Modularized intelligent pressurizing and oxygenating method for high-altitude building

Abstract

The invention relates to the technical field of pressurization and oxygen supplementation, in particular to a modular intelligent pressurization and oxygen supplementation method for a high-altitude building, which comprises the following steps: when the duration of entering the high-altitude building by a person is less than a set duration, carrying out collaborative scheduling on a pressurization module, a building oxygen supplementation module and a sleep oxygen supplementation module through a collaborative optimization model, wherein the collaborative optimization model is constructed based on a minimum operation cost and climbing energy consumption objective function and an operation condition constraint function, and is solved through a multi-objective evolutionary algorithm; when the entering duration is larger than or equal to the set duration, the pressurizing module is controlled to increase the pressure to the target indoor air pressure at the first speed and the building oxygen supplementation module is started at intervals in the active mode, and the pressurizing module is controlled to increase the pressure to the target indoor air pressure at the second speed and the sleep oxygen supplementation module is started at the set speed in the sleep mode. According to the invention, modular intelligent control of the high-altitude building is realized, so that the high-altitude building better conforms to the human body adaptation condition.

Description

Technical Field

[0001] The present invention relates to the technical field of pressurized oxygen supplementation, and particularly to a modular intelligent pressurized oxygen supplementation method for high-altitude buildings. Background Art

[0002] Due to its high altitude, low air pressure and special geographical features, the cold chain plateau region has significantly different climatic conditions from those of the plains. The volume fraction of oxygen in the atmosphere in the plateau region mostly remains at about 20.9%, and the partial pressure of oxygen decreases with the increase in altitude, resulting in an oxygen content about 60% lower than that in the plains. In addition, due to the decrease in atmospheric pressure in the plateau region, the partial pressure of oxygen in the alveoli and the partial pressure of arterial blood oxygen in the human body are easily much lower than the normal levels in the plains, leading to hypoxia in the plateau. This has brought many challenges to the lives of plateau residents and tourists, especially having a significant impact on sleep quality.

[0003] Currently, through the "zero-altitude house", scientific name: high-altitude pressurized livable building, pressurization and oxygen supplementation of the indoor environment are realized to simulate the environment of the plains and solve the problems of the impact of harsh conditions such as low pressure and hypoxia in the plateau on the human body.

[0004] For example, the patent document with the application number 202311035219.0 discloses an indoor air pressurized oxygen supplementation system that simultaneously adjusts the air pressure and oxygen content in the room through an air compressor. However, it cannot accurately perform coordinated control of pressurization and oxygen supplementation according to the physical conditions of personnel.

[0005] Therefore, how to perform intelligent control of the modular pressurization module and oxygen supplementation system of high-altitude buildings in line with the situation of people entering from the outside is a technical problem to be solved currently. Summary of the Invention

[0006] To this end, the present invention provides a modular intelligent pressurized oxygen supplementation method for high-altitude buildings. By performing refined coordinated scheduling of the pressurization module and oxygen supplementation system when people first enter a high-altitude building, and performing refined control according to the activity or sleep state of people after entering for a period of time, modular intelligent control of high-altitude buildings is realized to make it more in line with the human adaptation situation.

[0007] To achieve the above object, the present invention proposes a modular intelligent pressurized oxygen supplementation method for high-altitude buildings. The high-altitude building is provided with a diffused pressurization module and a building oxygen supplementation module that communicate with the internal space of the building, and a local centralized sleep oxygen supplementation module that communicates with the sleep utensils inside the building, including:

[0008] When the time that a person enters the high-altitude building is less than the set time, the pressurization module, the building oxygen supplementation module, and the sleep oxygen supplementation module are coordinately scheduled through a cooperative optimization model, where the cooperative optimization model is constructed based on an objective function of minimizing operating costs and climbing energy consumption and an operating condition constraint function, and is solved by a multi-objective evolutionary algorithm;

[0009] When the time that a person enters the high-altitude building is greater than or equal to the set time, if the high-altitude building is in the active mode, control the pressurization module to boost the pressure to the target indoor air pressure at the first rate, the building oxygen supplementation module to be turned on intermittently, and the sleep oxygen supplementation module to be turned off; if the high-altitude building is in the sleep mode, control the pressurization module to boost the pressure to the target indoor air pressure at a second rate less than the first rate, the sleep oxygen supplementation module to be turned on at the set rate, and the building oxygen supplementation module to be turned off.

