Oxygen respirator monitoring and alarming method and system

Abstract

The invention provides an oxygen respirator monitoring and alarming method and system, and relates to the technical field of oxygen respirator monitoring and alarming, and the technical scheme is characterized in that a response result of a firefighter to a preset state confirmation request and monitoring parameters related to the physiological discomfort state of the firefighter are acquired; judging whether a preset relevance indicating that the fireman cannot effectively respond to the state confirmation request due to physiological discomfort exists between the obtained response result and the abnormal fluctuation of the monitoring parameters or not; when it is judged that the relevance meets a preset relevance analysis rule, it is determined that the firefighter has the early disability risk that oxygen supply is still sufficient but the action ability is possibly damaged due to non-hypoxia, and early warning or alarming is conducted. The oxygen respirator monitoring and alarming method and system have the advantages that the alarming accuracy is improved, and the defect that a traditional oxygen respirator alarming system mainly pays attention to oxygen supply safety is overcome.

Description

Technical Field

[0001] This application relates to the technical field of oxygen breathing apparatus monitoring and alarm, and in particular, to an oxygen breathing apparatus monitoring and alarm method and system. Background Art

[0002] In fire fighting and rescue operations, especially in environments such as underground commercial complexes with low visibility, complex internal structures, and narrow passageways, firefighters face extremely high risks. To ensure the safety of firefighters, an integrated firefighter safety protection system is usually equipped. This system integrates multiple monitoring and communication modules. For example, a fire fighting oxygen breathing apparatus for real-time monitoring of cylinder pressure, valve status, and calculation of the estimated remaining working time; a fire fighting suit integrated with environmental temperature and humidity sensors, multiple gas concentration sensors, and physiological parameter sensors such as heart rate and body surface temperature; a fire fighting helmet integrated with communication, information display, and lighting functions; and a module for providing indoor positioning information. The data collected by these modules is aggregated to a personal processing unit worn by the firefighter and communicated with the rear command center.

[0003] However, during actual rescue operations, firefighters may encounter various complex situations, posing potential threats to their safety, and existing monitoring and alarm systems may not be able to identify these risks in a timely and effective manner. For example, when passing through a narrow passage, a firefighter may inhale a small amount of irritating gas that has not been completely removed by the filtration system, causing severe coughing and brief dizziness, resulting in body shaking and slow movement. In this case, even if key indicators such as the cylinder pressure of the oxygen breathing apparatus are still within the safe range and the low-oxygen alarm has not been triggered, the physiological state and mobility of the firefighter may be affected. At the same time, the posture sensor on the fire fighting suit may only record short-term irregular activities, which do not reach the preset alarm conditions representing a distress state (such as falling or long-term stillness).

[0004] Existing integrated safety protection systems usually have an active firefighter status confirmation mechanism, which sends a status confirmation request to the firefighter through a voice assistant or a mask display screen. However, in the case of restricted movement due to physiological discomfort (such as dizziness and coughing) as described above, the firefighter may not be able to effectively respond to the status confirmation request within the preset response window period, resulting in a "status confirmation timeout" event.

[0005] Furthermore, although the cylinder pressure of the oxygen breathing apparatus remains normal, the firefighter's coughing and discomfort may cause short-term and irregular fluctuations in the breathing rate, resulting in a slightly higher instantaneous oxygen consumption rate than normal, but the amplitude of this fluctuation is not sufficient to cause the estimated remaining working time to rapidly drop to the alarm threshold. At the same time, the gas sensor on the fire fighting suit may detect a weak upward trend in the concentration of a certain irritating gas in the environment, but it also does not reach the concentration threshold for independently triggering a gas alarm.

[0006] The existing monitoring and alarm methods for fire-fighting oxygen breathing apparatuses mainly rely on single or independent threshold judgments such as oxygen pressure. In specific situations where multiple indicators (such as status confirmation responses, fluctuations in breathing parameters, slight changes in ambient gas concentration, irregular postural activities, etc.) all show abnormal fluctuations, and these abnormal combinations may indicate that the firefighter fails to effectively respond to the status confirmation request due to physical discomfort, the traditional alarm logic has significant deficiencies. If only relying on the cylinder pressure threshold alarm of the oxygen breathing apparatus itself, no warning will be issued at this time because the oxygen stock is still sufficient. If simply equating the "status confirmation timeout" event with the most urgent distress signal and immediately notifying the command center and teammates, false alarms may occur when the firefighter is only temporarily inconvenient to respond or there is a brief communication interference, disrupting the rescue order. However, if there is no effective identification and early warning mechanism for the state composed of various physiological index abnormalities and environmental factors that do not reach the clear alarm standard and may gradually develop into the incapability of the firefighter, it may delay the early attention and necessary assistance to the firefighter. Once the firefighter's condition deteriorates further, such as falling due to dizziness or losing consciousness, even if the oxygen breathing apparatus supplies oxygen normally, it cannot guarantee their safety until the oxygen is exhausted or a more serious secondary accident occurs to trigger a clear alarm, and the best rescue opportunity may have been missed at this time. Especially for the alarm system of the oxygen breathing apparatus, its core goal is to ensure oxygen supply safety. However, in the above scenarios, the safety risk of the firefighter does not directly stem from the oxygen supply itself, but from the abnormal state of its user. This requires that the monitoring and alarm method of the oxygen breathing apparatus can be integrated with a wider range of firefighter status monitoring information, identify such risks that do not directly act on oxygen supply but can lead to oxygen safety problems (due to the user's incapability to effectively utilize oxygen) at the initial stage of the event development, and give appropriate early warning or alarm according to the risk level.

[0007] In view of the above problems, the existing technology urgently needs to be improved. Summary of the Invention

[0008] The purpose of this application is to provide a monitoring and alarm method and system for oxygen breathing apparatuses, which have the advantages of improving the accuracy of alarm and making up for the deficiency that the traditional alarm system of oxygen breathing apparatuses mainly focuses on oxygen supply safety.

[0009] In the first aspect, this application provides a monitoring and alarm method for oxygen breathing apparatuses, and the technical solution is as follows:

[0010] Including:

[0011] Obtaining the response result of the firefighter to the preset status confirmation request;

[0012] Obtaining multiple monitoring parameters from different sources related to the physical discomfort state of the firefighter;

[0013] Based on a preset correlation analysis rule, determine whether there is a preset correlation between the obtained response result and the abnormal fluctuations of the monitoring parameters from multiple different sources, which indicates that the firefighter fails to effectively respond to the status confirmation request due to the physical discomfort.

[0014] When the determined correlation meets the preset correlation analysis rule, determine that the firefighter has an early disability risk caused by non-hypoxia reasons, with sufficient oxygen supply but possible impaired mobility, and trigger a warning or alarm for the early disability risk based on the determined early disability risk.

[0015] Further, in the present application, the step of determining whether there is a preset correlation between the obtained response result and the abnormal fluctuations of the monitoring parameters from multiple different sources, which indicates that the firefighter fails to effectively respond to the status confirmation request due to the physical discomfort, based on a preset correlation analysis rule includes:

[0016] Obtain real-time physiological data indicating the current individual physiological characteristics of the firefighter, and real-time environmental parameters indicating the current environmental state of the rescue site.

[0017] According to the difference between the real-time physiological data and the baseline physiological data of the firefighter, or according to the change characteristics of the real-time environmental parameters, determine one or more rule adjustment instructions for the preset correlation analysis rule.

[0018] Apply the one or more rule adjustment instructions to adjust at least one parameter or logical condition in the preset correlation analysis rule to generate an adjusted correlation analysis rule.

[0019] Use the adjusted correlation analysis rule, combined with the obtained response result and the abnormal fluctuations of the monitoring parameters from multiple different sources, to determine whether there is a preset correlation between the response result and the abnormal fluctuations of the monitoring parameters from multiple different sources, which indicates that the firefighter fails to effectively respond to the status confirmation request due to the physical discomfort.

[0020] Further, in the present application, the step of determining one or more rule adjustment instructions for the preset correlation analysis rule according to the difference between the real-time physiological data and the baseline physiological data of the firefighter, or according to the change characteristics of the real-time environmental parameters includes:

[0021] Obtain a first set of candidate rule adjustment parameters determined by the difference between the real-time physiological data and the baseline physiological data of the firefighter.

[0022] Obtain a second set of candidate rule adjustment parameters determined by the change characteristics of the real-time environmental parameters.

[0023] According to the preset conflict determination rule, determine whether there is an indication conflict between the first set of candidate rule adjustment parameters and the second set of candidate rule adjustment parameters;

[0024] When it is determined that there is the indication conflict, then according to the preset conflict handling mechanism, combining the first set of candidate rule adjustment parameters and the second set of candidate rule adjustment parameters, determine the one or more rule adjustment instructions for the preset correlation analysis rule;

[0025] When it is determined that there is no such indication conflict, then determine the one or more rule adjustment instructions for the preset correlation analysis rule according to the first set of candidate rule adjustment parameters determined by the difference between the real-time physiological data and the reference physiological data of the firefighter, or according to the second set of candidate rule adjustment parameters determined by the change characteristics of the real-time environmental parameters, or by combining the first set of candidate rule adjustment parameters and the second set of candidate rule adjustment parameters.

