Thermal environment and air quality self-adjusting intelligent emergency rescue cabin and method

Abstract

The invention discloses a thermal environment and air quality self-adjusting intelligent emergency rescue cabin and method, and relates to the technical field of emergency rescue. The core of the method is that firstly, heat loss characteristics of the rescue cabin are learned through a simulated environment, and a standard loss rate is established; then, a target temperature interval is set, and based on comparison between the temperature loss rate monitored in real time and a standard value, hierarchical temperature control modes such as dormancy, low-power-consumption maintenance or dynamic balance are dynamically selected. Furthermore, by integrating vital sign monitoring, the target temperature can be dynamically adjusted according to parameters such as the core body temperature, blood oxygen and blood pressure of a patient, medical intervention modes such as active cooling and shock rewarming are started, and safe rewarming is achieved in the shock rewarming mode in the mode that a core area is heated through directional radiation. The system is also internally provided with a multi-rule priority arbitration mechanism. The cabin body comprises a sensor, an air conditioner, vital sign monitoring equipment and a directional heating plate. Unification of energy conservation, temperature stabilization and precise medical intervention is achieved, and the intelligent level and the safety guarantee capacity of emergency rescue are remarkably improved.

Description

Technical Field

[0001] This invention belongs to the field of emergency rescue technology, specifically an intelligent emergency rescue cabin and method with self-regulating thermal environment and air quality. Background Technology

[0002] In emergency rescue scenarios such as natural disasters and accidents, providing a safe and stable temporary shelter for the injured or affected is crucial. Traditional emergency rescue cabins or tents typically only have basic temperature control functions, and their air conditioning systems often use simple on / off or fixed-power operation modes. This approach has significant drawbacks: First, in harsh and variable outdoor environments, fixed temperature control strategies are either extremely energy-intensive and difficult to maintain for extended periods with emergency power, or they cannot effectively cope with rapid temperature loss, leading to drastic temperature fluctuations inside the cabin and exposing trapped individuals to the risk of secondary injuries such as hypothermia or heatstroke.

[0003] Secondly, current technologies generally lack the ability to perceive and respond to the physiological state of trapped individuals. For the injured, especially critically ill patients (such as those in shock, with high fever, or hypoxemia), the ideal ambient temperature is not a fixed value, but needs to be dynamically and precisely adjusted based on their core body temperature, circulatory status, and other vital signs. For example, inappropriate rapid rewarming of a patient with hemorrhagic shock may trigger "rewarming shock," leading to a further drop in core blood pressure and endangering their life.

[0004] Furthermore, existing emergency rescue equipment systems have relatively limited functionality, with environmental control, vital sign monitoring, and medical intervention operating in isolation, failing to form an intelligent and collaborative life support system. Simultaneously, the authenticity and completeness of rescue process data are difficult to guarantee, hindering subsequent medical analysis and accident tracing. Therefore, there is an urgent need in this field for an intelligent emergency rescue cabin and method capable of intelligently sensing the environment and personnel's physiological state, and achieving adaptive and precise control, to ensure personnel safety and comfort while improving energy efficiency and providing clinical auxiliary treatment functions. Summary of the Invention

[0005] This invention aims to solve at least one of the technical problems existing in the prior art;

[0006] Therefore, this invention proposes an intelligent emergency rescue method that self-regulates thermal environment and air quality, comprising:

[0007] Environmental heat loss characteristics learning phase: By simulating the rescue environment, temperature difference data inside and outside the rescue cabin is obtained, and the standard heat loss rate is calculated;

[0008] Set target temperature range: Set the initial target temperature maintenance range, including the upper temperature limit Th and the lower temperature limit Tw;

[0009] Implementation of graded temperature control strategy: Monitor the real-time temperature loss rate of the rescue cabin, compare it with the standard loss rate, and select different temperature control modes based on the comparison results, including mode A, mode B or mode C;

[0010] Cyclic execution: After the temperature reaches Tw or after running for a period of time, reassess the heat loss rate and adjust the control mode.

[0011] Furthermore, the method also includes:

[0012] The system acquires the patient's vital signs parameters in real time, including core body temperature (Tc) and mean skin temperature (Tsm). The target temperature range is adjusted based on the core body temperature (Tc): if Tc > 38.0℃, Th and Tw are lowered; if Tc < 36.0℃, Th and Tw are raised.

