A method and system for detecting high temperature resistance performance of a fire helmet

Abstract

The application provides a high-temperature resistance performance detection method and system of a fire helmet, and relates to the technical field of performance detection, comprising: performing structural decomposition and protection failure characteristic analysis on the fire helmet; placing the fire helmet in a preset detection chamber, performing incremental control of thermal radiation, and performing protection state monitoring; in the preset detection chamber, performing high-temperature exposure control on the fire helmet at a first temperature resistance threshold, and synchronously applying a physical field load or a chemical field load; analyzing the attenuation of the protection performance of the fire helmet under the coupling load, and determining a second temperature resistance threshold. Through the application, the technical problem that the protection performance of the fire helmet under multiple fire environment cannot be comprehensively evaluated due to the limitation of single environmental load test in the prior art, and the reliability of the safety evaluation of the fire helmet is affected, can be solved. Through comprehensive evaluation of the attenuation of the protection performance of the fire helmet in complex fire environment, the reliability of the safety evaluation of the fire helmet is improved.

Description

Technical Field

[0001] This application relates to the field of performance testing technology, specifically to a method and system for testing the high-temperature resistance performance of a fire helmet. Background Technology

[0002] Existing testing methods for fire helmets typically employ single environmental load tests, primarily focusing on the impact of simulated high temperatures or flames on the helmet during a fire. However, real-world fire environments are extremely complex. Fire helmets not only need to withstand high temperatures but also the combined effects of multiple physical and chemical fields. Single environmental load tests cannot comprehensively reflect the multiple pressures and loads a helmet may encounter in a complex fire environment, leading to limitations in protective performance assessments and affecting the accurate evaluation of the helmet's protective capabilities. Conversely, qualification conclusions derived from existing testing methods may deviate from the helmet's actual protective effectiveness in real-world fire scenes with multiple concurrent threats, resulting in insufficient reliability in fire helmet safety assessments.

[0003] In summary, the existing technology suffers from the limitation of single environmental load testing, which makes it impossible to fully evaluate the protective performance of fire helmets in multiple fire environments, further affecting the reliability of fire helmet safety assessment. Summary of the Invention

[0004] The purpose of this application is to provide a method and system for testing the high-temperature resistance performance of fire helmets, in order to solve the technical problem in the prior art that the protection performance of fire helmets under multiple fire environments cannot be fully evaluated due to the limitations of single environmental load testing, which further affects the reliability of fire helmet safety assessment.

[0005] To achieve the above objectives, this application provides a method and system for testing the high-temperature resistance of fire helmets.

[0006] In a first aspect, this application provides a method for testing the high-temperature resistance performance of a fire helmet. This method is implemented using a high-temperature resistance performance testing system for fire helmets. The method includes: performing structural decomposition and protective failure characteristic analysis on the fire helmet to obtain multiple protective failure characteristic information corresponding to multiple helmet components; placing the fire helmet in a preset testing chamber, implementing incremental control of thermal radiation, and monitoring the protective status using preset sensor components to determine a first temperature resistance threshold when the status of multiple helmet components reaches the multiple protective failure characteristic information; in the preset testing chamber, subjecting the fire helmet to high-temperature exposure control at the first temperature resistance threshold, simultaneously applying at least two interrelated physical field loads or chemical field loads, and monitoring the response of the fire helmet using preset sensor components to obtain multiple monitoring datasets related to helmet protective failure; based on the multiple monitoring datasets, analyzing the attenuation of the protective performance of the fire helmet under coupled loads, and determining a second temperature resistance threshold under coupled loads.

[0007] Optionally, the fire helmet is decomposed into multiple helmet components according to function, and multiple functional marking information is generated; based on the multiple functional marking information, the physical state or performance parameters under functional failure are matched to obtain the multiple protection failure characteristic information.

[0008] Optionally, the plurality of helmet components include an outer shell, a cushioning layer, a face shield, a wearing adjustment component, and component connection points.

[0009] Optionally, based on a preset holding time, the radiative heat flux in the preset detection chamber is controlled and increased multiple times according to a first preset step size, while the surface temperature of each helmet component is monitored simultaneously, and the protective status of each helmet component is monitored through preset sensor components; when the protective status of any helmet component first reaches the corresponding high-temperature failure characteristic information, the surface temperature of the corresponding component is recorded as the first detection temperature, and the second detection temperature at the time of the last control adjacent to the first detection temperature is extracted; the first detection temperature and the second detection temperature constitute the detection range, and the radiative heat flux control test and the surface temperature and protective status monitoring of each helmet component are continued according to a second preset step size based on a preset holding time, to determine the corresponding component surface temperature when the corresponding high-temperature failure characteristic information is reached, and multiple component failure temperatures are generated; the lowest value among the multiple component failure temperatures is defined as the first temperature resistance threshold of the fire helmet.

[0010] Optionally, the preset sensor assembly includes a functional sensor and a temperature sensor for detecting failure characteristics corresponding to multiple protection failure characteristic information; wherein, both the functional sensor and the temperature sensor are sensors configured according to the actual test environment of the preset detection chamber to meet the actual temperature resistance and corrosion resistance requirements.

[0011] Optionally, historical fire scene datasets are collected, physical field loads or chemical field loads other than temperature loads are extracted from the fire scene, and a combined recurrence probability analysis of the same fire scene is performed. At least two interrelated physical field loads or chemical field loads with a combined recurrence probability greater than a preset probability threshold are identified as coupling fields, and a coupling time sequence test scenario is configured based on historical coupling data. The fire helmet is subjected to high-temperature exposure control with the first temperature resistance threshold, and interactive coupling control is performed according to the coupling time sequence test scenario. Simultaneously, the surface temperature and protection status of each helmet component are monitored through preset sensor components to generate the multi-type monitoring dataset.

[0012] Optionally, physical field loads other than temperature loads include open flame, mechanical impact, and mechanical scratching; chemical fields other than temperature loads include liquid spray and exposure to corrosive chemical media.