[0026] Further, in the present application, when the preset conflict handling mechanism includes at least two conflict handling strategies, and the current conflict situation composed of the first set of candidate rule adjustment parameters and the second set of candidate rule adjustment parameters simultaneously satisfies the conditions for activating the at least two conflict handling strategies, or the conflict situation does not fully satisfy the activation conditions of any preset conflict handling strategy, the step of determining the one or more rule adjustment instructions according to the preset conflict handling mechanism, combining the first set of candidate rule adjustment parameters and the second set of candidate rule adjustment parameters includes:

[0027] Obtain the quantitative characteristics of the conflict situation;

[0028] Obtain the time series change trend of the first set of candidate rule adjustment parameters and the time series change trend of the second set of candidate rule adjustment parameters;

[0029] Obtain the feedback data on the execution effect of the historical rule adjustment instructions related to the historical conflict situation, historical parameter trend, and historical conflict handling strategy adopted;

[0030] According to the preset meta-rule set, and in combination with the obtained quantitative characteristics of the conflict situation, the obtained time series change trend, and the obtained feedback data, select one conflict handling strategy from the at least two conflict handling strategies included in the preset conflict handling mechanism, or combine the at least two conflict handling strategies to generate the current conflict handling solution;

[0031] Apply the current conflict handling solution, and combine the first set of candidate rule adjustment parameters and the second set of candidate rule adjustment parameters to determine the one or more rule adjustment instructions.

[0032] Further, in the present application, when the meta-rule indicates that at least two conflict handling strategies need to be combined to generate the current conflict handling solution, the steps of combining the at least two conflict handling strategies based on a preset set of meta-rules, and in combination with the quantified features of the obtained conflict situation, the obtained time series change trend, and the obtained feedback data to generate the current conflict handling solution include:

[0033] Identify the combinatorial parameters of the at least two conflict handling strategies that are greater than one and adjustable, where the combinatorial parameters are used to characterize the combination method of the at least two conflict handling strategies, and the combination method includes at least one of weight assignment, execution order, and fusion logic;

[0034] Based on the indication of the meta-rule, or with reference to the valid combinatorial parameter values recorded in the feedback data similar to the quantified features of the conflict situation and the time series change trend at this moment, set a set-form starting combinatorial parameter value for the obtained combinatorial parameters that are greater than one and adjustable;

[0035] By adjusting the value of at least one parameter among the obtained combinatorial parameters that are greater than one and adjustable, generate a set of combinatorial parameter values that are greater than one and different for selection;

[0036] Using a preset combination effect evaluation logic, for each set-form combinatorial parameter value for selection generated, and in combination with the quantified features of the conflict situation, the time series change trend, and the feedback data at this moment, calculate the numerical evaluation index of its corresponding expected effect, where the combination effect evaluation logic is used to output the numerical evaluation index of the expected effect of combining the combinatorial parameter values for selection according to the input quantified features of the conflict situation, the time series change trend, the feedback data, and the set-form combinatorial parameter values for selection;

[0037] From the numerical evaluation indexes of each set-form combinatorial parameter value for selection calculated, select a set-form combinatorial parameter value that makes the numerical evaluation index meet a preset optimization goal as the finally determined combinatorial parameter;

[0038] Apply the finally determined combinatorial parameter selected to combine the at least two conflict handling strategies to generate the current conflict handling solution.

[0039] Further, in the present application, the monitoring parameters from multiple different sources include at least two of the following: environmental gas concentration parameters, firefighters' breathing parameters, firefighters' posture parameters, firefighters' heart rate parameters, and firefighters' body surface temperature parameters.

[0040] Further, in the present application, the real-time environmental parameters include at least one of environmental temperature and gas concentration.

[0041] Further, in the present application, the meta-rule set is a set of rules that defines, based on the quantitative characteristics of the conflict situation, the trend of the time series change, and the feedback data, the logic of selecting one of the at least two conflict handling strategies included in the preset conflict handling mechanism, or determining the combination method of the at least two conflict handling strategies.

[0042] Further, in the present application, the preset combined effect evaluation logic at least includes:

[0043] Mapping the key elements in the quantitative characteristics of the input conflict situation, the trend of the time series change, the feedback data, and the alternative combined parameter values to be evaluated currently into a set of predefined scoring items;

[0044] Setting corresponding weight coefficients and scoring rules for each scoring item;

[0045] Obtaining the numerical evaluation index by calculating the scores of each scoring item and performing weighted summation or logical reasoning.

[0046] In a second aspect, the present application also proposes an oxygen breathing apparatus monitoring and alarm system, which includes:

[0047] A response result acquisition module, configured to acquire the response result of a firefighter to a preset status confirmation request;

[0048] A monitoring parameter acquisition module, configured to acquire monitoring parameters from multiple different sources related to the physiological discomfort state of the firefighter, where each of the monitoring parameters from the multiple different sources does not reach a preset independent alarm threshold, and the monitoring parameters from the multiple different sources show abnormal fluctuations indicating the physiological discomfort;

[0049] A relevance judgment module, configured to judge, based on a preset correlation analysis rule, whether there is a preset relevance indicating that the firefighter fails to effectively respond to the status confirmation request due to the physiological discomfort between the response result acquired by the response result acquisition module and the abnormal fluctuations of the monitoring parameters from multiple different sources acquired by the monitoring parameter acquisition module;

[0050] A risk determination and early warning module, which is configured to determine that the firefighter has an early disability risk caused by non-hypoxia reasons, with sufficient oxygen supply but possible impaired mobility, when the relevance determined by the relevance judgment module meets the preset correlation analysis rules, and trigger an early warning or alarm for the determined early disability risk based on the determined early disability risk.

[0051] As can be seen from the above, an oxygen breathing apparatus monitoring and alarm method and system provided by the present application obtain the response result of the firefighter to the status confirmation request and the abnormal fluctuations of multiple sub-threshold monitoring parameters, and judge whether there is a correlation of ineffective response due to physical discomfort based on correlation analysis, so as to identify the early disability risk caused by non-hypoxia reasons and trigger an early warning or alarm. It has the ability to effectively identify the early disability risk of firefighters caused by a combination of multiple abnormalities that are difficult to discover in the prior art, provide an early warning or alarm in the early stage when the oxygen supply of the firefighter is sufficient but the mobility may be impaired, thereby improving the accuracy of the alarm and making up for the deficiency that the traditional oxygen breathing apparatus alarm system mainly focuses on oxygen supply safety. Brief Description of the Drawings

[0052] Figure 1 It is a schematic flowchart of an oxygen breathing apparatus monitoring and alarm method provided by the present application.

[0053] Figure 2 It is a schematic structural diagram of an oxygen breathing apparatus monitoring and alarm system provided by the present application.

[0054] In the figure: 1. The first acquisition module; 2. The second acquisition module; 3. The judgment module; 4. The early warning module. Detailed Embodiments

[0055] Next, the technical solutions in the present application will be clearly and completely described in conjunction with the drawings in the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the present application usually described and illustrated in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the present application to be protected, but only represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0056] It should be noted that: similar reference numerals and letters denote similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of the present application, the terms "first", "second", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0057] In traditional existing methods, when a firefighter has sufficient oxygen supply but fails to effectively respond to a status confirmation request due to physical discomfort, and there are abnormal fluctuations in multiple threshold monitoring parameters, it is impossible to effectively identify the early disability risk and give appropriate warnings or alarms. This limitation stems from the fact that existing systems mainly rely on single or independent monitoring parameter thresholds for alarm judgment. When multiple relevant parameters do not reach their respective independent alarm thresholds, even if the combined abnormal fluctuations of these parameters and the user's non-response behavior jointly indicate the potential risks of physical discomfort and impaired mobility, the system cannot identify it as an event that requires attention. As a result, the system may not be able to promptly perceive the early deterioration of the user's status, affecting the accurate assessment of the user's safety status and the classification of risk levels, and further delaying necessary intervention measures.

[0058] For example, in a rescue operation in a complex environment, a firefighter wears a personal safety protection system integrating multiple monitoring functions. The system includes an oxygen respirator monitoring module, a physiological status monitoring module, and an environmental monitoring module. The system sends a status confirmation request to the user according to a preset strategy, but the user fails to respond in time through the system interface or voice command due to a short-term physical discomfort caused by inhaling trace amounts of irritating substances, and the system records a response timeout event. At the same time, the oxygen respirator monitors fluctuations in the user's breathing frequency and instantaneous oxygen consumption rate, but the amplitudes of these fluctuations do not reach the thresholds for triggering oxygen-related alarms. The physiological status monitoring module detects a slight irregular change in the user's posture, but it does not meet the preset distress posture alarm conditions such as falling or staying still for a long time. The environmental monitoring module detects a slight increase in the concentration of a specific gas in the environment, but the concentration value is far below the threshold for triggering a gas alarm. In this case, despite the user's non-response and abnormal fluctuations in multiple monitoring parameters, the existing system, due to the lack of the ability to comprehensively correlate and analyze these scattered signals, cannot determine whether there is a correlation between these events indicating that the user's mobility may be impaired due to physical discomfort. As a result, the system will not trigger any alarms or only trigger low-level and unclear prompts, failing to effectively reflect the specific early risks faced by the user.

[0059] If the above problems are not solved, the system will not be able to issue an effective warning when the user has sufficient oxygen supply but shows signs of early physical discomfort and impaired mobility. This may cause the user's status to continue to deteriorate without being noticed until a traditional alarm usually associated with a more serious situation (such as an oxygen depletion alarm or a distress posture alarm) is triggered. By then, the user may have completely lost their mobility or consciousness, missing the best opportunity for early intervention and rescue. This delayed risk identification and inappropriate alarm mechanism directly affect the safety protection level of the user in a complex and dangerous environment, increasing the risk of rescue operations.

