A carbon dioxide ventilator

Abstract

The present invention provides a carbon dioxide ventilator, belonging to the technical field of ventilators, comprising: a CO2 gas circuit, a normal air circuit, a mixed air circuit, an O2 gas circuit, a humidifying and heating device, an end monitoring system and an environmental monitoring system; a first fan, a CO2 concentration sensor, a flow sensor and a CO2 gas circuit switch are arranged in the CO2 gas circuit; air filtration intake silencing, a second fan and a differential pressure sensor are arranged in the normal air circuit; an O2 concentration sensor, a CO2 concentration sensor and a flow sensor are arranged in the mixed air circuit; an O2 gas source oxygen generator, a third fan, an O2 concentration sensor, a flow sensor and an O2 gas circuit switch are arranged in the O2 gas circuit; the end monitoring system includes a breathing mask, a blood oxygen concentration tester and a PETCO2 sensor; the environmental monitoring system includes a CO2 concentration sensor, an O2 concentration sensor, a barometer and a temperature and humidity sensor.

Description

Technical Field

[0001] The invention belongs to the technical field of ventilators, and in particular relates to a carbon dioxide ventilator. Background Art

[0002] In the field of respiratory therapy and assisted sleep, ventilation therapy is currently generally performed by using compressed air from a fan. With the development of medical technology, ventilators have been widely used in the treatment of a variety of diseases such as sleep apnea syndrome, chronic obstructive pulmonary disease, and altitude sickness. In special cases, an oxygen concentrator is usually connected to the breathing tube or mask to increase the oxygen concentration and achieve the purpose of auxiliary treatment. In the prior art, ventilators mainly adjust ventilation parameters by monitoring blood oxygen saturation. However, this single monitoring method has obvious limitations. Studies have shown that even patients with chronic obstructive pulmonary disease with normal blood oxygen saturation at rest during the day may experience hypoxemia at night, and this is more significant during the acute exacerbation period. In this case, relying solely on monitoring blood oxygen saturation cannot detect potential respiratory function abnormalities in a timely manner. Especially in plateau environments, human exposure to a low oxygen environment can lead to a rapid increase in ventilation. As ventilation increases, excessive discharge of carbon dioxide in the body forms hypocarbia, which in turn causes respiratory alkalosis. This pathological change inhibits the initial enhanced ventilation response and affects the human body's adaptation process to the plateau environment. Existing ventilators lack an effective regulatory mechanism for this complex physiological process. In addition, in patients with sleep apnea syndrome, especially those with central sleep apnea associated with heart failure, frequent episodes of apnea occur due to reduced levels of carbon dioxide in the blood. This not only causes intermittent hypoxia, but also further damages heart function and increases the risk of death. Although existing technologies can improve symptoms by providing continuous positive airway pressure ventilation, they cannot fundamentally solve the problem of regulating respiratory rhythm.

[0003] The main technical problem at present is that the ventilator lacks the ability to accurately regulate the partial pressure of carbon dioxide, which may lead to complications such as carbon dioxide retention or insufficiency during long-term mechanical ventilation. This problem is particularly prominent in the treatment of high altitude environments and sleep apnea, which directly affects the treatment effect and patient prognosis. Summary of the invention

[0004] In view of this, the present invention provides a carbon dioxide ventilator, which can solve the problem that the existing ventilators have insufficient ability to regulate the carbon dioxide partial pressure.

[0005] The present invention is achieved in that:

[0006] The present invention provides a carbon dioxide ventilator, which includes: a CO2 gas circuit, a normal air circuit, a mixed air circuit, an O2 gas circuit, a humidifying and heating device, an end monitoring system and an environmental monitoring system; a first fan, a CO2 concentration sensor, a flow sensor and a CO2 gas circuit switch are arranged in the CO2 gas circuit; an air filter intake silencer, a second fan and a differential pressure sensor are arranged in the normal air circuit; an O2 concentration sensor, a CO2 concentration sensor and a flow sensor are arranged in the mixed air circuit; an O2 gas source oxygen generator, a third fan, an O2 concentration sensor, a flow sensor and an O2 gas circuit switch are arranged in the O2 gas circuit; the end monitoring system includes a breathing mask, a blood oxygen concentration tester and a PETCO2 sensor; the environmental monitoring system includes a CO2 concentration sensor, an O2 concentration sensor, a barometer and a temperature and humidity sensor.

[0007] On the basis of the above technical solutions, the carbon dioxide ventilator of the present invention can be further improved as follows:

[0008] Among them, one side of the CO2 gas circuit is connected to the CO2 gas source, and the other side converges with the mixed air circuit. The first fan is arranged between the CO2 concentration sensor and the flow sensor; the normal air circuit is connected to the mixed air circuit through a grille, and the second fan is arranged between the air filter intake silencer and the differential pressure sensor.

[0009] Furthermore, one side of the O2 gas circuit is connected to the O2 gas source oxygen generator, and the other side converges with the mixed air circuit. The third fan is arranged between the O2 concentration sensor and the flow sensor; the mixed air circuit is connected to the humidifying and heating device.

[0010] Furthermore, the grille is composed of a plurality of parallel diversion plates, and equal-spacing air flow channels are formed between the diversion plates. Both ends of the diversion plates are fixedly connected to the inner wall of the normal air circuit and the inner wall of the mixed air circuit respectively.

[0011] Furthermore, the humidifying and heating device includes a heating cavity and a heating unit. A water storage tank is arranged in the heating cavity, and the heating unit is arranged at the bottom of the water storage tank; a delivery pipeline is arranged between the heating cavity and the breathing mask.

[0012] Furthermore, the breathing mask includes a mask body and a fixing structure. The mask body is bowl-shaped, and a sealing soft pad is arranged at the edge; the fixing structure includes a headband and a strap buckle, and the headband is connected to both sides of the mask body through the strap buckle.

[0013] Furthermore, the CO2 gas source is a high-pressure gas cylinder, and the high-pressure gas cylinder is connected to the CO2 gas circuit switch through a pressure reducing valve; the O2 gas source oxygen generator is connected to the O2 gas circuit switch through a pressure stabilizing valve.

[0014] Further, the CO2 concentration sensor, O2 concentration sensor, barometer and temperature and humidity sensor in the environmental monitoring system are all fixedly installed on the surface of the housing; the housing is composed of an upper housing and a lower housing that are buckled together, and a partition is provided inside to divide the space into an air path area and a monitoring area.

[0015] Further, the PETCO2 sensor is arranged at the connection between the breathing mask and the humidifying and heating device; the blood oxygen concentration tester includes a measurement probe and a numerical display unit, and the measurement probe is electrically connected to the numerical display unit through a signal line.

[0016] Further, it further includes a main control single-chip microcomputer, and the main control single-chip microcomputer is connected to the CO2 concentration sensor, O2 concentration sensor, PETCO2 sensor, blood oxygen concentration tester, temperature and humidity sensor and barometer through a terminal block; the main control single-chip microcomputer is provided with a signal acquisition and output processing control module, and the signal acquisition and output processing control module is respectively connected to the first fan, the second fan and the third fan through a drive circuit; the main control single-chip microcomputer is respectively connected to the CO2 gas path switch and the O2 gas path switch through a solenoid valve drive circuit; the measured value of the blood oxygen concentration tester and the measured value of the PETCO2 sensor are fed back to the signal acquisition and output processing control module, and the shortest path problem of a directed graph is solved to adjust the fan speed.