[0013] Optionally, the first type of load field and the second type of load field are extracted from the coupling field; the historical coupling data are clustered according to the coupling strength of the first type of load field and the second type of load field, and multiple sets of coupling scenarios are generated with the cluster center of each cluster; the coupling time series test scenario is constructed with the multiple sets of coupling scenarios.

[0014] Optionally, using the multi-class monitoring datasets and corresponding coupled time-series test scenarios as modeling data, a temperature resistance attenuation twin model of the fire helmet under multi-field coupling is constructed; the temperature resistance attenuation twin model is run to simulate the temperature resistance attenuation value inside the helmet under different input coupled loads, and a temperature resistance threshold attenuation table corresponding to multiple coupled loads is determined; the temperature resistance threshold attenuation table is matched with the second temperature resistance threshold corresponding to the target coupled load.

[0015] Secondly, this application also provides a high-temperature resistance performance testing system for fire helmets, used to perform a high-temperature resistance performance testing method for fire helmets as described in the first aspect, wherein the high-temperature resistance performance testing system for fire helmets includes: a protection failure characteristic analysis module, used to perform structural decomposition and protection failure characteristic analysis on the fire helmet to obtain multiple protection failure characteristic information corresponding to multiple helmet components; a protection status monitoring module, used to place the fire helmet in a preset testing chamber, perform incremental control of thermal radiation, monitor the protection status through preset sensor components, and determine a first temperature resistance threshold when the status of multiple helmet components reaches the multiple protection failure characteristic information; a high-temperature exposure control module, used to perform high-temperature exposure control on the fire helmet in the preset testing chamber at the first temperature resistance threshold, simultaneously apply at least two interrelated physical field loads or chemical field loads, and monitor the response of the fire helmet through preset sensor components to obtain multiple monitoring datasets related to helmet protection failure; and a protection performance attenuation analysis module, used to analyze the attenuation of the protection performance of the fire helmet under coupled load based on the multiple monitoring datasets, and determine a second temperature resistance threshold under coupled load.

[0016] One or more technical solutions provided in this application have at least the following technical effects or advantages: By performing structural decomposition and protective failure characteristic analysis on the fire helmet, multiple protective failure characteristic information corresponding to multiple helmet components is obtained. The fire helmet is placed in a preset detection chamber, and incremental control of thermal radiation is implemented. The protective status is monitored by preset sensor components to determine a first temperature resistance threshold when the status of multiple helmet components reaches the multiple protective failure characteristic information. In the preset detection chamber, the fire helmet is subjected to high-temperature exposure control with the first temperature resistance threshold. At least two interrelated physical field loads or chemical field loads are applied simultaneously, and the response of the fire helmet is monitored by preset sensor components to obtain multiple monitoring datasets related to helmet protective failure. Based on the multiple monitoring datasets, the attenuation of the protective performance of the fire helmet under coupled loads is analyzed to determine a second temperature resistance threshold under coupled loads. In other words, by disassembling the fire helmet into multiple independent components and analyzing the protective failure characteristics of each component independently, increasing thermal radiation is controlled in a pre-set detection chamber, and at least two interrelated physical or chemical field loads are applied simultaneously. The response data of the helmet under multiple loads is collected through a multi-sensor assembly, and protective performance attenuation analysis is performed to determine the second temperature resistance threshold. This comprehensively evaluates the protective performance of the fire helmet in complex environments, improving the accuracy and reliability of fire helmet safety assessment.

[0017] The above description is merely an overview of the technical solution of this application. To better understand the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating a method for testing the high-temperature resistance of a fire helmet according to this application.

[0020] Figure 2 This is a schematic diagram of the high-temperature resistance performance testing system for a fire helmet according to this application.

[0021] Explanation of reference numerals in the attached diagram: Module 11 for protection failure characteristics analysis, Module 12 for protection status monitoring, Module 13 for high temperature exposure control, and Module 14 for protection performance degradation analysis. Detailed Implementation

[0022] This application provides a method and system for testing the high-temperature resistance performance of fire helmets, solving the technical problem in existing technologies where the limitation of single environmental load testing leads to an inability to comprehensively assess the protective performance of fire helmets under multiple fire environments, further affecting the reliability of fire helmet safety assessments. By disassembling the fire helmet into multiple independent components and independently analyzing the protective failure characteristics of each component, the method employs a pre-set testing chamber with increasing thermal radiation control, simultaneously applying at least two interrelated physical or chemical field loads. Multi-sensor components collect the helmet's response data under multiple loads, and protective performance attenuation analysis is performed to determine a second temperature resistance threshold. This comprehensive assessment of the fire helmet's protective performance in complex environments improves the accuracy and reliability of fire helmet safety assessments.

[0023] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0024] Example 1, please refer to the appendix. Figure 1 This application provides a method for testing the high-temperature resistance of a fire helmet. The method is applied to a high-temperature resistance testing system for fire helmets and specifically includes the following steps: Structural decomposition and protective failure characteristic analysis were performed on the fire helmet to obtain multiple protective failure characteristic information corresponding to multiple helmet components.

[0025] Furthermore, this application also includes the following steps: decomposing the fire helmet into multiple helmet components according to their functions, and generating multiple functional marking information; matching the physical state or performance parameters under functional failure based on the multiple functional marking information to obtain the multiple protective failure characteristic information.

[0026] Furthermore, this application also includes the following steps: the plurality of helmet components include a shell, a buffer layer, a face shield, a wearing adjustment component, and component connection points.

[0027] Specifically, based on the design principles of fire helmets, a function-oriented structural decomposition is performed. The overall protective function of the fire helmet is broken down into several core sub-functions, and the physical components that realize these sub-functions are identified, resulting in multiple helmet components, including the outer shell, buffer layer, face shield, wearing adjustment components, and component connection points. For example, the overall protection is decomposed into five major sub-functions: external resistance, energy buffering, visual protection, stable wearing, and structural interconnection. The helmet entity is then labeled as five major component clusters: outer shell, buffer layer, face shield, wearing adjustment components (including chin strap, headband, etc.), and component connection points (such as rivets and buckle interfaces).