[0060] In response, referring to Figure 1, this application proposes a method for monitoring and alarming an oxygen breathing apparatus, including:

[0061] S110. Obtain the response result of the firefighter to the preset status confirmation request;

[0062] S120. Obtain multiple monitoring parameters from different sources related to the physiological discomfort state of the firefighter;

[0063] S130. Based on the preset correlation analysis rule, determine whether there is a preset correlation between the obtained response result and the abnormal fluctuations of the multiple monitoring parameters from different sources, which indicates that the firefighter fails to effectively respond to the status confirmation request due to physiological discomfort;

[0064] S140. When the judged correlation meets the preset correlation analysis rule, determine that the firefighter has an early disability risk caused by non-hypoxia reasons, with sufficient oxygen supply but possible impaired mobility, and trigger a warning or alarm for the early disability risk based on the determined early disability risk.

[0065] Among them, obtaining the response result of the firefighter to the preset status confirmation request means the feedback information received by the system after sending a signal to ask the firefighter about the current status. Specifically, it can be implemented by means such as voice recognition, button confirmation, and mask display screen interaction.

[0066] Among them, obtaining multiple monitoring parameters from different sources related to the physiological discomfort state of the firefighter means collecting data from different sensors on the firefighter's equipment, and these data reflect the physiological condition of the firefighter or the environment where the firefighter is located. Specifically, devices such as a respiratory rate sensor, a gas concentration sensor, an attitude sensor, and a heart rate sensor can be used to obtain the data.

[0067] Among them, based on the preset correlation analysis rule, determining whether there is a preset correlation between the obtained response result and the abnormal fluctuations of the multiple monitoring parameters from different sources, which indicates that the firefighter fails to effectively respond to the status confirmation request due to physiological discomfort, means using a pre-set logic or model to analyze whether there is a specific connection pattern between the response result and the sub-threshold abnormal changes of the multi-source monitoring parameters. This pattern indicates that the firefighter's failure to respond is caused by physiological discomfort.

[0068] Among them, when the judged correlation meets the preset correlation analysis rule, determining that the firefighter has an early disability risk caused by non-hypoxia reasons, with sufficient oxygen supply but possible impaired mobility, means that when the previous correlation judgment result is true, the system concludes that the firefighter faces a specific danger. This danger is not due to insufficient oxygen, but due to other reasons that may cause a decline in their mobility, although the oxygen is still sufficient. Specifically, it can be implemented by means such as logical judgment and state machine conversion.

[0069] Among them, triggering an early disability risk warning or alarm based on the determined early disability risk means that according to the specific early risks determined previously, the system issues corresponding prompt information. This prompt information is issued according to the nature of the specific risk, and can be specifically implemented by means such as sound alarms, visual prompts, and sending information to the command center.

[0070] The core innovation of this application lies in comprehensively analyzing the correlation between the response results of firefighters to status confirmation requests and the abnormal fluctuations of monitoring parameters from multiple different sources, so as to identify the risk that firefighters may face early disability due to non-hypoxic reasons when the oxygen supply is sufficient, and trigger a warning or alarm based on this risk, which is different from the existing methods that only rely on a single or independent threshold for alarm.

[0071] The working process and principle of this application are that this method aims to identify the early disability risk that exists when firefighters, under the condition of sufficient oxygen supply, fail to effectively respond to the system status confirmation request due to physical discomfort, and at the same time, abnormal fluctuations occur in multiple monitoring parameters that do not reach the independent alarm threshold.

[0072] Specifically, first obtain the response result of the firefighter to the preset status confirmation request issued by the system. This request is used to actively detect the current status and interaction ability of the firefighter. The response result can be the active confirmation signal of the firefighter or the situation where no effective response is received within the preset time window. Failure to effectively respond indicates that the firefighter may have some abnormal status.

[0073] At the same time, obtain multiple monitoring parameters from different sources related to the physical discomfort status of the firefighter. These parameters can include respiratory rate, oxygen consumption rate, heart rate, body surface temperature, posture change, environmental gas concentration, etc., which are collected by different sensors or modules integrated on the firefighter's equipment. Obtaining parameters from multiple different sources is to provide more comprehensive information to reflect the subtle changes in the physiological state of the firefighter and the surrounding environment. The fluctuation range of each of these parameters may not be sufficient to trigger an independent alarm threshold, but their combined changes may indicate potential problems.

[0074] Furthermore, based on the pre-determined correlation analysis rules, determine whether there is a specific correlation between the obtained response result and the abnormal fluctuations of the multiple monitoring parameters obtained from different sources. This correlation is defined as indicating that the firefighter fails to effectively respond to the status confirmation request due to physical discomfort. The correlation analysis rules are used to identify specific patterns. For example, when a firefighter fails to respond to a request, if there are fluctuations in the respiratory rate and a slight increase in the environmental gas concentration at the same time, there may be a correlation indicating physical discomfort. Through this correlation judgment, the method can distinguish the real risk caused by physical discomfort from non-responses caused by other reasons (such as a brief communication interruption).

[0075] Thus, when the judged relevance meets the pre-determined correlation analysis rules, the method determines that the firefighter has an early disability risk caused by non-hypoxic reasons, with sufficient oxygen supply but possible impaired mobility. This determination clarifies the nature (non-oxygen reason) and stage (early, possible impaired mobility) of the risk, which is different from the traditional oxygen depletion alarm. The identification of this early risk enables the system to issue a warning before the problem worsens.

[0076] Finally, based on the determined early disability risk, the method triggers a warning or alarm for this early disability risk. The warning or alarm can be graded according to the risk level. For example, a local prompt is sent to the firefighter himself, or a specific type of alarm signal is sent to the rear command center, which indicates that the nature of the risk is "early disability risk caused by non-hypoxic reasons", rather than the traditional oxygen depletion alarm or distress posture alarm. Thus, the command center can understand the specific nature of the risk and take targeted support measures.

[0077] As a preferred embodiment, the solution of the present application is specifically implemented as follows:

[0078] In a personal safety protection system for firefighters, an oxygen respirator monitoring module, a physiological state monitoring module, and an environmental monitoring module are integrated. The system periodically or after detecting a specific event (such as entering a new area), sends a request of "please confirm the status" to the firefighter through a voice prompt inside the helmet. The system sets a response time window, such as several seconds. The response result acquisition module records whether the firefighter has confirmed through a voice command or a button on the helmet within this time window. If no valid confirmation is received, it is recorded as "response timeout".

[0079] Meanwhile, the monitoring parameter acquisition module continuously collects data from different sources. The oxygen respirator monitoring module provides respiratory rate and instantaneous oxygen consumption rate data. The physiological state monitoring module provides heart rate and posture sensor data. The environmental monitoring module provides environmental temperature, humidity, and various gas concentration data. The real-time values of these parameters are obtained.

[0080] The relevance judgment module receives the response result (such as "response timeout") and the real-time data of multiple monitoring parameters provided by the monitoring parameter acquisition module. A set of pre-determined correlation analysis rules is stored inside this module. For example, a rule may stipulate that: "If the response times out, and the fluctuation range of the respiratory rate exceeds a preset threshold A within the past 10 seconds, and the concentration of environmental gas X has increased by more than a preset threshold B within the past 30 seconds, then it is judged that there is a relevance indicating physical discomfort". The relevance judgment module matches and judges the obtained response result and monitoring parameters with the rule set.

[0081] When the relevance judgment module determines that the current situation meets one or more association analysis rules, the risk determination and early warning module is activated. Based on the judgment result, this module determines that there is an early disability risk for firefighters caused by non-hypoxia reasons, with sufficient oxygen supply but possible impaired mobility. For example, the system determines that the current situation conforms to the risk pattern of "impaired mobility due to physical discomfort".

[0082] Based on the determined early disability risk, the risk determination and early warning module triggers corresponding early warnings or alarms. For example, the system can send a text prompt "Attention: There may be a risk of early physical discomfort" to the display screen inside the firefighter's helmet, and at the same time send a specific type of alarm signal to the rear command center. This signal indicates that the nature of the risk is "early disability risk caused by non-hypoxia reasons", rather than the traditional "oxygen depletion alarm" or "distress posture alarm". Thus, the command center can understand the specific nature of the risk and take targeted support measures.

[0083] Through the above solution, this application solves the problem that when the oxygen supply of firefighters is sufficient but they fail to effectively respond to the status confirmation request due to physical discomfort, and there are abnormal fluctuations in multiple monitoring parameters, the existing methods cannot effectively identify the early disability risk and give appropriate early warnings or alarms. This method can identify the early disability risk caused by potential physical discomfort and non-oxygen reasons by comprehensively analyzing the relevance between the response status of firefighters and the abnormal fluctuations of various physiological and environmental monitoring data. Thus, the system can detect potential dangers in advance when the problem has not deteriorated severely and the oxygen supply is still sufficient, and trigger early warnings or alarms for specific risk natures, so as to achieve more timely and appropriate intervention and win time for ensuring the safety of firefighters.