[0017] The signal acquisition and output processing control module executes the following steps to achieve precise adjustment of the first fan, the second fan and the third fan:

[0018] S10. Construct a directed graph network structure, set the rotation speeds of the first fan, the second fan and the third fan as nodes in the graph, the connection lines between the nodes represent the conversion paths of the rotation speed combinations, and the weights of the connection lines represent the energy consumption values in the conversion process;

[0019] S20. Collect the measured values of the blood oxygen concentration tester and the PETCO2 sensor, map the measured values into the directed graph network structure, and obtain the starting node corresponding to the current rotation speed combination;

[0020] S30. Determine the target node in the directed graph network structure according to the preset blood oxygen concentration target value and PETCO2 concentration target value, and the target node corresponds to the target rotation speed combination;

[0021] S40. Use Dijkstra's algorithm to calculate the shortest path from the starting node to the target node, the shortest path corresponds to the optimal route for fan speed adjustment, and the path length corresponds to the total energy consumption of speed adjustment;

[0022] S50. Traverse all the nodes on the shortest path, record the rotational speed differences between every two adjacent nodes, and generate a rotational speed adjustment sequence, where the rotational speed adjustment sequence includes the adjustment direction and the adjustment amount.

[0023] S60. Adjust the rotational speeds of the first fan, the second fan, and the third fan step by step according to the rotational speed adjustment sequence, where each step of the adjustment plan is obtained by solving a rotational speed adjustment equation set; the rotational speed adjustment equation set includes a load balance equation, a power distribution equation, a ventilation volume constraint equation, and an efficiency maximization equation.

[0024] S70. After each rotational speed adjustment is completed, collect the blood oxygen concentration and the PETCO2 concentration, and define the combined value of the blood oxygen concentration and the PETCO2 concentration as the actual reached node.

[0025] S80. Judge the deviation value between the actual reached node and the path planning node. When the deviation value exceeds the threshold, use the actual reached node as the new starting node, and return to step S40 to re-plan the path.

[0026] S90. Repeat steps S40 to S80 until the deviation value between the actual reached node and the target node is less than the threshold.

[0027] The load balance equation is used to ensure a reasonable load distribution among the three fans. The input includes the current rotational speeds and the rated powers of the three fans, and the output is the load distribution coefficient among the three fans.

[0028] The power distribution equation is used to calculate the target power values of the three fans. The input includes the load distribution coefficient and the target rotational speed of the path planning, and the output is the target power value of each fan.

[0029] The ventilation volume constraint equation is used to limit the change range of the total ventilation volume. The input includes the differences between the current rotational speeds and the target rotational speeds of the fans, and the output is the maximum allowable rotational speed adjustment amount.

[0030] The efficiency maximization equation is used to achieve the best working efficiency of the three fans. The input includes the characteristic curve parameters of the fans and the target power values, and the output is the rotational speed increment value corresponding to the best efficiency point.

[0031] The time interval between two adjacent adjustments is 3 seconds, and the adjustment amount of each step does not exceed 20% of the current rotational speed.

[0032] Further, the shortest path calculation in step S40 adopts the Dijkstra algorithm, and its state transition equation is specifically expressed as follows:

[0033] ;

[0034] In the formula, is from the starting point to the node The shortest distance; is the node to the node The weight value, representing the energy consumption; is the node adjacent to The node number.

[0035] The rotational speed difference calculation in step S50 is specifically expressed as follows:

[0036] ;

[0037] In the formula, is the rotational speed adjustment amount of the th fan; is the target rotational speed of the th fan; is the current rotational speed of the th fan; takes values of 1, 2, 3, corresponding to the three fans respectively.

[0038] The rotational speed adjustment equation set in step S60 is specifically expressed as follows:

[0039] Load balance equation:

[0040] ;

[0041] In the formula, is the load distribution coefficient of the th fan; is the output power of the th fan; takes values of 1, 2, 3.

[0042] Power distribution equation:

[0043] ;

[0044] In the formula, is the rotational speed of the th fan; is the fitting coefficient of the fan characteristic curve; takes values of 1, 2, 3.

[0045] Ventilation volume constraint equation:

[0046] ;

[0047] In the formula, is the total ventilation volume of the system; is the ventilation volume of the th fan; is the ventilation efficiency of the th fan; The maximum ventilation volume allowed by the system.

[0048] Efficiency maximization equation:

[0049] ;

[0050] In the formula, is the efficiency of the th fan; is the output power; is the input power; is the air density; is the acceleration due to gravity; is the head; is the ventilation volume.

[0051] The methods for obtaining each parameter are as follows:

[0052] (1) Fan characteristic curve fitting coefficient Obtained through experiments:

[0053] Step 1: Measure the fan power at different speeds;

[0054] Step 2: Fit a cubic curve using the least squares method.

[0055] (2) Ventilation efficiency Measured through the following steps:

[0056] Step 1: Measure the input electrical power of the fan;

[0057] Step 2: Measure the outlet air flow pressure and flow rate;

[0058] Step 3: Calculate the output power and efficiency.

[0059] Principle of constructing the equation set:

[0060] (1) The load balance equation is based on the principle of multi-machine cooperation to ensure system stability;

[0061] (2) The power distribution equation uses a cubic polynomial to describe the fan characteristics, covering the performance of the fan under different working conditions;

[0062] (3) The ventilation volume constraint equation considers safety requirements to prevent excessive ventilation;

[0063] (4) The efficiency maximization equation is based on the basic principles of fluid mechanics to optimize system energy consumption.

[0064] The above speed regulation equation set introduces a load distribution coefficient to achieve multi-machine dynamic balance; uses a cubic polynomial to describe the fan characteristics to improve the model accuracy; and considers the ventilation efficiency to optimize system energy consumption.

[0065] Compared with the prior art, a carbon dioxide ventilator provided by the present invention effectively solves the problem of insufficient regulation of carbon dioxide partial pressure in the prior art. Specifically, it is reflected in the following aspects:

[0066] First of all, the present invention adopts a dual-parameter real-time monitoring strategy of PETCO2 and blood oxygen saturation, and establishes a more comprehensive respiratory function evaluation system. By using a PETCO2 sensor to real-time monitor the end-tidal carbon dioxide partial pressure, it can accurately reflect the carbon dioxide level in the blood, avoiding the monitoring blind area that may be caused by relying solely on blood oxygen saturation.

[0067] Secondly, the present invention innovatively adopts a multi-dimensional parameter optimization control strategy based on the Dijkstra algorithm to realize the coordinated regulation of the speeds of three fans. By constructing a directed graph network considering energy consumption, the system can find the optimal regulation path with the lowest energy consumption while ensuring the treatment effect. Test data shows that compared with the traditional control scheme

[0068] Thirdly, the present invention realizes the active regulation of the respiratory rhythm by precisely controlling the carbon dioxide concentration in the inhaled gas. For patients with sleep apnea, the system can adjust the gas composition in advance according to the change trend of PETCO2 to prevent the occurrence of apnea.

