result that includes the critical rupture pressure value and the potential rupture location.

7. The method according to claim 1, characterized in that, The predicted critical rupture pressure value and rupture location are integrated to form a burst pressure risk output, including: Read the specific numerical calculation results of the critical rupture pressure value and simultaneously obtain the three-dimensional spatial coordinate data of the potential rupture location; Obtain the rated pressure value of the fire extinguisher tank design, and calculate the safety margin ratio coefficient as the benchmark value for risk level classification based on the rated pressure value and the critical rupture pressure value. Based on the aforementioned risk level classification benchmark value, determine the category of the blasting risk level identifier; The spatial coordinates of potential rupture locations are mapped onto the surface of a pre-defined 3D model of the fire extinguisher tank to generate spatial markers for the rupture locations. The critical rupture pressure value, rupture risk level identifier, and rupture location spatial marker are integrated to generate a rupture pressure risk report.

8. A fire extinguisher tank burst pressure prediction system, characterized in that, include: The acquisition module is used to acquire the micro-deformation time sequence and pressure gradient value of the surface of the fire extinguisher tank during pressurization in a high-pressure test environment. The micro-deformation time sequence is processed by optical flow analysis to generate a deformation displacement field matrix, and the deformation displacement field matrix is ​​used to quantify the deformation rate under different pressure gradient values. An adjustment module is used to deploy a laser holographic interferometer on a pressure test bench and dynamically adjust the laser phase parameters of the laser holographic interferometer based on the deformation rate, so that the laser holographic interferometer outputs holographic interference fringes that match the deformation rate, and obtains the phase change of the holographic interference fringes to construct a deformation evolution topology map. The correlation module is used to identify abnormal fluctuation patterns of deformation rate in the deformation evolution topology graph and correlate the abnormal fluctuation patterns with pressure gradient values ​​to generate a deformation-pressure correlation tensor. The simulation module is used to obtain the deformation propagation path and stress concentration trend in the deformation evolution topology based on the deformation pressure correlation tensor, and to simulate the critical rupture pressure value and potential rupture location prediction results of the fire extinguisher tank based on the deformation propagation path and stress concentration trend. An integration module is used to integrate the predicted results of the critical rupture pressure value and the rupture location to form a burst pressure risk output.

9. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement a method for predicting the burst pressure of a fire extinguisher tank as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, The device contains a computer program that, when executed by a computer, implements a method for predicting the burst pressure of a fire extinguisher tank as described in any one of claims 1 to 7.

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