[0084] Specifically, in some of the above solutions of this application, it is proposed to judge whether there is a relevance between the failure of firefighters to effectively respond to the status confirmation request and the abnormal fluctuations of monitoring parameters related to physical discomfort based on preset association analysis rules, so as to identify the early disability risk of firefighters. However, due to the dynamic changes in the individual physiological characteristics of firefighters and the environmental conditions at the rescue scene, a fixed preset association analysis rule may not be able to fully adapt to these changes, resulting in inaccurate judgment of relevance, thus affecting the reliability of early disability risk identification and possibly causing false alarms or missed alarms.

[0085] In response to this, this application further proposes steps for judging whether there is a preset relevance indicating that firefighters fail to effectively respond to the status confirmation request due to physical discomfort between the obtained response result and the abnormal fluctuations of multiple monitoring parameters from different sources based on the preset association analysis rules, including:

[0086] Obtain real-time physiological data indicating the current individual physiological characteristics of a firefighter, and real-time environmental parameters indicating the current environmental state of the rescue scene;

[0087] Determine one or more rule adjustment instructions for a preset correlation analysis rule based on the difference between the real-time physiological data and the baseline physiological data of the firefighter, or based on the change characteristics of the real-time environmental parameters;

[0088] Apply one or more rule adjustment instructions to adjust at least one parameter or logical condition in the preset correlation analysis rule to generate an adjusted correlation analysis rule;

[0089] Use the adjusted correlation analysis rule to combine the obtained response result with the abnormal fluctuations of the monitoring parameters from multiple different sources to determine whether there is a preset correlation indicating that the firefighter fails to effectively respond to the status confirmation request due to physical discomfort between the response result and the abnormal fluctuations of the monitoring parameters from multiple different sources.

[0090] Among them, the purpose of this method is to solve the problem of insufficient judgment accuracy that may occur when using a fixed preset correlation analysis rule for judgment, which may be caused by not considering the individual differences of firefighters and real-time environmental changes. Its core lies in introducing real-time dynamic information and adaptively adjusting the correlation analysis rule based on this information, so as to improve the accuracy and reliability of early disability risk judgment.

[0091] Specifically, this solution first obtains real-time physiological data indicating the current individual physiological characteristics of a firefighter, and real-time environmental parameters indicating the current environmental state of the rescue scene. Obtaining real-time physiological data can reflect the current physical state of the firefighter, such as heart rate, respiratory rate, body temperature, etc. These data are affected by various factors such as individual basic differences, fatigue level, and health status. Obtaining real-time environmental parameters can reflect the external environmental conditions where the firefighter is located, such as temperature, humidity, specific gas concentration, etc. These environmental factors also have an impact on the physiological state and monitoring parameters of the firefighter. By obtaining these real-time and dynamic data, it provides basic information for subsequent rule adjustment. Real-time physiological data can be obtained through physiological sensors worn on the firefighter, such as heart rate belts, thermometers, respiratory sensors, etc. Real-time environmental parameters can be obtained through environmental sensors integrated in the fire suit or helmet, such as gas sensors, temperature and humidity sensors, etc. These data are transmitted to the personal processing unit or the rear command center for processing.

[0092] Next, based on the differences between the real-time physiological data and the baseline physiological data of the firefighter, or according to the changing characteristics of the real-time environmental parameters, one or more rule adjustment instructions for the preset correlation analysis rules are determined. Comparing the real-time physiological data with the baseline physiological data of the firefighter can quantify the deviation of the current physiological state from its normal or baseline state, and this difference may indicate the current physiological load or potential abnormalities of the firefighter. For example, if the real-time heart rate is significantly higher than the baseline resting heart rate or the average heart rate during the task of this firefighter, it may indicate an increase in physiological load. At the same time, analyzing the time series of the real-time environmental parameters can evaluate the possible impact of environmental factors on the physiological state and parameter performance of the firefighter. For example, a rapid increase in environmental temperature may lead to an increase in body temperature and heart rate. Based on these physiological differences or environmental change characteristics, the system can intelligently generate instructions to adjust the preset correlation analysis rules. For example, in a high-temperature and high-humidity environment, it may be necessary to adjust the threshold for judging abnormal fluctuations in physiological parameters, or when detecting trace amounts of irritating gases, it is necessary to pay more attention to the relevance between the respiratory system-related indicators and the unresponsive state more sensitively. Determining the rule adjustment instruction is a key step in achieving the dynamic adaptability of the rules. The rule adjustment instruction can be a numerical value indicating that a certain threshold needs to be increased or decreased by an amount, or it can be a logical identifier indicating that a certain logical condition needs to be activated or disabled.

[0093] Then, apply one or more rule adjustment instructions to adjust at least one parameter or logical condition in the preset correlation analysis rules to generate an adjusted correlation analysis rule. This step is to specifically implement the adjustment instructions determined in the previous step onto the preset correlation analysis rules. The adjustment can be to modify the numerical parameters used to judge the relevance in the rules. For example, adjust the judgment threshold for abnormal fluctuations in a certain physiological parameter from the preset X to X + ΔX, where ΔX is determined by the rule adjustment instruction. The adjustment can also be to add, delete, or modify the weights of the logical conditions in the rules. For example, in a specific environment, add a judgment condition that requires both abnormal fluctuations in physiological parameter A and abnormal changes in environmental parameter B to be met to judge the existence of relevance, or increase the weight of abnormal fluctuations in a certain parameter in the relevance judgment. Through this adjustment, a general preset rule is transformed into an adjusted rule that is more in line with the current individual physiological state and environmental characteristics of the firefighter and is more targeted.

[0094] Finally, using the adjusted association analysis rules, combined with the abnormal fluctuations of the obtained response results and the monitoring parameters from multiple different sources, it is determined whether there is a preset association indicating that the firefighter fails to effectively respond to the status confirmation request due to physical discomfort between the response results and the abnormal fluctuations of the monitoring parameters from multiple different sources. Different from directly using fixed preset rules for judgment, the rules used here are dynamically adjusted. This adjusted rule can more accurately evaluate the strength and nature of the association between the firefighter's failure to respond to the status confirmation request and the abnormal fluctuations of the monitoring parameters under the current individual physiological state and environmental conditions, so as to more reliably judge whether there is an early disability risk caused by physical discomfort. For example, when the environmental temperature is high, the adjusted rule may allow a higher heart rate fluctuation range, and when trace amounts of toxic gases are detected, the adjusted rule may be more sensitive to small fluctuations in the breathing rate. By using the adjusted rule for judgment, misjudgments caused by individual differences or environmental changes can be reduced, and the accuracy of early disability risk identification can be improved. The judgment result is then used to determine whether there is an early disability risk for the firefighter and trigger corresponding warnings or alarms.

[0095] Through the above steps, this solution realizes the dynamic adaptive adjustment of the association analysis rules, enabling them to better adapt to the individual differences of firefighters and the changes in the rescue site environment, improving the accuracy and robustness of early disability risk identification, and thus being able to issue warnings or alarms more timely and reliably, providing a more effective technical means for ensuring the safety of firefighters.

[0096] Specifically, in some of the above solutions of this application, it is proposed to determine one or more rule adjustment instructions for the pre-set association analysis rules according to the difference between the real-time physiological data and the benchmark physiological data of the firefighter, or according to the change characteristics of the real-time environmental parameters, so that the association analysis rules have stronger adaptability to individual differences and environmental changes. However, in this process, the real-time physiological data and the real-time environmental parameters may change simultaneously, and the rule adjustment directions or degrees indicated by them may be inconsistent or even conflict with each other. Simply describing the method of determining the rule adjustment instructions fails to handle the potential conflicts between the adjustment requirements from these two different sources or comprehensively utilize the information of both according to specific goals, which may lead to the determined rule adjustment instructions not accurately reflecting the actual situation or considering all relevant factors, affecting the credibility of the subsequent association judgment based on the changed rules, and may increase the possibility of false reports or missed reports.

[0097] In response to this, this application further proposes that the steps of determining one or more rule adjustment instructions for the pre-set association analysis rules according to the difference between the real-time physiological data and the benchmark physiological data of the firefighter, or according to the change characteristics of the real-time environmental parameters, include:

[0098] Obtain a first set of candidate rule adjustment parameters determined by the differences between real-time physiological data and the baseline physiological data of the firefighter;

[0099] Obtain a second set of candidate rule adjustment parameters determined by the change characteristics of real-time environmental parameters;

[0100] According to a pre-set conflict determination rule, determine whether there is an indication conflict between the first set of candidate rule adjustment parameters and the second set of candidate rule adjustment parameters;

[0101] When it is determined that there is an indication conflict, then according to a pre-set conflict handling mechanism, combine the first set of candidate rule adjustment parameters and the second set of candidate rule adjustment parameters to determine one or more rule adjustment instructions for a pre-set association analysis rule;

[0102] When it is determined that there is no indication conflict, then determine one or more rule adjustment instructions for a pre-set association analysis rule according to the first set of candidate rule adjustment parameters determined by the differences between real-time physiological data and the baseline physiological data of the firefighter, or according to the second set of candidate rule adjustment parameters determined by the change characteristics of real-time environmental parameters, or by combining the first set of candidate rule adjustment parameters and the second set of candidate rule adjustment parameters.

[0103] Among them, this solution has detailed regulations and improvements for the steps of determining rule adjustment instructions, aiming to accurately and stably generate rule adjustment instructions and be able to handle situations when there are conflicts in adjustment requirements from different data sources.