[0069] Finally, the present invention integrates an environmental monitoring system, which can automatically adjust the control strategy according to environmental parameters. This feature enables the device to have better adaptability in high-altitude environments and can help users complete the physiological adaptation process faster. In summary, the present invention solves the problem of insufficient regulation ability of the existing ventilator for carbon dioxide partial pressure. Brief Description of the Drawings

[0070] Figure 1 is a schematic structural diagram of the carbon dioxide ventilator provided by the present invention;

[0071] Figure 2 is a schematic diagram of the steps executed by the signal acquisition and output processing control module. Detailed Embodiments

[0072] To make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention.

[0073] Such as Figure 1, which is a schematic diagram of the structure of a carbon dioxide ventilator provided by the present invention, including a CO2 gas circuit 1, a normal air circuit 2, a mixed air circuit 3, an O2 gas circuit 4, a humidifying and heating device 12, a terminal monitoring system 18 and an environmental monitoring system 29. This embodiment integrates CO2 and O2 dual gas source inputs and adopts a multi-sensor real-time monitoring method to achieve precise control of respiratory gas components, and can provide personalized respiratory support solutions according to the actual needs of users.

[0074] The CO2 gas circuit 1 is made of medical-grade 316L stainless steel pipe with a diameter range of 8 to 12 mm and a wall thickness of 0.8 to 1.2 mm. The material has excellent corrosion resistance and biocompatibility, and can operate stably for a long time without oxidation or release of harmful substances. The inner wall of the pipeline is precisely polished, and the surface roughness is controlled below 0.4 microns. This smooth inner wall can effectively reduce airflow resistance and reduce energy consumption. The first fan A set in the gas circuit is driven by a brushless DC motor with a rated power of 35 to 55 watts, a maximum speed of 4500 to 5000 rpm, and a stepless speed regulation function. This configuration ensures the accuracy and stability of CO2 delivery, and the delivery volume can be adjusted in real time according to demand. The measurement range of the CO2 concentration sensor 5 is 0 to 50000 ppm, the accuracy reaches ±50ppm, and the response time is less than 60 seconds, which can accurately monitor the concentration change of CO2 in the gas circuit. The flow sensor 6 has a range of 0 to 120 cubic meters per hour and an accuracy of ±1.5%, which can accurately control the gas flow. The CO2 gas circuit switch 24 has a rated pressure of 0.5 to 1.0 MPa, a response time of less than 25 milliseconds, and good sealing and reliability.

[0075] The normal air path 2 uses food-grade PP pipes or medical-grade silicone pipes with a diameter of 15 to 20 mm. This type of material has good toughness and weather resistance and can withstand repeated bending and disinfection. The air filtration intake silencer 7 adopts a multi-layer composite filter element structure, with an outer layer of 80 to 120 grams per square meter of non-woven fabric for filtering larger particles; the middle layer is a HEPA filter with a filtration efficiency of not less than 99.97%, which can effectively block particles larger than 0.3 microns; the inner layer is an activated carbon layer with a specific surface area of ​​not less than 800 square meters per gram, which can absorb odors and harmful gases in the air. This multi-layer filtration structure not only ensures the cleanliness of the intake air, but also reduces noise through the sound absorption characteristics of the material. The second fan B adopts a centrifugal structure with a rated power of 75 to 95 watts and a maximum air volume of 600 to 700 cubic meters per hour. The differential pressure sensor 8 has a range of ±5 to 10 millibars and an accuracy of ±0.25%, which is used to monitor the degree of blockage of the filtration system.

[0076] The mixed air gas path 3 is made of medical-grade silicone hose with an inner diameter of 18 to 25 mm, and the material complies with the ISO 10993 biocompatibility standard. This material has good elasticity and chemical stability and can adapt to different usage environments. The measurement range of the O2 concentration sensor 9 is 0 to 100%, with an accuracy of ±1 to 2%, and it can monitor the oxygen content in the mixed gas in real time. The performance parameters of the CO2 concentration sensor 10 and the flow sensor 11 are the same as those described above. The design of the mixed gas path adopts a special geometric structure, the length of the mixing section is not less than 10 times the pipe diameter, and a vortex promotion device is set to ensure the uniformity of gas mixing, and the air flow distribution deviation is controlled within 5%.

[0077] The O2 gas path 4 is made of special oxygen materials with a pipe diameter of 10 to 15 mm, and the surface roughness of the inner wall is not greater than 0.4 microns. This smooth inner wall can reduce the interaction between oxygen molecules and the pipe wall and reduce oxygen loss. The oxygen production range of the O2 gas source oxygen generator 19 is 0.5 to 5 liters per minute, and the oxygen concentration is 93±3%. The third fan C adopts an external rotor structure with a rated power of 35 to 45 watts, featuring low noise and high efficiency. The technical indicators of the O2 concentration sensor 20 and the flow sensor 21 are the same as those described above. The O2 gas path switch 23 adopts a special sealing structure to ensure safe and reliable operation in a high-concentration oxygen environment.

[0078] The housing of the humidifying and heating device 12 is made of transparent material with a volume of 300 to 500 ml. The rated power of the heating element is 35 to 45 watts, and the surface temperature is accurately controlled within the range of 30 to 40 °C. The water tank is made of corrosion-resistant material and has an automatic liquid level compensation function to maintain a stable humidifying effect. This device can heat the gas to 35 to 37 °C, and the relative humidity reaches more than 95%, significantly improving the breathing comfort.

[0079] In the end monitoring system 18, the breathing mask 13 is made of medical-grade silicone material with a hardness of 40 to 60 Shore A. The design of the mask fully considers the ergonomic principle and adopts an anatomical contour, which can fit the facial contour and reduce air leakage. The measurement range of the blood oxygen concentration tester 15 is 70 to 100%, with an accuracy of ±2%. It adopts a high-sensitivity photoelectric sensor and can accurately monitor blood oxygen changes. The sampling flow of the PETCO2 sensor 16 is 45 to 55 ml per minute, and the response time is less than 3 seconds, enabling accurate measurement of end-tidal CO2.

[0080] The environmental monitoring system 29 adopts a distributed layout, and each sensor is connected through a digital bus to achieve real-time data acquisition and transmission. The CO2 concentration sensor 25 and the O2 concentration sensor 26 are used to monitor the components of environmental gases. The measurement range of the barometer 27 is 300 to 1250 hPa, and the accuracy is ±0.5 to 1.0 hPa, which can accurately reflect altitude changes. The temperature measurement range of the temperature and humidity sensor 28 is -40 to 125 °C, the accuracy is ±0.1 to 0.2 °C, the humidity measurement range is 0 to 100%, and the accuracy is ±1.5 to 2%. The monitoring of these environmental parameters provides a basis for the intelligent adjustment of the equipment.

[0081] The control system uses a 32-bit processor with a main frequency of not less than 150 MHz and is equipped with sufficient storage space. The display module uses a 5- to 7-inch display screen with a resolution of not less than 800×480, is equipped with a capacitive touch screen, and provides an intuitive human-machine interaction interface. The power supply uses a medical-grade switching power supply with multiple protection functions to ensure the safe and stable operation of the equipment.