[0028] Based on functional decomposition, functional marking information is generated for each component, detailing the performance of each component under specific conditions, such as temperature range, impact resistance, and tensile strength. For example, the functional marking information for the outer shell is to resist direct impact and insulate against primary heat loads, while the functional marking information for the face shield is to maintain clear vision and provide facial protection.

[0029] For each component's functional markings, its failure mechanism is analyzed, and corresponding physical or performance parameters are selected as failure characteristics. For example, for the outer shell and buffer layer, which provide protection, failure may manifest as softening and deformation of the material itself, cracking, dripping, or bubbling; for the face mask, which provides visual protection, failure manifests as degradation of optical performance, such as a decrease in light transmittance to a certain dangerous threshold; for the adjustment components, which provide stable wearing, failure manifests as loss of mechanical properties, such as the tensile strength of the webbing material decreasing to a certain preset percentage, such as 50% of the initial strength, or the unlocking force of the buckle mechanism exceeding the allowable range for safe operation, such as less than 15 Newtons for accidental unlocking, or greater than 50 Newtons for difficulty in detachment in an emergency. Matching the physical state or performance parameters under functional failure involves reverse-engineering the specific morphological changes or key performance index values ​​that the component will inevitably exhibit when the function fails, such as softening and deformation, cracking, dripping, decreased light transmittance, bubbling, tensile strength decreasing to a preset percentage, or the unlocking force of the buckle mechanism exceeding the allowable range.

[0030] By functionally decomposing the structure of fire helmets and generating corresponding functional marking information, we can understand the protective performance of each component in high-temperature fire environments, predict the failure points of each component in advance, effectively improve the design accuracy of fire helmets, ensure their effective protection in fires, and greatly improve the safety and reliability of helmets.

[0031] The fire helmet is placed in a preset detection chamber, and the heat radiation is incrementally controlled. The protective status is monitored by a preset sensor assembly to determine the first temperature resistance threshold when the status of multiple helmet components reaches the multiple protective failure characteristic information.

[0032] Furthermore, this application also includes the following steps: based on a preset holding time, the radiative heat flux in the preset detection chamber is controlled and increased multiple times according to a first preset step size, while simultaneously monitoring the surface temperature of each helmet component, and monitoring the protection status of each helmet component through a preset sensor component; when the protection status of any helmet component first reaches the corresponding high-temperature failure characteristic information, the surface temperature of the corresponding component is recorded as the first detection temperature, and the second detection temperature at the last control adjacent to the first detection temperature is extracted; using the first detection temperature and the second detection temperature to form a detection range, the radiative heat flux control test and the surface temperature and protection status monitoring of each helmet component are continued according to a second preset step size based on a preset holding time, to determine the corresponding component surface temperature when the corresponding high-temperature failure characteristic information is reached, and to generate multiple component failure temperatures; the lowest value among the multiple component failure temperatures is defined as the first temperature resistance threshold of the fire helmet.

[0033] Specifically, a fire helmet was placed in a pre-set testing chamber. Using a thermal radiation increment control device within the chamber, the initial radiant heat flux was gradually increased to simulate the helmet's protective performance under different temperature conditions, particularly its response to high temperatures during a fire. Based on a pre-set holding time, the radiant heat flux within the testing chamber was repeatedly increased in a large initial step. Pre-set sensor components continuously and synchronously monitored the surface temperature and current protective status of each helmet component, observing the response of each component and identifying any physical failures or performance degradation. The pre-set holding time is the duration for which the operating condition is maintained constant at each set radiant heat flux level. Its purpose is to ensure that the helmet components have sufficient time to absorb heat and reach the quasi-steady-state temperature under the given heat load, thereby observing the true, non-instantaneous material response.

[0034] When the protection status of any component first reaches the preset high-temperature failure characteristic information, that is, when the component's performance shows a significant degradation, the surface temperature of the component at this time is recorded as the first detection temperature. The system automatically traces back to the previous heat flux step, that is, the last stable step before the failure occurred, and extracts the surface temperature of the component corresponding to that step as the second detection temperature, which is the second detection temperature at the time of the previous control adjacent to the first detection temperature.

[0035] Based on the first and second detection temperatures, a temperature detection range is defined, and the fine measurement phase begins. The heat flux is adjusted back to the operating condition corresponding to the second detection temperature, and then a step-by-step temperature increase test is performed within the detection range using a very small second preset step size. Each step maintains sufficient duration and is closely monitored. The surface temperature and current protective status of each helmet component are continuously and synchronously monitored through preset sensor components, observing the response of each component to detect any physical failures or performance degradation. The surface temperature at which the component just reaches the failure criterion is precisely determined, i.e., the component failure temperature. This fine measurement process is repeated for multiple helmet components to obtain a series of component failure temperatures. Following the barrel principle, the lowest value among the multiple component failure temperatures is defined as the first temperature resistance threshold of the fire helmet.

[0036] In the stepped temperature rise test, different increments of radiative heat flux are set in two stages: a first preset step size and a second preset step size. The first preset step size is larger and used for rapid, wide-range scanning and positioning of the helmet's temperature resistance range to efficiently identify the critical range for failure. The second preset step size is smaller and used for precise measurements within the identified, narrow critical range to determine the accurate failure temperature threshold. The second preset step size is smaller than the first preset step size.