[0104] First, by obtaining a first set of candidate rule adjustment parameters determined by the differences between real-time physiological data and the baseline physiological data of the firefighter, and obtaining a second set of candidate rule adjustment parameters determined by the change characteristics of real-time environmental parameters, this solution distinguishes adjustment suggestions from physiological states and environmental states, which is the basis for handling potential conflicts. For example, the first set of candidate rule adjustment parameters may include rule weight adjustment suggestions calculated based on the deviation degrees of heart rate and respiratory rate from the baseline values, while the second set of candidate rule adjustment parameters may include rule threshold adjustment suggestions calculated based on the change rates of environmental temperature and toxic gas concentration.

[0105] Next, according to the pre-set conflict determination rules, it is determined whether there is an indication conflict between these two sets of candidate rule adjustment parameters. This step identifies the situations that require special handling. The conflict determination rules can be a set of logical conditions. For example, if the first set of parameters indicates that a certain judgment threshold should be lowered, while the second set of parameters indicates that the threshold should be raised, then a conflict is determined. When it is determined that there is an indication conflict, this solution no longer simply selects one of the sources. Instead, according to the pre-set conflict handling mechanism, combining the first set of candidate rule adjustment parameters and the second set of candidate rule adjustment parameters, one or more final rule adjustment instructions are determined. This means that the system will fuse, coordinate, or selectively apply the adjustment suggestions from different sources to generate an adjustment instruction that is logical and meets specific goals, thus solving the problem of conflicting adjustment requirements from different data sources. For example, the conflict handling mechanism can adopt a weighted average method, allocate weights according to the current credibility of physiological data and environmental data, and calculate the final adjustment parameters; or, according to the pre-set priority rules, preferentially adopt the suggestions of physiological data or environmental data in a specific conflict mode.

[0106] When it is determined that there is no indication conflict, this solution determines one or more rule adjustment instructions according to the first set of candidate rule adjustment parameters determined by the difference between the real-time physiological data and the baseline physiological data of the firefighter, or according to the second set of candidate rule adjustment parameters determined by the change characteristics of the real-time environmental parameters, or by combining the first set of candidate rule adjustment parameters and the second set of candidate rule adjustment parameters. This provides the possibility of selection, allowing to choose the adjustment source that meets specific goals according to the situation or comprehensively consider both when there is no conflict. For example, when there is no conflict, the first set of parameters can be directly adopted, or the second set of parameters can be directly adopted, or the two sets of parameters can be simply superimposed or averaged. By introducing candidate parameters, conflict determination, and conflict handling mechanisms, this solution can handle the adjustment requirements from different monitoring sources in more detail. When there are conflicts among these requirements, it can be coordinated according to the pre-set solution and the numerical indicators can be made to reach specific goals, thus generating more accurate and stable rule adjustment instructions.

[0107] Thus, the ability of the changed association analysis rule to achieve the expected function is improved, which helps to more accurately identify the early disability risk of firefighters. By explicitly identifying and handling the potential conflicts between physiological data and environmental data in the process of determining rule adjustment instructions, this solution solves the complex situations that cannot be handled by the simple "or" logic, improves the accuracy and stability of rule adjustment, and further enhances the credibility of early disability risk judgment, reducing the possibility of false reporting or missed reporting.

[0108] Specifically, in some of the above solutions of the present application, it is proposed to determine one or more rule adjustment instructions for a preset association analysis rule based on a preset conflict handling mechanism, in combination with a first set of candidate rule adjustment parameters determined by the difference between real-time physiological data and reference physiological data and a second set of candidate rule adjustment parameters determined by the change characteristics of real-time environmental parameters, so as to handle and generate adjustment instructions when there are conflicts in the rule adjustment direction or degree indicated by physiological data and environmental parameters. However, in practical applications, the preset conflict handling mechanism may include multiple conflict handling strategies. When the conflict situation composed of the first set of candidate rule adjustment parameters and the second set of candidate rule adjustment parameters is complex, for example, when the conditions for activating multiple conflict handling strategies are simultaneously met, or when the activation conditions of any preset conflict handling strategy are not fully met, how to select the most suitable one from these strategies for the current situation, or how to effectively combine these strategies to generate a rule adjustment instruction that can more accurately and robustly reflect the current complex situation is a problem that existing solutions have not fully solved. Simply applying fixed strategies or lacking a flexible strategy selection / combination mechanism may result in inaccurate rule adjustment instructions, affecting the adjustment effect of subsequent association analysis rules, and further affecting the accuracy and timeliness of early disability risk identification.

[0109] In response to this, the present application further proposes that when the preset conflict handling mechanism includes at least two conflict handling strategies, and the current conflict situation composed of the first set of candidate rule adjustment parameters and the second set of candidate rule adjustment parameters simultaneously meets the conditions for activating the at least two conflict handling strategies, or the conflict situation does not fully meet the activation conditions of any preset conflict handling strategy, the steps of determining one or more rule adjustment instructions based on the preset conflict handling mechanism, in combination with the first set of candidate rule adjustment parameters and the second set of candidate rule adjustment parameters, include:

[0110] Obtain the quantitative characteristics of the conflict situation;

[0111] Obtain the time series change trend of the first set of candidate rule adjustment parameters and the time series change trend of the second set of candidate rule adjustment parameters;

[0112] Obtain the feedback data on the execution effect of historical rule adjustment instructions related to historical conflict situations, historical parameter trends, and historical conflict handling strategies adopted;

[0113] Based on a preset meta-rule set, and in combination with the obtained quantitative characteristics of the conflict situation, the obtained time series change trends, and the obtained feedback data, select one conflict handling strategy from the at least two conflict handling strategies included in the preset conflict handling mechanism, or combine the at least two conflict handling strategies to generate the current conflict handling solution;

[0114] Apply the current conflict handling solution, and combine the parameter adjustment of the first set of candidate rules and the parameter adjustment of the second set of candidate rules to determine one or more rule adjustment instructions.

[0115] Among them, when the preset conflict handling mechanism includes at least two conflict handling strategies, and the current conflict situation composed of the parameter adjustment of the first set of candidate rules and the parameter adjustment of the second set of candidate rules is complex or ambiguous, first obtain the quantitative characteristics of the conflict situation. The quantitative characteristics of the conflict situation can be a numerical or categorical representation of the conflict degree, conflict direction difference, number of parameters involved, etc. For example, the angular difference between the adjustment directions (such as increasing or decreasing the rule weight) indicated by the first and second sets of parameters can be calculated, or the ratio or difference between the adjustment amplitudes they indicate can be calculated. These quantitative characteristics provide an objective description of the current conflict state.

[0116] Furthermore, obtain the time series change trend of the parameter adjustment of the first set of candidate rules and the time series change trend of the parameter adjustment of the second set of candidate rules. The time series change trend can include the change rate, acceleration, volatility, periodicity, etc. of the parameter value over time. For example, it can be calculated whether the adjustment parameter indicated by the physiological parameter has been continuously increasing or fluctuating greatly in a recent period of time, and whether the adjustment parameter indicated by the environmental parameter is stable or changing rapidly. These trend information reflects the dynamic evolution process and potential development direction of the conflict.

[0117] In addition, obtain the feedback data on the execution effect of the historical rule adjustment instructions related to the historical conflict situation, historical parameter trend, and historical conflict handling strategies adopted. The feedback data can include what conflict handling strategy was adopted when a similar conflict situation and parameter trend occurred in the past, and the evaluation of the adjustment effect of the rule adjustment instruction finally generated by this strategy on the subsequent correlation analysis rules (for example, whether early risks were successfully identified, whether false alarms or missed alarms were reduced). These historical data constitute an empirical knowledge base for guiding decision-making in the current complex situation.

[0118] Based on a preset set of meta - rules, combined with the quantified characteristics of the obtained conflict situation, the obtained time - series change trend, and the obtained feedback data, select one conflict - handling strategy from at least two conflict - handling strategies included in the preset conflict - handling mechanism, or combine at least two conflict - handling strategies to generate the current conflict - handling solution. The set of meta - rules is a set of higher - level rules used to guide how to select or combine basic conflict - handling strategies according to the current specific situation (described by quantified characteristics and time trends) and historical experience (provided by feedback data). For example, the meta - rules can stipulate that: if the conflict level is high and the physiological parameter trend shows rapid deterioration, then preferentially select a strategy that tends to be conservative (i.e., more likely to trigger an alarm); if the conflict level is medium and historical data shows that a certain strategy has the best effect under a similar trend, then select that strategy; if the current situation has similarities with multiple situations in the historical record, or a single strategy is difficult to handle, then the meta - rules can indicate to combine multiple strategies in ways such as weighted averaging or sequential execution. The setting of the meta - rules can be based on the experience of domain experts.

[0119] That is, the set of meta - rules is a logical set, the content of which is the rules for guiding how to make strategy selection or combination, defining the logic of selecting one of the at least two conflict - handling strategies included in the preset conflict - handling mechanism according to the quantified characteristics of the conflict situation, the time - series change trend, and the feedback data, or determining the combination method of the at least two conflict - handling strategies.

[0120] Apply the current conflict - handling solution, and combine the parameter adjustment of the first set of candidate rules and the parameter adjustment of the second set of candidate rules to determine one or more rule - adjustment instructions. Once the current optimal conflict - handling solution (whether it is a single strategy or a strategy combination) is determined, apply this solution to the candidate adjustment parameters generated from physiological data and environmental data, and finally calculate the specific instructions for adjusting the association - analysis rules. For example, if the selected strategy is to take the average of the two, then the final instruction may be the average of the adjustment values indicated by the first set and the second set of parameters; if the selected strategy is a weighted combination, then calculate the weighted average according to the determined weights; if the selected strategy is to give priority to physiological data, then the final instruction is mainly determined based on the first set of parameters.