[0082] Through reasonable structural design and precise control strategies, this ventilator realizes the precise adjustment of respiratory parameters. In the treatment of sleep apnea, the respiratory rhythm can be stabilized by precisely controlling the CO2 concentration (1 to 3%), and at the same time, hypoxemia can be relieved by oxygen therapy (flow rate 0.5 to 5 liters per minute). When applied in a high-altitude environment, the system can automatically adjust the ventilation parameters according to changes in air pressure to help users better adapt to the hypoxic environment. The design of the entire system fully considers safety, reliability, and user comfort, and can meet the treatment needs in different scenarios.

[0083] The innovation of the present invention is reflected in the following aspects: First, through the coordinated control of multiple gas paths, the precise adjustment of the components of respiratory gases is realized; second, a comprehensive monitoring system is adopted to ensure the safety and effectiveness of the treatment process; third, through intelligent control algorithms, the adaptive adjustment of the equipment is realized; finally, a good human-machine interaction design improves the convenience of using the equipment. These innovative designs give this equipment significant technical advantages in the field of respiratory therapy.

[0084] As Figure 2 shown, for the specific implementation manner of the signal acquisition and output processing control module, the steps it executes are described in detail as follows: The specific implementation manner of step S10 is to construct a directed graph network structure for speed regulation. First, set the speeds of 3 fans as the nodes in the graph, and each node is represented by a 3D vector denoted, where , , respectively represent the rotational speeds of the 1st, 2nd, and 3rd fans, and the rotational speed range is from 0 to 5000 rpm. The directed edges between nodes represent the conversion paths from one rotational speed combination to another, and the weights of the edges are calculated using an energy consumption model. The energy consumption model takes into account the kinetic energy change and mechanical loss caused by the rotational speed change, and the weight calculation formula is , where is the moment of inertia of the kth fan, is the rotational speed change amount, is the mechanical damping coefficient. The network structure is stored using an adjacency matrix, and the matrix elements represent the energy consumption values between the corresponding rotational speed combinations. The elements without conversion paths are set to infinity. The network structure constructed in this way includes all possible rotational speed adjustment schemes, laying a foundation for finding the optimal adjustment path subsequently. When constructing the network, the rotational speed space is discretized into a 100×100×100 grid, and the rotational speed difference between adjacent grid points does not exceed 50 rpm, ensuring the smoothness of the adjustment. The total number of nodes is controlled within 1 million, ensuring the calculation efficiency.

[0085] The specific implementation of step S20 is to collect physiological parameters and map them to network nodes. First, the blood oxygen saturation is collected through a blood oxygen concentration tester, and the sampling frequency is 1 Hz. The data is filtered by median filtering to remove outliers. At the same time, the PETCO2 concentration is collected, and the sampling frequency is also 1 Hz. The Kalman filter is used to eliminate the measurement noise. The filtered data is converted into a fan rotational speed combination through a mapping function, and the mapping function is implemented using fuzzy control rules. The input variables of the fuzzy control are the blood oxygen saturation and the PETCO2 concentration, and the output variable is the target rotational speeds of the 3 fans. The fuzzy rules are set based on the experience of medical experts. For example, when the blood oxygen is low and the PETCO2 is normal, increase the rotational speeds of the 2nd and 3rd fans and keep the rotational speed of the 1st fan unchanged. The fuzzy inference uses the Mamdani algorithm, and the membership function selects the Gaussian function. Finally, the obtained rotational speed combination is mapped to the nearest node in the network as the starting point of the path planning. During the mapping process, the normal range of the blood oxygen saturation is from 92% to 100%, and the normal range of the PETCO2 is from 35 to 45 mmHg.

[0086] The specific implementation of step S30 is to determine the target node. According to the preset target parameter range, the same mapping method is used to convert the target physiological parameters into the target rotational speed combination. The target value of blood oxygen saturation is set to 96% to 98%, and the target value of PETCO2 is set to 38 to 42 mmHg. The output limiting of the fuzzy control rule ensures that the target rotational speed is within the allowable range of the device. The upper limit of the rotational speed of the first fan is 3000 rpm, the second fan is 4000 rpm, and the third fan is 3500 rpm. At the same time, the matching relationship between the fans is considered. For example, the rotational speed of the second fan should not be less than 1.2 times the rotational speed of the first fan and not greater than 1.5 times the rotational speed of the third fan. The rotational speed combination that meets the constraint conditions is used as the target node. If there are multiple nodes that meet the conditions, the node with the minimum energy consumption at the current working point is selected as the target node.

[0087] The specific implementation of step S40 is to calculate the shortest path based on Dijkstra's algorithm. First, initialize the distance array and the predecessor node array , where represents the shortest distance from the starting point to node . The initial values are infinite except for the starting point. A small root heap is used to store the nodes to be expanded, and the capacity of the heap does not exceed 1000 nodes. Each time, the top node of the heap is taken out, and all its adjacent nodes are scanned. According to the state transition equation , the shortest distance is updated. Among them, is the weight value from node to node . If the distance value of node is updated, its predecessor node is updated to , and node is added to the heap. The termination condition of the algorithm is that the heap is empty or the target node is found. Finally, the shortest path is obtained by backtracking according to the predecessor node array. The rotational speed change amount between adjacent nodes on the path is restricted, and the single-step rotational speed change of any fan does not exceed 20% of the current rotational speed.

[0088] The specific implementation of step S50 is to generate a rotational speed adjustment sequence. The nodes on the shortest path are processed in sequence, and the rotational speed difference between adjacent nodes is calculated. Among them, is the rotational speed adjustment amount of the th fan, is the target rotational speed, is the current rotational speed. Since the dynamic characteristics of the fan need to be considered in actual control, the rotational speed regulation is divided into several sub-intervals. The sub-intervals are divided with equal time intervals, each interval being 3 seconds. The rotational speed change within each sub-interval is planned using a parabola to ensure continuous acceleration. The adjustment sequence includes the starting rotational speed, ending rotational speed, and transition point rotational speed of each sub-interval. The length of the sequence is jointly determined by the path length and adjustment constraints, usually between 10 and 30 intervals.

[0089] The specific implementation of step S60 is to solve the rotational speed regulation equations. First, a load balance equation is established , where is the load distribution coefficient, is the output power. The power distribution equation uses a cubic polynomial model , where , , , are the fan characteristic coefficients. The ventilation volume constraint equation is , where is the total ventilation volume, is the ventilation efficiency, is the maximum allowable ventilation volume. The efficiency maximization equation is , where is the air density, is the acceleration due to gravity, is the head. The system of equations is solved using the Newton iteration method, and the initial iteration value is provided by the solution of the previous moment. The iteration convergence criterion is that the maximum relative error between adjacent two iteration solutions is less than 0.1%. During the solution process, fan characteristic constraints are introduced to ensure that the operating point is located in the stable region of the fan characteristic curve.

[0090] The specific implementation of step S70 is to perform real-time parameter monitoring. After each rotational speed adjustment is completed, wait for 1 second for the system to reach stability, and then collect blood oxygen concentration and PETCO2 data within 300 ms. The data collection uses a sampling rate of 100 Hz, and power frequency interference is removed through digital filtering. The processed data is subjected to outlier detection, and data points exceeding 3 times the standard deviation are removed. Then, the mean value is calculated as the characteristic value of the current operating point. The characteristic value is converted into the actual arrival node in the rotational speed space through the aforementioned fuzzy mapping method. The influence of measurement error is considered during the mapping process. The measurement error of blood oxygen concentration is within ±2%, and the measurement error of PETCO2 is within ±3 mmHg.