[0037] For example, the initial radiative heat flux of the detection chamber is 10 kW / m².2 The first preset step size is 5kW / m 2 The preset holding time is 5 minutes. The failure characteristics of the casing are defined as a local temperature exceeding 320℃ and the appearance of bubbles with a diameter >3mm. Rapid scanning phase: at 15kW / m 2 After holding for 5 minutes, the highest temperature of the outer casing was 245℃, with no bubbling; at 20kW / m 2 After holding for 5 minutes, the highest temperature of the outer casing was 295℃, with no bubbling; at 25kW / m 2 After maintaining the temperature for 3 minutes, the infrared thermal imager showed that the temperature at a certain point on the casing reached 328℃. Simultaneous high-definition imaging revealed a 4mm bulge at that point, indicating a failure had occurred. The first detected temperature was recorded as 328℃, corresponding to 25kW / m². 2 Operating conditions. Going back to the previous level of 20kW / m 2 The stable temperature at this point is 295℃, which is recorded as the second detection temperature. The precision measurement range is determined to be 295℃ to 328℃. The heat flux is adjusted back to 20kW / m². 2 Corresponding to 295℃, the second preset step size is changed to 1kW / m. 2 At 21kW / m 2 After holding for 5 minutes, the temperature at that point was 305℃, with no bubbling; at 22kW / m 2 After holding for 5 minutes, the temperature reached 315℃ with no bubbling; at 23kW / m 2 The temperature was maintained at 321℃ for 4 minutes, and tiny bubbles of about 3.2mm were observed, which was precisely determined to be the failure starting point. The failure temperature of the helmet components was recorded as 321℃. Assuming that the precise failure temperatures of the buffer layer and the visor are 335℃ and 310℃ respectively, the lowest value of 310℃ is taken. Therefore, the first temperature resistance threshold of this helmet is 310℃.

[0038] By gradually increasing the heat radiation flux and accurately monitoring the protection status of each component, the high-temperature resistance performance of each component can be meticulously evaluated. This not only accurately determines the failure temperature of each component, but also ensures that the first temperature resistance threshold obtained can truly reflect the overall high-temperature resistance capability of the fire helmet by comprehensively analyzing the failure points of multiple components, thus improving the accuracy and reliability of the high-temperature resistance test of the fire helmet.

[0039] Furthermore, this application also includes the following steps: the preset sensor assembly includes a functional sensor and a temperature sensor for detecting failure characteristics corresponding to multiple protection failure characteristic information; wherein, the functional sensor and the temperature sensor are both sensors configured according to the actual test environment of the preset detection chamber to meet the actual temperature resistance and corrosion resistance requirements.

[0040] Specifically, during the testing of fire helmets, multiple sensor components, i.e., multiple smart sensors, need to be installed in a pre-designed testing chamber. Functional sensors and temperature sensors are the core components. Functional sensors are used to monitor changes in the protective performance of each component of the helmet in real time, while temperature sensors are used to detect temperature changes of each component at high temperatures to determine the temperature conditions at which failure occurs. The pre-designed sensor components themselves, including their encapsulation, probes, and electronic components, undergo special design and material selection to withstand the highest temperatures and corrosive media that may occur in the test environment over a long period of time at their installation location without damage, performance drift, or measurement distortion. In other words, all selected sensors must be calibrated and customized according to the most severe conditions that the pre-designed testing chamber may encounter during the composite testing phase, such as continuous high temperatures up to 310°C, intermittent water mist spray, and a weakly acidic combustion gas environment. For example, active water cooling and corrosion-resistant optical windows are added to the camera; high-temperature ceramic insulation materials are used for electrical connections; and corrosion-resistant metals such as Hastelloy are selected for the sensor probes. This transforms the entire sensor component from an independent laboratory instrument into an embedded sensing system capable of surviving and accurately operating in the simulated extreme fire environment.

[0041] In the preset detection chamber, the fire helmet is subjected to high-temperature exposure control at the first temperature resistance threshold. At least two interrelated physical field loads or chemical field loads are applied simultaneously, and the response of the fire helmet is monitored by a preset sensor assembly to obtain multiple monitoring datasets related to helmet protection failure.

[0042] Furthermore, this application also includes the following steps: collecting historical fire scene datasets, extracting physical field loads or chemical field loads other than temperature loads from the fire scene, and performing a combined recurrence probability analysis of the same fire scene, identifying at least two interrelated physical field loads or chemical field loads with a combined recurrence probability greater than a preset probability threshold as coupling fields, and configuring a coupling time-series test scenario based on historical coupling data; performing high-temperature exposure control on the fire helmet using the first temperature resistance threshold, performing interactive coupling control according to the coupling time-series test scenario, and simultaneously monitoring the surface temperature and protection status of each helmet component through preset sensor components to generate the multi-class monitoring dataset.

[0043] Furthermore, this application also includes the following steps: physical field loads other than temperature loads include open flame, mechanical impact, and mechanical scratching; chemical fields other than temperature loads include liquid spraying and exposure to corrosive chemical media.

[0044] Furthermore, this application also includes the following steps: extracting the first type of load field and the second type of load field from the coupling field; clustering the historical coupling data according to the coupling strength of the first type of load field and the second type of load field, generating multiple sets of coupling scenarios with the cluster centers of each cluster; and constructing the coupling time series test scenario with the multiple sets of coupling scenarios.

[0045] Specifically, based on the first temperature resistance threshold, the temperature control of the preset testing chamber is activated, placing the fire helmet in a high-temperature thermal background environment. This means that the high-temperature exposure is controlled under the extreme temperature environment of the first temperature resistance threshold. For example, if the first temperature resistance threshold is 310℃, the control system will adjust the heat flux inside the chamber to a state that just keeps the surface temperature of the weakest component of the helmet stable at around 310℃, thereby creating a stable thermodynamic background environment at the material failure critical point.

[0046] Historical fire scene datasets were collected and organized, containing temporal or correlated information on environmental parameters (such as temperature, heat flux, and gas composition) and load conditions (such as object collapse, water jet, and chemical leakage) at each stage of fire development. This included fire accident investigation reports, laboratory fire simulation results, and on-site data from firefighters. All non-temperature load types, such as open flame, impact, scratching, spraying, and chemical exposure, were traversed to calculate the joint probability of any two or more loads occurring in the same scenario. Physical field loads are physical forces or pressures acting on fire helmets, such as open flame, mechanical impact, and mechanical scratching, representing the physical environment fire helmets may encounter in actual fires. Chemical field loads are chemical environmental factors acting on fire helmets, such as liquid spraying and exposure to corrosive chemical media, affecting the helmet's material properties; in particular, contact with chemical substances in a fire environment accelerates material degradation. Combined recurrence probability analysis was used to analyze the frequency of various physical or chemical loads (excluding high temperature) occurring simultaneously in the same fire or within the same time period in historical data. By calculating the probability of such co-occurrence, it is possible to identify which load combinations exhibit significant correlation in real fire scenarios, rather than being random independent events. For example, in a building interior flashover scenario, the probability of high-temperature water mist / steam spray and debris impact occurring simultaneously is as high as 65%. When the probability of such a combination exceeds a preset probability threshold, such as 40%, the combination is marked as a typical interrelated coupled field. The preset probability threshold is a set statistical significance threshold value. Only when the recurrence probability of a combination of two or more loads exceeds this threshold is it considered to have sufficient universality and representativeness to be worth reproducing as a coupled field in the test. This ensures that the test scenario is derived from practice, rather than theoretical assumptions.