[0010] Further, the process of constructing the objective function of minimizing operating costs and climbing energy consumption includes:

[0011] Calculate the pressurization maintenance cost and the pressurization electricity cost based on the pressurization speed level, the pressurization operation duration, the pressurization level power, the steady-state operation level, the steady-state operation duration, and the steady-state level operation power of the pressurization module;

[0012] Calculate the building oxygen supplementation maintenance cost and the building oxygen supplementation electricity cost based on the air intake speed level, the oxygen generator flow rate, and the air intake operation duration of the building oxygen supplementation module;

[0013] Calculate the sleep oxygen supplementation operation cost based on the air extraction speed level, the air extraction operation duration, and the oxygen supply amount of the sleep oxygen supplementation module;

[0014] Construct the operating cost by summing up the pressurization maintenance cost, the pressurization electricity cost, the building oxygen supplementation maintenance cost, the building oxygen supplementation electricity cost, and the sleep oxygen supplementation operation cost.

[0015] Further, the climbing energy consumption is calculated based on the pressurization speed level, the pressurization operation duration, the pressurization level power, the air intake speed level, the air intake operation duration, the air extraction speed level, and the oxygen supply amount;

[0016] The objective function of minimizing operating costs and climbing energy consumption takes the pressurization speed level, the steady-state operation level, the air intake speed level, the oxygen generator flow rate, the air extraction speed level, and the oxygen supply amount as decision variables.

[0017] Further, the operating condition constraint function includes a pressurization constraint condition, a building oxygen supplementation constraint condition, and a sleep oxygen supplementation constraint condition. The process of constructing the operating condition constraint function includes:

[0018] Construct a pressurization constraint condition based on the boost speed level, the boost operation duration, the steady-state operation level, the steady-state operation duration, and the operating power of the dehumidification and noise reduction equipment;

[0019] Construct a building oxygen supplementation constraint condition based on the inlet air speed level, the oxygen generator flow rate, the inlet air operation duration, and the oxygen supply flow rate, the exhaust air volume, the oxygen concentration in the building, the volume of the building, and the target amount of oxygen concentration;

[0020] Construct a sleep oxygen supplementation constraint condition based on circular free jet based on the exhaust air speed level, the exhaust air operation duration, and the oxygen supply amount.

[0021] In the above solution, through the intelligent scheduling of the pressurization module, the building oxygen supplementation module, and the sleep oxygen supplementation module, while ensuring the health, safety, and comfort of personnel in the high-altitude building, the cost is significantly reduced.

[0022] Furthermore, the process of solving the collaborative optimization model by the multi-objective evolutionary algorithm includes:

[0023] Encode and represent the solution of the collaborative optimization model through a vector model with a set initialization strategy to generate a solution vector;

[0024] Solve the solution vector through the multi-objective evolutionary algorithm to determine the set parameters of the decision variables, and use the set parameters for the collaborative scheduling of the pressurization module, the building oxygen supplementation module, and the sleep oxygen supplementation module.

[0025] Furthermore, the vector model is a two-layer vector model, and the vector coding sequences of each layer are the speed level sequence and the flow oxygen supply sequence respectively.

[0026] Furthermore, the multi-objective evolutionary algorithm is a multi-objective evolutionary algorithm based on a hybrid meta-heuristic, and an adaptive mechanism is used to select operators, including:

[0027] Calculate the weight of this iteration according to the number of times the operator is used, the weight of the previous iteration, and the total score of the iteration;

[0028] Calculate the selection probability of the operator according to the weight of this iteration, and use the selection probability for the operator selection of the multi-objective evolutionary algorithm.

[0029] In the above solution, encoding is performed through a vector model that conforms to the scheduling situation, and the solution is obtained through the multi-objective evolutionary algorithm. For dynamic environments such as altitude mutations and oxygen supply demand fluctuations, the adaptive mechanism can quickly adjust the operator strategy, improving the quality of the solution obtained through the multi-objective evolutionary algorithm.

[0030] Further, the pressurization module includes a compressor and a noise reduction device, the building oxygen supply module includes an oxygen generation device, an oxygen storage device, a blower, and an inlet pipeline, the sleep oxygen supply module includes an extraction pipeline connected to the inlet pipeline and having a smaller diameter than the diameter of the inlet pipeline, and the outlet of the extraction pipeline is arranged inside the pillow.

[0031] Further, the first rate is calculated and determined based on the volume inside the building, the maximum number of people inside the building, and the oxygen consumption rate per person, so as to ensure sufficient oxygen supply by the oxygen generation device, the oxygen storage device, and the blower in the active mode.

[0032] The second rate is calculated and determined based on the first rate, the maximum extraction noise, and the sleep oxygen consumption reduction coefficient, so as to ensure the quiet operation of the noise reduction device in the sleep mode.

[0033] Further, when the duration of a person entering the high-altitude building is greater than or equal to the set duration, if the high-altitude building is in the active mode, the interval duration is calculated according to the target indoor air pressure.