[0121] By introducing the quantified characteristics of the conflict situation, the time - series trend of the parameters, and the historical feedback data, and using the meta - rules for strategy selection or combination, the system no longer simply executes fixed conflict - handling logic, but can learn and adapt, dynamically adjusting the processing method according to the specific situation. This dynamic, experience - based strategy - decision - making process enables the generated rule - adjustment instructions to more accurately reflect the current complex physiological and environmental states, thereby improving the accuracy and robustness of the adjustment of the association - analysis rules.

[0122] Specifically, in some of the above solutions of the present application, it is proposed to select one conflict handling strategy from at least two conflict handling strategies included in a preset conflict handling mechanism, or combine at least two conflict handling strategies, based on a preset set of meta-rules, in combination with the quantified characteristics of the obtained conflict situation, the obtained time series change trend, and the obtained feedback data, so as to generate a current conflict handling solution for handling an indicated conflict existing between a first set of candidate rule adjustment parameters determined by the difference between real-time physiological data and the reference physiological data of a firefighter and a second set of candidate rule adjustment parameters determined by the change characteristics of real-time environmental parameters. However, when the meta-rules indicate that at least two conflict handling strategies need to be combined, how to determine the specific combination method (such as weights, order, fusion logic, etc.) so that the generated current conflict handling solution can most effectively resolve the current conflict situation and ultimately generate an optimal rule adjustment instruction is an issue not elaborated in detail or may pose a challenge in the above solutions. Simply combining strategies may not fully utilize the current conflict characteristics, trends, and historical experience, resulting in the generated rule adjustment instruction not being optimal, thereby affecting the accuracy of subsequent associated analysis rule adjustment and the timeliness and accuracy of early disability risk determination.

[0123] In response to this, the present application further proposes that when the meta-rules indicate that at least two conflict handling strategies need to be combined to generate a current conflict handling solution, the steps of combining at least two conflict handling strategies based on a preset set of meta-rules, in combination with the quantified characteristics of the obtained conflict situation, the obtained time series change trend, and the obtained feedback data, to generate a current conflict handling solution include:

[0124] Identify combinable parameters of at least two conflict handling strategies, where the number of combinable parameters is greater than one and can be adjusted. The combinable parameters are used to characterize the combination method of at least two conflict handling strategies, and the combination method includes at least one of weight assignment, execution order, and fusion logic;

[0125] Based on the indication of the meta-rules, or with reference to the effective combinable parameter values recorded in the feedback data similar to the quantified characteristics and time series change trend of the conflict situation at this moment, set a set-form starting combinable parameter value for the obtained combinable parameters with a number greater than one and can be adjusted;

[0126] By adjusting the value of at least one of the obtained combinable parameters with a number greater than one and can be adjusted, generate a set of alternative combinable parameter values with a number greater than one and different from each other;

[0127] Using the preset combined effect evaluation logic, for each set - form alternative combined parameter value generated, and in combination with the quantitative characteristics of the conflict situation at this moment, the time - series change trend, and the feedback data, calculate the numerical evaluation index of its corresponding expected effect. The combined effect evaluation logic is used to output the numerical evaluation index of the expected effect of combining with the alternative combined parameter value according to the input quantitative characteristics of the conflict situation at this moment, the time - series change trend, the feedback data, and the set - form alternative combined parameter value;

[0128] From the numerical evaluation indexes of each set - form alternative combined parameter value calculated, select a set - form combined parameter value that makes the numerical evaluation index meet the preset optimization goal as the finally determined combined parameter;

[0129] Apply the finally determined combined parameter selected to combine at least two conflict - handling strategies to generate the current conflict - handling plan.

[0130] Among them, this solution provides a systematic, data - based optimization method for the problem of how to determine the optimal combination method when the meta - rule indicates that multiple conflict - handling strategies need to be combined.

[0131] First, identify the combined parameters of at least two conflict - handling strategies that are greater than one and adjustable. These parameters characterize the combination method of the strategies, such as the weights of different strategies, the execution order, or the result fusion logic. This step is the basis for combined optimization, clarifying the adjustable variables. Specifically, for two strategies A and B, the combined parameters can include the weight of strategy A (e.g., a value between 0 and 1), the weight of strategy B, a boolean value indicating whether strategy A is executed before strategy B, or specific function parameters for fusing the output results of strategies A and B. The number of these parameters is greater than one and can be adjusted within a certain range.

[0132] Next, based on the indication of the meta - rule or referring to the effective combined parameters in the historical feedback data in similar situations, set a set - form starting value for these adjustable combined parameters. This provides a reasonable starting point and helps improve the efficiency of subsequent optimization searches. For example, if the meta - rule indicates a certain strategy combination tendency based on the current conflict characteristics, or the historical feedback data shows that a certain specific weight distribution has better effects in similar conflict situations, then these indications or historical data can be used as the initial combined parameter values. This step utilizes the meta - rule and feedback data obtained in the previous solution, providing useful prior information for the subsequent optimization process.

[0133] Then, by adjusting the value of at least one of these combined parameters, multiple distinct sets of alternative combined parameter values, each with a quantity greater than one, are generated. This step is to explore different combination possibilities and provide alternative options for subsequent selection. For example, if the initial weight combination is (0.5, 0.5), multiple different weight combination sets such as (0.6, 0.4), (0.4, 0.6), etc. can be generated by increasing or decreasing one of the weights. If the combination method includes an execution order, different order sets such as strategy A executed first and strategy B executed first can be generated.

[0134] Subsequently, using a preset combined effect evaluation logic, for each generated set of alternative combined parameter values, in combination with the current conflict situation quantification characteristics, time series change trends, and historical feedback data, a numerical evaluation index of the expected effect of using this combined parameter value for combination is calculated. The combined effect evaluation logic is the core, which can predict the possible effect of this combination scheme according to the current specific situation and the set combined parameters, and transform the qualitative combination method into a quantifiable evaluation index. This evaluation logic receives the quantification characteristics of the current conflict, the trend (obtained from the previous scheme), and historical feedback data as inputs, and combines with the set of combined parameter values to be evaluated to output a numerical value, such as the accuracy of the predicted rule adjustment instruction, the timeliness of the early disability risk determination, or the false alarm rate, etc. By combining these context information (characteristics, trends, feedback) with the combined parameters to be evaluated for evaluation, this solution can dynamically and context-awarely predict the effects of different combination methods, which is different from simply fixing the combination method or selecting a strategy based on a single factor, reflecting the adaptability to complex situations.

[0135] Furthermore, from the multiple calculated numerical evaluation indexes, select the set of combined parameter values that makes the evaluation index meet a preset optimization goal (for example, maximizing the expected effect or minimizing the expected risk), and determine it as the combined parameter to be finally adopted. This step is to make a decision based on quantitative evaluation to ensure that the selected combination scheme is optimal. For example, if the optimization goal is to maximize the predicted rule adjustment accuracy, then select the set of combined parameter values corresponding to the highest evaluation index.

[0136] Finally, apply the selected and finalized combined parameters to combine at least two conflict handling strategies, thereby generating an optimal conflict handling solution for the current specific conflict situation. This step is to transform the optimization result into an actually executable solution for guiding the generation of subsequent rule adjustment instructions. Through the above steps, this solution can dynamically and data-drivenly determine the best combination method of multiple conflict handling strategies according to the current specific situation and historical experience, improve the effectiveness and accuracy of conflict handling, and then optimize the adjustment of association analysis rules, ultimately enhancing the reliability of early disability risk warning. By using the conflict characteristics, trends, and feedback data obtained in the previous solution to drive the optimization evaluation of the combination method, this solution can generate a conflict handling solution that is more adaptable to the current situation than a fixed combination or simple selection, thus more effectively solving the rule adjustment problem caused by physiological and environmental parameter conflicts and improving the performance of the overall warning system.

[0137] As a preferred embodiment, the solution of this application is specifically implemented as follows:

[0138] When the meta-rule indicates that at least two conflict handling strategies need to be combined to generate the current conflict handling solution, for example, when there is a conflict between the rule adjustment direction indicated by physiological data and the rule adjustment direction indicated by environmental parameters, and the meta-rule determines that multiple handling methods need to be integrated based on the current conflict intensity and historical pattern, this solution is applied. First, identify the combination parameters of the conflict handling strategies to be combined. For example, Strategy 1 may focus on adjustment based on physiological trends, and Strategy 2 may focus on adjustment based on the environmental change amplitude. Their combination method can be characterized by weight parameters. For example, assign weight W1 to Strategy 1 and weight W2 to Strategy 2, where W1 + W2 = 1. These weight parameters are combination parameters that are greater than one and can be adjusted.

[0139] Next, based on the indication of the meta-rule, or referring to the effective combination parameters recorded in the historical feedback data similar to the quantitative characteristics (such as physiological fluctuation amplitude, environmental change rate) and time series change trends (such as continuous deterioration of physiological indicators, environmental indicators tending to be stable) of the current conflict situation, set a starting value in the form of a set for the identified combination parameters. For example, if the historical data shows that the strategy focusing on physiological trends is more effective in a similar situation, the starting weight value set can be set as {W1 = 0.7, W2 = 0.3}.