[0047] From each identified coupled field, the first-type load field and the second-type load field constituting it are separated. Then, all events recording this load combination are retrieved from historical data, and the specific intensity values ​​of the two loads in each event are extracted, such as the energy value of the impact and the flow rate value of the spray, forming a series of intensity pair data points. The first-type load field and the second-type load field are designations for the two associated loads in the identified coupled fields, usually distinguished according to their physical nature or the way they interact with the helmet. For example, in a coupled field of mechanical impact + liquid spray, the mechanical impact can be called the first-type load field (physical contact mechanical energy load), and the liquid spray can be called the second-type load field (fluid medium load).

[0048] Clustering is performed based on coupling strength. Clustering algorithms analyze the intensity of data points to automatically identify natural groupings within the data. For example, the data can be divided into three clusters: one representing high-intensity impact coupled with low-intensity spraying, one representing medium-intensity impact coupled with medium-intensity spraying, and another representing low-intensity impact coupled with high-intensity spraying. The coordinates of the cluster center define the standard intensity parameters for that typical pattern. For example, cluster 1 center: impact 45 joules, spray 5 liters / minute; cluster 2 center: impact 25 joules, spray 15 liters / minute, etc. Each cluster center defines a set of standard coupling scenarios.

[0049] Coupled timing test scenarios are constructed based on multiple coupled scenarios, describing how different load combinations are applied alternately or synchronously within a certain time period. Timing control ensures that the load environment experienced by the fire helmet changes dynamically at different time points, simulating the complexity and dynamic changes in the fire environment. The coupled timing test scenario is a specific test procedure built based on multiple coupled scenarios, gradually applying different load combinations at different time points to simulate the performance of the fire helmet under multiple environmental pressures.

[0050] In the coupled timing test scenario, the fire helmet is subjected to high-temperature exposure control based on a first temperature resistance threshold. This means that during the test, the helmet's temperature is constantly controlled, approaching but not exceeding the temperature resistance limit, while other loads are simultaneously applied to the helmet. Pre-set sensor components monitor the surface temperature and protective status of each helmet component in real time, recording the helmet's performance under high temperature and other environmental pressures, particularly whether components exhibit softening, cracking, melting, or other failure phenomena. Throughout the test, the obtained temperature and protective status data are integrated into multiple monitoring datasets, including the helmet's response under each load scenario. Interactive coupling control dynamically adjusts the order and intensity of load combinations during the test, allowing multiple loads to act on the helmet simultaneously, simulating the multiple pressure interactions in a real fire.

[0051] By constructing multiple coupled scenarios and coupled time-series test scenarios, the performance of fire helmets in complex environments during actual fires is simulated. Especially in extreme environments, the test considers not only single environmental loads but also the effects of multiple loads acting simultaneously, improving the realism and comprehensiveness of the test. By monitoring the temperature and protective status of each component of the helmet in real time, its performance in complex environments is accurately evaluated, the helmet's high-temperature resistance and damage resistance are determined, and the helmet design is ultimately optimized to ensure that it can provide effective protection under multiple fire pressures.

[0052] Based on the aforementioned multi-class monitoring datasets, the attenuation of the protective performance of the fire helmet under coupled load is analyzed, and the second temperature resistance threshold under coupled load is determined.

[0053] Furthermore, this application also includes the following steps: using the multi-class monitoring dataset and the corresponding coupled time-series test scenarios as modeling data, constructing a temperature resistance attenuation twin model of the fire helmet under multi-field coupling; running the temperature resistance attenuation twin model to simulate the temperature resistance attenuation value inside the helmet under different input coupled loads, and determining a temperature resistance threshold attenuation table corresponding to multiple coupled loads; matching the temperature resistance threshold attenuation table with the second temperature resistance threshold corresponding to the target coupled load.

[0054] Specifically, multi-class monitoring datasets are associated and paired with coupled time-series test scenarios that generate them to create twin models. The multi-class monitoring datasets include data on temperature, pressure, chemical corrosion, mechanical shock, and thermal radiation, which serve as input data for the model. These datasets are formatted and standardized through a data preprocessing module to ensure effective integration with the model. Each coupled time-series test scenario describes the combination patterns of different loads over time, used for model training and providing environmental change data under different scenarios. The multi-class monitoring datasets undergo data cleaning, transformation, and standardization. Key features, such as temperature decay rates, pressure changes, chemical corrosion rates, and the impact of mechanical shock, are extracted from the final datasets and transformed into input features that the model can process, becoming the input data for the twin model.

[0055] This system combines Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to process time-series data. CNNs handle local features in the monitoring data, such as surface temperature changes, while LSTMs handle time dependencies, such as the long-term impact of load changes on temperature. CNN layers extract local features from multi-dimensional data, capturing key factors like stress and temperature changes within the helmet's local area. LSTM layers capture the long-term impact of load variations on the helmet, such as the temperature decay trend of helmet materials under continuous high-temperature environments. The features extracted by the CNN and LSTM layers are merged and mapped to the output layer to output the helmet's temperature decay value. The input data consists of multi-class monitoring datasets and coupled time-series test scenarios; the output data is the simulated temperature decay value, including changes in the helmet's temperature threshold under different loads and time stages. Mean squared error (MSE) is used as the loss function to quantify the difference between the predicted results and the actual observed data. The Adam optimization algorithm is used to automatically adjust the learning rate during training to accelerate convergence and improve the prediction accuracy of the model. The training data is augmented by adding noise, varying load intensity, and simulating different types of fire scenarios to improve the generalization ability of the model.