[0013] Furthermore, it also includes:

[0014] Calculate the difference between core body temperature and mean skin temperature: AT = Tc - Tsm;

[0015] Predictive temperature control is implemented based on the changing trends of AT and Tc values: if AT is low and Tc is high, ventilation is enhanced to assist heat dissipation; if AT is high and Tc is high, cooling power and fan speed are limited.

[0016] Furthermore, it also includes:

[0017] Monitor blood oxygen saturation (SpO2);

[0018] When SpO2 < 90%, the target temperature range is locked in the neutral range, and mode C is preferred to minimize temperature fluctuations.

[0019] Furthermore, it also includes:

[0020] Monitor blood pressure (BP) and core body temperature (Tc);

[0021] When systolic blood pressure is <90 mmHg and Tc is <36.0℃, the shock rewarming mode is activated, raising Th and Tw to the preset high temperature range, and controlling the directional infrared radiation heating plate to concentrate heating on the core area of ​​the torso.

[0022] Furthermore, it also includes:

[0023] A multi-rule priority arbitration mechanism is set up, with the priority order as follows: shock rewarming mode > SpO2 metabolism optimization mode > active temperature control mode based on core body temperature.

[0024] Furthermore, it also includes:

[0025] Package environmental parameters, vital signs data, and system control commands into treatment data;

[0026] The treatment data is encrypted, including its structured chunking and conversion into encrypted data packets using binary tree path encoding.

[0027] Furthermore, the tiered temperature control strategy is being implemented:

[0028] If the real-time loss rate is less than the standard loss rate, activate Mode A and turn off or put the air conditioner into standby mode.

[0029] If the real-time power loss rate is between the standard power loss rate and 1.35 times the standard power loss rate, activate mode B and start the air conditioner's low power consumption mode.

[0030] If the real-time loss rate is greater than 1.35 times the standard loss rate, activate Mode C to start the air conditioner's high-power mode to maintain temperature stability.

[0031] Furthermore, the learning phase of environmental heat loss characteristics includes:

[0032] In a simulation environment, multiple real-time temperature difference values ​​are set to obtain the time from the upper limit of the cabin temperature to the lower limit of the cabin temperature, and the average time of loss Ts is calculated.

[0033] The standard loss rate is obtained by comparing the real-time temperature difference with the average loss rate.

[0034] A smart emergency rescue cabin with self-regulating thermal environment and air quality, wherein the smart emergency rescue cabin uses the aforementioned method to control the environment inside the cabin.

[0035] Compared with the prior art, the beneficial effects of the present invention are:

[0036] This application achieves high efficiency, energy saving, and temperature stability: by learning and predicting the characteristics of environmental heat loss and adopting a graded temperature control strategy, the system can intelligently select the most energy-efficient and stable control mode according to the severity of the actual environment, effectively avoiding the problems of high energy consumption and large temperature fluctuations of traditional methods, and is particularly suitable for emergency rescue scenarios with limited power.

[0037] This application also enables patient-centered precision medical intervention: by integrating vital sign monitoring, the system can dynamically adjust the target temperature range and execution strategy according to the patient's pathophysiological state, upgrading the temperature control system from simple environmental regulation to an active and safe clinical auxiliary treatment method. Attached Figure Description

[0038] Figure 1 The method flowchart provided by the present invention. Detailed Implementation

[0039] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] Please see Figure 1 This application provides an intelligent emergency rescue method that self-regulates thermal environment and air quality;

[0041] As an embodiment of this application, this embodiment provides a basic thermal environment self-regulation method, the core of which is to perform predictive and graded temperature control based on the temperature loss characteristics of the emergency cabin under the current environment, so as to achieve energy saving and stability.

[0042] Includes the following steps:

[0043] S101: Learning phase of environmental heat loss characteristics;

[0044] The emergency rescue cabin is placed in a simulated rescue environment. Temperature sensors are installed inside and outside the cabin, and different temperature difference values ​​are set. Here, it is necessary to obtain the minimum and maximum values ​​of the internal and external temperatures of the rescue cabin during several past rescue processes, and mark them as the lower and upper temperature difference values. The several rescue processes here are simulated in real time for each past rescue environment, including wind and snow conditions. The air conditioning in the rescue cabin is not turned on initially, and the initial temperature is set to the upper temperature value by default.