[0140] Then, by adjusting the value of at least one of these combination parameters, generate a set of alternative combination parameter values that are greater than one and different from each other. For example, starting from the starting value {W1 = 0.7, W2 = 0.3}, a series of alternative sets can be generated, such as {W1 = 0.6, W2 = 0.4}, {W1 = 0.8, W2 = 0.2}, {W1 = 0.5, W2 = 0.5}, etc.

[0141] Subsequently, using the preset combined effect evaluation logic, for each set-form alternative combined parameter value generated, and in combination with the quantitative characteristics of the conflict situation at that moment, the time series change trend, and the feedback data, a numerical evaluation index of the corresponding expected effect is calculated. Based on this combined effect evaluation logic, the current situation data (conflict characteristics, trends, feedback) and the set of combined parameter values to be evaluated are input, and a numerical value is output, such as the accuracy score of the predicted rule adjustment instruction, or the comprehensive score of the predicted false alarm / missed alarm risk.

[0142] Furthermore, from the numerical evaluation indexes of each set-form alternative combined parameter value calculated, a set-form combined parameter value that makes the numerical evaluation index meet the preset optimization goal is selected as the finally determined combined parameter. For example, if the optimization goal is to maximize the accuracy score of the rule adjustment instruction, the set of combined parameter values with the highest score is selected.

[0143] Finally, the finally determined combined parameter selected is applied to combine at least two conflict handling strategies to generate the current conflict handling solution. For example, if the finally determined combined parameter is {W1 = 0.75, W2 = 0.25}, the outputs of Strategy 1 and Strategy 2 are fused in the ratio of 75% and 25% to form the final rule adjustment instruction.

[0144] Through the above technical solution, the present application solves the problem of how to determine the specific combination method to make the generated current conflict handling solution most effectively solve the current conflict situation and finally generate the optimal rule adjustment instruction when the meta-rule indicates that multiple conflict handling strategies need to be combined. By identifying adjustable combined parameters, setting a starting point based on historical experience and the current situation, systematically exploring different combination possibilities, and using data-driven evaluation logic to quantitatively predict the effects of each combination scheme, the optimal combined parameters are finally selected. Thereby, the generated rule adjustment instruction can more accurately reflect the current complex situation, improve the effectiveness of subsequent correlation analysis rule adjustment, and further enhance the timeliness and accuracy of early disability risk determination.

[0145] Specifically, the preset combined effect evaluation logic at least includes:

[0146] Mapping the quantitative characteristics of the input conflict situation, the key elements in the time series change trend, the feedback data, and the current alternative combined parameter values to be evaluated into a set of predefined scoring items;

[0147] Setting corresponding weight coefficients and scoring rules for each scoring item;

[0148] A numerical evaluation index is obtained by calculating the scores of each scoring item and performing weighted summation or logical reasoning.

[0149] For the quantitative features of the input conflict situation, the time series change trend, the key elements in the feedback data, and the alternative combined parameter values to be evaluated currently, they are mapped to a set of predefined scoring items:

[0150] The function of this step is to transform the complex input information of the combined effect evaluation logic (including the quantitative features of the current conflict, the time series change trend of the parameters, the key information in the historical feedback data, and the specific combined parameter values being evaluated) into a series of standardized and quantifiable scoring items. The predefined scoring items are determined in advance according to fire rescue professional knowledge and system design goals, and can reflect the evaluation dimensions of different aspects of the combined effect. For example, one scoring item can correspond to the urgency of the current conflict, another scoring item can correspond to the success rate of the selected combined parameters in historical similar high-risk scenarios, and another scoring item can correspond to the impact of the combined parameters on the system response time. By mapping the diverse input information to these standardized scoring items, it lays a foundation for subsequent quantitative evaluation and comprehensive calculation, enabling information from different sources and of different natures to be considered within a unified framework.

[0151] For each scoring item, set corresponding weight coefficients and scoring rules:

[0152] The function of this step is to assign the relative importance of each scoring item defined in the previous step in the overall evaluation and clarify how to give specific scores according to the actual situation of the scoring item. The weight coefficient reflects the influence degree of different scoring items on the final combined effect evaluation result. For example, in some scenarios, the weight of the scoring item indicating a high success rate in the historical feedback data may be higher than the calculation complexity of the combined parameters. The scoring rule defines how to calculate the score of this item according to the input value of each scoring item (for example, the specific value of the conflict urgency, the specific percentage of the historical success rate), which can be a simple linear mapping, or a more complex non-linear function or logical judgment. By setting the weight coefficient and scoring rule, the system can conduct focused and standardized quantification of each influencing factor according to the preset evaluation strategy and professional knowledge.

[0153] For by calculating the scores of each scoring item and performing weighted summation or logical reasoning, a numerical evaluation index is obtained;

[0154] The purpose of this step is to synthesize the quantified scores of each scoring item to form a final numerical evaluation index that can represent the expected effect of the current alternative combined parameter values to be evaluated. The scores of each scoring item are calculated according to the scoring rules set in the previous step by scoring the actual input values of each scoring item. Then, by performing weighted summation, that is, multiplying the score of each scoring item by its corresponding weight coefficient and then adding them up, a comprehensive value is obtained. Or, through logical reasoning, such as based on a set of "if-then" rules, the final evaluation index value is inferred according to the combination of the scores of each scoring item. Whether using weighted summation or logical reasoning, the purpose is to converge multi-dimensional scoring information into a single numerical evaluation index that can intuitively reflect the quality of the combined effect, compare and select among multiple sets of alternative combined parameter values to determine the best combination method for conflict handling strategies.

[0155] In the solution of this application, the monitoring parameters from multiple different sources include at least two of the environmental gas concentration parameter, the firefighter's breathing parameter, the firefighter's posture parameter, the firefighter's heart rate parameter, and the firefighter's body surface temperature parameter.

[0156] The environmental gas concentration parameter refers to the concentration data of specific gases or unknown gases in the surrounding environment monitored by the gas sensors on the fire suit. A slight increase in the concentration of trace irritating gases is the direct environmental factor causing the physiological discomfort of firefighters. Even if it does not reach the independent gas alarm threshold, its change is also important information indicating potential risks.

[0157] The firefighter's breathing parameter refers to the data such as the breathing rate and the instantaneous oxygen consumption rate monitored by the oxygen respirator. Coughing and discomfort will cause fluctuations in the breathing rate and the instantaneous consumption rate. These fluctuations, even if they do not trigger the low-oxygen alarm, are also important information indicating physiological abnormalities.

[0158] The firefighter's posture parameter refers to the data such as body sway and activity level monitored by the posture sensors on the fire suit. Dizziness will cause body sway and slow movement. These posture changes, even if they do not reach the alarm conditions of falling or staying stationary for a long time, are also important information indicating that the mobility may be impaired.

[0159] The firefighter's heart rate parameter and the firefighter's body surface temperature parameter refer to the physiological indicators monitored by the fire suit. These parameters can reflect the physiological load and stress state of firefighters, and their abnormal fluctuations may also be associated with physiological discomfort.

[0160] Taking these parameters from different sensors and different categories as the monitoring parameters from multiple different sources ensures that the system can obtain sub-threshold abnormal information related to firefighters' physiological discomfort and environmental factors from multiple dimensions. The combination and correlation analysis of these parameters are the basis for identifying early and hidden disability risks that are difficult to detect by traditional single-threshold alarm methods.

[0161] In the solution of the present application, the real-time environmental parameters include at least one of the environmental temperature and the gas concentration.

[0162] The environmental temperature and the gas concentration are key environmental factors that firefighters may directly perceive in a complex rescue environment and affect their physiological states. These specific parameters are inputs for determining the rule adjustment instruction, and their contribution lies in providing objective data characterizing the microenvironment state where the firefighters are located, enabling the system to adjust the correlation analysis rule according to environmental changes and improving the compliance degree of risk judgment.

[0163] In a second aspect, referring to Figure 2 , the present application further proposes an oxygen breathing apparatus monitoring and alarming system, which includes:

[0164] A first acquisition module 1 for acquiring the response result of the firefighter to the preset status confirmation request;

[0165] A second acquisition module 2 for acquiring monitoring parameters from multiple different sources related to the physiological discomfort state of the firefighter. The monitoring parameters from multiple different sources do not reach the preset independent alarm threshold respectively, and the monitoring parameters from multiple different sources show abnormal fluctuations indicating physiological discomfort;

[0166] A judgment module 3 for judging, based on a preset correlation analysis rule, whether there is a preset correlation indicating that the firefighter fails to effectively respond to the status confirmation request due to physiological discomfort between the response result acquired by the response result acquisition module and the abnormal fluctuations of the monitoring parameters from multiple different sources acquired by the monitoring parameter acquisition module;

[0167] An early warning module 4 for, when the correlation judged by the correlation judgment module meets the preset correlation analysis rule, determining that the firefighter has an early disability risk caused by non-hypoxia reasons, with sufficient oxygen supply but possible impaired mobility, and triggering an early warning or alarm for the early disability risk based on the determined early disability risk.

[0168] By acquiring the response result of the firefighter to the status confirmation request and the abnormal fluctuations of multiple sub-threshold monitoring parameters, and judging whether there is a correlation of ineffective response due to physiological discomfort based on the correlation analysis, so as to identify the early disability risk caused by non-hypoxia reasons and trigger an early warning or alarm, it has the ability to effectively identify the early disability risk of firefighters caused by a combination of multiple abnormalities that are difficult to discover in the prior art, provide an early warning or alarm in the early stage when the oxygen supply of the firefighter is sufficient but the mobility may be impaired, thereby improving the accuracy of the alarm and making up for the deficiency that the traditional oxygen breathing apparatus alarm system mainly focuses on oxygen supply safety.