[0056] Data is processed in batches, and model weights are updated using batch gradient descent. After each iteration, the error between the model's predicted values ​​and the actual monitoring data is calculated, and parameters are updated via backpropagation. After training, the model's performance is checked using a validation set to avoid overfitting. Training parameters include a learning rate of 0.001 (the optimal value obtained through tuning), a batch size of 64, and 100 iterations; the training set accounts for 80% of the model, and the validation set for 20%. The model is validated using test set data to evaluate its predictive ability on unseen data, outputting the temperature resistance decay value of the helmet and comparing it with the actual monitoring data to calculate the prediction error. The mean absolute error is used to evaluate the model's accuracy and goodness of fit. The model outputs a table of temperature resistance threshold decay data, recording the decay values ​​corresponding to various coupled loads. The table includes the changes in the temperature resistance threshold of the fire helmet under different load combinations.

[0057] A temperature resistance decay twin model was run to simulate the temperature resistance decay process inside a fire helmet under different input coupled loads. Based on the input load intensity and duration, the temperature resistance decay values ​​of each component of the helmet were calculated, revealing the temperature decay of the helmet under these loads. Thousands of such simulation results were compiled to generate a temperature resistance threshold decay table covering a broad load parameter space.

[0058] In practical assessments, when it is necessary to determine the protection limit of a helmet in a specific target fire environment, there is no need to conduct complex and dangerous physical tests again. Simply input the target load parameters into the temperature resistance threshold decay table to obtain the temperature resistance decay value. Then, subtract the temperature resistance decay value from the first temperature resistance threshold to obtain the second temperature resistance threshold. The second temperature resistance threshold is the second temperature resistance threshold corresponding to the target coupled load matched from the temperature resistance threshold decay table; that is, the maximum temperature at which the fire helmet can still maintain effective protection under specific environmental conditions.

[0059] The input consists of a monitoring dataset and time-series data from coupled time-series test scenarios; the output is a table of temperature resistance decay values ​​and temperature threshold decay. By simulating different load combinations, the temperature resistance performance of the helmet in fire scenarios is predicted. The temperature resistance decay twin model simulates the temperature resistance decay behavior of fire helmets in complex fire environments, and can predict changes in the protective performance of fire helmets under multiple loads, such as high temperature, mechanical impact, and chemical corrosion. Using the temperature resistance decay twin model, designers can accurately assess the performance of fire helmets in actual fires, ensuring that they still provide effective protection under multiple pressures.

[0060] After data input, it first enters the data preprocessing module for cleaning and standardization. Then, it passes through the feature extraction module to extract key features and transmit them to the twin model module for training and prediction. The trained temperature attenuation twin model is then validated in the testing and evaluation module, ultimately outputting prediction results, including temperature attenuation values ​​and a temperature threshold attenuation table. In practical applications, the temperature attenuation twin model can be dynamically adjusted based on real-time collected fire scene data, thus providing real-time feedback for helmet design and improvement.

[0061] By constructing a twin model of temperature resistance decay and generating a temperature resistance threshold decay table, it is possible to accurately simulate the performance of fire helmets in multiple fire environments, especially the temperature resistance performance under the simultaneous action of multiple physical and chemical loads. This not only improves the accuracy of fire helmet performance evaluation, but also enables the rapid adjustment of fire helmet design and production standards according to different fire environments, thereby improving the adaptability and safety of helmets under extreme fire conditions.

[0062] In summary, the high-temperature resistance testing method for fire helmets provided in this application has the following technical advantages: By performing structural decomposition and protective failure characteristic analysis on the fire helmet, multiple protective failure characteristic information corresponding to multiple helmet components is obtained. The fire helmet is placed in a preset detection chamber, and incremental control of thermal radiation is implemented. The protective status is monitored by preset sensor components to determine a first temperature resistance threshold when the status of multiple helmet components reaches the multiple protective failure characteristic information. In the preset detection chamber, the fire helmet is subjected to high-temperature exposure control with the first temperature resistance threshold. At least two interrelated physical field loads or chemical field loads are applied simultaneously, and the response of the fire helmet is monitored by preset sensor components to obtain multiple monitoring datasets related to helmet protective failure. Based on the multiple monitoring datasets, the attenuation of the protective performance of the fire helmet under coupled loads is analyzed to determine a second temperature resistance threshold under coupled loads. In other words, by disassembling the fire helmet into multiple independent components and analyzing the protective failure characteristics of each component independently, increasing thermal radiation is controlled in a pre-set detection chamber, and at least two interrelated physical or chemical field loads are applied simultaneously. The response data of the helmet under multiple loads is collected through a multi-sensor assembly, and protective performance attenuation analysis is performed to determine the second temperature resistance threshold. This comprehensively evaluates the protective performance of the fire helmet in complex environments, improving the accuracy and reliability of fire helmet safety assessment.

[0063] Example 2: Based on the same inventive concept as the high-temperature resistance testing method for a fire helmet in Example 1, this application also provides a high-temperature resistance testing system for a fire helmet. Please refer to the appendix. Figure 2 The high-temperature resistance testing system for a fire helmet includes: The protective failure characteristic analysis module 11 is used to perform structural decomposition and protective failure characteristic analysis on the fire helmet to obtain multiple protective failure characteristic information corresponding to multiple helmet components; the protective status monitoring module 12 is used to place the fire helmet in a preset detection chamber, perform incremental control of thermal radiation, monitor the protective status through preset sensor components, and determine a first temperature resistance threshold when the status of multiple helmet components reaches the multiple protective failure characteristic information; the high temperature exposure control module 13 is used to perform high temperature exposure control on the fire helmet in the preset detection chamber with the first temperature resistance threshold, simultaneously apply at least two interrelated physical field loads or chemical field loads, and monitor the response of the fire helmet through preset sensor components to obtain multiple monitoring datasets related to helmet protective failure; the protective performance attenuation analysis module 14 is used to analyze the attenuation of the protective performance of the fire helmet under coupled load based on the multiple monitoring datasets, and determine a second temperature resistance threshold under coupled load.