[0091] The specific implementation of step S80 is to evaluate the control effect. Calculate the deviation value between the actual arrival node and the path planning node. The deviation value includes the node coordinate deviation and the corresponding physiological parameter deviation. The node coordinate deviation is measured by the Euclidean distance, and the threshold is set to 5% of the maximum rotational speed. The physiological parameter deviation is measured by the weighted sum of squares. The weight of the blood oxygen concentration deviation is 0.7, and the weight of the PETCO2 deviation is 0.3. The threshold of the total deviation value is set to 0.15. When any deviation exceeds the threshold, the actual arrival node is used as the new starting node, and the process returns to step S40 to re-plan the path. During the deviation evaluation process, the system stability is also checked. If the deviation does not show a decreasing trend after 3 consecutive adjustments, the diagonal elements of the weight matrix are increased to improve the robustness of the control.

[0092] The specific implementation of step S90 is iterative optimization control. Repeat steps S40 to S80 until the deviation value between the actual arrival node and the target node is less than the threshold. Adaptive step size control is adopted during the iteration. The initial step size is 80% of the maximum allowable adjustment amount, and then it is dynamically adjusted according to the deviation change trend. If the deviation continuously decreases, the step size remains unchanged; if the deviation fluctuates, the step size is reduced to 0.8 times the current value; if the deviation continuously increases, the step size is reduced to 0.5 times the current value. At the same time, a time constraint is introduced. If convergence has not been achieved after 90 seconds, the deviation threshold is increased to 1.2 times the original value. Throughout the control process, the system parameters are always kept within the safe range, the blood oxygen saturation is not less than 90%, and the PETCO2 does not exceed 50 mmHg. When the system reaches the target state, it enters the steady-state control mode, and the path planning is only re-triggered when the parameters change significantly.

[0093] Specifically, the principle of the present invention is: based on the physiological mechanism of human respiratory regulation, the active regulation of respiratory function is achieved by precisely controlling the composition of the inhaled gas. As the most important chemical regulator of the respiratory center, carbon dioxide affects respiratory activities through two pathways: one is to stimulate the peripheral chemoreceptors and affect the respiratory rhythm through neural reflexes; the other is to directly act on the central chemoreceptors by crossing the blood-brain barrier. The present invention is precisely based on this physiological mechanism, and realizes the precise regulation of respiratory function by precisely controlling the carbon dioxide concentration in the inhaled gas.

[0094] In the design of the control strategy, the present invention adopts a multi-objective optimization method based on graph theory. By mapping the rotational speed combinations of the three fans to network nodes, the change process of the system state is transformed into a path search problem in the network. This mathematical model not only considers the achievement of the control objectives, but also incorporates the energy consumption as the weight of the edge into the optimization scope, ensuring the economy of the control process.

[0095] In specific implementation, the present invention uses fuzzy control rules to map physiological parameters to the fan speed space, and this mapping relationship fully considers the non-linear characteristics of human respiratory regulation. At the same time, the Dijkstra algorithm is used to find the optimal adjustment path, ensuring the smoothness of the adjustment process and minimizing energy consumption. The system also introduces an adaptive step size control mechanism, which can dynamically adjust the adjustment strategy according to the control effect, improving the robustness of the system.

[0096] From a physiological perspective, the solution of the present invention avoids the respiratory alkalosis that may be caused by traditional ventilation treatment by maintaining an appropriate partial pressure of carbon dioxide. At the same time, apnea caused by too low carbon dioxide level is prevented through preventive adjustment. This regulation mechanism is coordinated with the human body's own respiratory regulation mechanism, so better treatment effects can be achieved.

[0097] A specific embodiment 1 of the present invention is provided below: A carbon dioxide ventilator, including a CO2 gas pipeline 1, a normal air pipeline 2, a mixed air pipeline 3, an O2 gas pipeline 4, a humidifying and heating device 12, an end monitoring system 18 and an environmental monitoring system 29. In this embodiment, through the integration of dual gas sources of CO2 and O2 input and the use of multi-sensor real-time monitoring, precise regulation of the respiratory gas components is achieved, and a personalized respiratory support plan can be provided according to the actual needs of the user.

[0098] The CO2 gas pipeline 1 is made of medical-grade 316L stainless steel pipe, with a pipe diameter range of 8 - 12 mm and a wall thickness of 0.8 - 1.2 mm. The first fan A installed in this pipeline can be a brushless DC fan, with a rated power range of 35 - 55 W, a maximum speed of 4500 - 5000 rpm, and having a stepless speed regulation function. The selectable fan models include the D1225C series, the G1225M series, the R1225M series, etc. The measurement range of the CO2 concentration sensor 5 should be 0 - 50000 ppm, with an accuracy requirement of ±(50 ppm + 5% reading) and a response time requirement of less than 60 s. NDIR double-beam sensors or photoacoustic spectroscopy sensors can be selected, and the specific models can be the SCD40 series, the K33 series, the CM1106 series, etc. The measurement range of the flow sensor 6 is 0 - 120 m³ / h, with an accuracy requirement of ±1.5%. Thermal mass flow sensors or vortex street flow sensors can be selected, such as the FLOWSIC series, the VA series, the D6F series, etc. The CO2 gas pipeline switch 24 can be an electromagnetic valve, with a rated pressure requirement of 0.5 - 1.0 MPa and a response time requirement within 25 ms. The VXZ series, the 2W series, the PU220 series, etc. can be selected.

[0099] The normal air circuit 2 uses food-grade PP pipes or medical-grade silicone pipes with a pipe diameter of 15 - 20 mm. The air filtration and intake silencing device 7 adopts a multi-layer composite filter element structure. The outer layer is a primary filter layer (using non-woven fabric of 80 - 120 g / m²), the middle layer is a HEPA filter (the filtration efficiency requirement is ≥99.97%@0.3μm), and the inner layer is an activated carbon layer (the specific surface area requirement is ≥800 m² / g). The second fan B is preferably a centrifugal fan with a rated power range of 75 - 95 W and a maximum air volume requirement of 600 - 700 m³ / h. Models such as R3G series, D4E series, or G3G series can be selected. The pressure difference sensor 8 has a measurement range requirement of ±5 - 10 mbar and an accuracy requirement of ±0.25%. Models such as HSC series, MS series, or DPS series can be selected.

[0100] The mixed air circuit 3 uses medical-grade silicone hoses with an inner diameter of 18 - 25 mm, and the material needs to meet the ISO 10993 biocompatibility standard. The measurement range of the O2 concentration sensor 9 is required to be 0 - 100%, and the accuracy requirement is ±1 - 2%. Electrochemical sensors or fluorescence quenching type sensors can be selected, such as R-17 series, KE series, or OOM series, etc. The technical requirements of the CO2 concentration sensor 10 and the flow sensor 11 are the same as those described above. The geometric design of the mixed gas circuit requires that the length of the mixing section is not less than 10 times the pipe diameter, and the deviation of the air flow distribution uniformity is controlled within 5%.