[0045] Then, starting from the lower temperature value, select the real-time temperature difference value. Increase the set temperature each time until the real-time temperature difference value reaches the upper temperature value, and obtain a temperature difference table composed of several real-time temperature difference values.

[0046] Obtain the temperature difference table, then select from the minimum value in the temperature difference table, place the rescue cabin in the corresponding real-time temperature difference environment, and set an internal temperature range from the lower limit to the upper limit of the cabin temperature.

[0047] Then, the time it takes for the upper limit of the cabin temperature to drop to the lower limit of the cabin temperature in several simulations is automatically marked as the average loss time Ts, and the real-time value of the temperature difference is divided by the average loss time to obtain the value marked as the standard loss rate.

[0048] Ts is a dynamic parameter that can be preset by the system administrator based on the severity of the environment and continuously adjusted through learning during actual operation. For example, in a cold and windy environment, Ts may be only 10 minutes; while in a mild indoor environment, Ts may be several hours long.

[0049] S102: Set the target temperature range;

[0050] Set an initial, comfortable target temperature range, for example, Th=26℃, Tw=24℃;

[0051] S103: Execution of graded temperature control strategy;

[0052] Starting with Th, monitor the trend and rate of temperature drop to Tw, compare it with the baseline loss time Ts, and dynamically adjust the power of the air conditioning system.

[0053] The initial real-time temperature difference of the rescue cabin when the air conditioning is not turned on is obtained, and compared with the corresponding standard temperature loss rate in the temperature difference table. Then, the average temperature loss rate when the rescue cabin temperature drops to the set value is monitored and marked as the real-time temperature loss rate. Based on the relationship between the real-time temperature loss rate and the standard temperature loss rate, different modes are selected to regulate the cabin temperature. Specifically:

[0054] If the real-time loss rate is less than the standard loss rate, then mode A will be activated to control the temperature inside the rescue cabin.

[0055] If the standard loss rate ≤ real-time loss rate ≤ 1.35 times the standard loss rate, then mode B is activated to control the temperature inside the rescue cabin.

[0056] If the real-time loss rate exceeds 1.35 times the standard loss rate, mode C will be activated to control the temperature inside the rescue chamber. The specific definitions of modes A, B, and C are as follows:

[0057] Mode A: This mode employs a hibernation mode for slow temperature loss: the control system will completely shut down the air conditioning or put it into standby mode, allowing the cabin temperature to drop naturally and slowly to Tw; when the temperature drops to Tw, the system will restart the air conditioning to quickly raise the temperature back to Th; this mode saves energy to the greatest extent.

[0058] Mode B is designed for moderate temperature loss and employs a low-power maintenance mode: the air conditioner is activated in low-power mode to control its heating power, so that the actual temperature rise rate inside the cabin is slightly lower than the rate of temperature loss caused by the environment; the effect is that the cabin temperature will still slowly drop to Tw, but the drop process is more gradual.

[0059] When the temperature drops to Tw, the system switches to full power to raise the temperature to Th; this "soft landing" method avoids drastic temperature fluctuations and frequent system starts and stops.

[0060] Mode C is for situations where temperature loss is rapid. It adopts a dynamic balance mode: directly activates the temperature maintenance mode, controls the air conditioner to run at a higher power, so that the heating effect of the air conditioner basically cancels out the temperature loss caused by the environment, thereby stabilizing the cabin temperature in a narrow range around Th, such as 25.5℃-26℃, to prioritize the stability of the cabin environment.

[0061] S104: Execute in a loop.

[0062] After the temperature reaches Tw and is reheated, or after running in mode C for a period of time, the system will return to step S101 to reassess the current heat loss rate and select the corresponding control mode to achieve continuous adaptive adjustment.

[0063] This embodiment effectively solves the problems of high energy consumption and large temperature fluctuations in traditional temperature control methods in emergency rescue scenarios by predicting the rate of temperature loss and implementing graded control.

[0064] Example 2, building upon Example 1, further introduces the monitoring of the patient's core vital signs, upgrading the temperature control strategy from environmental adaptation to patient-centered precision medical intervention. Its core lies in dynamically adjusting the target temperature range (Th, Tw) from Example 1 based on the patient's physiological state.