[0034] In the above solution, the function of fully utilizing the sleep oxygen supply module to supply oxygen directionally through the pillow to increase the local oxygen concentration is realized, and the best balance of efficient oxygen supply, low energy consumption operation, and user experience in the high-altitude building under extreme environments is achieved.

[0035] Compared with the prior art, the beneficial effects of the present invention are as follows.

[0036] 1. By carrying out coordinated scheduling of the pressurization module and the oxygen supply system in a refined manner when a person first enters the high-altitude building, and after entering for a period of time, carrying out refined control according to the activity or sleep state of the person respectively, modular intelligent control of the high-altitude building is realized, so that it better conforms to the human adaptation situation.

[0037] 2. Through the intelligent scheduling of the pressurization module, the building oxygen supply module, and the sleep oxygen supply module, while ensuring the health, safety, and comfort of the personnel in the high-altitude building, the cost is significantly reduced.

[0038] 3. By encoding with a vector model that conforms to the scheduling situation and solving through a multi-objective evolutionary algorithm, for dynamic environments such as altitude mutations and oxygen supply demand fluctuations, the adaptive mechanism can quickly adjust the operator strategy, improving the quality of the solution obtained by the multi-objective evolutionary algorithm.

[0039] 4. The function of fully utilizing the sleep oxygen supply module to supply oxygen directionally through the pillow to increase the local oxygen concentration is realized, and the best balance of efficient oxygen supply, low energy consumption operation, and user experience in the high-altitude building under extreme environments is achieved. Description of the Drawings

[0040] Figure 1 Schematic flow chart of the modular intelligent pressurization and oxygen supplementation method for high-altitude buildings according to an embodiment of the present invention;

[0041] Figure 2 Schematic flow chart of the collaborative optimization model of the modular intelligent pressurization and oxygen supplementation method for high-altitude buildings according to an embodiment of the present invention;

[0042] Figure 3 Schematic flow chart of the mode adjustment of the modular intelligent pressurization and oxygen supplementation method for high-altitude buildings according to an embodiment of the present invention;

[0043] Figure 4 Schematic diagram of the structure of each module of the modular intelligent pressurization and oxygen supplementation method for high-altitude buildings according to an embodiment of the present invention. Detailed implementation manners

[0044] In order to make the objectives and advantages of the present invention clearer and more understandable, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0045] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present invention and do not limit the protection scope of the present invention.

[0046] It should be noted that in the description of the present invention, the terms indicating directions or positional relationships such as "upper", "lower", "left", "right", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings. This is only for convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention.

[0047] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installation", "connection", and "connection" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0048] Such as Figures 1 to 4As shown in the figure, the present invention provides a modular intelligent pressurization and oxygen supplementation method for high-altitude buildings. By carrying out coordinated scheduling of the pressurization module and the oxygen supplementation system in a refined manner when people first enter a high-altitude building, and after a period of entry, carrying out refined control according to the activity or sleep state of people respectively, modular intelligent control of high-altitude buildings is realized to make it more in line with the human adaptation situation.

[0049] As Figures 1 to 4 shown in the figure, this embodiment proposes a modular intelligent pressurization and oxygen supplementation method for high-altitude buildings. The high-altitude building is provided with a diffused pressurization module and a building oxygen supplementation module leading to the interior space of the building, and a local centralized sleep oxygen supplementation module leading to the sleep utensils inside the building, including:

[0050] When the time for people to enter the high-altitude building is less than the set time, the pressurization module, the building oxygen supplementation module and the sleep oxygen supplementation module are coordinately scheduled through a coordinated optimization model, wherein the coordinated optimization model is constructed based on the objective functions of minimizing operation cost and climbing energy consumption and the operation condition constraint function, and is solved through a multi-objective evolutionary algorithm;

[0051] When the time for people to enter the high-altitude building is greater than or equal to the set time, if the high-altitude building is in the activity mode, control the pressurization module to boost the pressure to the target indoor air pressure at the first rate, the building oxygen supplementation module is turned on at intervals, and the sleep oxygen supplementation module is turned off; if the high-altitude building is in the sleep mode, control the pressurization module to boost the pressure to the target indoor air pressure at the second rate which is less than the first rate, the sleep oxygen supplementation module is turned on at the set rate, and the building oxygen supplementation module is turned off.

[0052] It can be understood that through the coordinated optimization model, quickly and intelligently reach the optimal state of the pressurization module, the building oxygen supplementation module and the sleep oxygen supplementation module, so that high-altitude buildings, preferably high-altitude pressurized livable buildings, the "zero-altitude room" can quickly relax the human body.