[0064] Furthermore, the protective failure characteristic analysis module 11 in the high temperature resistance performance testing system for a fire helmet is also used to: decompose the fire helmet into multiple helmet components according to function and generate multiple functional marking information; and perform physical state or performance parameter matching under functional failure based on the multiple functional marking information to obtain the multiple protective failure characteristic information.

[0065] Furthermore, the protective failure characteristic analysis module 11 in the high temperature resistance performance testing system of the fire helmet is also used for: the plurality of helmet components include a shell, a buffer layer, a face mask, a wearing adjustment component, and component connection points.

[0066] Furthermore, the protective status monitoring module 12 in the high-temperature resistance performance testing system for fire helmets is also used for: controlling and increasing the radiant heat flux in the preset detection chamber multiple times according to a first preset step size based on a preset holding time, and simultaneously monitoring the surface temperature of each helmet component, and monitoring the protective status of each helmet component through a preset sensor component; when the protective status of any helmet component first reaches the corresponding high-temperature failure characteristic information, recording the surface temperature of the corresponding component as the first detection temperature, and extracting the second detection temperature at the last control adjacent to the first detection temperature; using the first detection temperature and the second detection temperature as the detection range, continuing to perform radiant heat flux control testing and monitoring of the surface temperature and protective status of each helmet component according to a second preset step size based on a preset holding time, determining the corresponding component surface temperature when the corresponding high-temperature failure characteristic information is reached, and generating multiple component failure temperatures; defining the lowest value among the multiple component failure temperatures as the first temperature resistance threshold of the fire helmet.

[0067] Furthermore, the protection status monitoring module 12 in the high temperature resistance performance testing system for a fire helmet is also used for: the preset sensor assembly includes a functional sensor and a temperature sensor for detecting failure characteristics corresponding to multiple protection failure characteristic information; wherein, the functional sensor and the temperature sensor are both sensors configured according to the actual test environment of the preset test chamber to meet the actual temperature resistance and corrosion resistance requirements.

[0068] Furthermore, the high-temperature exposure control module 13 in the high-temperature resistance performance testing system for a fire helmet is also used for: collecting historical fire scene datasets, extracting physical field loads or chemical field loads other than temperature loads in the fire scene, and performing a combined recurrence probability analysis of the same fire scene, identifying at least two interrelated physical field loads or chemical field loads with a combined recurrence probability greater than a preset probability threshold as coupling fields, and configuring a coupling time sequence test scene based on historical coupling data; performing high-temperature exposure control on the fire helmet with the first temperature resistance threshold, performing interactive coupling control according to the coupling time sequence test scene, and simultaneously monitoring the surface temperature and protection status of each helmet component through preset sensor components to generate the multi-type monitoring dataset.

[0069] Furthermore, the high-temperature exposure control module 13 in the high-temperature resistance performance testing system of the fire helmet is also used for: physical field loads other than temperature loads, including open flame, mechanical impact, and mechanical scratching; and chemical fields other than temperature loads, including liquid spray and exposure to corrosive chemical media.

[0070] Furthermore, the high-temperature exposure control module 13 in the high-temperature resistance performance testing system for a fire helmet is also used to: extract the first type of load field and the second type of load field in the coupling field; cluster the historical coupling data according to the coupling strength of the first type of load field and the second type of load field, and generate multiple sets of coupling scenarios with the cluster center of each cluster; and construct the coupling time sequence test scenario with the multiple sets of coupling scenarios.

[0071] Furthermore, the protective performance attenuation analysis module 14 in the high-temperature resistance performance testing system for a fire helmet is also used to: construct a temperature resistance attenuation twin model of the fire helmet under multi-field coupling using the multi-class monitoring dataset and the corresponding coupled time-series test scenarios as modeling data; run the temperature resistance attenuation twin model to simulate the temperature resistance attenuation value inside the helmet under different input coupled loads, and determine the temperature resistance threshold attenuation table corresponding to multiple coupled loads; and match the second temperature resistance threshold corresponding to the target coupled load with the temperature resistance threshold attenuation table.

[0072] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The method and specific examples for testing the high-temperature resistance of a fire helmet in Embodiment 1 described above are also applicable to the high-temperature resistance testing system for a fire helmet in this embodiment. Through the foregoing detailed description of the method for testing the high-temperature resistance of a fire helmet, those skilled in the art can clearly understand the high-temperature resistance testing system for a fire helmet in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here. As for the systems / devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple, and relevant details can be found in the method section.

[0073] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0074] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for testing the high-temperature resistance of a fire helmet, characterized in that, include: Structural decomposition and protective failure characteristic analysis were performed on the fire helmet to obtain multiple protective failure characteristic information for multiple helmet components; The fire helmet is placed in a preset detection chamber, and incremental control of heat radiation is implemented. The protective status is monitored using preset sensor components to determine a first temperature resistance threshold when the status of multiple helmet components reaches the multiple protective failure characteristic information, including: Based on the preset holding time, the radiative heat flux in the preset detection chamber is controlled to increase multiple times according to the first preset step size, and the surface temperature of each helmet component is monitored simultaneously. The protective status of each helmet component is monitored through the preset sensor component. When the protective state of any helmet component reaches the corresponding high-temperature failure characteristic information for the first time, the surface temperature of the corresponding component is recorded as the first detection temperature, and the second detection temperature of the previous control adjacent to the first detection temperature is extracted. Using the first detection temperature and the second detection temperature as the detection range, continue to perform radiant heat flux control test and monitor the surface temperature and protection status of each helmet component according to the second preset step size based on the preset holding time, determine the corresponding component surface temperature when the corresponding high temperature failure characteristic information is reached, and generate multiple component failure temperatures. The lowest value among the failure temperatures of the plurality of components is defined as the first temperature resistance threshold of the fire helmet; In the preset detection chamber, the fire helmet is subjected to high-temperature exposure control at the first temperature resistance threshold. At least two interrelated physical or chemical field loads are applied simultaneously, and the response of the fire helmet is monitored by a preset sensor assembly to obtain multiple monitoring datasets related to helmet protection failure, including: Collect historical fire scene datasets, extract physical field loads or chemical field loads other than temperature loads in the fire scene, and perform combined recurrence probability analysis of the same fire scene. Identify at least two interrelated physical field loads or chemical field loads with a combined recurrence probability greater than a preset probability threshold as coupling fields, and configure coupling time series test scenarios based on historical coupling data. The fire helmet is subjected to high temperature exposure control based on the first temperature resistance threshold. Interactive coupling control is performed according to the coupling timing test scenario. Simultaneously, the surface temperature and protection status of each helmet component are monitored through preset sensor components to generate the multi-type monitoring dataset. Based on the aforementioned multi-class monitoring datasets, the attenuation of the protective performance of the fire helmet under coupled load is analyzed, and the second temperature resistance threshold under coupled load is determined.