[0101] The O2 gas circuit 4 uses special oxygen copper pipes or stainless steel pipes with a pipe diameter of 10 - 15 mm, and the surface roughness requirement of the inner wall surface is Ra≤0.4μm. The oxygen production range of the O2 gas source / oxygen generator 19 is required to be 0.5 - 5 L / min, and the oxygen concentration is 93% ± 3%. Molecular sieve pressure swing adsorption type oxygen generators can be selected, such as 5L series, 8L series, or 10L series, etc. The third fan C can be an external rotor fan with a rated power of 35 - 45 W. Models such as K3G series, D3G series, or R3G series can be selected. The technical requirements of the O2 concentration sensor 20 and the flow sensor 21 are the same as those described above. The O2 gas circuit switch 23 uses a solenoid valve, and the technical requirements are the same as those of the CO2 gas circuit switch.

[0102] The housing of the humidifying and heating device 12 is made of transparent PC material or acrylic material with a volume of 300 - 500 ml. The heating element uses a PTC ceramic heating sheet or a silicone heating sheet with a rated power of 35 - 45 W, and the surface temperature control range is 30 - 40℃. The water tank can be made of a stainless steel inner liner or food-grade PP material and has an automatic water replenishment function. The performance index requirements of this device are: the gas outlet temperature is 35 - 37℃, and the relative humidity is ≥95%.

[0103] In the end - point monitoring system 18, the breathing mask 13 is made of medical - grade silicone material, with a hardness range of 40 - 60 Shore A. The measurement range of the blood oxygen concentration tester 15 is required to be 70 - 100%, and the accuracy requirement is ±2%. A reflective or transmissive pulse oximetry sensor can be selected. The sampling flow rate requirement of the PETCO2 sensor 16 is 45 - 55 ml / min, and the response time requirement is <3 s. A mainstream or sidestream sensor can be selected, such as the NomoLine series, LoFlo series, or Capnostat series, etc.

[0104] Each sensor in the environmental monitoring system 29 is connected through an RS485 bus or a CAN bus. The technical requirements of the CO2 concentration sensor 25 and the O2 concentration sensor 26 are the same as those described above. The measurement range requirement of the barometer 27 is 300 - 1250 hPa, and the accuracy requirement is ±0.5 - 1.0 hPa. The BMP series, MS series, or HP series, etc. can be selected. The temperature measurement range of the temperature and humidity sensor 28 is required to be - 40 to 125 °C, and the accuracy requirement is ±0.1 - 0.2 °C. The humidity measurement range is 0 - 100% RH, and the accuracy requirement is ±1.5 - 2% RH. The SHT series, HTS series, or AM series, etc. can be selected.

[0105] The control system can select a processor with a 32 - bit ARM Cortex - M4 or M7 core, with a main frequency requirement of not less than 150 MHz, a Flash capacity of ≥1 MB, and a RAM capacity of ≥128 KB. The display screen uses a 5 - 7 - inch LCD (resolution not less than 800×480) and is equipped with a capacitive touch screen. The power supply requires a medical - grade switching power supply, with an input of 220V AC and outputs of 24V DC / 8 - 12A and 12V DC / 3 - 5A, and it needs to have over - voltage, over - current, short - circuit and other protection functions.

[0106] The connection between each gas path uses a quick - connector. The material can be selected from 316L stainless steel or brass, and the sealing ring uses fluororubber or ethylene propylene diene monomer rubber. After system integration, the overall dimensions of the machine are recommended to be in the range of 350 - 450 mm×250 - 350 mm×200 - 300 mm (length×width×height), and the weight is controlled within 7 - 9 kg. All materials need to meet the requirements of medical device biocompatibility and can withstand wiping with conventional disinfectants.

[0107] In this Embodiment 1, through the precise delivery and monitoring of CO2 gas, the regulation of respiratory rhythm is achieved; through the real-time regulation of O2 concentration, the hypoxia symptoms are alleviated; through the monitoring and compensation of environmental parameters, the adaptability of the device in different scenarios is improved. In the treatment of sleep apnea syndrome, the device can stabilize the respiratory rhythm through the precise control of CO2 concentration (1 - 3%), and at the same time alleviate hypoxemia through oxygen therapy (flow rate 0.5 - 5 L / min). When applied in the plateau environment, the device can automatically adjust the ventilation parameters according to the air pressure change to help the user better adapt to the hypoxic environment. The multiple redundant settings of each sensor ensure the reliability of the system operation, and multiple safety protection measures ensure the use safety.

[0108] Embodiment 2: This embodiment provides a specific implementation scheme of a carbon dioxide ventilator device for the treatment of sleep apnea syndrome. This device is mainly applied to the respiratory department of medical institutions for treating patients with moderate to severe sleep apnea syndrome.

[0109] The carbon dioxide ventilator in this embodiment adopts a modular design, mainly including four parts: a gas supply system, a mixing control system, a monitoring system, and a human-machine interaction system. Among them, the gas supply system includes a CO2 gas source, an oxygen generator, and an air filtration device; the mixing control system includes three independently controlled fans and a corresponding sensor array; the monitoring system includes a blood oxygen and end-tidal CO2 monitoring device; the human-machine interaction system includes a touch display screen and an alarm device.

[0110] In terms of the gas supply system, the CO2 gas source uses a medical-grade 40L high-pressure gas cylinder, the CO2 purity reaches 99.99%, and the gas cylinder pressure is 15 MPa. The pressure reducing valve adopts a two-stage pressure reducing structure, reducing the pressure to 0.8 MPa in the first stage and to 0.2 MPa in the second stage. The oxygen generator adopts the molecular sieve pressure swing adsorption technology, with a rated oxygen production of 3 L / min and an oxygen concentration of up to 93% ± 3%. The air filtration device adopts a three-stage filtration structure, including primary, intermediate, and high-efficiency filters, with filtration accuracies of 10 μm, 5 μm, and 0.3 μm respectively.

[0111] In terms of the mixing control system, all three fans adopt centrifugal fans driven by brushless DC motors, and the specific parameters are shown in the following table.

[0112] Table 1 Fan Parameter Specification Table

[0113]

[0114] The sensor system configuration is shown in the following table.

[0115] Table 2 Sensor Configuration Parameter Table

[0116]

[0117] In terms of the monitoring system, a reflective pulse oximeter is used for blood oxygen saturation monitoring, with a measurement range of 35% - 100% and an accuracy of ±2% within the range of 70% - 100%. An infrared absorption sensor is used for end-tidal CO2 monitoring, with a measurement range of 0 - 100 mmHg and an accuracy of ±2 mmHg. The sampling frequency of the monitoring data is 100 Hz, and the data update rate is 1 Hz.

[0118] The humidification and heating system uses a PTC heating element, with a heating power of 200 W, a water tank capacity of 500 ml, and a maximum humidification amount of 400 ml / h. The heating temperature can be adjusted within the range of 30 - 40 °C, and the relative humidity can reach 95%RH. The delivery pipeline is made of medical-grade silicone material, with heating wires inside the wall to maintain the gas temperature and prevent the formation of condensed water.

[0119] The breathing mask is made of medical-grade silicone material and provides three sizes: S, M, and L. The mask edge adopts a double-layer airbag sealing structure, and the inner layer inflation pressure is 0.2 kPa. The headband is made of elastic nylon material, with a width of 25 mm, and the tightness can be adjusted through a quick adjustment buckle.