[0053] Furthermore, as Figure 2 shown in the figure, the process of constructing the objective functions of minimizing operation cost and climbing energy consumption includes:

[0054] Calculate the pressurization maintenance cost and pressurization electricity cost based on the pressurization speed level, pressurization operation duration, pressurization level power, steady-state operation level, steady-state operation duration and steady-state level operation power of the pressurization module;

[0055] Calculate the building oxygen supplementation maintenance cost and building oxygen supplementation electricity cost based on the air inlet speed level, oxygen generator flow rate and air inlet operation duration of the building oxygen supplementation module;

[0056] Calculate the sleep oxygen supplementation operation cost based on the air extraction speed level, air extraction operation duration and oxygen supply amount of the sleep oxygen supplementation module;

[0057] Construct the operating cost from the sum of the pressurization maintenance cost, pressurization power consumption cost, building oxygen supplementation maintenance cost, building oxygen supplementation power consumption cost, and sleep oxygen supplementation operation cost.

[0058] Specifically, the objective function for minimizing the operating cost and climbing energy consumption is:

[0059] min(C 0 ,E 0 )

[0060] Wherein, C 0 represents the operating cost, and E 0 represents the climbing energy consumption. Therefore, this objective function comprehensively considers two key optimization objectives, namely the operating cost and the climbing energy consumption, meeting the requirements of optimizing and balancing multiple objectives in the pressurization and oxygen supplementation project for high-altitude buildings.

[0061] Among them, the operating cost is:

[0062] C 0 = TEA s + TEA U + TEA L + TEA P + TEA R

[0063] Wherein, C 0 represents the operating cost, TEA s represents the pressurization maintenance cost, TEA U represents the pressurization power consumption cost, TEA L represents the building oxygen supplementation maintenance cost, TEA P represents the building oxygen supplementation power consumption cost, TEA R represents the sleep oxygen supplementation operation cost.

[0064] Among them, the calculation formula for the pressurization maintenance cost is:

[0065]

[0066] Wherein, TEA s represents the pressurization maintenance cost (yuan), T u represents the climbing duration (h), S i represents the pressure increase speed corresponding to the pressure increase speed level i, C s represents the maintenance cost per hour per unit level during the pressure increase stage (yuan / level·h), L j represents the steady-state operation level j, T w represents the steady-state operation duration (h), C l represents the maintenance cost per hour per unit level during the steady-state stage (yuan / level·h).

[0067] Among them, the electricity cost for pressurization is:

[0068]

[0069] In the formula, TEA U represents the electricity cost for pressurization (yuan), T U represents the operation duration of voltage boost, T w represents the steady-state operation duration (h), W Ui represents the boost power level corresponding to the boost speed level i (kW), W wj is the steady-state operation power corresponding to the steady-state operation level j (kW), and P is the local electricity price (yuan / kWh).

[0070] Among them, the calculation formula for the building oxygen replenishment and maintenance cost is:

[0071] TEA L =(C 0 +AL 0 T 0 )T x

[0072] In the formula, TEA L represents the building oxygen replenishment and maintenance cost (yuan), C 0 represents the fixed cost per maintenance (yuan), such as manual inspection and replacement of basic consumables, A represents the flow loss coefficient (yuan / m 3 ·h), that is, the additional loss cost generated per hour of operation when the oxygen generator flow increases by 1 m 3 / h, such as filter wear and oxygen separation membrane consumption, L o represents the oxygen generator flow (m 3 ·h), T o represents the operation duration of the oxygen generator (h), T x is the maintenance cycle coefficient, that is, maintenance is performed once every T x hours of operation.

[0073] Among them, the calculation formula for the building oxygen replenishment electricity cost is:

[0074]

[0075] In the formula, TEA P represents the building oxygen replenishment electricity cost (yuan), V i represents the fan speed corresponding to the air inlet speed level i, L o represents the oxygen generator flow (m 3 ·h), P Li and P respectively represent the unit level power coefficient and the local electricity price corresponding to the air inlet speed level i, T L 、T orespectively represent the inlet air operation duration of the fan and the operation duration of the oxygen generator, W 1 , W 0 respectively represent the energy efficiency ratio, power / flow kW / (m 3 / h), and the base power (kW). It can be understood that V i P L , L o W 1 +W 0 can be used to calculate the fan power (kW) and the oxygen generator power (kW) respectively.

[0076] Among them, the calculation formula for the electricity cost of building oxygen supplementation is:

[0077] TEA R =P Ri T R P + QC 1 Y

[0078] In the formula, TEA R represents the operation cost of sleep oxygen supplementation (yuan), P Ri , P respectively represent the exhaust power corresponding to the exhaust speed level i, T R represents the exhaust operation duration, Q represents the oxygen supply amount, Y represents the oxygen supply cost, and C 1 represents the outlet oxygen concentration.

[0079] Therefore, through the subdivision of the operation cost C 0 by the above formulas, it covers the core cost sources of the pressurized oxygen supplementation energy consumption system of high-altitude buildings, can accurately reflect the actual cost structure, and the decision variables in the formula will not be too many to cause the collaborative optimization model to be too complex and reduce the reliability of the solution results.