[0065] This embodiment specifically includes the following steps:

[0066] S201: Perform basic environmental temperature control and physiological parameter acquisition;

[0067] As described in Example 1, the system first establishes and runs an adaptive temperature control based on the prediction of ambient temperature loss; at the same time, it acquires the patient's core body temperature Tc and multiple skin temperatures in real time, and calculates their average value as the average skin temperature Tsm.

[0068] S202: Target temperature range resetting based on core body temperature.

[0069] The system compares the core body temperature Tc with a preset threshold, and based on the comparison result, covers the initial target temperature range (Th, Tw) set by S102 in Example 1:

[0070] If Tc > 38.0℃, it is determined to be high heat, and the system will start the active cooling mode, and simultaneously lower the upper limit Th and lower limit Tw of the target temperature range, for example, reset to Th' = 24℃ and Tw' = 22℃.

[0071] If Tc < 36.0℃, it is determined to be hypothermia. The system then activates the active rewarming mode, simultaneously raising the upper limit Th and lower limit Tw of the target temperature range. For example, it can be reset to Th' = 28℃ and Tw' = 26℃.

[0072] If Tc is within the normal range of 36.0℃-37.3℃, then maintain the current or default comfortable temperature range (Th, Tw).

[0073] S203: Refined regulation based on the relationship between skin temperature and core body temperature;

[0074] The system calculates the difference between core body temperature and average skin temperature, AT, where AT = Tc - Tsm, and uses the real-time trend of Tc to perform predictive temperature control.

[0075] Scenario 1: High Tc with low AT (i.e., high skin temperature and moist skin). This indicates that the patient is effectively dissipating heat through sweating. In this case, based on the active cooling mode, the system can control the ventilation system in the cabin to appropriately enhance airflow (such as starting a low-speed circulating fan) to assist evaporative cooling and improve cooling efficiency;

[0076] Scenario 2: High Tc accompanied by high ΔT, i.e., low skin temperature but high core body temperature; this indicates peripheral vasoconstriction and poor heat dissipation, potentially posing a risk of serious infection or sepsis. In this case, the system should adopt a gentle cooling strategy; specifically, the control system will limit the maximum cooling power and fan speed of the air conditioner to make the cooling process more gradual, avoiding shivering caused by drastic cooling (shivering generates heat, which is detrimental to cooling).

[0077] Trend prediction: The system calculates the rate of change of Tc (dTc / dt) in real time. For example, if dTc / dt is detected to be greater than +0.1℃ / minute, even if the current Tc has not yet exceeded 38.0℃, the system can enter the preparation state for active cooling mode in advance, or take more aggressive cooling measures to achieve proactive intervention.

[0078] S204: Integrated control loop.

[0079] After the target temperature range (Th, Tw) is reset in S202, S103 and S104 of Example 1 will continue to operate, but using the new target temperature range (Th', Tw'). The system will select and switch between modes A, B, and C based on the new temperature range and the real-time calculated heat loss rate, thereby achieving intelligent temperature control that meets the patient's physiological needs while also taking into account energy efficiency and environmental adaptability.

[0080] This embodiment deeply integrates the patient's core body temperature, skin temperature, and their changing trends, enabling the emergency rescue cabin's temperature control system to possess clinical auxiliary treatment functions, and to respond to different pathological states of patients more intelligently and safely.

[0081] Example 3 is a further development and expansion of Example 2. By introducing key vital signs parameters such as blood pressure (BP) and blood oxygen saturation (SpO2) and adding a directional heating device, a comprehensive life support system for critically ill patients in shock is constructed.

[0082] The method provided in this embodiment adds the following steps to the method in embodiment two:

[0083] S301: Multi-parameter monitoring and status determination.

[0084] The system continuously monitors the patient's blood pressure (BP) and blood oxygen saturation (SpO2).

[0085] S302: SpO2-based metabolic optimization temperature control.

[0086] Rule: When SpO2 < 90%, indicating hypoxemia, the system determines the patient is in a high-risk state. At this time, excessively high or low temperatures will increase the body's metabolic rate, thereby increasing oxygen consumption. Therefore, the system will activate metabolic stabilization mode. In this mode:

[0087] 1. The system forcibly locks the target temperature range (Th, Tw) inside the cabin into a neutral temperature range, for example, Th=26℃, Tw=24℃;

[0088] 2. In terms of temperature control strategy, mode C in Example 1 is preferred, with minimizing temperature fluctuations as the primary goal, to avoid increasing the patient's oxygen consumption due to temperature changes.