[0120] The gas mixing control adopts a three-stage adjustment strategy. The first stage is the startup stage, and the system gradually adjusts the mixed gas to the initial set value within 60 seconds. The second stage is the stable operation stage, and the system makes real-time fine-tuning according to the patient's blood oxygen saturation and end-tidal CO2 levels. The third stage is the end stage, and the system gradually adjusts the mixed gas back to the normal air composition within 30 seconds. The specific operating parameters are shown in the following table.

[0121] Table 3 Gas Mixing Control Parameter Table

[0122]

[0123] In actual operation, the system automatically adjusts according to the patient's physiological indicators. The target value of blood oxygen saturation is set at 95%, with an allowable fluctuation range of ±2%. The target value of end-tidal CO2 is set at 40 mmHg, with an allowable fluctuation range of ±5 mmHg. When the monitored value exceeds the range, the system automatically adjusts the composition and flow rate of the mixed gas.

[0124] The device in this embodiment also includes multiple safety protection mechanisms, mainly including the following aspects:

[0125] 1. Pressure protection: When the pipeline pressure exceeds 40 kPa, the system automatically reduces the fan speed. When the pressure exceeds 50 kPa, the system immediately shuts down and alarms.

[0126] 2. Concentration protection: When the CO2 concentration exceeds 3.5%, the system automatically cuts off the CO2 supply and alarms. When the O2 concentration is lower than 19%, the system automatically increases the output of the oxygen generator.

[0127] 3. Flow protection: When the total flow rate is below 15 L / min, the system automatically increases the fan speed. When the flow rate exceeds 80 L / min, the system automatically decreases the fan speed.

[0128] 4. Temperature protection: When the airway temperature exceeds 42 °C, the system automatically reduces the heating power. When the temperature is below 28 °C, the system automatically increases the heating power.

[0129] The device of this embodiment shows the following advantages in clinical applications:

[0130] 1. High gas mixing precision: The control precision of CO2 concentration reaches ±0.1%, and the control precision of O2 concentration reaches ±1%, meeting the clinical use requirements.

[0131] 2. Fast response speed: The response time of the system to changes in blood oxygen saturation is less than 3 seconds, and the response time to changes in end-tidal CO2 is less than 2 seconds.

[0132] 3. Good noise control: Under normal working conditions, the equipment noise level is controlled below 45 dB, without affecting the patient's sleep.

[0133] 4. High safety: Multiple protection mechanisms ensure the safety of the treatment process and effectively prevent accidents.

[0134] 5. Easy to operate: Adopting touch screen control, with a friendly interface and simple operation steps, suitable for medical staff to use.

[0135] In the actual operation test, the device of this embodiment treated 10 patients with moderate to severe sleep apnea for 7 days, achieving good results. The various indicators during the treatment are shown in the following table.

[0136] Table 4 Data table of clinical application effects

[0137]

[0138] Through reasonable structural design and parameter configuration, this embodiment realizes a safe and reliable carbon dioxide ventilator device. While ensuring the treatment effect, the device has a good user experience and safety performance. Through clinical application verification, the practical value of the device in the treatment of sleep apnea syndrome is proved.

[0139] Embodiment 3: Optimization embodiment of carbon dioxide ventilator based on intelligent control algorithm

[0140] Based on Embodiment 2, this embodiment focuses on the optimization design of the signal acquisition and output processing control module. By introducing an intelligent control algorithm, precise adjustment of the fan speed and optimization of system energy consumption are achieved. The main improvements include the following aspects.

[0141] I. Optimization of System Control Structure

[0142] This embodiment adopts a hierarchical control structure, including three levels: the decision-making layer, the coordination layer, and the execution layer. The decision-making layer is responsible for determining the control objectives based on the patient's physiological parameters, the coordination layer is responsible for calculating the optimal control strategy, and the execution layer is responsible for implementing specific control actions. The function configurations of each level are shown in the following table.

[0143] Table 5 Hierarchical Structure Table of Control System

[0144]

[0145] II. Construction of Directed Graph Network

[0146] This embodiment constructs a directed graph network containing 125 nodes, and each node represents a specific speed combination of three fans. The connections between nodes are established according to the following rules:

[0147] 1. The speed difference between adjacent nodes does not exceed 20% of the current speed. 2. The weight of the connection between nodes is determined by the energy consumption value, and the calculation formula is the weighted sum of the total power and the speed change amount. 3. Each node is connected to at most 26 other nodes to ensure an appropriate search space.

[0148] The specific node distribution parameters are shown in the following table.

[0149] Table 6 Table of Network Node Distribution Parameters

[0150]

[0151] III. Modeling of Fan Characteristic Curve

[0152] The operating parameters of the fan at different speeds are obtained through experimental measurement, and the characteristic curve is obtained by fitting with a cubic polynomial. The measurement data and fitting results are shown in the following table.

[0153] Table 7 Measurement Data Table of Fan Characteristic Curve

[0154]

[0155] IV. Implementation of Control Algorithm

[0156] This embodiment adopts an iterative optimization method to achieve precise control of the fan speed. The main steps include:

[0157] 1. State parameter acquisition: The system operating parameters are acquired every 3 seconds, including blood oxygen saturation, end-tidal CO2, fan speed, etc.

[0158] 2. Determination of target values: Set the target parameter range according to the treatment requirements, with the blood oxygen saturation being 95% ± 2% and the end-tidal CO2 being 40 ± 5 mmHg.

[0159] 3. Path planning: Use Dijkstra's algorithm to calculate the shortest path and determine the fan speed adjustment sequence.

[0160] 4. Speed adjustment: Gradually adjust the fan speed according to the calculation results, with each adjustment amplitude not exceeding 20%.

[0161] The specific control parameter settings are shown in the following table.

[0162] Table 8 Control parameter configuration table

[0163]

[0164] V. System performance comparison

[0165] This embodiment was compared and tested with Embodiment 1, mainly examining three aspects: control accuracy, energy consumption, and treatment effect. The test results are shown in the following table.

[0166] Table 9 System performance comparison data table

[0167]

[0168] VI. Clinical application effect

[0169] This embodiment conducted a 14-day treatment test on 15 patients and compared the treatment effects with those of Embodiment 1. The results showed that this embodiment had significant improvements in both treatment effect and patient comfort. The specific data are shown in the following table.

[0170] Table 10 Clinical effect comparison data table

[0171]

[0172] VII. System reliability verification

[0173] This embodiment conducted a 30-day continuous reliability test, and the test contents included the control accuracy maintenance ability, system stability, and fault response ability. The test results are shown in the following table.

[0174] Table 11 System reliability test data table

[0175]

[0176] VIII. Energy-saving effect analysis

[0177] This embodiment significantly reduced the system energy consumption by optimizing the control algorithm. The specific energy-saving effects are shown in the following table.

[0178] Table 12 System Energy Saving Effect Data Sheet

[0179]

[0180] From the comparison of the above data, it can be seen that significant improvements have been achieved in multiple aspects in this embodiment:

[0181] 1. Improvement in control accuracy: The control accuracy of CO2 and O2 concentrations has increased by 50%, and the system response is more accurate.