[0080] Furthermore, as Figure 2 shown, the climbing energy consumption is calculated based on the boost speed level, boost operation duration, boost level power, inlet air speed level, inlet air operation duration, exhaust speed level, and oxygen supply amount, specifically as:

[0081] E 0 =P Ui T U +P Lj T L +P Rk T R +QY

[0082] In the formula, E 0 represents the climbing energy consumption, P Ui represents the boost level power corresponding to the boost speed level i, T U represents the boost operation duration, P LjRepresents the unit - level power coefficient corresponding to the incoming air speed level j, T L Represents the incoming air operation duration of the fan, P Rk Respectively represent the exhaust power corresponding to the exhaust air speed level k, T R Represents the exhaust operation duration, Q represents the oxygen supply amount, and Y represents the oxygen supply cost. Among them, each power is determined by mapping through the speed level.

[0083] The objective function of minimizing the operation cost and climbing energy consumption takes the boost speed level, steady - state operation level, incoming air speed level, oxygen generator flow rate, exhaust air speed level, and oxygen supply amount as decision variables.

[0084] Furthermore, as Figure 2 shown, the operation condition constraint function includes a pressurization constraint condition, a building oxygen supplementation constraint condition, and a sleep oxygen supplementation constraint condition. The process of constructing the operation condition constraint function includes:

[0085] Based on the boost speed level, the boost operation duration, the steady - state operation level, the steady - state operation duration, and the operation power of the dehumidification and noise reduction equipment, construct the pressurization constraint condition;

[0086] Based on the incoming air speed level, the oxygen generator flow rate, the incoming air operation duration, and the oxygen supply flow rate, exhaust air volume, oxygen concentration in the building, building volume, and target oxygen concentration amount, construct the building oxygen supplementation constraint condition;

[0087] Based on the exhaust air speed level, exhaust operation duration, and oxygen supply amount, construct the sleep oxygen supplementation constraint condition based on circular free jet.

[0088] Specifically, the pressurization constraint condition is:

[0089] P Ui +P d ≤P max

[0090] P wj +P d ≤P max

[0091] P Ui T U +P Wj T W +P d (T U +T W )≤E max

[0092] In the formula, P Ui 、P d 、P max 、P wjrespectively represent the boosting-level power, dehumidification and noise reduction power, power supply capacity safety power corresponding to the boosting speed level i, and the steady-state level power corresponding to the steady-state operation level j; T U and T W respectively represent the boosting operation duration and the steady-state operation duration; E max represents the energy consumption constraint of the pressurization system.

[0093] In the above solution, through the intelligent scheduling of the pressurization module, building oxygen supplementation module and sleep oxygen supplementation module, while ensuring the health, safety and comfort of personnel in high-altitude buildings, the cost is significantly reduced.

[0094] Specifically, the building oxygen supplementation constraint conditions are as follows:

[0095]

[0096] Q in (C out - C) ≥ Q target

[0097] Q in ≥ NV

[0098] In the formula, Q in , Q out , Q target , Q 3 respectively represent the incoming air flow rate, exhaust air volume, target incoming air volume, and current oxygen production amount corresponding to the incoming air speed level i, C out and C are the indoor incoming air oxygen concentration and the current oxygen concentration respectively, N is the minimum air change rate that the incoming air volume needs to meet, and V is the indoor space volume.

[0099] Specifically, the sleep oxygen supplementation constraint conditions include:

[0100] Based on the jet velocity constraint of circular free jet, to ensure that the oxygen ejected from the pillow can effectively enter the nose of the person:

[0101]

[0102] Q e ≤ kQ

[0103] In the formula, u, u 0 , u min respectively represent the actual jet velocity received by the person, the jet outlet velocity corresponding to the exhaust air speed level j, and the minimum jet velocity received by the person, D represents the jet outlet diameter, and d represents the distance between the person and the jet outlet, that is, the pillow thickness.

[0104] Based on the oxygen concentration diffusion constraint of the oxygen concentration diffusion model, to ensure that the amount of oxygen received by the person on the pillow is sufficient:

[0105]

[0106] In the formula, C, C 0 , C req respectively represent the actual oxygen concentration received by personnel, the initial oxygen supply concentration, and the required oxygen concentration in the sleep area. Q, Q e respectively represent the oxygen supply amount and the exhaust air flow rate corresponding to the exhaust air speed level. φ(d) represents the jet diffusion coefficient at the pillow thickness d.