2. The method for testing the high-temperature resistance of a fire helmet as described in claim 1, characterized in that, Structural decomposition and protective failure characteristic analysis were performed on the fire helmet to obtain multiple protective failure characteristic information for multiple helmet components, including: The fire helmet is decomposed into multiple helmet components according to their functions, and multiple functional marking information is generated; Based on the multiple functional marker information, the physical state or performance parameters under functional failure are matched to obtain the multiple protection failure characteristic information.

3. The method for testing the high-temperature resistance of a fire helmet as described in claim 2, characterized in that, The multiple helmet components include an outer shell, a cushioning layer, a face shield, a wearing adjustment component, and component connection points.

4. The method for testing the high-temperature resistance of a fire helmet as described in claim 1, characterized in that, Physical field loads other than temperature loads include open flames, mechanical impacts, and mechanical scratches; chemical field loads other than temperature loads include liquid sprays and exposure to corrosive chemical media.

5. The method for testing the high-temperature resistance of a fire helmet as described in claim 1, characterized in that, Configure coupled timing test scenarios based on historical coupled data, including: Extract the first type of load field and the second type of load field from the coupled field; The historical coupling data is clustered according to the coupling strength of the first type of load field and the second type of load field, and multiple sets of coupling scenarios are generated with the cluster center of each cluster. The coupled timing test scenario is constructed using the multiple sets of coupled scenarios.

6. The method for testing the high-temperature resistance of a fire helmet as described in claim 1, characterized in that, Based on the aforementioned multi-class monitoring datasets, the attenuation of the protective performance of the fire helmet under coupled load is analyzed, and the second temperature resistance threshold under coupled load is determined, including: Using the aforementioned multi-class monitoring datasets and corresponding coupled time-series test scenarios as modeling data, a twin model of the temperature attenuation resistance of the fire helmet under multi-field coupling is constructed. Run the temperature resistance decay twin model to simulate the temperature resistance decay value inside the helmet under different input coupled loads, and determine the temperature resistance threshold decay table corresponding to various coupled loads. The second temperature threshold corresponding to the target coupled load is matched with the temperature threshold decay table.

7. The method for testing the high-temperature resistance of a fire helmet as described in claim 1, characterized in that, The preset sensor assembly includes a functional sensor and a temperature sensor for detecting failure characteristics corresponding to multiple protection failure characteristic information; Among them, the functional sensor and the temperature sensor are both sensors configured according to the actual test environment of the preset detection chamber to meet the actual temperature resistance and corrosion resistance requirements.

8. A high-temperature resistance testing system for fire helmets, characterized in that, The step of implementing the high-temperature resistance performance testing method for a fire helmet according to any one of claims 1 to 7, wherein the high-temperature resistance performance testing system for the fire helmet comprises: The protection failure characteristic analysis module is used to perform structural decomposition and protection failure characteristic analysis on the fire helmet, and obtain multiple protection failure characteristic information corresponding to multiple helmet components. The protective status monitoring module is used to place the fire helmet in a preset detection chamber, perform incremental control of heat radiation, monitor the protective status through preset sensor components, and determine the first temperature resistance threshold when the status of multiple helmet components reaches the multiple protective failure characteristic information. The high-temperature exposure control module is used to control the high-temperature exposure of the fire helmet in the preset detection chamber at the first temperature resistance threshold, simultaneously apply at least two interrelated physical field loads or chemical field loads, and monitor the response of the fire helmet through a preset sensor assembly to obtain multiple monitoring datasets related to helmet protection failure. The protective performance attenuation analysis module is used to analyze the attenuation of the protective performance of the fire helmet under coupled load based on the multi-type monitoring dataset, and to determine the second temperature resistance threshold under coupled load. The protection status monitoring module is also used for: Based on the preset holding time, the radiative heat flux in the preset detection chamber is controlled to increase multiple times according to the first preset step size, and the surface temperature of each helmet component is monitored simultaneously. The protective status of each helmet component is monitored through the preset sensor component. When the protective state of any helmet component reaches the corresponding high-temperature failure characteristic information for the first time, the surface temperature of the corresponding component is recorded as the first detection temperature, and the second detection temperature of the previous control adjacent to the first detection temperature is extracted. Using the first detection temperature and the second detection temperature as the detection range, continue to perform radiant heat flux control test and monitor the surface temperature and protection status of each helmet component according to the second preset step size based on the preset holding time, determine the corresponding component surface temperature when the corresponding high temperature failure characteristic information is reached, and generate multiple component failure temperatures. The lowest value among the failure temperatures of the plurality of components is defined as the first temperature resistance threshold of the fire helmet; The high-temperature exposure control module is also used for: Collect historical fire scene datasets, extract physical field loads or chemical field loads other than temperature loads in the fire scene, and perform combined recurrence probability analysis of the same fire scene. Identify at least two interrelated physical field loads or chemical field loads with a combined recurrence probability greater than a preset probability threshold as coupling fields, and configure coupling time series test scenarios based on historical coupling data. The fire helmet is subjected to high-temperature exposure control based on the first temperature resistance threshold. Interactive coupling control is performed according to the coupling timing test scenario. Simultaneously, the surface temperature and protection status of each helmet component are monitored through preset sensor components to generate the multi-type monitoring dataset.

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