[0182] 2. Reduction in energy consumption: By optimizing the control algorithm, the daily average energy consumption of the system has decreased by 33.3%, significantly reducing the operating cost.

[0183] 3. Improvement in treatment effect: The treatment compliance rate has increased by 10.9%, and the average compliance time has been shortened by 37%.

[0184] 4. Optimization of the user experience: The patient comfort score has increased by 14.1%, indicating that the system runs more smoothly.

[0185] 5. System reliability: Through 30 days of continuous operation testing, it has been proved that the system has good reliability and stability.

[0186] In this embodiment, through the introduction of an intelligent control algorithm, a comprehensive improvement in system performance has been achieved on the basis of Embodiment 2. This solution not only improves the treatment effect but also reduces energy consumption, having good application value and promotion prospects. Practice has proved that this control solution can meet the clinical use requirements and provide better technical support for the treatment of sleep apnea syndrome.

[0187] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention.

Claims

1. A carbon dioxide ventilator, characterized in that, Including: A CO2 gas pipeline, a normal air pipeline, a mixed air pipeline, an O2 gas pipeline, a humidifying and heating device, an end monitoring system, and an environmental monitoring system; a first fan, a CO2 concentration sensor, a flow sensor, and a CO2 pipeline switch are arranged in the CO2 gas pipeline; an air filtering and intake silencer, a second fan, and a differential pressure sensor are arranged in the normal air pipeline; an O2 concentration sensor, a CO2 concentration sensor, and a flow sensor are arranged in the mixed air pipeline; an O2 gas source oxygen generator, a third fan, an O2 concentration sensor, a flow sensor, and an O2 pipeline switch are arranged in the O2 gas pipeline; the end monitoring system includes a breathing mask, a blood oxygen concentration tester, and a PETCO2 sensor; the environmental monitoring system includes a CO2 concentration sensor, an O2 concentration sensor, a barometer, and a temperature and humidity sensor; it also includes a main control single-chip microcomputer, and the main control single-chip microcomputer is provided with a signal acquisition and output processing control module for performing the following steps to achieve precise adjustment of the first fan, the second fan, and the third fan: S10. Construct a directed graph network structure, set the rotation speeds of the first fan, the second fan, and the third fan as nodes in the graph, the connections between the nodes represent the conversion paths of the rotation speed combinations, and the weights of the connections represent the energy consumption values of the conversion process; S20. Collect the measurement values of the blood oxygen concentration tester and the PETCO2 sensor, map the measurement values into the directed graph network structure, and obtain the starting node corresponding to the current rotation speed combination; S30. Determine the target node in the directed graph network structure according to the preset blood oxygen concentration target value and PETCO2 concentration target value, and the target node corresponds to the target rotation speed combination; S40. Use Dijkstra's algorithm to calculate the shortest path from the starting node to the target node. The shortest path corresponds to the optimal route for the fan rotation speed adjustment, and the path length corresponds to the total energy consumption of the rotation speed adjustment; S50. Traverse all the nodes on the shortest path, record the rotation speed difference between every two adjacent nodes, and generate a rotation speed adjustment sequence. The rotation speed adjustment sequence includes the adjustment direction and the adjustment amount; S60. Adjust the rotation speeds of the first fan, the second fan, and the third fan step by step according to the rotation speed adjustment sequence, and each step adjustment scheme is obtained by solving the rotation speed adjustment equation set; the rotation speed adjustment equation set includes a load balance equation, a power distribution equation, a ventilation volume constraint equation, and an efficiency maximization equation; S70. Collect the blood oxygen concentration and PETCO2 concentration after each rotation speed adjustment, and define the combined value of the blood oxygen concentration and PETCO2 concentration as the actually reached node; S80. Judge the deviation value between the actually reached node and the path planning node. When the deviation value exceeds the threshold, use the actually reached node as the new starting node, and return to step S40 to re-plan the path; S90. Repeat steps S40 to S80 until the deviation value between the actually reached node and the target node is less than the threshold.

2. The carbon dioxide ventilator according to claim 1, characterized in that One side of the CO2 gas circuit is connected to a CO2 gas source, and the other side converges with the mixed air circuit. The first fan is arranged between the CO2 concentration sensor and the flow sensor. The normal air circuit is connected to the mixed air circuit through a grille, and the second fan is arranged between the air filter intake silencer and the differential pressure sensor.

3. The carbon dioxide ventilator according to claim 2, characterized in that, One side of the O2 gas circuit is connected to an O2 gas source oxygen generator, and the other side converges with the mixed air circuit. The third fan is arranged between the O2 concentration sensor and the flow sensor. The mixed air circuit is connected to a humidifying and heating device.

4. The carbon dioxide ventilator according to claim 3, characterized in that, The grille is composed of a plurality of parallel deflector plates. Equal-spacing air flow channels are formed between the deflector plates. Both ends of the deflector plates are fixedly connected to the inner wall of the normal air circuit and the inner wall of the mixed air circuit respectively.

5. The carbon dioxide ventilator according to claim 4, characterized in that, The humidifying and heating device includes a heating cavity and a heating unit. A water storage tank is arranged in the heating cavity, and the heating unit is arranged at the bottom of the water storage tank. A delivery pipeline is arranged between the heating cavity and the breathing mask.

6. The carbon dioxide ventilator according to claim 5, characterized in that, The breathing mask includes a mask body and a fixing structure. The mask body is bowl-shaped, and a sealing soft pad is arranged at the edge. The fixing structure includes a headband and a strap buckle. The headband is connected to both sides of the mask body through the strap buckle.

7. The carbon dioxide ventilator according to claim 6, wherein, The CO2 gas source is a high-pressure gas cylinder, and the high-pressure gas cylinder is connected to a CO2 gas circuit switch through a pressure reducing valve. The O2 gas source oxygen generator is connected to an O2 gas circuit switch through a pressure stabilizing valve.

8. A carbon dioxide ventilator according to claim 7, characterized in that, The CO2 concentration sensor, O2 concentration sensor, barometer, and temperature and humidity sensor in the environmental monitoring system are all fixedly installed on the surface of the outer shell. The outer shell is composed of an upper shell and a lower shell buckled together, and a partition is arranged inside to divide the space into a gas circuit area and a monitoring area.

9. The carbon dioxide ventilator according to claim 8, characterized in that, The PETCO2 sensor is arranged at the connection between the breathing mask and the humidifying and heating device. The blood oxygen concentration tester includes a measurement probe and a numerical display unit. The measurement probe is electrically connected to the numerical display unit through a signal line.

10. The carbon dioxide ventilator according to claim 9, characterized in that, The main control single-chip microcomputer is connected to the CO2 concentration sensor, O2 concentration sensor, PETCO2 sensor, blood oxygen concentration tester, temperature and humidity sensor, and barometer through a terminal block. The signal acquisition and output processing control module is respectively connected to the first fan, the second fan, and the third fan through a drive circuit. The main control single-chip microcomputer is respectively connected to the CO2 gas circuit switch and the O2 gas circuit switch through a solenoid valve drive circuit. The measured values of the blood oxygen concentration tester and the measured values of the PETCO2 sensor are fed back to the signal acquisition and output processing control module.

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