[0107] The exhaust air and oxygen supply flow ratio constraint is used to avoid excessive extraction of oxygen:

[0108] Q e ≤kQ

[0109] In the formula, Q, Q e respectively represent the oxygen supply amount and the exhaust air flow rate corresponding to the exhaust air speed level. k is an empirical coefficient used to define the upper limit of the exhaust air and oxygen supply flow ratio, which can avoid excessive extraction of oxygen, ensure that the system for oxygen supplementation operates at an appropriate oxygen supply and exhaust air flow ratio, and guarantee the stability of the process. Preferably, it is taken as 12.

[0110] Furthermore, the process of solving the collaborative optimization model through the multi-objective evolutionary algorithm includes:

[0111] Encoding and representing the solution of the collaborative optimization model through a vector model with a set initialization strategy to generate a solution vector;

[0112] Solving the solution vector through the multi-objective evolutionary algorithm to determine the set parameters of the decision variables, and using the set parameters for the collaborative scheduling of the pressurization module, building oxygen supplementation module, and sleep oxygen supplementation module.

[0113] Furthermore, the vector model is a two-layer vector model, and the vector encoding sequences of each layer are the speed level sequence and the flow oxygen supply sequence respectively.

[0114] Furthermore, the multi-objective evolutionary algorithm is a multi-objective evolutionary algorithm based on a hybrid metaheuristic, and an adaptive mechanism is used to select operators, including:

[0115] Calculating the weight of this iteration according to the number of times the operator is used, the weight of the previous iteration, and the total score of the iteration;

[0116] Calculating the selection probability of the operator according to the weight of this iteration, and using the selection probability for the operator selection of the multi-objective evolutionary algorithm.

[0117] Specifically, the way the multi-objective evolutionary algorithm is embedded in the genetic algorithm framework of Pareto is as Figure 4As shown in Figure 1, it is applicable to the multi-objective combinatorial optimization problem of the objective function in the collaborative optimization model constructed by the above formulas. By adaptively adjusting the weights of the destruction and repair operators and using the Metropolis criterion to accept inferior solutions, the algorithm's search ability and solution quality are improved.

[0118] Specifically, the specific setting parameters of the genetic algorithm embedded in the adaptive large neighborhood search algorithm include: population size 100, termination condition iteration 100 times, fitness function is the operating cost and climbing energy consumption of the objective function, the smaller the two objectives, the higher the fitness, the initial temperature setting needs to be obtained based on preliminary experiments, and the temperature drop strategy: T k+1 =T k *θ where θ∈[0,1] is the annealing rate coefficient.

[0119] Specifically, the probability that the Metropolis criterion accepts an inferior solution is:

[0120]

[0121] The initial temperature is determined through preliminary experiments and directly affects the accuracy of the algorithm. The annealing rate is closely related to the probability of accepting inferior solutions. As the iterations proceed, the temperature gradually decreases, the probability of accepting inferior solutions decreases, and the algorithm gradually focuses on local area search.

[0122] In order to adaptively select operators, a roulette wheel method is used to select the operator to be selected based on the current weight. The score calculation formula for each operator i in each iteration is:

[0123] S i (t) = S i (t-1)+ΔS i (t)

[0124] Where S i (t) represents the total score of the operator after the tth iteration, ΔS i (t) represents the score obtained by the operator in the tth iteration. The selection weight w of each operator i i (t) will be updated based on its historical performance, and the weight update formula is:

[0125]

[0126] α is a decay parameter used to balance the weight of historical performance and current performance. i (t) is the number of times the operator is used. i (t) represents the weight of the operator after the tth iteration. The probability of each operator being selected is calculated based on the updated weight, and the probability is calculated as:

[0127]

[0128] where p i (t) represents the probability of selecting operator i in the t-th iteration, m is the total number of operators. During one iteration, the better-performing operators get higher scores and increased weights, thus increasing the number of times they are selected. At the beginning of ALNS, the weight of each operator is set to 1.

[0129] In the above solution, encoding is performed through a vector model that conforms to the scheduling situation, and the solution is obtained through a multi-objective evolutionary algorithm. For dynamic environments such as sudden altitude changes and fluctuating oxygen supply demands, the adaptive mechanism can quickly adjust the operator strategy, improving the quality of the solution obtained through the multi-objective evolutionary algorithm.

[0130] Furthermore, the pressurization module includes a compressor and a noise reduction device, the building oxygen supplementation module includes an oxygen generation device, an oxygen storage device, a fan, and an inlet pipeline, and the sleep oxygen supplementation module includes an extraction pipeline connected to the inlet pipeline and having a smaller diameter than the inlet pipeline, and the outlet of the extraction pipeline is arranged inside the pillow.