[0089] S303: Synergistic management of hypotension and hypothermia, i.e. shock rewarming mode;

[0090] When the system simultaneously detects low blood pressure, such as systolic blood pressure <90 mmHg and low body temperature Tc <36.0℃, it determines that the patient may be in a state of hemorrhagic or hypothermic shock. At this time, the system activates a dedicated shock rewarming mode, which has the highest control priority.

[0091] 1. Target resetting and priority arbitration: The system covers all other temperature control commands, raising the upper limit Th of the target temperature range to 30℃ and the lower limit Tw to 28℃;

[0092] 2. Differentiated rewarming: The core of this mode is to prioritize keeping the core area of ​​the torso warm;

[0093] Core area heating: The system controls the directional infrared radiation heating plates deployed above the patient's torso, such as the precordial region and abdomen, to set the power, for example, 70%-90% of the rated power, to provide concentrated and directional radiation heating to the core area;

[0094] Gentle treatment of the extremities: For the extremities, the air conditioning system only keeps them warm or uses mode B in Example 1, low power consumption maintenance mode; it provides light heating and strictly avoids rapid and intense heating of the extremities.

[0095] 3. Control Logic: This differentiated rewarming strategy, which prioritizes the core and moderates the extremities, aims to rapidly raise the core body temperature and improve cardiac and cerebral perfusion, while avoiding rewarming shock caused by sudden vasodilation in the limbs, which would redistribute blood to the extremities and lead to a further drop in core blood pressure.

[0096] S304: Multi-rule priority arbitration.

[0097] In the complex system of this embodiment, multiple control rules may be triggered simultaneously. Therefore, the system has a built-in rule arbitrator; its priority is set as follows: shock rewarming mode S303 > SpO2 metabolism optimization mode S302 > active temperature control mode based on core body temperature S202.

[0098] Example: When a patient simultaneously experiences hypotension, hypothermia, and hypoxemia, the system will prioritize the highest priority shock rewarming mode S303. In this mode, even if SpO2 is below 90%, the system prioritizes rapidly stabilizing core body temperature and will not execute the neutral temperature locking operation of S302.

[0099] This embodiment integrates blood pressure and blood oxygen parameters and introduces directional radiant heating technology and a multi-rule arbitration mechanism, enabling the system to cope with the most critical emergency rescue scenarios and buy precious time to save lives. This demonstrates the high integration and clinical applicability of this invention as an intelligent emergency rescue system.

[0100] Of course, as a fourth embodiment of this application, this embodiment, based on embodiment three, packages all environmental parameters, vital sign data, and system control commands into treatment data, and encrypts the treatment data. The specific encryption method is as follows:

[0101] S401: Treatment data structuring and partitioning;

[0102] The system organizes the real-time generated treatment data packets according to the structure of data headers and data tables; the data header is a collection of data names, such as core body temperature, cabin temperature, air conditioning mode, and blood oxygen saturation.

[0103] The data table contains specific numerical parameters that correspond one-to-one with the data header, such as 36.5, 26.0, 98, etc.

[0104] S402: Construct a binary tree encoding 0-9;

[0105] The system constructs a fixed 0-9 binary tree structure in memory, arranging nodes from top to bottom and from left to right, as follows:

[0106] The root node is 0;

[0107] The second layer is numbered 1 and 2 from left to right;

[0108] The third layer, from left to right, consists of numbers 3, 4, 5, and 6;

[0109] The fourth layer consists of 7, 8, and 9 from left to right; this binary tree structure is fixed and the node numbers are unique, serving as a reference for subsequent encoding.

[0110] S403: Convert numerical values ​​into path data, specifically as follows:

[0111] Decompose the numerical value into individual numeric characters bit by bit;

[0112] For each numeric character, determine its position in the binary tree;

[0113] Generate a transformation path from the original binary tree rooted at 0 to the binary tree rooted at the corresponding node of the numeric character. Each transformation involves a left or right rotation around the root node.