[0169] In addition, in some preferred embodiments, an oxygen respirator monitoring and alarm system proposed by the present application can perform any one of the steps in the above method.

[0170] The above are only the embodiments of the present application and are not intended to limit the protection scope of the present application. For those skilled in the art, the present application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for monitoring and alarming an oxygen breathing apparatus, characterized in that, Including: Obtaining the response result of the firefighter to the preset status confirmation request; Obtaining monitoring parameters from multiple different sources related to the physiological discomfort state of the firefighter; Based on a preset correlation analysis rule, determining whether there is a preset correlation indicating that the firefighter fails to effectively respond to the status confirmation request due to the physiological discomfort between the obtained response result and the abnormal fluctuations of the obtained monitoring parameters from multiple different sources; When the determined correlation meets the preset correlation analysis rule, determining that the firefighter has an early disability risk caused by non-hypoxia reasons, with sufficient oxygen supply but possible impaired mobility, and triggering a warning or alarm for the early disability risk based on the determined early disability risk.

2. The oxygen breathing apparatus monitoring and alarming method according to claim 1, characterized in that, The step of, based on a preset correlation analysis rule, determining whether there is a preset correlation indicating that the firefighter fails to effectively respond to the status confirmation request due to the physiological discomfort between the obtained response result and the abnormal fluctuations of the obtained monitoring parameters from multiple different sources includes: Obtaining real-time physiological data indicating the current individual physiological characteristics of the firefighter and real-time environmental parameters indicating the current environmental state of the rescue scene; Determining one or more rule adjustment instructions for the preset correlation analysis rule according to the difference between the real-time physiological data and the baseline physiological data of the firefighter, or according to the change characteristics of the real-time environmental parameters; Applying the one or more rule adjustment instructions to adjust at least one parameter or logical condition in the preset correlation analysis rule to generate an adjusted correlation analysis rule; Using the adjusted correlation analysis rule, combined with the obtained response result and the abnormal fluctuations of the obtained monitoring parameters from multiple different sources, determining whether there is a preset correlation indicating that the firefighter fails to effectively respond to the status confirmation request due to the physiological discomfort between the response result and the abnormal fluctuations of the monitoring parameters from multiple different sources.

3. A method for monitoring and alarming an oxygen breathing apparatus according to claim 2, characterized in that, The step of determining one or more rule adjustment instructions for the preset correlation analysis rule according to the difference between the real-time physiological data and the baseline physiological data of the firefighter, or according to the change characteristics of the real-time environmental parameters includes: Obtaining a first set of candidate rule adjustment parameters determined by the difference between the real-time physiological data and the baseline physiological data of the firefighter; Obtaining a second set of candidate rule adjustment parameters determined by the change characteristics of the real-time environmental parameters; Judging whether there is an indication of conflict between the first set of candidate rule adjustment parameters and the second set of candidate rule adjustment parameters according to a preset conflict determination rule; When it is determined that there is the indication of conflict, then according to a preset conflict handling mechanism, combining the first set of candidate rule adjustment parameters and the second set of candidate rule adjustment parameters to determine the one or more rule adjustment instructions for the preset correlation analysis rule. When it is determined that there is no such indication conflict, the parameter of the first set of candidate rules determined according to the difference between the real-time physiological data and the reference physiological data of the firefighter, or the parameter of the second set of candidate rules determined according to the change characteristics of the real-time environmental parameters, or the combination of the parameter of the first set of candidate rules and the parameter of the second set of candidate rules is used to determine the one or more rule adjustment instructions for the preset association analysis rules.

4. The oxygen breathing apparatus monitoring and alarming method according to claim 3, characterized in that, When the preset conflict handling mechanism includes at least two conflict handling strategies, and the current conflict situation composed of the parameter of the first set of candidate rules and the parameter of the second set of candidate rules simultaneously meets the conditions for activating the at least two conflict handling strategies, or the conflict situation does not fully meet the activation conditions of any preset conflict handling strategy, the step of determining the one or more rule adjustment instructions according to the preset conflict handling mechanism and combining the parameter of the first set of candidate rules and the parameter of the second set of candidate rules includes: Obtain the quantitative characteristics of the conflict situation; Obtain the time series change trend of the parameter of the first set of candidate rules and the time series change trend of the parameter of the second set of candidate rules; Obtain the feedback data on the execution effect of the historical rule adjustment instructions related to the historical conflict situation, historical parameter trend, and historical conflict handling strategy adopted; According to the preset meta-rule set, and in combination with the obtained quantitative characteristics of the conflict situation, the obtained time series change trend, and the obtained feedback data, select one conflict handling strategy from the at least two conflict handling strategies included in the preset conflict handling mechanism, or combine the at least two conflict handling strategies to generate the current conflict handling plan; Apply the current conflict handling plan, and in combination with the parameter of the first set of candidate rules and the parameter of the second set of candidate rules, determine the one or more rule adjustment instructions.

5. A method for monitoring and alarming an oxygen breathing apparatus according to claim 4, characterized in that, When the meta-rule indicates that the at least two conflict handling strategies need to be combined to generate the current conflict handling plan, the step of combining the at least two conflict handling strategies according to the preset meta-rule set, and in combination with the obtained quantitative characteristics of the conflict situation, the obtained time series change trend, and the obtained feedback data to generate the current conflict handling plan includes: Identify the combination parameters of the at least two conflict handling strategies that are greater than one and adjustable. The combination parameters are used to represent the combination method of the at least two conflict handling strategies, and the combination method includes at least one of weight allocation, execution order, and fusion logic; Based on the indication of the meta-rule, or referring to the effective combination parameters recorded in the feedback data similar to the quantitative characteristics of the conflict situation and the time series change trend at this moment, set the combination parameter values in the form of a set as the starting point for the obtained combination parameters that are greater than one and adjustable; By adjusting the value of at least one of the combinable parameters whose obtained quantity is greater than one and can be adjusted, a set of combinable parameter values that are greater than one and different from each other and are for selection is generated; Using a preset combined effect evaluation logic, for each set of combinable parameter values for selection generated, and in combination with the quantitative characteristics of the conflict situation, the time series change trend, and the feedback data at this moment, a numerical evaluation index of the corresponding expected effect is calculated. The combined effect evaluation logic is used to output a numerical evaluation index of the expected effect of combining the combinable parameter values for selection according to the input quantitative characteristics of the conflict situation, the time series change trend, the feedback data, and the combinable parameter values for selection in set form; From the numerical evaluation indexes of each set of combinable parameter values for selection calculated, a set of combinable parameter values that makes the numerical evaluation index meet a preset optimization goal is selected as the finally determined combinable parameters; Applying the finally determined combinable parameters selected to combine the at least two conflict handling strategies to generate the current conflict handling solution.

6. The oxygen breathing apparatus monitoring and alarming method according to claim 1, characterized in that, The multiple monitoring parameters from different sources include at least two of environmental gas concentration parameters, firefighter breathing parameters, firefighter posture parameters, firefighter heart rate parameters, and firefighter body surface temperature parameters.

7. A method for monitoring and alarming an oxygen respirator according to claim 2, characterized in that, The real-time environmental parameters include at least one of environmental temperature and gas concentration.

8. A method for monitoring and alarming an oxygen breathing apparatus according to claim 4, characterized in that, The meta-rule set is a set of rules that defines the logic of selecting one of the at least two conflict handling strategies included in the preset conflict handling mechanism or determining the combination method of the at least two conflict handling strategies according to the quantitative characteristics of the conflict situation, the time series change trend, and the feedback data.

9. The oxygen breathing apparatus monitoring and alarming method according to claim 1, characterized in that, The preset combined effect evaluation logic at least includes: Mapping the key elements in the input quantitative characteristics of the conflict situation, the time series change trend, the feedback data, and the combinable parameter values for selection currently to be evaluated into a set of predefined scoring items; Setting corresponding weight coefficients and scoring rules for each scoring item; By calculating the scores of each scoring item and performing weighted summation or logical reasoning, the numerical evaluation index is obtained.

10. An oxygen breathing apparatus monitoring and alarming system, characterized in that, The system includes: A first acquisition module for acquiring the response result of the firefighter to the preset status confirmation request; A second acquisition module for acquiring multiple monitoring parameters from different sources related to the physiological discomfort state of the firefighter. Each of the multiple monitoring parameters from different sources does not reach a preset independent alarm threshold, and the multiple monitoring parameters from different sources show abnormal fluctuations indicating the physiological discomfort; A judgment module for judging whether there is a preset relevance indicating that the firefighter fails to effectively respond to the status confirmation request due to the physiological discomfort between the response result acquired by the response result acquisition module and the abnormal fluctuations of the multiple monitoring parameters from different sources acquired by the monitoring parameter acquisition module based on a preset correlation analysis rule; An early warning module, configured to determine that the firefighter has an early disability risk caused by non-hypoxia reasons, with sufficient oxygen supply but possible impaired mobility, when the relevance judged by the relevance judgment module meets the preset correlation analysis rules, and trigger an early warning or alarm for the early disability risk based on the determined early disability risk.

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