[0131] Specifically, the compressor is used to pressurize oxygen to ensure that oxygen can be efficiently transported to the target area through the pipeline. The noise reduction device is used to reduce the mechanical vibration and airflow noise generated during the operation of the compressor, ensuring quiet operation in the sleep mode. The oxygen generation device is used to store the oxygen produced by the oxygen generation device. The fan is used to drive the gas flow and transport oxygen from the oxygen storage device or the oxygen generation device to the target area through the pipeline. The inlet pipeline is used to connect the oxygen generation device, the oxygen storage device, the fan, and the target area to form a complete oxygen transportation network.

[0132] Furthermore, the first rate is calculated and determined based on the volume of the building, the maximum number of people in the building, and the single-person oxygen consumption rate, so as to ensure sufficient oxygen transportation by the oxygen generation device, the oxygen storage device, and the fan in the active mode;

[0133] The second rate is calculated and determined based on the first rate, the maximum extraction noise, and the sleep oxygen consumption reduction coefficient, so as to ensure quiet operation of the noise reduction device in the sleep mode.

[0134] Specifically, the calculation formula for the first rate is:

[0135]

[0136] In the formula, v 1 represents the first rate, n represents the maximum number of people in the building, R o represents the single-person oxygen consumption rate, ρ represents the air density, V represents the volume of the building, and K represents the leakage compensation coefficient.

[0137] Specifically, the calculation formula for the second rate is:

[0138] v 2 = v 1 N max α s

[0139] Wherein, v 1 represents the first rate, v 2 represents the second rate, N max represents the maximum exhaust noise, α s represents the coefficient of reduction in sleep oxygen consumption.

[0140] Further, when the duration of a person entering the high-altitude building is greater than or equal to a set duration, if the high-altitude building is in an active mode, the interval duration is calculated according to the target indoor air pressure.

[0141] Specifically, the calculation formula for the interval duration is:

[0142]

[0143] Wherein, t s represents the interval duration, n represents the maximum number of people in the building, R o represents the oxygen consumption rate per person, Q g represents the oxygen supply system, V represents the volume inside the building, ΔP m represents the difference between the target indoor air pressure and the current indoor air pressure.

[0144] In the above solution, the function of fully utilizing the sleep oxygen supply module to supply oxygen directionally through the pillow to increase the local oxygen concentration is realized, and the best balance of efficient oxygen supply, low energy consumption operation and user experience of the high-altitude building in an extreme environment is achieved.

[0145] In this embodiment, through the coordinated scheduling of the refined pressurization module and the oxygen supply system when a person first enters the high-altitude building, and after a period of entry, refined control is carried out according to the activity or sleep state of the person, modular intelligent control of the high-altitude building is realized to make it more in line with the human adaptation situation. Through the intelligent scheduling of the pressurization module, the building oxygen supply module and the sleep oxygen supply module, while ensuring the health, safety and comfort of the personnel in the high-altitude building, the cost is significantly reduced. By encoding through a vector model that conforms to the scheduling situation and solving through a multi-objective evolutionary algorithm, for dynamic environments such as altitude mutation and oxygen supply demand fluctuation, the adaptive mechanism can quickly adjust the operator strategy, improving the quality of the solution obtained by the multi-objective evolutionary algorithm. The function of fully utilizing the sleep oxygen supply module to supply oxygen directionally through the pillow to increase the local oxygen concentration is realized, and the best balance of efficient oxygen supply, low energy consumption operation and user experience of the high-altitude building in an extreme environment is achieved.

[0146] So far, the technical solution of the present invention has been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easily understood by those skilled in the art that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0147] The above are only the preferred embodiments of the present invention and are not used to limit the present invention; for those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A modular intelligent pressurization and oxygen supplementation method for high-altitude buildings, characterized in that: The high-altitude building is equipped with a diffuse pressurization module and a building oxygenation module that are connected to the interior space of the building, as well as a local centralized sleep oxygenation module that is connected to the sleeping equipment inside the building, including: When the time a person has been in the high-altitude building is less than the set time, the pressurization module, the building oxygen supplementation module and the sleep oxygen supplementation module are collaboratively scheduled through a collaborative optimization model, wherein the collaborative optimization model is constructed based on the objective function of minimizing the operating cost and climbing energy consumption and the operating condition constraint function, and is solved by a multi-objective evolutionary algorithm; When the time a person enters the high-altitude building is greater than or equal to the set time, if the high-altitude building is in active mode, the pressurization module is controlled to increase the pressure to the target indoor air pressure at a first rate, the building oxygen supplementation module is turned on at intervals, and the sleep oxygen supplementation module is turned off; if the high-altitude building is in sleep mode, the pressurization module is controlled to increase the pressure to the target indoor air pressure at a second rate less than the first rate, the sleep oxygen supplementation module is turned on at a set rate, and the building oxygen supplementation module is turned off.