[0114] Moving to the left subtree is represented by the character L, and moving to the right subtree is represented by the character R;

[0115] For numerical values ​​containing decimal points, a specific separator D is used to separate the path sequences of the integer part and the decimal part;

[0116] Obtain the path data corresponding to each value;

[0117] S404: Pair and combine path data with data header to form the final encrypted data packet, which is then transmitted to the user to facilitate subsequent medical analysis and accident tracing, while preventing data from being tampered with.

[0118] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. An intelligent emergency rescue method with self-regulation of thermal environment and air quality, characterized in that, include: Environmental heat loss characteristics learning phase: By simulating the rescue environment, temperature difference data inside and outside the rescue cabin is obtained, and the standard heat loss rate is calculated; Set target temperature range: Set the initial target temperature maintenance range, including the upper temperature limit Th and the lower temperature limit Tw; Implementation of graded temperature control strategy: Monitor the real-time temperature loss rate of the rescue cabin, compare it with the standard loss rate, and select different temperature control modes based on the comparison results, including mode A, mode B or mode C; Cyclic execution: After the temperature reaches Tw or after running for a period of time, reassess the heat loss rate and adjust the control mode.

2. The intelligent emergency rescue method for self-regulation of thermal environment and air quality according to claim 1, characterized in that, The method also includes: The system acquires the patient's vital signs parameters in real time, including core body temperature (Tc) and mean skin temperature (Tsm). The target temperature range is adjusted based on the core body temperature (Tc): if Tc > 38.0℃, Th and Tw are lowered; if Tc < 36.0℃, Th and Tw are raised.

3. The intelligent emergency rescue method for self-regulation of thermal environment and air quality according to claim 2, characterized in that, Also includes: Calculate the difference between core body temperature and mean skin temperature: AT = Tc - Tsm; Predictive temperature control is implemented based on the changing trends of AT and Tc values: if AT is low and Tc is high, ventilation is enhanced to assist heat dissipation; if AT is high and Tc is high, cooling power and fan speed are limited.

4. The intelligent emergency rescue method for self-regulation of thermal environment and air quality according to claim 2, characterized in that, Also includes: Monitor blood oxygen saturation (SpO2); When SpO2 < 90%, the target temperature range is locked in the neutral range, and mode C is preferred to minimize temperature fluctuations.

5. The intelligent emergency rescue method for self-regulation of thermal environment and air quality according to claim 2, characterized in that, Also includes: Monitor blood pressure (BP) and core body temperature (Tc); When systolic blood pressure is <90 mmHg and Tc is <36.0℃, the shock rewarming mode is activated, raising Th and Tw to the preset high temperature range, and controlling the directional infrared radiation heating plate to concentrate heating on the core area of ​​the torso.

6. The intelligent emergency rescue method for self-regulation of thermal environment and air quality according to claim 5, characterized in that, Also includes: A multi-rule priority arbitration mechanism is set up, with the priority order as follows: shock rewarming mode > SpO2 metabolism optimization mode > active temperature control mode based on core body temperature.

7. The intelligent emergency rescue method for self-regulation of thermal environment and air quality according to claim 1, characterized in that, Also includes: Package environmental parameters, vital signs data, and system control commands into treatment data; The treatment data is encrypted, including its structured chunking and conversion into encrypted data packets using binary tree path encoding.

8. The intelligent emergency rescue method for self-regulation of thermal environment and air quality according to claim 1, characterized in that, The tiered temperature control strategy is in progress: If the real-time loss rate is less than the standard loss rate, activate Mode A and turn off or put the air conditioner into standby mode. If the real-time power loss rate is between the standard power loss rate and 1.35 times the standard power loss rate, activate mode B and start the air conditioner's low power consumption mode. If the real-time loss rate is greater than 1.35 times the standard loss rate, activate Mode C to start the air conditioner's high-power mode to maintain temperature stability.

9. The intelligent emergency rescue method for self-regulation of thermal environment and air quality according to claim 1, characterized in that, The learning phase for environmental heat loss characteristics includes: In a simulation environment, multiple real-time temperature difference values ​​are set to obtain the time from the upper limit of the cabin temperature to the lower limit of the cabin temperature, and the average time of loss Ts is calculated. The standard loss rate is obtained by comparing the real-time temperature difference with the average loss rate.

10. An intelligent emergency rescue cabin with self-regulating thermal environment and air quality, characterized in that, The intelligent emergency rescue cabin uses any one of the methods described in claims 1-9 to control the environment inside the cabin.

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