2. The modular intelligent pressurization and oxygen supplementation method for high-altitude buildings according to claim 1, characterized in that: The process of constructing the objective function to minimize operating cost and climbing energy consumption includes: Calculate the boost maintenance cost and boost electricity cost based on the boost speed level, boost operation time, boost level power, steady-state operation level, steady-state operation time and steady-state level operation power of the boost module; Calculate the maintenance cost of building oxygen supplementation and the electricity cost of building oxygen supplementation based on the air inlet speed level of the building oxygen supplementation module, the oxygen generator flow rate and the air inlet operation time; The operating cost of the sleep oxygen supplementation module is calculated based on the ventilation speed level, ventilation operation time and oxygen supply of the sleep oxygen supplementation module; The operating cost is constructed by summing the pressurization maintenance fee, the pressurization electricity fee, the building oxygen supplementation maintenance fee, the building oxygen supplementation electricity fee and the sleep oxygen supplementation operation fee.

3. The modular intelligent pressurization and oxygen supplementation method for high-altitude buildings according to claim 2 is characterized in that: The climbing energy consumption is calculated based on the boost speed level, boost operation time, boost level power, air intake speed level, air intake operation time, exhaust speed level and oxygen supply; The objective function of minimizing operating cost and climbing energy consumption takes the boost speed level, steady-state operation level, air inlet speed level, oxygen generator flow, exhaust speed level and oxygen supply as decision variables.

4. The modular intelligent pressurization and oxygen supplementation method for high-altitude buildings according to claim 3 is characterized in that: The operating condition constraint function includes a pressurization constraint condition, a building oxygen supplement constraint condition, and a sleep oxygen supplement constraint condition. The process of constructing the operating condition constraint function includes: Constructing a boost constraint condition based on the boost speed level, the boost operation duration, the steady-state operation level, the steady-state operation duration, and the dehumidification and noise reduction equipment operation power; Building oxygen supplementation constraint conditions are established based on the air intake speed level, the oxygen concentrator flow rate, the air intake operation time, and the oxygen supply flow rate, exhaust air volume, oxygen concentration in the building, volume in the building, and oxygen concentration target value; The sleeping oxygen supplementation constraint condition based on circular free jet is constructed based on the ventilation speed level, ventilation operation time and oxygen supply.

5. The modular intelligent pressurization and oxygen supplementation method for high-altitude buildings according to claim 2, characterized in that: The process of solving the collaborative optimization model construction through multi-objective evolutionary algorithm includes: The solution of the collaborative optimization model is encoded by a vector model with an initialization strategy to generate a solution vector; The solution vector is solved by a multi-objective evolutionary algorithm to determine the setting parameters of the decision variables, and the setting parameters are used for the coordinated scheduling of the boosting module, the building oxygen supplementation module and the sleep oxygen supplementation module.

6. The modular intelligent pressurization and oxygen supplementation method for high-altitude buildings according to claim 5, characterized in that: The vector model is a two-layer vector model, and the vector encoding sequence of each layer is a speed level sequence and a flow oxygen supply sequence.

7. The modular intelligent pressurization and oxygen supplementation method for high-altitude buildings according to claim 5, characterized in that: The multi-objective evolutionary algorithm is a multi-objective evolutionary algorithm based on hybrid meta-heuristics, which adopts an adaptive mechanism to select operators, including: The weight of this iteration is calculated based on the number of times the operator is used, the weight of the previous iteration, and the total score of the iteration; The selection probability of the operator is calculated according to the weight of this iteration, and the selection probability is used for operator selection of the multi-objective evolutionary algorithm.

8. The modular intelligent pressurization and oxygen supplementation method for high-altitude buildings according to claim 1, characterized in that: The boosting module includes a compressor and a noise reduction device, the building oxygen supplementation module includes an oxygen production device, an oxygen storage device, a fan and an access pipeline, and the sleep oxygen supplementation module includes an exhaust pipeline connected to the access pipeline and having a smaller diameter than the access pipeline, and the air outlet of the exhaust pipeline is arranged inside the pillow.

9. The modular intelligent pressurization and oxygen supplementation method for high-altitude buildings according to claim 8, characterized in that: The first rate is calculated and determined based on the volume of the building, the maximum number of people in the building, and the oxygen consumption rate of a single person, so as to ensure that the oxygen production device, the oxygen storage device, and the fan deliver sufficient oxygen in the activity mode; The second rate is calculated and determined based on the first rate, the maximum ventilation noise and the sleep oxygen consumption reduction coefficient, so as to make the noise reduction device in the sleep mode run quietly.

10. The modular intelligent pressurization and oxygen supplementation method for high-altitude buildings according to any one of claims 1 to 9, characterized in that: When the time a person has entered the high-altitude building is greater than or equal to the set time, if the high-altitude building is in an active mode, the interval time is calculated according to the target indoor air pressure.

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